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PyTorch/SpeechSynthesis/FastPitch/triton | triton | run_online_performance_test_on_triton | #!/usr/bin/env python3
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""
For models with variable-sized inputs you must provide the --input-shape argument so that perf_analyzer knows
what shape tensors to use. For example, for a model that has an input called IMAGE that has shape [ 3, N, M ],
where N and M are variable-size dimensions, to tell perf_analyzer to send batch-size 4 requests of shape [ 3, 224, 224 ]
`--shape IMAGE:3,224,224`.
"""
import argparse
import csv
import os
import sys
from pathlib import Path
from typing import List, Optional
# method from PEP-366 to support relative import in executed modules
if __package__ is None:
__package__ = Path(__file__).parent.name
from .deployment_toolkit.report import save_results, show_results, sort_results
from .deployment_toolkit.warmup import warmup
def calculate_average_latency(r):
avg_sum_fields = [
"Client Send",
"Network+Server Send/Recv",
"Server Queue",
"Server Compute",
"Server Compute Input",
"Server Compute Infer",
"Server Compute Output",
"Client Recv",
]
avg_latency = sum([int(r.get(f, 0)) for f in avg_sum_fields])
return avg_latency
def update_performance_data(results: List, performance_file: str):
with open(performance_file, "r") as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
row["avg latency"] = calculate_average_latency(row)
results.append(row)
def _parse_batch_sizes(batch_sizes: str):
batches = batch_sizes.split(sep=",")
return list(map(lambda x: int(x.strip()), batches))
def online_performance(
model_name: str,
batch_sizes: List[int],
result_path: str,
input_shapes: Optional[List[str]] = None,
profiling_data: str = "random",
triton_instances: int = 1,
triton_gpu_engine_count: int = 1,
server_url: str = "localhost",
measurement_window: int = 10000,
shared_memory: bool = False
):
print("\n")
print(f"==== Dynamic batching analysis start ====")
print("\n")
input_shapes = " ".join(map(lambda shape: f" --shape {shape}", input_shapes)) if input_shapes else ""
print(f"Running performance tests for dynamic batching")
performance_file = f"triton_performance_dynamic_partial.csv"
max_batch_size = max(batch_sizes)
max_total_requests = 2 * max_batch_size * triton_instances * triton_gpu_engine_count
max_concurrency = min(256, max_total_requests)
batch_size = max(1, max_total_requests // 256)
step = max(1, max_concurrency // 32)
min_concurrency = step
exec_args = f"""-m {model_name} \
-x 1 \
-p {measurement_window} \
-v \
-i http \
-u {server_url}:8000 \
-b {batch_size} \
-f {performance_file} \
--concurrency-range {min_concurrency}:{max_concurrency}:{step} \
--input-data {profiling_data} {input_shapes}"""
if shared_memory:
exec_args += " --shared-memory=cuda"
result = os.system(f"perf_client {exec_args}")
if result != 0:
print(f"Failed running performance tests. Perf client failed with exit code {result}")
sys.exit(1)
results = list()
update_performance_data(results=results, performance_file=performance_file)
results = sort_results(results=results)
save_results(filename=result_path, data=results)
show_results(results=results)
os.remove(performance_file)
print("Performance results for dynamic batching stored in: {0}".format(result_path))
print("\n")
print(f"==== Analysis done ====")
print("\n")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model-name", type=str, required=True, help="Name of the model to test")
parser.add_argument(
"--input-data", type=str, required=False, default="random", help="Input data to perform profiling."
)
parser.add_argument(
"--input-shape",
action="append",
required=False,
help="Input data shape in form INPUT_NAME:<full_shape_without_batch_axis>.",
)
parser.add_argument("--batch-sizes", type=str, required=True, help="List of batch sizes to tests. Comma separated.")
parser.add_argument("--triton-instances", type=int, default=1, help="Number of Triton Server instances")
parser.add_argument(
"--number-of-model-instances", type=int, default=1, help="Number of models instances on Triton Server"
)
parser.add_argument("--result-path", type=str, required=True, help="Path where result file is going to be stored.")
parser.add_argument("--server-url", type=str, required=False, default="localhost", help="Url to Triton server")
parser.add_argument(
"--measurement-window", required=False, help="Time which perf_analyzer will wait for results", default=10000
)
parser.add_argument("--shared-memory", help="Use shared memory for communication with Triton", action="store_true",
default=False)
args = parser.parse_args()
warmup(
server_url=args.server_url,
model_name=args.model_name,
batch_sizes=_parse_batch_sizes(args.batch_sizes),
triton_instances=args.triton_instances,
triton_gpu_engine_count=args.number_of_model_instances,
profiling_data=args.input_data,
input_shapes=args.input_shape,
measurement_window=args.measurement_window,
shared_memory=args.shared_memory
)
online_performance(
server_url=args.server_url,
model_name=args.model_name,
batch_sizes=_parse_batch_sizes(args.batch_sizes),
triton_instances=args.triton_instances,
triton_gpu_engine_count=args.number_of_model_instances,
profiling_data=args.input_data,
input_shapes=args.input_shape,
result_path=args.result_path,
measurement_window=args.measurement_window,
shared_memory=args.shared_memory
)
if __name__ == "__main__":
main()
|
TensorFlow2/Classification/ConvNets/model | model | efficientnet_model_v1 | # Lint as: python3
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains definitions for EfficientNet v1 model.
[1] Mingxing Tan, Quoc V. Le
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
ICML'19, https://arxiv.org/abs/1905.11946
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
from typing import Any, Dict, Optional, List, Text, Tuple
import copy
import tensorflow as tf
import tensorflow.keras.backend as K
import horovod.tensorflow as hvd
from utils.optimizer_factory import GradientAccumulator
from model.layers import simple_swish, hard_swish, identity, gelu, get_activation
from model.blocks import conv2d_block, mb_conv_block
from model.common_modules import round_filters, round_repeats, load_weights
from dataloader import preprocessing
from dataloader.dataset_factory import mixing_lite
DENSE_KERNEL_INITIALIZER = {
'class_name': 'VarianceScaling',
'config': {
'scale': 1 / 3.0,
'mode': 'fan_in',
'distribution': 'uniform'
}
}
@tf.keras.utils.register_keras_serializable(package='Vision')
class Model(tf.keras.Model):
"""Wrapper class for an EfficientNet v1 Keras model.
Contains helper methods to build, manage, and save metadata about the model.
"""
def __init__(self,
config: Dict[Text, Any] = None):
"""Create an EfficientNet v1 model.
Args:
config: (optional) the main model parameters to create the model
overrides: (optional) a dict containing keys that can override
config
"""
super().__init__()
self.config = config
if self.config.grad_accum_steps > 1:
self.grad_accumulator = GradientAccumulator()
self.gradients_gnorm = tf.Variable(0, trainable=False, dtype=tf.float32)
self.local_step = tf.Variable(initial_value=0, dtype=tf.int64, trainable=False, aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA)
input_channels = config.mparams.input_channels
# Consistent with channels last format. will be permuted in _build, if channels first requested.
input_shape = (None, None, input_channels) # Should handle any image size
image_input = tf.keras.layers.Input(shape=input_shape)
is_training ="predict" not in config.mode
if is_training:
mixup_input = tf.keras.layers.Input(shape=(1, 1, 1))
cutmix_input = tf.keras.layers.Input(shape=(None, None, 1))
is_tr_split = tf.keras.layers.Input(shape=(1)) # indicates whether we use tr or eval data loader
inputs = [image_input,mixup_input,cutmix_input,is_tr_split]
else:
inputs = [image_input]
output = self._build(inputs)
# Cast to float32 in case we have a different model dtype
output = tf.cast(output, tf.float32)
# defining a Model object within another Model object is not the best design idea,
# but I wanted to make use of existing functional API code from Subhankar
self.model = tf.keras.Model(inputs=inputs,outputs=output)
def call(self,data):
is_predict ="predict" in self.config.mode
if not is_predict:
x=data['image']
mixup_weights = data['mixup_weight']
cutmix_masks = data['cutmix_mask']
is_tr_split = data['is_tr_split']
return self.model([x,mixup_weights,cutmix_masks,is_tr_split])
else:
return self.model([data])
def _build(self,
input: List[tf.keras.layers.Input]):
"""Creates an EfficientNet v1 graph given the model parameters.
This function is wrapped by the `EfficientNet_v1` class to make a tf.keras.Model.
Args:
image_input: the input batch of images
Returns:
the output of efficientnet v1
"""
config = self.config
depth_coefficient = config.mparams.depth_coefficient
blocks = config.mparams.blocks
stem_base_filters = config.mparams.stem_base_filters
top_base_filters = config.mparams.top_base_filters
activation = get_activation(config.mparams.activation)
dropout_rate = config.mparams.dropout_rate
drop_connect_rate = config.mparams.drop_connect_rate
num_classes = config.mparams.num_classes
input_channels = config.mparams.input_channels
rescale_input = config.mparams.rescale_input
data_format = tf.keras.backend.image_data_format()
dtype = config.mparams.dtype
weight_decay = config.weight_decay
weight_init = config.mparams.weight_init
train_batch_size = config.train_batch_size
do_mixup = config.mixup_alpha > 0
do_cutmix = config.cutmix_alpha > 0
def cond_mixing(args):
images,mixup_weights,cutmix_masks,is_tr_split = args
return tf.cond(tf.keras.backend.equal(is_tr_split[0],0),
lambda: images, # eval phase
lambda: mixing_lite(images,mixup_weights,cutmix_masks, train_batch_size, do_mixup, do_cutmix)) # tr phase
images = input[0]
x = images
if len(input) > 1:
# we get here only during train or train_and_eval modes
if self.config.defer_img_mixing:
# we get here only if we chose not to perform image mixing in the data loader
# image mixing on device further accelrates training
mixup_weights = input[1]
cutmix_masks = input[2]
is_tr_split = input[3]
x = tf.keras.layers.Lambda(cond_mixing)([images,mixup_weights,cutmix_masks,is_tr_split])
# data loader outputs data in the channels last format
if data_format == 'channels_first':
# Happens on GPU/TPU if available.
x = tf.keras.layers.Permute((3, 1, 2))(x)
if rescale_input:
# x-mean/std
x = preprocessing.normalize_images(x,
mean_rgb=config.mparams.mean_rgb,
stddev_rgb=config.mparams.std_rgb,
num_channels=input_channels,
dtype=dtype,
data_format=data_format)
# Build stem
x = conv2d_block(x,
round_filters(stem_base_filters, config),
config,
kernel_size=[3, 3],
strides=[2, 2],
activation=activation,
name='stem')
# Build blocks
num_blocks_total = sum(
round_repeats(block['num_repeat'], depth_coefficient) for block in blocks)
block_num = 0
for stack_idx, block in enumerate(blocks):
assert block['num_repeat'] > 0
# Update block input and output filters based on depth multiplier
block.update({
'input_filters':round_filters(block['input_filters'], config),
'output_filters':round_filters(block['output_filters'], config),
'num_repeat':round_repeats(block['num_repeat'], depth_coefficient)})
# The first block needs to take care of stride and filter size increase
drop_rate = drop_connect_rate * float(block_num) / num_blocks_total
config.mparams.update({'drop_connect_rate': drop_rate}) # TODO(Sugh) replace
block_prefix = 'stack_{}/block_0/'.format(stack_idx)
x = mb_conv_block(x, block, config, block_prefix)
block_num += 1
if block['num_repeat'] > 1:
block.update({
'input_filters':block['output_filters'],
'strides':(1, 1)
})
for block_idx in range(block['num_repeat'] - 1):
drop_rate = drop_connect_rate * float(block_num) / num_blocks_total
config.mparams.update({'drop_connect_rate': drop_rate})
block_prefix = 'stack_{}/block_{}/'.format(stack_idx, block_idx + 1)
x = mb_conv_block(x, block, config, prefix=block_prefix)
block_num += 1
# Build top
x = conv2d_block(x,
round_filters(top_base_filters, config),
config,
activation=activation,
name='top')
# Build classifier
DENSE_KERNEL_INITIALIZER['config']['mode'] = weight_init
x = tf.keras.layers.GlobalAveragePooling2D(name='top_pool')(x)
if dropout_rate and dropout_rate > 0:
x = tf.keras.layers.Dropout(dropout_rate, name='top_dropout')(x)
x = tf.keras.layers.Dense(
num_classes,
kernel_initializer=DENSE_KERNEL_INITIALIZER,
kernel_regularizer=tf.keras.regularizers.l2(weight_decay),
bias_regularizer=tf.keras.regularizers.l2(weight_decay),
name='logits')(x)
x = tf.keras.layers.Activation('softmax', name='probs', dtype=tf.float32)(x)
return x
|
PyTorch/SpeechSynthesis/FastPitch/common | common | tb_dllogger | import atexit
import glob
import re
from itertools import product
from pathlib import Path
import dllogger
import torch
import numpy as np
from dllogger import StdOutBackend, JSONStreamBackend, Verbosity
from torch.utils.tensorboard import SummaryWriter
tb_loggers = {}
class TBLogger:
"""
xyz_dummies: stretch the screen with empty plots so the legend would
always fit for other plots
"""
def __init__(self, enabled, log_dir, name, interval=1, dummies=True):
self.enabled = enabled
self.interval = interval
self.cache = {}
if self.enabled:
self.summary_writer = SummaryWriter(
log_dir=Path(log_dir, name), flush_secs=120, max_queue=200)
atexit.register(self.summary_writer.close)
if dummies:
for key in ('_', '✕'):
self.summary_writer.add_scalar(key, 0.0, 1)
def log(self, step, data):
for k, v in data.items():
self.log_value(step, k, v.item() if type(v) is torch.Tensor else v)
def log_value(self, step, key, val, stat='mean'):
if self.enabled:
if key not in self.cache:
self.cache[key] = []
self.cache[key].append(val)
if len(self.cache[key]) == self.interval:
agg_val = getattr(np, stat)(self.cache[key])
self.summary_writer.add_scalar(key, agg_val, step)
del self.cache[key]
def log_grads(self, step, model):
if self.enabled:
norms = [p.grad.norm().item() for p in model.parameters()
if p.grad is not None]
for stat in ('max', 'min', 'mean'):
self.log_value(step, f'grad_{stat}', getattr(np, stat)(norms),
stat=stat)
def unique_log_fpath(fpath):
"""Have a unique log filename for every separate run"""
log_num = max([0] + [int(re.search("\.(\d+)", Path(f).suffix).group(1))
for f in glob.glob(f"{fpath}.*")])
return f"{fpath}.{log_num + 1}"
def stdout_step_format(step):
if isinstance(step, str):
return step
fields = []
if len(step) > 0:
fields.append("epoch {:>4}".format(step[0]))
if len(step) > 1:
fields.append("iter {:>3}".format(step[1]))
if len(step) > 2:
fields[-1] += "/{}".format(step[2])
return " | ".join(fields)
def stdout_metric_format(metric, metadata, value):
name = metadata.get("name", metric + " : ")
unit = metadata.get("unit", None)
format = f'{{{metadata.get("format", "")}}}'
fields = [name, format.format(value) if value is not None else value, unit]
fields = [f for f in fields if f is not None]
return "| " + " ".join(fields)
def init(log_fpath, log_dir, enabled=True, tb_subsets=[], **tb_kw):
if enabled:
backends = [
JSONStreamBackend(Verbosity.DEFAULT, log_fpath, append=True),
JSONStreamBackend(Verbosity.DEFAULT, unique_log_fpath(log_fpath)),
StdOutBackend(Verbosity.VERBOSE, step_format=stdout_step_format,
metric_format=stdout_metric_format)
]
else:
backends = []
dllogger.init(backends=backends)
dllogger.metadata("train_lrate", {"name": "lrate", "unit": None, "format": ":>3.2e"})
for id_, pref in [('train', ''), ('train_avg', 'avg train '),
('val', ' avg val '), ('val_ema', ' EMA val ')]:
dllogger.metadata(f"{id_}_loss",
{"name": f"{pref}loss", "unit": None, "format": ":>5.2f"})
dllogger.metadata(f"{id_}_mel_loss",
{"name": f"{pref}mel loss", "unit": None, "format": ":>5.2f"})
dllogger.metadata(f"{id_}_kl_loss",
{"name": f"{pref}kl loss", "unit": None, "format": ":>5.5f"})
dllogger.metadata(f"{id_}_kl_weight",
{"name": f"{pref}kl weight", "unit": None, "format": ":>5.5f"})
dllogger.metadata(f"{id_}_frames/s",
{"name": None, "unit": "frames/s", "format": ":>10.2f"})
dllogger.metadata(f"{id_}_took",
{"name": "took", "unit": "s", "format": ":>3.2f"})
global tb_loggers
tb_loggers = {s: TBLogger(enabled, log_dir, name=s, **tb_kw)
for s in tb_subsets}
def init_inference_metadata(batch_size=None):
modalities = [('latency', 's', ':>10.5f'), ('RTF', 'x', ':>10.2f'),
('frames/s', 'frames/s', ':>10.2f'), ('samples/s', 'samples/s', ':>10.2f'),
('letters/s', 'letters/s', ':>10.2f'), ('tokens/s', 'tokens/s', ':>10.2f')]
if batch_size is not None:
modalities.append((f'RTF@{batch_size}', 'x', ':>10.2f'))
percs = ['', 'avg', '90%', '95%', '99%']
models = ['', 'fastpitch', 'waveglow', 'hifigan']
for perc, model, (mod, unit, fmt) in product(percs, models, modalities):
name = f'{perc} {model} {mod}'.strip().replace(' ', ' ')
dllogger.metadata(name.replace(' ', '_'),
{'name': f'{name: <26}', 'unit': unit, 'format': fmt})
def log(step, tb_total_steps=None, data={}, subset='train'):
if tb_total_steps is not None:
tb_loggers[subset].log(tb_total_steps, data)
if subset != '':
data = {f'{subset}_{key}': v for key, v in data.items()}
dllogger.log(step, data=data)
def log_grads_tb(tb_total_steps, grads, tb_subset='train'):
tb_loggers[tb_subset].log_grads(tb_total_steps, grads)
def parameters(data, verbosity=0, tb_subset=None):
for k, v in data.items():
dllogger.log(step="PARAMETER", data={k: v}, verbosity=verbosity)
if tb_subset is not None and tb_loggers[tb_subset].enabled:
tb_data = {k: v for k, v in data.items()
if type(v) in (str, bool, int, float)}
tb_loggers[tb_subset].summary_writer.add_hparams(tb_data, {})
def flush():
dllogger.flush()
for tbl in tb_loggers.values():
if tbl.enabled:
tbl.summary_writer.flush()
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/plugins/taco2ModulationRemovalPlugin | taco2ModulationRemovalPlugin | taco2ModulationRemovalKernel | /*
* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "taco2ModulationRemovalKernel.h"
#include "taco2Utils.h"
#include <algorithm>
#include <cassert>
#include <cfloat>
namespace nvinfer1
{
namespace plugin
{
/******************************************************************************
* CONSTANTS ******************************************************************
*****************************************************************************/
namespace
{
constexpr const int WINDOW_SIZE = 1024;
} // namespace
/******************************************************************************
* KERNELS ********************************************************************
*****************************************************************************/
__global__ void modulationRemovalKernel(const int batchSize, const float* const weightsDevice,
const float* const inputDevice, float* const outputDevice, const int inputLength, const int hopLength,
const float scale)
{
// load weights into shared memory
__shared__ float localWeights[WINDOW_SIZE];
for (int i = threadIdx.x; i < WINDOW_SIZE; i += blockDim.x)
{
localWeights[i] = weightsDevice[i];
}
__syncthreads();
const int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx < inputLength - WINDOW_SIZE)
{
const int inIdx = idx + (WINDOW_SIZE / 2);
// start the window over the first overlap, and slide it until the last
// overlap for this point
float sum = 0.0f;
const int windowOffset = inIdx % hopLength;
for (int j = windowOffset; j < WINDOW_SIZE; j += hopLength)
{
if (inIdx - j >= 0)
{
sum += localWeights[j];
}
}
// normal all non-zero values
for (int i = 0; i < batchSize; ++i)
{
float val = inputDevice[inIdx + inputLength * i];
if (sum > FLT_MIN)
{
val /= sum;
}
val *= scale;
outputDevice[idx + (inputLength - WINDOW_SIZE) * i] = val;
}
}
}
/******************************************************************************
* PUBLIC STATIC METHODS ******************************************************
*****************************************************************************/
void Taco2ModulationRemovalKernel::compute(const int batchSize, const float* const weightsDevice,
const float* const inputDevice, float* const outputDevice, const int inputLength, const int filterLength,
const int hopLength, cudaStream_t stream)
{
assert(filterLength == WINDOW_SIZE);
const dim3 grid(taco2::Taco2Utils::roundUpBlocks(inputLength - filterLength, WINDOW_SIZE));
const dim3 block(WINDOW_SIZE);
modulationRemovalKernel<<<grid, block, 0, stream>>>(batchSize, weightsDevice, inputDevice, outputDevice,
inputLength, hopLength, static_cast<float>(filterLength) / static_cast<float>(hopLength));
}
} // namespace plugin
} // namespace nvinfer1
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/tacotron2 | tacotron2 | maskGenerator | /*
* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef TT2I_MASKGENERATOR_H
#define TT2I_MASKGENERATOR_H
#include "cuda_runtime.h"
#include <cstdint>
namespace tts
{
class MaskGenerator
{
public:
/**
* @brief Generate the mask vectors ([1.0, ..., 1.0, 0.0 ... 0.0]) for the
* various input lengths, such that all ones will appear up to the given
* length for each item, and all zeros will follow.
*
* @param lengthsDevice The input lengths on the device (must be of length
* batchSize).
* @param maskLength The length of each mask.
* @param batchSize The size of the batch.
* @param maskDevice The location to output the mask (must be of length
* maskLength*batchSize).
* @param stream The stream to execute on.
*/
static void generate(
const int32_t* lengthsDevice, int maskLength, int batchSize, float* maskDevice, cudaStream_t stream);
};
} // namespace tts
#endif
|
PyTorch/Classification/ConvNets/efficientnet/training/AMP | AMP | DGXA100_efficientnet-b4_AMP | python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode convergence --platform DGXA100 /imagenet --workspace ${1:-./} --raport-file raport.json
|
TensorFlow2/Segmentation/UNet_Medical/examples | examples | unet_TRAIN_FULL | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script launches U-Net run in FP32 and runs 5-fold cross-validation training for 6400 iterations.
# Usage:
# bash unet_TRAIN_FULL_TF-AMP.sh <number of GPUs> <path to dataset> <path to results directory> <batch size>
horovodrun -np $1 python main.py --data_dir $2 --model_dir $3 --log_every 100 --max_steps 6400 --batch_size $4 --exec_mode train_and_evaluate --fold 0 --augment --xla > $3/log_FP32_${1}GPU_fold0.txt
horovodrun -np $1 python main.py --data_dir $2 --model_dir $3 --log_every 100 --max_steps 6400 --batch_size $4 --exec_mode train_and_evaluate --fold 1 --augment --xla > $3/log_FP32_${1}GPU_fold1.txt
horovodrun -np $1 python main.py --data_dir $2 --model_dir $3 --log_every 100 --max_steps 6400 --batch_size $4 --exec_mode train_and_evaluate --fold 2 --augment --xla > $3/log_FP32_${1}GPU_fold2.txt
horovodrun -np $1 python main.py --data_dir $2 --model_dir $3 --log_every 100 --max_steps 6400 --batch_size $4 --exec_mode train_and_evaluate --fold 3 --augment --xla > $3/log_FP32_${1}GPU_fold3.txt
horovodrun -np $1 python main.py --data_dir $2 --model_dir $3 --log_every 100 --max_steps 6400 --batch_size $4 --exec_mode train_and_evaluate --fold 4 --augment --xla > $3/log_FP32_${1}GPU_fold4.txt
python runtime/parse_results.py --model_dir $3 --exec_mode convergence --env FP32_${1}GPU |
Tools/PyTorch/TimeSeriesPredictionPlatform/conf | conf | train_derived_fields | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# @package _global_
dataset:
config:
# The line below is equivalent to python's `model.config.get(model_type, 'default') == 'graph' and dataset.config.get('graph', False)`
# For more info on resolvers see: https://omegaconf.readthedocs.io/en/2.1_branch/custom_resolvers.html
# We cannot reuse `graph: ...` because during resolution it queries dataset.config.graph which causes infinite recursion
construct_graph: ${and:${cmp:${oc.select:model.config.model_type,default},graph},${oc.select:dataset.config.graph,false}}
xgb: ${cont.lower:${oc.select:trainer._target_, ctltrainer}, xgbtrainer}
stat: ${cont.lower:${oc.select:trainer._target_, ctltrainer}, stattrainer}
trainer:
criterion:
reduction: ${if:${feature.selector:${dataset.config.features}, WEIGHT, CONTINUOUS},none,mean}
config:
encoder_length: ${dataset.config.encoder_length}
example_length: ${dataset.config.example_length}
model_type: ${oc.select:model.config.model_type,default}
evaluator:
config:
preprocessor_state_path: ${dataset.config.dest_path}/tspp_preprocess.bin
device: ${trainer.config.device}
encoder_length: ${dataset.config.encoder_length}
output_selector: ${oc.select:model.config.output_selector,0}
model_type: ${oc.select:model.config.model_type,default}
# We want to inform model about shape of the data
model:
config:
device: ${trainer.config.device}
encoder_length: ${dataset.config.encoder_length}
example_length: ${dataset.config.example_length}
num_ts: ${dataset.config.time_series_count}
temporal_known_continuous_inp_size: ${len:${feature.selector:${dataset.config.features}, KNOWN, CONTINUOUS}}
temporal_observed_continuous_inp_size: ${if:${dataset.config.MultiID},${add:${len:${feature.selector:${dataset.config.features}, OBSERVED, CONTINUOUS}},${dataset.config.time_series_count}},${len:${feature.selector:${dataset.config.features}, OBSERVED, CONTINUOUS}}}
static_continuous_inp_size: ${len:${feature.selector:${dataset.config.features}, STATIC, CONTINUOUS}}
temporal_target_size: ${len:${feature.selector:${dataset.config.features}, TARGET, CONTINUOUS}} # XXX: we currently support only continuous targets
static_categorical_inp_lens: ${feature.cardinalities:${feature.selector:${dataset.config.features}, STATIC, CATEGORICAL}}
temporal_known_categorical_inp_lens: ${feature.cardinalities:${feature.selector:${dataset.config.features}, KNOWN, CATEGORICAL}}
temporal_observed_categorical_inp_lens: ${feature.cardinalities:${feature.selector:${dataset.config.features}, OBSERVED, CATEGORICAL}}
num_static_vars: ${sum:${model.config.static_continuous_inp_size},${len:${model.config.static_categorical_inp_lens}}}
num_future_vars: ${sum:${model.config.temporal_known_continuous_inp_size},${len:${model.config.temporal_known_categorical_inp_lens}}}
num_historic_vars: ${sum:${model.config.num_future_vars},${model.config.temporal_observed_continuous_inp_size},${model.config.temporal_target_size},${len:${model.config.temporal_observed_categorical_inp_lens}}}
|
TensorFlow2/LanguageModeling/BERT/official/nlp/modeling/networks | networks | encoder_scaffold_test | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for transformer-based text encoder network."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.python.keras import keras_parameterized # pylint: disable=g-direct-tensorflow-import
from official.modeling import activations
from official.nlp.modeling import layers
from official.nlp.modeling.networks import encoder_scaffold
# Test class that wraps a standard transformer layer. If this layer is called
# at any point, the list passed to the config object will be filled with a
# boolean 'True'. We register this class as a Keras serializable so we can
# test serialization below.
# @tf.keras.utils.register_keras_serializable(package="TestOnly")
class ValidatedTransformerLayer(layers.Transformer):
def __init__(self, call_list, **kwargs):
super(ValidatedTransformerLayer, self).__init__(**kwargs)
self.list = call_list
def call(self, inputs):
self.list.append(True)
return super(ValidatedTransformerLayer, self).call(inputs)
def get_config(self):
config = super(ValidatedTransformerLayer, self).get_config()
config["call_list"] = []
return config
# This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It
# guarantees forward compatibility of this code for the V2 switchover.
@keras_parameterized.run_all_keras_modes
class EncoderScaffoldLayerClassTest(keras_parameterized.TestCase):
def test_network_creation(self):
hidden_size = 32
sequence_length = 21
num_hidden_instances = 3
embedding_cfg = {
"vocab_size": 100,
"type_vocab_size": 16,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
call_list = []
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dtype":
"float32",
"call_list":
call_list
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=num_hidden_instances,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cls=ValidatedTransformerLayer,
hidden_cfg=hidden_cfg,
embedding_cfg=embedding_cfg)
# Create the inputs (note that the first dimension is implicit).
word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
data, pooled = test_network([word_ids, mask, type_ids])
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, data.dtype)
self.assertAllEqual(tf.float32, pooled.dtype)
# If call_list[0] exists and is True, the passed layer class was
# instantiated from the given config properly.
self.assertNotEmpty(call_list)
self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
def test_network_creation_with_float16_dtype(self):
tf.keras.mixed_precision.experimental.set_policy("mixed_float16")
hidden_size = 32
sequence_length = 21
embedding_cfg = {
"vocab_size": 100,
"type_vocab_size": 16,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
"dtype": "float16",
}
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dtype":
"float16",
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
classification_layer_dtype=tf.float16,
hidden_cfg=hidden_cfg,
embedding_cfg=embedding_cfg)
# Create the inputs (note that the first dimension is implicit).
word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
data, pooled = test_network([word_ids, mask, type_ids])
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# If float_dtype is set to float16, the output should always be float16.
self.assertAllEqual(tf.float16, data.dtype)
self.assertAllEqual(tf.float16, pooled.dtype)
def test_network_invocation(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
embedding_cfg = {
"vocab_size": vocab_size,
"type_vocab_size": num_types,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dtype":
"float32",
}
tf.keras.mixed_precision.experimental.set_policy("float32")
print(hidden_cfg)
print(embedding_cfg)
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cfg=embedding_cfg)
# Create the inputs (note that the first dimension is implicit).
word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
data, pooled = test_network([word_ids, mask, type_ids])
# Create a model based off of this network:
model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
# Invoke the model. We can't validate the output data here (the model is too
# complex) but this will catch structural runtime errors.
batch_size = 3
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
type_id_data = np.random.randint(
num_types, size=(batch_size, sequence_length))
_ = model.predict([word_id_data, mask_data, type_id_data])
# Creates a EncoderScaffold with max_sequence_length != sequence_length
num_types = 7
embedding_cfg = {
"vocab_size": vocab_size,
"type_vocab_size": num_types,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length * 2,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cfg=embedding_cfg)
model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
_ = model.predict([word_id_data, mask_data, type_id_data])
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
hidden_size = 32
sequence_length = 21
embedding_cfg = {
"vocab_size": 100,
"type_vocab_size": 16,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dtype":
"float32",
}
# Create a small EncoderScaffold for testing.
network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cfg=embedding_cfg)
# Create another network object from the first object's config.
new_network = encoder_scaffold.EncoderScaffold.from_config(
network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
@keras_parameterized.run_all_keras_modes
class EncoderScaffoldEmbeddingNetworkTest(keras_parameterized.TestCase):
def test_network_invocation(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
# Build an embedding network to swap in for the default network. This one
# will have 2 inputs (mask and word_ids) instead of 3, and won't use
# positional embeddings.
word_ids = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name="input_word_ids")
mask = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name="input_mask")
embedding_layer = layers.OnDeviceEmbedding(
vocab_size=vocab_size,
embedding_width=hidden_size,
initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),
name="word_embeddings")
word_embeddings = embedding_layer(word_ids)
network = tf.keras.Model([word_ids, mask], [word_embeddings, mask])
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dtype":
"float32",
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cls=network,
embedding_data=embedding_layer.embeddings)
# Create the inputs (note that the first dimension is implicit).
word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
data, pooled = test_network([word_ids, mask])
# Create a model based off of this network:
model = tf.keras.Model([word_ids, mask], [data, pooled])
# Invoke the model. We can't validate the output data here (the model is too
# complex) but this will catch structural runtime errors.
batch_size = 3
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
_ = model.predict([word_id_data, mask_data])
# Test that we can get the embedding data that we passed to the object. This
# is necessary to support standard language model training.
self.assertIs(embedding_layer.embeddings,
test_network.get_embedding_table())
def test_serialize_deserialize(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
# Build an embedding network to swap in for the default network. This one
# will have 2 inputs (mask and word_ids) instead of 3, and won't use
# positional embeddings.
word_ids = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name="input_word_ids")
mask = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name="input_mask")
embedding_layer = layers.OnDeviceEmbedding(
vocab_size=vocab_size,
embedding_width=hidden_size,
initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),
name="word_embeddings")
word_embeddings = embedding_layer(word_ids)
network = tf.keras.Model([word_ids, mask], [word_embeddings, mask])
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dtype":
"float32",
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cls=network,
embedding_data=embedding_layer.embeddings)
# Create another network object from the first object's config.
new_network = encoder_scaffold.EncoderScaffold.from_config(
test_network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(test_network.get_config(), new_network.get_config())
# Create a model based off of the old and new networks:
word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
data, pooled = new_network([word_ids, mask])
new_model = tf.keras.Model([word_ids, mask], [data, pooled])
data, pooled = test_network([word_ids, mask])
model = tf.keras.Model([word_ids, mask], [data, pooled])
# Copy the weights between models.
new_model.set_weights(model.get_weights())
# Invoke the models.
batch_size = 3
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
data, cls = model.predict([word_id_data, mask_data])
new_data, new_cls = new_model.predict([word_id_data, mask_data])
# The output should be equal.
self.assertAllEqual(data, new_data)
self.assertAllEqual(cls, new_cls)
# We should not be able to get a reference to the embedding data.
with self.assertRaisesRegex(RuntimeError, ".*does not have a reference.*"):
new_network.get_embedding_table()
@keras_parameterized.run_all_keras_modes
class EncoderScaffoldHiddenInstanceTest(keras_parameterized.TestCase):
def test_network_invocation(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
embedding_cfg = {
"vocab_size": vocab_size,
"type_vocab_size": num_types,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
"dtype": "float32",
}
call_list = []
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dtype":
"float32",
"call_list":
call_list
}
# Create a small EncoderScaffold for testing. This time, we pass an already-
# instantiated layer object.
xformer = ValidatedTransformerLayer(**hidden_cfg)
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cls=xformer,
embedding_cfg=embedding_cfg)
# Create the inputs (note that the first dimension is implicit).
word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
data, pooled = test_network([word_ids, mask, type_ids])
# Create a model based off of this network:
model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
# Invoke the model. We can't validate the output data here (the model is too
# complex) but this will catch structural runtime errors.
batch_size = 3
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
type_id_data = np.random.randint(
num_types, size=(batch_size, sequence_length))
_ = model.predict([word_id_data, mask_data, type_id_data])
# If call_list[0] exists and is True, the passed layer class was
# called as part of the graph creation.
self.assertNotEmpty(call_list)
self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
def test_serialize_deserialize(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
embedding_cfg = {
"vocab_size": vocab_size,
"type_vocab_size": num_types,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
"dtype": "float32",
}
call_list = []
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dtype":
"float32",
"call_list":
call_list
}
# Create a small EncoderScaffold for testing. This time, we pass an already-
# instantiated layer object.
xformer = ValidatedTransformerLayer(**hidden_cfg)
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cls=xformer,
embedding_cfg=embedding_cfg)
# Create another network object from the first object's config.
new_network = encoder_scaffold.EncoderScaffold.from_config(
test_network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(test_network.get_config(), new_network.get_config())
# Create a model based off of the old and new networks:
word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
data, pooled = new_network([word_ids, mask, type_ids])
new_model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
data, pooled = test_network([word_ids, mask, type_ids])
model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
# Copy the weights between models.
new_model.set_weights(model.get_weights())
# Invoke the models.
batch_size = 3
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
type_id_data = np.random.randint(
num_types, size=(batch_size, sequence_length))
data, cls = model.predict([word_id_data, mask_data, type_id_data])
new_data, new_cls = new_model.predict(
[word_id_data, mask_data, type_id_data])
# The output should be equal.
self.assertAllEqual(data, new_data)
self.assertAllEqual(cls, new_cls)
if __name__ == "__main__":
assert tf.version.VERSION.startswith('2.')
tf.test.main()
|
PyTorch/Recommendation/DLRM/triton | triton | README | # Deploying the DLRM model using Triton Inference Server
This folder contains instructions for deploment and exemplary client application to run inference on
Triton Inference Server as well as detailed performance analysis.
## Table Of Contents
* [Solution Overview](#solution-overview)
* [Quick Start Guide](#quick-start-guide)
* [Running Triton Inference Server and client](#running-triton-inference-server-and-client)
* [Performance](#performance)
* [Latency vs Throughput](#throughputlatency-results)
* [Advanced](#advanced)
* [Dynamic batching support](#dynamic-batching-support)
## Solution Overview
The [NVIDIA Triton Inference Server](https://github.com/NVIDIA/triton-inference-server) provides a datacenter and cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or gRPC endpoint, allowing remote clients to request inferencing for any number of GPU or CPU models being managed by the server.
## Quick Start Guide
### Running Triton Inference Server and client
The very first step of deployment is to acquire trained checkpoint and model configuration for this
checkpoint. Default model configuration are stored inside `dlrm/config` directory.
**Currently, our implementation only supports TorchScript deployment for models that fit into the memory of a single GPU.**
You can read more about training DLRM models on different dataset configurations based on frequency threshold in the preprocessing step in [README](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Recommendation/DLRM/README.md#preprocessing-on-gpu).
#### Inference container
Every command below is called from special inference container. To build that container go to main
repository folder and call
`docker build -t dlrm-inference . -f triton/Dockerfile`
This command will download dependencies and build inference container. Then run shell inside the
container:
`docker run -it --rm --gpus device=0 --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --net=host -v <PATH_TO_MODEL_REPOSITORY>:/repository -v <PATH_TO_MODEL_CHECKPOINT>:/results/checkpoint -v <PATH_TO_DATASET>:/data dlrm-inference bash`
Here `--gpus '"device=0,1,2,3"'` selects GPUs indexed by ordinals `0,1,2` and `3`, respectively. The server will see only these GPUs. If you write `device=all`, then the server will see all the available GPUs. `PATH_TO_MODEL_REPOSITORY` indicates location where
deployed models were stored.
#### Deploying the model
To deploy the model into Triton compatible format use `deployer.py`. This script is
meant to be run from the deployment docker container.
```
usage: deployer.py [-h] (--ts-script | --ts-trace | --onnx) [--triton-no-cuda]
[--triton-model-name TRITON_MODEL_NAME]
[--triton-model-version TRITON_MODEL_VERSION]
[--triton-max-batch-size TRITON_MAX_BATCH_SIZE]
[--triton-dyn-batching-delay TRITON_DYN_BATCHING_DELAY]
[--triton-engine-count TRITON_ENGINE_COUNT]
[--save-dir SAVE_DIR] [--deploy_cpu]
...
optional arguments:
-h, --help show this help message and exit
--ts-script convert to torchscript using torch.jit.script
--ts-trace convert to torchscript using torch.jit.trace
--onnx convert to onnx using torch.onnx.export
--deploy_cpu
triton related flags:
--triton-no-cuda Use the CPU for tracing.
--triton-model-name TRITON_MODEL_NAME
exports to appropriate directory structure for triton
--triton-model-version TRITON_MODEL_VERSION
exports to appropriate directory structure for triton
--triton-max-batch-size TRITON_MAX_BATCH_SIZE
Specifies the 'max_batch_size' in the triton model
config. See the triton documentation for more info.
--triton-dyn-batching-delay TRITON_DYN_BATCHING_DELAY
Determines the dynamic_batching queue delay in
milliseconds(ms) for the triton model config. Use '0'
or '-1' to specify static batching. See the triton
documentation for more info.
--triton-engine-count TRITON_ENGINE_COUNT
Specifies the 'instance_group' count value in the
triton model config. See the triton documentation for
more info.
--save-dir SAVE_DIR Saved model directory
other flags:
model_arguments arguments that will be ignored by deployer lib and
will be forwarded to your deployer script
```
Following model specific arguments have to be specified for model deployment:
```
--embedding_dim EMBEDDING_DIM
Embedding dimensionality.
--top_mlp_sizes TOP_MLP_SIZES [TOP_MLP_SIZES ...]
Units in layers of top MLP (default: 1024 1024 512 256 1).
--bottom_mlp_sizes BOTTOM_MLP_SIZES [BOTTOM_MLP_SIZES ...]
Units in layers of bottom MLP (default: 512 256 128).
--interaction_op {cat,dot}
Interaction operator to use.
--dataset DATASET
Path to dataset directory contaning model_size.json file
describing input sizes for each embedding layer.
--batch_size BATCH_SIZE
Internal dataloader batch size, usually it is the same as batch size
specified in --triton-max-batch_size flag.
--fp16
Set a model for fp16 deployment.
--dump_perf_data DIRECTORY_NAME
Dump binary performance data that can by loaded by perf client.
--model_checkpoint MODEL_CHECKPOINT
Checkpoint file with trained model that is going to be deployed.
--cpu Export cpu model instead of gpu.
```
For example, to deploy model into TorchScript format, using half precision and max batch size 4096 called
`dlrm-ts-trace-16` execute:
`python -m triton.deployer --ts-trace --triton-model-name dlrm-ts-trace-16 --triton-max-batch-size 4096 --save-dir /repository -- --model_checkpoint /results/checkpoint --fp16 --batch_size 4096 --num_numerical_features 13 --embedding_dim 128 --top_mlp_sizes 1024 1024 512 256 1 --bottom_mlp_sizes 512 256 128 --interaction_op dot --dataset /data`
Where `model_checkpoint` is a checkpoint for a trained model with the same configuration as used during export and dataset (or at least dataset configuration)
is mounted under `/data`
#### Running the Triton server
**NOTE: This step is executed outside inference container**
1. `docker pull nvcr.io/nvidia/tritonserver:20.09-py3`
2. `docker run -d --rm --gpus device=0 --ipc=host --network=host [--cpuset-cpus=0-15] -p 8000:8000 -p 8001:8001 -p 8002:8002 -v <PATH_TO_MODEL_REPOSITORY>:/models nvcr.io/nvidia/tritonserver:20.09-py3 tritonserver --model-repository=/models --log-verbose=1 --model-control-mode=explicit`
Here `--gpus '"device=0,1,2,3"'` selects GPUs indexed by ordinals `0,1,2` and `3`, respectively. The server will see only these GPUs. If you write `device=all`, then the server will see all the available GPUs. `PATH_TO_MODEL_REPOSITORY` indicates location where
deployed models were stored. Additional `--model-control-mode` option allows to manually load and
unload models. This is especially useful when dealing with numerous large models like DLRM.
For models exported to onnx format and hosted inside onnx runtime it might be required to limit visible cpu to fully utlize gpu acceleration. Use `--cpuset-cpus` docker option for that.
#### Running the client
Exemplary client `client.py` allows to check model performance against synthetic or real validation
data. Client connects to Triton server and perform inference.
```
usage: client.py [-h] --triton-server-url TRITON_SERVER_URL
--triton-model-name TRITON_MODEL_NAME
[--triton-model-version TRITON_MODEL_VERSION] [-v]
[-H HTTP_HEADER] --dataset_config DATASET_CONFIG
[--inference_data INFERENCE_DATA] [--batch_size BATCH_SIZE]
[--drop_last_batch DROP_LAST_BATCH] [--fp16]
[--test_batches TEST_BATCHES]
optional arguments:
-h, --help show this help message and exit
--triton-server-url TRITON_SERVER_URL
URL adress of triton server (with port)
--triton-model-name TRITON_MODEL_NAME
Triton deployed model name
--triton-model-version TRITON_MODEL_VERSION
Triton model version
-v, --verbose Enable verbose output
-H HTTP_HEADER HTTP headers to add to inference server requests.
Format is -H"Header:Value".
--dataset_config DATASET_CONFIG
--inference_data INFERENCE_DATA
Path to file with inference data.
--batch_size BATCH_SIZE
Inference request batch size
--drop_last_batch DROP_LAST_BATCH
Drops the last batch size if it's not full
--fp16 Use 16bit for numerical input
--test_batches TEST_BATCHES
Specifies number of batches used in the inference
```
To run inference on the model exported in previous steps, using data located under
`/data/test` execute:
`python -m triton.client --triton-server-url localhost:8000 --triton-model-name dlrm-ts-trace-16 --dataset_config /data/model_size.json --inference_data /data/test --batch_size 4096 --fp16`
#### Gathering performance data
Performance data can be gathered using `perf_client` tool. To use this tool, performance data needs
to be dumped during deployment. To do that, use `--dump_perf_data` option for the deployer:
`python -m triton.deployer --ts-trace --triton-model-name dlrm-ts-trace-16 --triton-max-batch-size 4096 --save-dir /repository -- --model_checkpoint /results/checkpoint --fp16 --batch_size 4096 --num_numerical_features 13 --embedding_dim 128 --top_mlp_sizes 1024 1024 512 256 1 --bottom_mlp_sizes 512 256 128 --interaction_op dot --dataset /data --dump_perf_data /location/for/perfdata`
When perf data are saved, `perf_client` can be used with following command:
`/workspace/bin/perf_client --max-threads 10 -m dlrm-ts-trace-16 -x 1 -p 5000 -v -i gRPC -u localhost:8001 -b 4096 -l 5000 --concurrency-range 1 --input-data /location/for/perfdata -f result.csv`
For more information about `perf_client` please refer to [official documentation](https://docs.nvidia.com/deeplearning/sdk/triton-inference-server-master-branch-guide/docs/optimization.html#perf-client).
## Performance
The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA’s latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).
### Throughput/Latency results
Throughput is measured in recommendations/second, and latency in milliseconds.
**TorchScript FP16 inference (V100-32G)**
| Batch Size| Throughput [samples / s] | Median Latency [ms]| 95% latency [ms]| 99% latency [ms]|
|--------:|--------------------:|--------------:|--------------:|---------------:|
| 1 | 1019 | 0.966 | 1.027 | 1.082 |
| 2 | 2119 | 0.989 | 1.047 | 1.086 |
| 4 | 3340 | 1.199 | 1.277 | 1.290 |
| 8 | 6641 | 1.197 | 1.284 | 1.314 |
| 16 | 12.5k | 1.082 | 1.196 | 1.214 |
| 32 | 28k | 1.133 | 1.271 | 1.291 |
| 64 | 45k | 1.413 | 1.489 | 1.551 |
| 128 | 105k | 1.223 | 1.270 | 1.290 |
| 256 | 193.6k | 1.320 | 1.471 | 1.518 |
| 512 | 376k | 1.367 | 1.449 | 1.486 |
| 1024 | 740k | 1.379 | 1.463 | 1.536 |
| 2048 | 1.105M | 1.817 | 2.106 | 2.195 |
| 4096 | 1.488M | 2.730 | 2.851 | 3.266 |
| 8192 | 1.676M | 4.851 | 5.174 | 5.486 |
| 16384 | 1.831M | 8.926 | 9.127 | 9.415 |
| 32768 | 1.756M | 18.543 | 19.625 | 20.223 |
| 65536 | 1.678M | 38.950 | 41.112 | 41.985 |
| 131072 | 1.547M | 86.258 | 90.772 | 92.511 |
**TorchScript FP32 inference (V100-32G)**
| Batch Size| Throughput [samples / s] | Median Latency [ms]| 95% latency [ms]| 99% latency [ms]|
|--------:|--------------------:|--------------:|--------------:|---------------:|
| 1 | 1153 | 0.855 | 0.909 | 0.929 |
| 2 | 2084 | 0.950 | 1.042 | 1.199 |
| 4 | 4105 | 0.955 | 1.033 | 1.177 |
| 8 | 8356 | 0.943 | 1.029 | 1.179 |
| 16 | 16.8k | 0.942 | 1.009 | 1.158 |
| 32 | 28.3k | 1.134 | 1.274 | 1.336 |
| 64 | 54.7k | 1.214 | 1.307 | 1.353 |
| 128 | 118k | 1.036 | 1.255 | 1.303 |
| 256 | 202k | 1.275 | 1.404 | 1.449 |
| 512 | 401k | 1.286 | 1.365 | 1.397 |
| 1024 | 707k | 1.448 | 1.518 | 1.550 |
| 2048 | 833k | 2.450 | 2.547 | 2.610 |
| 4096 | 1.013M | 3.996 | 4.361 | 4.683 |
| 8192 | 1.091M | 7.333 | 7.951 | 8.115 |
| 16384 | 1.180M | 13.8 | 14.462 | 15.053 |
| 32768 | 1.173M | 27.927 | 28.655 | 28.841 |
| 65536 | 1.140M | 57.046 | 58.627 | 58.861 |
| 131072 | 1.074M | 120.982 | 122.193 | 122.337 |
**TorchScript FP32 inference CPU (2x E5-2698 v4 @ 2.20GHz)**
| Batch Size| Throughput [samples / s] | Avg Latency [ms]| 95% latency [ms]| 99% latency [ms]|
|--------:|--------------------:|--------------:|--------------:|---------------:|
| 1 | 923.2 | 1.051 | 1.195 | 1.225 |
| 2 | 1660.8 | 1.204 | 1.486 | 1.597 |
| 4 | 3553.6 | 1.095 | 1.456 | 1.65 |
| 8 | 6692.8 | 1.112 | 1.56 | 1.787 |
| 16 | 11.8k | 1.317 | 1.545 | 1.698 |
| 32 | 19.2k | 1.636 | 1.851 | 2.261 |
| 64 | 28.6k | 2.203 | 2.403 | 2.615 |
| 128 | 37.3k | 3.333 | 3.968 | 4.143 |
| 256 | 46.5k | 5.286 | 6.538 | 7.102 |
| 512 | 63.5k | 7.962 | 8.256 | 10.052 |
| 1024 | 85.8k | 10.777 | 16.563 | 17.917 |
| 2048 | 117k | 17.169 | 19.441 | 26.955 |
| 4096 | 95.8k | 41.988 | 45.996 | 50.483 |
| 8192 | 85.1k | 92.251 | 131.333 | 133.578 |
| 16384 | 88.5k | 187.677 | 204.676 | 231.393 |
| 32768 | 78.6k | 408.815 | 428.574 | 429.58 |
| 65536 | 91.8k | 804.059 | 810.328 | 810.328 |
| 131072 | 78.6k| 1606.89 | 1635.36 | 1635.36 |
The plot above shows, that the GPU is saturated with batch size 4096. However, running inference with larger batches
might be faster, than running several inference requests. Therefore, we choose 65536 as the optimal batch size.
## Advanced
### Dynamic batching support
The Triton server has a dynamic batching mechanism built in, that can be enabled. When it is enabled, then the server creates
inference batches from the received requests. Since the output of the model is a single probability, the batch size of a
single request may be large. Here it is assumed to be 4096. With dynamic batching enabled, the server will concatenate requests of this size into
an inference batch. The upper bound of the size of the inference batch is set to 65536. All these parameters are configurable.
Performance results on a single V100-32G (ONNX FP16 model) for various numbers of simultaneous requests are shown in the figure below.
The plot above shows, that if we have a 20ms upper bound on latency, then a single GPU can handle up to 8 concurrent requests.
This leads to total throughput of 1.776.030 recommendations/sec. This means 35520 recommendations within 20ms, on a single GPU.
|
TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking | benchmarking | UNet_trainbench_AMP_1GPU | #!/usr/bin/env bash
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script launches UNet training benchmark in TF-AMP on 1 GPU using 16 batch size (16 per GPU)
# Usage ./UNet_trainbench_AMP_1GPU.sh <path to dataset> <dagm classID (1-10)>
BASEDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
export TF_CPP_MIN_LOG_LEVEL=3
# Cleaning up for benchmark
RESULT_DIR="/tmp"
rm -rf "${RESULT_DIR}"
python "${BASEDIR}/../../main.py" \
--unet_variant='tinyUNet' \
--activation_fn='relu' \
--exec_mode='training_benchmark' \
--iter_unit='batch' \
--num_iter=1500 \
--batch_size=16 \
--warmup_step=500 \
--results_dir="${RESULT_DIR}" \
--data_dir="${1}" \
--dataset_name='DAGM2007' \
--dataset_classID="${2}" \
--data_format='NCHW' \
--use_auto_loss_scaling \
--amp \
--xla \
--learning_rate=1e-4 \
--learning_rate_decay_factor=0.8 \
--learning_rate_decay_steps=500 \
--rmsprop_decay=0.9 \
--rmsprop_momentum=0.8 \
--loss_fn_name='adaptive_loss' \
--weight_decay=1e-5 \
--weight_init_method='he_uniform' \
--augment_data \
--display_every=250 \
--debug_verbosity=0
|
PyTorch/Segmentation/MaskRCNN/pytorch/configs/quick_schedules | quick_schedules | e2e_mask_rcnn_R_50_FPN_quick | MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
BACKBONE:
CONV_BODY: "R-50-FPN"
OUT_CHANNELS: 256
RPN:
USE_FPN: True
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
PRE_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TEST: 1000
ROI_HEADS:
USE_FPN: True
BATCH_SIZE_PER_IMAGE: 256
ROI_BOX_HEAD:
POOLER_RESOLUTION: 7
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POOLER_SAMPLING_RATIO: 2
FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
PREDICTOR: "FPNPredictor"
ROI_MASK_HEAD:
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
PREDICTOR: "MaskRCNNC4Predictor"
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
RESOLUTION: 28
SHARE_BOX_FEATURE_EXTRACTOR: False
MASK_ON: True
DATASETS:
TRAIN: ("coco_2014_minival",)
TEST: ("coco_2014_minival",)
INPUT:
MIN_SIZE_TRAIN: 600
MAX_SIZE_TRAIN: 1000
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1000
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
BASE_LR: 0.005
WEIGHT_DECAY: 0.0001
STEPS: (1500,)
MAX_ITER: 2000
IMS_PER_BATCH: 4
TEST:
IMS_PER_BATCH: 2
|
TensorFlow/Detection/SSD/models/research/slim/nets | nets | mobilenet_v1_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
"""Tests for MobileNet v1."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from nets import mobilenet_v1
slim = tf.contrib.slim
class MobilenetV1Test(tf.test.TestCase):
def testBuildClassificationNetwork(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith(
'MobilenetV1/Logits/SpatialSqueeze'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertTrue('Predictions' in end_points)
self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
[batch_size, num_classes])
def testBuildPreLogitsNetwork(self):
batch_size = 5
height, width = 224, 224
num_classes = None
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(net.op.name.startswith('MobilenetV1/Logits/AvgPool'))
self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1024])
self.assertFalse('Logits' in end_points)
self.assertFalse('Predictions' in end_points)
def testBuildBaseNetwork(self):
batch_size = 5
height, width = 224, 224
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(inputs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 7, 7, 1024])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
def testBuildOnlyUptoFinalEndpoint(self):
batch_size = 5
height, width = 224, 224
endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
for index, endpoint in enumerate(endpoints):
with tf.Graph().as_default():
inputs = tf.random_uniform((batch_size, height, width, 3))
out_tensor, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint=endpoint)
self.assertTrue(out_tensor.op.name.startswith(
'MobilenetV1/' + endpoint))
self.assertItemsEqual(endpoints[:index+1], end_points.keys())
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
def testBuildAndCheckAllEndPointsUptoConv2d_13(self):
batch_size = 5
height, width = 224, 224
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
normalizer_fn=slim.batch_norm):
_, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_13_pointwise')
_, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_13_pointwise',
use_explicit_padding=True)
endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
'Conv2d_6_depthwise': [batch_size, 14, 14, 256],
'Conv2d_6_pointwise': [batch_size, 14, 14, 512],
'Conv2d_7_depthwise': [batch_size, 14, 14, 512],
'Conv2d_7_pointwise': [batch_size, 14, 14, 512],
'Conv2d_8_depthwise': [batch_size, 14, 14, 512],
'Conv2d_8_pointwise': [batch_size, 14, 14, 512],
'Conv2d_9_depthwise': [batch_size, 14, 14, 512],
'Conv2d_9_pointwise': [batch_size, 14, 14, 512],
'Conv2d_10_depthwise': [batch_size, 14, 14, 512],
'Conv2d_10_pointwise': [batch_size, 14, 14, 512],
'Conv2d_11_depthwise': [batch_size, 14, 14, 512],
'Conv2d_11_pointwise': [batch_size, 14, 14, 512],
'Conv2d_12_depthwise': [batch_size, 7, 7, 512],
'Conv2d_12_pointwise': [batch_size, 7, 7, 1024],
'Conv2d_13_depthwise': [batch_size, 7, 7, 1024],
'Conv2d_13_pointwise': [batch_size, 7, 7, 1024]}
self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
for endpoint_name, expected_shape in endpoints_shapes.items():
self.assertTrue(endpoint_name in end_points)
self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
expected_shape)
self.assertItemsEqual(endpoints_shapes.keys(),
explicit_padding_end_points.keys())
for endpoint_name, expected_shape in endpoints_shapes.items():
self.assertTrue(endpoint_name in explicit_padding_end_points)
self.assertListEqual(
explicit_padding_end_points[endpoint_name].get_shape().as_list(),
expected_shape)
def testOutputStride16BuildAndCheckAllEndPointsUptoConv2d_13(self):
batch_size = 5
height, width = 224, 224
output_stride = 16
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
normalizer_fn=slim.batch_norm):
_, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, output_stride=output_stride,
final_endpoint='Conv2d_13_pointwise')
_, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
inputs, output_stride=output_stride,
final_endpoint='Conv2d_13_pointwise', use_explicit_padding=True)
endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
'Conv2d_6_depthwise': [batch_size, 14, 14, 256],
'Conv2d_6_pointwise': [batch_size, 14, 14, 512],
'Conv2d_7_depthwise': [batch_size, 14, 14, 512],
'Conv2d_7_pointwise': [batch_size, 14, 14, 512],
'Conv2d_8_depthwise': [batch_size, 14, 14, 512],
'Conv2d_8_pointwise': [batch_size, 14, 14, 512],
'Conv2d_9_depthwise': [batch_size, 14, 14, 512],
'Conv2d_9_pointwise': [batch_size, 14, 14, 512],
'Conv2d_10_depthwise': [batch_size, 14, 14, 512],
'Conv2d_10_pointwise': [batch_size, 14, 14, 512],
'Conv2d_11_depthwise': [batch_size, 14, 14, 512],
'Conv2d_11_pointwise': [batch_size, 14, 14, 512],
'Conv2d_12_depthwise': [batch_size, 14, 14, 512],
'Conv2d_12_pointwise': [batch_size, 14, 14, 1024],
'Conv2d_13_depthwise': [batch_size, 14, 14, 1024],
'Conv2d_13_pointwise': [batch_size, 14, 14, 1024]}
self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
for endpoint_name, expected_shape in endpoints_shapes.items():
self.assertTrue(endpoint_name in end_points)
self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
expected_shape)
self.assertItemsEqual(endpoints_shapes.keys(),
explicit_padding_end_points.keys())
for endpoint_name, expected_shape in endpoints_shapes.items():
self.assertTrue(endpoint_name in explicit_padding_end_points)
self.assertListEqual(
explicit_padding_end_points[endpoint_name].get_shape().as_list(),
expected_shape)
def testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13(self):
batch_size = 5
height, width = 224, 224
output_stride = 8
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
normalizer_fn=slim.batch_norm):
_, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, output_stride=output_stride,
final_endpoint='Conv2d_13_pointwise')
_, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
inputs, output_stride=output_stride,
final_endpoint='Conv2d_13_pointwise', use_explicit_padding=True)
endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32],
'Conv2d_1_depthwise': [batch_size, 112, 112, 32],
'Conv2d_1_pointwise': [batch_size, 112, 112, 64],
'Conv2d_2_depthwise': [batch_size, 56, 56, 64],
'Conv2d_2_pointwise': [batch_size, 56, 56, 128],
'Conv2d_3_depthwise': [batch_size, 56, 56, 128],
'Conv2d_3_pointwise': [batch_size, 56, 56, 128],
'Conv2d_4_depthwise': [batch_size, 28, 28, 128],
'Conv2d_4_pointwise': [batch_size, 28, 28, 256],
'Conv2d_5_depthwise': [batch_size, 28, 28, 256],
'Conv2d_5_pointwise': [batch_size, 28, 28, 256],
'Conv2d_6_depthwise': [batch_size, 28, 28, 256],
'Conv2d_6_pointwise': [batch_size, 28, 28, 512],
'Conv2d_7_depthwise': [batch_size, 28, 28, 512],
'Conv2d_7_pointwise': [batch_size, 28, 28, 512],
'Conv2d_8_depthwise': [batch_size, 28, 28, 512],
'Conv2d_8_pointwise': [batch_size, 28, 28, 512],
'Conv2d_9_depthwise': [batch_size, 28, 28, 512],
'Conv2d_9_pointwise': [batch_size, 28, 28, 512],
'Conv2d_10_depthwise': [batch_size, 28, 28, 512],
'Conv2d_10_pointwise': [batch_size, 28, 28, 512],
'Conv2d_11_depthwise': [batch_size, 28, 28, 512],
'Conv2d_11_pointwise': [batch_size, 28, 28, 512],
'Conv2d_12_depthwise': [batch_size, 28, 28, 512],
'Conv2d_12_pointwise': [batch_size, 28, 28, 1024],
'Conv2d_13_depthwise': [batch_size, 28, 28, 1024],
'Conv2d_13_pointwise': [batch_size, 28, 28, 1024]}
self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
for endpoint_name, expected_shape in endpoints_shapes.items():
self.assertTrue(endpoint_name in end_points)
self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
expected_shape)
self.assertItemsEqual(endpoints_shapes.keys(),
explicit_padding_end_points.keys())
for endpoint_name, expected_shape in endpoints_shapes.items():
self.assertTrue(endpoint_name in explicit_padding_end_points)
self.assertListEqual(
explicit_padding_end_points[endpoint_name].get_shape().as_list(),
expected_shape)
def testBuildAndCheckAllEndPointsApproximateFaceNet(self):
batch_size = 5
height, width = 128, 128
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
normalizer_fn=slim.batch_norm):
_, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75)
_, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75,
use_explicit_padding=True)
# For the Conv2d_0 layer FaceNet has depth=16
endpoints_shapes = {'Conv2d_0': [batch_size, 64, 64, 24],
'Conv2d_1_depthwise': [batch_size, 64, 64, 24],
'Conv2d_1_pointwise': [batch_size, 64, 64, 48],
'Conv2d_2_depthwise': [batch_size, 32, 32, 48],
'Conv2d_2_pointwise': [batch_size, 32, 32, 96],
'Conv2d_3_depthwise': [batch_size, 32, 32, 96],
'Conv2d_3_pointwise': [batch_size, 32, 32, 96],
'Conv2d_4_depthwise': [batch_size, 16, 16, 96],
'Conv2d_4_pointwise': [batch_size, 16, 16, 192],
'Conv2d_5_depthwise': [batch_size, 16, 16, 192],
'Conv2d_5_pointwise': [batch_size, 16, 16, 192],
'Conv2d_6_depthwise': [batch_size, 8, 8, 192],
'Conv2d_6_pointwise': [batch_size, 8, 8, 384],
'Conv2d_7_depthwise': [batch_size, 8, 8, 384],
'Conv2d_7_pointwise': [batch_size, 8, 8, 384],
'Conv2d_8_depthwise': [batch_size, 8, 8, 384],
'Conv2d_8_pointwise': [batch_size, 8, 8, 384],
'Conv2d_9_depthwise': [batch_size, 8, 8, 384],
'Conv2d_9_pointwise': [batch_size, 8, 8, 384],
'Conv2d_10_depthwise': [batch_size, 8, 8, 384],
'Conv2d_10_pointwise': [batch_size, 8, 8, 384],
'Conv2d_11_depthwise': [batch_size, 8, 8, 384],
'Conv2d_11_pointwise': [batch_size, 8, 8, 384],
'Conv2d_12_depthwise': [batch_size, 4, 4, 384],
'Conv2d_12_pointwise': [batch_size, 4, 4, 768],
'Conv2d_13_depthwise': [batch_size, 4, 4, 768],
'Conv2d_13_pointwise': [batch_size, 4, 4, 768]}
self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
for endpoint_name, expected_shape in endpoints_shapes.items():
self.assertTrue(endpoint_name in end_points)
self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
expected_shape)
self.assertItemsEqual(endpoints_shapes.keys(),
explicit_padding_end_points.keys())
for endpoint_name, expected_shape in endpoints_shapes.items():
self.assertTrue(endpoint_name in explicit_padding_end_points)
self.assertListEqual(
explicit_padding_end_points[endpoint_name].get_shape().as_list(),
expected_shape)
def testModelHasExpectedNumberOfParameters(self):
batch_size = 5
height, width = 224, 224
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
normalizer_fn=slim.batch_norm):
mobilenet_v1.mobilenet_v1_base(inputs)
total_params, _ = slim.model_analyzer.analyze_vars(
slim.get_model_variables())
self.assertAlmostEqual(3217920, total_params)
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')]
_, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
inputs, num_classes, scope='depth_multiplied_net',
depth_multiplier=0.5)
for key in endpoint_keys:
original_depth = end_points[key].get_shape().as_list()[3]
new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
self.assertEqual(0.5 * original_depth, new_depth)
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
endpoint_keys = [key for key in end_points.keys()
if key.startswith('Mixed') or key.startswith('Conv')]
_, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
inputs, num_classes, scope='depth_multiplied_net',
depth_multiplier=2.0)
for key in endpoint_keys:
original_depth = end_points[key].get_shape().as_list()[3]
new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
self.assertEqual(2.0 * original_depth, new_depth)
def testRaiseValueErrorWithInvalidDepthMultiplier(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
with self.assertRaises(ValueError):
_ = mobilenet_v1.mobilenet_v1(
inputs, num_classes, depth_multiplier=-0.1)
with self.assertRaises(ValueError):
_ = mobilenet_v1.mobilenet_v1(
inputs, num_classes, depth_multiplier=0.0)
def testHalfSizeImages(self):
batch_size = 5
height, width = 112, 112
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Conv2d_13_pointwise']
self.assertListEqual(pre_pool.get_shape().as_list(),
[batch_size, 4, 4, 1024])
def testUnknownImageShape(self):
tf.reset_default_graph()
batch_size = 2
height, width = 224, 224
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Conv2d_13_pointwise']
feed_dict = {inputs: input_np}
tf.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
def testGlobalPoolUnknownImageShape(self):
tf.reset_default_graph()
batch_size = 1
height, width = 250, 300
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes,
global_pool=True)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Conv2d_13_pointwise']
feed_dict = {inputs: input_np}
tf.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 10, 1024])
def testUnknowBatchSize(self):
batch_size = 1
height, width = 224, 224
num_classes = 1000
inputs = tf.placeholder(tf.float32, (None, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[None, num_classes])
images = tf.random_uniform((batch_size, height, width, 3))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEquals(output.shape, (batch_size, num_classes))
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
is_training=False)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (batch_size,))
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 150, 150
num_classes = 1000
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
mobilenet_v1.mobilenet_v1(train_inputs, num_classes)
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
reuse=True)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (eval_batch_size,))
def testLogitsNotSqueezed(self):
num_classes = 25
images = tf.random_uniform([1, 224, 224, 3])
logits, _ = mobilenet_v1.mobilenet_v1(images,
num_classes=num_classes,
spatial_squeeze=False)
with self.test_session() as sess:
tf.global_variables_initializer().run()
logits_out = sess.run(logits)
self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=None)
self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
slim.batch_norm)])
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=True)
self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=False)
self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
sc = mobilenet_v1.mobilenet_v1_arg_scope()
self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
if __name__ == '__main__':
tf.test.main()
|
PyTorch/LanguageModeling/BERT/triton/runner/maintainer | maintainer | maintainer_factory | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pathlib
if __name__ == "__main__" and __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from .docker.maintainer import DockerMaintainer
class MaintainerFactory:
@staticmethod
def create_docker_maintainer():
return DockerMaintainer()
|
PyTorch/Classification/GPUNet/triton/runner | runner | pipeline | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pathlib
from typing import Dict, Tuple
# method from PEP-366 to support relative import in executed modules
if __name__ == "__main__" and __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from .stages import (
ConversionStage,
DeployStage,
ExportStage,
ResultsType,
TritonPerformanceOfflineStage,
TritonPerformanceOnlineStage,
TritonPreparePerformanceProfilingDataStage,
)
class Pipeline:
"""
Definition of stages that has to be executed before and during experiments
"""
# Stages to execute as part of single experiment
_experiment_stages = [
ExportStage.label,
ConversionStage.label,
DeployStage.label,
TritonPreparePerformanceProfilingDataStage.label,
TritonPerformanceOfflineStage.label,
TritonPerformanceOnlineStage.label,
]
def __init__(self):
"""
Initialize pipeline
"""
self._stages: Dict = dict()
def model_export(self, commands: Tuple[str, ...]) -> None:
"""
Model export stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = ExportStage(commands=commands)
self._stages[stage.label] = stage
def model_conversion(self, commands: Tuple[str, ...]) -> None:
"""
Model conversion stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = ConversionStage(commands=commands)
self._stages[stage.label] = stage
def model_deploy(self, commands: Tuple[str, ...]) -> None:
"""
Model deployment stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = DeployStage(commands=commands)
self._stages[stage.label] = stage
def triton_prepare_performance_profiling_data(self, commands: Tuple[str, ...]) -> None:
"""
Model profiling data creation stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = TritonPreparePerformanceProfilingDataStage(commands=commands)
self._stages[stage.label] = stage
def triton_performance_offline_tests(self, commands: Tuple[str, ...], result_path: str) -> None:
"""
Model performance offline test stage
Args:
commands: Commands to be executed as part of stage
result_path: Path where results file is stored
Returns:
None
"""
stage = TritonPerformanceOfflineStage(
commands=commands,
result_path=result_path,
result_type=ResultsType.TRITON_PERFORMANCE_OFFLINE,
)
self._stages[stage.label] = stage
def triton_performance_online_tests(self, commands: Tuple[str, ...], result_path: str) -> None:
"""
Model performance online test stage
Args:
commands: Commands to be executed as part of stage
result_path: Path where results file is stored
Returns:
None
"""
stage = TritonPerformanceOnlineStage(
commands=commands,
result_path=result_path,
result_type=ResultsType.TRITON_PERFORMANCE_ONLINE,
)
self._stages[stage.label] = stage
def stages(self):
"""
Generate stages which should be run per experiment
Returns:
Generator with stages object
"""
for stage_name in self._experiment_stages:
stage = self._stages.get(stage_name)
if not stage:
continue
yield stage
|
TensorFlow/Translation/GNMT | GNMT | attention_wrapper | # Copyright 2018 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A powerful dynamic attention wrapper object."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import math
import os
import numpy as np
import tensorflow as tf
from utils import math_utils
from utils import misc_utils
from tensorflow.contrib.framework.python.framework import tensor_util
from tensorflow.python.framework import function
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import rnn_cell_impl
_zero_state_tensors = rnn_cell_impl._zero_state_tensors # pylint: disable=protected-access
nest = tf.contrib.framework.nest
class AttentionMechanism(object):
@property
def alignments_size(self):
raise NotImplementedError
@property
def state_size(self):
raise NotImplementedError
def _prepare_memory(memory, memory_sequence_length, check_inner_dims_defined):
"""Convert to tensor and possibly mask `memory`.
Args:
memory: `Tensor`, shaped `[batch_size, max_time, ...]`.
memory_sequence_length: `int32` `Tensor`, shaped `[batch_size]`.
check_inner_dims_defined: Python boolean. If `True`, the `memory`
argument's shape is checked to ensure all but the two outermost
dimensions are fully defined.
Returns:
A (possibly masked), checked, new `memory`.
Raises:
ValueError: If `check_inner_dims_defined` is `True` and not
`memory.shape[2:].is_fully_defined()`.
"""
memory = nest.map_structure(
lambda m: tf.convert_to_tensor(m, name="memory"), memory)
if memory_sequence_length is not None:
memory_sequence_length = tf.convert_to_tensor(
memory_sequence_length, name="memory_sequence_length")
if check_inner_dims_defined:
def _check_dims(m):
if not m.get_shape()[2:].is_fully_defined():
raise ValueError("Expected memory %s to have fully defined inner dims, "
"but saw shape: %s" % (m.name, m.get_shape()))
nest.map_structure(_check_dims, memory)
if memory_sequence_length is None:
seq_len_mask = None
else:
seq_len_mask = tf.sequence_mask(
memory_sequence_length,
maxlen=tf.shape(nest.flatten(memory)[0])[1],
dtype=nest.flatten(memory)[0].dtype)
seq_len_batch_size = (
memory_sequence_length.shape[0].value
or tf.shape(memory_sequence_length)[0])
def _maybe_mask(m, seq_len_mask):
rank = m.get_shape().ndims
rank = rank if rank is not None else tf.rank(m)
extra_ones = tf.ones(rank - 2, dtype=tf.int32)
m_batch_size = m.shape[0].value or tf.shape(m)[0]
if memory_sequence_length is not None:
seq_len_mask = tf.reshape(
seq_len_mask,
tf.concat((tf.shape(seq_len_mask), extra_ones), 0))
return m * seq_len_mask
else:
return m
return nest.map_structure(lambda m: _maybe_mask(m, seq_len_mask), memory)
def _maybe_mask_score(score, memory_sequence_length, score_mask_value):
if memory_sequence_length is None:
return score
if score_mask_value is None:
score_mask_value = tf.as_dtype(score.dtype).as_numpy_dtype(-np.inf)
score_mask = tf.sequence_mask(
memory_sequence_length, maxlen=tf.shape(score)[1])
score_mask_values = score_mask_value * tf.ones_like(score)
return tf.where(score_mask, score, score_mask_values)
class _BaseAttentionMechanism(AttentionMechanism):
"""A base AttentionMechanism class providing common functionality.
Common functionality includes:
1. Storing the query and memory layers.
2. Preprocessing and storing the memory.
"""
def __init__(self,
query_layer,
memory,
probability_fn,
memory_sequence_length=None,
memory_layer=None,
check_inner_dims_defined=True,
score_mask_value=None,
name=None):
"""Construct base AttentionMechanism class.
Args:
query_layer: Callable. Instance of `tf.layers.Layer`. The layer's depth
must match the depth of `memory_layer`. If `query_layer` is not
provided, the shape of `query` must match that of `memory_layer`.
memory: The memory to query; usually the output of an RNN encoder. This
tensor should be shaped `[batch_size, max_time, ...]`.
probability_fn: A `callable`. Converts the score and previous alignments
to probabilities. Its signature should be:
`probabilities = probability_fn(score, state)`.
memory_sequence_length (optional): Sequence lengths for the batch entries
in memory. If provided, the memory tensor rows are masked with zeros
for values past the respective sequence lengths.
memory_layer: Instance of `tf.layers.Layer` (may be None). The layer's
depth must match the depth of `query_layer`.
If `memory_layer` is not provided, the shape of `memory` must match
that of `query_layer`.
check_inner_dims_defined: Python boolean. If `True`, the `memory`
argument's shape is checked to ensure all but the two outermost
dimensions are fully defined.
score_mask_value: (optional): The mask value for score before passing into
`probability_fn`. The default is -inf. Only used if
`memory_sequence_length` is not None.
name: Name to use when creating ops.
"""
if (query_layer is not None
and not isinstance(query_layer, tf.layers.Layer)):
raise TypeError(
"query_layer is not a Layer: %s" % type(query_layer).__name__)
if (memory_layer is not None
and not isinstance(memory_layer, tf.layers.Layer)):
raise TypeError(
"memory_layer is not a Layer: %s" % type(memory_layer).__name__)
self._query_layer = query_layer
self._memory_layer = memory_layer
self.dtype = memory_layer.dtype
if not callable(probability_fn):
raise TypeError("probability_fn must be callable, saw type: %s" %
type(probability_fn).__name__)
self._probability_fn = lambda score, prev: ( # pylint:disable=g-long-lambda
probability_fn(
_maybe_mask_score(score, memory_sequence_length, score_mask_value),
prev))
with tf.name_scope(
name, "BaseAttentionMechanismInit", nest.flatten(memory)):
self._values = _prepare_memory(
memory, memory_sequence_length,
check_inner_dims_defined=check_inner_dims_defined)
self._keys = (
self.memory_layer(self._values) if self.memory_layer # pylint: disable=not-callable
else self._values)
self._batch_size = (
self._keys.shape[0].value or tf.shape(self._keys)[0])
self._alignments_size = (self._keys.shape[1].value or
tf.shape(self._keys)[1])
def build(self, query_shape):
self._query_layer.build((None, query_shape[-1]))
# memory_layer is built in the constructor.
self.built = True
@property
def memory_layer(self):
return self._memory_layer
@property
def query_layer(self):
return self._query_layer
@property
def values(self):
return self._values
@property
def keys(self):
return self._keys
@property
def batch_size(self):
return self._batch_size
@property
def alignments_size(self):
return self._alignments_size
@property
def state_size(self):
return self._alignments_size
def initial_alignments(self, batch_size, dtype):
"""Creates the initial alignment values for the `AttentionWrapper` class.
This is important for AttentionMechanisms that use the previous alignment
to calculate the alignment at the next time step (e.g. monotonic attention).
The default behavior is to return a tensor of all zeros.
Args:
batch_size: `int32` scalar, the batch_size.
dtype: The `dtype`.
Returns:
A `dtype` tensor shaped `[batch_size, alignments_size]`
(`alignments_size` is the values' `max_time`).
"""
max_time = self._alignments_size
return _zero_state_tensors(max_time, batch_size, dtype)
def initial_state(self, batch_size, dtype):
"""Creates the initial state values for the `AttentionWrapper` class.
This is important for AttentionMechanisms that use the previous alignment
to calculate the alignment at the next time step (e.g. monotonic attention).
The default behavior is to return the same output as initial_alignments.
Args:
batch_size: `int32` scalar, the batch_size.
dtype: The `dtype`.
Returns:
A structure of all-zero tensors with shapes as described by `state_size`.
"""
return self.initial_alignments(batch_size, dtype)
def _bahdanau_score(processed_query, keys, normalize, v, g, b):
"""Implements Bahdanau-style (additive) scoring function.
This attention has two forms. The first is Bhandanau attention,
as described in:
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
"Neural Machine Translation by Jointly Learning to Align and Translate."
ICLR 2015. https://arxiv.org/abs/1409.0473
The second is the normalized form. This form is inspired by the
weight normalization article:
Tim Salimans, Diederik P. Kingma.
"Weight Normalization: A Simple Reparameterization to Accelerate
Training of Deep Neural Networks."
https://arxiv.org/abs/1602.07868
To enable the second form, set `normalize=True`.
Args:
processed_query: Tensor, shape `[batch_size, num_units]` to compare to keys.
keys: Processed memory, shape `[batch_size, max_time, num_units]`.
normalize: Whether to normalize the score function.
Returns:
A `[batch_size, max_time]` tensor of unnormalized score values.
"""
# Reshape from [batch_size, ...] to [batch_size, 1, ...] for broadcasting.
processed_query = tf.expand_dims(processed_query, 1)
if normalize:
# normed_v = g * v / ||v||
def NormalizedAttenFwd(keys, processed_query, g, v, b):
"""normalized atten."""
normed_v = g * v * tf.rsqrt(
tf.reduce_sum(tf.square(v)))
batch = tf.shape(keys)[0]
max_time = tf.shape(keys)[1]
units = tf.shape(keys)[-1]
# [batch, time, dim]
activation = tf.tanh(keys + processed_query + b)
# [batch * time, dim]
activation = tf.reshape(activation, [batch * max_time, units])
# [dim, 1]
v = tf.expand_dims(normed_v, -1)
# [batch * time, 1] -> [batch * time]
y = tf.squeeze(tf.matmul(activation, v), axis=1)
y = tf.reshape(y, [batch, max_time])
return y
use_xla = os.environ["use_xla"] == "true"
def NormalizedAtten(keys, processed_query, g, v, b):
return NormalizedAttenFwd(keys, processed_query, g, v, b)
fn = NormalizedAtten
if os.environ["use_defun"] == "true":
fn = function.Defun(compiled=use_xla)(fn)
res = fn(keys, processed_query, g, v, b)
res.set_shape((None, keys.shape[1]))
return res
else:
def _Atten(keys, processed_query, v):
"""atten."""
batch = tf.shape(keys)[0]
max_time = tf.shape(keys)[1]
units = tf.shape(keys)[-1]
activation = tf.tanh(keys + processed_query)
activation = tf.reshape(activation, [batch * max_time, units])
v = tf.expand_dims(v, -1)
y = tf.squeeze(tf.matmul(activation, v), axis=1)
y = tf.reshape(y, [batch, max_time])
return y
fn = _Atten
if os.environ["use_defun"] == "true":
fn = function.Defun()(fn)
return fn(keys, processed_query, v)
class BahdanauAttention(_BaseAttentionMechanism):
"""Implements Bahdanau-style (additive) attention.
This attention has two forms. The first is Bahdanau attention,
as described in:
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
"Neural Machine Translation by Jointly Learning to Align and Translate."
ICLR 2015. https://arxiv.org/abs/1409.0473
The second is the normalized form. This form is inspired by the
weight normalization article:
Tim Salimans, Diederik P. Kingma.
"Weight Normalization: A Simple Reparameterization to Accelerate
Training of Deep Neural Networks."
https://arxiv.org/abs/1602.07868
To enable the second form, construct the object with parameter
`normalize=True`.
"""
def __init__(self,
num_units,
memory,
memory_sequence_length=None,
normalize=False,
probability_fn=None,
score_mask_value=None,
dtype=None,
name="BahdanauAttention"):
"""Construct the Attention mechanism.
Args:
num_units: The depth of the query mechanism.
memory: The memory to query; usually the output of an RNN encoder. This
tensor should be shaped `[batch_size, max_time, ...]`.
memory_sequence_length (optional): Sequence lengths for the batch entries
in memory. If provided, the memory tensor rows are masked with zeros
for values past the respective sequence lengths.
normalize: Python boolean. Whether to normalize the energy term.
probability_fn: (optional) A `callable`. Converts the score to
probabilities. The default is `tf.nn.softmax`. Other options include
`tf.contrib.seq2seq.hardmax` and `tf.contrib.sparsemax.sparsemax`.
Its signature should be: `probabilities = probability_fn(score)`.
score_mask_value: (optional): The mask value for score before passing into
`probability_fn`. The default is -inf. Only used if
`memory_sequence_length` is not None.
dtype: The data type for the query and memory layers of the attention
mechanism.
name: Name to use when creating ops.
"""
if probability_fn is None:
probability_fn = tf.nn.softmax
if dtype is None:
dtype = tf.float32
wrapped_probability_fn = lambda score, _: probability_fn(score)
super(BahdanauAttention, self).__init__(
query_layer=tf.layers.Dense(
num_units, name="query_layer", use_bias=False, dtype=dtype),
memory_layer=tf.layers.Dense(
num_units, name="memory_layer", use_bias=False, dtype=dtype),
memory=memory,
probability_fn=wrapped_probability_fn,
memory_sequence_length=memory_sequence_length,
score_mask_value=score_mask_value,
name=name)
self._num_units = num_units
self._normalize = normalize
self._name = name
self.v = tf.get_variable("attention_v", [num_units], dtype=dtype)
# TODO(jamesqin): I have no idea why the following is happening.
# If not doing read_value(), later in backprop it would complain about
# AddN op is fed with "resource" dtype input, which shouldn't be happening.
if isinstance(self.v, resource_variable_ops.ResourceVariable):
self.v = self.v.read_value()
self.g, self.b = None, None
if self._normalize:
# Scalar used in weight normalization
self.g = tf.get_variable(
"attention_g", dtype=dtype,
initializer=tf.constant_initializer(math.sqrt((1. / num_units))),
shape=())
# Same as above self.g
if isinstance(self.g, resource_variable_ops.ResourceVariable):
self.g = self.g.read_value()
# Bias added prior to the nonlinearity
self.b = tf.get_variable(
"attention_b", [num_units], dtype=dtype,
initializer=tf.zeros_initializer())
# Same as above self.g
if isinstance(self.b, resource_variable_ops.ResourceVariable):
self.b = self.b.read_value()
def __call__(self, query, state):
"""Score the query based on the keys and values.
Args:
query: Tensor of dtype matching `self.values` and shape
`[batch_size, query_depth]`.
state: Tensor of dtype matching `self.values` and shape
`[batch_size, alignments_size]`
(`alignments_size` is memory's `max_time`).
Returns:
alignments: Tensor of dtype matching `self.values` and shape
`[batch_size, alignments_size]` (`alignments_size` is memory's
`max_time`).
"""
query_dim = query.shape[-1].value
assert query_dim
def _compute_alignments(query, state):
with tf.variable_scope(None, "bahdanau_attention", [query]):
# TODO(jamesqin): figure out the shape implications of Defun.
query.set_shape((None, query_dim))
processed_query = self.query_layer(query) if self.query_layer else query
score = _bahdanau_score(processed_query, self._keys, self._normalize,
self.v, self.g, self.b)
alignments = self._probability_fn(score, state)
next_state = alignments
return alignments, next_state
return _compute_alignments(query, state)
class AttentionWrapperState(
collections.namedtuple("AttentionWrapperState",
("cell_state", "attention", "time", "alignments",
"alignment_history", "attention_state"))):
"""`namedtuple` storing the state of a `AttentionWrapper`.
Contains:
- `cell_state`: The state of the wrapped `RNNCell` at the previous time
step.
- `attention`: The attention emitted at the previous time step.
- `time`: int32 scalar containing the current time step.
- `alignments`: A single or tuple of `Tensor`(s) containing the alignments
emitted at the previous time step for each attention mechanism.
- `alignment_history`: (if enabled) a single or tuple of `TensorArray`(s)
containing alignment matrices from all time steps for each attention
mechanism. Call `stack()` on each to convert to a `Tensor`.
- `attention_state`: A single or tuple of nested objects
containing attention mechanism state for each attention mechanism.
The objects may contain Tensors or TensorArrays.
"""
def clone(self, **kwargs):
"""Clone this object, overriding components provided by kwargs.
The new state fields' shape must match original state fields' shape. This
will be validated, and original fields' shape will be propagated to new
fields.
Example:
```python
initial_state = attention_wrapper.zero_state(dtype=..., batch_size=...)
initial_state = initial_state.clone(cell_state=encoder_state)
```
Args:
**kwargs: Any properties of the state object to replace in the returned
`AttentionWrapperState`.
Returns:
A new `AttentionWrapperState` whose properties are the same as
this one, except any overridden properties as provided in `kwargs`.
"""
def with_same_shape(old, new):
"""Check and set new tensor's shape."""
xla_compile = (os.environ["xla_compile"] == "true")
if not xla_compile:
if isinstance(old, tf.Tensor) and isinstance(new, tf.Tensor):
return tensor_util.with_same_shape(old, new)
return new
return nest.map_structure(
with_same_shape,
self,
super(AttentionWrapperState, self)._replace(**kwargs))
def _compute_attention(attention_mechanism, cell_output, attention_state,
attention_layer):
"""Computes the attention and alignments for a given attention_mechanism."""
alignments, next_attention_state = attention_mechanism(
cell_output, state=attention_state)
# Context is the inner product of alignments and values along the
# memory time dimension.
# alignments shape is
# [batch_size, 1, memory_time]
# attention_mechanism.values shape is
# [batch_size, memory_time, memory_size]
# the batched matmul is over memory_time, so the output shape is
# [batch_size, 1, memory_size].
# we then squeeze out the singleton dim.
expanded_alignments = tf.expand_dims(alignments, 1)
context = math_utils.BatchMatMul(expanded_alignments,
attention_mechanism.values)
context = tf.squeeze(context, [1])
if attention_layer is not None:
attention = attention_layer(tf.concat([cell_output, context], 1))
else:
attention = context
return attention, alignments, next_attention_state
class AttentionWrapper(tf.nn.rnn_cell.RNNCell):
"""Wraps another `RNNCell` with attention.
"""
def __init__(self,
cell,
attention_mechanism,
attention_layer_size=None,
alignment_history=False,
cell_input_fn=None,
output_attention=True,
initial_cell_state=None,
name=None,
attention_layer=None):
"""Construct the `AttentionWrapper`.
**NOTE** If you are using the `BeamSearchDecoder` with a cell wrapped in
`AttentionWrapper`, then you must ensure that:
- The encoder output has been tiled to `beam_width` via
`tf.contrib.seq2seq.tile_batch` (NOT `tf.tile`).
- The `batch_size` argument passed to the `zero_state` method of this
wrapper is equal to `true_batch_size * beam_width`.
- The initial state created with `zero_state` above contains a
`cell_state` value containing properly tiled final state from the
encoder.
An example:
```
tiled_encoder_outputs = tf.contrib.seq2seq.tile_batch(
encoder_outputs, multiplier=beam_width)
tiled_encoder_final_state = tf.conrib.seq2seq.tile_batch(
encoder_final_state, multiplier=beam_width)
tiled_sequence_length = tf.contrib.seq2seq.tile_batch(
sequence_length, multiplier=beam_width)
attention_mechanism = MyFavoriteAttentionMechanism(
num_units=attention_depth,
memory=tiled_inputs,
memory_sequence_length=tiled_sequence_length)
attention_cell = AttentionWrapper(cell, attention_mechanism, ...)
decoder_initial_state = attention_cell.zero_state(
dtype, batch_size=true_batch_size * beam_width)
decoder_initial_state = decoder_initial_state.clone(
cell_state=tiled_encoder_final_state)
```
Args:
cell: An instance of `RNNCell`.
attention_mechanism: A list of `AttentionMechanism` instances or a single
instance.
attention_layer_size: A list of Python integers or a single Python
integer, the depth of the attention (output) layer(s). If None
(default), use the context as attention at each time step. Otherwise,
feed the context and cell output into the attention layer to generate
attention at each time step. If attention_mechanism is a list,
attention_layer_size must be a list of the same length. If
attention_layer is set, this must be None.
alignment_history: Python boolean, whether to store alignment history
from all time steps in the final output state (currently stored as a
time major `TensorArray` on which you must call `stack()`).
cell_input_fn: (optional) A `callable`. The default is:
`lambda inputs, attention: tf.concat([inputs, attention], -1)`.
output_attention: Python bool. If `True` (default), the output at each
time step is the attention value. This is the behavior of Luong-style
attention mechanisms. If `False`, the output at each time step is
the output of `cell`. This is the behavior of Bhadanau-style
attention mechanisms. In both cases, the `attention` tensor is
propagated to the next time step via the state and is used there.
This flag only controls whether the attention mechanism is propagated
up to the next cell in an RNN stack or to the top RNN output.
initial_cell_state: The initial state value to use for the cell when
the user calls `zero_state()`. Note that if this value is provided
now, and the user uses a `batch_size` argument of `zero_state` which
does not match the batch size of `initial_cell_state`, proper
behavior is not guaranteed.
name: Name to use when creating ops.
attention_layer: A list of `tf.layers.Layer` instances or a
single `tf.layers.Layer` instance taking the context and cell output as
inputs to generate attention at each time step. If None (default), use
the context as attention at each time step. If attention_mechanism is a
list, attention_layer must be a list of the same length. If
attention_layers_size is set, this must be None.
Raises:
TypeError: `attention_layer_size` is not None and (`attention_mechanism`
is a list but `attention_layer_size` is not; or vice versa).
ValueError: if `attention_layer_size` is not None, `attention_mechanism`
is a list, and its length does not match that of `attention_layer_size`;
if `attention_layer_size` and `attention_layer` are set simultaneously.
"""
super(AttentionWrapper, self).__init__(name=name)
rnn_cell_impl.assert_like_rnncell("cell", cell)
if isinstance(attention_mechanism, (list, tuple)):
self._is_multi = True
attention_mechanisms = attention_mechanism
for attention_mechanism in attention_mechanisms:
if not isinstance(attention_mechanism, AttentionMechanism):
raise TypeError(
"attention_mechanism must contain only instances of "
"AttentionMechanism, saw type: %s"
% type(attention_mechanism).__name__)
else:
self._is_multi = False
if not isinstance(attention_mechanism, AttentionMechanism):
raise TypeError(
"attention_mechanism must be an AttentionMechanism or list of "
"multiple AttentionMechanism instances, saw type: %s"
% type(attention_mechanism).__name__)
attention_mechanisms = (attention_mechanism,)
if cell_input_fn is None:
cell_input_fn = (
lambda inputs, attention: tf.concat([inputs, attention], -1))
else:
if not callable(cell_input_fn):
raise TypeError(
"cell_input_fn must be callable, saw type: %s"
% type(cell_input_fn).__name__)
if attention_layer_size is not None and attention_layer is not None:
raise ValueError("Only one of attention_layer_size and attention_layer "
"should be set")
if attention_layer_size is not None:
attention_layer_sizes = tuple(
attention_layer_size
if isinstance(attention_layer_size, (list, tuple))
else (attention_layer_size,))
if len(attention_layer_sizes) != len(attention_mechanisms):
raise ValueError(
"If provided, attention_layer_size must contain exactly one "
"integer per attention_mechanism, saw: %d vs %d"
% (len(attention_layer_sizes), len(attention_mechanisms)))
self._attention_layers = tuple(
tf.layers.Dense(
attention_layer_size,
name="attention_layer",
use_bias=False,
dtype=attention_mechanisms[i].dtype)
for i, attention_layer_size in enumerate(attention_layer_sizes))
self._attention_layer_size = sum(attention_layer_sizes)
elif attention_layer is not None:
self._attention_layers = tuple(
attention_layer
if isinstance(attention_layer, (list, tuple))
else (attention_layer,))
if len(self._attention_layers) != len(attention_mechanisms):
raise ValueError(
"If provided, attention_layer must contain exactly one "
"layer per attention_mechanism, saw: %d vs %d"
% (len(self._attention_layers), len(attention_mechanisms)))
self._attention_layer_size = sum(
layer.compute_output_shape(
[None,
cell.output_size + mechanism.values.shape[-1].value])[-1].value
for layer, mechanism in zip(
self._attention_layers, attention_mechanisms))
else:
self._attention_layers = None
self._attention_layer_size = sum(
attention_mechanism.values.get_shape()[-1].value
for attention_mechanism in attention_mechanisms)
self._cell = cell
self._attention_mechanisms = attention_mechanisms
self._cell_input_fn = cell_input_fn
self._output_attention = output_attention
self._alignment_history = alignment_history
with tf.name_scope(name, "AttentionWrapperInit"):
if initial_cell_state is None:
self._initial_cell_state = None
else:
final_state_tensor = nest.flatten(initial_cell_state)[-1]
state_batch_size = (
final_state_tensor.shape[0].value
or tf.shape(final_state_tensor)[0])
error_message = (
"When constructing AttentionWrapper %s: " % self._base_name +
"Non-matching batch sizes between the memory "
"(encoder output) and initial_cell_state. Are you using "
"the BeamSearchDecoder? You may need to tile your initial state "
"via the tf.contrib.seq2seq.tile_batch function with argument "
"multiple=beam_width.")
with tf.control_dependencies(
self._batch_size_checks(state_batch_size, error_message)):
self._initial_cell_state = nest.map_structure(
lambda s: tf.identity(s, name="check_initial_cell_state"),
initial_cell_state)
def force_build(self, cell_input_shape, query_shape):
def _build_cell(cell, input_shape):
if isinstance(cell, tf.nn.rnn_cell.DropoutWrapper):
_build_cell(cell._cell, input_shape)
elif isinstance(cell, tf.nn.rnn_cell.ResidualWrapper):
_build_cell(cell._cell, input_shape)
else:
cell.build(input_shape)
_build_cell(self._cell,
(cell_input_shape[-1] + self._attention_layer_size))
for am in self._attention_mechanisms:
am.build(query_shape)
self.built = True
def _batch_size_checks(self, batch_size, error_message):
return []
def _item_or_tuple(self, seq):
"""Returns `seq` as tuple or the singular element.
Which is returned is determined by how the AttentionMechanism(s) were passed
to the constructor.
Args:
seq: A non-empty sequence of items or generator.
Returns:
Either the values in the sequence as a tuple if AttentionMechanism(s)
were passed to the constructor as a sequence or the singular element.
"""
t = tuple(seq)
if self._is_multi:
return t
else:
return t[0]
@property
def output_size(self):
if self._output_attention:
return self._attention_layer_size
else:
return self._cell.output_size
@property
def state_size(self):
"""The `state_size` property of `AttentionWrapper`.
Returns:
An `AttentionWrapperState` tuple containing shapes used by this object.
"""
return AttentionWrapperState(
cell_state=self._cell.state_size,
time=tf.TensorShape([]),
attention=self._attention_layer_size,
alignments=self._item_or_tuple(
a.alignments_size for a in self._attention_mechanisms),
attention_state=self._item_or_tuple(
a.state_size for a in self._attention_mechanisms),
alignment_history=self._item_or_tuple(
a.alignments_size if self._alignment_history else ()
for a in self._attention_mechanisms)) # sometimes a TensorArray
def zero_state(self, batch_size, dtype):
"""Return an initial (zero) state tuple for this `AttentionWrapper`.
**NOTE** Please see the initializer documentation for details of how
to call `zero_state` if using an `AttentionWrapper` with a
`BeamSearchDecoder`.
Args:
batch_size: `0D` integer tensor: the batch size.
dtype: The internal state data type.
Returns:
An `AttentionWrapperState` tuple containing zeroed out tensors and,
possibly, empty `TensorArray` objects.
Raises:
ValueError: (or, possibly at runtime, InvalidArgument), if
`batch_size` does not match the output size of the encoder passed
to the wrapper object at initialization time.
"""
with tf.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
if self._initial_cell_state is not None:
cell_state = self._initial_cell_state
else:
cell_state = self._cell.zero_state(batch_size, dtype)
error_message = (
"When calling zero_state of AttentionWrapper %s: " % self._base_name +
"Non-matching batch sizes between the memory "
"(encoder output) and the requested batch size. Are you using "
"the BeamSearchDecoder? If so, make sure your encoder output has "
"been tiled to beam_width via tf.contrib.seq2seq.tile_batch, and "
"the batch_size= argument passed to zero_state is "
"batch_size * beam_width.")
with tf.control_dependencies(
self._batch_size_checks(batch_size, error_message)):
cell_state = nest.map_structure(
lambda s: tf.identity(s, name="checked_cell_state"),
cell_state)
initial_alignments = [
attention_mechanism.initial_alignments(batch_size, dtype)
for attention_mechanism in self._attention_mechanisms]
return AttentionWrapperState(
cell_state=cell_state,
time=tf.zeros([], dtype=tf.int64),
attention=_zero_state_tensors(self._attention_layer_size, batch_size,
dtype),
alignments=self._item_or_tuple(initial_alignments),
attention_state=self._item_or_tuple(
attention_mechanism.initial_state(batch_size, dtype)
for attention_mechanism in self._attention_mechanisms),
alignment_history=self._item_or_tuple(
tf.TensorArray(
dtype,
size=0,
dynamic_size=True,
element_shape=alignment.shape)
if self._alignment_history else ()
for alignment in initial_alignments))
def call(self, inputs, state):
"""Perform a step of attention-wrapped RNN.
- Step 1: Mix the `inputs` and previous step's `attention` output via
`cell_input_fn`.
- Step 2: Call the wrapped `cell` with this input and its previous state.
- Step 3: Score the cell's output with `attention_mechanism`.
- Step 4: Calculate the alignments by passing the score through the
`normalizer`.
- Step 5: Calculate the context vector as the inner product between the
alignments and the attention_mechanism's values (memory).
- Step 6: Calculate the attention output by concatenating the cell output
and context through the attention layer (a linear layer with
`attention_layer_size` outputs).
Args:
inputs: (Possibly nested tuple of) Tensor, the input at this time step.
state: An instance of `AttentionWrapperState` containing
tensors from the previous time step.
Returns:
A tuple `(attention_or_cell_output, next_state)`, where:
- `attention_or_cell_output` depending on `output_attention`.
- `next_state` is an instance of `AttentionWrapperState`
containing the state calculated at this time step.
Raises:
TypeError: If `state` is not an instance of `AttentionWrapperState`.
"""
if not isinstance(state, AttentionWrapperState):
raise TypeError("Expected state to be instance of AttentionWrapperState. "
"Received type %s instead." % type(state))
# Step 1: Calculate the true inputs to the cell based on the
# previous attention value.
cell_inputs = self._cell_input_fn(inputs, state.attention)
cell_state = state.cell_state
cell_output, next_cell_state = self._cell(cell_inputs, cell_state)
cell_batch_size = (
cell_output.shape[0].value or tf.shape(cell_output)[0])
error_message = (
"When applying AttentionWrapper %s: " % self.name +
"Non-matching batch sizes between the memory "
"(encoder output) and the query (decoder output). Are you using "
"the BeamSearchDecoder? You may need to tile your memory input via "
"the tf.contrib.seq2seq.tile_batch function with argument "
"multiple=beam_width.")
with tf.control_dependencies(
self._batch_size_checks(cell_batch_size, error_message)):
cell_output = tf.identity(
cell_output, name="checked_cell_output")
if self._is_multi:
previous_attention_state = state.attention_state
previous_alignment_history = state.alignment_history
else:
previous_attention_state = [state.attention_state]
previous_alignment_history = [state.alignment_history]
all_alignments = []
all_attentions = []
all_attention_states = []
maybe_all_histories = []
for i, attention_mechanism in enumerate(self._attention_mechanisms):
attention, alignments, next_attention_state = _compute_attention(
attention_mechanism, cell_output, previous_attention_state[i],
self._attention_layers[i] if self._attention_layers else None)
alignment_history = previous_alignment_history[i].write(
state.time, alignments) if self._alignment_history else ()
all_attention_states.append(next_attention_state)
all_alignments.append(alignments)
all_attentions.append(attention)
maybe_all_histories.append(alignment_history)
attention = tf.concat(all_attentions, 1)
next_state = AttentionWrapperState(
time=state.time + 1,
cell_state=next_cell_state,
attention=attention,
attention_state=self._item_or_tuple(all_attention_states),
alignments=self._item_or_tuple(all_alignments),
alignment_history=self._item_or_tuple(maybe_all_histories))
if self._output_attention:
return attention, next_state
else:
return cell_output, next_state
class BahdanauAttentionFusedLayer(object):
"""Fused attention layer using Bahdanau attention.
Only used during training.
"""
def __init__(self,
num_units,
memory,
memory_sequence_length=None,
dtype=None,
name="BahdanauAttention"):
self.v = tf.get_variable("attention_v", [num_units], dtype=dtype)
# TODO(jamesqin): I have no idea why the following is happening.
# If not doing read_value(), later in backprop it would complain about
# AddN op is fed with "resource" dtype input, which shouldn't be happening.
if isinstance(self.v, resource_variable_ops.ResourceVariable):
self.v = self.v.read_value()
# Scalar used in weight normalization
self.g = tf.get_variable(
"attention_g", dtype=dtype,
initializer=tf.constant_initializer(math.sqrt((1. / num_units))),
shape=())
if isinstance(self.g, resource_variable_ops.ResourceVariable):
self.g = self.g.read_value()
# Bias added prior to the nonlinearity
self.b = tf.get_variable(
"attention_b", [num_units], dtype=dtype,
initializer=tf.zeros_initializer())
if isinstance(self.b, resource_variable_ops.ResourceVariable):
self.b = self.b.read_value()
self.query_layer = tf.layers.Dense(
num_units, name="query_layer", use_bias=False, dtype=dtype)
self.memory_layer = tf.layers.Dense(
num_units, name="memory_layer", use_bias=False, dtype=dtype)
def _mask_score(score, memory_sequence_length):
# score is [batch, query_max_t, memory_t]
if memory_sequence_length is None:
return score
# [batch, 1]
memory_sequence_length = tf.expand_dims(memory_sequence_length, -1)
query_max_t = tf.shape(score)[1]
# [batch, query_t]
memory_sequence_length = tf.broadcast_to(
memory_sequence_length,
[tf.shape(memory_sequence_length)[0], query_max_t])
# [batch, query_t, memory_t]
score_mask = tf.sequence_mask(
memory_sequence_length, maxlen=tf.shape(score)[-1])
score_mask_value = tf.as_dtype(score.dtype).as_numpy_dtype(-np.inf)
score_mask_values = score_mask_value * tf.ones_like(score)
return tf.where(score_mask, score, score_mask_values)
# score: [batch, query_max_t, memory_t]
# memory_seq_length: [batch]
self._probability_fn = lambda score, prev: ( # pylint:disable=g-long-lambda
tf.nn.softmax(_mask_score(score, memory_sequence_length)))
with tf.name_scope(name, "BaseAttentionMechanismInit", [memory]):
# Mask padded memory.
# [batch, memory_t, units]
self.values = _prepare_memory(
memory, memory_sequence_length, check_inner_dims_defined=True)
# [batch, memory_t, units]
self.keys = self.memory_layer(self.values)
def __call__(self, queries):
"""Invoke the layer.
Args:
queries: [batch, queryt_t, query_size]
Returns:
attention: [batch, query_t, num_units]
"""
# Compute score for all queries, returns [batch, query_t, memory_t].
# The var scope naming is chosen to build inference graph correctly.
with tf.variable_scope(
"multi_rnn_cell/cell_0_attention/attention/bahdanau_attention",
[queries]):
# [batch, query_t, query_size] -> [batch, query_t, num_units]
processed_queries = self.query_layer(queries)
# [batch, memory_t, num_units] -> [batch, 1, memory_t, num_units]
keys = tf.expand_dims(self.keys, axis=1)
# [batch, query_t, num_units] -> [batch, query_t, 1, num_units]
processed_queries = tf.expand_dims(processed_queries, axis=2)
# [batch, 1, memory_t, units] + [batch, query_t, 1, units] + [units] ->
# [batch, query_t, memory_t, units]
activation = tf.tanh(keys + processed_queries + self.b)
# [units, 1]
normed_v = self.g * self.v * tf.rsqrt(tf.reduce_sum(tf.square(self.v)))
v = tf.expand_dims(normed_v, -1)
# [batch, query_t, memory_t, units] * [units, 1] ->
# [batch, query_t, memory_t, 1]
# [batch, query_t, memory_t, 1] --(squeeze)--> [batch, query_t, memory_t]
score = tf.squeeze(tf.tensordot(activation, v, axes=1), axis=-1)
# Compute alignment
# bahdanau attention doesn't use the attention state in prob func (softmax)
unused_state = None
# [batch, query_t, memory_t]
alignments = self._probability_fn(score, unused_state)
# Note: slow batched matmul in fp16
# [batch, query_t, memory_t] * [ batch, memory_t, units] ->
# [batch, query_t, units]
attention = math_utils.BatchMatMul(alignments, self.values)
return attention
|
TensorFlow/Detection/SSD/models/research/slim/nets/nasnet | nasnet | pnasnet | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains the definition for the PNASNet classification networks.
Paper: https://arxiv.org/abs/1712.00559
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import tensorflow as tf
from nets.nasnet import nasnet
from nets.nasnet import nasnet_utils
arg_scope = tf.contrib.framework.arg_scope
slim = tf.contrib.slim
def large_imagenet_config():
"""Large ImageNet configuration based on PNASNet-5."""
return tf.contrib.training.HParams(
stem_multiplier=3.0,
dense_dropout_keep_prob=0.5,
num_cells=12,
filter_scaling_rate=2.0,
num_conv_filters=216,
drop_path_keep_prob=0.6,
use_aux_head=1,
num_reduction_layers=2,
data_format='NHWC',
skip_reduction_layer_input=1,
total_training_steps=250000,
use_bounded_activation=False,
)
def mobile_imagenet_config():
"""Mobile ImageNet configuration based on PNASNet-5."""
return tf.contrib.training.HParams(
stem_multiplier=1.0,
dense_dropout_keep_prob=0.5,
num_cells=9,
filter_scaling_rate=2.0,
num_conv_filters=54,
drop_path_keep_prob=1.0,
use_aux_head=1,
num_reduction_layers=2,
data_format='NHWC',
skip_reduction_layer_input=1,
total_training_steps=250000,
use_bounded_activation=False,
)
def pnasnet_large_arg_scope(weight_decay=4e-5, batch_norm_decay=0.9997,
batch_norm_epsilon=0.001):
"""Default arg scope for the PNASNet Large ImageNet model."""
return nasnet.nasnet_large_arg_scope(
weight_decay, batch_norm_decay, batch_norm_epsilon)
def pnasnet_mobile_arg_scope(weight_decay=4e-5,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001):
"""Default arg scope for the PNASNet Mobile ImageNet model."""
return nasnet.nasnet_mobile_arg_scope(weight_decay, batch_norm_decay,
batch_norm_epsilon)
def _build_pnasnet_base(images,
normal_cell,
num_classes,
hparams,
is_training,
final_endpoint=None):
"""Constructs a PNASNet image model."""
end_points = {}
def add_and_check_endpoint(endpoint_name, net):
end_points[endpoint_name] = net
return final_endpoint and (endpoint_name == final_endpoint)
# Find where to place the reduction cells or stride normal cells
reduction_indices = nasnet_utils.calc_reduction_layers(
hparams.num_cells, hparams.num_reduction_layers)
# pylint: disable=protected-access
stem = lambda: nasnet._imagenet_stem(images, hparams, normal_cell)
# pylint: enable=protected-access
net, cell_outputs = stem()
if add_and_check_endpoint('Stem', net):
return net, end_points
# Setup for building in the auxiliary head.
aux_head_cell_idxes = []
if len(reduction_indices) >= 2:
aux_head_cell_idxes.append(reduction_indices[1] - 1)
# Run the cells
filter_scaling = 1.0
# true_cell_num accounts for the stem cells
true_cell_num = 2
activation_fn = tf.nn.relu6 if hparams.use_bounded_activation else tf.nn.relu
for cell_num in range(hparams.num_cells):
is_reduction = cell_num in reduction_indices
stride = 2 if is_reduction else 1
if is_reduction: filter_scaling *= hparams.filter_scaling_rate
if hparams.skip_reduction_layer_input or not is_reduction:
prev_layer = cell_outputs[-2]
net = normal_cell(
net,
scope='cell_{}'.format(cell_num),
filter_scaling=filter_scaling,
stride=stride,
prev_layer=prev_layer,
cell_num=true_cell_num)
if add_and_check_endpoint('Cell_{}'.format(cell_num), net):
return net, end_points
true_cell_num += 1
cell_outputs.append(net)
if (hparams.use_aux_head and cell_num in aux_head_cell_idxes and
num_classes and is_training):
aux_net = activation_fn(net)
# pylint: disable=protected-access
nasnet._build_aux_head(aux_net, end_points, num_classes, hparams,
scope='aux_{}'.format(cell_num))
# pylint: enable=protected-access
# Final softmax layer
with tf.variable_scope('final_layer'):
net = activation_fn(net)
net = nasnet_utils.global_avg_pool(net)
if add_and_check_endpoint('global_pool', net) or not num_classes:
return net, end_points
net = slim.dropout(net, hparams.dense_dropout_keep_prob, scope='dropout')
logits = slim.fully_connected(net, num_classes)
if add_and_check_endpoint('Logits', logits):
return net, end_points
predictions = tf.nn.softmax(logits, name='predictions')
if add_and_check_endpoint('Predictions', predictions):
return net, end_points
return logits, end_points
def build_pnasnet_large(images,
num_classes,
is_training=True,
final_endpoint=None,
config=None):
"""Build PNASNet Large model for the ImageNet Dataset."""
hparams = copy.deepcopy(config) if config else large_imagenet_config()
# pylint: disable=protected-access
nasnet._update_hparams(hparams, is_training)
# pylint: enable=protected-access
if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
tf.logging.info('A GPU is available on the machine, consider using NCHW '
'data format for increased speed on GPU.')
if hparams.data_format == 'NCHW':
images = tf.transpose(images, [0, 3, 1, 2])
# Calculate the total number of cells in the network.
# There is no distinction between reduction and normal cells in PNAS so the
# total number of cells is equal to the number normal cells plus the number
# of stem cells (two by default).
total_num_cells = hparams.num_cells + 2
normal_cell = PNasNetNormalCell(hparams.num_conv_filters,
hparams.drop_path_keep_prob, total_num_cells,
hparams.total_training_steps,
hparams.use_bounded_activation)
with arg_scope(
[slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
is_training=is_training):
with arg_scope([slim.avg_pool2d, slim.max_pool2d, slim.conv2d,
slim.batch_norm, slim.separable_conv2d,
nasnet_utils.factorized_reduction,
nasnet_utils.global_avg_pool,
nasnet_utils.get_channel_index,
nasnet_utils.get_channel_dim],
data_format=hparams.data_format):
return _build_pnasnet_base(
images,
normal_cell=normal_cell,
num_classes=num_classes,
hparams=hparams,
is_training=is_training,
final_endpoint=final_endpoint)
build_pnasnet_large.default_image_size = 331
def build_pnasnet_mobile(images,
num_classes,
is_training=True,
final_endpoint=None,
config=None):
"""Build PNASNet Mobile model for the ImageNet Dataset."""
hparams = copy.deepcopy(config) if config else mobile_imagenet_config()
# pylint: disable=protected-access
nasnet._update_hparams(hparams, is_training)
# pylint: enable=protected-access
if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
tf.logging.info('A GPU is available on the machine, consider using NCHW '
'data format for increased speed on GPU.')
if hparams.data_format == 'NCHW':
images = tf.transpose(images, [0, 3, 1, 2])
# Calculate the total number of cells in the network.
# There is no distinction between reduction and normal cells in PNAS so the
# total number of cells is equal to the number normal cells plus the number
# of stem cells (two by default).
total_num_cells = hparams.num_cells + 2
normal_cell = PNasNetNormalCell(hparams.num_conv_filters,
hparams.drop_path_keep_prob, total_num_cells,
hparams.total_training_steps,
hparams.use_bounded_activation)
with arg_scope(
[slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
is_training=is_training):
with arg_scope(
[
slim.avg_pool2d, slim.max_pool2d, slim.conv2d, slim.batch_norm,
slim.separable_conv2d, nasnet_utils.factorized_reduction,
nasnet_utils.global_avg_pool, nasnet_utils.get_channel_index,
nasnet_utils.get_channel_dim
],
data_format=hparams.data_format):
return _build_pnasnet_base(
images,
normal_cell=normal_cell,
num_classes=num_classes,
hparams=hparams,
is_training=is_training,
final_endpoint=final_endpoint)
build_pnasnet_mobile.default_image_size = 224
class PNasNetNormalCell(nasnet_utils.NasNetABaseCell):
"""PNASNet Normal Cell."""
def __init__(self, num_conv_filters, drop_path_keep_prob, total_num_cells,
total_training_steps, use_bounded_activation=False):
# Configuration for the PNASNet-5 model.
operations = [
'separable_5x5_2', 'max_pool_3x3', 'separable_7x7_2', 'max_pool_3x3',
'separable_5x5_2', 'separable_3x3_2', 'separable_3x3_2', 'max_pool_3x3',
'separable_3x3_2', 'none'
]
used_hiddenstates = [1, 1, 0, 0, 0, 0, 0]
hiddenstate_indices = [1, 1, 0, 0, 0, 0, 4, 0, 1, 0]
super(PNasNetNormalCell, self).__init__(
num_conv_filters, operations, used_hiddenstates, hiddenstate_indices,
drop_path_keep_prob, total_num_cells, total_training_steps,
use_bounded_activation)
|
PyTorch/LanguageModeling/BERT/data | data | SquadDownloader | # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import bz2
import os
import urllib.request
import sys
class SquadDownloader:
def __init__(self, save_path):
self.save_path = save_path + '/squad'
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
if not os.path.exists(self.save_path + '/v1.1'):
os.makedirs(self.save_path + '/v1.1')
if not os.path.exists(self.save_path + '/v2.0'):
os.makedirs(self.save_path + '/v2.0')
self.download_urls = {
'https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json' : 'v1.1/train-v1.1.json',
'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json' : 'v1.1/dev-v1.1.json',
'https://worksheets.codalab.org/rest/bundles/0xbcd57bee090b421c982906709c8c27e1/contents/blob/' : 'v1.1/evaluate-v1.1.py',
'https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json' : 'v2.0/train-v2.0.json',
'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json' : 'v2.0/dev-v2.0.json',
'https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/' : 'v2.0/evaluate-v2.0.py',
}
def download(self):
for item in self.download_urls:
url = item
file = self.download_urls[item]
print('Downloading:', url)
if os.path.isfile(self.save_path + '/' + file):
print('** Download file already exists, skipping download')
else:
response = urllib.request.urlopen(url)
with open(self.save_path + '/' + file, "wb") as handle:
handle.write(response.read())
|
TensorFlow2/LanguageModeling/BERT/official/nlp/modeling/networks | networks | span_labeling_test | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for span_labeling network."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.python.keras import keras_parameterized # pylint: disable=g-direct-tensorflow-import
from official.nlp.modeling.networks import span_labeling
# This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It
# guarantees forward compatibility of this code for the V2 switchover.
@keras_parameterized.run_all_keras_modes
class SpanLabelingTest(keras_parameterized.TestCase):
def test_network_creation(self):
"""Validate that the Keras object can be created."""
sequence_length = 15
input_width = 512
test_network = span_labeling.SpanLabeling(
input_width=input_width, output='predictions')
# Create a 3-dimensional input (the first dimension is implicit).
sequence_data = tf.keras.Input(
shape=(sequence_length, input_width), dtype=tf.float32)
start_outputs, end_outputs = test_network(sequence_data)
# Validate that the outputs are of the expected shape.
expected_output_shape = [None, sequence_length]
self.assertEqual(expected_output_shape, start_outputs.shape.as_list())
self.assertEqual(expected_output_shape, end_outputs.shape.as_list())
def test_network_invocation(self):
"""Validate that the Keras object can be invoked."""
sequence_length = 15
input_width = 512
test_network = span_labeling.SpanLabeling(input_width=input_width)
# Create a 3-dimensional input (the first dimension is implicit).
sequence_data = tf.keras.Input(
shape=(sequence_length, input_width), dtype=tf.float32)
outputs = test_network(sequence_data)
model = tf.keras.Model(sequence_data, outputs)
# Invoke the network as part of a Model.
batch_size = 3
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, input_width))
start_outputs, end_outputs = model.predict(input_data)
# Validate that the outputs are of the expected shape.
expected_output_shape = (batch_size, sequence_length)
self.assertEqual(expected_output_shape, start_outputs.shape)
self.assertEqual(expected_output_shape, end_outputs.shape)
def test_network_invocation_with_internal_logit_output(self):
"""Validate that the logit outputs are correct."""
sequence_length = 15
input_width = 512
test_network = span_labeling.SpanLabeling(
input_width=input_width, output='predictions')
# Create a 3-dimensional input (the first dimension is implicit).
sequence_data = tf.keras.Input(
shape=(sequence_length, input_width), dtype=tf.float32)
output = test_network(sequence_data)
model = tf.keras.Model(sequence_data, output)
logit_model = tf.keras.Model(
test_network.inputs,
[test_network.start_logits, test_network.end_logits])
batch_size = 3
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, input_width))
start_outputs, end_outputs = model.predict(input_data)
start_logits, end_logits = logit_model.predict(input_data)
# Ensure that the tensor shapes are correct.
expected_output_shape = (batch_size, sequence_length)
self.assertEqual(expected_output_shape, start_outputs.shape)
self.assertEqual(expected_output_shape, end_outputs.shape)
self.assertEqual(expected_output_shape, start_logits.shape)
self.assertEqual(expected_output_shape, end_logits.shape)
# Ensure that the logits, when softmaxed, create the outputs.
input_tensor = tf.keras.Input(expected_output_shape[1:])
output_tensor = tf.keras.layers.Activation(tf.nn.log_softmax)(input_tensor)
softmax_model = tf.keras.Model(input_tensor, output_tensor)
start_softmax = softmax_model.predict(start_logits)
self.assertAllClose(start_outputs, start_softmax)
end_softmax = softmax_model.predict(end_logits)
self.assertAllClose(end_outputs, end_softmax)
def test_network_invocation_with_external_logit_output(self):
"""Validate that the logit outputs are correct."""
sequence_length = 15
input_width = 512
test_network = span_labeling.SpanLabeling(
input_width=input_width, output='predictions')
logit_network = span_labeling.SpanLabeling(
input_width=input_width, output='logits')
logit_network.set_weights(test_network.get_weights())
# Create a 3-dimensional input (the first dimension is implicit).
sequence_data = tf.keras.Input(
shape=(sequence_length, input_width), dtype=tf.float32)
output = test_network(sequence_data)
logit_output = logit_network(sequence_data)
model = tf.keras.Model(sequence_data, output)
logit_model = tf.keras.Model(sequence_data, logit_output)
batch_size = 3
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, input_width))
start_outputs, end_outputs = model.predict(input_data)
start_logits, end_logits = logit_model.predict(input_data)
# Ensure that the tensor shapes are correct.
expected_output_shape = (batch_size, sequence_length)
self.assertEqual(expected_output_shape, start_outputs.shape)
self.assertEqual(expected_output_shape, end_outputs.shape)
self.assertEqual(expected_output_shape, start_logits.shape)
self.assertEqual(expected_output_shape, end_logits.shape)
# Ensure that the logits, when softmaxed, create the outputs.
input_tensor = tf.keras.Input(expected_output_shape[1:])
output_tensor = tf.keras.layers.Activation(tf.nn.log_softmax)(input_tensor)
softmax_model = tf.keras.Model(input_tensor, output_tensor)
start_softmax = softmax_model.predict(start_logits)
self.assertAllClose(start_outputs, start_softmax)
end_softmax = softmax_model.predict(end_logits)
self.assertAllClose(end_outputs, end_softmax)
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
network = span_labeling.SpanLabeling(
input_width=128,
activation='relu',
initializer='zeros',
output='predictions')
# Create another network object from the first object's config.
new_network = span_labeling.SpanLabeling.from_config(network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
def test_unknown_output_type_fails(self):
with self.assertRaisesRegex(ValueError, 'Unknown `output` value "bad".*'):
_ = span_labeling.SpanLabeling(input_width=10, output='bad')
if __name__ == '__main__':
tf.test.main()
|
Tools/PyTorch/TimeSeriesPredictionPlatform/conf/model | model | xgboost | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
_target_: models.tspp_xgboost.TSPPXGBoost
config:
max_depth: 10
learning_rate: 0.2
subsample: 1.0
colsample_bytree: 0.8
tree_method: gpu_hist
n_rounds: 400
objective: reg:squarederror
defaults:
- _self_
- /trainer@_global_/trainer: xgbtrainer
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/utils/types | types | array_type | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TypeVar
import cupy as cp
import numpy as np
NDArray = TypeVar('NDArray', np.ndarray, cp.ndarray)
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | static_shape | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Helper functions to access TensorShape values.
The rank 4 tensor_shape must be of the form [batch_size, height, width, depth].
"""
def get_batch_size(tensor_shape):
"""Returns batch size from the tensor shape.
Args:
tensor_shape: A rank 4 TensorShape.
Returns:
An integer representing the batch size of the tensor.
"""
tensor_shape.assert_has_rank(rank=4)
return tensor_shape[0].value
def get_height(tensor_shape):
"""Returns height from the tensor shape.
Args:
tensor_shape: A rank 4 TensorShape.
Returns:
An integer representing the height of the tensor.
"""
tensor_shape.assert_has_rank(rank=4)
return tensor_shape[1].value
def get_width(tensor_shape):
"""Returns width from the tensor shape.
Args:
tensor_shape: A rank 4 TensorShape.
Returns:
An integer representing the width of the tensor.
"""
tensor_shape.assert_has_rank(rank=4)
return tensor_shape[2].value
def get_depth(tensor_shape):
"""Returns depth from the tensor shape.
Args:
tensor_shape: A rank 4 TensorShape.
Returns:
An integer representing the depth of the tensor.
"""
tensor_shape.assert_has_rank(rank=4)
return tensor_shape[3].value
|
TensorFlow/Detection/SSD/models/research/object_detection/metrics | metrics | offline_eval_map_corloc_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for utilities in offline_eval_map_corloc binary."""
import tensorflow as tf
from object_detection.metrics import offline_eval_map_corloc as offline_eval
class OfflineEvalMapCorlocTest(tf.test.TestCase):
def test_generateShardedFilenames(self):
test_filename = '/path/to/file'
result = offline_eval._generate_sharded_filenames(test_filename)
self.assertEqual(result, [test_filename])
test_filename = '/path/to/file-00000-of-00050'
result = offline_eval._generate_sharded_filenames(test_filename)
self.assertEqual(result, [test_filename])
result = offline_eval._generate_sharded_filenames('/path/to/@3.record')
self.assertEqual(result, [
'/path/to/-00000-of-00003.record', '/path/to/-00001-of-00003.record',
'/path/to/-00002-of-00003.record'
])
result = offline_eval._generate_sharded_filenames('/path/to/abc@3')
self.assertEqual(result, [
'/path/to/abc-00000-of-00003', '/path/to/abc-00001-of-00003',
'/path/to/abc-00002-of-00003'
])
result = offline_eval._generate_sharded_filenames('/path/to/@1')
self.assertEqual(result, ['/path/to/-00000-of-00001'])
def test_generateFilenames(self):
test_filenames = ['/path/to/file', '/path/to/@3.record']
result = offline_eval._generate_filenames(test_filenames)
self.assertEqual(result, [
'/path/to/file', '/path/to/-00000-of-00003.record',
'/path/to/-00001-of-00003.record', '/path/to/-00002-of-00003.record'
])
if __name__ == '__main__':
tf.test.main()
|
TensorFlow/LanguageModeling | LanguageModeling | README | # Language Modeling
Language modeling (LM) is a natural language processing (NLP) task that determines the probability of a given sequence of words occurring in a sentence.
In an era where computers, smartphones and other electronic devices increasingly need to interact with humans, language modeling has become an indispensable technique for teaching devices how to communicate in natural languages in human-like ways.
But how does language modeling work? And what can you build with it? What are the different approaches, what are its potential benefits and limitations, and how might you use it in your business?
In this guide, you’ll find answers to all of those questions and more. Whether you’re an experienced machine learning engineer considering implementation, a developer wanting to learn more, or a product manager looking to explore what’s possible with natural language processing and language modeling, this guide is for you.
Here’s a look at what we’ll cover:
- Language modeling – the basics
- How does language modeling work?
- Use cases and applications
- Getting started
## Language modeling – the basics
### What is language modeling?
"*Language modeling is the task of assigning a probability to sentences in a language. […]
Besides assigning a probability to each sequence of words, the language models also assign a
probability for the likelihood of a given word (or a sequence of words) to follow a sequence
of words.*" Source: Page 105, [Neural Network Methods in Natural Language Processing](http://amzn.to/2wt1nzv), 2017.
### Types of language models
There are primarily two types of Language Models:
- Statistical Language Models: These models use traditional statistical techniques like N-grams, Hidden Markov Models (HMM), and certain linguistic rules to learn the probability distribution of words.
- Neural Language Models: They use different kinds of Neural Networks to model language, and have surpassed the statistical language models in their effectiveness.
"*We provide ample empirical evidence to suggest that connectionist language models are
superior to standard n-gram techniques, except their high computational (training)
complexity.*" Source: [Recurrent neural network based language model](http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf), 2010.
Given the superior performance of neural language models, we include in the container two popular state-of-the-art neural language models: BERT and Transformer-XL.
### Why is language modeling important?
Language modeling is fundamental in modern NLP applications. It enables machines to understand qualitative information, and enables people to communicate with machines in the natural languages that humans use to communicate with each other.
Language modeling is used directly in a variety of industries, including tech, finance, healthcare, transportation, legal, military, government, and more -- actually, you probably have just interacted with a language model today, whether it be through Google search, engaging with a voice assistant, or using text autocomplete features.
## How does language modeling work?
The roots of modern language modeling can be traced back to 1948, when Claude Shannon
published a paper titled "A Mathematical Theory of Communication", laying the foundation for information theory and language modeling. In the paper, Shannon detailed the use of a stochastic model called the Markov chain to create a statistical model for the sequences of letters in English text. The Markov models, along with n-gram, are still among the most popular statistical language models today.
However, simple statistical language models have serious drawbacks in scalability and fluency because of its sparse representation of language. Overcoming the problem by representing language units (eg. words, characters) as a non-linear, distributed combination of weights in continuous space, neural language models can learn to approximate words without being misled by rare or unknown values.
Therefore, as mentioned above, we introduce two popular state-of-the-art neural language models, BERT and Transformer-XL, in Tensorflow and PyTorch. More details can be found in the [NVIDIA Deep Learning Examples Github Repository ](https://github.com/NVIDIA/DeepLearningExamples)
## Use cases and applications
### Speech Recognition
Imagine speaking a phrase to the phone, expecting it to convert the speech to text. How does
it know if you said "recognize speech" or "wreck a nice beach"? Language models help figure it out
based on the context, enabling machines to process and make sense of speech audio.
### Spelling Correction
Language-models-enabled spellcheckers can point to spelling errors and possibly suggest alternatives.
### Machine translation
Imagine you are translating the Chinese sentence "我在开车" into English. Your translation system gives you several choices:
- I at open car
- me at open car
- I at drive
- me at drive
- I am driving
- me am driving
A language model tells you which translation sounds the most natural.
## Getting started
NVIDIA provides examples for Language Modeling on [Deep Learning Examples Github Repository](https://github.com/NVIDIA/DeepLearningExamples). These examples provide you with easy to consume and highly optimized scripts for both training and inferencing. The quick start guide at our GitHub repository will help you in setting up the environment using NGC Docker Images, download pre-trained models from NGC and adapt the model training and inference for your application/use-case.
These models are tested and maintained by NVIDIA, leveraging mixed precision using tensor cores on our latest GPUs for faster training times while maintaining accuracy.
|
TensorFlow/Segmentation/UNet_Industrial/model/layers | layers | padding | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# ==============================================================================
#
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# ==============================================================================
import tensorflow as tf
from model.layers.utils import _log_hparams
__all__ = ['pad']
def pad(inputs, paddings, mode='CONSTANT', name='padding', constant_values=0):
if mode.upper() not in ['CONSTANT', 'REFLECT', 'SYMMETRIC']:
raise ValueError("Unknown padding mode: `%s` (accepted: ['CONSTANT', 'REFLECT', 'SYMMETRIC'])" % mode)
net = tf.pad(inputs, paddings=paddings, mode=mode, name=name, constant_values=constant_values)
_log_hparams(
classname='Padding',
layername=net.name,
paddings=paddings,
mode=mode,
constant_values=constant_values,
out_shape=str(net.get_shape()),
out_dtype=net.dtype
)
return net
|
PyTorch/LanguageModeling/Transformer-XL/pytorch | pytorch | run_wt103_base | #!/bin/bash
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
export OMP_NUM_THREADS=1
if [[ "$1" == 'train' ]]; then
echo 'Run training...'
python -m torch.distributed.launch --nproc_per_node="$2" train.py \
--config_file wt103_base.yaml \
"${@:3}"
elif [[ "$1" == 'eval' ]]; then
echo 'Run evaluation...'
python -m torch.distributed.launch --nproc_per_node="$2" eval.py \
--config_file wt103_base.yaml \
"${@:3}"
else
echo 'unknown argment 1'
fi
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/generator/tabular/transforms | transforms | base_transform | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC
class BaseTransform(ABC):
"""Base class for all transforms.
The `BaseTransform` class contains methods that must be implemented
by specific transforms objects. The `fit` method is optional.
"""
def fit(self, data):
"""Fits the transform on the data.
Args:
data (pandas.Series or cudf.Series or numpy.array or cupy.array):
Data to transform.
Returns:
None
"""
pass
def transform(self, data):
"""Transform the data.
Args:
data (pandas.Series or cudf.Series or numpy.array or cupy.array):
Data to transform.
Returns:
numpy.array: Transformed data.
"""
raise NotImplementedError()
def fit_transform(self, data):
"""Fit to the data and then return the transformed data.
Args:
data (pandas.Series or cudf.Series or numpy.array or cupy.array):
Data to fit and transform
Returns:
Transformed data.
"""
self.fit(data)
return self.transform(data)
def inverse_transform(self, data):
"""Reverses the transformation done on the data back to original values.
Args:
data (pandas.Series or cudf.Series or numpy.array or cupy.array): Data to inverse-transform.
Returns:
Inverse transformed data.
"""
raise NotImplementedError()
|
TensorFlow2/Recommendation/SIM/preprocessing | preprocessing | sim_preprocessing | # Copyright (c) 2022 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Preprocessing script for SIM models."""
import logging
import multiprocessing
import os
import click
import cudf
import cupy
import dask.dataframe
import dask_cudf
import rmm
from preprocessing.io import load_metadata, load_review_data, save_metadata
from preprocessing.ops import ExplodeSequence, add_negative_sequence, list_slice, slice_and_pad_left
DASK_TRAIN_DATASET_CHUNKSIZE = 15_000
TRAIN_DATA_DIR = "train"
TEST_DATA_DIR = "test"
TEST_DATA_FILE = "part.0.parquet"
CATEGORIZED_METADATA_FILE = "metadata.json"
OUTPUT_META = {
"label": "int8",
"uid": "int64",
"item": "int32",
"cat": "int32",
"item_sequence": "list",
"cat_sequence": "list",
"neg_item_sequence": "list",
"neg_cat_sequence": "list",
}
logging.basicConfig(
level=logging.INFO,
format="[%(asctime)s] %(levelname)s: %(message)s",
)
def add_categorified_column(df, col_name, id_col_name):
unique_values = df[col_name].unique().to_frame()
unique_values[id_col_name] = cupy.arange(len(unique_values), dtype="int32") + 1
df = df.merge(unique_values, how="left", on=col_name)
return df
def categorify_items(all_items_unique: cudf.DataFrame, metadata: cudf.DataFrame) -> cudf.DataFrame:
unique_item_with_category = all_items_unique.merge(metadata, how="left", on="item")
unique_item_with_category = unique_item_with_category.fillna("no_category")
df = add_categorified_column(unique_item_with_category, "item", "item_id")
df = add_categorified_column(df, "cat", "cat_id")
return df
def filter_too_short_sequences(reviews: cudf.DataFrame, min_seq_length: int) -> cudf.DataFrame:
user_counts = reviews["user"].value_counts()
user_counts_filtered = user_counts[user_counts >= min_seq_length]
valid_users = user_counts_filtered.index
reviews = reviews[reviews["user"].isin(valid_users)]
reviews.reset_index(drop=True, inplace=True)
return reviews
def add_items_and_categories_indices(
reviews: cudf.DataFrame,
item_and_cat_with_ids: cudf.DataFrame,
) -> cudf.DataFrame:
return reviews.merge(item_and_cat_with_ids, how="left", on="item")
def categorify_users(reviews: cudf.DataFrame) -> cudf.DataFrame:
return add_categorified_column(reviews, "user", "uid")
def create_sampling_df(
all_items: cudf.DataFrame,
item_and_cat_with_ids: cudf.DataFrame
) -> cudf.DataFrame:
sampling_df = all_items.merge(item_and_cat_with_ids, how="left", on="item")
sampling_df = sampling_df[["item_id", "cat_id"]]
sampling_df = sampling_df.sort_values(by="item_id")
sampling_df.reset_index(drop=True, inplace=True)
return sampling_df
def aggregate_per_user(df):
df = df.sort_values(by=["unixReviewTime", "item"])
df = df.groupby("uid").agg({
"item_id": list,
"cat_id": list,
})
df.reset_index(inplace=True)
df = df.rename(columns={
"item_id": "item_sequence",
"cat_id": "cat_sequence",
})
df["item"] = df["item_sequence"].list.get(-1)
df["cat"] = df["cat_sequence"].list.get(-1)
df["item_sequence"] = list_slice(df["item_sequence"], 0, -1)
df["cat_sequence"] = list_slice(df["cat_sequence"], 0, -1)
return df
def explode_sequence(df: cudf.DataFrame, min_elements: int, max_elements: int) -> cudf.DataFrame:
df = ExplodeSequence(
col_names=["item_sequence", "cat_sequence"],
keep_cols=["uid"],
max_elements=max_elements + 1,
).transform(df)
df["item"] = df["item_sequence"].list.get(-1)
df["cat"] = df["cat_sequence"].list.get(-1)
df["item_sequence"] = list_slice(df["item_sequence"], 0, -1)
df["cat_sequence"] = list_slice(df["cat_sequence"], 0, -1)
df = df[df.item_sequence.list.len() >= min_elements]
return df
def add_negative_label(pos_df: cudf.DataFrame, sampling_df: cudf.DataFrame) -> cudf.DataFrame:
neg_df = pos_df.copy()
pos_df["label"] = cupy.int8(1)
neg_df["label"] = cupy.int8(0)
neg = cupy.random.randint(
low=0,
high=len(sampling_df),
size=len(neg_df),
dtype=int,
)
neg_item_ids = sampling_df["item_id"].iloc[neg].values
neg_df["item"] = neg_item_ids
neg_cat_ids = sampling_df["cat_id"].iloc[neg].values
neg_df["cat"] = neg_cat_ids
df = cudf.concat([pos_df, neg_df])
return df
def add_negative_sampling(df: cudf.DataFrame, sampling_df: cudf.DataFrame) -> cudf.DataFrame:
df = add_negative_label(df, sampling_df)
neg = cupy.random.randint(
low=0,
high=len(sampling_df),
size=int(df.item_sequence.list.len().sum()),
dtype=int,
)
item_samples = sampling_df["item_id"].iloc[neg]
cat_samples = sampling_df["cat_id"].iloc[neg]
df["neg_item_sequence"] = add_negative_sequence(df["item_sequence"], item_samples)
df["neg_cat_sequence"] = add_negative_sequence(df["cat_sequence"], cat_samples)
return df
def pad_with_zeros(df: cudf.DataFrame, max_elements: int) -> cudf.DataFrame:
df["item_sequence"] = slice_and_pad_left(df["item_sequence"], max_elements)
df["cat_sequence"] = slice_and_pad_left(df["cat_sequence"], max_elements)
df["neg_item_sequence"] = slice_and_pad_left(df["neg_item_sequence"], max_elements)
df["neg_cat_sequence"] = slice_and_pad_left(df["neg_cat_sequence"], max_elements)
return df
def create_train_dataset(
df: cudf.DataFrame,
sampling_df: cudf.DataFrame,
min_elements: int,
max_elements: int,
output_path: str,
seed: int,
dask_scheduler: str = "processes",
) -> None:
def transform(df, sampling_df, partition_info):
part_seed = seed + partition_info["number"] + 1
cupy.random.seed(part_seed)
df = explode_sequence(df, min_elements, max_elements)
df = add_negative_sampling(df, sampling_df)
df = pad_with_zeros(df, max_elements)
df = df.sort_values(by=["uid"])
df.reset_index(drop=True, inplace=True)
df = df[list(OUTPUT_META)]
return df
ddf = dask_cudf.from_cudf(df, chunksize=DASK_TRAIN_DATASET_CHUNKSIZE)
ddf = ddf.map_partitions(transform, meta=OUTPUT_META, sampling_df=sampling_df)
ddf = ddf.clear_divisions()
with dask.config.set(scheduler=dask_scheduler):
ddf.to_parquet(output_path, write_index=False, overwrite=True)
def create_test_dataset(
df: cudf.DataFrame,
sampling_df: cudf.DataFrame,
max_elements: int,
output_path: str,
) -> None:
df = add_negative_sampling(df, sampling_df)
df = pad_with_zeros(df, max_elements)
df = df.sort_values(by=["uid"])
df.reset_index(drop=True, inplace=True)
df = df[list(OUTPUT_META)]
os.makedirs(output_path, exist_ok=True)
output_file = os.path.join(output_path, TEST_DATA_FILE)
df.to_parquet(output_file, index=False)
@click.command()
@click.option(
"--amazon_dataset_path",
required=True,
help="Path to the dataset. Must contain both reviews and metadata json files.",
type=str,
)
@click.option(
"--output_path",
required=True,
help="Path where preprocessed dataset is saved.",
type=str,
)
@click.option(
"--metadata_file_name",
default="meta_Books.json",
help="Path to the dataset. Must contain both reviews and metadata json files.",
type=str,
)
@click.option(
"--reviews_file_name",
default="reviews_Books.json",
help="Path where preprocessed dataset is saved.",
type=str,
)
@click.option(
"--max_sequence_length",
default=100,
help="Take only `max_sequence_length` last elements of a sequence.",
)
@click.option(
"--shortest_sequence_for_user",
default=20,
help="Specifies what is a minimal length of a sequence. "
"Every user with a sequence shorter than this value will be discarded."
)
@click.option(
"--shortest_sequence_for_training",
default=1,
help="Specifies what is a minimal length of a sequence in a training set.",
)
@click.option(
"--metadata_loader_n_proc",
default=multiprocessing.cpu_count(),
help="Specifies the number of processes used to parse metadata.",
)
@click.option(
"--review_loader_num_workers",
default=20,
help="Specifies the number of dask workers used to read reviews data. "
"Note that, as each worker is a new process, too high value might cause GPU OOM errors."
)
@click.option(
"--seed",
default=12345,
help="Seed for reproducibility."
"Note that the results can still differ between machines because of dask/cudf non-determinism.",
type=int,
)
def main(
amazon_dataset_path: str,
output_path: str,
metadata_file_name: str,
reviews_file_name: str,
max_sequence_length: int,
shortest_sequence_for_user: int,
shortest_sequence_for_training: int,
metadata_loader_n_proc: int,
review_loader_num_workers: int,
seed: int,
):
cupy.random.seed(seed)
rmm.reinitialize(managed_memory=True)
metadata_path = os.path.join(amazon_dataset_path, metadata_file_name)
reviews_path = os.path.join(amazon_dataset_path, reviews_file_name)
logging.info("Loading metadata")
metadata = load_metadata(metadata_path, metadata_loader_n_proc)
assert len(metadata) == metadata["item"].nunique(), "metadata should contain unique items"
logging.info("Loading review data")
reviews = load_review_data(reviews_path, review_loader_num_workers)
logging.info("Removing short user sequences")
reviews = filter_too_short_sequences(reviews, shortest_sequence_for_user)
logging.info("Categorifying users, items, categories")
all_items_unique = reviews["item"].unique().to_frame()
item_and_cat_with_ids = categorify_items(all_items_unique, metadata)
reviews = add_items_and_categories_indices(reviews, item_and_cat_with_ids)
reviews = categorify_users(reviews)
logging.info("Aggregating data per user")
df = aggregate_per_user(reviews)
logging.info("Preparing dataframe for negative sampling")
all_items = reviews["item"].to_frame()
sampling_df = create_sampling_df(all_items, item_and_cat_with_ids)
os.makedirs(output_path, exist_ok=True)
logging.info("Creating train dataset")
create_train_dataset(
df,
sampling_df,
min_elements=shortest_sequence_for_training,
max_elements=max_sequence_length,
output_path=os.path.join(output_path, TRAIN_DATA_DIR),
seed=seed,
)
logging.info("Creating test dataset")
create_test_dataset(
df,
sampling_df,
max_elements=max_sequence_length,
output_path=os.path.join(output_path, TEST_DATA_DIR),
)
logging.info("Saving metadata")
save_metadata(
number_of_items=len(item_and_cat_with_ids),
number_of_categories=item_and_cat_with_ids["cat_id"].nunique(),
number_of_users=len(df),
output_path=os.path.join(output_path, CATEGORIZED_METADATA_FILE),
)
if __name__ == "__main__":
main()
|
MxNet/Classification/RN50v1.5 | RN50v1.5 | README | # ResNet-50 v1.5 for MXNet
This repository provides a script and recipe to train the ResNet-50 v1.5 model to achieve state-of-the-art accuracy, and is tested and maintained by NVIDIA.
## Table Of Contents
- [Model overview](#model-overview)
* [Model architecture](#model-architecture)
* [Default configuration](#default-configuration)
* [Feature support matrix](#feature-support-matrix)
* [Features](#features)
* [Mixed precision training](#mixed-precision-training)
* [Enabling mixed precision](#enabling-mixed-precision)
* [Enabling TF32](#enabling-tf32)
- [Setup](#setup)
* [Requirements](#requirements)
- [Quick Start Guide](#quick-start-guide)
- [Advanced](#advanced)
* [Scripts and sample code](#scripts-and-sample-code)
* [Parameters](#parameters)
* [Command-line options](#command-line-options)
* [Getting the data](#getting-the-data)
* [Dataset guidelines](#dataset-guidelines)
* [Multi-dataset](#multi-dataset)
* [Training process](#training-process)
* [Inference process](#inference-process)
- [Performance](#performance)
* [Benchmarking](#benchmarking)
* [Training performance benchmark](#training-performance-benchmark)
* [Inference performance benchmark](#inference-performance-benchmark)
* [Results](#results)
* [Training accuracy results](#training-accuracy-results)
* [Training accuracy: NVIDIA DGX A100 (8x A100 80GB)](#training-accuracy-nvidia-dgx-a100-8x-a100-80gb)
* [Training accuracy: NVIDIA DGX-1 (8x V100 16GB)](#training-accuracy-nvidia-dgx-1-8x-v100-16gb)
* [Training stability test](#training-stability-test)
* [Training performance results](#training-performance-results)
* [Training performance: NVIDIA DGX A100 (8x A100 80GB)](#training-performance-nvidia-dgx-a100-8x-a100-80gb)
* [Training performance: NVIDIA DGX-1 (8x V100 16GB)](#training-performance-nvidia-dgx-1-8x-v100-16gb)
* [Training performance: NVIDIA DGX-2 (16x V100 32GB)](#training-performance-nvidia-dgx-2-16x-v100-32gb)
* [Inference performance results](#inference-performance-results)
* [Inference performance: NVIDIA DGX A100 (1x A100 80GB)](#inference-performance-nvidia-dgx-a100-1x-a100-80gb)
* [Inference performance: NVIDIA DGX-1 (1x V100 16GB)](#inference-performance-nvidia-dgx-1-1x-v100-16gb)
* [Inference performance: NVIDIA T4](#inference-performance-nvidia-t4)
- [Release notes](#release-notes)
* [Changelog](#changelog)
* [Known issues](#known-issues)
## Model overview
The ResNet-50 v1.5 model is a modified version of the [original ResNet-50 v1 model](https://arxiv.org/abs/1512.03385).
The difference between v1 and v1.5 is in the bottleneck blocks which require downsampling. ResNet v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.
This difference makes ResNet-50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec).
This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 3.5x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
### Model architecture
The model architecture was present in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) paper. The main advantage of the model is the usage of residual layers as a building block that helps with gradient propagation during training.
_Image source: [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)_
### Default configuration
**Optimizer**
* SGD with momentum (0.875)
* Learning rate = 0.256 for 256 batch size, for other batch sizes we linearly scale the learning rate
* Learning rate schedule - we use cosine LR schedule
* Linear warmup of the learning rate during the first 5 epochs according to [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677).
* Weight decay: 3.0517578125e-05 (1/32768)
* We do not apply WD on batch norm trainable parameters (gamma/bias)
* Label Smoothing: 0.1
* We train for:
* 50 Epochs - configuration that reaches 75.9% top1 accuracy
* 90 Epochs - 90 epochs is a standard for ResNet-50
* 250 Epochs - best possible accuracy. For 250 epoch training we also use [MixUp regularization](https://arxiv.org/pdf/1710.09412.pdf).
**Data augmentation**
For training:
* Normalization
* Random resized crop to 224x224
* Scale from 8% to 100%
* Aspect ratio from 3/4 to 4/3
* Random horizontal flip
For inference:
* Normalization
* Scale to 256x256
* Center crop to 224x224
### Feature support matrix
| **Feature** | **ResNet-50 MXNet** |
|:---:|:--------:|
|[DALI](https://docs.nvidia.com/deeplearning/sdk/dali-release-notes/index.html)|yes|
|Horovod Multi-GPU|yes|
#### Features
The following features are supported by this model.
**NVIDIA DALI**
NVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks, and an execution engine, to accelerate the pre-processing of the input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be easily integrated into different deep learning training and inference applications.
**Horovod Multi-GPU**
Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the [Horovod: Official repository](https://github.com/horovod/horovod).
### Mixed precision training
Mixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:
1. Porting the model to use the FP16 data type where appropriate.
2. Adding loss scaling to preserve small gradient values.
The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in [CUDA 8](https://devblogs.nvidia.com/parallelforall/tag/fp16/) in the NVIDIA Deep Learning SDK.
For information about:
- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) documentation.
- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.
#### Enabling mixed precision
Using the Gluon API, ensure you perform the following steps to convert a model that supports computation with float16.
1. Cast Gluon Block‘s parameters and expected input type to float16 by calling the cast method of the Block representing the network.
```python
net = net.cast('float16')
```
2. Ensure the data input to the network is of float16 type. If your DataLoader or Iterator produces output in another datatype, then you have to cast your data. There are different ways you can do this. The easiest way is to use the `astype` method of NDArrays.
```python
data = data.astype('float16', copy=False)
```
3. If you are using images and DataLoader, you can also use a Cast transform. It is preferable to use `multi_precision` mode of optimizer when training in float16. This mode of optimizer maintains a master copy of the weights in float32 even when the training (forward and backward pass) is in float16. This helps increase precision of the weight updates and can lead to faster convergence in some scenarios.
```python
optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01)
```
#### Enabling TF32
TensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.
TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.
For more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.
TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.
## Setup
The following section lists the requirements that you need to meet in order to start training the ResNet-50 v1.5 model.
### Requirements
This repository contains Dockerfile which extends the MXNet NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:
- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
- [MXNet 22.10-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia%2Fmxnet)
Supported GPUs:
- [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
- [NVIDIA Turing architecture](https://www.nvidia.com/en-us/design-visualization/technologies/turing-architecture/)
- [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)
For more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:
- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
- [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#accessing_registry)
- [Running MXNet](https://docs.nvidia.com/deeplearning/frameworks/mxnet-release-notes/running.html#running)
For those unable to use the MXNet NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).
## Quick Start Guide
To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the ResNet-50 model on the ImageNet 1k dataset. For the specifics concerning training and inference, see the [Advanced](#advanced) section.
1. Clone the repository.
```bash
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/MxNet/Classification/RN50v1.5
```
2. Build the ResNet-50 MXNet NGC container.
After Docker is set up, you can build the ResNet-50 image with:
```bash
docker build . -t nvidia_rn50_mx
```
3. Start an interactive session in the NGC container to run preprocessing/training/inference.
```bash
nvidia-docker run --rm -it --ipc=host -v <path to dataset>:/data/imagenet/train-val-recordio-passthrough nvidia_rn50_mx
```
4. Download the data.
* Download the images from `http://image-net.org/download-images`.
* Extract the training and validation data:
```bash
mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done
cd ..
mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash
```
5. Preprocess the ImageNet 1k dataset.
```bash
./scripts/prepare_imagenet.sh <path to raw imagenet> <path where processed dataset will be created>
```
6. Start training.
```bash
./runner -n <number of gpus> -b <batch size per GPU (default 192)>
```
7. Start validation/evaluation.
```bash
./runner -n <number of gpus> -b <batch size per GPU (default 192)> --load <path to trained model> --mode val
```
8. Start inference/predictions.
```bash
./runner --load <path to trained model> --mode pred --data-pred <path to the image>
```
## Advanced
The following sections provide greater details of the dataset, running training and inference, and the training results.
### Scripts and sample code
In the root directory, the most important files are:
* `runner`: A wrapper on the `train.py` script which is the main executable script for training/validation/predicting.
* `benchmark.py`: A script for benchmarking.
* `Dockerfile`: Container to build the container.
* `fit.py`: A file containing most of the training and validation logic.
* `data.py`: Data loading and preprocessing code.
* `dali.py`: Data loading and preprocessing code using DALI.
* `models.py`: The model architecture.
* `report.py`: A file containing JSON report structure and description of fields.
In the `scripts` directory, the most important files are:
* `prepare_imagenet.sh`: A script that converts raw dataset format to RecordIO format.
### Parameters
The complete list of available parameters contains:
```
Model:
--arch {resnetv1,resnetv15,resnextv1,resnextv15,xception}
model architecture (default: resnetv15)
--num-layers NUM_LAYERS
number of layers in the neural network, required by
some networks such as resnet (default: 50)
--num-groups NUM_GROUPS
number of groups for grouped convolutions, required by
some networks such as resnext (default: 32)
--num-classes NUM_CLASSES
the number of classes (default: 1000)
--batchnorm-eps BATCHNORM_EPS
the amount added to the batchnorm variance to prevent
output explosion. (default: 1e-05)
--batchnorm-mom BATCHNORM_MOM
the leaky-integrator factor controling the batchnorm
mean and variance. (default: 0.9)
--fuse-bn-relu FUSE_BN_RELU
have batchnorm kernel perform activation relu
(default: 0)
--fuse-bn-add-relu FUSE_BN_ADD_RELU
have batchnorm kernel perform add followed by
activation relu (default: 0)
Training:
--mode {train_val,train,val,pred}
mode (default: train_val)
--seed SEED random seed (default: None)
--gpus GPUS list of gpus to run, e.g. 0 or 0,2,5 (default: [0])
--kv-store {device,horovod}
key-value store type (default: device)
--dtype {float32,float16}
precision (default: float16)
--amp If enabled, turn on AMP (Automatic Mixed Precision)
(default: False)
--batch-size BATCH_SIZE
the batch size (default: 192)
--num-epochs NUM_EPOCHS
number of epochs (default: 90)
--lr LR initial learning rate (default: 0.1)
--lr-schedule {multistep,cosine}
learning rate schedule (default: cosine)
--lr-factor LR_FACTOR
the ratio to reduce lr on each step (default: 0.256)
--lr-steps LR_STEPS the epochs to reduce the lr, e.g. 30,60 (default: [])
--warmup-epochs WARMUP_EPOCHS
the epochs to ramp-up lr to scaled large-batch value
(default: 5)
--optimizer OPTIMIZER
the optimizer type (default: sgd)
--mom MOM momentum for sgd (default: 0.875)
--wd WD weight decay for sgd (default: 3.0517578125e-05)
--label-smoothing LABEL_SMOOTHING
label smoothing factor (default: 0.1)
--mixup MIXUP alpha parameter for mixup (if 0 then mixup is not
applied) (default: 0)
--disp-batches DISP_BATCHES
show progress for every n batches (default: 20)
--model-prefix MODEL_PREFIX
model checkpoint prefix (default: model)
--save-frequency SAVE_FREQUENCY
frequency of saving model in epochs (--model-prefix
must be specified). If -1 then save only best model.
If 0 then do not save anything. (default: -1)
--begin-epoch BEGIN_EPOCH
start the model from an epoch (default: 0)
--load LOAD checkpoint to load (default: None)
--test-io test reading speed without training (default: False)
--test-io-mode {train,val}
data to test (default: train)
--log LOG file where to save the log from the experiment
(default: log.log)
--dllogger-log DLLOGGER_LOG
file where to save the dllogger log from the
experiment (default: dllogger_log.log)
--workspace WORKSPACE
path to directory where results will be stored
(default: ./)
--no-metrics do not calculate evaluation metrics (for benchmarking)
(default: False)
--benchmark-iters BENCHMARK_ITERS
run only benchmark-iters iterations from each epoch
(default: None)
Data:
--data-train DATA_TRAIN
the training data (default: None)
--data-train-idx DATA_TRAIN_IDX
the index of training data (default: )
--data-val DATA_VAL the validation data (default: None)
--data-val-idx DATA_VAL_IDX
the index of validation data (default: )
--data-pred DATA_PRED
the image on which run inference (only for pred mode)
(default: None)
--data-backend {dali-gpu,dali-cpu,mxnet,synthetic}
set data loading & augmentation backend (default:
dali-gpu)
--image-shape IMAGE_SHAPE
the image shape feed into the network (default: [3,
224, 224])
--rgb-mean RGB_MEAN a tuple of size 3 for the mean rgb (default: [123.68,
116.779, 103.939])
--rgb-std RGB_STD a tuple of size 3 for the std rgb (default: [58.393,
57.12, 57.375])
--input-layout {NCHW,NHWC}
the layout of the input data (default: NCHW)
--conv-layout {NCHW,NHWC}
the layout of the data assumed by the conv operation
(default: NCHW)
--batchnorm-layout {NCHW,NHWC}
the layout of the data assumed by the batchnorm
operation (default: NCHW)
--pooling-layout {NCHW,NHWC}
the layout of the data assumed by the pooling
operation (default: NCHW)
--num-examples NUM_EXAMPLES
the number of training examples (doesn't work with
mxnet data backend) (default: 1281167)
--data-val-resize DATA_VAL_RESIZE
base length of shorter edge for validation dataset
(default: 256)
DALI data backend:
entire group applies only to dali data backend
--dali-separ-val each process will perform independent validation on
whole val-set (default: False)
--dali-threads DALI_THREADS
number of threadsper GPU for DALI (default: 3)
--dali-validation-threads DALI_VALIDATION_THREADS
number of threadsper GPU for DALI for validation
(default: 10)
--dali-prefetch-queue DALI_PREFETCH_QUEUE
DALI prefetch queue depth (default: 2)
--dali-nvjpeg-memory-padding DALI_NVJPEG_MEMORY_PADDING
Memory padding value for nvJPEG (in MB) (default: 64)
--dali-fuse-decoder DALI_FUSE_DECODER
0 or 1 whether to fuse decoder or not (default: 1)
MXNet data backend:
entire group applies only to mxnet data backend
--data-mxnet-threads DATA_MXNET_THREADS
number of threads for data decoding for mxnet data
backend (default: 40)
--random-crop RANDOM_CROP
if or not randomly crop the image (default: 0)
--random-mirror RANDOM_MIRROR
if or not randomly flip horizontally (default: 1)
--max-random-h MAX_RANDOM_H
max change of hue, whose range is [0, 180] (default:
0)
--max-random-s MAX_RANDOM_S
max change of saturation, whose range is [0, 255]
(default: 0)
--max-random-l MAX_RANDOM_L
max change of intensity, whose range is [0, 255]
(default: 0)
--min-random-aspect-ratio MIN_RANDOM_ASPECT_RATIO
min value of aspect ratio, whose value is either None
or a positive value. (default: 0.75)
--max-random-aspect-ratio MAX_RANDOM_ASPECT_RATIO
max value of aspect ratio. If min_random_aspect_ratio
is None, the aspect ratio range is
[1-max_random_aspect_ratio,
1+max_random_aspect_ratio], otherwise it is
[min_random_aspect_ratio, max_random_aspect_ratio].
(default: 1.33)
--max-random-rotate-angle MAX_RANDOM_ROTATE_ANGLE
max angle to rotate, whose range is [0, 360] (default:
0)
--max-random-shear-ratio MAX_RANDOM_SHEAR_RATIO
max ratio to shear, whose range is [0, 1] (default: 0)
--max-random-scale MAX_RANDOM_SCALE
max ratio to scale (default: 1)
--min-random-scale MIN_RANDOM_SCALE
min ratio to scale, should >= img_size/input_shape.
otherwise use --pad-size (default: 1)
--max-random-area MAX_RANDOM_AREA
max area to crop in random resized crop, whose range
is [0, 1] (default: 1)
--min-random-area MIN_RANDOM_AREA
min area to crop in random resized crop, whose range
is [0, 1] (default: 0.05)
--min-crop-size MIN_CROP_SIZE
Crop both width and height into a random size in
[min_crop_size, max_crop_size] (default: -1)
--max-crop-size MAX_CROP_SIZE
Crop both width and height into a random size in
[min_crop_size, max_crop_size] (default: -1)
--brightness BRIGHTNESS
brightness jittering, whose range is [0, 1] (default:
0)
--contrast CONTRAST contrast jittering, whose range is [0, 1] (default: 0)
--saturation SATURATION
saturation jittering, whose range is [0, 1] (default:
0)
--pca-noise PCA_NOISE
pca noise, whose range is [0, 1] (default: 0)
--random-resized-crop RANDOM_RESIZED_CROP
whether to use random resized crop (default: 1)
```
### Command-line options
To see the full list of available options and their descriptions, use the `-h` or `--help` command line option:
`./runner --help` and `python train.py --help`
`./runner` acts as a wrapper on `train.py` and all additional flags will be passed to `train.py`.
### Getting the data
The MXNet ResNet-50 v1.5 script operates on ImageNet 1k, a widely popular image classification dataset from ILSVRC challenge. You can download the images from `http://image-net.org/download-images`.
The recommended data format is
[RecordIO](https://mxnet.apache.org/versions/1.7.0/api/architecture/note_data_loading), which
concatenates multiple examples into seekable binary files for better read
efficiency. MXNet provides a tool called `im2rec.py` located in the `/opt/mxnet/tools/` directory.
The tool converts individual images into `.rec` files.
To prepare a RecordIO file containing ImageNet data, we first need to create `.lst` files
which consist of the labels and image paths. We assume that the original images were
downloaded to `/data/imagenet/raw/train-jpeg` and `/data/imagenet/raw/val-jpeg`.
```bash
python /opt/mxnet/tools/im2rec.py --list --recursive train /data/imagenet/raw/train-jpeg
python /opt/mxnet/tools/im2rec.py --list --recursive val /data/imagenet/raw/val-jpeg
```
Next, we generate the `.rec` (RecordIO files with data) and `.idx` (indexes required by DALI
to speed up data loading) files. To obtain the best training accuracy we do not preprocess the images when creating the RecordIO file.
```bash
python /opt/mxnet/tools/im2rec.py --pass-through --num-thread 40 train /data/imagenet/raw/train-jpeg
python /opt/mxnet/tools/im2rec.py --pass-through --num-thread 40 val /data/imagenet/raw/val-jpeg
```
#### Dataset guidelines
The process of loading, normalizing, and augmenting the data contained in the dataset can be found in the `data.py` and `dali.py` files.
The data is read from RecordIO format, which concatenates multiple examples into seekable binary files for better read efficiency.
Data augmentation techniques are described in the [Default configuration](#default-configuration) section.
#### Multi-dataset
In most cases, to train a model on a different dataset, no changes in the code are required, but the dataset has to be converted into RecordIO format.
To convert a custom dataset, follow the steps from [Getting the data](#getting-the-data) section, and refer to the `scripts/prepare_dataset.py` script.
### Training process
To start training, run:
`./runner -n <number of gpus> -b <batch size per GPU> --data-root <path to imagenet> --dtype <float32 or float16>`
By default, the training script runs the validation after each epoch:
* The best checkpoint will be stored in the `model_best.params` file in the working directory.
* The log from training will be saved in the `log.log` file in the working directory.
* The JSON report with statistics will be saved in the `report.json` file in the working directory.
If ImageNet is mounted in the `/data/imagenet/train-val-recordio-passthrough` directory, you don't have to specify the `--data-root` flag.
### Inference process
To start validation, run:
`./runner -n <number of gpus> -b <batch size per GPU> --data-root <path to imagenet> --dtype <float32 or float16> --mode val`
By default:
* The log from validation will be saved in the `log.log` file in the working directory.
* The JSON report with statistics will be saved in the `report.json` file in the working directory.
## Performance
The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA’s latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).
### Benchmarking
To benchmark training and inference, run:
`python benchmark.py -n <numbers of gpus separated by comma> -b <batch sizes per GPU separated by comma> --data-root <path to imagenet> --dtype <float32 or float16> -o <path to benchmark report>`
* To control the benchmark length per epoch, use the `-i` flag (defaults to 100 iterations).
* To control the number of epochs, use the `-e` flag.
* To control the number of warmup epochs (epochs which are not taken into account), use the `-w` flag.
* To limit the length of the dataset, use the `--num-examples` flag.
By default, the same parameters as in `./runner` will be used. Additional flags will be passed to `./runner`.
#### Training performance benchmark
To benchmark only training, use the `--mode train` flag.
#### Inference performance benchmark
To benchmark only inference, use the `--mode val` flag.
### Results
The following sections provide details on how we achieved our performance and accuracy in training and inference.
#### Training accuracy results
##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)
**90 epochs configuration**
Our results were obtained by running 8 times the `./runner -n <number of gpus> -b 512 --dtype float32` script for TF32 and the `./runner -n <number of gpus> -b 512` script for mixed precision in the mxnet-22.10-py3 NGC container on NVIDIA DGX A100 with (8x A100 80GB) GPUs.
| **GPUs** | **Accuracy - mixed precision** | **Accuracy - TF32** | **Time to train - mixed precision** | **Time to train - TF32** | **Time to train - speedup** |
|:---:|:---:|:---:|:--:|:---:|:---:|
|1|77.185|77.184|8.75|29.39|3.36|
|8|77.185|77.184|1.14|3.82|3.35|
##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)
**90 epochs configuration**
Our results were obtained by running the `./runner -n <number of gpus> -b 96 --dtype float32` training script for FP32 and the `./runner -n <number of gpus> -b 192` training script for mixed precision in the mxnet-22.10-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
| **GPUs** | **Accuracy - mixed precision** | **Accuracy - FP32** | **Time to train - mixed precision** | **Time to train - FP32** | **Time to train - speedup** |
|:---:|:---:|:---:|:---:|:---:|:---:|
|1|77.342|77.160|24.2|84.5|3.49|
|4|77.196|77.290|6.0|21.4|3.59|
|8|77.150|77.313|3.0|10.7|3.54|
##### Training stability test
Our results were obtained by running the following commands 8 times with different seeds.
* For 50 epochs
* `./runner -n 8 -b 96 --dtype float32 --num-epochs 50` for FP32
* `./runner -n 8 -b 192 --num-epochs 50` for mixed precision
* For 90 epochs
* `./runner -n 8 -b 96 --dtype float32` for FP32
* `./runner -n 8 -b 192` for mixed precision
* For 250 epochs
* `./runner -n 8 -b 96 --dtype float32 --num-epochs 250 --mixup 0.2` for FP32
* `./runner -n 8 -b 192 --num-epochs 250 --mixup 0.2` for mixed precision
| **# of epochs** | **mixed precision avg top1** | **FP32 avg top1** | **mixed precision standard deviation** | **FP32 standard deviation** | **mixed precision minimum top1** | **FP32 minimum top1** | **mixed precision maximum top1** | **FP32 maximum top1** |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|50|76.308|76.329|0.00073|0.00094|76.230|76.234|76.440|76.470|
|90|77.150|77.313|0.00098|0.00085|76.972|77.228|77.266|77.474|
|250|78.460|78.483|0.00078|0.00065|78.284|78.404|78.560|78.598|
**Plots for 250 epoch configuration**
Here are example graphs of FP32 and mixed precision training on 8 GPU 250 epochs configuration:
#### Training performance results
##### Training performance: NVIDIA DGX A100 (8x A100 80GB)
The following results were obtained by running the
`python benchmark.py -n 1,4,8 -b 512 --dtype float32 -o benchmark_report_tf32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for TF32 and the
`python benchmark.py -n 1,4,8 -b 512 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for mixed precision in the mxnet-22.10-py3 NGC container on NVIDIA DGX A100 with (8x A100 80GB) GPUs.
Training performance reported as Total IPS (data + compute time taken into account).
Weak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU.
| **GPUs** | **Throughput - mixed precision** | **Throughput - TF32** | **Throughput speedup (TF32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - TF32** |
|:---:|:---:|:---:|:---:|:---:|:---:|
|1|3410.52 |1055.78 |2.18 |1.00 |1.00 |
|4|13442.66 |4182.30 |3.24 |3.97 |3.96 |
|8|26673.72|8247.44 |3.23 |7.82 |7.81 |
##### Training performance: NVIDIA DGX-1 (8x V100 16GB)
The following results were obtained by running the
`python benchmark.py -n 1,2,4,8 -b 192 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for mixed precision and the
`python benchmark.py -n 1,2,4,8 -b 96 --dtype float32 -o benchmark_report_fp32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for FP32 in the mxnet-20.12-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
Training performance reported as Total IPS (data + compute time taken into account).
Weak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU.
| **GPUs** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** |
|:---:|:---:|:---:|:---:|:---:|:---:|
|1|1376 |384 |3.58 |1.00 |1.00 |
|2|2768 |763 |3.62 |2.01 |1.98 |
|4|5357 |1513 |3.54 |3.89 |3.94 |
|8|10723 |3005 |3.56 |7.79 |7.82 |
##### Training performance: NVIDIA DGX-2 (16x V100 32GB)
The following results were obtained by running the
`python benchmark.py -n 1,2,4,8,16 -b 256 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for mixed precision and the
`python benchmark.py -n 1,2,4,8,16 -b 128 --dtype float32 -o benchmark_report_fp32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for FP32 in the mxnet-20.12-py3 NGC container on NVIDIA DGX-2 with (16x V100 32GB) GPUs.
Training performance reported as Total IPS (data + compute time taken into account).
Weak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU.
| **GPUs** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** |
|:---:|:---:|:---:|:---:|:---:|:---:|
|1 |1492 |417 |3.57 |1.00 |1.00 |
|2 |2935 |821 |3.57 |1.96 |1.96 |
|4 |5726 |1623 |3.52 |3.83 |3.92 |
|8 |11368|3223 |3.52 |7.61 |7.72 |
|16|21484|6338 |3.38 |14.39|15.19|
#### Inference performance results
##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)
The following results were obtained by running the
`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val` script for mixed precision and the
`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_tf32.json -i 500 -e 3 -w 1 --mode val` script for TF32 in the mxnet-22.10-py3 NGC container on NVIDIA DGX A100 with (8x A100 80GB) GPUs.
Inference performance reported as Total IPS (data + compute time taken into account).
Reported mixed precision speedups are relative to TF32 numbers for corresponding configuration.
| **Batch size** | **Throughput (img/sec) - mixed precision** | **Throughput - speedup** | **Avg latency (ms) - mixed precision** | **Avg latency - speedup** | **50% latency (ms) - mixed precision** | **50% latency - speedup** | **90% latency (ms) - mixed precision** | **90% latency - speedup** | **95% latency (ms) - mixed precision** | **95% latency - speedup** | **99% latency (ms) - mixed precision** | **99% latency - speedup** |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| 1 | 1431.99 | 1.9 | 0.7 | 1.9 | 0.68 | 1.95 | 0.71 | 1.9 | 0.84 | 1.65 | 0.88 | 1.7 |
| 2 | 2530.66 | 2.19 | 0.79 | 2.19 | 0.74 | 2.31 | 0.86 | 2.05 | 0.93 | 2.0 | 2.0 | 0.97 |
| 4 | 3680.74 | 2.11 | 1.09 | 2.11 | 0.92 | 2.49 | 1.21 | 1.98 | 1.64 | 1.51 | 6.03 | 0.45 |
| 8 | 2593.88 | 1.11 | 3.08 | 1.11 | 2.89 | 1.17 | 4.09 | 0.89 | 4.72 | 0.8 | 9.85 | 0.55 |
| 16 | 4340.08 | 1.52 | 3.69 | 1.52 | 3.31 | 1.68 | 4.73 | 1.24 | 6.3 | 0.95 | 12.31 | 0.54 |
| 32 | 6808.22 | 2.1 | 4.7 | 2.1 | 4.0 | 2.46 | 6.44 | 1.58 | 9.01 | 1.15 | 15.88 | 0.68 |
| 64 | 7659.96 | 2.21 | 8.36 | 2.21 | 7.44 | 2.48 | 10.76 | 1.75 | 13.91 | 1.37 | 21.96 | 0.9 |
| 128 | 8017.67 | 2.23 | 15.96 | 2.23 | 15.0 | 2.37 | 18.95 | 1.9 | 21.65 | 1.67 | 30.36 | 1.23 |
| 192 | 8240.8 | 2.26 | 23.3 | 2.26 | 22.49 | 2.33 | 25.65 | 2.07 | 27.54 | 1.94 | 37.19 | 1.5 |
| 256 | 7909.62 | 2.15 | 32.37 | 2.15 | 31.66 | 2.2 | 34.27 | 2.05 | 37.02 | 1.9 | 42.83 | 1.66 |
| 512 | 7213.43 | 2.07 | 70.98 | 2.07 | 70.48 | 2.08 | 73.21 | 2.04 | 74.38 | 2.03 | 79.15 | 1.99 |
##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)
The following results were obtained by running the
`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val` script for mixed precision and the
`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_fp32.json -i 500 -e 3 -w 1 --mode val` script for FP32 in the mxnet-20.12-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
Inference performance reported as Total IPS (data + compute time taken into account).
Reported mixed precision speedups are relative to FP32 numbers for corresponding configuration.
| **Batch size** | **Throughput (img/sec) - mixed precision** | **Throughput - speedup** | **Avg latency (ms) - mixed precision** | **Avg latency - speedup** | **50% latency (ms) - mixed precision** | **50% latency - speedup** | **90% latency (ms) - mixed precision** | **90% latency - speedup** | **95% latency (ms) - mixed precision** | **95% latency - speedup** | **99% latency (ms) - mixed precision** | **99% latency - speedup** |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| 1 | 286 | 1.27 | 3.48 | 1.27 | 3.45 | 1.27 | 3.61 | 1.26| 3.68 | 1.26| 3.86 | 1.24|
| 2 | 519 | 1.34 | 3.84 | 1.34 | 3.77 | 1.35 | 4.05 | 1.31| 4.16 | 1.29| 4.59 | 1.27|
| 4 | 910 | 1.60 | 4.39 | 1.60 | 4.35 | 1.61 | 4.59 | 1.56| 4.66 | 1.56| 5.19 | 1.47|
| 8 | 1642| 2.20 | 4.87 | 2.20 | 4.68 | 2.29 | 5.35 | 2.05| 6.01 | 1.84| 11.06| 1.04|
| 16 | 2359| 2.55 | 6.78 | 2.55 | 6.49 | 2.66 | 7.07 | 2.48| 8.33 | 2.12| 13.89| 1.30|
| 32 | 2902| 2.86 | 11.02| 2.86 | 10.43| 3.02 | 12.25| 2.60| 13.88| 2.31| 21.41| 1.55|
| 64 | 3234| 2.74 | 19.78| 2.74 | 18.89| 2.86 | 22.50| 2.44| 25.38| 2.17| 30.78| 1.81|
| 128 | 3362| 2.69 | 38.06| 2.69 | 37.20| 2.75 | 42.32| 2.44| 45.12| 2.30| 50.59| 2.07|
| 192 | 3178| 2.52 | 60.40| 2.52 | 59.62| 2.55 | 65.56| 2.35| 68.16| 2.25| 73.72| 2.10|
| 256 | 3057| 2.38 | 83.73| 2.38 | 82.77| 2.40 | 92.26| 2.24| 92.26| 2.17|100.84| 2.23|
##### Inference performance: NVIDIA T4
The following results were obtained by running the
`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val` script for mixed precision and the
`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_fp32.json -i 500 -e 3 -w 1 --mode val` script for FP32 in the mxnet-20.12-py3 NGC container on an NVIDIA T4 GPU.
Inference performance reported as Total IPS (data + compute time taken into account).
Reported mixed precision speedups are relative to FP32 numbers for corresponding configuration.
| **Batch size** | **Throughput (img/sec) - mixed precision** | **Throughput - speedup** | **Avg latency (ms) - mixed precision** | **Avg latency - speedup** | **50% latency (ms) - mixed precision** | **50% latency - speedup** | **90% latency (ms) - mixed precision** | **90% latency - speedup** | **95% latency (ms) - mixed precision** | **95% latency - speedup** | **99% latency (ms) - mixed precision** | **99% latency - speedup** |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| 1 | 131 | 1.11 | 7.61 | 1.17 | 7.10 | 0.97 | 10.28 | 0.92 | 11.35 | 0.95 | 15.05 | 0.96 |
| 2 | 277 | 1.48 | 7.20 | 1.53 | 7.30 | 1.19 | 7.74 | 1.48 | 8.82 | 1.49 | 12.09 | 1.58 |
| 4 | 374 | 1.47 | 10.67| 1.50 | 10.20| 1.40 | 13.51 | 1.09 | 14.82 | 1.03 | 22.36 | 0.74 |
| 8 | 672 | 2.21 | 11.90| 2.23 | 11.21| 2.21 | 14.54 | 1.74 | 17.24 | 1.48 | 28.65 | 0.92 |
| 16 | 1267| 3.57 | 12.62| 3.58 | 12.02| 3.59 | 14.02 | 3.13 | 16.02 | 2.76 | 22.28 | 2.01 |
| 32 | 1473| 3.85 | 21.71| 3.86 | 21.67| 3.76 | 22.63 | 3.64 | 22.98 | 3.60 | 23.85 | 3.52 |
| 64 | 1561| 3.70 | 40.98| 3.70 | 40.87| 3.64 | 41.98 | 3.57 | 42.56 | 3.53 | 43.85 | 3.46 |
| 128 | 1555| 3.60 | 82.26| 3.60 | 81.86| 3.57 | 83.87 | 3.51 | 84.63 | 3.49 | 96.56 | 3.09 |
| 192 | 1545| 3.64 |124.26| 3.64 |123.67| 3.61 |125.76 | 3.58 |126.73 | 3.56 |143.27 | 3.19 |
| 256 | 1559| 3.71 |164.15| 3.71 |163.97| 3.71 |166.28 | 3.70 |167.01 | 3.70 |168.54 | 3.69 |
## Release notes
### Changelog
1. Dec, 2018
* Initial release (based on https://github.com/apache/incubator-mxnet/tree/v1.8.x/example/image-classification)
2. June, 2019
* Code refactor
* Label smoothing
* Cosine LR schedule
* MixUp regularization
* Better configurations
3. February, 2021
* DGX-A100 performance results
* Container version upgraded to 20.12
4. December, 2022
* Container version upgraded to 22.10
* Updated the A100 performance results. V100 and T4 performance results reflect the performance using the 20.12 container
### Known Issues
There are no known issues with this model.
|
TensorFlow2/LanguageModeling/ELECTRA/data/glue | glue | download_mrpc | #!/usr/bin/env bash
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
echo "Downloading MRPC data"
wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py
python download_glue_data.py --data_dir . --tasks MRPC
|
PyTorch/Recommendation/DLRM/triton | triton | client | #!/usr/bin/env python
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import argparse
import json
import sys
import numpy as np
import torch
import tritonclient.http as http_client
from sklearn.metrics import roc_auc_score
from tqdm import tqdm
from dlrm.data.datasets import SyntheticDataset, SplitCriteoDataset
from dlrm.utils.distributed import get_device_mapping
def get_data_loader(batch_size, *, data_path, model_config):
with open(model_config.dataset_config) as f:
categorical_sizes = list(json.load(f).values())
categorical_sizes = [s + 1 for s in categorical_sizes]
device_mapping = get_device_mapping(categorical_sizes, num_gpus=1)
if data_path:
data = SplitCriteoDataset(
data_path=data_path,
batch_size=batch_size,
numerical_features=True,
categorical_features=device_mapping['embedding'][0],
categorical_feature_sizes=categorical_sizes,
prefetch_depth=1,
drop_last_batch=model_config.drop_last_batch
)
else:
data = SyntheticDataset(
num_entries=batch_size * 1024,
batch_size=batch_size,
numerical_features=model_config.num_numerical_features,
categorical_feature_sizes=categorical_sizes,
device="cpu"
)
if model_config.test_batches > 0:
data = torch.utils.data.Subset(data, list(range(model_config.test_batches)))
return torch.utils.data.DataLoader(data,
batch_size=None,
num_workers=0,
pin_memory=False)
def run_infer(model_name, model_version, numerical_features, categorical_features, headers=None):
inputs = []
outputs = []
num_type = "FP16" if numerical_features.dtype == np.float16 else "FP32"
inputs.append(http_client.InferInput('input__0', numerical_features.shape, num_type))
inputs.append(http_client.InferInput('input__1', categorical_features.shape, "INT64"))
# Initialize the data
inputs[0].set_data_from_numpy(numerical_features, binary_data=True)
inputs[1].set_data_from_numpy(categorical_features, binary_data=False)
outputs.append(http_client.InferRequestedOutput('output__0', binary_data=True))
results = triton_client.infer(model_name,
inputs,
model_version=str(model_version) if model_version != -1 else '',
outputs=outputs,
headers=headers)
return results
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--triton-server-url',
type=str,
required=True,
help='URL adress of triton server (with port)')
parser.add_argument('--triton-model-name', type=str, required=True,
help='Triton deployed model name')
parser.add_argument('--triton-model-version', type=int, default=-1,
help='Triton model version')
parser.add_argument('-v',
'--verbose',
action="store_true",
required=False,
default=False,
help='Enable verbose output')
parser.add_argument('-H', dest='http_headers', metavar="HTTP_HEADER",
required=False, action='append',
help='HTTP headers to add to inference server requests. ' +
'Format is -H"Header:Value".')
parser.add_argument("--dataset_config", type=str, required=True)
parser.add_argument("--inference_data", type=str,
help="Path to file with inference data.")
parser.add_argument("--batch_size", type=int, default=1,
help="Inference request batch size")
parser.add_argument("--drop_last_batch", type=bool, default=True,
help="Drops the last batch size if it's not full")
parser.add_argument("--fp16", action="store_true", default=False,
help="Use 16bit for numerical input")
parser.add_argument("--test_batches", type=int, default=0,
help="Specifies number of batches used in the inference")
FLAGS = parser.parse_args()
try:
triton_client = http_client.InferenceServerClient(url=FLAGS.triton_server_url, verbose=FLAGS.verbose)
except Exception as e:
print("channel creation failed: " + str(e))
sys.exit(1)
if FLAGS.http_headers is not None:
headers_dict = {l.split(':')[0]: l.split(':')[1]
for l in FLAGS.http_headers}
else:
headers_dict = None
triton_client.load_model(FLAGS.triton_model_name)
if not triton_client.is_model_ready(FLAGS.triton_model_name):
sys.exit(1)
dataloader = get_data_loader(FLAGS.batch_size,
data_path=FLAGS.inference_data,
model_config=FLAGS)
results = []
tgt_list = []
for numerical_features, categorical_features, target in tqdm(dataloader):
numerical_features = numerical_features.cpu().numpy()
numerical_features = numerical_features.astype(np.float16 if FLAGS.fp16 else np.float32)
categorical_features = categorical_features.long().cpu().numpy()
output = run_infer(FLAGS.triton_model_name, FLAGS.triton_model_version,
numerical_features, categorical_features, headers_dict)
results.append(output.as_numpy('output__0'))
tgt_list.append(target.cpu().numpy())
results = np.concatenate(results).squeeze()
tgt_list = np.concatenate(tgt_list)
score = roc_auc_score(tgt_list, results)
print(f"Model score: {score}")
statistics = triton_client.get_inference_statistics(model_name=FLAGS.triton_model_name, headers=headers_dict)
print(statistics)
if len(statistics['model_stats']) != 1:
print("FAILED: Inference Statistics")
sys.exit(1)
|
TensorFlow2/Classification/ConvNets/efficientnet_v2/S/evaluation | evaluation | evaluation_AMP_V100-32G | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
python main.py \
--cfg config/efficientnet_v2/s_cfg.py \
--mode eval \
--use_amp \
--use_xla \
--eval_batch_size 128 \
--eval_img_size 384 \
--model_dir ./output/expXX \
--n_repeat_eval 4 \
--moving_average_decay 0.9999 # enables evaluation using EMA weights too
|
TensorFlow2/Recommendation/WideAndDeep/triton/runner | runner | pipeline | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pathlib
from typing import Dict, Tuple
# method from PEP-366 to support relative import in executed modules
if __name__ == "__main__" and __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from .stages import (
ConversionStage,
DeployStage,
ExportStage,
ResultsType,
TritonPerformanceOfflineStage,
TritonPerformanceOnlineStage,
TritonPreparePerformanceProfilingDataStage,
)
class Pipeline:
"""
Definition of stages that has to be executed before and during experiments
"""
# Stages to execute as part of single experiment
_experiment_stages = [
ExportStage.label,
ConversionStage.label,
DeployStage.label,
TritonPreparePerformanceProfilingDataStage.label,
TritonPerformanceOfflineStage.label,
TritonPerformanceOnlineStage.label,
]
def __init__(self):
"""
Initialize pipeline
"""
self._stages: Dict = dict()
def model_export(self, commands: Tuple[str, ...]) -> None:
"""
Model export stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = ExportStage(commands=commands)
self._stages[stage.label] = stage
def model_conversion(self, commands: Tuple[str, ...]) -> None:
"""
Model conversion stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = ConversionStage(commands=commands)
self._stages[stage.label] = stage
def model_deploy(self, commands: Tuple[str, ...]) -> None:
"""
Model deployment stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = DeployStage(commands=commands)
self._stages[stage.label] = stage
def triton_prepare_performance_profiling_data(self, commands: Tuple[str, ...]) -> None:
"""
Model profiling data creation stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = TritonPreparePerformanceProfilingDataStage(commands=commands)
self._stages[stage.label] = stage
def triton_performance_offline_tests(self, commands: Tuple[str, ...], result_path: str) -> None:
"""
Model performance offline test stage
Args:
commands: Commands to be executed as part of stage
result_path: Path where results file is stored
Returns:
None
"""
stage = TritonPerformanceOfflineStage(
commands=commands,
result_path=result_path,
result_type=ResultsType.TRITON_PERFORMANCE_OFFLINE,
)
self._stages[stage.label] = stage
def triton_performance_online_tests(self, commands: Tuple[str, ...], result_path: str) -> None:
"""
Model performance online test stage
Args:
commands: Commands to be executed as part of stage
result_path: Path where results file is stored
Returns:
None
"""
stage = TritonPerformanceOnlineStage(
commands=commands,
result_path=result_path,
result_type=ResultsType.TRITON_PERFORMANCE_ONLINE,
)
self._stages[stage.label] = stage
def stages(self):
"""
Generate stages which should be run per experiment
Returns:
Generator with stages object
"""
for stage_name in self._experiment_stages:
stage = self._stages.get(stage_name)
if not stage:
continue
yield stage
|
PyTorch/SpeechSynthesis/HiFiGAN/platform | platform | DGXA100_HiFi-GAN_AMP_8GPU | #!/bin/bash
set -a
: ${NUM_GPUS:=8}
: ${BATCH_SIZE:=16}
: ${GRAD_ACCUMULATION:=1}
: ${AMP:=true}
bash scripts/train_lj22khz.sh "$@" --no_amp_grouped_conv
|
TensorFlow2/Classification/ConvNets/utils | utils | setup | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import tensorflow as tf
import horovod.tensorflow as hvd
def set_flags(params):
# os.environ['CUDA_CACHE_DISABLE'] = '1'
os.environ['HOROVOD_GPU_ALLREDUCE'] = 'NCCL'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
# os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '0'
os.environ['TF_ADJUST_HUE_FUSED'] = '1'
os.environ['TF_ADJUST_SATURATION_FUSED'] = '1'
os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
# os.environ['TF_SYNC_ON_FINISH'] = '0'
os.environ['TF_AUTOTUNE_THRESHOLD'] = '2'
os.environ['HOROVOD_CACHE_CAPACITY'] = "0"
os.environ['HOROVOD_CYCLE_TIME'] = "1.0"
if params.intraop_threads:
os.environ['TF_NUM_INTRAOP_THREADS'] = str(params.intraop_threads)
if params.interop_threads:
os.environ['TF_NUM_INTEROP_THREADS'] = str(params.interop_threads)
if params.use_xla:
# it turns out tf_xla_enable_lazy_compilation is used before running main.py, so setting this flag
# in the current function would have no effect. Thus, this flag is already set in Dockerfile. The
# remaining XLA flags are set here.
TF_XLA_FLAGS = os.environ['TF_XLA_FLAGS'] # contains tf_xla_enable_lazy_compilation
# we set tf_xla_async_io_level=0 for 2 reasons: 1) It turns out that XLA doesn't like
# hvd.allreduce ops used in the custom train_step. Because of this issue, training never started.
# 2) XLA doesn't like the tf.cond used in conditional mixing (model module).
# remove async flag since it's obsolete
#os.environ['TF_XLA_FLAGS'] = TF_XLA_FLAGS + " --tf_xla_auto_jit=1 --tf_xla_async_io_level=0"
os.environ['TF_XLA_FLAGS'] = TF_XLA_FLAGS + " --tf_xla_auto_jit=1"
os.environ['TF_EXTRA_PTXAS_OPTIONS'] = "-sw200428197=true"
tf.keras.backend.clear_session()
tf.config.optimizer.set_jit(True)
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_visible_devices(gpus, 'GPU')
if params.memory_limit:
for gpu in gpus:
tf.config.experimental.set_virtual_device_configuration(gpu, [
tf.config.experimental.VirtualDeviceConfiguration(memory_limit=params.memory_limit)])
else:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
assert tf.config.experimental.get_memory_growth(gpu)
if gpus:
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
np.random.seed(params.seed)
tf.random.set_seed(params.seed)
if params.use_amp:
# Model.compile will automatically wrap an optimizer with a tf.keras.mixed_precision.LossScaleOptimizer
# if you use the 'mixed_float16' policy. If you use a custom training loop instead of calling Model.compile,
# you should explicitly use a tf.keras.mixed_precision.LossScaleOptimizer to avoid numeric underflow with float16.
policy = tf.keras.mixed_precision.experimental.Policy('mixed_float16', loss_scale='dynamic')
tf.keras.mixed_precision.experimental.set_policy(policy)
else:
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '0'
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | ops_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for object_detection.utils.ops."""
import numpy as np
import tensorflow as tf
from object_detection.core import standard_fields as fields
from object_detection.utils import ops
from object_detection.utils import test_case
slim = tf.contrib.slim
class NormalizedToImageCoordinatesTest(tf.test.TestCase):
def test_normalized_to_image_coordinates(self):
normalized_boxes = tf.placeholder(tf.float32, shape=(None, 1, 4))
normalized_boxes_np = np.array([[[0.0, 0.0, 1.0, 1.0]],
[[0.5, 0.5, 1.0, 1.0]]])
image_shape = tf.convert_to_tensor([1, 4, 4, 3], dtype=tf.int32)
absolute_boxes = ops.normalized_to_image_coordinates(normalized_boxes,
image_shape,
parallel_iterations=2)
expected_boxes = np.array([[[0, 0, 4, 4]],
[[2, 2, 4, 4]]])
with self.test_session() as sess:
absolute_boxes = sess.run(absolute_boxes,
feed_dict={normalized_boxes:
normalized_boxes_np})
self.assertAllEqual(absolute_boxes, expected_boxes)
class ReduceSumTrailingDimensions(tf.test.TestCase):
def test_reduce_sum_trailing_dimensions(self):
input_tensor = tf.placeholder(tf.float32, shape=[None, None, None])
reduced_tensor = ops.reduce_sum_trailing_dimensions(input_tensor, ndims=2)
with self.test_session() as sess:
reduced_np = sess.run(reduced_tensor,
feed_dict={input_tensor: np.ones((2, 2, 2),
np.float32)})
self.assertAllClose(reduced_np, 2 * np.ones((2, 2), np.float32))
class MeshgridTest(tf.test.TestCase):
def test_meshgrid_numpy_comparison(self):
"""Tests meshgrid op with vectors, for which it should match numpy."""
x = np.arange(4)
y = np.arange(6)
exp_xgrid, exp_ygrid = np.meshgrid(x, y)
xgrid, ygrid = ops.meshgrid(x, y)
with self.test_session() as sess:
xgrid_output, ygrid_output = sess.run([xgrid, ygrid])
self.assertAllEqual(xgrid_output, exp_xgrid)
self.assertAllEqual(ygrid_output, exp_ygrid)
def test_meshgrid_multidimensional(self):
np.random.seed(18)
x = np.random.rand(4, 1, 2).astype(np.float32)
y = np.random.rand(2, 3).astype(np.float32)
xgrid, ygrid = ops.meshgrid(x, y)
grid_shape = list(y.shape) + list(x.shape)
self.assertEqual(xgrid.get_shape().as_list(), grid_shape)
self.assertEqual(ygrid.get_shape().as_list(), grid_shape)
with self.test_session() as sess:
xgrid_output, ygrid_output = sess.run([xgrid, ygrid])
# Check the shape of the output grids
self.assertEqual(xgrid_output.shape, tuple(grid_shape))
self.assertEqual(ygrid_output.shape, tuple(grid_shape))
# Check a few elements
test_elements = [((3, 0, 0), (1, 2)),
((2, 0, 1), (0, 0)),
((0, 0, 0), (1, 1))]
for xind, yind in test_elements:
# These are float equality tests, but the meshgrid op should not introduce
# rounding.
self.assertEqual(xgrid_output[yind + xind], x[xind])
self.assertEqual(ygrid_output[yind + xind], y[yind])
class OpsTestFixedPadding(tf.test.TestCase):
def test_3x3_kernel(self):
tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
padded_tensor = ops.fixed_padding(tensor, 3)
with self.test_session() as sess:
padded_tensor_out = sess.run(padded_tensor)
self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape)
def test_5x5_kernel(self):
tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
padded_tensor = ops.fixed_padding(tensor, 5)
with self.test_session() as sess:
padded_tensor_out = sess.run(padded_tensor)
self.assertEqual((1, 6, 6, 1), padded_tensor_out.shape)
def test_3x3_atrous_kernel(self):
tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
padded_tensor = ops.fixed_padding(tensor, 3, 2)
with self.test_session() as sess:
padded_tensor_out = sess.run(padded_tensor)
self.assertEqual((1, 6, 6, 1), padded_tensor_out.shape)
class OpsTestPadToMultiple(tf.test.TestCase):
def test_zero_padding(self):
tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
padded_tensor = ops.pad_to_multiple(tensor, 1)
with self.test_session() as sess:
padded_tensor_out = sess.run(padded_tensor)
self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
def test_no_padding(self):
tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
padded_tensor = ops.pad_to_multiple(tensor, 2)
with self.test_session() as sess:
padded_tensor_out = sess.run(padded_tensor)
self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
def test_non_square_padding(self):
tensor = tf.constant([[[[0.], [0.]]]])
padded_tensor = ops.pad_to_multiple(tensor, 2)
with self.test_session() as sess:
padded_tensor_out = sess.run(padded_tensor)
self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
def test_padding(self):
tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
padded_tensor = ops.pad_to_multiple(tensor, 4)
with self.test_session() as sess:
padded_tensor_out = sess.run(padded_tensor)
self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape)
class OpsTestPaddedOneHotEncoding(tf.test.TestCase):
def test_correct_one_hot_tensor_with_no_pad(self):
indices = tf.constant([1, 2, 3, 5])
one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=0)
expected_tensor = np.array([[0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1]], np.float32)
with self.test_session() as sess:
out_one_hot_tensor = sess.run(one_hot_tensor)
self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
atol=1e-10)
def test_correct_one_hot_tensor_with_pad_one(self):
indices = tf.constant([1, 2, 3, 5])
one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=1)
expected_tensor = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1]], np.float32)
with self.test_session() as sess:
out_one_hot_tensor = sess.run(one_hot_tensor)
self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
atol=1e-10)
def test_correct_one_hot_tensor_with_pad_three(self):
indices = tf.constant([1, 2, 3, 5])
one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=3)
expected_tensor = np.array([[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1]], np.float32)
with self.test_session() as sess:
out_one_hot_tensor = sess.run(one_hot_tensor)
self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
atol=1e-10)
def test_correct_padded_one_hot_tensor_with_empty_indices(self):
depth = 6
pad = 2
indices = tf.constant([])
one_hot_tensor = ops.padded_one_hot_encoding(
indices, depth=depth, left_pad=pad)
expected_tensor = np.zeros((0, depth + pad))
with self.test_session() as sess:
out_one_hot_tensor = sess.run(one_hot_tensor)
self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
atol=1e-10)
def test_return_none_on_zero_depth(self):
indices = tf.constant([1, 2, 3, 4, 5])
one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=0, left_pad=2)
self.assertEqual(one_hot_tensor, None)
def test_raise_value_error_on_rank_two_input(self):
indices = tf.constant(1.0, shape=(2, 3))
with self.assertRaises(ValueError):
ops.padded_one_hot_encoding(indices, depth=6, left_pad=2)
def test_raise_value_error_on_negative_pad(self):
indices = tf.constant(1.0, shape=(2, 3))
with self.assertRaises(ValueError):
ops.padded_one_hot_encoding(indices, depth=6, left_pad=-1)
def test_raise_value_error_on_float_pad(self):
indices = tf.constant(1.0, shape=(2, 3))
with self.assertRaises(ValueError):
ops.padded_one_hot_encoding(indices, depth=6, left_pad=0.1)
def test_raise_value_error_on_float_depth(self):
indices = tf.constant(1.0, shape=(2, 3))
with self.assertRaises(ValueError):
ops.padded_one_hot_encoding(indices, depth=0.1, left_pad=2)
class OpsDenseToSparseBoxesTest(tf.test.TestCase):
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
def test_return_only_valid_boxes_when_input_contains_invalid_boxes(self):
num_classes = 4
num_valid_boxes = 3
num_boxes = 10
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32, shape=(num_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = (feed_dict[dense_location_placeholder]
[:num_valid_boxes])
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
class OpsTestIndicesToDenseVector(tf.test.TestCase):
def test_indices_to_dense_vector(self):
size = 10000
num_indices = np.random.randint(size)
rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
expected_output = np.zeros(size, dtype=np.float32)
expected_output[rand_indices] = 1.
tf_rand_indices = tf.constant(rand_indices)
indicator = ops.indices_to_dense_vector(tf_rand_indices, size)
with self.test_session() as sess:
output = sess.run(indicator)
self.assertAllEqual(output, expected_output)
self.assertEqual(output.dtype, expected_output.dtype)
def test_indices_to_dense_vector_size_at_inference(self):
size = 5000
num_indices = 250
all_indices = np.arange(size)
rand_indices = np.random.permutation(all_indices)[0:num_indices]
expected_output = np.zeros(size, dtype=np.float32)
expected_output[rand_indices] = 1.
tf_all_indices = tf.placeholder(tf.int32)
tf_rand_indices = tf.constant(rand_indices)
indicator = ops.indices_to_dense_vector(tf_rand_indices,
tf.shape(tf_all_indices)[0])
feed_dict = {tf_all_indices: all_indices}
with self.test_session() as sess:
output = sess.run(indicator, feed_dict=feed_dict)
self.assertAllEqual(output, expected_output)
self.assertEqual(output.dtype, expected_output.dtype)
def test_indices_to_dense_vector_int(self):
size = 500
num_indices = 25
rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
expected_output = np.zeros(size, dtype=np.int64)
expected_output[rand_indices] = 1
tf_rand_indices = tf.constant(rand_indices)
indicator = ops.indices_to_dense_vector(
tf_rand_indices, size, 1, dtype=tf.int64)
with self.test_session() as sess:
output = sess.run(indicator)
self.assertAllEqual(output, expected_output)
self.assertEqual(output.dtype, expected_output.dtype)
def test_indices_to_dense_vector_custom_values(self):
size = 100
num_indices = 10
rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
indices_value = np.random.rand(1)
default_value = np.random.rand(1)
expected_output = np.float32(np.ones(size) * default_value)
expected_output[rand_indices] = indices_value
tf_rand_indices = tf.constant(rand_indices)
indicator = ops.indices_to_dense_vector(
tf_rand_indices,
size,
indices_value=indices_value,
default_value=default_value)
with self.test_session() as sess:
output = sess.run(indicator)
self.assertAllClose(output, expected_output)
self.assertEqual(output.dtype, expected_output.dtype)
def test_indices_to_dense_vector_all_indices_as_input(self):
size = 500
num_indices = 500
rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
expected_output = np.ones(size, dtype=np.float32)
tf_rand_indices = tf.constant(rand_indices)
indicator = ops.indices_to_dense_vector(tf_rand_indices, size)
with self.test_session() as sess:
output = sess.run(indicator)
self.assertAllEqual(output, expected_output)
self.assertEqual(output.dtype, expected_output.dtype)
def test_indices_to_dense_vector_empty_indices_as_input(self):
size = 500
rand_indices = []
expected_output = np.zeros(size, dtype=np.float32)
tf_rand_indices = tf.constant(rand_indices)
indicator = ops.indices_to_dense_vector(tf_rand_indices, size)
with self.test_session() as sess:
output = sess.run(indicator)
self.assertAllEqual(output, expected_output)
self.assertEqual(output.dtype, expected_output.dtype)
class GroundtruthFilterTest(tf.test.TestCase):
def test_filter_groundtruth(self):
input_image = tf.placeholder(tf.float32, shape=(None, None, 3))
input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
input_classes = tf.placeholder(tf.int32, shape=(None,))
input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
input_area = tf.placeholder(tf.float32, shape=(None,))
input_difficult = tf.placeholder(tf.float32, shape=(None,))
input_label_types = tf.placeholder(tf.string, shape=(None,))
valid_indices = tf.placeholder(tf.int32, shape=(None,))
input_tensors = {
fields.InputDataFields.image: input_image,
fields.InputDataFields.groundtruth_boxes: input_boxes,
fields.InputDataFields.groundtruth_classes: input_classes,
fields.InputDataFields.groundtruth_is_crowd: input_is_crowd,
fields.InputDataFields.groundtruth_area: input_area,
fields.InputDataFields.groundtruth_difficult: input_difficult,
fields.InputDataFields.groundtruth_label_types: input_label_types
}
output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
image_tensor = np.random.rand(224, 224, 3)
feed_dict = {
input_image: image_tensor,
input_boxes:
np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
input_classes:
np.array([1, 2], dtype=np.int32),
input_is_crowd:
np.array([False, True], dtype=np.bool),
input_area:
np.array([32, 48], dtype=np.float32),
input_difficult:
np.array([True, False], dtype=np.bool),
input_label_types:
np.array(['APPROPRIATE', 'INCORRECT'], dtype=np.string_),
valid_indices:
np.array([0], dtype=np.int32)
}
expected_tensors = {
fields.InputDataFields.image:
image_tensor,
fields.InputDataFields.groundtruth_boxes:
[[0.2, 0.4, 0.1, 0.8]],
fields.InputDataFields.groundtruth_classes:
[1],
fields.InputDataFields.groundtruth_is_crowd:
[False],
fields.InputDataFields.groundtruth_area:
[32],
fields.InputDataFields.groundtruth_difficult:
[True],
fields.InputDataFields.groundtruth_label_types:
['APPROPRIATE']
}
with self.test_session() as sess:
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
for key in [fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_area]:
self.assertAllClose(expected_tensors[key], output_tensors[key])
for key in [fields.InputDataFields.groundtruth_classes,
fields.InputDataFields.groundtruth_is_crowd,
fields.InputDataFields.groundtruth_label_types]:
self.assertAllEqual(expected_tensors[key], output_tensors[key])
def test_filter_with_missing_fields(self):
input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
input_classes = tf.placeholder(tf.int32, shape=(None,))
input_tensors = {
fields.InputDataFields.groundtruth_boxes: input_boxes,
fields.InputDataFields.groundtruth_classes: input_classes
}
valid_indices = tf.placeholder(tf.int32, shape=(None,))
feed_dict = {
input_boxes:
np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
input_classes:
np.array([1, 2], dtype=np.int32),
valid_indices:
np.array([0], dtype=np.int32)
}
expected_tensors = {
fields.InputDataFields.groundtruth_boxes:
[[0.2, 0.4, 0.1, 0.8]],
fields.InputDataFields.groundtruth_classes:
[1]
}
output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
with self.test_session() as sess:
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
for key in [fields.InputDataFields.groundtruth_boxes]:
self.assertAllClose(expected_tensors[key], output_tensors[key])
for key in [fields.InputDataFields.groundtruth_classes]:
self.assertAllEqual(expected_tensors[key], output_tensors[key])
def test_filter_with_empty_fields(self):
input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
input_classes = tf.placeholder(tf.int32, shape=(None,))
input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
input_area = tf.placeholder(tf.float32, shape=(None,))
input_difficult = tf.placeholder(tf.float32, shape=(None,))
valid_indices = tf.placeholder(tf.int32, shape=(None,))
input_tensors = {
fields.InputDataFields.groundtruth_boxes: input_boxes,
fields.InputDataFields.groundtruth_classes: input_classes,
fields.InputDataFields.groundtruth_is_crowd: input_is_crowd,
fields.InputDataFields.groundtruth_area: input_area,
fields.InputDataFields.groundtruth_difficult: input_difficult
}
output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
feed_dict = {
input_boxes:
np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
input_classes:
np.array([1, 2], dtype=np.int32),
input_is_crowd:
np.array([False, True], dtype=np.bool),
input_area:
np.array([], dtype=np.float32),
input_difficult:
np.array([], dtype=np.float32),
valid_indices:
np.array([0], dtype=np.int32)
}
expected_tensors = {
fields.InputDataFields.groundtruth_boxes:
[[0.2, 0.4, 0.1, 0.8]],
fields.InputDataFields.groundtruth_classes:
[1],
fields.InputDataFields.groundtruth_is_crowd:
[False],
fields.InputDataFields.groundtruth_area:
[],
fields.InputDataFields.groundtruth_difficult:
[]
}
with self.test_session() as sess:
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
for key in [fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_area]:
self.assertAllClose(expected_tensors[key], output_tensors[key])
for key in [fields.InputDataFields.groundtruth_classes,
fields.InputDataFields.groundtruth_is_crowd]:
self.assertAllEqual(expected_tensors[key], output_tensors[key])
def test_filter_with_empty_groundtruth_boxes(self):
input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
input_classes = tf.placeholder(tf.int32, shape=(None,))
input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
input_area = tf.placeholder(tf.float32, shape=(None,))
input_difficult = tf.placeholder(tf.float32, shape=(None,))
valid_indices = tf.placeholder(tf.int32, shape=(None,))
input_tensors = {
fields.InputDataFields.groundtruth_boxes: input_boxes,
fields.InputDataFields.groundtruth_classes: input_classes,
fields.InputDataFields.groundtruth_is_crowd: input_is_crowd,
fields.InputDataFields.groundtruth_area: input_area,
fields.InputDataFields.groundtruth_difficult: input_difficult
}
output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
feed_dict = {
input_boxes:
np.array([], dtype=np.float).reshape(0, 4),
input_classes:
np.array([], dtype=np.int32),
input_is_crowd:
np.array([], dtype=np.bool),
input_area:
np.array([], dtype=np.float32),
input_difficult:
np.array([], dtype=np.float32),
valid_indices:
np.array([], dtype=np.int32)
}
with self.test_session() as sess:
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
for key in input_tensors:
if key == fields.InputDataFields.groundtruth_boxes:
self.assertAllEqual([0, 4], output_tensors[key].shape)
else:
self.assertAllEqual([0], output_tensors[key].shape)
class RetainGroundTruthWithPositiveClasses(tf.test.TestCase):
def test_filter_groundtruth_with_positive_classes(self):
input_image = tf.placeholder(tf.float32, shape=(None, None, 3))
input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
input_classes = tf.placeholder(tf.int32, shape=(None,))
input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
input_area = tf.placeholder(tf.float32, shape=(None,))
input_difficult = tf.placeholder(tf.float32, shape=(None,))
input_label_types = tf.placeholder(tf.string, shape=(None,))
valid_indices = tf.placeholder(tf.int32, shape=(None,))
input_tensors = {
fields.InputDataFields.image: input_image,
fields.InputDataFields.groundtruth_boxes: input_boxes,
fields.InputDataFields.groundtruth_classes: input_classes,
fields.InputDataFields.groundtruth_is_crowd: input_is_crowd,
fields.InputDataFields.groundtruth_area: input_area,
fields.InputDataFields.groundtruth_difficult: input_difficult,
fields.InputDataFields.groundtruth_label_types: input_label_types
}
output_tensors = ops.retain_groundtruth_with_positive_classes(input_tensors)
image_tensor = np.random.rand(224, 224, 3)
feed_dict = {
input_image: image_tensor,
input_boxes:
np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
input_classes:
np.array([1, 0], dtype=np.int32),
input_is_crowd:
np.array([False, True], dtype=np.bool),
input_area:
np.array([32, 48], dtype=np.float32),
input_difficult:
np.array([True, False], dtype=np.bool),
input_label_types:
np.array(['APPROPRIATE', 'INCORRECT'], dtype=np.string_),
valid_indices:
np.array([0], dtype=np.int32)
}
expected_tensors = {
fields.InputDataFields.image:
image_tensor,
fields.InputDataFields.groundtruth_boxes:
[[0.2, 0.4, 0.1, 0.8]],
fields.InputDataFields.groundtruth_classes:
[1],
fields.InputDataFields.groundtruth_is_crowd:
[False],
fields.InputDataFields.groundtruth_area:
[32],
fields.InputDataFields.groundtruth_difficult:
[True],
fields.InputDataFields.groundtruth_label_types:
['APPROPRIATE']
}
with self.test_session() as sess:
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
for key in [fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_area]:
self.assertAllClose(expected_tensors[key], output_tensors[key])
for key in [fields.InputDataFields.groundtruth_classes,
fields.InputDataFields.groundtruth_is_crowd,
fields.InputDataFields.groundtruth_label_types]:
self.assertAllEqual(expected_tensors[key], output_tensors[key])
class ReplaceNaNGroundtruthLabelScoresWithOnes(tf.test.TestCase):
def test_replace_nan_groundtruth_label_scores_with_ones(self):
label_scores = tf.constant([np.nan, 1.0, np.nan])
output_tensor = ops.replace_nan_groundtruth_label_scores_with_ones(
label_scores)
expected_tensor = [1.0, 1.0, 1.0]
with self.test_session():
output_tensor = output_tensor.eval()
self.assertAllClose(expected_tensor, output_tensor)
def test_input_equals_output_when_no_nans(self):
input_label_scores = [0.5, 1.0, 1.0]
label_scores_tensor = tf.constant(input_label_scores)
output_label_scores = ops.replace_nan_groundtruth_label_scores_with_ones(
label_scores_tensor)
with self.test_session():
output_label_scores = output_label_scores.eval()
self.assertAllClose(input_label_scores, output_label_scores)
class GroundtruthFilterWithCrowdBoxesTest(tf.test.TestCase):
def test_filter_groundtruth_with_crowd_boxes(self):
input_tensors = {
fields.InputDataFields.groundtruth_boxes:
[[0.1, 0.2, 0.6, 0.8], [0.2, 0.4, 0.1, 0.8]],
fields.InputDataFields.groundtruth_classes:
[1, 2],
fields.InputDataFields.groundtruth_is_crowd:
[True, False],
fields.InputDataFields.groundtruth_area:
[100.0, 238.7]
}
expected_tensors = {
fields.InputDataFields.groundtruth_boxes:
[[0.2, 0.4, 0.1, 0.8]],
fields.InputDataFields.groundtruth_classes:
[2],
fields.InputDataFields.groundtruth_is_crowd:
[False],
fields.InputDataFields.groundtruth_area:
[238.7]
}
output_tensors = ops.filter_groundtruth_with_crowd_boxes(
input_tensors)
with self.test_session() as sess:
output_tensors = sess.run(output_tensors)
for key in [fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_area]:
self.assertAllClose(expected_tensors[key], output_tensors[key])
for key in [fields.InputDataFields.groundtruth_classes,
fields.InputDataFields.groundtruth_is_crowd]:
self.assertAllEqual(expected_tensors[key], output_tensors[key])
class GroundtruthFilterWithNanBoxTest(tf.test.TestCase):
def test_filter_groundtruth_with_nan_box_coordinates(self):
input_tensors = {
fields.InputDataFields.groundtruth_boxes:
[[np.nan, np.nan, np.nan, np.nan], [0.2, 0.4, 0.1, 0.8]],
fields.InputDataFields.groundtruth_classes:
[1, 2],
fields.InputDataFields.groundtruth_is_crowd:
[False, True],
fields.InputDataFields.groundtruth_area:
[100.0, 238.7]
}
expected_tensors = {
fields.InputDataFields.groundtruth_boxes:
[[0.2, 0.4, 0.1, 0.8]],
fields.InputDataFields.groundtruth_classes:
[2],
fields.InputDataFields.groundtruth_is_crowd:
[True],
fields.InputDataFields.groundtruth_area:
[238.7]
}
output_tensors = ops.filter_groundtruth_with_nan_box_coordinates(
input_tensors)
with self.test_session() as sess:
output_tensors = sess.run(output_tensors)
for key in [fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_area]:
self.assertAllClose(expected_tensors[key], output_tensors[key])
for key in [fields.InputDataFields.groundtruth_classes,
fields.InputDataFields.groundtruth_is_crowd]:
self.assertAllEqual(expected_tensors[key], output_tensors[key])
class OpsTestNormalizeToTarget(tf.test.TestCase):
def test_create_normalize_to_target(self):
inputs = tf.random_uniform([5, 10, 12, 3])
target_norm_value = 4.0
dim = 3
with self.test_session():
output = ops.normalize_to_target(inputs, target_norm_value, dim)
self.assertEqual(output.op.name, 'NormalizeToTarget/mul')
var_name = tf.contrib.framework.get_variables()[0].name
self.assertEqual(var_name, 'NormalizeToTarget/weights:0')
def test_invalid_dim(self):
inputs = tf.random_uniform([5, 10, 12, 3])
target_norm_value = 4.0
dim = 10
with self.assertRaisesRegexp(
ValueError,
'dim must be non-negative but smaller than the input rank.'):
ops.normalize_to_target(inputs, target_norm_value, dim)
def test_invalid_target_norm_values(self):
inputs = tf.random_uniform([5, 10, 12, 3])
target_norm_value = [4.0, 4.0]
dim = 3
with self.assertRaisesRegexp(
ValueError, 'target_norm_value must be a float or a list of floats'):
ops.normalize_to_target(inputs, target_norm_value, dim)
def test_correct_output_shape(self):
inputs = tf.random_uniform([5, 10, 12, 3])
target_norm_value = 4.0
dim = 3
with self.test_session():
output = ops.normalize_to_target(inputs, target_norm_value, dim)
self.assertEqual(output.get_shape().as_list(),
inputs.get_shape().as_list())
def test_correct_initial_output_values(self):
inputs = tf.constant([[[[3, 4], [7, 24]],
[[5, -12], [-1, 0]]]], tf.float32)
target_norm_value = 10.0
dim = 3
expected_output = [[[[30/5.0, 40/5.0], [70/25.0, 240/25.0]],
[[50/13.0, -120/13.0], [-10, 0]]]]
with self.test_session() as sess:
normalized_inputs = ops.normalize_to_target(inputs, target_norm_value,
dim)
sess.run(tf.global_variables_initializer())
output = normalized_inputs.eval()
self.assertAllClose(output, expected_output)
def test_multiple_target_norm_values(self):
inputs = tf.constant([[[[3, 4], [7, 24]],
[[5, -12], [-1, 0]]]], tf.float32)
target_norm_value = [10.0, 20.0]
dim = 3
expected_output = [[[[30/5.0, 80/5.0], [70/25.0, 480/25.0]],
[[50/13.0, -240/13.0], [-10, 0]]]]
with self.test_session() as sess:
normalized_inputs = ops.normalize_to_target(inputs, target_norm_value,
dim)
sess.run(tf.global_variables_initializer())
output = normalized_inputs.eval()
self.assertAllClose(output, expected_output)
class OpsTestPositionSensitiveCropRegions(tf.test.TestCase):
def test_position_sensitive(self):
num_spatial_bins = [3, 2]
image_shape = [3, 2, 6]
# First channel is 1's, second channel is 2's, etc.
image = tf.constant(range(1, 3 * 2 + 1) * 6, dtype=tf.float32,
shape=image_shape)
boxes = tf.random_uniform((2, 4))
# The result for both boxes should be [[1, 2], [3, 4], [5, 6]]
# before averaging.
expected_output = np.array([3.5, 3.5]).reshape([2, 1, 1, 1])
for crop_size_mult in range(1, 3):
crop_size = [3 * crop_size_mult, 2 * crop_size_mult]
ps_crop_and_pool = ops.position_sensitive_crop_regions(
image, boxes, crop_size, num_spatial_bins, global_pool=True)
with self.test_session() as sess:
output = sess.run(ps_crop_and_pool)
self.assertAllClose(output, expected_output)
def test_position_sensitive_with_equal_channels(self):
num_spatial_bins = [2, 2]
image_shape = [3, 3, 4]
crop_size = [2, 2]
image = tf.constant(range(1, 3 * 3 + 1), dtype=tf.float32,
shape=[3, 3, 1])
tiled_image = tf.tile(image, [1, 1, image_shape[2]])
boxes = tf.random_uniform((3, 4))
box_ind = tf.constant([0, 0, 0], dtype=tf.int32)
# All channels are equal so position-sensitive crop and resize should
# work as the usual crop and resize for just one channel.
crop = tf.image.crop_and_resize(tf.expand_dims(image, axis=0), boxes,
box_ind, crop_size)
crop_and_pool = tf.reduce_mean(crop, [1, 2], keep_dims=True)
ps_crop_and_pool = ops.position_sensitive_crop_regions(
tiled_image,
boxes,
crop_size,
num_spatial_bins,
global_pool=True)
with self.test_session() as sess:
expected_output, output = sess.run((crop_and_pool, ps_crop_and_pool))
self.assertAllClose(output, expected_output)
def test_raise_value_error_on_num_bins_less_than_one(self):
num_spatial_bins = [1, -1]
image_shape = [1, 1, 2]
crop_size = [2, 2]
image = tf.constant(1, dtype=tf.float32, shape=image_shape)
boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32)
with self.assertRaisesRegexp(ValueError, 'num_spatial_bins should be >= 1'):
ops.position_sensitive_crop_regions(
image, boxes, crop_size, num_spatial_bins, global_pool=True)
def test_raise_value_error_on_non_divisible_crop_size(self):
num_spatial_bins = [2, 3]
image_shape = [1, 1, 6]
crop_size = [3, 2]
image = tf.constant(1, dtype=tf.float32, shape=image_shape)
boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32)
with self.assertRaisesRegexp(
ValueError, 'crop_size should be divisible by num_spatial_bins'):
ops.position_sensitive_crop_regions(
image, boxes, crop_size, num_spatial_bins, global_pool=True)
def test_raise_value_error_on_non_divisible_num_channels(self):
num_spatial_bins = [2, 2]
image_shape = [1, 1, 5]
crop_size = [2, 2]
image = tf.constant(1, dtype=tf.float32, shape=image_shape)
boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32)
with self.assertRaisesRegexp(
ValueError, 'Dimension size must be evenly divisible by 4 but is 5'):
ops.position_sensitive_crop_regions(
image, boxes, crop_size, num_spatial_bins, global_pool=True)
def test_position_sensitive_with_global_pool_false(self):
num_spatial_bins = [3, 2]
image_shape = [3, 2, 6]
num_boxes = 2
# First channel is 1's, second channel is 2's, etc.
image = tf.constant(range(1, 3 * 2 + 1) * 6, dtype=tf.float32,
shape=image_shape)
boxes = tf.random_uniform((num_boxes, 4))
expected_output = []
# Expected output, when crop_size = [3, 2].
expected_output.append(np.expand_dims(
np.tile(np.array([[1, 2],
[3, 4],
[5, 6]]), (num_boxes, 1, 1)),
axis=-1))
# Expected output, when crop_size = [6, 4].
expected_output.append(np.expand_dims(
np.tile(np.array([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4],
[5, 5, 6, 6],
[5, 5, 6, 6]]), (num_boxes, 1, 1)),
axis=-1))
for crop_size_mult in range(1, 3):
crop_size = [3 * crop_size_mult, 2 * crop_size_mult]
ps_crop = ops.position_sensitive_crop_regions(
image, boxes, crop_size, num_spatial_bins, global_pool=False)
with self.test_session() as sess:
output = sess.run(ps_crop)
self.assertAllEqual(output, expected_output[crop_size_mult - 1])
def test_position_sensitive_with_global_pool_false_and_do_global_pool(self):
num_spatial_bins = [3, 2]
image_shape = [3, 2, 6]
num_boxes = 2
# First channel is 1's, second channel is 2's, etc.
image = tf.constant(range(1, 3 * 2 + 1) * 6, dtype=tf.float32,
shape=image_shape)
boxes = tf.random_uniform((num_boxes, 4))
expected_output = []
# Expected output, when crop_size = [3, 2].
expected_output.append(np.mean(
np.expand_dims(
np.tile(np.array([[1, 2],
[3, 4],
[5, 6]]), (num_boxes, 1, 1)),
axis=-1),
axis=(1, 2), keepdims=True))
# Expected output, when crop_size = [6, 4].
expected_output.append(np.mean(
np.expand_dims(
np.tile(np.array([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4],
[5, 5, 6, 6],
[5, 5, 6, 6]]), (num_boxes, 1, 1)),
axis=-1),
axis=(1, 2), keepdims=True))
for crop_size_mult in range(1, 3):
crop_size = [3 * crop_size_mult, 2 * crop_size_mult]
# Perform global_pooling after running the function with
# global_pool=False.
ps_crop = ops.position_sensitive_crop_regions(
image, boxes, crop_size, num_spatial_bins, global_pool=False)
ps_crop_and_pool = tf.reduce_mean(
ps_crop, reduction_indices=(1, 2), keep_dims=True)
with self.test_session() as sess:
output = sess.run(ps_crop_and_pool)
self.assertAllEqual(output, expected_output[crop_size_mult - 1])
def test_raise_value_error_on_non_square_block_size(self):
num_spatial_bins = [3, 2]
image_shape = [3, 2, 6]
crop_size = [6, 2]
image = tf.constant(1, dtype=tf.float32, shape=image_shape)
boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32)
with self.assertRaisesRegexp(
ValueError, 'Only support square bin crop size for now.'):
ops.position_sensitive_crop_regions(
image, boxes, crop_size, num_spatial_bins, global_pool=False)
class OpsTestBatchPositionSensitiveCropRegions(tf.test.TestCase):
def test_position_sensitive_with_single_bin(self):
num_spatial_bins = [1, 1]
image_shape = [2, 3, 3, 4]
crop_size = [2, 2]
image = tf.random_uniform(image_shape)
boxes = tf.random_uniform((2, 3, 4))
box_ind = tf.constant([0, 0, 0, 1, 1, 1], dtype=tf.int32)
# When a single bin is used, position-sensitive crop and pool should be
# the same as non-position sensitive crop and pool.
crop = tf.image.crop_and_resize(image, tf.reshape(boxes, [-1, 4]), box_ind,
crop_size)
crop_and_pool = tf.reduce_mean(crop, [1, 2], keepdims=True)
crop_and_pool = tf.reshape(crop_and_pool, [2, 3, 1, 1, 4])
ps_crop_and_pool = ops.batch_position_sensitive_crop_regions(
image, boxes, crop_size, num_spatial_bins, global_pool=True)
with self.test_session() as sess:
expected_output, output = sess.run((crop_and_pool, ps_crop_and_pool))
self.assertAllClose(output, expected_output)
def test_position_sensitive_with_global_pool_false_and_known_boxes(self):
num_spatial_bins = [2, 2]
image_shape = [2, 2, 2, 4]
crop_size = [2, 2]
images = tf.constant(range(1, 2 * 2 * 4 + 1) * 2, dtype=tf.float32,
shape=image_shape)
# First box contains whole image, and second box contains only first row.
boxes = tf.constant(np.array([[[0., 0., 1., 1.]],
[[0., 0., 0.5, 1.]]]), dtype=tf.float32)
# box_ind = tf.constant([0, 1], dtype=tf.int32)
expected_output = []
# Expected output, when the box containing whole image.
expected_output.append(
np.reshape(np.array([[4, 7],
[10, 13]]),
(1, 2, 2, 1))
)
# Expected output, when the box containing only first row.
expected_output.append(
np.reshape(np.array([[3, 6],
[7, 10]]),
(1, 2, 2, 1))
)
expected_output = np.stack(expected_output, axis=0)
ps_crop = ops.batch_position_sensitive_crop_regions(
images, boxes, crop_size, num_spatial_bins, global_pool=False)
with self.test_session() as sess:
output = sess.run(ps_crop)
self.assertAllEqual(output, expected_output)
def test_position_sensitive_with_global_pool_false_and_single_bin(self):
num_spatial_bins = [1, 1]
image_shape = [2, 3, 3, 4]
crop_size = [1, 1]
images = tf.random_uniform(image_shape)
boxes = tf.random_uniform((2, 3, 4))
# box_ind = tf.constant([0, 0, 0, 1, 1, 1], dtype=tf.int32)
# Since single_bin is used and crop_size = [1, 1] (i.e., no crop resize),
# the outputs are the same whatever the global_pool value is.
ps_crop_and_pool = ops.batch_position_sensitive_crop_regions(
images, boxes, crop_size, num_spatial_bins, global_pool=True)
ps_crop = ops.batch_position_sensitive_crop_regions(
images, boxes, crop_size, num_spatial_bins, global_pool=False)
with self.test_session() as sess:
pooled_output, unpooled_output = sess.run((ps_crop_and_pool, ps_crop))
self.assertAllClose(pooled_output, unpooled_output)
class ReframeBoxMasksToImageMasksTest(tf.test.TestCase):
def testZeroImageOnEmptyMask(self):
box_masks = tf.constant([[[0, 0],
[0, 0]]], dtype=tf.float32)
boxes = tf.constant([[0.0, 0.0, 1.0, 1.0]], dtype=tf.float32)
image_masks = ops.reframe_box_masks_to_image_masks(box_masks, boxes,
image_height=4,
image_width=4)
np_expected_image_masks = np.array([[[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]]], dtype=np.float32)
with self.test_session() as sess:
np_image_masks = sess.run(image_masks)
self.assertAllClose(np_image_masks, np_expected_image_masks)
def testZeroBoxMasks(self):
box_masks = tf.zeros([0, 3, 3], dtype=tf.float32)
boxes = tf.zeros([0, 4], dtype=tf.float32)
image_masks = ops.reframe_box_masks_to_image_masks(box_masks, boxes,
image_height=4,
image_width=4)
with self.test_session() as sess:
np_image_masks = sess.run(image_masks)
self.assertAllEqual(np_image_masks.shape, np.array([0, 4, 4]))
def testMaskIsCenteredInImageWhenBoxIsCentered(self):
box_masks = tf.constant([[[1, 1],
[1, 1]]], dtype=tf.float32)
boxes = tf.constant([[0.25, 0.25, 0.75, 0.75]], dtype=tf.float32)
image_masks = ops.reframe_box_masks_to_image_masks(box_masks, boxes,
image_height=4,
image_width=4)
np_expected_image_masks = np.array([[[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 1, 0],
[0, 0, 0, 0]]], dtype=np.float32)
with self.test_session() as sess:
np_image_masks = sess.run(image_masks)
self.assertAllClose(np_image_masks, np_expected_image_masks)
def testMaskOffCenterRemainsOffCenterInImage(self):
box_masks = tf.constant([[[1, 0],
[0, 1]]], dtype=tf.float32)
boxes = tf.constant([[0.25, 0.5, 0.75, 1.0]], dtype=tf.float32)
image_masks = ops.reframe_box_masks_to_image_masks(box_masks, boxes,
image_height=4,
image_width=4)
np_expected_image_masks = np.array([[[0, 0, 0, 0],
[0, 0, 0.6111111, 0.16666669],
[0, 0, 0.3888889, 0.83333337],
[0, 0, 0, 0]]], dtype=np.float32)
with self.test_session() as sess:
np_image_masks = sess.run(image_masks)
self.assertAllClose(np_image_masks, np_expected_image_masks)
class MergeBoxesWithMultipleLabelsTest(tf.test.TestCase):
def testMergeBoxesWithMultipleLabels(self):
boxes = tf.constant(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
[0.25, 0.25, 0.75, 0.75]],
dtype=tf.float32)
class_indices = tf.constant([0, 4, 2], dtype=tf.int32)
class_confidences = tf.constant([0.8, 0.2, 0.1], dtype=tf.float32)
num_classes = 5
merged_boxes, merged_classes, merged_confidences, merged_box_indices = (
ops.merge_boxes_with_multiple_labels(
boxes, class_indices, class_confidences, num_classes))
expected_merged_boxes = np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=np.float32)
expected_merged_classes = np.array(
[[1, 0, 1, 0, 0], [0, 0, 0, 0, 1]], dtype=np.int32)
expected_merged_confidences = np.array(
[[0.8, 0, 0.1, 0, 0], [0, 0, 0, 0, 0.2]], dtype=np.float32)
expected_merged_box_indices = np.array([0, 1], dtype=np.int32)
with self.test_session() as sess:
(np_merged_boxes, np_merged_classes, np_merged_confidences,
np_merged_box_indices) = sess.run(
[merged_boxes, merged_classes, merged_confidences,
merged_box_indices])
self.assertAllClose(np_merged_boxes, expected_merged_boxes)
self.assertAllClose(np_merged_classes, expected_merged_classes)
self.assertAllClose(np_merged_confidences, expected_merged_confidences)
self.assertAllClose(np_merged_box_indices, expected_merged_box_indices)
def testMergeBoxesWithMultipleLabelsCornerCase(self):
boxes = tf.constant(
[[0, 0, 1, 1], [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1], [1, 0, 1, 1], [0, 1, 1, 1], [0, 0, 1, 1]],
dtype=tf.float32)
class_indices = tf.constant([0, 1, 2, 3, 2, 1, 0, 3], dtype=tf.int32)
class_confidences = tf.constant([0.1, 0.9, 0.2, 0.8, 0.3, 0.7, 0.4, 0.6],
dtype=tf.float32)
num_classes = 4
merged_boxes, merged_classes, merged_confidences, merged_box_indices = (
ops.merge_boxes_with_multiple_labels(
boxes, class_indices, class_confidences, num_classes))
expected_merged_boxes = np.array(
[[0, 0, 1, 1], [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 1, 1]],
dtype=np.float32)
expected_merged_classes = np.array(
[[1, 0, 0, 1], [1, 1, 0, 0], [0, 1, 1, 0], [0, 0, 1, 1]],
dtype=np.int32)
expected_merged_confidences = np.array(
[[0.1, 0, 0, 0.6], [0.4, 0.9, 0, 0],
[0, 0.7, 0.2, 0], [0, 0, 0.3, 0.8]], dtype=np.float32)
expected_merged_box_indices = np.array([0, 1, 2, 3], dtype=np.int32)
with self.test_session() as sess:
(np_merged_boxes, np_merged_classes, np_merged_confidences,
np_merged_box_indices) = sess.run(
[merged_boxes, merged_classes, merged_confidences,
merged_box_indices])
self.assertAllClose(np_merged_boxes, expected_merged_boxes)
self.assertAllClose(np_merged_classes, expected_merged_classes)
self.assertAllClose(np_merged_confidences, expected_merged_confidences)
self.assertAllClose(np_merged_box_indices, expected_merged_box_indices)
def testMergeBoxesWithEmptyInputs(self):
boxes = tf.zeros([0, 4], dtype=tf.float32)
class_indices = tf.constant([], dtype=tf.int32)
class_confidences = tf.constant([], dtype=tf.float32)
num_classes = 5
merged_boxes, merged_classes, merged_confidences, merged_box_indices = (
ops.merge_boxes_with_multiple_labels(
boxes, class_indices, class_confidences, num_classes))
with self.test_session() as sess:
(np_merged_boxes, np_merged_classes, np_merged_confidences,
np_merged_box_indices) = sess.run(
[merged_boxes, merged_classes, merged_confidences,
merged_box_indices])
self.assertAllEqual(np_merged_boxes.shape, [0, 4])
self.assertAllEqual(np_merged_classes.shape, [0, 5])
self.assertAllEqual(np_merged_confidences.shape, [0, 5])
self.assertAllEqual(np_merged_box_indices.shape, [0])
class NearestNeighborUpsamplingTest(test_case.TestCase):
def test_upsampling_with_single_scale(self):
def graph_fn(inputs):
custom_op_output = ops.nearest_neighbor_upsampling(inputs, scale=2)
return custom_op_output
inputs = np.reshape(np.arange(4).astype(np.float32), [1, 2, 2, 1])
custom_op_output = self.execute(graph_fn, [inputs])
expected_output = [[[[0], [0], [1], [1]],
[[0], [0], [1], [1]],
[[2], [2], [3], [3]],
[[2], [2], [3], [3]]]]
self.assertAllClose(custom_op_output, expected_output)
def test_upsampling_with_separate_height_width_scales(self):
def graph_fn(inputs):
custom_op_output = ops.nearest_neighbor_upsampling(inputs,
height_scale=2,
width_scale=3)
return custom_op_output
inputs = np.reshape(np.arange(4).astype(np.float32), [1, 2, 2, 1])
custom_op_output = self.execute(graph_fn, [inputs])
expected_output = [[[[0], [0], [0], [1], [1], [1]],
[[0], [0], [0], [1], [1], [1]],
[[2], [2], [2], [3], [3], [3]],
[[2], [2], [2], [3], [3], [3]]]]
self.assertAllClose(custom_op_output, expected_output)
class MatmulGatherOnZerothAxis(test_case.TestCase):
def test_gather_2d(self):
def graph_fn(params, indices):
return ops.matmul_gather_on_zeroth_axis(params, indices)
params = np.array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[0, 1, 0, 0]], dtype=np.float32)
indices = np.array([2, 2, 1], dtype=np.int32)
expected_output = np.array([[9, 10, 11, 12], [9, 10, 11, 12], [5, 6, 7, 8]])
gather_output = self.execute(graph_fn, [params, indices])
self.assertAllClose(gather_output, expected_output)
def test_gather_3d(self):
def graph_fn(params, indices):
return ops.matmul_gather_on_zeroth_axis(params, indices)
params = np.array([[[1, 2], [3, 4]],
[[5, 6], [7, 8]],
[[9, 10], [11, 12]],
[[0, 1], [0, 0]]], dtype=np.float32)
indices = np.array([0, 3, 1], dtype=np.int32)
expected_output = np.array([[[1, 2], [3, 4]],
[[0, 1], [0, 0]],
[[5, 6], [7, 8]]])
gather_output = self.execute(graph_fn, [params, indices])
self.assertAllClose(gather_output, expected_output)
def test_gather_with_many_indices(self):
def graph_fn(params, indices):
return ops.matmul_gather_on_zeroth_axis(params, indices)
params = np.array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[0, 1, 0, 0]], dtype=np.float32)
indices = np.array([0, 0, 0, 0, 0, 0], dtype=np.int32)
expected_output = np.array(6*[[1, 2, 3, 4]])
gather_output = self.execute(graph_fn, [params, indices])
self.assertAllClose(gather_output, expected_output)
def test_gather_with_dynamic_shape_input(self):
params_placeholder = tf.placeholder(tf.float32, shape=[None, 4])
indices_placeholder = tf.placeholder(tf.int32, shape=[None])
gather_result = ops.matmul_gather_on_zeroth_axis(
params_placeholder, indices_placeholder)
params = np.array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[0, 1, 0, 0]], dtype=np.float32)
indices = np.array([0, 0, 0, 0, 0, 0])
expected_output = np.array(6*[[1, 2, 3, 4]])
with self.test_session() as sess:
gather_output = sess.run(gather_result, feed_dict={
params_placeholder: params, indices_placeholder: indices})
self.assertAllClose(gather_output, expected_output)
class OpsTestMatMulCropAndResize(test_case.TestCase):
def testMatMulCropAndResize2x2To1x1(self):
def graph_fn(image, boxes):
return ops.matmul_crop_and_resize(image, boxes, crop_size=[1, 1])
image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
boxes = np.array([[[0, 0, 1, 1]]], dtype=np.float32)
expected_output = [[[[[2.5]]]]]
crop_output = self.execute(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
def testMatMulCropAndResize2x2To1x1Flipped(self):
def graph_fn(image, boxes):
return ops.matmul_crop_and_resize(image, boxes, crop_size=[1, 1])
image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
boxes = np.array([[[1, 1, 0, 0]]], dtype=np.float32)
expected_output = [[[[[2.5]]]]]
crop_output = self.execute(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
def testMatMulCropAndResize2x2To3x3(self):
def graph_fn(image, boxes):
return ops.matmul_crop_and_resize(image, boxes, crop_size=[3, 3])
image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
boxes = np.array([[[0, 0, 1, 1]]], dtype=np.float32)
expected_output = [[[[[1.0], [1.5], [2.0]],
[[2.0], [2.5], [3.0]],
[[3.0], [3.5], [4.0]]]]]
crop_output = self.execute(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
def testMatMulCropAndResize2x2To3x3Flipped(self):
def graph_fn(image, boxes):
return ops.matmul_crop_and_resize(image, boxes, crop_size=[3, 3])
image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
boxes = np.array([[[1, 1, 0, 0]]], dtype=np.float32)
expected_output = [[[[[4.0], [3.5], [3.0]],
[[3.0], [2.5], [2.0]],
[[2.0], [1.5], [1.0]]]]]
crop_output = self.execute(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
def testMatMulCropAndResize3x3To2x2(self):
def graph_fn(image, boxes):
return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
image = np.array([[[[1], [2], [3]],
[[4], [5], [6]],
[[7], [8], [9]]]], dtype=np.float32)
boxes = np.array([[[0, 0, 1, 1],
[0, 0, .5, .5]]], dtype=np.float32)
expected_output = [[[[[1], [3]], [[7], [9]]],
[[[1], [2]], [[4], [5]]]]]
crop_output = self.execute(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
def testMatMulCropAndResize3x3To2x2_2Channels(self):
def graph_fn(image, boxes):
return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
image = np.array([[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
boxes = np.array([[[0, 0, 1, 1],
[0, 0, .5, .5]]], dtype=np.float32)
expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
[[[1, 0], [2, 1]], [[4, 3], [5, 4]]]]]
crop_output = self.execute(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
def testBatchMatMulCropAndResize3x3To2x2_2Channels(self):
def graph_fn(image, boxes):
return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
image = np.array([[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]],
[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
boxes = np.array([[[0, 0, 1, 1],
[0, 0, .5, .5]],
[[1, 1, 0, 0],
[.5, .5, 0, 0]]], dtype=np.float32)
expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
[[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
[[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
[[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
crop_output = self.execute(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
def testMatMulCropAndResize3x3To2x2Flipped(self):
def graph_fn(image, boxes):
return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
image = np.array([[[[1], [2], [3]],
[[4], [5], [6]],
[[7], [8], [9]]]], dtype=np.float32)
boxes = np.array([[[1, 1, 0, 0],
[.5, .5, 0, 0]]], dtype=np.float32)
expected_output = [[[[[9], [7]], [[3], [1]]],
[[[5], [4]], [[2], [1]]]]]
crop_output = self.execute(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
def testInvalidInputShape(self):
image = tf.constant([[[1], [2]], [[3], [4]]], dtype=tf.float32)
boxes = tf.constant([[-1, -1, 1, 1]], dtype=tf.float32)
crop_size = [4, 4]
with self.assertRaises(ValueError):
_ = ops.matmul_crop_and_resize(image, boxes, crop_size)
class OpsTestCropAndResize(test_case.TestCase):
def testBatchCropAndResize3x3To2x2_2Channels(self):
def graph_fn(image, boxes):
return ops.native_crop_and_resize(image, boxes, crop_size=[2, 2])
image = np.array([[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]],
[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
boxes = np.array([[[0, 0, 1, 1],
[0, 0, .5, .5]],
[[1, 1, 0, 0],
[.5, .5, 0, 0]]], dtype=np.float32)
expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
[[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
[[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
[[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
crop_output = self.execute_cpu(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
if __name__ == '__main__':
tf.test.main()
|
TensorFlow/Detection/SSD/models/research/object_detection/models | models | faster_rcnn_inception_resnet_v2_feature_extractor_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for models.faster_rcnn_inception_resnet_v2_feature_extractor."""
import tensorflow as tf
from object_detection.models import faster_rcnn_inception_resnet_v2_feature_extractor as frcnn_inc_res
class FasterRcnnInceptionResnetV2FeatureExtractorTest(tf.test.TestCase):
def _build_feature_extractor(self, first_stage_features_stride):
return frcnn_inc_res.FasterRCNNInceptionResnetV2FeatureExtractor(
is_training=False,
first_stage_features_stride=first_stage_features_stride,
batch_norm_trainable=False,
reuse_weights=None,
weight_decay=0.0)
def test_extract_proposal_features_returns_expected_size(self):
feature_extractor = self._build_feature_extractor(
first_stage_features_stride=16)
preprocessed_inputs = tf.random_uniform(
[1, 299, 299, 3], maxval=255, dtype=tf.float32)
rpn_feature_map, _ = feature_extractor.extract_proposal_features(
preprocessed_inputs, scope='TestScope')
features_shape = tf.shape(rpn_feature_map)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
features_shape_out = sess.run(features_shape)
self.assertAllEqual(features_shape_out, [1, 19, 19, 1088])
def test_extract_proposal_features_stride_eight(self):
feature_extractor = self._build_feature_extractor(
first_stage_features_stride=8)
preprocessed_inputs = tf.random_uniform(
[1, 224, 224, 3], maxval=255, dtype=tf.float32)
rpn_feature_map, _ = feature_extractor.extract_proposal_features(
preprocessed_inputs, scope='TestScope')
features_shape = tf.shape(rpn_feature_map)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
features_shape_out = sess.run(features_shape)
self.assertAllEqual(features_shape_out, [1, 28, 28, 1088])
def test_extract_proposal_features_half_size_input(self):
feature_extractor = self._build_feature_extractor(
first_stage_features_stride=16)
preprocessed_inputs = tf.random_uniform(
[1, 112, 112, 3], maxval=255, dtype=tf.float32)
rpn_feature_map, _ = feature_extractor.extract_proposal_features(
preprocessed_inputs, scope='TestScope')
features_shape = tf.shape(rpn_feature_map)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
features_shape_out = sess.run(features_shape)
self.assertAllEqual(features_shape_out, [1, 7, 7, 1088])
def test_extract_proposal_features_dies_on_invalid_stride(self):
with self.assertRaises(ValueError):
self._build_feature_extractor(first_stage_features_stride=99)
def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
feature_extractor = self._build_feature_extractor(
first_stage_features_stride=16)
preprocessed_inputs = tf.random_uniform(
[224, 224, 3], maxval=255, dtype=tf.float32)
with self.assertRaises(ValueError):
feature_extractor.extract_proposal_features(
preprocessed_inputs, scope='TestScope')
def test_extract_box_classifier_features_returns_expected_size(self):
feature_extractor = self._build_feature_extractor(
first_stage_features_stride=16)
proposal_feature_maps = tf.random_uniform(
[2, 17, 17, 1088], maxval=255, dtype=tf.float32)
proposal_classifier_features = (
feature_extractor.extract_box_classifier_features(
proposal_feature_maps, scope='TestScope'))
features_shape = tf.shape(proposal_classifier_features)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
features_shape_out = sess.run(features_shape)
self.assertAllEqual(features_shape_out, [2, 8, 8, 1536])
if __name__ == '__main__':
tf.test.main()
|
PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker | docker | interactive | #!/bin/bash
PATH_TO_COCO=$1
MOUNT_LOCATION='/datasets/data'
NAME='maskrcnn_interactive'
docker run --runtime=nvidia -v $PATH_TO_COCO:/$MOUNT_LOCATION --rm --name=$NAME --shm-size=10g --ulimit memlock=-1 --ulimit stack=67108864 --ipc=host -t -i nvidia_joc_maskrcnn_pt bash
|
TensorFlow/LanguageModeling/BERT/scripts | scripts | finetune_inference_benchmark | #!/bin/bash
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
task=${1:-"squad"}
#Edit to save logs & checkpoints in a different directory
RESULTS_DIR=/results
if [ ! -d "$RESULTS_DIR" ] ; then
echo "Error! $RESULTS_DIR directory missing."
exit -1
fi
echo "Results directory set as " $RESULTS_DIR
LOGFILE="${RESULTS_DIR}/${task}_inference_benchmark_bert_${bert_model}.log"
tmp_file="/tmp/${task}_inference_benchmark.log"
if [ "$task" = "squad" ] ; then
export SQUAD_DIR=data/download/squad/v1.1
echo "Squad directory set as " $SQUAD_DIR
echo "Inference performance benchmarking for BERT $bert_model from $BERT_DIR" >> $LOGFILE
for bert_model in "base" "large"; do
echo "Model Sequence-Length Batch-size Precision Throughput-Average(sent/sec) Latency-Average(ms) Latency-50%(ms) Latency-90%(ms) Latency-95%(ms) Latency-99%(ms) Latency-100%(ms)" >> $LOGFILE
if [ "$bert_model" = "large" ] ; then
export BERT_DIR=data/download/nvidia_pretrained/bert_tf_pretraining_large_lamb
else
export BERT_DIR=data/download/nvidia_pretrained/bert_tf_squad11_base_128
fi
echo "BERT directory set as " $BERT_DIR
init_checkpoint="$BERT_DIR/model.ckpt"
for seq_len in 128 384; do
for bs in 1 2 4 8; do
for use_fp16 in "--amp" "--noamp"; do
python run_squad.py \
--vocab_file=$BERT_DIR/vocab.txt \
--bert_config_file=$BERT_DIR/bert_config.json \
--init_checkpoint=$init_checkpoint \
--do_predict=True \
--predict_file=$SQUAD_DIR/dev-v1.1.json \
--predict_batch_size=$bs \
--max_seq_length=$seq_len \
--doc_stride=128 \
--output_dir=${RESULTS_DIR} \
"$use_fp16" \
--use_xla --num_eval_iterations=1024 |& tee $tmp_file
perf=`cat $tmp_file | grep -F 'INFO:tensorflow:Throughput Average (sentences/sec) =' | tail -1 | awk -F'= ' '{print $2}'`
la=`cat $tmp_file | grep -F 'INFO:tensorflow:Latency Average (ms)' | awk -F'= ' '{print $2}'`
l50=`cat $tmp_file | grep -F 'INFO:tensorflow:Latency Confidence Level 50 (ms)' | awk -F'= ' '{print $2}'`
l90=`cat $tmp_file | grep -F 'INFO:tensorflow:Latency Confidence Level 90 (ms)' | awk -F'= ' '{print $2}'`
l95=`cat $tmp_file | grep -F 'INFO:tensorflow:Latency Confidence Level 95 (ms)' | awk -F'= ' '{print $2}'`
l99=`cat $tmp_file | grep -F 'INFO:tensorflow:Latency Confidence Level 99 (ms)' | awk -F'= ' '{print $2}'`
l100=`cat $tmp_file | grep -F 'INFO:tensorflow:Latency Confidence Level 100 (ms)' | awk -F'= ' '{print $2}'`
echo "$bert_model $seq_len $bs $use_fp16 $perf $la $l50 $l90 $l95 $l99 $l100" >> $LOGFILE
done
done
done
done
else
echo "Benchmarking for " $task "currently not supported. Sorry!"
fi |
TensorFlow2/Segmentation/nnUNet/data_preprocessing | data_preprocessing | preprocessor | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import json
import math
import os
import pickle
from pathlib import Path
import nibabel
import numpy as np
from joblib import Parallel, delayed
from runtime.utils import get_task_code, make_empty_dir
from skimage.transform import resize
from data_preprocessing import configs, transforms
class Preprocessor:
def __init__(self, args):
self.args = args
self.ct_min = 0
self.ct_max = 0
self.ct_mean = 0
self.ct_std = 0
self.target_spacing = None
self.task = args.task
self.task_code = get_task_code(args)
self.patch_size = configs.patch_size[self.task_code]
self.training = args.exec_mode == "training"
self.data_path = args.data / configs.task[args.task]
self.results = args.results / self.task_code
if not self.training:
self.results /= self.args.exec_mode
metadata_path = self.data_path / "dataset.json"
if self.args.exec_mode == "val":
dataset_json = json.load(open(metadata_path, "r"))
dataset_json["val"] = dataset_json["training"]
with open(metadata_path, "w") as outfile:
json.dump(dataset_json, outfile)
self.metadata = json.load(open(metadata_path, "r"))
self.modality = self.metadata["modality"]["0"]
def run(self):
print(f"Preprocessing {self.data_path}")
make_empty_dir(self.results, force=self.args.force)
if self.task_code in configs.spacings:
self.target_spacing = configs.spacings[self.task_code]
else:
self.collect_spacings()
print(f"Target spacing {self.target_spacing}")
if self.modality == "CT":
try:
self.ct_min = configs.ct_min[self.task]
self.ct_max = configs.ct_max[self.task]
self.ct_mean = configs.ct_mean[self.task]
self.ct_std = configs.ct_std[self.task]
except KeyError:
self.collect_intensities()
_mean = round(self.ct_mean, 2)
_std = round(self.ct_std, 2)
print(f"[CT] min: {self.ct_min}, max: {self.ct_max}, mean: {_mean}, std: {_std}")
self.run_parallel(self.preprocess_pair, self.args.exec_mode)
pickle.dump(
{
"patch_size": self.patch_size,
"spacings": self.target_spacing,
"n_class": len(self.metadata["labels"]),
"in_channels": len(self.metadata["modality"]),
},
open(os.path.join(self.results, "config.pkl"), "wb"),
)
def preprocess_pair(self, pair):
fname = os.path.basename(pair["image"] if isinstance(pair, dict) else pair)
image, label, image_spacings = self.load_pair(pair)
original_size = image.shape[1:]
image, label, bbox = transforms.crop_foreground(image, label)
test_metadata = np.vstack([bbox, original_size]) if not self.training else None
if self.args.dim == 3:
image, label = self.resample(image, label, image_spacings)
if self.modality == "CT":
image = np.clip(image, self.ct_min, self.ct_max)
image = self.normalize(image)
if self.training:
image, label = self.standardize(image, label)
image, label = np.transpose(image, (1, 2, 3, 0)), np.transpose(label, (1, 2, 3, 0))
self.save(image, label, fname, test_metadata)
def resample(self, image, label, image_spacings):
if self.target_spacing != image_spacings:
image, label = self.resample_pair(image, label, image_spacings)
return image, label
def standardize(self, image, label):
pad_shape = self.calculate_pad_shape(image)
image_shape = image.shape[1:]
if pad_shape != image_shape:
paddings = [(pad_sh - image_sh) / 2 for (pad_sh, image_sh) in zip(pad_shape, image_shape)]
image = self.pad(image, paddings)
label = self.pad(label, paddings)
if self.args.dim == 2: # Center cropping 2D images.
_, _, height, weight = image.shape
start_h = (height - self.patch_size[0]) // 2
start_w = (weight - self.patch_size[1]) // 2
image = image[:, :, start_h : start_h + self.patch_size[0], start_w : start_w + self.patch_size[1]]
label = label[:, :, start_h : start_h + self.patch_size[0], start_w : start_w + self.patch_size[1]]
return image, label
def normalize(self, image):
if self.modality == "CT":
return (image - self.ct_mean) / self.ct_std
return transforms.normalize_intensity(image, nonzero=True, channel_wise=True)
def save(self, image, label, fname, test_metadata):
mean, std = np.round(np.mean(image, (0, 1, 2)), 2), np.round(np.std(image, (0, 1, 2)), 2)
print(f"Saving {fname} shape {image.shape} mean {mean} std {std}")
self.save_npy(image, fname, "_x.npy")
if label is not None:
self.save_npy(label, fname, "_y.npy")
if test_metadata is not None:
self.save_npy(test_metadata, fname, "_meta.npy")
def load_pair(self, pair):
image = self.load_nifty(pair["image"] if isinstance(pair, dict) else pair)
image_spacing = self.load_spacing(image)
image = image.get_fdata().astype(np.float32)
image = self.standardize_layout(image)
if self.training:
label = self.load_nifty(pair["label"]).get_fdata().astype(np.uint8)
label = self.standardize_layout(label)
else:
label = None
return image, label, image_spacing
def resample_pair(self, image, label, spacing):
shape = self.calculate_new_shape(spacing, image.shape[1:])
if self.check_anisotrophy(spacing):
image = self.resample_anisotrophic_image(image, shape)
if label is not None:
label = self.resample_anisotrophic_label(label, shape)
else:
image = self.resample_regular_image(image, shape)
if label is not None:
label = self.resample_regular_label(label, shape)
image = image.astype(np.float32)
if label is not None:
label = label.astype(np.uint8)
return image, label
def calculate_pad_shape(self, image):
min_shape = self.patch_size[:]
image_shape = image.shape[1:]
if len(min_shape) == 2: # In 2D case we don't want to pad depth axis.
min_shape.insert(0, image_shape[0])
pad_shape = [max(mshape, ishape) for mshape, ishape in zip(min_shape, image_shape)]
return pad_shape
def get_intensities(self, pair):
image = self.load_nifty(pair["image"]).get_fdata().astype(np.float32)
label = self.load_nifty(pair["label"]).get_fdata().astype(np.uint8)
foreground_idx = np.where(label > 0)
intensities = image[foreground_idx].tolist()
return intensities
def collect_intensities(self):
intensities = self.run_parallel(self.get_intensities, "training")
intensities = list(itertools.chain.from_iterable(intensities))
self.ct_min, self.ct_max = np.percentile(intensities, [0.5, 99.5])
self.ct_mean, self.ct_std = np.mean(intensities), np.std(intensities)
def get_spacing(self, pair):
image = nibabel.load(self.data_path / pair["image"])
spacing = self.load_spacing(image)
return spacing
def collect_spacings(self):
spacing = self.run_parallel(self.get_spacing, "training")
spacing = np.array(spacing)
target_spacing = np.median(spacing, axis=0)
if max(target_spacing) / min(target_spacing) >= 3:
lowres_axis = np.argmin(target_spacing)
target_spacing[lowres_axis] = np.percentile(spacing[:, lowres_axis], 10)
self.target_spacing = list(target_spacing)
def check_anisotrophy(self, spacing):
def check(spacing):
return np.max(spacing) / np.min(spacing) >= 3
return check(spacing) or check(self.target_spacing)
def calculate_new_shape(self, spacing, shape):
spacing_ratio = np.array(spacing) / np.array(self.target_spacing)
new_shape = (spacing_ratio * np.array(shape)).astype(int).tolist()
return new_shape
def save_npy(self, image, fname, suffix):
np.save(os.path.join(self.results, fname.replace(".nii.gz", suffix)), image, allow_pickle=False)
def run_parallel(self, func, exec_mode):
return Parallel(n_jobs=self.args.n_jobs)(delayed(func)(pair) for pair in self.metadata[exec_mode])
def load_nifty(self, fname):
return nibabel.load(os.path.join(self.data_path, fname))
@staticmethod
def load_spacing(image):
return image.header["pixdim"][1:4].tolist()[::-1]
@staticmethod
def pad(image, padding):
pad_d, pad_w, pad_h = padding
return np.pad(
image,
(
(0, 0),
(math.floor(pad_d), math.ceil(pad_d)),
(math.floor(pad_w), math.ceil(pad_w)),
(math.floor(pad_h), math.ceil(pad_h)),
),
)
@staticmethod
def standardize_layout(data):
if len(data.shape) == 3:
data = np.expand_dims(data, 3)
return np.transpose(data, (3, 2, 1, 0))
@staticmethod
def resize_fn(image, shape, order, mode):
return resize(image, shape, order=order, mode=mode, cval=0, clip=True, anti_aliasing=False)
def resample_anisotrophic_image(self, image, shape):
resized_channels = []
for image_c in image:
resized = [self.resize_fn(i, shape[1:], 3, "edge") for i in image_c]
resized = np.stack(resized, axis=0)
resized = self.resize_fn(resized, shape, 0, "constant")
resized_channels.append(resized)
resized = np.stack(resized_channels, axis=0)
return resized
def resample_regular_image(self, image, shape):
resized_channels = []
for image_c in image:
resized_channels.append(self.resize_fn(image_c, shape, 3, "edge"))
resized = np.stack(resized_channels, axis=0)
return resized
def resample_anisotrophic_label(self, label, shape):
depth = label.shape[1]
reshaped = np.zeros(shape, dtype=np.uint8)
shape_2d = shape[1:]
reshaped_2d = np.zeros((depth, *shape_2d), dtype=np.uint8)
n_class = np.max(label)
for class_ in range(1, n_class + 1):
for depth_ in range(depth):
mask = label[0, depth_] == class_
resized_2d = self.resize_fn(mask.astype(float), shape_2d, 1, "edge")
reshaped_2d[depth_][resized_2d >= 0.5] = class_
for class_ in range(1, n_class + 1):
mask = reshaped_2d == class_
resized = self.resize_fn(mask.astype(float), shape, 0, "constant")
reshaped[resized >= 0.5] = class_
reshaped = np.expand_dims(reshaped, 0)
return reshaped
def resample_regular_label(self, label, shape):
reshaped = np.zeros(shape, dtype=np.uint8)
n_class = np.max(label)
for class_ in range(1, n_class + 1):
mask = label[0] == class_
resized = self.resize_fn(mask.astype(float), shape, 1, "edge")
reshaped[resized >= 0.5] = class_
reshaped = np.expand_dims(reshaped, 0)
return reshaped
|
. | DeepLearningExamples | .gitignore | repos.cfg
repos_init.cfg
nvtool*
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | label_map_util | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Label map utility functions."""
import logging
import tensorflow as tf
from google.protobuf import text_format
from object_detection.protos import string_int_label_map_pb2
def _validate_label_map(label_map):
"""Checks if a label map is valid.
Args:
label_map: StringIntLabelMap to validate.
Raises:
ValueError: if label map is invalid.
"""
for item in label_map.item:
if item.id < 0:
raise ValueError('Label map ids should be >= 0.')
if (item.id == 0 and item.name != 'background' and
item.display_name != 'background'):
raise ValueError('Label map id 0 is reserved for the background label')
def create_category_index(categories):
"""Creates dictionary of COCO compatible categories keyed by category id.
Args:
categories: a list of dicts, each of which has the following keys:
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category name
e.g., 'cat', 'dog', 'pizza'.
Returns:
category_index: a dict containing the same entries as categories, but keyed
by the 'id' field of each category.
"""
category_index = {}
for cat in categories:
category_index[cat['id']] = cat
return category_index
def get_max_label_map_index(label_map):
"""Get maximum index in label map.
Args:
label_map: a StringIntLabelMapProto
Returns:
an integer
"""
return max([item.id for item in label_map.item])
def convert_label_map_to_categories(label_map,
max_num_classes,
use_display_name=True):
"""Given label map proto returns categories list compatible with eval.
This function converts label map proto and returns a list of dicts, each of
which has the following keys:
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category name
e.g., 'cat', 'dog', 'pizza'.
We only allow class into the list if its id-label_id_offset is
between 0 (inclusive) and max_num_classes (exclusive).
If there are several items mapping to the same id in the label map,
we will only keep the first one in the categories list.
Args:
label_map: a StringIntLabelMapProto or None. If None, a default categories
list is created with max_num_classes categories.
max_num_classes: maximum number of (consecutive) label indices to include.
use_display_name: (boolean) choose whether to load 'display_name' field as
category name. If False or if the display_name field does not exist, uses
'name' field as category names instead.
Returns:
categories: a list of dictionaries representing all possible categories.
"""
categories = []
list_of_ids_already_added = []
if not label_map:
label_id_offset = 1
for class_id in range(max_num_classes):
categories.append({
'id': class_id + label_id_offset,
'name': 'category_{}'.format(class_id + label_id_offset)
})
return categories
for item in label_map.item:
if not 0 < item.id <= max_num_classes:
logging.info(
'Ignore item %d since it falls outside of requested '
'label range.', item.id)
continue
if use_display_name and item.HasField('display_name'):
name = item.display_name
else:
name = item.name
if item.id not in list_of_ids_already_added:
list_of_ids_already_added.append(item.id)
categories.append({'id': item.id, 'name': name})
return categories
def load_labelmap(path):
"""Loads label map proto.
Args:
path: path to StringIntLabelMap proto text file.
Returns:
a StringIntLabelMapProto
"""
with tf.gfile.GFile(path, 'r') as fid:
label_map_string = fid.read()
label_map = string_int_label_map_pb2.StringIntLabelMap()
try:
text_format.Merge(label_map_string, label_map)
except text_format.ParseError:
label_map.ParseFromString(label_map_string)
_validate_label_map(label_map)
return label_map
def get_label_map_dict(label_map_path,
use_display_name=False,
fill_in_gaps_and_background=False):
"""Reads a label map and returns a dictionary of label names to id.
Args:
label_map_path: path to StringIntLabelMap proto text file.
use_display_name: whether to use the label map items' display names as keys.
fill_in_gaps_and_background: whether to fill in gaps and background with
respect to the id field in the proto. The id: 0 is reserved for the
'background' class and will be added if it is missing. All other missing
ids in range(1, max(id)) will be added with a dummy class name
("class_<id>") if they are missing.
Returns:
A dictionary mapping label names to id.
Raises:
ValueError: if fill_in_gaps_and_background and label_map has non-integer or
negative values.
"""
label_map = load_labelmap(label_map_path)
label_map_dict = {}
for item in label_map.item:
if use_display_name:
label_map_dict[item.display_name] = item.id
else:
label_map_dict[item.name] = item.id
if fill_in_gaps_and_background:
values = set(label_map_dict.values())
if 0 not in values:
label_map_dict['background'] = 0
if not all(isinstance(value, int) for value in values):
raise ValueError('The values in label map must be integers in order to'
'fill_in_gaps_and_background.')
if not all(value >= 0 for value in values):
raise ValueError('The values in the label map must be positive.')
if len(values) != max(values) + 1:
# there are gaps in the labels, fill in gaps.
for value in range(1, max(values)):
if value not in values:
label_map_dict['class_' + str(value)] = value
return label_map_dict
def create_categories_from_labelmap(label_map_path, use_display_name=True):
"""Reads a label map and returns categories list compatible with eval.
This function converts label map proto and returns a list of dicts, each of
which has the following keys:
'id': an integer id uniquely identifying this category.
'name': string representing category name e.g., 'cat', 'dog'.
Args:
label_map_path: Path to `StringIntLabelMap` proto text file.
use_display_name: (boolean) choose whether to load 'display_name' field
as category name. If False or if the display_name field does not exist,
uses 'name' field as category names instead.
Returns:
categories: a list of dictionaries representing all possible categories.
"""
label_map = load_labelmap(label_map_path)
max_num_classes = max(item.id for item in label_map.item)
return convert_label_map_to_categories(label_map, max_num_classes,
use_display_name)
def create_category_index_from_labelmap(label_map_path, use_display_name=True):
"""Reads a label map and returns a category index.
Args:
label_map_path: Path to `StringIntLabelMap` proto text file.
use_display_name: (boolean) choose whether to load 'display_name' field
as category name. If False or if the display_name field does not exist,
uses 'name' field as category names instead.
Returns:
A category index, which is a dictionary that maps integer ids to dicts
containing categories, e.g.
{1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}, ...}
"""
categories = create_categories_from_labelmap(label_map_path, use_display_name)
return create_category_index(categories)
def create_class_agnostic_category_index():
"""Creates a category index with a single `object` class."""
return {1: {'id': 1, 'name': 'object'}}
|
TensorFlow/Detection/SSD/models/research/object_detection/g3doc | g3doc | preparing_inputs | # Preparing Inputs
Tensorflow Object Detection API reads data using the TFRecord file format. Two
sample scripts (`create_pascal_tf_record.py` and `create_pet_tf_record.py`) are
provided to convert from the PASCAL VOC dataset and Oxford-IIIT Pet dataset to
TFRecords.
## Generating the PASCAL VOC TFRecord files.
The raw 2012 PASCAL VOC data set is located
[here](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar).
To download, extract and convert it to TFRecords, run the following commands
below:
```bash
# From tensorflow/models/research/
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
tar -xvf VOCtrainval_11-May-2012.tar
python object_detection/dataset_tools/create_pascal_tf_record.py \
--label_map_path=object_detection/data/pascal_label_map.pbtxt \
--data_dir=VOCdevkit --year=VOC2012 --set=train \
--output_path=pascal_train.record
python object_detection/dataset_tools/create_pascal_tf_record.py \
--label_map_path=object_detection/data/pascal_label_map.pbtxt \
--data_dir=VOCdevkit --year=VOC2012 --set=val \
--output_path=pascal_val.record
```
You should end up with two TFRecord files named `pascal_train.record` and
`pascal_val.record` in the `tensorflow/models/research/` directory.
The label map for the PASCAL VOC data set can be found at
`object_detection/data/pascal_label_map.pbtxt`.
## Generating the Oxford-IIIT Pet TFRecord files.
The Oxford-IIIT Pet data set is located
[here](http://www.robots.ox.ac.uk/~vgg/data/pets/). To download, extract and
convert it to TFRecrods, run the following commands below:
```bash
# From tensorflow/models/research/
wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
tar -xvf annotations.tar.gz
tar -xvf images.tar.gz
python object_detection/dataset_tools/create_pet_tf_record.py \
--label_map_path=object_detection/data/pet_label_map.pbtxt \
--data_dir=`pwd` \
--output_dir=`pwd`
```
You should end up with two 10-sharded TFRecord files named
`pet_faces_train.record-?????-of-00010` and
`pet_faces_val.record-?????-of-00010` in the `tensorflow/models/research/`
directory.
The label map for the Pet dataset can be found at
`object_detection/data/pet_label_map.pbtxt`.
|
TensorFlow2/LanguageModeling/BERT/official/utils/misc | misc | distribution_utils | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Helper functions for running models in a distributed setting."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import random
import string
import tensorflow as tf
from official.utils.misc import tpu_lib
def _collective_communication(all_reduce_alg):
"""Return a CollectiveCommunication based on all_reduce_alg.
Args:
all_reduce_alg: a string specifying which collective communication to pick,
or None.
Returns:
tf.distribute.experimental.CollectiveCommunication object
Raises:
ValueError: if `all_reduce_alg` not in [None, 'ring', 'nccl']
"""
collective_communication_options = {
None: tf.distribute.experimental.CollectiveCommunication.AUTO,
"ring": tf.distribute.experimental.CollectiveCommunication.RING,
"nccl": tf.distribute.experimental.CollectiveCommunication.NCCL
}
if all_reduce_alg not in collective_communication_options:
raise ValueError(
"When used with `multi_worker_mirrored`, valid values for "
"all_reduce_alg are ['ring', 'nccl']. Supplied value: {}".format(
all_reduce_alg))
return collective_communication_options[all_reduce_alg]
def _mirrored_cross_device_ops(all_reduce_alg, num_packs):
"""Return a CrossDeviceOps based on all_reduce_alg and num_packs.
Args:
all_reduce_alg: a string specifying which cross device op to pick, or None.
num_packs: an integer specifying number of packs for the cross device op.
Returns:
tf.distribute.CrossDeviceOps object or None.
Raises:
ValueError: if `all_reduce_alg` not in [None, 'nccl', 'hierarchical_copy'].
"""
if all_reduce_alg is None:
return None
mirrored_all_reduce_options = {
"nccl": tf.distribute.NcclAllReduce,
"hierarchical_copy": tf.distribute.HierarchicalCopyAllReduce
}
if all_reduce_alg not in mirrored_all_reduce_options:
raise ValueError(
"When used with `mirrored`, valid values for all_reduce_alg are "
"['nccl', 'hierarchical_copy']. Supplied value: {}".format(
all_reduce_alg))
cross_device_ops_class = mirrored_all_reduce_options[all_reduce_alg]
return cross_device_ops_class(num_packs=num_packs)
def get_distribution_strategy(distribution_strategy="mirrored",
num_gpus=0,
num_workers=1,
all_reduce_alg=None,
num_packs=1,
tpu_address=None):
"""Return a DistributionStrategy for running the model.
Args:
distribution_strategy: a string specifying which distribution strategy to
use. Accepted values are 'off', 'one_device', 'mirrored',
'parameter_server', 'multi_worker_mirrored', and 'tpu' -- case insensitive.
'off' means not to use Distribution Strategy; 'tpu' means to use
TPUStrategy using `tpu_address`.
num_gpus: Number of GPUs to run this model.
num_workers: Number of workers to run this model.
all_reduce_alg: Optional. Specifies which algorithm to use when performing
all-reduce. For `MirroredStrategy`, valid values are "nccl" and
"hierarchical_copy". For `MultiWorkerMirroredStrategy`, valid values are
"ring" and "nccl". If None, DistributionStrategy will choose based on
device topology.
num_packs: Optional. Sets the `num_packs` in `tf.distribute.NcclAllReduce`
or `tf.distribute.HierarchicalCopyAllReduce` for `MirroredStrategy`.
tpu_address: Optional. String that represents TPU to connect to. Must not
be None if `distribution_strategy` is set to `tpu`.
Returns:
tf.distribute.DistibutionStrategy object.
Raises:
ValueError: if `distribution_strategy` is 'off' or 'one_device' and
`num_gpus` is larger than 1; or `num_gpus` is negative or if
`distribution_strategy` is `tpu` but `tpu_address` is not specified.
"""
if num_gpus < 0:
raise ValueError("`num_gpus` can not be negative.")
distribution_strategy = distribution_strategy.lower()
if distribution_strategy == "off":
return None
if distribution_strategy == "tpu":
# When tpu_address is an empty string, we communicate with local TPUs.
cluster_resolver = tpu_lib.tpu_initialize(tpu_address)
return tf.distribute.experimental.TPUStrategy(cluster_resolver)
if distribution_strategy == "multi_worker_mirrored":
return tf.distribute.experimental.MultiWorkerMirroredStrategy(
communication=_collective_communication(all_reduce_alg))
if distribution_strategy == "one_device":
if num_gpus == 0:
return tf.distribute.OneDeviceStrategy("device:CPU:0")
if num_gpus > 1:
raise ValueError("`OneDeviceStrategy` can not be used for more than "
"one device.")
return tf.distribute.OneDeviceStrategy("device:GPU:0")
if distribution_strategy == "mirrored":
if num_gpus == 0:
devices = ["device:CPU:0"]
else:
devices = ["device:GPU:%d" % i for i in range(num_gpus)]
return tf.distribute.MirroredStrategy(
devices=devices,
cross_device_ops=_mirrored_cross_device_ops(all_reduce_alg, num_packs))
if distribution_strategy == "parameter_server":
return tf.distribute.experimental.ParameterServerStrategy()
raise ValueError(
"Unrecognized Distribution Strategy: %r" % distribution_strategy)
def per_replica_batch_size(batch_size, num_gpus):
"""For multi-gpu, batch-size must be a multiple of the number of GPUs.
Note that distribution strategy handles this automatically when used with
Keras. For using with Estimator, we need to get per GPU batch.
Args:
batch_size: Global batch size to be divided among devices. This should be
equal to num_gpus times the single-GPU batch_size for multi-gpu training.
num_gpus: How many GPUs are used with DistributionStrategies.
Returns:
Batch size per device.
Raises:
ValueError: if batch_size is not divisible by number of devices
"""
if num_gpus <= 1:
return batch_size
remainder = batch_size % num_gpus
if remainder:
err = ('When running with multiple GPUs, batch size '
'must be a multiple of the number of available GPUs. Found {} '
'GPUs with a batch size of {}; try --batch_size={} instead.'
).format(num_gpus, batch_size, batch_size - remainder)
raise ValueError(err)
return int(batch_size / num_gpus)
# The `SyntheticDataset` is a temporary solution for generating synthetic data
# directly on devices. It is only useful for Keras with Distribution
# Strategies. We will have better support in `tf.data` or Distribution Strategy
# later.
class SyntheticDataset(object):
"""A dataset that generates synthetic data on each device."""
def __init__(self, dataset, split_by=1):
# dataset.take(1) doesn't have GPU kernel.
with tf.device('device:CPU:0'):
tensor = tf.data.experimental.get_single_element(dataset.take(1))
flat_tensor = tf.nest.flatten(tensor)
variable_data = []
initializers = []
for t in flat_tensor:
rebatched_t = tf.split(t, num_or_size_splits=split_by, axis=0)[0]
assert rebatched_t.shape.is_fully_defined(), rebatched_t.shape
v = tf.compat.v1.get_local_variable(self._random_name(),
initializer=rebatched_t)
variable_data.append(v)
initializers.append(v.initializer)
input_data = tf.nest.pack_sequence_as(tensor, variable_data)
self._iterator = SyntheticIterator(input_data, initializers)
def _random_name(self, size=10, chars=string.ascii_uppercase + string.digits):
return ''.join(random.choice(chars) for _ in range(size))
def __iter__(self):
return self._iterator
def make_one_shot_iterator(self):
return self._iterator
def make_initializable_iterator(self):
return self._iterator
class SyntheticIterator(object):
"""A dataset that generates synthetic data on each device."""
def __init__(self, input_data, initializers):
self._input_data = input_data
self._initializers = initializers
def get_next(self):
return self._input_data
def next(self):
return self.__next__()
def __next__(self):
try:
return self.get_next()
except tf.errors.OutOfRangeError:
raise StopIteration
def initialize(self):
if tf.executing_eagerly():
return tf.no_op()
else:
return self._initializers
def _monkey_patch_dataset_method(strategy):
"""Monkey-patch `strategy`'s `make_dataset_iterator` method."""
def make_dataset(self, dataset):
tf.compat.v1.logging.info('Using pure synthetic data.')
with self.scope():
if self.extended._global_batch_size: # pylint: disable=protected-access
return SyntheticDataset(dataset, self.num_replicas_in_sync)
else:
return SyntheticDataset(dataset)
def make_iterator(self, dataset):
dist_dataset = make_dataset(self, dataset)
return iter(dist_dataset)
strategy.orig_make_dataset_iterator = strategy.make_dataset_iterator
strategy.make_dataset_iterator = make_iterator
strategy.orig_distribute_dataset = strategy.experimental_distribute_dataset
strategy.experimental_distribute_dataset = make_dataset
def _undo_monkey_patch_dataset_method(strategy):
if hasattr(strategy, 'orig_make_dataset_iterator'):
strategy.make_dataset_iterator = strategy.orig_make_dataset_iterator
if hasattr(strategy, 'orig_distribute_dataset'):
strategy.make_dataset_iterator = strategy.orig_distribute_dataset
def set_up_synthetic_data():
_monkey_patch_dataset_method(tf.distribute.OneDeviceStrategy)
_monkey_patch_dataset_method(tf.distribute.MirroredStrategy)
_monkey_patch_dataset_method(
tf.distribute.experimental.MultiWorkerMirroredStrategy)
def undo_set_up_synthetic_data():
_undo_monkey_patch_dataset_method(tf.distribute.OneDeviceStrategy)
_undo_monkey_patch_dataset_method(tf.distribute.MirroredStrategy)
_undo_monkey_patch_dataset_method(
tf.distribute.experimental.MultiWorkerMirroredStrategy)
def configure_cluster(worker_hosts=None, task_index=-1):
"""Set multi-worker cluster spec in TF_CONFIG environment variable.
Args:
worker_hosts: comma-separated list of worker ip:port pairs.
Returns:
Number of workers in the cluster.
"""
tf_config = json.loads(os.environ.get('TF_CONFIG', '{}'))
if tf_config:
num_workers = (len(tf_config['cluster'].get('chief', [])) +
len(tf_config['cluster'].get('worker', [])))
elif worker_hosts:
workers = worker_hosts.split(',')
num_workers = len(workers)
if num_workers > 1 and task_index < 0:
raise ValueError('Must specify task_index when number of workers > 1')
task_index = 0 if num_workers == 1 else task_index
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': workers
},
'task': {'type': 'worker', 'index': task_index}
})
else:
num_workers = 1
return num_workers
def get_strategy_scope(strategy):
if strategy:
strategy_scope = strategy.scope()
else:
strategy_scope = DummyContextManager()
return strategy_scope
class DummyContextManager(object):
def __enter__(self):
pass
def __exit__(self, *args):
pass
|
TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools | dataset_tools | tf_record_creation_util_test | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tf_record_creation_util.py."""
import os
import contextlib2
import tensorflow as tf
from object_detection.dataset_tools import tf_record_creation_util
class OpenOutputTfrecordsTests(tf.test.TestCase):
def test_sharded_tfrecord_writes(self):
with contextlib2.ExitStack() as tf_record_close_stack:
output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
tf_record_close_stack,
os.path.join(tf.test.get_temp_dir(), 'test.tfrec'), 10)
for idx in range(10):
output_tfrecords[idx].write('test_{}'.format(idx))
for idx in range(10):
tf_record_path = '{}-{:05d}-of-00010'.format(
os.path.join(tf.test.get_temp_dir(), 'test.tfrec'), idx)
records = list(tf.python_io.tf_record_iterator(tf_record_path))
self.assertAllEqual(records, ['test_{}'.format(idx)])
if __name__ == '__main__':
tf.test.main()
|
TensorFlow2/LanguageModeling/ELECTRA/vocab | vocab | vocab | [PAD]
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security
interest
saying
allowed
master
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phone
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smith
winning
try
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moving
campaign
los
##ley
breath
nearly
mid
1987
certain
girls
date
italian
african
standing
fell
artist
##ted
shows
deal
mine
industry
1986
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everyone
republic
provide
collection
library
student
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primary
owned
older
via
heavy
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makes
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attention
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africa
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stated
length
ended
fingers
command
staff
skin
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opening
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okay
medal
kill
sun
cover
job
1985
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hell
feeling
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success
meet
reason
standard
meeting
novel
1984
trade
source
buildings
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rose
guy
goal
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chapter
native
husband
previously
unit
limited
entered
weeks
producer
operations
mountain
takes
covered
forced
related
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key
texas
cold
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channel
1980
traditional
films
dance
clear
approximately
500
nine
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prince
question
active
tracks
ireland
regional
silver
author
personal
sense
operation
##ine
economic
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holding
twenty
isbn
additional
speed
hour
edition
regular
historic
places
whom
shook
movie
km²
secretary
prior
report
chicago
read
foundation
view
engine
scored
1982
units
ask
airport
property
ready
immediately
lady
month
listed
contract
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manager
themselves
lines
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navy
writer
meant
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practice
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singer
glass
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required
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starting
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generally
giving
access
attended
test
couple
stand
catholic
martin
caught
executive
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eye
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thinking
chair
quite
shoulder
1979
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decision
plays
defeated
municipality
whether
structure
offered
slowly
pain
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direction
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paper
mission
1981
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200
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individual
managed
nature
lives
plant
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helped
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studied
computer
figure
relationship
issue
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loss
die
smiled
gun
ago
highest
1972
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male
bring
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distance
commercial
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location
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drive
1976
neck
1978
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caused
italy
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greek
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hotel
comes
appearance
joseph
double
issues
musical
companies
castle
income
review
assembly
bass
initially
parliament
artists
experience
1974
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walk
foot
engineering
talking
window
dropped
##ter
miss
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boys
break
1975
stars
edge
remember
policy
carried
train
stadium
bar
sex
angeles
evidence
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becoming
assistant
soviet
1977
upper
step
wing
1970
youth
financial
reach
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actor
numerous
##se
##st
nodded
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minute
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believed
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complex
beautiful
victory
associated
temple
1968
1973
chance
perhaps
metal
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1945
bishop
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lee
launched
particularly
tree
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retired
subject
prize
contains
yeah
theory
empire
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trust
recording
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happy
terms
camp
champion
1971
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pass
zealand
names
2nd
port
ancient
tom
corner
represented
watch
legal
anti
justice
cause
watched
brothers
45
material
changes
simply
response
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fast
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answer
60
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1969
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feature
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virginia
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cultural
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basketball
legs
guard
beyond
cast
doctor
mm
flight
results
remains
cost
effect
winter
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problems
chairman
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1967
pay
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selected
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box
regiment
majority
journal
35
edward
plans
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pretty
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characters
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operated
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65
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please
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1964
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1965
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1960
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1944
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machine
1963
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1962
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appear
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planned
nations
mass
broadcast
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share
image
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wild
offer
emperor
electric
reading
headed
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promoted
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ministry
1942
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politician
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80
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1961
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vote
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needs
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featuring
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shirt
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serious
turning
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lieutenant
firm
maria
amount
mixed
daniel
proposed
perfect
agreement
affairs
3rd
seconds
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paid
1943
prison
save
kitchen
label
administrative
intended
constructed
academic
nice
teacher
races
1956
formerly
corporation
ben
nation
issued
shut
1958
drums
housing
victoria
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opera
1959
graduated
function
von
mentioned
picked
build
recognized
shortly
protection
picture
notable
exchange
elections
1980s
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percent
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fish
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1941
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settled
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1940
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1948
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1957
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tonight
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effects
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screen
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1939
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1946
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1950
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pair
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33
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1970s
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70
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ed
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1955
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1952
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1947
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44
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murder
36
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website
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albums
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grabbed
protect
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boat
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1954
1953
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driver
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berlin
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account
freedom
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simon
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learning
steps
1949
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34
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1960s
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1951
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1990s
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64
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38
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fields
brigade
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clothes
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weapons
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flat
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1938
37
relations
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plants
suffered
1936
wilson
kids
begins
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1918
seats
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internet
models
worth
laws
400
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classes
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thanks
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75
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1937
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42
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1935
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1920
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intelligence
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39
sunday
48
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1930
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1919
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1933
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explained
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netherlands
clean
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1914
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1934
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1917
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41
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1929
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1932
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47
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52
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114
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witches
117
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121
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2020
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175
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210
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116
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clusters
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newark
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118
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127
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succeeding
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1808
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122
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123
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1794
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119
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124
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280
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5000
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sparhawk
320
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145
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270
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165
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gymnastics
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taipei
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126
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bundle
glorious
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132
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installations
1797
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330
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1791
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xii
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lionel
129
debates
lemon
tiffany
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dom
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133
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131
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144
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1600
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cooler
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thickness
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137
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225
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prostitution
152
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investigative
coated
healed
138
communal
hai
xiii
academics
boone
psychologist
restless
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stephens
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printer
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sustainability
134
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136
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141
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143
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physiology
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finch
methodology
ked
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1777
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finances
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bremen
translates
ensured
waterfront
29th
forrest
manny
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reigning
cookies
simpler
absorption
205
engraved
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1778
haze
146
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crossover
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suppression
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201
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rd
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1900s
killings
textbook
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cinematography
wharf
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setup
creed
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signatures
fallon
370
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laurie
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excavation
threads
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148
frantically
icc
utilize
determines
respiratory
thoughtful
receptions
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chandra
seine
147
builders
builds
diagnostic
dev
visibility
goddamn
analyses
dhaka
cho
proves
chancel
concurrent
curiously
canadians
pumped
restoring
1850s
turtles
jaguar
sinister
spinal
traction
declan
vows
1784
glowed
capitalism
swirling
install
universidad
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soloist
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coincidence
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dip
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combustion
infectious
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conclude
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jew
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surreal
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harmonica
151
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fishermen
intending
bravery
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klaus
titan
wembley
taiwanese
ransom
40th
incorrect
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eyelids
jp
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utilities
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eisenhower
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lana
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openings
concord
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connie
morality
sega
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earnings
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##17
nguyen
analyzed
warlord
hubbard
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behave
slovenian
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trance
caldwell
skye
unrest
coward
likelihood
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sci
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1700
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imagining
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contributes
dukes
instinctively
conan
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accessories
gradual
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sioux
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undertake
intercepted
sparkling
compressed
139
fungus
tombs
haley
imposing
rests
degradation
lincolnshire
retailers
wetlands
tulsa
distributor
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nun
greenhouse
convey
atlantis
aft
exits
oman
dresser
lyons
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joking
eddy
judgement
omitted
digits
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juniors
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cents
stricken
une
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wizards
weir
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nan
technician
fibers
liking
royalty
##cca
154
persia
terribly
magician
##rable
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vance
cafeteria
booker
camille
warmer
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consume
cavern
gaps
compass
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foyer
soothing
graveyard
maj
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blush
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cascade
demonstrates
ordinance
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boyle
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shaken
banjo
izzy
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breathless
vines
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alterations
chromosome
dwellings
feudal
mole
153
catalonia
relics
tenant
mandated
##fm
fridge
hats
honesty
patented
raul
heap
cruisers
accusing
enlightenment
infants
wherein
chatham
contractors
zen
affinity
hc
osborne
piston
156
traps
maturity
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lagos
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dealers
protocols
subset
prospects
biographical
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insignia
nuns
endured
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cromwell
crossroads
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grounded
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marseille
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choke
https
catalyst
moldova
italians
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disastrous
wee
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wwf
nope
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expresses
thumbs
167
##nza
coca
1781
cheating
##ption
skipped
sensory
heidelberg
spies
satan
dangers
semifinal
202
bohemia
whitish
confusing
shipbuilding
relies
surgeons
landings
ravi
baku
moor
suffix
alejandro
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litre
upheld
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rajasthan
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coaster
insists
posture
scenarios
etienne
favoured
appoint
transgender
elephants
poked
greenwood
defences
fulfilled
militant
somali
1758
chalk
potent
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migrants
wink
assistants
nos
restriction
activism
niger
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colon
shaun
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daphne
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swam
congregations
reprise
considerations
magnet
playable
xvi
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overthrow
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knob
chavez
coding
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propped
katrina
orient
newcomer
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farmhouse
interrogation
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committing
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forthcoming
strawberry
joaquin
macau
ponds
shocking
siberia
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chant
contributors
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sped
absorb
hail
1782
spared
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barbados
karate
opus
originates
saul
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evergreen
leaped
##rock
correlation
exaggerated
weekday
unification
bump
tracing
brig
afb
pathways
utilizing
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mod
mb
disturbance
kneeling
##stad
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100th
pune
##thy
decreasing
168
manipulation
miriam
academia
ecosystem
occupational
rbi
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rift
##14
rotary
stacked
incorporation
awakening
generators
guerrero
racist
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cyber
derivatives
culminated
allie
annals
panzer
sainte
wikipedia
pops
zu
austro
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politely
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mornings
educate
tastes
thrill
dartmouth
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db
##jee
regan
differing
concentrating
choreography
divinity
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pledged
alexandre
routing
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madeline
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apocalypse
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gunfire
culminating
elves
fined
liang
lam
programmed
tar
guessing
transparency
gabrielle
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cancellation
flexibility
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accession
shea
stronghold
nets
specializes
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abused
hasan
sgt
ling
exceeding
##₄
admiration
supermarket
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photographers
specialised
tilt
resonance
hmm
perfume
380
sami
threatens
garland
botany
guarding
boiled
greet
puppy
russo
supplier
wilmington
vibrant
vijay
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paralympic
grumbled
paige
faa
licking
margins
hurricanes
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fest
grenade
ripping
##uz
counseling
weigh
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needles
wiltshire
edison
costly
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fulton
tramway
redesigned
staffordshire
cache
gasping
watkins
sleepy
candidacy
##group
monkeys
timeline
throbbing
##bid
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berth
uzbekistan
vanderbilt
bothering
overturned
ballots
gem
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sunglasses
subscribers
hooker
compelling
ang
exceptionally
saloon
stab
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carla
terrifying
rom
##vision
coil
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satisfying
vendors
31st
mackay
deities
overlooked
ambient
bahamas
felipe
olympia
whirled
botanist
advertised
tugging
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disciples
morales
unionist
rites
foley
morse
motives
creepy
##₀
soo
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bargain
highness
frightening
turnpike
tory
reorganization
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depict
biographer
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unopposed
manifesto
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institut
emile
accidental
kapoor
##dam
kilkenny
cortex
lively
##13
romanesque
jain
shan
cannons
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petrol
echoing
amalgamated
disappears
cautious
proposes
sanctions
trenton
##ر
flotilla
aus
contempt
tor
canary
cote
theirs
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conceptual
deleted
fascinating
paso
blazing
elf
honourable
hutchinson
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surveyor
tee
amidst
wooded
reissue
intro
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cobb
shelters
newsletter
hanson
brace
encoding
confiscated
dem
caravan
marino
scroll
melodic
cows
imam
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northward
searches
biodiversity
cora
310
roaring
##bers
connell
theologian
halo
compose
pathetic
unmarried
dynamo
##oot
az
calculation
toulouse
deserves
humour
nr
forgiveness
tam
undergone
martyr
pamela
myths
whore
counselor
hicks
290
heavens
battleship
electromagnetic
##bbs
stellar
establishments
presley
hopped
##chin
temptation
90s
wills
nas
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nhs
##nya
seminars
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adaptations
gong
asher
lex
indicator
sikh
tobago
cites
goin
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satirical
##gies
characterised
correspond
bubbles
lure
participates
##vid
eruption
skate
therapeutic
1785
canals
wholesale
defaulted
sac
460
petit
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virgil
leak
ravens
256
portraying
##yx
ghetto
creators
dams
portray
vicente
##rington
fae
namesake
bounty
##arium
joachim
##ota
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aforementioned
axle
snout
depended
dismantled
reuben
480
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gallagher
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##pd
earnest
##ieu
##iary
inflicted
objections
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asa
gritted
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jericho
##sea
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flick
underside
ceramics
undead
substituted
195
eastward
undoubtedly
wheeled
chimney
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guinness
cb
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siding
##bell
traitor
baptiste
disguised
inauguration
149
tipperary
choreographer
perched
warmed
stationary
eco
##ike
##ntes
bacterial
##aurus
flores
phosphate
##core
attacker
invaders
alvin
intersects
a1
indirectly
immigrated
businessmen
cornelius
valves
narrated
pill
sober
ul
nationale
monastic
applicants
scenery
##jack
161
motifs
constitutes
cpu
##osh
jurisdictions
sd
tuning
irritation
woven
##uddin
fertility
gao
##erie
antagonist
impatient
glacial
hides
boarded
denominations
interception
##jas
cookie
nicola
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algebraic
marquess
bahn
parole
buyers
bait
turbines
paperwork
bestowed
natasha
renee
oceans
purchases
157
vaccine
215
##tock
fixtures
playhouse
integrate
jai
oswald
intellectuals
##cky
booked
nests
mortimer
##isi
obsession
sept
##gler
##sum
440
scrutiny
simultaneous
squinted
##shin
collects
oven
shankar
penned
remarkably
##я
slips
luggage
spectral
1786
collaborations
louie
consolidation
##ailed
##ivating
420
hoover
blackpool
harness
ignition
vest
tails
belmont
mongol
skinner
##nae
visually
mage
derry
##tism
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stevie
transitional
##rdy
redskins
drying
prep
prospective
##21
annoyance
oversee
##loaded
fills
##books
##iki
announces
fda
scowled
respects
prasad
mystic
tucson
##vale
revue
springer
bankrupt
1772
aristotle
salvatore
habsburg
##geny
dal
natal
nut
pod
chewing
darts
moroccan
walkover
rosario
lenin
punjabi
##ße
grossed
scattering
wired
invasive
hui
polynomial
corridors
wakes
gina
portrays
##cratic
arid
retreating
erich
irwin
sniper
##dha
linen
lindsey
maneuver
butch
shutting
socio
bounce
commemorative
postseason
jeremiah
pines
275
mystical
beads
bp
abbas
furnace
bidding
consulted
assaulted
empirical
rubble
enclosure
sob
weakly
cancel
polly
yielded
##emann
curly
prediction
battered
70s
vhs
jacqueline
render
sails
barked
detailing
grayson
riga
sloane
raging
##yah
herbs
bravo
##athlon
alloy
giggle
imminent
suffers
assumptions
waltz
##itate
accomplishments
##ited
bathing
remixed
deception
prefix
##emia
deepest
##tier
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balkan
frogs
##rong
slab
##pate
philosophers
peterborough
grains
imports
dickinson
rwanda
##atics
1774
dirk
lan
tablets
##rove
clone
##rice
caretaker
hostilities
mclean
##gre
regimental
treasures
norms
impose
tsar
tango
diplomacy
variously
complain
192
recognise
arrests
1779
celestial
pulitzer
##dus
bing
libretto
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adele
splash
##rite
expectation
lds
confronts
##izer
spontaneous
harmful
wedge
entrepreneurs
buyer
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bilingual
translate
rugged
conner
circulated
uae
eaton
##gra
##zzle
lingered
lockheed
vishnu
reelection
alonso
##oom
joints
yankee
headline
cooperate
heinz
laureate
invading
##sford
echoes
scandinavian
##dham
hugging
vitamin
salute
micah
hind
trader
##sper
radioactive
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militants
poisoned
ratified
remark
campeonato
deprived
wander
prop
##dong
outlook
##tani
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chiang
darcy
##oping
mandolin
spice
statesman
babylon
182
walled
forgetting
afro
##cap
158
giorgio
buffer
##polis
planetary
##gis
overlap
terminals
kinda
centenary
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arising
manipulate
elm
ke
1770
ak
##tad
chrysler
mapped
moose
pomeranian
quad
macarthur
assemblies
shoreline
recalls
stratford
##rted
noticeable
##evic
imp
##rita
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accustomed
supplying
tents
disgusted
vogue
sipped
filters
khz
reno
selecting
luftwaffe
mcmahon
tyne
masterpiece
carriages
collided
dunes
exercised
flare
remembers
muzzle
##mobile
heck
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burgess
lunged
middleton
boycott
bilateral
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hazardous
lumpur
multiplayer
spotlight
jackets
goldman
liege
porcelain
rag
waterford
benz
attracts
hopeful
battling
ottomans
kensington
baked
hymns
cheyenne
lattice
levine
borrow
polymer
clashes
michaels
monitored
commitments
denounced
##25
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cavity
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hobby
akin
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futures
intricate
cornish
patty
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illegally
dolphin
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barlow
yellowish
maddie
apologized
luton
plagued
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nana
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sway
fanny
łodz
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psi
suspicions
hanged
##eding
initiate
charlton
##por
nak
competent
235
analytical
annex
wardrobe
reservations
##rma
sect
162
fairfax
hedge
piled
buckingham
uneven
bauer
simplicity
snyder
interpret
accountability
donors
moderately
byrd
continents
##cite
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disciple
hr
jamaican
ping
nominees
##uss
mongolian
diver
attackers
eagerly
ideological
pillows
miracles
apartheid
revolver
sulfur
clinics
moran
163
##enko
ile
katy
rhetoric
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chronology
recycling
##hrer
elongated
mughal
pascal
profiles
vibration
databases
domination
##fare
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matthias
digest
rehearsal
polling
weiss
initiation
reeves
clinging
flourished
impress
ngo
##hoff
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buckley
symposium
rhythms
weed
emphasize
transforming
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##gence
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accountant
analyze
flicker
foil
priesthood
voluntarily
decreases
##80
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slater
sv
charting
mcgill
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moreno
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besieged
zur
robes
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admitting
api
deported
turmoil
peyton
earthquakes
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nationalists
beau
clair
brethren
interrupt
welch
curated
galerie
requesting
164
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impending
steward
viper
##vina
complaining
beautifully
brandy
foam
nl
1660
##cake
alessandro
punches
laced
explanations
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attribute
clit
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discomfort
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smoothed
whales
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adler
countered
duffy
disciplinary
widening
recipe
reliance
conducts
goats
gradient
preaching
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matilda
quasi
striped
meridian
cannabis
cordoba
certificates
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graffiti
hangs
pilgrims
repeats
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revive
urine
etat
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fueled
belts
fuzzy
susceptible
##hang
mauritius
salle
sincere
beers
hooks
##cki
arbitration
entrusted
advise
sniffed
seminar
junk
donnell
processors
principality
strapped
celia
mendoza
everton
fortunes
prejudice
starving
reassigned
steamer
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tuck
evenly
foreman
##ffen
dans
375
envisioned
slit
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baseman
liberia
rosemary
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electrified
periodically
potassium
stride
contexts
sperm
slade
mariners
influx
bianca
subcommittee
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spilling
icao
estuary
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delivers
iphone
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isa
mira
bohemian
dessert
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welcoming
proudly
slowing
##chs
musee
ascension
russ
##vian
waits
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africans
exploit
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gov
eccentric
crab
peck
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entrances
formidable
marketplace
groom
bolted
metabolism
patton
robbins
courier
payload
endure
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andes
refrigerator
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ornate
##uca
ruthless
illegitimate
masonry
strasbourg
bikes
adobe
##³
apples
quintet
willingly
niche
bakery
corpses
energetic
##cliffe
##sser
##ards
177
centimeters
centro
fuscous
cretaceous
rancho
##yde
andrei
telecom
tottenham
oasis
ordination
vulnerability
presiding
corey
cp
penguins
sims
##pis
malawi
piss
##48
correction
##cked
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##ryn
countdown
detectives
psychiatrist
psychedelic
dinosaurs
blouse
##get
choi
vowed
##oz
randomly
##pol
49ers
scrub
blanche
bruins
dusseldorf
##using
unwanted
##ums
212
dominique
elevations
headlights
om
laguna
##oga
1750
famously
ignorance
shrewsbury
##aine
ajax
breuning
che
confederacy
greco
overhaul
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paz
skirts
disagreement
cruelty
jagged
phoebe
shifter
hovered
viruses
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mandy
##lined
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landlord
squirrel
dashed
##ι
ornamental
gag
wally
grange
literal
spurs
undisclosed
proceeding
yin
##text
billie
orphan
spanned
humidity
indy
weighted
presentations
explosions
lucian
##tary
vaughn
hindus
##anga
##hell
psycho
171
daytona
protects
efficiently
rematch
sly
tandem
##oya
rebranded
impaired
hee
metropolis
peach
godfrey
diaspora
ethnicity
prosperous
gleaming
dar
grossing
playback
##rden
stripe
pistols
##tain
births
labelled
##cating
172
rudy
alba
##onne
aquarium
hostility
##gb
##tase
shudder
sumatra
hardest
lakers
consonant
creeping
demos
homicide
capsule
zeke
liberties
expulsion
pueblo
##comb
trait
transporting
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##yna
depart
gregg
mold
ledge
hangar
oldham
playboy
termination
analysts
gmbh
romero
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insist
cradle
filthy
brightness
slash
shootout
deposed
bordering
##truct
isis
microwave
tumbled
sheltered
cathy
werewolves
messy
andersen
convex
clapped
clinched
satire
wasting
edo
vc
rufus
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mont
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poznan
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restructuring
transverse
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azerbaijani
slovene
gestures
roommate
choking
shear
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vanguard
oblivious
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disagreed
baptism
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coliseum
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salvage
societe
cory
locke
relocation
relying
versailles
ahl
swelling
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cheerful
##word
##edes
gin
sarajevo
obstacle
diverted
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messed
thoroughbred
fluttered
utrecht
chewed
acquaintance
assassins
dispatch
mirza
##wart
nike
salzburg
swell
yen
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idle
ligue
samson
##nds
##igh
playful
spawned
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tease
##case
burgundy
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stirring
skeptical
interceptions
marathi
##dies
bedrooms
aroused
pinch
##lik
preferences
tattoos
buster
digitally
projecting
rust
##ital
kitten
priorities
addison
pseudo
##guard
dusk
icons
sermon
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bt
##lift
##xt
ju
truce
rink
##dah
##wy
defects
psychiatry
offences
calculate
glucose
##iful
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##unda
francaise
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richest
warwickshire
carly
1763
purity
redemption
lending
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muse
bruises
cerebral
aero
carving
##name
preface
terminology
invade
monty
##int
anarchist
blurred
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rossi
treats
guts
shu
foothills
ballads
undertaking
premise
cecilia
affiliates
blasted
conditional
wilder
minors
drone
rudolph
buffy
swallowing
horton
attested
##hop
rutherford
howell
primetime
livery
penal
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minimize
hydro
wrecked
wrought
palazzo
##gling
cans
vernacular
friedman
nobleman
shale
walnut
danielle
##ection
##tley
sears
##kumar
chords
lend
flipping
streamed
por
dracula
gallons
sacrifices
gamble
orphanage
##iman
mckenzie
##gible
boxers
daly
##balls
##ان
208
##ific
##rative
##iq
exploited
slated
##uity
circling
hillary
pinched
goldberg
provost
campaigning
lim
piles
ironically
jong
mohan
successors
usaf
##tem
##ught
autobiographical
haute
preserves
##ending
acquitted
comparisons
203
hydroelectric
gangs
cypriot
torpedoes
rushes
chrome
derive
bumps
instability
fiat
pets
##mbe
silas
dye
reckless
settler
##itation
info
heats
##writing
176
canonical
maltese
fins
mushroom
stacy
aspen
avid
##kur
##loading
vickers
gaston
hillside
statutes
wilde
gail
kung
sabine
comfortably
motorcycles
##rgo
169
pneumonia
fetch
##sonic
axel
faintly
parallels
##oop
mclaren
spouse
compton
interdisciplinary
miner
##eni
181
clamped
##chal
##llah
separates
versa
##mler
scarborough
labrador
##lity
##osing
rutgers
hurdles
como
166
burt
divers
##100
wichita
cade
coincided
##erson
bruised
mla
##pper
vineyard
##ili
##brush
notch
mentioning
jase
hearted
kits
doe
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pomerania
##ady
ronan
seizure
pavel
problematic
##zaki
domenico
##ulin
catering
penelope
dependence
parental
emilio
ministerial
atkinson
##bolic
clarkson
chargers
colby
grill
peeked
arises
summon
##aged
fools
##grapher
faculties
qaeda
##vial
garner
refurbished
##hwa
geelong
disasters
nudged
bs
shareholder
lori
algae
reinstated
rot
##ades
##nous
invites
stainless
183
inclusive
##itude
diocesan
til
##icz
denomination
##xa
benton
floral
registers
##ider
##erman
##kell
absurd
brunei
guangzhou
hitter
retaliation
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##eve
blanc
nh
consistency
contamination
##eres
##rner
dire
palermo
broadcasters
diaries
inspire
vols
brewer
tightening
ky
mixtape
hormone
##tok
stokes
##color
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##ssi
pg
##ometer
##lington
sanitation
##tility
intercontinental
apps
##adt
¹⁄₂
cylinders
economies
favourable
unison
croix
gertrude
odyssey
vanity
dangling
##logists
upgrades
dice
middleweight
practitioner
##ight
206
henrik
parlor
orion
angered
lac
python
blurted
##rri
sensual
intends
swings
angled
##phs
husky
attain
peerage
precinct
textiles
cheltenham
shuffled
dai
confess
tasting
bhutan
##riation
tyrone
segregation
abrupt
ruiz
##rish
smirked
blackwell
confidential
browning
amounted
##put
vase
scarce
fabulous
raided
staple
guyana
unemployed
glider
shay
##tow
carmine
troll
intervene
squash
superstar
##uce
cylindrical
len
roadway
researched
handy
##rium
##jana
meta
lao
declares
##rring
##tadt
##elin
##kova
willem
shrubs
napoleonic
realms
skater
qi
volkswagen
##ł
tad
hara
archaeologist
awkwardly
eerie
##kind
wiley
##heimer
##24
titus
organizers
cfl
crusaders
lama
usb
vent
enraged
thankful
occupants
maximilian
##gaard
possessing
textbooks
##oran
collaborator
quaker
##ulo
avalanche
mono
silky
straits
isaiah
mustang
surged
resolutions
potomac
descend
cl
kilograms
plato
strains
saturdays
##olin
bernstein
##ype
holstein
ponytail
##watch
belize
conversely
heroine
perpetual
##ylus
charcoal
piedmont
glee
negotiating
backdrop
prologue
##jah
##mmy
pasadena
climbs
ramos
sunni
##holm
##tner
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anand
deficiency
hertfordshire
stout
##avi
aperture
orioles
##irs
doncaster
intrigued
bombed
coating
otis
##mat
cocktail
##jit
##eto
amir
arousal
sar
##proof
##act
##ories
dixie
pots
##bow
whereabouts
159
##fted
drains
bullying
cottages
scripture
coherent
fore
poe
appetite
##uration
sampled
##ators
##dp
derrick
rotor
jays
peacock
installment
##rro
advisors
##coming
rodeo
scotch
##mot
##db
##fen
##vant
ensued
rodrigo
dictatorship
martyrs
twenties
##н
towed
incidence
marta
rainforest
sai
scaled
##cles
oceanic
qualifiers
symphonic
mcbride
dislike
generalized
aubrey
colonization
##iation
##lion
##ssing
disliked
lublin
salesman
##ulates
spherical
whatsoever
sweating
avalon
contention
punt
severity
alderman
atari
##dina
##grant
##rop
scarf
seville
vertices
annexation
fairfield
fascination
inspiring
launches
palatinate
regretted
##rca
feral
##iom
elk
nap
olsen
reddy
yong
##leader
##iae
garment
transports
feng
gracie
outrage
viceroy
insides
##esis
breakup
grady
organizer
softer
grimaced
222
murals
galicia
arranging
vectors
##rsten
bas
##sb
##cens
sloan
##eka
bitten
ara
fender
nausea
bumped
kris
banquet
comrades
detector
persisted
##llan
adjustment
endowed
cinemas
##shot
sellers
##uman
peek
epa
kindly
neglect
simpsons
talon
mausoleum
runaway
hangul
lookout
##cic
rewards
coughed
acquainted
chloride
##ald
quicker
accordion
neolithic
##qa
artemis
coefficient
lenny
pandora
tx
##xed
ecstasy
litter
segunda
chairperson
gemma
hiss
rumor
vow
nasal
antioch
compensate
patiently
transformers
##eded
judo
morrow
penis
posthumous
philips
bandits
husbands
denote
flaming
##any
##phones
langley
yorker
1760
walters
##uo
##kle
gubernatorial
fatty
samsung
leroy
outlaw
##nine
unpublished
poole
jakob
##ᵢ
##ₙ
crete
distorted
superiority
##dhi
intercept
crust
mig
claus
crashes
positioning
188
stallion
301
frontal
armistice
##estinal
elton
aj
encompassing
camel
commemorated
malaria
woodward
calf
cigar
penetrate
##oso
willard
##rno
##uche
illustrate
amusing
convergence
noteworthy
##lma
##rva
journeys
realise
manfred
##sable
410
##vocation
hearings
fiance
##posed
educators
provoked
adjusting
##cturing
modular
stockton
paterson
vlad
rejects
electors
selena
maureen
##tres
uber
##rce
swirled
##num
proportions
nanny
pawn
naturalist
parma
apostles
awoke
ethel
wen
##bey
monsoon
overview
##inating
mccain
rendition
risky
adorned
##ih
equestrian
germain
nj
conspicuous
confirming
##yoshi
shivering
##imeter
milestone
rumours
flinched
bounds
smacked
token
##bei
lectured
automobiles
##shore
impacted
##iable
nouns
nero
##leaf
ismail
prostitute
trams
##lace
bridget
sud
stimulus
impressions
reins
revolves
##oud
##gned
giro
honeymoon
##swell
criterion
##sms
##uil
libyan
prefers
##osition
211
preview
sucks
accusation
bursts
metaphor
diffusion
tolerate
faye
betting
cinematographer
liturgical
specials
bitterly
humboldt
##ckle
flux
rattled
##itzer
archaeologists
odor
authorised
marshes
discretion
##ов
alarmed
archaic
inverse
##leton
explorers
##pine
drummond
tsunami
woodlands
##minate
##tland
booklet
insanity
owning
insert
crafted
calculus
##tore
receivers
##bt
stung
##eca
##nched
prevailing
travellers
eyeing
lila
graphs
##borne
178
julien
##won
morale
adaptive
therapist
erica
cw
libertarian
bowman
pitches
vita
##ional
crook
##ads
##entation
caledonia
mutiny
##sible
1840s
automation
##ß
flock
##pia
ironic
pathology
##imus
remarried
##22
joker
withstand
energies
##att
shropshire
hostages
madeleine
tentatively
conflicting
mateo
recipes
euros
ol
mercenaries
nico
##ndon
albuquerque
augmented
mythical
bel
freud
##child
cough
##lica
365
freddy
lillian
genetically
nuremberg
calder
209
bonn
outdoors
paste
suns
urgency
vin
restraint
tyson
##cera
##selle
barrage
bethlehem
kahn
##par
mounts
nippon
barony
happier
ryu
makeshift
sheldon
blushed
castillo
barking
listener
taped
bethel
fluent
headlines
pornography
rum
disclosure
sighing
mace
doubling
gunther
manly
##plex
rt
interventions
physiological
forwards
emerges
##tooth
##gny
compliment
rib
recession
visibly
barge
faults
connector
exquisite
prefect
##rlin
patio
##cured
elevators
brandt
italics
pena
173
wasp
satin
ea
botswana
graceful
respectable
##jima
##rter
##oic
franciscan
generates
##dl
alfredo
disgusting
##olate
##iously
sherwood
warns
cod
promo
cheryl
sino
##ة
##escu
twitch
##zhi
brownish
thom
ortiz
##dron
densely
##beat
carmel
reinforce
##bana
187
anastasia
downhill
vertex
contaminated
remembrance
harmonic
homework
##sol
fiancee
gears
olds
angelica
loft
ramsay
quiz
colliery
sevens
##cape
autism
##hil
walkway
##boats
ruben
abnormal
ounce
khmer
##bbe
zachary
bedside
morphology
punching
##olar
sparrow
convinces
##35
hewitt
queer
remastered
rods
mabel
solemn
notified
lyricist
symmetric
##xide
174
encore
passports
wildcats
##uni
baja
##pac
mildly
##ease
bleed
commodity
mounds
glossy
orchestras
##omo
damian
prelude
ambitions
##vet
awhile
remotely
##aud
asserts
imply
##iques
distinctly
modelling
remedy
##dded
windshield
dani
xiao
##endra
audible
powerplant
1300
invalid
elemental
acquisitions
##hala
immaculate
libby
plata
smuggling
ventilation
denoted
minh
##morphism
430
differed
dion
kelley
lore
mocking
sabbath
spikes
hygiene
drown
runoff
stylized
tally
liberated
aux
interpreter
righteous
aba
siren
reaper
pearce
millie
##cier
##yra
gaius
##iso
captures
##ttering
dorm
claudio
##sic
benches
knighted
blackness
##ored
discount
fumble
oxidation
routed
##ς
novak
perpendicular
spoiled
fracture
splits
##urt
pads
topology
##cats
axes
fortunate
offenders
protestants
esteem
221
broadband
convened
frankly
hound
prototypes
isil
facilitated
keel
##sher
sahara
awaited
bubba
orb
prosecutors
186
hem
520
##xing
relaxing
remnant
romney
sorted
slalom
stefano
ulrich
##active
exemption
folder
pauses
foliage
hitchcock
epithet
204
criticisms
##aca
ballistic
brody
hinduism
chaotic
youths
equals
##pala
pts
thicker
analogous
capitalist
improvised
overseeing
sinatra
ascended
beverage
##tl
straightforward
##kon
curran
##west
bois
325
induce
surveying
emperors
sax
unpopular
##kk
cartoonist
fused
##mble
unto
##yuki
localities
##cko
##ln
darlington
slain
academie
lobbying
sediment
puzzles
##grass
defiance
dickens
manifest
tongues
alumnus
arbor
coincide
184
appalachian
mustafa
examiner
cabaret
traumatic
yves
bracelet
draining
heroin
magnum
baths
odessa
consonants
mitsubishi
##gua
kellan
vaudeville
##fr
joked
null
straps
probation
##ław
ceded
interfaces
##pas
##zawa
blinding
viet
224
rothschild
museo
640
huddersfield
##vr
tactic
##storm
brackets
dazed
incorrectly
##vu
reg
glazed
fearful
manifold
benefited
irony
##sun
stumbling
##rte
willingness
balkans
mei
wraps
##aba
injected
##lea
gu
syed
harmless
##hammer
bray
takeoff
poppy
timor
cardboard
astronaut
purdue
weeping
southbound
cursing
stalls
diagonal
##neer
lamar
bryce
comte
weekdays
harrington
##uba
negatively
##see
lays
grouping
##cken
##henko
affirmed
halle
modernist
##lai
hodges
smelling
aristocratic
baptized
dismiss
justification
oilers
##now
coupling
qin
snack
healer
##qing
gardener
layla
battled
formulated
stephenson
gravitational
##gill
##jun
1768
granny
coordinating
suites
##cd
##ioned
monarchs
##cote
##hips
sep
blended
apr
barrister
deposition
fia
mina
policemen
paranoid
##pressed
churchyard
covert
crumpled
creep
abandoning
tr
transmit
conceal
barr
understands
readiness
spire
##cology
##enia
##erry
610
startling
unlock
vida
bowled
slots
##nat
##islav
spaced
trusting
admire
rig
##ink
slack
##70
mv
207
casualty
##wei
classmates
##odes
##rar
##rked
amherst
furnished
evolve
foundry
menace
mead
##lein
flu
wesleyan
##kled
monterey
webber
##vos
wil
##mith
##на
bartholomew
justices
restrained
##cke
amenities
191
mediated
sewage
trenches
ml
mainz
##thus
1800s
##cula
##inski
caine
bonding
213
converts
spheres
superseded
marianne
crypt
sweaty
ensign
historia
##br
spruce
##post
##ask
forks
thoughtfully
yukon
pamphlet
ames
##uter
karma
##yya
bryn
negotiation
sighs
incapable
##mbre
##ntial
actresses
taft
##mill
luce
prevailed
##amine
1773
motionless
envoy
testify
investing
sculpted
instructors
provence
kali
cullen
horseback
##while
goodwin
##jos
gaa
norte
##ldon
modify
wavelength
abd
214
skinned
sprinter
forecast
scheduling
marries
squared
tentative
##chman
boer
##isch
bolts
swap
fisherman
assyrian
impatiently
guthrie
martins
murdoch
194
tanya
nicely
dolly
lacy
med
##45
syn
decks
fashionable
millionaire
##ust
surfing
##ml
##ision
heaved
tammy
consulate
attendees
routinely
197
fuse
saxophonist
backseat
malaya
##lord
scowl
tau
##ishly
193
sighted
steaming
##rks
303
911
##holes
##hong
ching
##wife
bless
conserved
jurassic
stacey
unix
zion
chunk
rigorous
blaine
198
peabody
slayer
dismay
brewers
nz
##jer
det
##glia
glover
postwar
int
penetration
sylvester
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airlift
heiress
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viva
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390
macon
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belongings
leicestershire
196
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inverted
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sobbing
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4000
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diversion
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landowners
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melville
quantitative
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downed
unavailable
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approximation
hiroshima
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valle
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traveler
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tonga
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gigantic
serum
enjoyment
retailer
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technicians
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arithmetic
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backstage
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tao
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aces
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bandwidth
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540
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sacrificed
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vamp
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schedules
sindh
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beaux
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gliding
unidentified
216
collaborating
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jesuits
oro
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anal
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231
enthusiasts
newscast
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janata
dewey
parkinson
179
ankara
biennial
towering
dd
inconsistent
950
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furiously
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grassland
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loneliness
217
memorandum
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popularized
rhodesia
60s
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sans
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biotechnology
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pains
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1830s
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guggenheim
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305
hectare
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hutton
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verify
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longitude
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skeletal
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pokemon
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divides
convictions
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pausing
burying
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mathias
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kite
hunched
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189
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disadvantage
liza
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greedy
paradox
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cary
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rowland
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shafts
lawsuits
activate
conor
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culturally
garlic
265
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furrowed
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meteorological
rendezvous
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sums
wentworth
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vineyards
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scandinavia
jiang
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adjunct
rhyme
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hopeless
surrounds
tremble
32nd
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oils
medicinal
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steer
wilkes
219
255
concessions
hue
uniquely
blinded
landon
yahoo
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commemorating
dex
specify
chicks
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intercity
1400
morley
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pang
oblique
stalled
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flirting
newborn
1769
bishopric
shaved
232
currie
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dharma
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favorites
smug
novella
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abusive
creations
espana
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paradigm
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renew
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weimar
grunt
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lordship
plotting
expenditure
infringement
lbs
refrain
av
mimi
mistakenly
postmaster
1771
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ras
motorsports
tito
199
subjective
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bully
stew
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prescott
1a
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bids
styling
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reeve
sneaking
exploding
katz
akbar
migrant
syllables
indefinitely
##ogical
destroys
replaces
applause
##phine
pest
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218
articulated
bertie
##thing
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courtroom
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aesthetics
cummings
tehsil
hormones
titanic
dangerously
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stadion
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ciudad
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ely
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password
pasture
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minsk
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##ologies
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assessments
martian
royalist
1765
examines
##mani
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nino
223
parry
scooped
relativity
##eli
##uting
##cao
congregational
noisy
traverse
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strikeouts
nickelodeon
obituary
transylvania
binds
depictions
polk
trolley
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breeders
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dryly
hokkaido
1762
strengths
stacks
bonaparte
connectivity
neared
prostitutes
stamped
anaheim
gutierrez
sinai
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bram
fresno
madhya
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proton
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wanda
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distinguishing
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grasping
sermons
tomato
bland
stimulation
avenues
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distortion
fatalities
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correctional
pricing
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prom
dammit
ying
collaborate
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welterweight
33rd
pointer
substitution
bonded
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communicating
multitude
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federally
intimacy
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betray
ssr
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airbus
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ud
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biomedical
netflix
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macy
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jelly
lavender
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60th
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hottest
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predictions
fuji
bu
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barangay
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clocks
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cereal
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xml
forcibly
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petra
sarcasm
hartley
peeled
storytelling
##42
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fig
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greenville
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geographically
nell
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cemeteries
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crossings
nah
holloway
reptiles
chun
sichuan
snowy
660
corrections
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zheng
ambassadors
blacksmith
fielded
fluids
hardcover
turnover
medications
melvin
academies
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ro
roach
absorbing
spaniards
colton
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outsider
espionage
kelsey
245
edible
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dora
establishes
##sham
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contracting
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cinematic
costello
nesting
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connolly
duff
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mma
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fergus
sexes
gi
optics
spectator
woodstock
banning
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differentiate
outfielder
refinery
226
312
gerhard
horde
lair
drastically
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landfall
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motorsport
odi
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predominant
quay
skins
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edna
harshly
complementary
murdering
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wreckage
##90
ono
outstretched
lennox
munitions
galen
reconcile
470
scalp
bicycles
gillespie
questionable
rosenberg
guillermo
hostel
jarvis
kabul
volvo
opium
yd
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abuses
decca
outpost
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sensible
neutrality
##64
ponce
anchorage
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turrets
inadvertently
disagree
libre
vodka
reassuring
weighs
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jumper
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outs
stain
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envy
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smashing
heightened
policing
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mixes
lai
prima
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lucrative
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kc
manually
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stature
staffed
bun
bastards
nairobi
priced
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thatcher
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comune
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brasil
incentives
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hereford
musica
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benedictine
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rb
knocks
sha
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efficacy
ventura
masonic
sanford
maize
leverage
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capacities
santana
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novelty
vanilla
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benin
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neptune
drafting
tallinn
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humiliation
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schleswig
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liturgy
spectacle
sweeney
pont
routledge
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cosmos
ut
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sleek
universally
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typed
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favors
allegheny
glaciers
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recalling
aziz
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parasite
requiem
auf
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illumination
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festivities
bows
govern
vibe
vp
333
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leaping
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gaping
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craven
fantasies
houghton
engined
reigned
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supervising
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commentaries
unnatural
fingernails
spirituality
tighten
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canadiens
protesting
intentional
cheers
sparta
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widen
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controllers
dodd
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navarre
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defect
squire
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whisky
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560
inevitably
tome
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chew
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elastic
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streaked
alliances
jailed
regal
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czechoslovak
narration
absently
##uld
bluegrass
guangdong
quran
criticizing
hose
hari
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skier
streaks
deploy
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raft
bose
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huff
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haifa
simplest
bursting
endings
ib
sultanate
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franks
whitman
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collaborators
forster
organising
ui
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battleships
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geologist
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reflective
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bayern
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lakshmi
shortlisted
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camouflage
donate
af
christi
lau
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disclosed
nemesis
1761
assemble
straining
northamptonshire
tal
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bernardino
premature
heidi
42nd
coefficients
galactic
reproduce
buzzed
sensations
zionist
monsieur
myrtle
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pee
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overland
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massey
gatherings
haitian
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disapproval
flaws
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1766
npr
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boroughs
creole
forums
techno
1755
dent
abdominal
streetcar
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procurement
gemini
predictable
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acheron
christoph
feeder
fronts
vendor
bernhard
jammu
tumors
slang
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goaltender
twists
curving
manson
vuelta
mer
peanut
confessions
pouch
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allowance
theodor
vascular
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authenticity
metabolic
coughing
nanjing
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pembroke
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splendid
36th
ff
hourly
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experimentation
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##46
caressed
entertained
steak
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biologist
orphans
baroness
oyster
stepfather
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mirage
reefs
speeding
##31
barons
1764
227
inhabit
preached
repealed
##tral
honoring
boogie
captives
administer
johanna
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gel
suspiciously
1767
sobs
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backbone
hayward
garry
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maxi
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galileo
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crimea
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amour
bumper
matrices
natalia
baking
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palestinians
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smack
conveyed
ensembles
gardening
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1640
harvesting
topography
jing
shifters
dormitory
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ist
skulls
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dolores
jewellery
sarawak
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christy
confinement
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credibility
fir
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fulfilling
anthropologist
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milky
olson
proprietor
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guarantees
intercollegiate
ljubljana
hilda
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sardinia
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frontman
privileged
witchcraft
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laude
poking
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bracket
amazement
yunnan
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maharaja
linnaeus
264
commissioning
milano
peacefully
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akira
rani
regulator
##36
grasses
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luzon
crows
compiler
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tab
buccaneers
ellington
hamlets
whig
socialists
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mythological
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rhineland
semantic
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succeeds
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doses
bleak
madam
predatory
relentless
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reluctance
##vir
hailey
crore
silvery
1759
monstrous
swimmers
transmissions
hawthorn
informing
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toilets
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crouch
kb
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295
cartel
hadley
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maximus
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beverages
galaxies
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swarm
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buckinghamshire
fen
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auditions
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1740
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nightingale
tamara
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weaknesses
silhouette
285
compatibility
withdrawing
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giggling
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bookstore
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southernmost
tilting
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economical
rf
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dreadful
hinted
projections
shattering
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analogue
indicted
periodical
fullback
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haynes
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1745
thirst
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vigorous
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disposed
sheath
chickens
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fitzroy
sas
silesia
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odisha
1650
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pe
spokane
ratios
athena
massage
shen
dilemma
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niall
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warship
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cyclists
constructions
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stomped
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relevance
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outnumbered
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fairs
totals
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262
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stairway
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commodities
grease
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artworks
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supremacy
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234
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manufactures
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238
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quo
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xx
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38th
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302
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1100
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233
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254
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228
stimuli
twenty20
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490
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241
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configurations
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1756
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246
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commended
outspoken
242
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annette
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mosquito
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hampered
1648
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reservoirs
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summarized
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porn
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sweetly
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ich
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legitimacy
brushes
expressive
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systemic
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gettysburg
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dialogues
muttering
251
housekeeper
sicilian
discouraged
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beamed
kaladin
halftime
kidnap
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1754
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depleted
instituto
insulin
reprised
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interrupting
radcliffe
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medici
1715
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playfully
turbulent
##47
starvation
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rebellious
petersen
verification
merits
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cakes
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1757
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shortages
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fidelity
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harvested
seismic
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theoretically
barbie
lynx
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poisonous
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reactive
residues
obedience
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conjecture
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emancipation
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professorship
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internship
terence
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executions
legislators
berries
peninsular
tinged
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1689
amplifier
corvette
ribbons
lavish
pennant
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worthless
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expenditures
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chesterfield
mandir
tailor
39th
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willed
aristocracy
devotees
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harcourt
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startup
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kern
palaces
helium
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princely
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faso
bazaar
warmly
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229
pairing
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wien
freaked
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rebirth
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tha
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archival
professions
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shadowy
shrink
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glued
migrate
stereotypes
devoid
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625
evacuate
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infancy
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optic
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sachs
kingsley
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carvings
clockwise
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intruder
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bourgeois
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penthouse
sane
720
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stakeholders
neumann
bb
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comb
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catchment
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rallies
typing
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forefront
freiburg
sweetie
giacomo
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goodwill
worshipped
aspirations
midday
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fishery
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bournemouth
turk
243
hearth
ethanol
guadalajara
murmurs
sl
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afforded
scripted
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wah
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coroner
translucent
252
memorials
puck
progresses
clumsy
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315
candace
recounted
##27
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filtering
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leveled
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dubious
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citations
exhibiting
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turkic
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injunction
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antibodies
##44
organise
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cardiovascular
cushion
inverness
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dia
cocoa
sibling
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expanse
feasible
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algiers
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rus
bloomberg
dso
westphalia
bro
tacoma
281
downloads
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konrad
duran
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continuum
jett
compares
legislator
secession
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reacher
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drumming
postage
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superiors
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ignacio
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kaplan
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bratislava
mori
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disturb
haas
313
cartridges
gilmore
radiated
salford
tunic
hades
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archeological
delilah
magistrates
auditioned
brewster
charters
empowerment
blogs
cappella
dynasties
iroquois
whipping
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raceway
truths
myra
weaken
judah
mcgregor
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mic
refueling
37th
burnley
bosses
markus
premio
query
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dunbar
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darkest
lyndon
sealing
commendation
reappeared
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addicted
ezio
slaughtered
satisfactory
shuffle
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fortification
warrington
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resurrected
fargo
mane
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foreword
ox
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abrams
hua
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sakura
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sentimental
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midfield
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sturdy
scrolls
macleod
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entropy
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mitochondrial
cicero
excelled
thinner
convoys
perceive
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systematically
grind
burkina
287
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ops
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guantanamo
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forcefully
wavy
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pointless
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portico
superficial
clerical
outlaws
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burials
muir
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creditors
hauling
rattle
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monde
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dwell
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hellenic
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furnishings
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neurological
novice
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contemplated
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nightstand
saratoga
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documenting
pulsing
taluk
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busted
marital
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disagreements
wasps
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mcdonnell
mimic
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pendant
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congratulations
argent
darrell
concussion
losers
regrets
thessaloniki
reversal
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hardwood
thence
achilles
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horizontally
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intercourse
seduce
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ferris
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amar
nik
unarmed
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evaluating
kyrgyzstan
sweetness
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meiji
notoriety
stimulate
disrupt
figuring
instructional
mcgrath
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groundbreaking
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khorasan
agrarian
bengals
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radiating
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pitchers
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tariff
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appellate
lehigh
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brawl
duct
texans
##ciation
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skipper
speculative
vomit
doctrines
stresses
253
davy
graders
whitehead
jozef
timely
cumulative
haryana
paints
appropriately
boon
cactus
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dow
legions
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perceptions
1730
picturesque
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periphery
rune
wr
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celtics
sentencing
whoa
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confirms
variance
425
moines
mathews
spade
rave
m1
fronted
fx
blending
alleging
reared
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237
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grassroots
eroded
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directs
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rooftop
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buys
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repetition
warehouses
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exclude
nix
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ito
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glands
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longed
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chattanooga
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syrup
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careless
precedence
frescoes
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consult
modelled
snatch
peat
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caucasian
humane
relaxation
spins
temperance
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occupations
lambda
hybrids
moons
mp3
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247
rolf
societal
yerevan
ness
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befriended
mechanized
nominate
trough
boasted
cues
seater
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bends
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emptiness
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anxiously
lark
propellers
chichester
jock
ev
2a
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credible
recounts
tori
loyalist
abduction
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steered
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dietary
trojan
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lifeboat
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infirmary
nearing
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boyer
mutter
trillion
joanne
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sinks
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uruguayan
clasp
sirius
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accelerator
prohibit
sunken
byu
chronological
diplomats
ochreous
510
symmetrical
1644
maia
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salts
reigns
atrocities
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hess
bared
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voyager
dyer
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fountains
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rollins
atheist
ominous
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herr
chariot
martina
strung
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horrific
sahib
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saetan
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flushing
lauderdale
analytic
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530
navarro
beak
gorilla
herrera
broom
guadalupe
raiding
sykes
311
bsc
deliveries
1720
invasions
carmichael
tajikistan
thematic
ecumenical
sentiments
onstage
##rians
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catastrophic
flanks
molten
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waller
aimee
terminating
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alternately
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nehru
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outraged
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empires
template
banners
repetitive
za
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vegetarian
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guiana
opt
cavendish
lucknow
synthesized
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finalized
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fictitious
mayoral
unreliable
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embracing
peppers
rbis
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inhibition
slashed
togo
orderly
embroidered
safari
salty
236
barron
benito
totaled
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pubs
simulated
caden
devin
tolkien
momma
welding
sesame
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gottingen
hardness
630
shaman
temeraire
620
adequately
pediatric
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ck
assertion
radicals
composure
cadence
seafood
beaufort
lazarus
mani
warily
cunning
kurdistan
249
cantata
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ares
##41
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nape
townland
geared
insulted
flutter
boating
violate
draper
dumping
malmo
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firearm
alta
bono
obscured
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exceeds
panorama
unbelievable
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preschool
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disconnected
installing
rescuing
secretaries
accessibility
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bouts
slug
waterway
mindanao
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halves
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liter
maternity
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bragg
electrification
mcc
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roxy
schizophrenia
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munoz
kaye
whaling
239
mil
tingling
tolerant
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unconventional
volcanoes
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deportivo
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robson
kaufman
neuroscience
wai
deportation
masovian
scraping
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hacking
bulge
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administratively
yao
580
amp
mammoth
booster
claremont
hooper
nomenclature
pursuits
mclaughlin
melinda
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catfish
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substrates
taxa
zee
originals
kimberly
packets
padma
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borrowing
ostensibly
solvent
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lukas
shreveport
veracruz
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tt
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hobbs
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cyclic
radiant
alistair
greenish
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dat
independents
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hyper
applicant
bradshaw
spores
telangana
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inexpensive
nuclei
322
jang
nme
soho
spd
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cradled
receptionist
pow
##43
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fascism
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experimenting
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345
jocelyn
maris
stair
nocturnal
toro
constabulary
elgin
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msc
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doherty
doping
sarcastically
batter
maneuvers
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intrinsic
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1753
showtime
cafes
gasps
lviv
ushered
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fours
restart
astonishment
transmitting
flyer
shrugs
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intriguing
cones
dictated
mushrooms
medial
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escorting
gaped
##26
godfather
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djs
recaptured
timetable
vila
1710
3a
aerodrome
mortals
scientology
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angelina
mag
convection
unpaid
insertion
intermittent
lego
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endeavor
kota
pereira
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304
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glamorgan
insults
agatha
fey
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protruding
steamship
zeta
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urges
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802
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replacements
hawker
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casts
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680
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strategically
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handkerchief
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christophe
insulting
316
nakamura
romani
asiatic
magdalena
palma
cruises
stripping
405
konstantin
soaring
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colloquially
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sincerity
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ornaments
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usable
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321
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monograph
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airplanes
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charms
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1661
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tnt
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transcript
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raining
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clauses
menacing
1642
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dealings
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seekers
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1688
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horne
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435
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hermes
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chapels
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jamal
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compute
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warp
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mcgraw
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hannibal
mein
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cher
christensen
greenberg
interruption
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jem
244
mocked
bottoms
cambridgeshire
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synth
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advisers
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nominally
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272
308
41st
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soundtracks
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patrolling
wharton
plainly
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eastman
schuster
topographic
engages
immensely
unbearable
fairchild
1620
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indictment
hahn
bangladeshi
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expects
indoors
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revisited
constituents
rallied
rotate
loomed
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amateurs
augsburg
auschwitz
crowns
skeletons
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bonnet
257
dummy
globalization
simeon
sleeper
mandal
differentiated
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milne
bundled
exasperated
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owes
segregated
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dentist
piracy
props
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malicious
paws
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dependency
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258
pistons
rourke
jed
grammatical
tres
maha
wig
512
ghostly
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clones
showdown
throttle
twain
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metz
nagoya
335
braking
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385
roaming
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amin
crippled
##37
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indifferent
hoffmann
idols
intimidating
1751
261
influenza
memo
onions
1748
bandage
consciously
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clandestine
observes
swiped
tangle
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hugs
congresses
josiah
spirited
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filmmaking
inmate
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ur
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royale
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levant
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visas
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snuck
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kerman
res
fairbanks
jarrett
abstraction
saharan
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1746
procedural
clearer
kincaid
sash
luciano
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crunch
helmut
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revolutionaries
##tute
creamy
leach
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1747
permitting
nes
plight
wendell
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contra
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clancy
ipa
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karin
pontiac
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haunt
leto
bergman
expands
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wal
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canning
celine
cords
obesity
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intrusion
planner
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reasoned
sequencing
307
harrow
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marred
mcintyre
repay
tarzan
darting
248
harrisburg
margarita
repulsed
##hur
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belinda
hamburger
novo
compliant
runways
bingham
registrar
skyscraper
ic
cuthbert
improvisation
livelihood
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admiring
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believer
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popcorn
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rectory
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sonar
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boutique
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guerre
bathed
upbringing
vaulted
sandals
blessings
##naut
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1680
306
foxes
pia
corrosion
hesitantly
confederates
crystalline
footprints
shapiro
tirana
valentin
drones
45th
microscope
shipments
texted
inquisition
wry
guernsey
unauthorized
resigning
760
ripple
schubert
stu
reassure
felony
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brittle
koreans
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implicit
tyres
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magnolia
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fei
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familiarity
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indicative
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fundamentally
jimmie
overrun
395
anchors
moans
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britannia
armagh
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purposely
seizing
##vao
bewildered
mundane
avoidance
cosmopolitan
geometridae
quartermaster
caf
415
chatter
engulfed
gleam
purge
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juliette
jurisprudence
guerra
revisions
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brew
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1749
clapton
cloudy
conde
hermitage
278
simulations
torches
vincenzo
matteo
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westbound
accomplishment
tentacles
unaffected
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flopped
sloping
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dreamer
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vu
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consecration
copying
messaging
breaker
climates
hospitalized
1752
torino
afternoons
winfield
witnessing
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choirs
sawmill
coldly
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conical
bibliography
pamphlets
severn
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sis
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1690
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civilizations
paces
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skaters
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pharmaceuticals
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stocked
yearbook
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hamid
moors
pastures
summons
jihad
nectar
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bayou
untitled
pleasing
vastly
republics
intellect
##η
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stylistic
sb
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consolation
frequented
h₂o
walden
widows
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404
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chunks
improves
288
grit
recited
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snarl
sociological
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inquired
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bruise
clube
consultancy
homogeneous
hornets
multiplication
pasta
prick
savior
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vanishing
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bn
distillery
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dockyard
massif
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voss
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floats
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kv
annapolis
madden
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noctuidae
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avenge
steamboat
forte
illustrates
erika
##trip
570
dew
nationalities
bran
manifested
thirsty
diversified
muscled
reborn
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arson
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willoughby
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286
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dashboard
yuki
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granville
myspace
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enthusiastically
indications
saxe
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consolidate
iec
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ramps
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mana
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rei
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ns
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lew
seamus
1630
cocky
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mcdowell
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condensed
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invincible
missy
pious
balthazar
ci
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myriad
1707
silenced
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netball
yourselves
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clarify
heller
peg
durban
etudes
offender
roast
blackmail
curvature
##woods
vile
309
illicit
suriname
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overture
1685
bubbling
gymnast
tucking
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maldives
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75th
jens
respondents
analytics
cavaliers
papacy
raju
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funnel
271
disneyland
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sociologist
##iam
2500
faulkner
louvre
menon
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276
##ower
afterlife
mannheim
peptide
referees
comedians
meaningless
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fabrics
hurley
renal
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clover
disdain
unsafe
redesign
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firth
barnsley
portage
reset
narrows
268
commandos
expansive
speechless
tubular
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essendon
eyelashes
smashwords
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craved
sprinted
chet
somme
astor
wrocław
orton
266
bane
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mischief
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offenses
undermine
moi
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590
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frs
uniformed
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259
powerless
priscilla
stimulated
980
qc
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frustrating
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regimes
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kickoff
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rockwell
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crusader
glue
revolutions
scrambling
1714
grover
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englishman
aztec
263
contemplating
coven
ipad
preach
triumphant
tufts
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rotational
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328
falkland
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strewn
clarissa
rejoin
environmentally
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v6
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airfields
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1741
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amelie
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friar
morals
passions
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vis
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burnham
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rudd
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murderous
textual
wainwright
armada
bam
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timer
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noticeably
wynn
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crises
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patterned
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accessory
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entourage
stitches
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headwaters
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interlude
distraught
draught
1727
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sy
transient
triad
subgenus
adapting
kidd
shortstop
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dimly
spiked
mcleod
reprint
nellie
pretoria
windmill
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singled
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273
reunite
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747
bankers
outlying
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apologies
cosmetics
patsy
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bender
nantes
serene
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lucha
mmm
323
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juarez
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juveniles
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dq
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ref
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ua
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psychologists
stryker
sw
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turkmenistan
wits
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screenings
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advises
fredrik
interpreting
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spectroscopy
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conqueror
kgb
leukemia
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mammal
unitarian
urgently
chopin
sanitary
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spicy
drugged
hinges
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tipping
trier
impoverished
westchester
##caster
267
epoch
nonstop
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aromatic
centrally
cerro
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billions
modulation
sedimentary
283
facilitating
outrageous
goldstein
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fleets
spaceship
vertebrae
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alcoholism
als
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1643
670
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bulbs
synchronized
vito
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cumbria
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brood
hilltop
luminous
carts
keynote
larkin
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lilith
tinted
277
wrestle
mobilization
##uses
sequential
siam
bloomfield
takahashi
274
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presenters
ringo
blazed
witty
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devastation
haydn
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newt
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molina
rabbis
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001
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wb
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enroll
hypothetical
hysterical
1743
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indefinite
warped
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exchanging
525
unsuitable
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1603
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cobalt
homemade
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durable
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ligament
succumbed
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spawn
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filmfare
rochelle
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1725
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saber
supplements
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crowe
wellesley
carrot
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primate
drury
sincerely
topical
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callahan
kyiv
smarter
tits
undo
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announcements
anthologies
barrio
nebula
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bodyguards
2021
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barns
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scully
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demoted
gorman
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writ
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alla
invertebrates
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1641
amman
1742
align
healy
redistribution
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insulation
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adherents
hezbollah
vitro
ferns
yanking
269
php
registering
uppsala
cheerleading
confines
mischievous
tully
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49th
docked
roam
stipulated
pumpkin
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prompt
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blindly
shuddering
craftsmen
frail
scented
katharine
scramble
shaggy
sponge
helix
zaragoza
279
##52
43rd
backlash
fontaine
seizures
posse
cowan
nonfiction
telenovela
wwii
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encircled
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infinitely
psyche
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docking
evicted
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unmarked
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lettering
litres
musique
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benevolent
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blackened
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mccall
racers
tingle
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##orestation
introductions
radically
292
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##باد
1610
1739
munchen
plead
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condo
scissors
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apprehension
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watering
formulas
sequels
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aggravated
bae
commencing
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enfield
prohibits
marne
vedic
civilized
euclidean
jagger
beforehand
blasts
dumont
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740
conversions
hierarchical
rios
simulator
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hedges
oleg
thrusts
shadowed
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maximize
1744
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wellness
marlon
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casket
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ley
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char
influencing
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412
ammonia
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incompatible
violins
cornered
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grooves
astronauts
columbian
rampant
fabrication
kyushu
mahmud
vanish
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mesopotamia
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pitted
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999
grimace
roanoke
tchaikovsky
twinned
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xinjiang
arias
clemson
kazakh
sizable
1662
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plunge
tatum
vittorio
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bracing
indifference
projectile
superliga
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realises
upgrading
299
porte
retribution
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nk
stil
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ama
bureaucracy
blackberry
bosch
testosterone
collapses
greer
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fifties
malls
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baskets
adolescents
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detecting
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fledgling
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telecommunication
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6000
controversies
outdated
taxis
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restrictive
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mundo
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burroughs
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valuation
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quivering
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alteration
faltered
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51st
hydra
ticked
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recommends
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arjun
stagecoach
wilfred
trickle
pronouns
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nighttime
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gall
pea
stitch
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leung
milos
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starved
snowfall
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parasitic
cot
discus
hana
strikers
appleton
kitchens
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disclose
metis
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1701
tesla
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fitch
1735
blooded
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decimal
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cyclones
eun
bottled
peas
pensacola
basha
bolivian
crabs
boil
lanterns
partridge
roofed
1645
necks
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opined
patting
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chuckles
volta
whereupon
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suicidal
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inherently
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puppets
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southend
stills
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deterioration
pointe
prowess
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billings
subcontinent
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psalms
degraded
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generosity
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frankish
plastics
accommodations
zoological
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motorized
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canucks
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dail
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grasslands
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judgments
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luz
supplementary
tianjin
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ric
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croats
transcribed
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arden
cv
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357
seventies
staggering
alam
horticultural
hs
regression
timbers
blasting
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montagu
manipulating
##cit
catalytic
1550
troopers
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condemnation
fitzpatrick
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inexperienced
1670
castes
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outing
314
dubois
flicking
quarrel
ste
learners
1625
iq
whistled
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282
classify
tariffs
temperament
355
folly
liszt
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ceasefire
apparel
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maru
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assortment
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transmitters
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solemnly
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repairing
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groceries
ultraviolet
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susquehanna
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bayer
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craving
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barbecue
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delightful
smartphone
newtown
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hissing
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cisco
44th
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lowlands
stockings
thierry
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maison
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provocative
1724
4a
utopia
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gujarati
purcell
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swallows
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respectful
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declines
empathy
stigma
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taxpayers
gui
volga
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lifespan
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enactment
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peng
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hardship
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detainees
planck
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kayla
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mortally
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cognition
radha
319
liechtenstein
meade
richly
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harpsichord
liberalism
trumpets
lauded
tyrant
salsa
tiled
lear
promoters
reused
slicing
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checkpoint
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mammalian
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nirvana
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delgado
headlining
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jug
tko
1649
naga
intersections
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benfica
nawab
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ashford
gulp
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brentford
frazier
pleasures
dunne
potsdam
shenzhen
dentistry
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flanagan
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chorale
dinah
prem
quezon
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relinquished
sutra
terri
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flaps
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poly
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homme
aback
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linger
womb
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doorstep
orthodoxy
threaded
westfield
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dioceses
fridays
subsided
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loyalists
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letterman
lunatic
prelate
tenderly
invariably
souza
thug
winslow
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furlongs
gogh
jeopardy
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pegasus
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humiliated
standalone
tagged
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freshmen
klan
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attaining
initiating
transatlantic
logged
viz
##uance
1723
combatants
intervening
stephane
chieftain
despised
grazed
317
cdc
galveston
godzilla
macro
simulate
##planes
parades
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960
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equator
overdose
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joshi
epstein
sonora
treacherous
aquatics
manchu
responsive
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supervisory
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karlsruhe
mab
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phonetic
reuters
spaghetti
820
almighty
danzig
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designations
lured
outset
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supermarkets
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grupo
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susanna
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comprehension
genealogy
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redding
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1722
bowing
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lest
sharpened
fulbright
valkyrie
sikhs
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swans
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merritt
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commuted
redhead
clerks
leasing
cesare
dea
hazy
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greenfield
servicemen
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armando
blackout
dt
sagged
downloadable
intra
potion
pods
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xp
attendants
gambia
stale
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plump
asteroids
rediscovered
buds
flea
hive
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1737
classifications
debuts
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olympus
scala
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visuals
wiggled
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clench
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snort
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churning
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lodges
riches
sima
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accountable
socrates
regulates
mueller
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1702
avoids
solids
himalayas
nutrient
pup
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squat
fades
nec
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tequila
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immortals
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cleansing
dario
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etymology
servicing
zhejiang
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horned
erasmus
rayon
relocating
£10
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escalated
promenade
stubble
2010s
artisans
axial
liquids
mora
sho
yoo
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bundles
oldies
##nally
notification
bastion
##ths
sparkle
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1728
leash
pathogen
highs
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immature
880
gonzaga
ignatius
mansions
monterrey
sweets
bryson
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polled
regatta
brightest
pei
rosy
squid
hatfield
payroll
addict
meath
cornerback
heaviest
lodging
##mage
capcom
rippled
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barnet
mayhem
ymca
snuggled
rousseau
##cute
blanchard
284
fragmented
leighton
chromosomes
risking
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corinne
coyotes
cynical
hiroshi
yeomanry
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ebook
grading
mandela
plume
agustin
magdalene
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bea
femme
trafford
##coll
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52nd
fourier
upton
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camilla
gust
iihf
islamabad
longevity
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feldman
netting
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endeavour
foraging
mfa
orr
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greyish
contradiction
graz
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handicapped
marlene
tweed
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spp
campos
miocene
pri
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cooks
pluto
cozy
pornographic
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70th
fairness
glided
jonny
lynne
rounding
sired
##emon
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remade
uncover
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complied
lei
newsweek
##jured
##parts
##enting
##pg
293
finer
guerrillas
athenian
deng
disused
stepmother
accuse
gingerly
seduction
521
confronting
##walker
##going
gora
nostalgia
sabres
virginity
wrenched
##minated
syndication
wielding
eyre
##56
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behaved
taxpayer
sweeps
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childless
gallant
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amplified
geraldine
scrape
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babylonian
fresco
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1718
restricting
tack
fukuoka
osborn
selector
partnering
##dlow
318
gnu
kia
tak
whitley
gables
##54
##mania
mri
softness
immersion
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1713
chilling
insignificant
pcs
##uis
elites
lina
purported
supplemental
teaming
##americana
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proficient
rouen
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niccolo
selects
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fluffy
1621
gruff
knotted
mukherjee
polgara
thrash
nicholls
secluded
smoothing
thru
corsica
loaf
whitaker
inquiries
##rrier
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indochina
289
marlins
myles
peking
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extracts
pastry
superhuman
connacht
vogel
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quarries
refit
teaser
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20s
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camped
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tung
tracker
pursuant
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soc
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overboard
workout
chores
fares
orphaned
stains
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fenton
surpassing
joyah
triggers
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grandmaster
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clapping
fraudulent
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nagasaki
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tun
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dax
heroism
ina
wrexham
onboard
unsigned
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moshe
galley
winnie
droplets
exiles
praises
watered
noodles
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multicultural
stink
bingo
comets
erskine
modernized
canned
constraint
domestically
chemotherapy
featherweight
stifled
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darkly
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refreshing
hasty
isolate
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kitchener
planners
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cages
yarn
implant
toulon
elects
childbirth
yue
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cn
rightful
sportsman
junctions
remodeled
specifies
##rgh
291
##oons
complimented
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lister
ot
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bequeathed
cheekbones
fontana
gabby
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amadeus
corrugated
maverick
resented
triangles
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nazareth
tyrol
1675
assent
poorer
sectional
aegean
##cous
296
nylon
ghanaian
##egorical
##weig
cushions
forbid
fusiliers
obstruction
somerville
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dime
earrings
elliptical
leyte
oder
polymers
timmy
atm
midtown
piloted
settles
continual
externally
mayfield
##uh
enrichment
henson
keane
persians
1733
benji
braden
pep
324
##efe
contenders
pepsi
valet
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298
##asse
##earing
goofy
stroll
##amen
authoritarian
occurrences
adversary
ahmedabad
tangent
toppled
dorchester
1672
modernism
marxism
islamist
charlemagne
exponential
racks
unicode
brunette
mbc
pic
skirmish
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hoisted
messina
shatter
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jedi
vantage
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clemens
mahmoud
corrupted
authentication
lowry
nils
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omnibus
wounding
jillian
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serialized
narcotics
handheld
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intersecting
stimulating
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crate
fellowships
hemingway
casinos
climatic
fordham
copeland
drip
beatty
leaflets
robber
brothel
madeira
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ultrasound
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valor
forbade
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villas
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duane
marquez
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disadvantaged
forearms
kawasaki
reacts
consular
lax
uncles
uphold
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concepcion
dorsey
lass
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arching
passageway
1708
researches
tia
internationals
##graphs
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distinguishes
javanese
divert
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plotted
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affirmative
signifies
validation
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kari
felicity
georgina
zulu
##eros
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##rath
overcoming
##dot
argyll
##rbin
1734
chiba
ratification
windy
earls
parapet
##marks
hunan
pristine
astrid
punta
##gart
brodie
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malaga
minerva
rouse
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bellowed
pagoda
portals
reclamation
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##⁄₄
parentheses
quoting
allergic
palette
showcases
benefactor
heartland
nonlinear
##tness
bladed
cheerfully
scans
##ety
##hone
1666
girlfriends
pedersen
hiram
sous
##liche
##nator
1683
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##orio
##umen
bobo
primaries
smiley
##cb
unearthed
uniformly
fis
metadata
1635
ind
##oted
recoil
##titles
##tura
##ια
406
hilbert
jamestown
mcmillan
tulane
seychelles
##frid
antics
coli
fated
stucco
##grants
1654
bulky
accolades
arrays
caledonian
carnage
optimism
puebla
##tative
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enforcing
rotherham
seo
dunlop
aeronautics
chimed
incline
zoning
archduke
hellenistic
##oses
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candi
thong
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magnate
rustic
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projective
slant
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danes
hollis
vocalists
##ammed
congenital
contend
gesellschaft
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douglass
quieter
##cm
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howled
salim
spontaneously
townsville
buena
southport
##bold
kato
1638
faerie
stiffly
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297
flawless
realising
taboo
##7th
bytes
straightening
356
jena
##hid
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cartwright
berber
bertram
soloists
411
noses
417
coping
fission
hardin
inca
##cen
1717
mobilized
vhf
##raf
biscuits
curate
##85
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331
gaunt
neighbourhoods
1540
##abas
blanca
bypassed
sockets
behold
coincidentally
##bane
nara
shave
splinter
terrific
##arion
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commonplace
juris
redwood
waistband
boxed
caitlin
fingerprints
jennie
naturalized
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balfour
craters
jody
bungalow
hugely
quilt
glitter
pigeons
undertaker
bulging
constrained
goo
##sil
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assimilation
reworked
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persuasion
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felicia
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1732
explodes
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lyman
vulture
hog
overlook
begs
northwards
ow
spoil
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fatima
favorably
accumulate
sargent
sorority
corresponded
dispersal
kochi
toned
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internacional
newfound
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booths
peanuts
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medicare
muriel
nur
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crates
millennia
pajamas
worsened
##breakers
jimi
vanuatu
yawned
##udeau
carousel
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hurdle
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rv
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airship
ambiguity
compulsion
recapture
##claiming
arthritis
##osomal
1667
asserting
ngc
sniffing
dade
discontent
glendale
ported
##amina
defamation
rammed
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fling
livingstone
##fleet
875
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apocalyptic
comrade
lcd
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cessna
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343
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egan
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402
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475
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341
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peacekeeping
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1656
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weathered
1731
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eruptions
1729
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327
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351
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telecast
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coveted
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870
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ideally
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505
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1709
friendships
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thumped
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339
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1651
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56th
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southland
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422
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rn
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contentious
1653
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1705
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381
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344
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starbucks
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|
TensorFlow2/Segmentation/MaskRCNN | MaskRCNN | requirements | opencv-python-headless
git+https://github.com/NVIDIA/cocoapi#egg=pycocotools&subdirectory=PythonAPI
git+https://github.com/NVIDIA/dllogger
|
PyTorch/SpeechSynthesis/HiFiGAN/platform | platform | DGX1_HiFi-GAN_FP32_8GPU | #!/bin/bash
set -a
: ${NUM_GPUS:=8}
: ${BATCH_SIZE:=16}
: ${GRAD_ACCUMULATION:=1}
: ${AMP:=false}
bash scripts/train_lj22khz.sh "$@"
|
TensorFlow2/Classification/ConvNets/efficientnet_v2/S/training/TF32 | TF32 | convergence_1xA100-80G | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
python3 main.py \
--cfg config/efficientnet_v2/s_cfg.py \
--mode train_and_eval \
--use_xla \
--model_dir ./output/ \
--data_dir /data/ \
--log_steps 500 \
--save_checkpoint_freq 10 \
--n_stages 4 \
--max_epochs 350 \
--train_batch_size 230 \
--train_img_size 300 \
--base_img_size 128 \
--lr_decay cosine \
--lr_init 0.005 \
--weight_decay .000005 \
--opt_epsilon 0.001 \
--moving_average_decay 0.9999 \
--eval_img_size 384 \
--eval_batch_size 100 \
--augmenter_name randaugment \
--raug_num_layers 2 \
--raug_magnitude 15 \
--cutmix_alpha 0 \
--mixup_alpha 0 \
--defer_img_mixing |
PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config | config | defaults | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import os
from yacs.config import CfgNode as CN
# -----------------------------------------------------------------------------
# Convention about Training / Test specific parameters
# -----------------------------------------------------------------------------
# Whenever an argument can be either used for training or for testing, the
# corresponding name will be post-fixed by a _TRAIN for a training parameter,
# or _TEST for a test-specific parameter.
# For example, the number of images during training will be
# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
# IMAGES_PER_BATCH_TEST
# -----------------------------------------------------------------------------
# Config definition
# -----------------------------------------------------------------------------
_C = CN()
_C.MODEL = CN()
_C.MODEL.RPN_ONLY = False
_C.MODEL.MASK_ON = False
_C.MODEL.DEVICE = "cuda"
_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN"
# If the WEIGHT starts with a catalog://, like :R-50, the code will look for
# the path in paths_catalog. Else, it will use it as the specified absolute
# path
_C.MODEL.WEIGHT = ""
# -----------------------------------------------------------------------------
# INPUT
# -----------------------------------------------------------------------------
_C.INPUT = CN()
# Size of the smallest side of the image during training
_C.INPUT.MIN_SIZE_TRAIN = 800 # (800,)
# Maximum size of the side of the image during training
_C.INPUT.MAX_SIZE_TRAIN = 1333
# Size of the smallest side of the image during testing
_C.INPUT.MIN_SIZE_TEST = 800
# Maximum size of the side of the image during testing
_C.INPUT.MAX_SIZE_TEST = 1333
# Values to be used for image normalization
_C.INPUT.PIXEL_MEAN = [102.9801, 115.9465, 122.7717]
# Values to be used for image normalization
_C.INPUT.PIXEL_STD = [1., 1., 1.]
# Convert image to BGR format (for Caffe2 models), in range 0-255
_C.INPUT.TO_BGR255 = True
# -----------------------------------------------------------------------------
# Dataset
# -----------------------------------------------------------------------------
_C.DATASETS = CN()
# List of the dataset names for training, as present in paths_catalog.py
_C.DATASETS.TRAIN = ()
# List of the dataset names for testing, as present in paths_catalog.py
_C.DATASETS.TEST = ()
# -----------------------------------------------------------------------------
# DataLoader
# -----------------------------------------------------------------------------
_C.DATALOADER = CN()
# Number of data loading threads
_C.DATALOADER.NUM_WORKERS = 4
# If > 0, this enforces that each collated batch should have a size divisible
# by SIZE_DIVISIBILITY
_C.DATALOADER.SIZE_DIVISIBILITY = 0
# If True, each batch should contain only images for which the aspect ratio
# is compatible. This groups portrait images together, and landscape images
# are not batched with portrait images.
_C.DATALOADER.ASPECT_RATIO_GROUPING = True
# If True, the custom Hybrid Dataloader is used.
# If not, torch.utils.data.DataLoader is used for dataloading.
_C.DATALOADER.HYBRID = True
# ---------------------------------------------------------------------------- #
# Backbone options
# ---------------------------------------------------------------------------- #
_C.MODEL.BACKBONE = CN()
# The backbone conv body to use
# The string must match a function that is imported in modeling.model_builder
# (e.g., 'FPN.add_fpn_ResNet101_conv5_body' to specify a ResNet-101-FPN
# backbone)
_C.MODEL.BACKBONE.CONV_BODY = "R-50-C4"
# Add StopGrad at a specified stage so the bottom layers are frozen
_C.MODEL.BACKBONE.FREEZE_CONV_BODY_AT = 2
_C.MODEL.BACKBONE.OUT_CHANNELS = 256 * 4
# GN for backbone
_C.MODEL.BACKBONE.USE_GN = False
# ---------------------------------------------------------------------------- #
# FPN options
# ---------------------------------------------------------------------------- #
_C.MODEL.FPN = CN()
_C.MODEL.FPN.USE_GN = False
_C.MODEL.FPN.USE_RELU = False
# ---------------------------------------------------------------------------- #
# Group Norm options
# ---------------------------------------------------------------------------- #
_C.MODEL.GROUP_NORM = CN()
# Number of dimensions per group in GroupNorm (-1 if using NUM_GROUPS)
_C.MODEL.GROUP_NORM.DIM_PER_GP = -1
# Number of groups in GroupNorm (-1 if using DIM_PER_GP)
_C.MODEL.GROUP_NORM.NUM_GROUPS = 32
# GroupNorm's small constant in the denominator
_C.MODEL.GROUP_NORM.EPSILON = 1e-5
# ---------------------------------------------------------------------------- #
# RPN options
# ---------------------------------------------------------------------------- #
_C.MODEL.RPN = CN()
_C.MODEL.RPN.USE_FPN = False
# Base RPN anchor sizes given in absolute pixels w.r.t. the scaled network input
_C.MODEL.RPN.ANCHOR_SIZES = (32, 64, 128, 256, 512)
# Stride of the feature map that RPN is attached.
# For FPN, number of strides should match number of scales
_C.MODEL.RPN.ANCHOR_STRIDE = (16,)
# RPN anchor aspect ratios
_C.MODEL.RPN.ASPECT_RATIOS = (0.5, 1.0, 2.0)
# Remove RPN anchors that go outside the image by RPN_STRADDLE_THRESH pixels
# Set to -1 or a large value, e.g. 100000, to disable pruning anchors
_C.MODEL.RPN.STRADDLE_THRESH = 0
# Minimum overlap required between an anchor and ground-truth box for the
# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD
# ==> positive RPN example)
_C.MODEL.RPN.FG_IOU_THRESHOLD = 0.7
# Maximum overlap allowed between an anchor and ground-truth box for the
# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD
# ==> negative RPN example)
_C.MODEL.RPN.BG_IOU_THRESHOLD = 0.3
# Total number of RPN examples per image
_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256
# Target fraction of foreground (positive) examples per RPN minibatch
_C.MODEL.RPN.POSITIVE_FRACTION = 0.5
# Number of top scoring RPN proposals to keep before applying NMS
# When FPN is used, this is *per FPN level* (not total)
_C.MODEL.RPN.PRE_NMS_TOP_N_TRAIN = 12000
_C.MODEL.RPN.PRE_NMS_TOP_N_TEST = 6000
# Number of top scoring RPN proposals to keep after applying NMS
_C.MODEL.RPN.POST_NMS_TOP_N_TRAIN = 2000
_C.MODEL.RPN.POST_NMS_TOP_N_TEST = 1000
# NMS threshold used on RPN proposals
_C.MODEL.RPN.NMS_THRESH = 0.7
# Proposal height and width both need to be greater than RPN_MIN_SIZE
# (a the scale used during training or inference)
_C.MODEL.RPN.MIN_SIZE = 0
# Number of top scoring RPN proposals to keep after combining proposals from
# all FPN levels
_C.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN = 2000
_C.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST = 2000
# Custom rpn head, empty to use default conv or separable conv
_C.MODEL.RPN.RPN_HEAD = "SingleConvRPNHead"
# ---------------------------------------------------------------------------- #
# ROI HEADS options
# ---------------------------------------------------------------------------- #
_C.MODEL.ROI_HEADS = CN()
_C.MODEL.ROI_HEADS.USE_FPN = False
# Overlap threshold for an RoI to be considered foreground (if >= FG_IOU_THRESHOLD)
_C.MODEL.ROI_HEADS.FG_IOU_THRESHOLD = 0.5
# Overlap threshold for an RoI to be considered background
# (class = 0 if overlap in [0, BG_IOU_THRESHOLD))
_C.MODEL.ROI_HEADS.BG_IOU_THRESHOLD = 0.5
# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets
# These are empirically chosen to approximately lead to unit variance targets
_C.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS = (10., 10., 5., 5.)
# RoI minibatch size *per image* (number of regions of interest [ROIs])
# Total number of RoIs per training minibatch =
# TRAIN.BATCH_SIZE_PER_IM * TRAIN.IMS_PER_BATCH * NUM_GPUS
# E.g., a common configuration is: 512 * 2 * 8 = 8192
_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)
_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25
# Only used on test mode
# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to
# balance obtaining high recall with not having too many low precision
# detections that will slow down inference post processing steps (like NMS)
_C.MODEL.ROI_HEADS.SCORE_THRESH = 0.05
# Overlap threshold used for non-maximum suppression (suppress boxes with
# IoU >= this threshold)
_C.MODEL.ROI_HEADS.NMS = 0.5
# Maximum number of detections to return per image (100 is based on the limit
# established for the COCO dataset)
_C.MODEL.ROI_HEADS.DETECTIONS_PER_IMG = 100
_C.MODEL.ROI_BOX_HEAD = CN()
_C.MODEL.ROI_BOX_HEAD.FEATURE_EXTRACTOR = "ResNet50Conv5ROIFeatureExtractor"
_C.MODEL.ROI_BOX_HEAD.PREDICTOR = "FastRCNNPredictor"
_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
_C.MODEL.ROI_BOX_HEAD.POOLER_SCALES = (1.0 / 16,)
_C.MODEL.ROI_BOX_HEAD.NUM_CLASSES = 81
# Hidden layer dimension when using an MLP for the RoI box head
_C.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM = 1024
# GN
_C.MODEL.ROI_BOX_HEAD.USE_GN = False
# Dilation
_C.MODEL.ROI_BOX_HEAD.DILATION = 1
_C.MODEL.ROI_BOX_HEAD.CONV_HEAD_DIM = 256
_C.MODEL.ROI_BOX_HEAD.NUM_STACKED_CONVS = 4
_C.MODEL.ROI_MASK_HEAD = CN()
_C.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR = "ResNet50Conv5ROIFeatureExtractor"
_C.MODEL.ROI_MASK_HEAD.PREDICTOR = "MaskRCNNC4Predictor"
_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14
_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0
_C.MODEL.ROI_MASK_HEAD.POOLER_SCALES = (1.0 / 16,)
_C.MODEL.ROI_MASK_HEAD.MLP_HEAD_DIM = 1024
_C.MODEL.ROI_MASK_HEAD.CONV_LAYERS = (256, 256, 256, 256)
_C.MODEL.ROI_MASK_HEAD.RESOLUTION = 14
_C.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR = True
# Whether or not resize and translate masks to the input image.
_C.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS = False
_C.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS_THRESHOLD = 0.5
# Dilation
_C.MODEL.ROI_MASK_HEAD.DILATION = 1
# GN
_C.MODEL.ROI_MASK_HEAD.USE_GN = False
# ---------------------------------------------------------------------------- #
# ResNe[X]t options (ResNets = {ResNet, ResNeXt}
# Note that parts of a resnet may be used for both the backbone and the head
# These options apply to both
# ---------------------------------------------------------------------------- #
_C.MODEL.RESNETS = CN()
# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
_C.MODEL.RESNETS.NUM_GROUPS = 1
# Baseline width of each group
_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64
# Place the stride 2 conv on the 1x1 filter
# Use True only for the original MSRA ResNet; use False for C2 and Torch models
_C.MODEL.RESNETS.STRIDE_IN_1X1 = True
# Residual transformation function
_C.MODEL.RESNETS.TRANS_FUNC = "BottleneckWithFixedBatchNorm"
# ResNet's stem function (conv1 and pool1)
_C.MODEL.RESNETS.STEM_FUNC = "StemWithFixedBatchNorm"
# Apply dilation in stage "res5"
_C.MODEL.RESNETS.RES5_DILATION = 1
_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
# ---------------------------------------------------------------------------- #
# Solver
# ---------------------------------------------------------------------------- #
_C.SOLVER = CN()
_C.SOLVER.MAX_ITER = 40000
_C.SOLVER.BASE_LR = 0.001
_C.SOLVER.BIAS_LR_FACTOR = 2
_C.SOLVER.MOMENTUM = 0.9
_C.SOLVER.WEIGHT_DECAY = 0.0005
_C.SOLVER.WEIGHT_DECAY_BIAS = 0
_C.SOLVER.GAMMA = 0.1
_C.SOLVER.STEPS = (30000,)
_C.SOLVER.WARMUP_FACTOR = 1.0 / 3
_C.SOLVER.WARMUP_ITERS = 500
_C.SOLVER.WARMUP_METHOD = "linear"
_C.SOLVER.CHECKPOINT_PERIOD = 2500
# Number of images per batch
# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will
# see 2 images per batch
_C.SOLVER.IMS_PER_BATCH = 16
# Parameters to accumulate gradient across steps
_C.SOLVER.ACCUMULATE_STEPS = 1
_C.SOLVER.ACCUMULATE_GRAD = False
# ---------------------------------------------------------------------------- #
# Specific test options
# ---------------------------------------------------------------------------- #
_C.TEST = CN()
_C.TEST.EXPECTED_RESULTS = []
_C.TEST.EXPECTED_RESULTS_SIGMA_TOL = 4
# Number of images per batch
# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will
# see 2 images per batch
_C.TEST.IMS_PER_BATCH = 8
# ---------------------------------------------------------------------------- #
# Misc options
# ---------------------------------------------------------------------------- #
_C.OUTPUT_DIR = "."
_C.PATHS_CATALOG = os.path.join(os.path.dirname(__file__), "paths_catalog.py")
_C.USE_TORCH_DDP = True
# ---------------------------------------------------------------------------- #
# Precision options
# ---------------------------------------------------------------------------- #
# Precision of input, allowable: (float32, float16)
_C.DTYPE = "float32"
# Use NATIVE NHWC/NCHW for eligible convolutions
_C.NHWC = True
# Evaluate every epoch
_C.PER_EPOCH_EVAL = False
_C.MIN_BBOX_MAP = 0.377
_C.MIN_MASK_MAP = 0.342
_C.SAVE_CHECKPOINT = True
|
PyTorch/Classification/ConvNets/image_classification | image_classification | smoothing | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
class LabelSmoothing(nn.Module):
"""
NLL loss with label smoothing.
"""
def __init__(self, smoothing=0.0):
"""
Constructor for the LabelSmoothing module.
:param smoothing: label smoothing factor
"""
super(LabelSmoothing, self).__init__()
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
def forward(self, x, target):
logprobs = torch.nn.functional.log_softmax(x, dim=-1)
nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
nll_loss = nll_loss.squeeze(1)
smooth_loss = -logprobs.mean(dim=-1)
loss = self.confidence * nll_loss + self.smoothing * smooth_loss
return loss.mean()
|
TensorFlow/Detection/SSD/models/research/object_detection/samples/configs | configs | faster_rcnn_resnet152_coco | # Faster R-CNN with Resnet-152 (v1), configuration for MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.
model {
faster_rcnn {
num_classes: 90
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
feature_extractor {
type: 'faster_rcnn_resnet152'
first_stage_features_stride: 16
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: {
batch_size: 1
optimizer {
momentum_optimizer: {
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 0.0003
schedule {
step: 900000
learning_rate: .00003
}
schedule {
step: 1200000
learning_rate: .000003
}
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
from_detection_checkpoint: true
# Note: The below line limits the training process to 200K steps, which we
# empirically found to be sufficient enough to train the pets dataset. This
# effectively bypasses the learning rate schedule (the learning rate will
# never decay). Remove the below line to train indefinitely.
num_steps: 200000
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
}
label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
}
eval_config: {
num_examples: 8000
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 10
}
eval_input_reader: {
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
}
label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
shuffle: false
num_readers: 1
}
|
PyTorch/Detection/Efficientdet/effdet | effdet | efficientnet | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from collections import namedtuple
import torch
from torch import nn
BlockParameters = namedtuple('BlockParameters',
['kernel_size', 'stride', 'num_repeat', 'in_channels', 'out_channels', 'expand_ratio'])
GlobalParameters = namedtuple('GlobalParameters',
['squeeze_excitation_ratio', 'batchnorm_momentum', 'batchnorm_epsilon',
'stochastic_depth_survival_prob', 'feature_channels', "weights_init_mode"])
efficientnet_configs = {
"fanin": GlobalParameters(
squeeze_excitation_ratio=0.25,
batchnorm_momentum=1-0.99, # batchnorm momentum definition is different in pytorch and original paper
batchnorm_epsilon=1e-3,
stochastic_depth_survival_prob=0.8,
feature_channels=1280,
weights_init_mode="fan_in"
),
"fanout": GlobalParameters(
squeeze_excitation_ratio=0.25,
batchnorm_momentum=1-0.99,
batchnorm_epsilon=1e-3,
stochastic_depth_survival_prob=0.8,
feature_channels=1280,
weights_init_mode="fan_out"
),
}
BASE_EFFICIENTNET_BLOCKS_CONFIG = [
BlockParameters(kernel_size=3, stride=1, num_repeat=1, in_channels=32, out_channels=16, expand_ratio=1),
BlockParameters(kernel_size=3, stride=2, num_repeat=2, in_channels=16, out_channels=24, expand_ratio=6),
BlockParameters(kernel_size=5, stride=2, num_repeat=2, in_channels=24, out_channels=40, expand_ratio=6),
BlockParameters(kernel_size=3, stride=2, num_repeat=3, in_channels=40, out_channels=80, expand_ratio=6),
BlockParameters(kernel_size=5, stride=1, num_repeat=3, in_channels=80, out_channels=112, expand_ratio=6),
BlockParameters(kernel_size=5, stride=2, num_repeat=4, in_channels=112, out_channels=192, expand_ratio=6),
BlockParameters(kernel_size=3, stride=1, num_repeat=1, in_channels=192, out_channels=320, expand_ratio=6)
]
def _scale_width(num_channels, width_coeff, divisor=8):
num_channels *= width_coeff
# Rounding should not go down by more than 10%
rounded_num_channels = max(divisor, int(num_channels + divisor / 2) // divisor * divisor)
if rounded_num_channels < 0.9 * num_channels:
rounded_num_channels += divisor
return rounded_num_channels
def scaled_efficientnet_config(width_coeff, depth_coeff):
config = [
block._replace(
num_repeat=int(math.ceil(block.num_repeat * depth_coeff)),
in_channels=_scale_width(block.in_channels, width_coeff),
out_channels=_scale_width(block.out_channels, width_coeff),
)
for block in BASE_EFFICIENTNET_BLOCKS_CONFIG
]
return config
class SqueezeAndExcitation(nn.Module):
def __init__(self, in_channels, squeeze, activation):
super(SqueezeAndExcitation, self).__init__()
self.squeeze = nn.Linear(in_channels, squeeze)
self.expand = nn.Linear(squeeze, in_channels)
self.activation = activation
self.sigmoid = nn.Sigmoid()
def forward(self, x):
out = torch.mean(x.view(x.size(0), x.size(1), -1), 2)
out = self.squeeze(out)
out = self.activation(out)
out = self.expand(out)
out = self.sigmoid(out)
out = out.unsqueeze(2).unsqueeze(3)
return out
# Since torch.nn.SiLU is not supported in ONNX,
# it is required to use this implementation in exported model (15-20% more GPU memory is needed)
class MemoryInefficientSiLU(nn.Module):
def __init__(self, *args, **kwargs):
super(MemoryInefficientSiLU, self).__init__()
def forward(self, x):
return x * torch.sigmoid(x)
class ConvBN(nn.Sequential):
def __init__(self, kernel_size, stride, in_channels, out_channels, activation,
bn_momentum, bn_epsilon, groups=1):
layers = [
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
groups=groups, bias=False, padding=int((kernel_size - 1) / 2)),
nn.BatchNorm2d(out_channels, momentum=bn_momentum, eps=bn_epsilon),
]
if activation is not None:
layers.append(activation)
super(ConvBN, self).__init__(*layers)
class MBConvBlock(nn.Module):
def __init__(self, block_config, global_config, survival_prob, activation):
super(MBConvBlock, self).__init__()
self.in_channels = block_config.in_channels
self.out_channels = block_config.out_channels
self.hidden_dim = self.in_channels * block_config.expand_ratio
self.squeeze_dim = max(1, int(self.in_channels * global_config.squeeze_excitation_ratio))
self.kernel_size = block_config.kernel_size
self.stride = block_config.stride
self.stochastic_depth_survival_prob = survival_prob
bn_momentum = global_config.batchnorm_momentum
bn_epsilon = global_config.batchnorm_epsilon
if self.in_channels != self.hidden_dim:
self.expand_conv = ConvBN(1, 1, self.in_channels, self.hidden_dim, activation(),
bn_momentum=bn_momentum, bn_epsilon=bn_epsilon)
self.squeeze_and_excitation = SqueezeAndExcitation(self.hidden_dim, self.squeeze_dim, activation())
self.depthwise_conv = ConvBN(self.kernel_size, self.stride, self.hidden_dim, self.hidden_dim, activation(),
groups=self.hidden_dim, bn_momentum=bn_momentum, bn_epsilon=bn_epsilon)
self.project_conv = ConvBN(1, 1, self.hidden_dim, self.out_channels,
activation=None, bn_momentum=bn_momentum, bn_epsilon=bn_epsilon)
def _drop_connections(self, x, synchronized=False):
if not self.training:
return x
random_mask = torch.rand([x.shape[0], 1, 1, 1], dtype=x.dtype, device=x.device)
if synchronized:
torch.distributed.broadcast(random_mask, 0)
random_mask = (self.stochastic_depth_survival_prob + random_mask).floor()
scaled_x = x / self.stochastic_depth_survival_prob
return scaled_x * random_mask
def forward(self, inputs):
x = inputs
if self.in_channels != self.hidden_dim:
x = self.expand_conv(x)
x = self.depthwise_conv(x)
x = x * self.squeeze_and_excitation(x)
x = self.project_conv(x)
if self.stride == 1 and self.in_channels == self.out_channels:
if self.stochastic_depth_survival_prob != 1:
x = self._drop_connections(x)
x = x + inputs
return x
class EfficientNet(nn.Module):
def __init__(self, width_coeff, depth_coeff, dropout, num_classes, global_config, features_only=True, out_indices=None, onnx_exportable=False):
super(EfficientNet, self).__init__()
self.features_only = features_only
self.efficientnet_blocks_config = scaled_efficientnet_config(width_coeff, depth_coeff)
self.global_config = global_config
self.in_channels = 3
self.feature_channels = _scale_width(self.global_config.feature_channels, width_coeff)
self.activation = torch.nn.SiLU if not onnx_exportable else MemoryInefficientSiLU
self.input_conv = ConvBN(3, 2, self.in_channels, self.efficientnet_blocks_config[0].in_channels,
activation=self.activation(),
bn_momentum=self.global_config.batchnorm_momentum,
bn_epsilon=self.global_config.batchnorm_epsilon)
self.feature_info = []
self.mbconv_blocks = nn.Sequential(*self.mbconv_blocks_generator())
if not self.features_only:
self.features_conv = ConvBN(1, 1, self.efficientnet_blocks_config[-1].out_channels, self.feature_channels,
activation=self.activation(),
bn_momentum=self.global_config.batchnorm_momentum,
bn_epsilon=self.global_config.batchnorm_epsilon)
self.avg_pooling = nn.AdaptiveAvgPool2d(1)
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(self.feature_channels, num_classes)
if out_indices is not None:
self.feature_info = [v for i, v in enumerate(self.feature_info) if i in out_indices]
def mbconv_blocks_generator(self):
num_blocks = sum([block_config.num_repeat for block_config in self.efficientnet_blocks_config])
drop_rate = 1.0 - self.global_config.stochastic_depth_survival_prob
idx = 0
current_stride = 2
prev_block_config = None
for config_idx, block_config in enumerate(self.efficientnet_blocks_config):
for i in range(block_config.num_repeat):
# Conditions for feature extraction
if config_idx == len(self.efficientnet_blocks_config)-1 and i == block_config.num_repeat-1:
self.feature_info.append(dict(block_idx=idx, reduction=current_stride, num_chs=block_config.out_channels))
elif prev_block_config is not None and block_config.stride > 1:
self.feature_info.append(dict(block_idx=idx-1, reduction=current_stride, num_chs=prev_block_config.out_channels))
# Calculating the current stride
if block_config.stride > 1:
current_stride = current_stride * block_config.stride
survival_prob = 1.0 - drop_rate * float(idx) / num_blocks
yield MBConvBlock(block_config, self.global_config,
survival_prob=survival_prob, activation=self.activation)
idx += 1
prev_block_config = block_config
block_config = block_config._replace(in_channels=block_config.out_channels, stride=1)
def forward(self, inputs):
x = inputs
x = self.input_conv(x)
features = []
extraction_idx = 0
for i, b in enumerate(self.mbconv_blocks):
x = b(x)
if i == self.feature_info[extraction_idx]['block_idx']:
features.append(x)
extraction_idx += 1
return x, features
|
Tools/PyTorch/TimeSeriesPredictionPlatform/triton/deployment_toolkit | deployment_toolkit | __init__ | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License. |
TensorFlow2/Recommendation/WideAndDeep/triton/runner | runner | config | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pathlib
from typing import Dict, List, Optional, Union
import yaml
from ..deployment_toolkit.core import PerformanceTool
if __name__ == "__main__" and __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from .configuration import Configuration
from .core import DataObject
from .triton import Triton
class Checkpoint(DataObject):
"""
Checkpoint data placeholder
"""
name: str
url: str
def __init__(self, name: str, url: str):
self.name = name
self.url = url
class Dataset(DataObject):
"""
Dataset data placeholder
"""
name: str
def __init__(self, name: str):
self.name = name
class Config(DataObject):
"""
Configuration object for runner experiments
"""
def __init__(
self,
model_name: str,
framework: str,
container_version: str,
batching: str,
configurations: List[Configuration],
ensemble_model_name: Optional[str] = None,
datasets_dir: str = "datasets",
datasets: List[Dataset] = None,
checkpoints: List[Checkpoint] = None,
triton_dockerfile: Optional[str] = None,
triton_container_image: Optional[str] = None,
triton_custom_operations: Optional[str] = None,
triton_load_model_method: Optional[str] = Triton.LOAD_MODE.EXPLICIT,
measurement_steps_offline: int = 8,
measurement_steps_online: int = 32,
performance_tool: PerformanceTool = PerformanceTool.MODEL_ANALYZER,
):
"""
Args:
model_name: Name of model
framework: Framework used to create model
container_version: Version of Triton Inference Server container used for evaluation
batching: Mark if model support batching
configurations: List of experiments configurations
datasets_dir: Directory where datasets are stored
datasets: Datasets used for conversion/export
checkpoints: Checkpoints with trained model
triton_load_model_method: Triton Inference Server model loading mode
triton_dockerfile: Dockerfile for Triton to build custom image
triton_container_image: Custom image used for Triton Server - leave empty to use default or built from Dockerfile
triton_custom_operations: Path where custom operation library is stored
measurement_steps_offline: Number of measurement steps in offline performance stage
measurement_steps_online: Number of measurement steps in online performance stage
performance_tool: Performance Tool used for generating results
"""
self.model_name = model_name
self.ensemble_model_name = ensemble_model_name
self.framework = framework
self.container_version = container_version
self.batching = batching
self.configurations = configurations
self.datasets_dir = datasets_dir
self.datasets = datasets
self.checkpoints = checkpoints
self.triton_load_model_method = triton_load_model_method
self.triton_dockerfile = triton_dockerfile
self.triton_container_image = triton_container_image
self.triton_custom_operations = triton_custom_operations
self.measurement_steps_offline = measurement_steps_offline
self.measurement_steps_online = measurement_steps_online
self.performance_tool = performance_tool
def to_file(self, file_path: Union[pathlib.Path, str]) -> None:
"""
Save config data to file
Args:
file_path: path to file where config data is should be stored
Returns:
None
"""
data = self.to_dict()
with open(file_path, "w") as f:
yaml.safe_dump(data, f)
@staticmethod
def from_dict(config_data: Dict):
"""
Create configuration object from data stored in dictionary
Args:
config_data: dictionary with config data
Returns:
Config object
"""
configurations = []
for configuration_data in config_data["configurations"]:
configuration = Configuration(**configuration_data)
configurations.append(configuration)
checkpoints = []
for checkpoint_data in config_data.get("checkpoints", []):
checkpoint = Checkpoint(
name=checkpoint_data["name"],
url=checkpoint_data["url"],
)
checkpoints.append(checkpoint)
datasets = []
for dataset_data in config_data.get("datasets", []):
dataset = Dataset(name=dataset_data["name"])
datasets.append(dataset)
return Config(
model_name=config_data["model_name"],
framework=config_data["framework"],
container_version=config_data["container_version"],
batching=config_data["batching"],
configurations=configurations,
checkpoints=checkpoints,
datasets=datasets,
datasets_dir=config_data.get("datasets_dir"),
triton_load_model_method=config_data["triton_load_model_method"],
triton_dockerfile=config_data.get("triton_dockerfile"),
triton_custom_operations=config_data.get("triton_custom_operations"),
measurement_steps_offline=config_data["measurement_steps_offline"],
measurement_steps_online=config_data["measurement_steps_online"],
performance_tool=PerformanceTool(config_data["performance_tool"]),
)
@staticmethod
def from_file(file_path: Union[pathlib.Path, str]):
"""
Load experiment data from file
Args:
file_path: path to file where experiment data is stored
Returns:
Experiment object
"""
with open(file_path) as f:
config_data = yaml.safe_load(f)
return Config.from_dict(config_data)
|
TensorFlow2/Detection/Efficientdet/utils | utils | horovod_utils | # Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import horovod.tensorflow.keras as hvd
def get_rank():
try:
return hvd.rank()
except:
return 0
def get_world_size():
try:
return hvd.size()
except:
return 1
def is_main_process():
return get_rank() == 0 |
PyTorch/Segmentation/nnUNet | nnUNet | preprocess | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
from data_preprocessing.preprocessor import Preprocessor
from utils.utils import get_task_code
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument("--data", type=str, default="/data", help="Path to data directory")
parser.add_argument("--results", type=str, default="/data", help="Path for saving results directory")
parser.add_argument(
"--exec_mode",
type=str,
default="training",
choices=["training", "val", "test"],
help="Mode for data preprocessing",
)
parser.add_argument("--ohe", action="store_true", help="Add one-hot-encoding for foreground voxels (voxels > 0)")
parser.add_argument("--verbose", action="store_true")
parser.add_argument("--task", type=str, help="Number of task to be run. MSD uses numbers 01-10")
parser.add_argument("--dim", type=int, default=3, choices=[2, 3], help="Data dimension to prepare")
parser.add_argument("--n_jobs", type=int, default=-1, help="Number of parallel jobs for data preprocessing")
if __name__ == "__main__":
args = parser.parse_args()
start = time.time()
Preprocessor(args).run()
task_code = get_task_code(args)
path = os.path.join(args.data, task_code)
if args.exec_mode == "test":
path = os.path.join(path, "test")
end = time.time()
print(f"Pre-processing time: {(end - start):.2f}")
|
TensorFlow/Classification/ConvNets/triton/scripts | scripts | process_dataset | #!/usr/bin/env bash
# Copyright (c) 2021 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
if [ -d "${DATASETS_DIR}/imagenet" ]; then
echo "Dataset already downloaded and processed."
else
python triton/process_dataset.py
fi |
TensorFlow/Detection/SSD/models/research/object_detection/models | models | ssd_mobilenet_v1_feature_extractor_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for ssd_mobilenet_v1_feature_extractor."""
import numpy as np
import tensorflow as tf
from object_detection.models import ssd_feature_extractor_test
from object_detection.models import ssd_mobilenet_v1_feature_extractor
slim = tf.contrib.slim
class SsdMobilenetV1FeatureExtractorTest(
ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
is_training=True, use_explicit_padding=False):
"""Constructs a new feature extractor.
Args:
depth_multiplier: float depth multiplier for feature extractor
pad_to_multiple: the nearest multiple to zero pad the input height and
width dimensions to.
is_training: whether the network is in training mode.
use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
inputs so that the output dimensions are the same as if 'SAME' padding
were used.
Returns:
an ssd_meta_arch.SSDFeatureExtractor object.
"""
min_depth = 32
return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor(
is_training, depth_multiplier, min_depth, pad_to_multiple,
self.conv_hyperparams_fn,
use_explicit_padding=use_explicit_padding)
def test_extract_features_returns_correct_shapes_128(self):
image_height = 128
image_width = 128
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 8, 8, 512), (2, 4, 4, 1024),
(2, 2, 2, 512), (2, 1, 1, 256),
(2, 1, 1, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_explicit_padding=False)
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_explicit_padding=True)
def test_extract_features_returns_correct_shapes_299(self):
image_height = 299
image_width = 299
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 19, 19, 512), (2, 10, 10, 1024),
(2, 5, 5, 512), (2, 3, 3, 256),
(2, 2, 2, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_explicit_padding=False)
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_explicit_padding=True)
def test_extract_features_with_dynamic_image_shape(self):
image_height = 128
image_width = 128
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 8, 8, 512), (2, 4, 4, 1024),
(2, 2, 2, 512), (2, 1, 1, 256),
(2, 1, 1, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_explicit_padding=False)
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_explicit_padding=True)
def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
image_height = 299
image_width = 299
depth_multiplier = 0.5**12
pad_to_multiple = 1
expected_feature_map_shape = [(2, 19, 19, 32), (2, 10, 10, 32),
(2, 5, 5, 32), (2, 3, 3, 32),
(2, 2, 2, 32), (2, 1, 1, 32)]
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_explicit_padding=False)
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_explicit_padding=True)
def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
image_height = 299
image_width = 299
depth_multiplier = 1.0
pad_to_multiple = 32
expected_feature_map_shape = [(2, 20, 20, 512), (2, 10, 10, 1024),
(2, 5, 5, 512), (2, 3, 3, 256),
(2, 2, 2, 256), (2, 1, 1, 128)]
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_explicit_padding=False)
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_explicit_padding=True)
def test_extract_features_raises_error_with_invalid_image_size(self):
image_height = 32
image_width = 32
depth_multiplier = 1.0
pad_to_multiple = 1
self.check_extract_features_raises_error_with_invalid_image_size(
image_height, image_width, depth_multiplier, pad_to_multiple)
def test_preprocess_returns_correct_value_range(self):
image_height = 128
image_width = 128
depth_multiplier = 1
pad_to_multiple = 1
test_image = np.random.rand(2, image_height, image_width, 3)
feature_extractor = self._create_feature_extractor(depth_multiplier,
pad_to_multiple)
preprocessed_image = feature_extractor.preprocess(test_image)
self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
def test_variables_only_created_in_scope(self):
depth_multiplier = 1
pad_to_multiple = 1
scope_name = 'MobilenetV1'
self.check_feature_extractor_variables_under_scope(
depth_multiplier, pad_to_multiple, scope_name)
def test_has_fused_batchnorm(self):
image_height = 40
image_width = 40
depth_multiplier = 1
pad_to_multiple = 1
image_placeholder = tf.placeholder(tf.float32,
[1, image_height, image_width, 3])
feature_extractor = self._create_feature_extractor(depth_multiplier,
pad_to_multiple)
preprocessed_image = feature_extractor.preprocess(image_placeholder)
_ = feature_extractor.extract_features(preprocessed_image)
self.assertTrue(any(op.type == 'FusedBatchNorm'
for op in tf.get_default_graph().get_operations()))
if __name__ == '__main__':
tf.test.main()
|
PyTorch/SpeechRecognition/QuartzNet | QuartzNet | inference | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import math
import os
import random
import time
from heapq import nlargest
from itertools import chain, repeat
from pathlib import Path
from tqdm import tqdm
import dllogger
import torch
import numpy as np
import torch.distributed as distrib
from dllogger import JSONStreamBackend, StdOutBackend, Verbosity
from quartznet import config
from common import helpers
from common.dali.data_loader import DaliDataLoader
from common.dataset import (AudioDataset, FilelistDataset, get_data_loader,
SingleAudioDataset)
from common.features import BaseFeatures, FilterbankFeatures
from common.helpers import print_once, process_evaluation_epoch
from common.tb_dllogger import stdout_metric_format, unique_log_fpath
from nemo_dle_model_converter import load_nemo_ckpt
from quartznet.model import GreedyCTCDecoder, QuartzNet
def get_parser():
parser = argparse.ArgumentParser(description='QuartzNet inference')
parser.add_argument('--batch_size', default=16, type=int,
help='Data batch size')
parser.add_argument('--steps', default=0, type=int,
help='Eval this many steps for every worker')
parser.add_argument('--warmup_steps', default=0, type=int,
help='Burn-in period before measuring latencies')
parser.add_argument('--model_config', type=str, required=True,
help='Relative model config path given dataset folder')
parser.add_argument('--dataset_dir', type=str,
help='Absolute path to dataset folder')
parser.add_argument('--val_manifests', type=str, nargs='+',
help='Relative path to evaluation dataset manifest files')
parser.add_argument('--ckpt', default=None, type=str,
help='Path to model checkpoint')
parser.add_argument('--amp', '--fp16', action='store_true',
help='Use FP16 precision')
parser.add_argument('--cudnn_benchmark', action='store_true',
help='Enable cudnn benchmark')
parser.add_argument('--cpu', action='store_true',
help='Run inference on CPU')
parser.add_argument("--seed", default=None, type=int, help='Random seed')
parser.add_argument('--local_rank', default=os.getenv('LOCAL_RANK', 0),
type=int, help='GPU id used for distributed training')
io = parser.add_argument_group('feature and checkpointing setup')
io.add_argument('--dali_device', type=str, choices=['none', 'cpu', 'gpu'],
default='gpu', help='Use DALI pipeline for fast data processing')
io.add_argument('--save_predictions', type=str, default=None,
help='Save predictions in text form at this location')
io.add_argument('--save_logits', default=None, type=str,
help='Save output logits under specified path')
io.add_argument('--transcribe_wav', type=str,
help='Path to a single .wav file (16KHz)')
io.add_argument('--transcribe_filelist', type=str,
help='Path to a filelist with one .wav path per line')
io.add_argument('-o', '--output_dir', default='results/',
help='Output folder to save audio (file per phrase)')
io.add_argument('--log_file', type=str, default=None,
help='Path to a DLLogger log file')
io.add_argument('--ema', action='store_true',
help='Load averaged model weights')
io.add_argument('--torchscript', action='store_true',
help='Evaluate with a TorchScripted model')
io.add_argument('--torchscript_export', action='store_true',
help='Export the model with torch.jit to the output_dir')
io.add_argument('--override_config', type=str, action='append',
help='Overrides arbitrary config value.'
' Syntax: `--override_config nested.config.key=val`.')
return parser
def durs_to_percentiles(durations, ratios):
durations = np.asarray(durations) * 1000 # in ms
latency = durations
latency = latency[5:]
mean_latency = np.mean(latency)
latency_worst = nlargest(math.ceil((1 - min(ratios)) * len(latency)), latency)
latency_ranges = get_percentile(ratios, latency_worst, len(latency))
latency_ranges[0.5] = mean_latency
return latency_ranges
def get_percentile(ratios, arr, nsamples):
res = {}
for a in ratios:
idx = max(int(nsamples * (1 - a)), 0)
res[a] = arr[idx]
return res
def torchscript_export(data_loader, audio_processor, model, greedy_decoder,
output_dir, use_amp, use_conv_masks, model_config, device,
save):
audio_processor.to(device)
for batch in data_loader:
batch = [t.to(device, non_blocking=True) for t in batch]
audio, audio_len, _, _ = batch
feats, feat_lens = audio_processor(audio, audio_len)
break
print("\nExporting featurizer...")
print("\nNOTE: Dithering causes warnings about non-determinism.\n")
ts_feat = torch.jit.trace(audio_processor, (audio, audio_len))
print("\nExporting acoustic model...")
model(feats, feat_lens)
ts_acoustic = torch.jit.trace(model, (feats, feat_lens))
print("\nExporting decoder...")
log_probs = model(feats, feat_lens)
ts_decoder = torch.jit.script(greedy_decoder, log_probs)
print("\nJIT export complete.")
if save:
precision = "fp16" if use_amp else "fp32"
module_name = f'{os.path.basename(model_config)}_{precision}'
ts_feat.save(os.path.join(output_dir, module_name + "_feat.pt"))
ts_acoustic.save(os.path.join(output_dir, module_name + "_acoustic.pt"))
ts_decoder.save(os.path.join(output_dir, module_name + "_decoder.pt"))
return ts_feat, ts_acoustic, ts_decoder
def main():
parser = get_parser()
args = parser.parse_args()
log_fpath = args.log_file or str(Path(args.output_dir, 'nvlog_infer.json'))
dllogger.init(backends=[
JSONStreamBackend(Verbosity.DEFAULT, log_fpath, append=True),
JSONStreamBackend(Verbosity.DEFAULT, unique_log_fpath(log_fpath)),
StdOutBackend(Verbosity.VERBOSE, metric_format=stdout_metric_format)
])
[dllogger.log("PARAMETER", {k: v}) for k, v in vars(args).items()]
for step in ['DNN', 'data+DNN', 'data']:
for c in [0.99, 0.95, 0.9, 0.5]:
cs = 'avg' if c == 0.5 else f'{int(100*c)}%'
dllogger.metadata(f'{step.lower()}_latency_{c}',
{'name': f'{step} latency {cs}',
'format': ':>7.2f', 'unit': 'ms'})
dllogger.metadata(
'eval_wer', {'name': 'WER', 'format': ':>3.2f', 'unit': '%'})
if args.cpu:
device = torch.device('cpu')
else:
assert torch.cuda.is_available()
device = torch.device('cuda')
torch.backends.cudnn.benchmark = args.cudnn_benchmark
if args.seed is not None:
torch.manual_seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
random.seed(args.seed + args.local_rank)
# set up distributed training
multi_gpu = not args.cpu and int(os.environ.get('WORLD_SIZE', 1)) > 1
if multi_gpu:
torch.cuda.set_device(args.local_rank)
distrib.init_process_group(backend='nccl', init_method='env://')
print_once(f'Inference with {distrib.get_world_size()} GPUs')
if args.ckpt is not None:
print(f'Loading the model from {args.ckpt} ...')
print(f'{args.model_config} will be overriden.')
if args.ckpt.lower().endswith('.nemo'):
ckpt, cfg = load_nemo_ckpt(args.ckpt)
else:
cfg = config.load(args.model_config)
ckpt = torch.load(args.ckpt, map_location='cpu')
sd_key = 'ema_state_dict' if args.ema else 'state_dict'
if args.ema and 'ema_state_dict' not in ckpt:
print(f'WARNING: EMA weights are unavailable in {args.ckpt}.')
sd_key = 'state_dict'
state_dict = ckpt[sd_key]
else:
cfg = config.load(args.model_config)
state_dict = None
config.apply_config_overrides(cfg, args)
symbols = helpers.add_ctc_blank(cfg['labels'])
use_dali = args.dali_device in ('cpu', 'gpu')
dataset_kw, features_kw = config.input(cfg, 'val')
measure_perf = args.steps > 0
# dataset
if args.transcribe_wav or args.transcribe_filelist:
if use_dali:
print("DALI supported only with input .json files; disabling")
use_dali = False
assert not cfg['input_val']['audio_dataset'].get('pad_to_max_duration', False)
assert not (args.transcribe_wav and args.transcribe_filelist)
if args.transcribe_wav:
dataset = SingleAudioDataset(args.transcribe_wav)
else:
dataset = FilelistDataset(args.transcribe_filelist)
data_loader = get_data_loader(dataset,
batch_size=1,
multi_gpu=multi_gpu,
shuffle=False,
num_workers=0,
drop_last=(True if measure_perf else False))
_, features_kw = config.input(cfg, 'val')
feat_proc = FilterbankFeatures(**features_kw)
elif use_dali:
# pad_to_max_duration is not supported by DALI - have simple padders
if features_kw['pad_to_max_duration']:
feat_proc = BaseFeatures(
pad_align=features_kw['pad_align'],
pad_to_max_duration=True,
max_duration=features_kw['max_duration'],
sample_rate=features_kw['sample_rate'],
window_size=features_kw['window_size'],
window_stride=features_kw['window_stride'])
features_kw['pad_to_max_duration'] = False
else:
feat_proc = None
data_loader = DaliDataLoader(
gpu_id=args.local_rank or 0,
dataset_path=args.dataset_dir,
config_data=dataset_kw,
config_features=features_kw,
json_names=args.val_manifests,
batch_size=args.batch_size,
pipeline_type=("train" if measure_perf else "val"), # no drop_last
device_type=args.dali_device,
symbols=symbols)
else:
dataset = AudioDataset(args.dataset_dir,
args.val_manifests,
symbols,
**dataset_kw)
data_loader = get_data_loader(dataset,
args.batch_size,
multi_gpu=multi_gpu,
shuffle=False,
num_workers=4,
drop_last=False)
feat_proc = FilterbankFeatures(**features_kw)
model = QuartzNet(encoder_kw=config.encoder(cfg),
decoder_kw=config.decoder(cfg, n_classes=len(symbols)))
if state_dict is not None:
model.load_state_dict(state_dict, strict=True)
model.to(device)
model.eval()
if feat_proc is not None:
feat_proc.to(device)
feat_proc.eval()
if args.amp:
model = model.half()
if args.torchscript:
greedy_decoder = GreedyCTCDecoder()
feat_proc, model, greedy_decoder = torchscript_export(
data_loader, feat_proc, model, greedy_decoder, args.output_dir,
use_amp=args.amp, use_conv_masks=True, model_toml=args.model_toml,
device=device, save=args.torchscript_export)
if multi_gpu:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank)
agg = {'txts': [], 'preds': [], 'logits': []}
dur = {'data': [], 'dnn': [], 'data+dnn': []}
looped_loader = chain.from_iterable(repeat(data_loader))
greedy_decoder = GreedyCTCDecoder()
sync = lambda: torch.cuda.synchronize() if device.type == 'cuda' else None
steps = args.steps + args.warmup_steps or len(data_loader)
with torch.no_grad():
for it, batch in enumerate(tqdm(looped_loader, initial=1, total=steps)):
if use_dali:
feats, feat_lens, txt, txt_lens = batch
if feat_proc is not None:
feats, feat_lens = feat_proc(feats, feat_lens)
else:
batch = [t.to(device, non_blocking=True) for t in batch]
audio, audio_lens, txt, txt_lens = batch
feats, feat_lens = feat_proc(audio, audio_lens)
sync()
t1 = time.time()
if args.amp:
feats = feats.half()
if model.encoder.use_conv_masks:
log_probs, log_prob_lens = model(feats, feat_lens)
else:
log_probs = model(feats, feat_lens)
preds = greedy_decoder(log_probs)
sync()
t2 = time.time()
# burn-in period; wait for a new loader due to num_workers
if it >= 1 and (args.steps == 0 or it >= args.warmup_steps):
dur['data'].append(t1 - t0)
dur['dnn'].append(t2 - t1)
dur['data+dnn'].append(t2 - t0)
if txt is not None:
agg['txts'] += helpers.gather_transcripts([txt], [txt_lens],
symbols)
agg['preds'] += helpers.gather_predictions([preds], symbols)
agg['logits'].append(log_probs)
if it + 1 == steps:
break
sync()
t0 = time.time()
# communicate the results
if args.transcribe_wav:
for idx, p in enumerate(agg['preds']):
print_once(f'Prediction {idx+1: >3}: {p}')
elif args.transcribe_filelist:
pass
elif not multi_gpu or distrib.get_rank() == 0:
wer, _ = process_evaluation_epoch(agg)
dllogger.log(step=(), data={'eval_wer': 100 * wer})
if args.save_predictions:
with open(args.save_predictions, 'w') as f:
f.write('\n'.join(agg['preds']))
if args.save_logits:
logits = torch.cat(agg['logits'], dim=0).cpu()
torch.save(logits, args.save_logits)
# report timings
if len(dur['data']) >= 20:
ratios = [0.9, 0.95, 0.99]
for stage in dur:
lat = durs_to_percentiles(dur[stage], ratios)
for k in [0.99, 0.95, 0.9, 0.5]:
kk = str(k).replace('.', '_')
dllogger.log(step=(), data={f'{stage.lower()}_latency_{kk}': lat[k]})
else:
print_once('Not enough samples to measure latencies.')
if __name__ == "__main__":
main()
|
TensorFlow2/LanguageModeling/BERT | BERT | dllogger_class | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from dllogger import Logger, StdOutBackend, JSONStreamBackend, Verbosity
import numpy
class dllogger_class():
def format_step(self, step):
if isinstance(step, str):
return step
elif isinstance(step, int):
return "Iteration: {} ".format(step)
elif len(step) > 0:
return "Iteration: {} ".format(step[0])
else:
return ""
def __init__(self, log_path="bert_dllog.json"):
self.logger = Logger([
StdOutBackend(Verbosity.DEFAULT, step_format=self.format_step),
JSONStreamBackend(Verbosity.VERBOSE, log_path),
])
self.logger.metadata("mlm_loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
self.logger.metadata("nsp_loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
self.logger.metadata("avg_loss_step", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
self.logger.metadata("total_loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
self.logger.metadata("loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
self.logger.metadata("f1", {"unit": None, "format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"})
self.logger.metadata("precision", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"})
self.logger.metadata("recall", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"})
self.logger.metadata("mcc", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"})
self.logger.metadata("exact_match", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"})
self.logger.metadata(
"throughput_train",
{"unit": "sequences/s", "format": ":.3f", "GOAL": "MAXIMIZE", "STAGE": "TRAIN"},
)
self.logger.metadata(
"throughput_inf",
{"unit": "sequences/s", "format": ":.3f", "GOAL": "MAXIMIZE", "STAGE": "VAL"},
)
self.logger.metadata(
"throughput_val",
{"unit": "sequences/s", "format": ":.3f", "GOAL": "MAXIMIZE", "STAGE": "VAL"},
)
|
TensorFlow/Classification/ConvNets/resnext101-32x4d/training | training | DGX1_RNxt101-32x4d_AMP_90E | #!/bin/bash
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
WORKSPACE=${1:-"/workspace/rn50v15_tf"}
DATA_DIR=${2:-"/data"}
OTHER=${@:3}
if [[ ! -z "${BIND_TO_SOCKET}" ]]; then
BIND_TO_SOCKET="--bind-to socket"
fi
mpiexec --allow-run-as-root ${BIND_TO_SOCKET} -np 8 python3 main.py --arch=resnext101-32x4d \
--mode=train_and_evaluate --iter_unit=epoch --num_iter=90 \
--batch_size=128 --warmup_steps=100 --cosine_lr --label_smoothing 0.1 \
--lr_init=0.256 --lr_warmup_epochs=8 --momentum=0.875 --weight_decay=6.103515625e-05 \
--amp --static_loss_scale 128 \
--data_dir=${DATA_DIR}/tfrecords --data_idx_dir=${DATA_DIR}/dali_idx \
--results_dir=${WORKSPACE}/results --weight_init=fan_in ${OTHER}
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/generator/tabular/data_transformer | data_transformer | __init__ | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
|
PyTorch/Classification/ConvNets/resnet50v1.5 | resnet50v1.5 | README | # ResNet50 v1.5 For PyTorch
This repository provides a script and recipe to train the ResNet50 model to
achieve state-of-the-art accuracy, and is tested and maintained by NVIDIA.
## Table Of Contents
* [Model overview](#model-overview)
* [Default configuration](#default-configuration)
* [Optimizer](#optimizer)
* [Data augmentation](#data-augmentation)
* [DALI](#dali)
* [Feature support matrix](#feature-support-matrix)
* [Features](#features)
* [Mixed precision training](#mixed-precision-training)
* [Enabling mixed precision](#enabling-mixed-precision)
* [Enabling TF32](#enabling-tf32)
* [Setup](#setup)
* [Requirements](#requirements)
* [Quick Start Guide](#quick-start-guide)
* [Advanced](#advanced)
* [Scripts and sample code](#scripts-and-sample-code)
* [Command-line options](#command-line-options)
* [Dataset guidelines](#dataset-guidelines)
* [Training process](#training-process)
* [Inference process](#inference-process)
* [Performance](#performance)
* [Benchmarking](#benchmarking)
* [Training performance benchmark](#training-performance-benchmark)
* [Inference performance benchmark](#inference-performance-benchmark)
* [Results](#results)
* [Training accuracy results](#training-accuracy-results)
* [Training accuracy: NVIDIA DGX A100 (8x A100 80GB)](#training-accuracy-nvidia-dgx-a100-8x-a100-80gb)
* [Training accuracy: NVIDIA DGX-1 (8x V100 16GB)](#training-accuracy-nvidia-dgx-1-8x-v100-16gb)
* [Training accuracy: NVIDIA DGX-2 (16x V100 32GB)](#training-accuracy-nvidia-dgx-2-16x-v100-32gb)
* [Example plots](#example-plots)
* [Training performance results](#training-performance-results)
* [Training performance: NVIDIA DGX A100 (8x A100 80GB)](#training-performance-nvidia-dgx-a100-8x-a100-80gb)
* [Training performance: NVIDIA DGX-1 16GB (8x V100 16GB)](#training-performance-nvidia-dgx-1-16gb-8x-v100-16gb)
* [Training performance: NVIDIA DGX-1 32GB (8x V100 32GB)](#training-performance-nvidia-dgx-1-32gb-8x-v100-32gb)
* [Inference performance results](#inference-performance-results)
* [Inference performance: NVIDIA DGX-1 16GB (1x V100 16GB)](#inference-performance-nvidia-dgx-1-1x-v100-16gb)
* [Inference performance: NVIDIA T4](#inference-performance-nvidia-t4)
* [Release notes](#release-notes)
* [Changelog](#changelog)
* [Known issues](#known-issues)
## Model overview
The ResNet50 v1.5 model is a modified version of the [original ResNet50 v1 model](https://arxiv.org/abs/1512.03385).
The difference between v1 and v1.5 is that, in the bottleneck blocks which requires
downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.
This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a smallperformance drawback (~5% imgs/sec).
The model is initialized as described in [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification](https://arxiv.org/pdf/1502.01852.pdf)
This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
We are currently working on adding [NHWC data layout](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) support for Mixed Precision training.
### Default configuration
The following sections highlight the default configurations for the ResNet50 model.
#### Optimizer
This model uses SGD with momentum optimizer with the following hyperparameters:
* Momentum (0.875)
* Learning rate (LR) = 0.256 for 256 batch size, for other batch sizes we linearly
scale the learning rate.
* Learning rate schedule - we use cosine LR schedule
* For bigger batch sizes (512 and up) we use linear warmup of the learning rate
during the first couple of epochs
according to [Training ImageNet in 1 hour](https://arxiv.org/abs/1706.02677).
Warmup length depends on the total training length.
* Weight decay (WD)= 3.0517578125e-05 (1/32768).
* We do not apply WD on Batch Norm trainable parameters (gamma/bias)
* Label smoothing = 0.1
* We train for:
* 50 Epochs -> configuration that reaches 75.9% top1 accuracy
* 90 Epochs -> 90 epochs is a standard for ImageNet networks
* 250 Epochs -> best possible accuracy.
* For 250 epoch training we also use [MixUp regularization](https://arxiv.org/pdf/1710.09412.pdf).
#### Data augmentation
This model uses the following data augmentation:
* For training:
* Normalization
* Random resized crop to 224x224
* Scale from 8% to 100%
* Aspect ratio from 3/4 to 4/3
* Random horizontal flip
* For inference:
* Normalization
* Scale to 256x256
* Center crop to 224x224
#### Other training recipes
This script does not target any specific benchmark.
There are changes that others have made which can speed up convergence and/or increase accuracy.
One of the more popular training recipes is provided by [fast.ai](https://github.com/fastai/imagenet-fast).
The fast.ai recipe introduces many changes to the training procedure, one of which is progressive resizing of the training images.
The first part of training uses 128px images, the middle part uses 224px images, and the last part uses 288px images.
The final validation is performed on 288px images.
Training script in this repository performs validation on 224px images, just like the original paper described.
These two approaches can't be directly compared, since the fast.ai recipe requires validation on 288px images,
and this recipe keeps the original assumption that validation is done on 224px images.
Using 288px images means that a lot more FLOPs are needed during inference to reach the same accuracy.
### Feature support matrix
The following features are supported by this model:
| Feature | ResNet50
|-----------------------|--------------------------
|[DALI](https://docs.nvidia.com/deeplearning/sdk/dali-release-notes/index.html) | Yes
|[APEX AMP](https://nvidia.github.io/apex/amp.html) | Yes |
#### Features
- NVIDIA DALI - DALI is a library accelerating data preparation pipeline. To accelerate your input pipeline, you only need to define your data loader
with the DALI library. For more information about DALI, refer to the [DALI product documentation](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html).
- [APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains utility libraries, such as [Automatic Mixed Precision (AMP)](https://nvidia.github.io/apex/amp.html), which require minimal network code changes to leverage Tensor Cores performance. Refer to the [Enabling mixed precision](#enabling-mixed-precision) section for more details.
### DALI
We use [NVIDIA DALI](https://github.com/NVIDIA/DALI),
which speeds up data loading when CPU becomes a bottleneck.
DALI can use CPU or GPU, and outperforms the PyTorch native dataloader.
Run training with `--data-backends dali-gpu` or `--data-backends dali-cpu` to enable DALI.
For DGXA100 and DGX1 we recommend `--data-backends dali-cpu`, for DGX2 we recommend `--data-backends dali-gpu`.
### Mixed precision training
Mixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:
1. Porting the model to use the FP16 data type where appropriate.
2. Adding loss scaling to preserve small gradient values.
The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.
For information about:
- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.
- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.
- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/).
#### Enabling mixed precision
Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP), a library from [APEX](https://github.com/NVIDIA/apex) that casts variables to half-precision upon retrieval,
while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients.
In PyTorch, loss scaling can be easily applied by using scale_loss() method provided by AMP. The scaling value to be used can be [dynamic](https://nvidia.github.io/apex/fp16_utils.html#apex.fp16_utils.DynamicLossScaler) or fixed.
For an in-depth walk through on AMP, check out sample usage [here](https://github.com/NVIDIA/apex/tree/master/apex/amp#usage-and-getting-started). [APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage tensor cores performance.
To enable mixed precision, you can:
- Import AMP from APEX:
```python
from apex import amp
```
- Wrap model and optimizer in amp.initialize:
```python
model, optimizer = amp.initialize(model, optimizer, opt_level="O1", loss_scale="dynamic")
```
- Scale loss before backpropagation:
```python
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
```
#### Enabling TF32
TensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.
TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.
For more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.
TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.
## Setup
The following section lists the requirements that you need to meet in order to start training the ResNet50 model.
### Requirements
This repository contains Dockerfile which extends the PyTorch NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:
* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
* [PyTorch 21.03-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch) or newer
* Supported GPUs:
* [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
* [NVIDIA Turing architecture](https://www.nvidia.com/en-us/geforce/turing/)
* [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)
For more information about how to get started with NGC containers, see the
following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning
DGX Documentation:
* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)
* [Running PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running)
For those unable to use the PyTorch NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).
## Quick Start Guide
### 1. Clone the repository.
```
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/PyTorch/Classification/
```
### 2. Download and preprocess the dataset.
The ResNet50 script operates on ImageNet 1k, a widely popular image classification dataset from the ILSVRC challenge.
PyTorch can work directly on JPEGs, therefore, preprocessing/augmentation is not needed.
To train your model using mixed or TF32 precision with Tensor Cores or using FP32,
perform the following steps using the default parameters of the resnet50 model on the ImageNet dataset.
For the specifics concerning training and inference, see the [Advanced](#advanced) section.
1. [Download the images](http://image-net.org/download-images).
2. Extract the training data:
```bash
mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done
cd ..
```
3. Extract the validation data and move the images to subfolders:
```bash
mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash
```
The directory in which the `train/` and `val/` directories are placed, is referred to as `<path to imagenet>` in this document.
### 3. Build the ResNet50 PyTorch NGC container.
```
docker build . -t nvidia_resnet50
```
### 4. Start an interactive session in the NGC container to run training/inference.
```
nvidia-docker run --rm -it -v <path to imagenet>:/imagenet --ipc=host nvidia_resnet50
```
### 5. Start training
To run training for a standard configuration (DGXA100/DGX1V/DGX2V, AMP/TF32/FP32, 90/250 Epochs),
run one of the scripts in the `./resnet50v1.5/training` directory
called `./resnet50v1.5/training/{AMP, TF32, FP32}/{ DGXA100, DGX1V, DGX2V }_resnet50_{AMP, TF32, FP32}_{ 90, 250 }E.sh`.
Ensure ImageNet is mounted in the `/imagenet` directory.
Example:
`bash ./resnet50v1.5/training/AMP/DGX1_resnet50_AMP_250E.sh <path were to store checkpoints and logs>`
### 6. Start inference
You can download pretrained weights from NGC:
```bash
wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/resnet50_pyt_amp/versions/20.06.0/zip -O resnet50_pyt_amp_20.06.0.zip
unzip resnet50_pyt_amp_20.06.0.zip
```
To run inference on ImageNet, run:
`python ./main.py --arch resnet50 --evaluate --epochs 1 --pretrained-from-file nvidia_resnet50_200821.pth.tar -b <batch size> <path to imagenet>`
To run inference on JPEG image using pretrained weights:
`python classify.py --arch resnet50 --pretrained-from-file nvidia_resnet50_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
## Advanced
The following sections provide greater details of the dataset, running training and inference, and the training results.
### Scripts and sample code
To run a non standard configuration use:
* For 1 GPU
* FP32
`python ./main.py --arch resnet50 -c fanin --label-smoothing 0.1 <path to imagenet>`
`python ./main.py --arch resnet50 -c fanin --label-smoothing 0.1 --amp --static-loss-scale 256 <path to imagenet>`
* For multiple GPUs
* FP32
`python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnet50 -c fanin --label-smoothing 0.1 <path to imagenet>`
* AMP
`python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnet50 -c fanin --label-smoothing 0.1 --amp --static-loss-scale 256 <path to imagenet>`
Use `python ./main.py -h` to obtain the list of available options in the `main.py` script.
### Command-line options:
To see the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:
`python main.py -h`
```
usage: main.py [-h] [--data-backend BACKEND] [--arch ARCH]
[--model-config CONF] [-j N] [--epochs N]
[--run-epochs N] [-b N] [--optimizer-batch-size N] [--lr LR]
[--lr-schedule SCHEDULE] [--warmup E] [--label-smoothing S]
[--mixup ALPHA] [--momentum M] [--weight-decay W]
[--bn-weight-decay] [--nesterov] [--print-freq N]
[--resume PATH] [--pretrained-from-file PATH]
[--static-loss-scale STATIC_LOSS_SCALE] [--dynamic-loss-scale]
[--prof N] [--amp] [--seed SEED] [--gather-checkpoints]
[--raport-file RAPORT_FILE] [--evaluate] [--training-only]
[--no-checkpoints] [--checkpoint-filename CHECKPOINT_FILENAME]
[--workspace DIR] [--memory-format {nchw,nhwc}]
DIR
PyTorch ImageNet Training
positional arguments:
DIR path to dataset
optional arguments:
-h, --help show this help message and exit
--data-backend BACKEND
data backend: pytorch | synthetic | dali-gpu | dali-cpu
(default: dali-cpu)
--arch ARCH, -a ARCH model architecture: resnet18 | resnet34 | resnet50 |
resnet101 | resnet152 | resnext50-32x4d |
resnext101-32x4d | resnext101-32x8d |
resnext101-32x8d-basic | se-resnext101-32x4d (default:
resnet50)
--model-config CONF, -c CONF
model configs: classic | fanin | grp-fanin | grp-
fanout(default: classic)
-j N, --workers N number of data loading workers (default: 5)
--epochs N number of total epochs to run
--run-epochs N run only N epochs, used for checkpointing runs
-b N, --batch-size N mini-batch size (default: 256) per gpu
--optimizer-batch-size N
size of a total batch size, for simulating bigger
batches using gradient accumulation
--lr LR, --learning-rate LR
initial learning rate
--lr-schedule SCHEDULE
Type of LR schedule: step, linear, cosine
--warmup E number of warmup epochs
--label-smoothing S label smoothing
--mixup ALPHA mixup alpha
--momentum M momentum
--weight-decay W, --wd W
weight decay (default: 1e-4)
--bn-weight-decay use weight_decay on batch normalization learnable
parameters, (default: false)
--nesterov use nesterov momentum, (default: false)
--print-freq N, -p N print frequency (default: 10)
--resume PATH path to latest checkpoint (default: none)
--pretrained-from-file PATH
load weights from here
--static-loss-scale STATIC_LOSS_SCALE
Static loss scale, positive power of 2 values can
improve amp convergence.
--dynamic-loss-scale Use dynamic loss scaling. If supplied, this argument
supersedes --static-loss-scale.
--prof N Run only N iterations
--amp Run model AMP (automatic mixed precision) mode.
--seed SEED random seed used for numpy and pytorch
--gather-checkpoints Gather checkpoints throughout the training, without
this flag only best and last checkpoints will be
stored
--raport-file RAPORT_FILE
file in which to store JSON experiment raport
--evaluate evaluate checkpoint/model
--training-only do not evaluate
--no-checkpoints do not store any checkpoints, useful for benchmarking
--checkpoint-filename CHECKPOINT_FILENAME
--workspace DIR path to directory where checkpoints will be stored
--memory-format {nchw,nhwc}
memory layout, nchw or nhwc
```
### Dataset guidelines
To use your own dataset, divide it in directories as in the following scheme:
- Training images - `train/<class id>/<image>`
- Validation images - `val/<class id>/<image>`
If your dataset's has number of classes different than 1000, you need to pass `--num_classes N` flag to the training script.
### Training process
All the results of the training will be stored in the directory specified with `--workspace` argument.
Script will store:
- most recent checkpoint - `checkpoint.pth.tar` (unless `--no-checkpoints` flag is used).
- checkpoint with best validation accuracy - `model_best.pth.tar` (unless `--no-checkpoints` flag is used).
- JSON log - in the file specified with `--raport-file` flag.
Metrics gathered through training:
- `train.loss` - training loss
- `train.total_ips` - training speed measured in images/second
- `train.compute_ips` - training speed measured in images/second, not counting data loading
- `train.data_time` - time spent on waiting on data
- `train.compute_time` - time spent in forward/backward pass
To restart training from checkpoint use `--resume` option.
To start training from pretrained weights (e.g. downloaded from NGC) use `--pretrained-from-file` option.
The difference between those two is that the pretrained weights contain only model weights,
and checkpoints, apart from model weights, contain optimizer state, LR scheduler state.
Checkpoints are suitable for dividing the training into parts, for example in order
to divide the training job into shorter stages, or restart training after infrastructure fail.
Pretrained weights can be used as a base for finetuning the model to a different dataset,
or as a backbone to detection models.
### Inference process
Validation is done every epoch, and can be also run separately on a checkpointed model.
`python ./main.py --arch resnet50 --evaluate --epochs 1 --resume <path to checkpoint> -b <batch size> <path to imagenet>`
Metrics gathered through training:
- `val.loss` - validation loss
- `val.top1` - validation top1 accuracy
- `val.top5` - validation top5 accuracy
- `val.total_ips` - inference speed measured in images/second
- `val.compute_ips` - inference speed measured in images/second, not counting data loading
- `val.data_time` - time spent on waiting on data
- `val.compute_time` - time spent on inference
To run inference on JPEG image, you have to first extract the model weights from checkpoint:
`python checkpoint2model.py --checkpoint-path <path to checkpoint> --weight-path <path where weights will be stored>`
Then run classification script:
`python classify.py --arch resnet50 --pretrained-from-file <path to weights from previous step> --precision AMP|FP32 --image <path to JPEG image>`
You can also run ImageNet validation on pretrained weights:
`python ./main.py --arch resnet50 --evaluate --epochs 1 --pretrained-from-file <path to pretrained weights> -b <batch size> <path to imagenet>`
#### NGC Pretrained weights:
Pretrained weights can be downloaded from NGC:
```bash
wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/resnet50_pyt_amp/versions/20.06.0/zip -O resnet50_pyt_amp_20.06.0.zip
unzip resnet50_pyt_amp_20.06.0.zip
```
To run inference on ImageNet, run:
`python ./main.py --arch resnet50 --evaluate --epochs 1 --pretrained-from-file nvidia_resnet50_200821.pth.tar -b <batch size> <path to imagenet>`
To run inference on JPEG image using pretrained weights:
`python classify.py --arch resnet50 --weights nvidia_resnet50_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
## Performance
The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA’s latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).
### Benchmarking
The following section shows how to run benchmarks measuring the model performance in training and inference modes.
#### Training performance benchmark
To benchmark training, run:
* For 1 GPU
* FP32 (V100 GPUs only)
`python ./launch.py --model resnet50 --precision FP32 --mode benchmark_training --platform DGX1V <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* TF32 (A100 GPUs only)
`python ./launch.py --model resnet50 --precision TF32 --mode benchmark_training --platform DGXA100 <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* AMP
`python ./launch.py --model resnet50 --precision AMP --mode benchmark_training --platform <DGX1V|DGXA100> <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* For multiple GPUs
* FP32 (V100 GPUs only)
`python ./launch.py --model resnet50 --precision FP32 --mode benchmark_training --platform DGX1V <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* TF32 (A100 GPUs only)
`python ./multiproc.py --nproc_per_node 8 ./launch.py --model resnet50 --precision TF32 --mode benchmark_training --platform DGXA100 <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* AMP
`python ./multiproc.py --nproc_per_node 8 ./launch.py --model resnet50 --precision AMP --mode benchmark_training --platform <DGX1V|DGXA100> <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
Each of these scripts will run 100 iterations and save results in the `benchmark.json` file.
#### Inference performance benchmark
To benchmark inference, run:
* FP32 (V100 GPUs only)
`python ./launch.py --model resnet50 --precision FP32 --mode benchmark_inference --platform DGX1V <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* TF32 (A100 GPUs only)
`python ./launch.py --model resnet50 --precision TF32 --mode benchmark_inference --platform DGXA100 <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* AMP
`python ./launch.py --model resnet50 --precision AMP --mode benchmark_inference --platform <DGX1V|DGXA100> <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
Each of these scripts will run 100 iterations and save results in the `benchmark.json` file.
### Results
#### Training accuracy results
Our results were obtained by running the applicable training script in the pytorch-20.12 NGC container.
To achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).
##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)
| **Epochs** | **Mixed Precision Top1** | **TF32 Top1** |
|:----------:|:------------------------:|:--------------:|
| 90 | 77.12 +/- 0.11 | 76.95 +/- 0.18 |
| 250 | 78.43 +/- 0.11 | 78.38 +/- 0.17 |
##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)
| **Epochs** | **Mixed Precision Top1** | **FP32 Top1** |
|:----------:|:------------------------:|:--------------:|
| 90 | 76.88 +/- 0.16 | 77.01 +/- 0.16 |
| 250 | 78.25 +/- 0.12 | 78.30 +/- 0.16 |
##### Training accuracy: NVIDIA DGX-2 (16x V100 32GB)
| **epochs** | **Mixed Precision Top1** | **FP32 Top1** |
|:-:|:-:|:-:|
| 50 | 75.81 +/- 0.08 | 76.04 +/- 0.05 |
| 90 | 77.10 +/- 0.06 | 77.23 +/- 0.04 |
| 250 | 78.59 +/- 0.13 | 78.46 +/- 0.03 |
##### Example plots
The following images show a 250 epochs configuration on a DGX-1V.
#### Training performance results
Our results were obtained by running the applicable training script in the pytorch-21.03 NGC container.
To achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).
##### Training performance: NVIDIA DGX A100 (8x A100 80GB)
| **GPUs** | **Throughput - TF32** | **Throughput - mixed precision** | **Throughput speedup (TF32 to mixed precision)** | **TF32 Strong Scaling** | **Mixed Precision Strong Scaling** | **Mixed Precision Training Time (90E)** | **TF32 Training Time (90E)** |
|:--------:|:----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|:---------------------------------------:|:----------------------------:|
| 1 | 938 img/s | 2470 img/s | 2.63 x | 1.0 x | 1.0 x | ~14 hours | ~36 hours |
| 8 | 7248 img/s | 16621 img/s | 2.29 x | 7.72 x | 6.72 x | ~3 hours | ~5 hours |
##### Training performance: NVIDIA DGX-1 16GB (8x V100 16GB)
| **GPUs** | **Throughput - FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **FP32 Strong Scaling** | **Mixed Precision Strong Scaling** | **Mixed Precision Training Time (90E)** | **FP32 Training Time (90E)** |
|:--------:|:----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|:---------------------------------------:|:----------------------------:|
| 1 | 367 img/s | 1200 img/s | 3.26 x | 1.0 x | 1.0 x | ~29 hours | ~92 hours |
| 8 | 2855 img/s | 8322 img/s | 2.91 x | 7.76 x | 6.93 x | ~5 hours | ~12 hours |
##### Training performance: NVIDIA DGX-1 32GB (8x V100 32GB)
| **GPUs** | **Throughput - FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **FP32 Strong Scaling** | **Mixed Precision Strong Scaling** | **Mixed Precision Training Time (90E)** | **FP32 Training Time (90E)** |
|:--------:|:----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|:---------------------------------------:|:----------------------------:|
| 1 | 356 img/s | 1156 img/s | 3.24 x | 1.0 x | 1.0 x | ~30 hours | ~95 hours |
| 8 | 2766 img/s | 8056 img/s | 2.91 x | 7.75 x | 6.96 x | ~5 hours | ~13 hours |
#### Inference performance results
Our results were obtained by running the applicable training script in the pytorch-21.03 NGC container.
To achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).
##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)
###### FP32 Inference Latency
| **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |
|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|
| 1 | 96 img/s | 10.37 ms | 10.81 ms | 11.73 ms |
| 2 | 196 img/s | 10.24 ms | 11.18 ms | 12.89 ms |
| 4 | 386 img/s | 10.46 ms | 11.01 ms | 11.75 ms |
| 8 | 709 img/s | 11.5 ms | 12.36 ms | 13.12 ms |
| 16 | 1023 img/s | 16.07 ms | 15.69 ms | 15.97 ms |
| 32 | 1127 img/s | 29.37 ms | 28.53 ms | 28.67 ms |
| 64 | 1200 img/s | 55.4 ms | 53.5 ms | 53.71 ms |
| 128 | 1229 img/s | 109.26 ms | 104.04 ms | 104.34 ms |
| 256 | 1261 img/s | 214.48 ms | 202.51 ms | 202.88 ms |
###### Mixed Precision Inference Latency
| **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |
|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|
| 1 | 78 img/s | 12.78 ms | 13.27 ms | 14.36 ms |
| 2 | 154 img/s | 13.01 ms | 13.74 ms | 15.19 ms |
| 4 | 300 img/s | 13.41 ms | 14.25 ms | 15.68 ms |
| 8 | 595 img/s | 13.65 ms | 14.51 ms | 15.6 ms |
| 16 | 1178 img/s | 14.0 ms | 15.07 ms | 16.26 ms |
| 32 | 2146 img/s | 15.84 ms | 17.25 ms | 18.53 ms |
| 64 | 2984 img/s | 23.18 ms | 21.51 ms | 21.93 ms |
| 128 | 3249 img/s | 43.55 ms | 39.36 ms | 40.1 ms |
| 256 | 3382 img/s | 84.14 ms | 75.3 ms | 80.08 ms |
##### Inference performance: NVIDIA T4
###### FP32 Inference Latency
| **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |
|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|
| 1 | 98 img/s | 10.7 ms | 12.82 ms | 16.71 ms |
| 2 | 186 img/s | 11.26 ms | 13.79 ms | 16.99 ms |
| 4 | 325 img/s | 12.73 ms | 13.89 ms | 18.03 ms |
| 8 | 363 img/s | 22.41 ms | 22.57 ms | 22.9 ms |
| 16 | 409 img/s | 39.77 ms | 39.8 ms | 40.23 ms |
| 32 | 420 img/s | 77.62 ms | 76.92 ms | 77.28 ms |
| 64 | 428 img/s | 152.73 ms | 152.03 ms | 153.02 ms |
| 128 | 426 img/s | 309.26 ms | 303.38 ms | 305.13 ms |
| 256 | 415 img/s | 635.98 ms | 620.16 ms | 625.21 ms |
###### Mixed Precision Inference Latency
| **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |
|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|
| 1 | 79 img/s | 12.96 ms | 15.47 ms | 20.0 ms |
| 2 | 156 img/s | 13.18 ms | 14.9 ms | 18.73 ms |
| 4 | 317 img/s | 12.99 ms | 14.69 ms | 19.05 ms |
| 8 | 652 img/s | 12.82 ms | 16.04 ms | 19.43 ms |
| 16 | 1050 img/s | 15.8 ms | 16.57 ms | 20.62 ms |
| 32 | 1128 img/s | 29.54 ms | 28.79 ms | 28.97 ms |
| 64 | 1165 img/s | 57.41 ms | 55.67 ms | 56.11 ms |
| 128 | 1190 img/s | 114.24 ms | 109.17 ms | 110.41 ms |
| 256 | 1198 img/s | 225.95 ms | 215.28 ms | 222.94 ms |
## Release notes
### Changelog
1. September 2018
* Initial release
2. January 2019
* Added options Label Smoothing, fan-in initialization, skipping weight decay on batch norm gamma and bias.
3. May 2019
* Cosine LR schedule
* MixUp regularization
* DALI support
* DGX2 configurations
* gradients accumulation
4. July 2019
* DALI-CPU dataloader
* Updated README
5. July 2020
* Added A100 scripts
* Updated README
6. February 2021
* Moved from APEX AMP to Native AMP
### Known issues
There are no known issues with this model.
|
TensorFlow/LanguageModeling/Transformer-XL | Transformer-XL | getdata | # BSD 3-Clause License
#
# Copyright (c) 2017,
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
echo "=== Acquiring datasets ==="
echo "---"
mkdir -p data
cd data
echo "- Downloading WikiText-103 (WT2)"
if [[ ! -d 'wikitext-103' ]]; then
wget --continue https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip
unzip -q wikitext-103-v1.zip
cd wikitext-103
mv wiki.train.tokens train.txt
mv wiki.valid.tokens valid.txt
mv wiki.test.tokens test.txt
cd ..
fi
if [[ $1 != 'full' ]]; then
exit 0
fi
if [[ ! -d 'wikitext-2' ]]; then
echo "- Downloading WikiText-2 (WT2)"
wget --quiet --continue https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip
unzip -q wikitext-2-v1.zip
cd wikitext-2
mv wiki.train.tokens train.txt
mv wiki.valid.tokens valid.txt
mv wiki.test.tokens test.txt
cd ..
fi
echo "- Downloading enwik8 (Character)"
if [[ ! -d 'enwik8' ]]; then
mkdir -p enwik8
cd enwik8
wget --continue http://mattmahoney.net/dc/enwik8.zip
wget https://raw.githubusercontent.com/salesforce/awd-lstm-lm/master/data/enwik8/prep_enwik8.py
python3 prep_enwik8.py
cd ..
fi
echo "- Downloading text8 (Character)"
if [[ ! -d 'text8' ]]; then
mkdir -p text8
cd text8
wget --continue http://mattmahoney.net/dc/text8.zip
python ../../prep_text8.py
cd ..
fi
echo "- Downloading Penn Treebank (PTB)"
if [[ ! -d 'penn' ]]; then
wget --quiet --continue http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
tar -xzf simple-examples.tgz
mkdir -p penn
cd penn
mv ../simple-examples/data/ptb.train.txt train.txt
mv ../simple-examples/data/ptb.test.txt test.txt
mv ../simple-examples/data/ptb.valid.txt valid.txt
cd ..
echo "- Downloading Penn Treebank (Character)"
mkdir -p pennchar
cd pennchar
mv ../simple-examples/data/ptb.char.train.txt train.txt
mv ../simple-examples/data/ptb.char.test.txt test.txt
mv ../simple-examples/data/ptb.char.valid.txt valid.txt
cd ..
rm -rf simple-examples/
fi
echo "- Downloading 1B words"
if [[ ! -d 'one-billion-words' ]]; then
mkdir -p one-billion-words
cd one-billion-words
wget --no-proxy http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz
tar xzvf 1-billion-word-language-modeling-benchmark-r13output.tar.gz
path="1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/"
cat ${path}/news.en.heldout-00000-of-00050 > valid.txt
cat ${path}/news.en.heldout-00000-of-00050 > test.txt
wget https://github.com/rafaljozefowicz/lm/raw/master/1b_word_vocab.txt
cd ..
fi
echo "---"
echo "Happy language modeling :)"
|
TensorFlow2/Recommendation/WideAndDeep/triton/runner | runner | exporter | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pathlib
# method from PEP-366 to support relative import in executed modules
if __name__ == "__main__" and __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from .core import Command
from .exceptions import RunnerException
from .stages import Stage
class CommandsExporter:
"""
Command exported to BASH scripts
"""
def __init__(self, scripts_dir: pathlib.Path):
"""
Args:
scripts_dir: Paths where scripts should be stored
"""
self._scripts_dir = scripts_dir
def export(self, stage: Stage) -> Command:
"""
Export stage commands to script and return new command to execute
Args:
stage: Stage object with commands
Returns:
Command object with script execution command
"""
filename = self._get_filename(stage.label)
file_path = self._scripts_dir / filename
with open(file_path, "w+") as stagefile:
stagefile.write("set -x\n")
stagefile.write("set -e\n")
stagefile.write("export PYTHONUNBUFFERED=1\n")
stagefile.write("export PYTHONPATH=`pwd`\n")
for command in stage.commands:
for line in str(command).split("\n"):
stagefile.write(str(line.rstrip()))
stagefile.write("\n")
stagefile.write("\n")
result = os.system(f'ex +"set syn=sh" +"norm gg=G" -cwq {file_path}')
if result != 0:
raise RunnerException(f"Failed running {filename} script formatting. Exit code {result}")
command = Command(f"bash -xe {file_path.as_posix()}")
return command
def _get_filename(self, label: str):
"""
Generate filename for script based on label
Args:
label: String with stage label
Returns:
String with script filename
"""
filename = label.replace(" ", "_").lower()
filename = f"{filename}.sh"
return filename
|
TensorFlow2/Recommendation/WideAndDeep/triton | triton | tf_dataloader | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
import numpy as np
import pandas as pd
import tensorflow as tf
from data.outbrain.features import get_features_keys, MULTIHOT_COLUMNS
def prepare_df(df):
for multihot_key, value in MULTIHOT_COLUMNS.items():
multihot_col = df.pop(multihot_key)
for i in range(value):
df[f"{multihot_key}_{i}"] = multihot_col.apply(
lambda x: x[i] if len(x) > i else -1
)
df[f"{multihot_key}_nnzs"] = multihot_col.apply(lambda x: len(x))
for col in df.columns:
if np.issubdtype(df[col].dtype, np.integer):
df[col] = df[col].astype(np.int32)
if np.issubdtype(df[col].dtype, np.floating):
df[col] = df[col].astype(np.float32)
return df
def _merge_multihots(*multihots, axis=1):
expanded = [tf.expand_dims(multihot, axis) for multihot in multihots]
concatenated = tf.concat(expanded, axis)
reshaped = tf.reshape(concatenated, [-1])
mask = tf.math.not_equal(reshaped, -1)
filtered = tf.boolean_mask(reshaped, mask)
return tf.reshape(filtered, [-1, 1])
def _filter_batch(elem):
label = elem.pop("clicked")
label = tf.reshape(label, [-1, 1])
disp_id = elem.pop("display_id")
for multihot_key, value in MULTIHOT_COLUMNS.items():
multihot_values = [elem.pop(f"{multihot_key}_{i}") for i in range(value)]
multihot_nnzs = elem.pop(f"{multihot_key}_nnzs")
values = _merge_multihots(*multihot_values)
row_lengths = multihot_nnzs
values = tf.reshape(values, [-1])
row_lengths = tf.reshape(row_lengths, [-1])
x = tf.RaggedTensor.from_row_lengths(
values, row_lengths, validate=False
).to_tensor(default_value=-1, shape=[None, value])
elem[f"{multihot_key}"] = x
features = get_features_keys()
elem = {
key: (
tf.reshape(value, [-1, 1])
if "list" not in key
else tf.reshape(value, [-1, MULTIHOT_COLUMNS[key]])
)
for key, value in elem.items()
if key in features or "list" in key
}
return elem, label, disp_id
def eval_input_fn(files_path, records_batch_size):
frames = []
for file in files_path:
frames.append(pd.read_parquet(file))
if len(frames) > 1:
df = pd.concat(frames)
else:
df = frames[0]
full_df = prepare_df(df)
dataset = tf.data.Dataset.from_tensor_slices(dict(full_df))
dataset = dataset.batch(batch_size=records_batch_size, drop_remainder=False)
dataset = dataset.map(map_func=partial(_filter_batch), num_parallel_calls=None)
dataset = dataset.prefetch(buffer_size=2)
return dataset
|
TensorFlow/Segmentation/UNet_Medical/examples | examples | unet_8GPU | # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script launches U-Net run in FP32 on 8 GPUs and trains for 6400 iterations with batch_size 8. Usage:
# bash unet_FP32_8GPU.sh <path to dataset> <path to results directory>
horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size 8 --exec_mode train_and_evaluate --crossvalidation_idx 0 --augment --xla |
TensorFlow2/LanguageModeling/BERT/official/utils/data | data | file_io | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Convenience functions for managing dataset file buffers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import atexit
import multiprocessing
import os
import tempfile
import uuid
import numpy as np
import six
import tensorflow as tf
class _GarbageCollector(object):
"""Deletes temporary buffer files at exit.
Certain tasks (such as NCF Recommendation) require writing buffers to
temporary files. (Which may be local or distributed.) It is not generally safe
to delete these files during operation, but they should be cleaned up. This
class keeps track of temporary files created, and deletes them at exit.
"""
def __init__(self):
self.temp_buffers = []
def register(self, filepath):
self.temp_buffers.append(filepath)
def purge(self):
try:
for i in self.temp_buffers:
if tf.io.gfile.exists(i):
tf.io.gfile.remove(i)
tf.compat.v1.logging.info("Buffer file {} removed".format(i))
except Exception as e:
tf.compat.v1.logging.error("Failed to cleanup buffer files: {}".format(e))
_GARBAGE_COLLECTOR = _GarbageCollector()
atexit.register(_GARBAGE_COLLECTOR.purge)
_ROWS_PER_CORE = 50000
def write_to_temp_buffer(dataframe, buffer_folder, columns):
if buffer_folder is None:
_, buffer_path = tempfile.mkstemp()
else:
tf.io.gfile.makedirs(buffer_folder)
buffer_path = os.path.join(buffer_folder, str(uuid.uuid4()))
_GARBAGE_COLLECTOR.register(buffer_path)
return write_to_buffer(dataframe, buffer_path, columns)
def iter_shard_dataframe(df, rows_per_core=1000):
"""Two way shard of a dataframe.
This function evenly shards a dataframe so that it can be mapped efficiently.
It yields a list of dataframes with length equal to the number of CPU cores,
with each dataframe having rows_per_core rows. (Except for the last batch
which may have fewer rows in the dataframes.) Passing vectorized inputs to
a multiprocessing pool is much more effecient than iterating through a
dataframe in serial and passing a list of inputs to the pool.
Args:
df: Pandas dataframe to be sharded.
rows_per_core: Number of rows in each shard.
Returns:
A list of dataframe shards.
"""
n = len(df)
num_cores = min([multiprocessing.cpu_count(), n])
num_blocks = int(np.ceil(n / num_cores / rows_per_core))
max_batch_size = num_cores * rows_per_core
for i in range(num_blocks):
min_index = i * max_batch_size
max_index = min([(i + 1) * max_batch_size, n])
df_shard = df[min_index:max_index]
n_shard = len(df_shard)
boundaries = np.linspace(0, n_shard, num_cores + 1, dtype=np.int64)
yield [df_shard[boundaries[j]:boundaries[j+1]] for j in range(num_cores)]
def _shard_dict_to_examples(shard_dict):
"""Converts a dict of arrays into a list of example bytes."""
n = [i for i in shard_dict.values()][0].shape[0]
feature_list = [{} for _ in range(n)]
for column, values in shard_dict.items():
if len(values.shape) == 1:
values = np.reshape(values, values.shape + (1,))
if values.dtype.kind == "i":
feature_map = lambda x: tf.train.Feature(
int64_list=tf.train.Int64List(value=x))
elif values.dtype.kind == "f":
feature_map = lambda x: tf.train.Feature(
float_list=tf.train.FloatList(value=x))
else:
raise ValueError("Invalid dtype")
for i in range(n):
feature_list[i][column] = feature_map(values[i])
examples = [
tf.train.Example(features=tf.train.Features(feature=example_features))
for example_features in feature_list
]
return [e.SerializeToString() for e in examples]
def _serialize_shards(df_shards, columns, pool, writer):
"""Map sharded dataframes to bytes, and write them to a buffer.
Args:
df_shards: A list of pandas dataframes. (Should be of similar size)
columns: The dataframe columns to be serialized.
pool: A multiprocessing pool to serialize in parallel.
writer: A TFRecordWriter to write the serialized shards.
"""
# Pandas does not store columns of arrays as nd arrays. stack remedies this.
map_inputs = [{c: np.stack(shard[c].values, axis=0) for c in columns}
for shard in df_shards]
# Failure within pools is very irksome. Thus, it is better to thoroughly check
# inputs in the main process.
for inp in map_inputs:
# Check that all fields have the same number of rows.
assert len(set([v.shape[0] for v in inp.values()])) == 1
for val in inp.values():
assert hasattr(val, "dtype")
assert hasattr(val.dtype, "kind")
assert val.dtype.kind in ("i", "f")
assert len(val.shape) in (1, 2)
shard_bytes = pool.map(_shard_dict_to_examples, map_inputs)
for s in shard_bytes:
for example in s:
writer.write(example)
def write_to_buffer(dataframe, buffer_path, columns, expected_size=None):
"""Write a dataframe to a binary file for a dataset to consume.
Args:
dataframe: The pandas dataframe to be serialized.
buffer_path: The path where the serialized results will be written.
columns: The dataframe columns to be serialized.
expected_size: The size in bytes of the serialized results. This is used to
lazily construct the buffer.
Returns:
The path of the buffer.
"""
if (tf.io.gfile.exists(buffer_path) and
tf.io.gfile.stat(buffer_path).length > 0):
actual_size = tf.io.gfile.stat(buffer_path).length
if expected_size == actual_size:
return buffer_path
tf.compat.v1.logging.warning(
"Existing buffer {} has size {}. Expected size {}. Deleting and "
"rebuilding buffer.".format(buffer_path, actual_size, expected_size))
tf.io.gfile.remove(buffer_path)
if dataframe is None:
raise ValueError(
"dataframe was None but a valid existing buffer was not found.")
tf.io.gfile.makedirs(os.path.split(buffer_path)[0])
tf.compat.v1.logging.info("Constructing TFRecordDataset buffer: {}"
.format(buffer_path))
count = 0
pool = multiprocessing.Pool(multiprocessing.cpu_count())
try:
with tf.io.TFRecordWriter(buffer_path) as writer:
for df_shards in iter_shard_dataframe(df=dataframe,
rows_per_core=_ROWS_PER_CORE):
_serialize_shards(df_shards, columns, pool, writer)
count += sum([len(s) for s in df_shards])
tf.compat.v1.logging.info("{}/{} examples written."
.format(str(count).ljust(8), len(dataframe)))
finally:
pool.terminate()
tf.compat.v1.logging.info("Buffer write complete.")
return buffer_path
|
PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/backbone | backbone | resnet | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""
Variant of the resnet module that takes cfg as an argument.
Example usage. Strings may be specified in the config file.
model = ResNet(
"StemWithFixedBatchNorm",
"BottleneckWithFixedBatchNorm",
"ResNet50StagesTo4",
)
OR:
model = ResNet(
"StemWithGN",
"BottleneckWithGN",
"ResNet50StagesTo4",
)
Custom implementations may be written in user code and hooked in via the
`register_*` functions.
"""
from collections import namedtuple
import torch
import torch.nn.functional as F
from torch import nn
from maskrcnn_benchmark.layers import FrozenBatchNorm2d
from maskrcnn_benchmark.layers import Conv2d
from maskrcnn_benchmark.modeling.make_layers import group_norm
from maskrcnn_benchmark.utils.registry import Registry
# ResNet stage specification
StageSpec = namedtuple(
"StageSpec",
[
"index", # Index of the stage, eg 1, 2, ..,. 5
"block_count", # Numer of residual blocks in the stage
"return_features", # True => return the last feature map from this stage
],
)
# -----------------------------------------------------------------------------
# Standard ResNet models
# -----------------------------------------------------------------------------
# ResNet-50 (including all stages)
ResNet50StagesTo5 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, False), (4, 3, True))
)
# ResNet-50 up to stage 4 (excludes stage 5)
ResNet50StagesTo4 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, True))
)
# ResNet-101 (including all stages)
ResNet101StagesTo5 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, False), (4, 3, True))
)
# ResNet-101 up to stage 4 (excludes stage 5)
ResNet101StagesTo4 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, True))
)
# ResNet-50-FPN (including all stages)
ResNet50FPNStagesTo5 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 6, True), (4, 3, True))
)
# ResNet-101-FPN (including all stages)
ResNet101FPNStagesTo5 = tuple(
StageSpec(index=i, block_count=c, return_features=r)
for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 23, True), (4, 3, True))
)
class ResNet(nn.Module):
def __init__(self, cfg):
super(ResNet, self).__init__()
# If we want to use the cfg in forward(), then we should make a copy
# of it and store it for later use:
# self.cfg = cfg.clone()
# Translate string names to implementations
stem_module = _STEM_MODULES[cfg.MODEL.RESNETS.STEM_FUNC]
stage_specs = _STAGE_SPECS[cfg.MODEL.BACKBONE.CONV_BODY]
transformation_module = _TRANSFORMATION_MODULES[cfg.MODEL.RESNETS.TRANS_FUNC]
# Construct the stem module
self.stem = stem_module(cfg)
# Constuct the specified ResNet stages
num_groups = cfg.MODEL.RESNETS.NUM_GROUPS
width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
stage2_bottleneck_channels = num_groups * width_per_group
stage2_out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
self.stages = []
self.return_features = {}
for stage_spec in stage_specs:
name = "layer" + str(stage_spec.index)
stage2_relative_factor = 2 ** (stage_spec.index - 1)
bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
out_channels = stage2_out_channels * stage2_relative_factor
module = _make_stage(
transformation_module,
in_channels,
bottleneck_channels,
out_channels,
stage_spec.block_count,
num_groups,
cfg.MODEL.RESNETS.STRIDE_IN_1X1,
first_stride=int(stage_spec.index > 1) + 1,
)
in_channels = out_channels
self.add_module(name, module)
self.stages.append(name)
self.return_features[name] = stage_spec.return_features
# Optionally freeze (requires_grad=False) parts of the backbone
self._freeze_backbone(cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT)
def _freeze_backbone(self, freeze_at):
if freeze_at < 0:
return
for stage_index in range(freeze_at):
if stage_index == 0:
m = self.stem # stage 0 is the stem
else:
m = getattr(self, "layer" + str(stage_index))
for p in m.parameters():
p.requires_grad = False
def forward(self, x):
outputs = []
x = self.stem(x)
for stage_name in self.stages:
x = getattr(self, stage_name)(x)
if self.return_features[stage_name]:
outputs.append(x)
return outputs
class ResNetHead(nn.Module):
def __init__(
self,
block_module,
stages,
num_groups=1,
width_per_group=64,
stride_in_1x1=True,
stride_init=None,
res2_out_channels=256,
dilation=1
):
super(ResNetHead, self).__init__()
stage2_relative_factor = 2 ** (stages[0].index - 1)
stage2_bottleneck_channels = num_groups * width_per_group
out_channels = res2_out_channels * stage2_relative_factor
in_channels = out_channels // 2
bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
block_module = _TRANSFORMATION_MODULES[block_module]
self.stages = []
stride = stride_init
for stage in stages:
name = "layer" + str(stage.index)
if not stride:
stride = int(stage.index > 1) + 1
module = _make_stage(
block_module,
in_channels,
bottleneck_channels,
out_channels,
stage.block_count,
num_groups,
stride_in_1x1,
first_stride=stride,
dilation=dilation
)
stride = None
self.add_module(name, module)
self.stages.append(name)
def forward(self, x):
for stage in self.stages:
x = getattr(self, stage)(x)
return x
def _make_stage(
transformation_module,
in_channels,
bottleneck_channels,
out_channels,
block_count,
num_groups,
stride_in_1x1,
first_stride,
dilation=1
):
blocks = []
stride = first_stride
for _ in range(block_count):
blocks.append(
transformation_module(
in_channels,
bottleneck_channels,
out_channels,
num_groups,
stride_in_1x1,
stride,
dilation=dilation
)
)
stride = 1
in_channels = out_channels
return nn.Sequential(*blocks)
class Bottleneck(nn.Module):
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups,
stride_in_1x1,
stride,
dilation,
norm_func
):
super(Bottleneck, self).__init__()
self.downsample = None
if in_channels != out_channels:
down_stride = stride if dilation == 1 else 1
self.downsample = nn.Sequential(
Conv2d(
in_channels, out_channels,
kernel_size=1, stride=down_stride, bias=False
),
norm_func(out_channels),
)
for modules in [self.downsample,]:
for l in modules.modules():
if isinstance(l, Conv2d):
nn.init.kaiming_uniform_(l.weight, a=1)
if dilation > 1:
stride = 1 # reset to be 1
# The original MSRA ResNet models have stride in the first 1x1 conv
# The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have
# stride in the 3x3 conv
stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
self.conv1 = Conv2d(
in_channels,
bottleneck_channels,
kernel_size=1,
stride=stride_1x1,
bias=False,
)
self.bn1 = norm_func(bottleneck_channels)
# TODO: specify init for the above
self.conv2 = Conv2d(
bottleneck_channels,
bottleneck_channels,
kernel_size=3,
stride=stride_3x3,
padding=dilation,
bias=False,
groups=num_groups,
dilation=dilation
)
self.bn2 = norm_func(bottleneck_channels)
self.conv3 = Conv2d(
bottleneck_channels, out_channels, kernel_size=1, bias=False
)
self.bn3 = norm_func(out_channels)
for l in [self.conv1, self.conv2, self.conv3,]:
nn.init.kaiming_uniform_(l.weight, a=1)
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = F.relu_(out)
out = self.conv2(out)
out = self.bn2(out)
out = F.relu_(out)
out0 = self.conv3(out)
out = self.bn3(out0)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = F.relu_(out)
return out
class BaseStem(nn.Module):
def __init__(self, cfg, norm_func):
super(BaseStem, self).__init__()
out_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
self.conv1 = Conv2d(
3, out_channels, kernel_size=7, stride=2, padding=3, bias=False
)
self.bn1 = norm_func(out_channels)
for l in [self.conv1,]:
nn.init.kaiming_uniform_(l.weight, a=1)
def forward(self, x):
x = self.conv1(x.float())
x = self.bn1(x)
x = F.relu_(x)
x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
return x
class BottleneckWithFixedBatchNorm(Bottleneck):
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1
):
super(BottleneckWithFixedBatchNorm, self).__init__(
in_channels=in_channels,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
stride_in_1x1=stride_in_1x1,
stride=stride,
dilation=dilation,
norm_func=FrozenBatchNorm2d
)
class StemWithFixedBatchNorm(BaseStem):
def __init__(self, cfg):
super(StemWithFixedBatchNorm, self).__init__(
cfg, norm_func=FrozenBatchNorm2d
)
class BottleneckWithGN(Bottleneck):
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1
):
super(BottleneckWithGN, self).__init__(
in_channels=in_channels,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
stride_in_1x1=stride_in_1x1,
stride=stride,
dilation=dilation,
norm_func=group_norm
)
class StemWithGN(BaseStem):
def __init__(self, cfg):
super(StemWithGN, self).__init__(cfg, norm_func=group_norm)
_TRANSFORMATION_MODULES = Registry({
"BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm,
"BottleneckWithGN": BottleneckWithGN,
})
_STEM_MODULES = Registry({
"StemWithFixedBatchNorm": StemWithFixedBatchNorm,
"StemWithGN": StemWithGN,
})
_STAGE_SPECS = Registry({
"R-50-C4": ResNet50StagesTo4,
"R-50-C5": ResNet50StagesTo5,
"R-101-C4": ResNet101StagesTo4,
"R-101-C5": ResNet101StagesTo5,
"R-50-FPN": ResNet50FPNStagesTo5,
"R-101-FPN": ResNet101FPNStagesTo5,
})
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/util | util | pluginBuilder | /*
* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef TT2I_PLUGINBUILDER_H
#define TT2I_PLUGINBUILDER_H
#include "NvInfer.h"
#include "trtPtr.h"
#include <cstdint>
#include <memory>
#include <string>
#include <vector>
namespace tts
{
class PluginBuilder
{
public:
/**
* @brief Create a new PluginBuilder class.
*
* @param pluginName The name of the plugin.
* @param pluginVersion The version of the plugin.
*/
PluginBuilder(const std::string& pluginName, const std::string& pluginVersion);
// delete copy constructor and assignment operator
PluginBuilder(const PluginBuilder& other) = delete;
PluginBuilder& operator=(const PluginBuilder& other) = delete;
/**
* @brief Add a field to the plugin.
*
* @param name The name of the field.
* @param data The data for the field.
* @param length The length of the field.
*/
void setField(const std::string& name, const nvinfer1::Weights& weights);
/**
* @brief Add a scalar field to the plugin.
*
* @param name The name of the field.
* @param value The value of the field.
*/
void setField(const std::string& name, int32_t value);
/**
* @brief Add a scalar field to the plugin.
*
* @param name The name of the field.
* @param value The value of the field.
*/
void setField(const std::string& name, float value);
/**
* @brief Build the plugin instance.
*
* @param name The name of the instance.
*
* @return The instantiated plugin.
*/
TRTPtr<nvinfer1::IPluginV2> make(const std::string& name);
private:
union scalar_t
{
int32_t i;
float f;
scalar_t(const int32_t value)
: i(value)
{
}
scalar_t(const float value)
: f(value)
{
}
};
nvinfer1::IPluginCreator* mCreator;
std::vector<nvinfer1::PluginField> mFields;
// use a set of unique_ptr's, so that as the array is expanded and
// reallocated, the memory addresses of the scalars do not change.
std::vector<std::unique_ptr<std::string>> mNames;
std::vector<std::unique_ptr<scalar_t>> mScalars;
/**
* @brief Set a field to the plugin.
*
* @param field The field to set.
*/
void setField(const nvinfer1::PluginField& field);
};
} // namespace tts
#endif
|
PyTorch/SpeechSynthesis/FastPitch/triton/scripts | scripts | process_dataset | #!/usr/bin/env bash
# Copyright (c) 2021 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
DATASET_DIR="${DATASETS_DIR}/LJSpeech-1.1/LJSpeech-1.1_fastpitch"
: ${F0_METHOD:="pyin"}
: ${ARGS="--extract-mels"}
if [ ! -d "${DATASET_DIR}/mels" ]; then
python prepare_dataset.py \
--wav-text-filelists filelists/ljs_audio_text_val.txt \
--n-workers 16 \
--batch-size 1 \
--dataset-path $DATASET_DIR \
--extract-pitch \
--f0-method $F0_METHOD \
$ARGS
fi
|
Tools/PyTorch/TimeSeriesPredictionPlatform/loggers | loggers | log_helper | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import json
import pandas as pd
import dllogger
from dllogger import JSONStreamBackend, Logger, StdOutBackend
from .backends import AggregatorBackend, TensorBoardBackend, AverageMeter
from omegaconf import OmegaConf
from distributed_utils import is_main_process
def jsonlog_2_df(path, keys):
with open(path, 'r') as f:
log = [json.loads(l[4:]) for l in f.readlines()]
log = [l for l in log if l['type'] == 'LOG' and isinstance(l['step'], (int, list))]
assert log[-1]['step'] == [], "Logfile is corrupted"
log[-1]['step']=log[-2]['step'] # Every log ends with step == []
log = [
{
**{k:v for k,v in l.items() if not isinstance(v, dict)},
**(l['data'] if 'data' in l else {}),
'timestamp':float(l['timestamp'])*1000
}
for l in log
]
log = [{k:v for k,v in l.items() if k in keys} for l in log]
df = pd.DataFrame(log)
df = df.groupby('step').mean()
return df
def empty_step_format(step):
return ""
def empty_prefix_format(timestamp):
return ""
def no_string_metric_format(metric, metadata, value):
unit = metadata["unit"] if "unit" in metadata.keys() else ""
format = "{" + metadata["format"] + "}" if "format" in metadata.keys() else "{}"
if metric == "String":
return "{} {}".format(format.format(value) if value is not None else value, unit)
return "{} : {} {}".format(metric, format.format(value) if value is not None else value, unit)
def setup_logger(config, resume_training=False):
log_filename = config.get("log_filename", "log.json")
if is_main_process():
backends = [
TensorBoardBackend(verbosity=dllogger.Verbosity.VERBOSE),
JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE, filename=log_filename, append=True),
AggregatorBackend(verbosity=dllogger.Verbosity.VERBOSE, agg_dict={"loss": AverageMeter}),
StdOutBackend(
verbosity=dllogger.Verbosity.DEFAULT,
step_format=empty_step_format,
metric_format=no_string_metric_format,
prefix_format=empty_prefix_format,
),
]
logger = Logger(backends=backends)
else:
logger = Logger(backends=[])
container_setup_info = get_framework_env_vars()
logger.log(step="PARAMETER", data=container_setup_info, verbosity=dllogger.Verbosity.VERBOSE)
if not resume_training:
logger.metadata("loss", {"unit": "nat", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
logger.metadata("val_loss", {"unit": "nat", "GOAL": "MINIMIZE", "STAGE": "VAL"})
return logger
def restart_logger(config, logger):
"""An utility function to nealty close every backend holding resources"""
for b in logger.backends:
if hasattr(b, 'close'):
b.close()
return setup_logger(config, resume_training=True)
def log_parameters(logger, config):
model_config = flatten_config(config.model)
trainer_config = flatten_config(config.trainer)
additional_fields = {'seed': config.seed}
logger.log(step="PARAMETER", data={**model_config, **trainer_config, **additional_fields}, verbosity=dllogger.Verbosity.VERBOSE)
def flatten_config(config):
config = OmegaConf.to_container(config, resolve=True)
if '_target_' in config:
del config['_target_']
if 'config' in config:
c = config['config']
config = {**c, **config}
del config['config']
config = pd.json_normalize(config, sep='.')
config = config.to_dict(orient='records')[0]
return config
def get_framework_env_vars():
return {
"NVIDIA_PYTORCH_VERSION": os.environ.get("NVIDIA_PYTORCH_VERSION"),
"PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION"),
"CUBLAS_VERSION": os.environ.get("CUBLAS_VERSION"),
"NCCL_VERSION": os.environ.get("NCCL_VERSION"),
"CUDA_DRIVER_VERSION": os.environ.get("CUDA_DRIVER_VERSION"),
"CUDNN_VERSION": os.environ.get("CUDNN_VERSION"),
"CUDA_VERSION": os.environ.get("CUDA_VERSION"),
"NVIDIA_PIPELINE_ID": os.environ.get("NVIDIA_PIPELINE_ID"),
"NVIDIA_BUILD_ID": os.environ.get("NVIDIA_BUILD_ID"),
"NVIDIA_TF32_OVERRIDE": os.environ.get("NVIDIA_TF32_OVERRIDE"),
}
|
TensorFlow2/Recommendation/DLRM_and_DCNv2/tensorflow-dot-based-interact/tensorflow_dot_based_interact/python | python | __init__ | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
|
TensorFlow2/Classification/ConvNets | ConvNets | README | This repository supports
- EfficientNet v1-B0 and v1-B4. Please see [here](efficientnet_v1/README.md) for the complete guide.
- EfficientNet v2-S. Please see [here](efficientnet_v2/README.md) for the complete guide.
|
PyTorch/Translation/Transformer/fairseq/modules/strided_batched_gemm | strided_batched_gemm | strided_batched_gemm_cuda | #pragma once
#include <iostream>
#include <vector>
#include <cuda.h>
#include <cuda_fp16.h>
#include <cuda_profiler_api.h>
#include <cuda_runtime.h>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/Exceptions.h>
#include "cutlass/cutlass.h"
#include "cutlass/gemm/gemm.h"
#include "cutlass/gemm/wmma_gemm_traits.h"
namespace {
cublasOperation_t convertTransToCublasOperation(char trans) {
if (trans == 't')
return CUBLAS_OP_T;
else if (trans == 'n')
return CUBLAS_OP_N;
else if (trans == 'c')
return CUBLAS_OP_C;
else {
AT_ERROR("trans must be one of: t, n, c");
return CUBLAS_OP_T;
}
}
void CublasStridedBatchedGemm(
char transa, char transb, long m, long n, long k,
float alpha, const half *a, long lda, long strideA, const half *b, long ldb,
long strideB, float beta, half *c, long ldc, long strideC, long batchCount,
cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP) {
cublasOperation_t opa = convertTransToCublasOperation(transa);
cublasOperation_t opb = convertTransToCublasOperation(transb);
cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
cublasSetStream(handle, stream);
float fAlpha = alpha;
float fBeta = beta;
// THCublasCheck(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
TORCH_CUDABLAS_CHECK(cublasGemmStridedBatchedEx(
handle, opa, opb, (int)m, (int)n, (int)k, (void *)&fAlpha, a, CUDA_R_16F,
(int)lda, strideA, b, CUDA_R_16F, (int)ldb, strideB, (void *)&fBeta, c,
CUDA_R_16F, (int)ldc, strideC, (int)batchCount, CUDA_R_32F, algo));
// THCublasCheck(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
}
} // namespace
template <cutlass::MatrixLayout::Kind A_LAYOUT,
cutlass::MatrixLayout::Kind B_LAYOUT, int SRC_A, int SRC_B, int DST_C>
void CutlassGemm_FP32Accum(cudaStream_t stream, long m, long n, long k,
float alpha, const half *a, long lda, long strideA,
const half *b, long ldb, long strideB, float beta,
half *c, long ldc, long strideC, long batchCount) {
// printf("CUTLASS-> %c%c M: %ld N: %ld K: %ld %d%d%d LDA: %ld LDB: %ld LDC:
// %ld strideA: %ld strideB: %ld strideC: %ld Alpha: %f Beta: %f\n",
// ((int)A_LAYOUT == 0 ? 'T' : 'N'), ((int)B_LAYOUT ==0 ? 'T' : 'N'), m, n, k,
// SRC_A,SRC_B,DST_C, lda, ldb, ldc, strideA, strideB, strideC, alpha, beta);
typedef cutlass::gemm::WmmaGemmTraits<
A_LAYOUT, B_LAYOUT, cutlass::Shape<32, 16, 16>, half, half, half,
cutlass::gemm::LinearScaling<float>, float,
typename cutlass::gemm::WmmaGemmAccumulatorsPerWarp<
typename cutlass::Shape<32, 16, 16>>::Shape,
typename cutlass::Shape<16, 16, 16>,
SRC_A, // kScalarsPerLdgA_
SRC_B, // kScalarsPerLdgB_
SRC_A, // KScalarsPerLdsA_
SRC_B, // KScalarsPerLdsB_
DST_C, // kScalarsPerLdgCAndStgD_
DST_C / 2, // kScalarsPerStsD_
DST_C / 2 // kScalarsPerLdsD_
>
WmmaGemmTraits;
typedef cutlass::gemm::Gemm<WmmaGemmTraits> Gemm;
typename Gemm::Params params;
int result = params.initialize(
m, // M dimension for each batch
n, // N dimension for each batch
k, // K dimension for each batch
alpha, // scalar alpha
a, lda,
strideA, // distance in memory between the first element of neighboring
// batch
b, ldb,
strideB, // distance in memory between the first element of neighboring
// batch
beta, // scalar beta
c, // source matrix C
ldc,
strideC, // distance in memory between the first element of neighboring
// batch
c, // destination matrix C (may be different memory than source C matrix)
ldc,
strideC, // distance in memory between the first element of neighboring
// batch
batchCount);
AT_ASSERTM(result == 0, "Failed to initialize CUTLASS Gemm::Params object.");
// batchCount in cutlass batched GEMM kernels maps to gridDim.z, which is
// limited to 16 bits. To implement batched GEMM with larger batch size, we
// fragment it into smaller batched GEMMs of gridDim.z <= 64k
long batchesLeft = batchCount;
long iterBatchCount = std::min(batchesLeft, static_cast<long>((1 << 16) - 1));
do {
// printf("CUTLASS-> %c%c M: %ld N: %ld K: %ld %d%d%d LDA: %ld LDB: %ld LDC:
// %ld strideA: %ld strideB: %ld strideC: %ld Alpha: %f Beta: %f
// TotalBatches: %ld iterBatchCount %ld\n", ((int)A_LAYOUT == 0 ? 'T' : 'N'),
// ((int)B_LAYOUT ==0 ? 'T' : 'N'), m, n, k, SRC_A,SRC_B,DST_C, lda, ldb,
// ldc, strideA, strideB, strideC, alpha, beta, batchesLeft, iterBatchCount);
int result =
params.initialize(m, // M dimension for each batch
n, // N dimension for each batch
k, // K dimension for each batch
alpha, // scalar alpha
a, lda,
strideA, // distance in memory between the first
// element of neighboring batch
b, ldb,
strideB, // distance in memory between the first
// element of neighboring batch
beta, // scalar beta
c, // source matrix C
ldc,
strideC, // distance in memory between the first
// element of neighboring batch
c, // destination matrix C (may be different memory
// than source C matrix)
ldc,
strideC, // distance in memory between the first
// element of neighboring batch
iterBatchCount);
AT_ASSERTM(result == 0,
"Failed to initialize CUTLASS Gemm::Params object.");
// Launch the CUTLASS GEMM kernel.
C10_CUDA_CHECK(Gemm::launch(params, stream));
// Update batched GEMM params based on completed work
batchesLeft = batchesLeft - iterBatchCount;
a += iterBatchCount * strideA;
b += iterBatchCount * strideB;
c += iterBatchCount * strideC;
;
iterBatchCount = std::min(batchesLeft, static_cast<long>((1 << 16) - 1));
} while (batchesLeft > 0);
}
namespace {
void gemm_switch_fp32accum(char transa, char transb, long m,
long n, long k, float alpha, const half *a, long lda,
long strideA, const half *b, long ldb, long strideB,
float beta, half *c, long ldc, long strideC,
long batchCount) {
auto stream = c10::cuda::getCurrentCUDAStream();
// printf("GEMM -> %c%c M: %i N: %i K: %i Alpha: %f Beta: %f\n", (transa ==
// 't' ? 'T' : 'N'), (transb =='t' ? 'T' : 'N'), m, n, k, alpha, beta);
if ((transa == 't') && (transb == 'n')) {
if (!(lda & 0x7) && !(ldb & 0x7) && !(ldc & 0x7)) {
CublasStridedBatchedGemm(transa, transb, m, n, k, alpha, a, lda,
strideA, b, ldb, strideB, beta, c, ldc, strideC,
batchCount, CUBLAS_GEMM_ALGO0_TENSOR_OP);
}
else if (!(lda & 0x7) && !(ldb & 0x7) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 8, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x7) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 8, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x3) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 4, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x3) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 4, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x3) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 4, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x1) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 2, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x1) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 2, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x1) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 2, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x7) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 8, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x7) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 8, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x7) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 8, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x3) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 4, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x3) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 4, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x3) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 4, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x1) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 2, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x1) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 2, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x1) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 2, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x7) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 8, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x7) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 8, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x7) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 8, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x3) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 4, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x3) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 4, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x3) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 4, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x1) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 2, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x1) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 2, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x1) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kRowMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 2, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else {
CublasStridedBatchedGemm(transa, transb, m, n, k, alpha, a, lda,
strideA, b, ldb, strideB, beta, c, ldc, strideC,
batchCount);
}
} else if ((transa == 'n') && (transb == 'n')) {
if (!(lda & 0x7) && !(ldb & 0x7) && !(ldc & 0x7)) {
CublasStridedBatchedGemm(transa, transb, m, n, k, alpha, a, lda,
strideA, b, ldb, strideB, beta, c, ldc, strideC,
batchCount, CUBLAS_GEMM_ALGO0_TENSOR_OP);
}
else if (!(lda & 0x7) && !(ldb & 0x7) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 8, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x7) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 8, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x3) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 4, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x3) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 4, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x3) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 4, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x1) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 2, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x1) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 2, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x1) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 8, 2, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x7) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 8, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x7) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 8, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x7) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 8, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x3) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 4, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x3) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 4, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x3) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 4, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x1) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 2, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x1) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 2, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x1) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 4, 2, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x7) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 8, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x7) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 8, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x7) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 8, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x3) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 4, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x3) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 4, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x3) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 4, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x1) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 2, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x1) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 2, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x1) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kColumnMajor, 2, 2, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else {
CublasStridedBatchedGemm(transa, transb, m, n, k, alpha, a, lda,
strideA, b, ldb, strideB, beta, c, ldc, strideC,
batchCount);
}
} else if ((transa == 'n') && (transb == 't')) {
if (!(lda & 0x7) && !(ldb & 0x7) && !(ldc & 0x7)) {
CublasStridedBatchedGemm(transa, transb, m, n, k, alpha, a, lda,
strideA, b, ldb, strideB, beta, c, ldc, strideC,
batchCount, CUBLAS_GEMM_ALGO0_TENSOR_OP);
}
else if (!(lda & 0x7) && !(ldb & 0x7) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 8, 8, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x7) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 8, 8, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x3) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 8, 4, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x3) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 8, 4, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x3) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 8, 4, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x1) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 8, 2, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x1) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 8, 2, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x7) && !(ldb & 0x1) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 8, 2, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x7) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 4, 8, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x7) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 4, 8, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x7) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 4, 8, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x3) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 4, 4, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x3) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 4, 4, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x1) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 4, 2, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x1) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 4, 2, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x3) && !(ldb & 0x1) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 4, 2, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x7) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 2, 8, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x7) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 2, 8, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x7) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 2, 8, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x3) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 2, 4, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x3) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 2, 4, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x3) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 2, 4, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x1) && !(ldc & 0x7)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 2, 2, 8>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x1) && !(ldc & 0x3)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 2, 2, 4>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else if (!(lda & 0x1) && !(ldb & 0x1) && !(ldc & 0x1)) {
CutlassGemm_FP32Accum<cutlass::MatrixLayout::kColumnMajor,
cutlass::MatrixLayout::kRowMajor, 2, 2, 2>(
stream, m, n, k, alpha, a, lda, strideA, b, ldb, strideB, beta, c,
ldc, strideC, batchCount);
} else {
CublasStridedBatchedGemm(transa, transb, m, n, k, alpha, a, lda,
strideA, b, ldb, strideB, beta, c, ldc, strideC,
batchCount);
}
} else {
AT_ASSERTM(false, "TransA and TransB are invalid");
}
}
void adjustLdLevel3(char transa, char transb, int64_t m, int64_t n, int64_t k,
int64_t *lda, int64_t *ldb, int64_t *ldc) {
int transa_ = ((transa == 't') || (transa == 'T'));
int transb_ = ((transb == 't') || (transb == 'T'));
// Note: leading dimensions generally are checked that they are > 0 and at
// least as big the result requires (even if the value won't be used).
if (n <= 1)
*ldc = std::max<int64_t>(m, 1);
if (transa_) {
if (m <= 1)
*lda = std::max<int64_t>(k, 1);
} else {
if (k <= 1)
*lda = std::max<int64_t>(m, 1);
}
if (transb_) {
if (k <= 1)
*ldb = std::max<int64_t>(n, 1);
} else {
if (n <= 1)
*ldb = std::max<int64_t>(k, 1);
}
}
void HgemmStridedBatched(char transa, char transb, long m,
long n, long k, float alpha, const half *a, long lda,
long strideA, const half *b, long ldb, long strideB,
float beta, half *c, long ldc, long strideC,
long batchCount) {
if ((m >= INT_MAX) || (n >= INT_MAX) || (k >= INT_MAX) || (lda >= INT_MAX) ||
(ldb >= INT_MAX) || (ldc >= INT_MAX) || (batchCount >= INT_MAX))
{
AT_ERROR("Cublas_SgemmStridedBatched only supports m, n, k, lda, ldb, ldc, "
"batchCount"
"with the bound [val] <= %d",
INT_MAX);
}
adjustLdLevel3(transa, transb, m, n, k, &lda, &ldb, &ldc);
gemm_switch_fp32accum(transa, transb, m, n, k, alpha, a, lda, strideA,
b, ldb, strideB, beta, c, ldc, strideC, batchCount);
}
} // namespace
at::Tensor strided_batched_gemm_cuda(
float beta,
at::Tensor in_result,
float alpha,
at::Tensor batch1,
at::Tensor batch2) {
bool transpose_result;
char transpose_batch1, transpose_batch2;
int64_t lda, ldb, ldc;
at::Tensor result, input1, input2;
if (in_result.stride(1) == 1)
{
transpose_result = false;
result = in_result;
ldc = result.stride(2);
}
else if (in_result.stride(2) == 1)
{
transpose_result = true;
at::Tensor swap = batch2;
batch2 = batch1;
batch1 = swap;
result = in_result;
ldc = result.stride(1);
} else {
AT_ASSERTM(false, "result should be contiguous");
}
if (batch1.stride(transpose_result ? 2 : 1) == 1 &&
batch1.stride(transpose_result ? 1 : 2) != 0) {
transpose_batch1 = 'n';
input1 = batch1;
lda = input1.stride(transpose_result ? 1 : 2);
} else if (batch1.stride(transpose_result ? 1 : 2) == 1 &&
batch1.stride(transpose_result ? 2 : 1) != 0) {
transpose_batch1 = 't';
input1 = batch1;
lda = input1.stride(transpose_result ? 2 : 1);
} else {
AT_ASSERTM(false, "input1 should be contiguous");
}
if (batch2.stride(transpose_result ? 2 : 1) == 1 &&
batch2.stride(transpose_result ? 1 : 2) != 0) {
transpose_batch2 = 'n';
input2 = batch2;
ldb = input2.stride(transpose_result ? 1 : 2);
} else if (batch2.stride(transpose_result ? 1 : 2) == 1 &&
batch2.stride(transpose_result ? 2 : 1) != 0) {
transpose_batch2 = 't';
input2 = batch2;
ldb = input2.stride(transpose_result ? 2 : 1);
} else {
AT_ASSERTM(false, "input2 should be contiguous");
}
int64_t num_batches = result.size(0);
HgemmStridedBatched(
transpose_batch1,
transpose_batch2,
result.size(transpose_result ? 2 : 1),
result.size(transpose_result ? 1 : 2),
input1.size(transpose_result ? 1 : 2),
alpha,
static_cast<const half*>(input1.data_ptr()), lda, input1.stride(0),
static_cast<const half*>(input2.data_ptr()), ldb, input2.stride(0),
beta,
static_cast<half*>(result.data_ptr()), ldc, result.stride(0),
num_batches);
return in_result;
}
|
PyTorch/LanguageModeling/Transformer-XL/pytorch | pytorch | mem_transformer | # Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.log_uniform_sampler import LogUniformSampler
from utils.log_uniform_sampler import sample_logits
from utils.proj_adaptive_softmax import ProjectedAdaptiveLogSoftmax
@torch.jit.script
def add_and_scale(tensor1, tensor2, alpha: float):
return alpha * (tensor1 + tensor2)
class PositionalEmbedding(nn.Module):
def __init__(self, demb):
super(PositionalEmbedding, self).__init__()
self.demb = demb
inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
self.register_buffer('inv_freq', inv_freq)
def forward(self, pos_seq, bsz=None):
sinusoid_inp = torch.ger(pos_seq, self.inv_freq)
pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1)
if bsz is not None:
return pos_emb[:, None, :].expand(-1, bsz, -1)
else:
return pos_emb[:, None, :]
class PositionwiseFF(nn.Module):
def __init__(self, d_model, d_inner, dropout, pre_lnorm=False):
super(PositionwiseFF, self).__init__()
self.d_model = d_model
self.d_inner = d_inner
self.dropout = dropout
self.CoreNet = nn.Sequential(
nn.Linear(d_model, d_inner), nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.Linear(d_inner, d_model),
nn.Dropout(dropout),
)
self.layer_norm = nn.LayerNorm(d_model)
self.pre_lnorm = pre_lnorm
def forward(self, inp):
if self.pre_lnorm:
# layer normalization + positionwise feed-forward
core_out = self.CoreNet(self.layer_norm(inp))
# residual connection
output = core_out + inp
else:
# positionwise feed-forward
core_out = self.CoreNet(inp)
# residual connection + layer normalization
output = self.layer_norm(inp + core_out)
return output
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
pre_lnorm=False):
super(MultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_head = d_head
self.dropout = dropout
self.q_net = nn.Linear(d_model, n_head * d_head, bias=False)
self.kv_net = nn.Linear(d_model, 2 * n_head * d_head, bias=False)
self.drop = nn.Dropout(dropout)
self.dropatt = nn.Dropout(dropatt)
self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
self.layer_norm = nn.LayerNorm(d_model)
self.scale = 1 / (d_head ** 0.5)
self.pre_lnorm = pre_lnorm
def forward(self, h, attn_mask=None, mems=None):
# multihead attention
# [hlen x bsz x n_head x d_head]
if mems is not None:
c = torch.cat([mems, h], 0)
else:
c = h
if self.pre_lnorm:
# layer normalization
c = self.layer_norm(c)
head_q = self.q_net(h)
head_k, head_v = torch.chunk(self.kv_net(c), 2, -1)
head_q = head_q.view(h.size(0), h.size(1), self.n_head, self.d_head)
head_k = head_k.view(c.size(0), c.size(1), self.n_head, self.d_head)
head_v = head_v.view(c.size(0), c.size(1), self.n_head, self.d_head)
# [bsz x n_head x qlen x klen]
attn_score = torch.einsum('ibnd,jbnd->bnij', head_q, head_k)
attn_score.mul_(self.scale)
if attn_mask is not None:
if attn_mask.dim() == 2:
attn_score.masked_fill_(attn_mask[None, None, :, :], -float('inf'))
elif attn_mask.dim() == 3:
attn_score.masked_fill_(attn_mask[:, None, :, :], -float('inf'))
# [bsz x qlen x klen x n_head]
attn_prob = F.softmax(attn_score, dim=3)
attn_prob = self.dropatt(attn_prob)
# [bsz x n_head x qlen x klen] * [klen x bsz x n_head x d_head] -> [qlen x bsz x n_head x d_head]
attn_vec = torch.einsum('bnij,jbnd->ibnd', attn_prob, head_v)
attn_vec = attn_vec.contiguous().view(
attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head)
# linear projection
attn_out = self.o_net(attn_vec)
attn_out = self.drop(attn_out)
if self.pre_lnorm:
# residual connection
output = h + attn_out
else:
# residual connection + layer normalization
output = self.layer_norm(h + attn_out)
return output
class RelMultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
tgt_len=None, ext_len=None, mem_len=None, pre_lnorm=False):
super(RelMultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_head = d_head
self.dropout = dropout
self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head, bias=False)
self.drop = nn.Dropout(dropout)
self.dropatt = nn.Dropout(dropatt)
self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
self.layer_norm = nn.LayerNorm(d_model)
self.scale = 1 / (d_head ** 0.5)
self.pre_lnorm = pre_lnorm
def _parallelogram_mask(self, h, w, left=False):
mask = torch.ones((h, w)).byte()
m = min(h, w)
mask[:m, :m] = torch.triu(mask[:m, :m])
mask[-m:, -m:] = torch.tril(mask[-m:, -m:])
if left:
return mask.bool()
else:
return mask.flip(0).bool()
def _shift(self, x, qlen, klen, mask, left=False):
if qlen > 1:
zero_pad = torch.zeros((x.size(0), qlen-1, x.size(2), x.size(3)),
device=x.device, dtype=x.dtype)
else:
zero_pad = torch.zeros(0, device=x.device, dtype=x.dtype)
if left:
mask = mask.flip(1)
x_padded = torch.cat([zero_pad, x], dim=1).expand(qlen, -1, -1, -1)
else:
x_padded = torch.cat([x, zero_pad], dim=1).expand(qlen, -1, -1, -1)
x = x_padded.masked_select(mask[:, :, None, None]) \
.view(qlen, klen, x.size(2), x.size(3))
return x
def _rel_shift(self, x, zero_triu=False):
zero_pad = torch.zeros((x.size(0), x.size(1), x.size(2), 1),
device=x.device, dtype=x.dtype)
x_padded = torch.cat([zero_pad, x], dim=3)
x_padded = x_padded.view(x.size(0), x.size(1), x.size(3) + 1, x.size(2))
x = x_padded.narrow(2, 1, x_padded.size(2) - 1).view_as(x)
if zero_triu:
ones = torch.ones((x.size(2), x.size(3)))
x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
return x
def forward(self, w, r, attn_mask=None, mems=None):
raise NotImplementedError
class RelPartialLearnableMultiHeadAttn(RelMultiHeadAttn):
def __init__(self, *args, **kwargs):
super(RelPartialLearnableMultiHeadAttn, self).__init__(*args, **kwargs)
self.r_net = nn.Linear(self.d_model, self.n_head * self.d_head, bias=False)
def forward(self, w, r, r_w_bias, r_r_bias, attn_mask=None, mems=None):
qlen, rlen, bsz = w.size(0), r.size(0), w.size(1)
if mems is not None:
cat = torch.cat([mems, w], 0)
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(cat))
else:
w_heads = self.qkv_net(cat)
r_head_k = self.r_net(r)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
w_head_q = w_head_q[-qlen:]
else:
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(w))
else:
w_heads = self.qkv_net(w)
r_head_k = self.r_net(r)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
klen = w_head_k.size(0)
w_head_q = w_head_q.view(qlen, bsz, self.n_head, self.d_head) # qlen x bsz x n_head x d_head
w_head_k = w_head_k.view(klen, bsz, self.n_head, self.d_head) # klen x bsz x n_head x d_head
w_head_v = w_head_v.view(klen, bsz, self.n_head, self.d_head) # klen x bsz x n_head x d_head
r_head_k = r_head_k.view(rlen, self.n_head, self.d_head) # qlen x n_head x d_head
# compute attention score
rw_head_q = w_head_q + r_w_bias # qlen x bsz x n_head x d_head
AC = torch.einsum('ibnd,jbnd->bnij', rw_head_q, w_head_k) # bsz x n_head x qlen x klen
rr_head_q = w_head_q + r_r_bias
BD = torch.einsum('ibnd,jnd->bnij', rr_head_q, r_head_k) # bsz x n_head x qlen x klen
BD = self._rel_shift(BD)
# [bsz x n_head x qlen x klen]
attn_score = add_and_scale(AC, BD, self.scale)
# compute attention probability
if attn_mask is not None:
if attn_mask.dim() == 2:
attn_score.masked_fill_(attn_mask[None, None, :, :], -float('inf'))
elif attn_mask.dim() == 3:
attn_score.masked_fill_(attn_mask[:, None, :, :], -float('inf'))
# [bsz x n_head x qlen x klen]
attn_prob = F.softmax(attn_score, dim=3)
attn_prob = self.dropatt(attn_prob)
# compute attention vector
attn_vec = torch.einsum('bnij,jbnd->ibnd', attn_prob, w_head_v)
# [qlen x bsz x n_head x d_head]
attn_vec = attn_vec.contiguous().view(
attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head)
# linear projection
attn_out = self.o_net(attn_vec)
attn_out = self.drop(attn_out)
if self.pre_lnorm:
# residual connection
output = w + attn_out
else:
# residual connection + layer normalization
output = self.layer_norm(w + attn_out)
return output
class RelLearnableMultiHeadAttn(RelMultiHeadAttn):
def __init__(self, *args, **kwargs):
super(RelLearnableMultiHeadAttn, self).__init__(*args, **kwargs)
def forward(self, w, r_emb, r_w_bias, r_bias, attn_mask=None, mems=None):
# r_emb: [klen, n_head, d_head], used for term B
# r_w_bias: [n_head, d_head], used for term C
# r_bias: [klen, n_head], used for term D
qlen, bsz = w.size(0), w.size(1)
if mems is not None:
cat = torch.cat([mems, w], 0)
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(cat))
else:
w_heads = self.qkv_net(cat)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
w_head_q = w_head_q[-qlen:]
else:
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(w))
else:
w_heads = self.qkv_net(w)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
klen = w_head_k.size(0)
w_head_q = w_head_q.view(qlen, bsz, self.n_head, self.d_head)
w_head_k = w_head_k.view(klen, bsz, self.n_head, self.d_head)
w_head_v = w_head_v.view(klen, bsz, self.n_head, self.d_head)
if klen > r_emb.size(0):
r_emb_pad = r_emb[0:1].expand(klen-r_emb.size(0), -1, -1)
r_emb = torch.cat([r_emb_pad, r_emb], 0)
r_bias_pad = r_bias[0:1].expand(klen-r_bias.size(0), -1)
r_bias = torch.cat([r_bias_pad, r_bias], 0)
else:
r_emb = r_emb[-klen:]
r_bias = r_bias[-klen:]
r_bias = r_bias.t()
# compute attention score
rw_head_q = w_head_q + r_w_bias[None] # qlen x bsz x n_head x d_head
AC = torch.einsum('ibnd,jbnd->bnij', rw_head_q, w_head_k) # bsz x n_head x qlen x klen
B_ = torch.einsum('ibnd,jnd->bnij', w_head_q, r_emb) # bsz x n_head x qlen x klen
D_ = r_bias[None, :, None, :] # 1 x n_head x 1 x klen
BD = self._rel_shift(B_ + D_)
# [bsz x qlen x klen x n_head]
attn_score = add_and_scale(AC, BD, self.scale)
# compute attention probability
if attn_mask is not None:
if attn_mask.dim() == 2:
attn_score.masked_fill_(attn_mask[None, None, :, :], -float('inf'))
elif attn_mask.dim() == 3:
attn_score.masked_fill_(attn_mask[:, None, :, :], -float('inf'))
# [bsz x n_head x qlen x klen]
attn_prob = F.softmax(attn_score, dim=3)
attn_prob = self.dropatt(attn_prob)
# compute attention vector
attn_vec = torch.einsum('bnij,jbnd->ibnd', attn_prob, w_head_v)
# [qlen x bsz x n_head x d_head]
attn_vec = attn_vec.contiguous().view(
attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head)
# linear projection
attn_out = self.o_net(attn_vec)
attn_out = self.drop(attn_out)
if self.pre_lnorm:
# residual connection
output = w + attn_out
else:
# residual connection + layer normalization
output = self.layer_norm(w + attn_out)
return output
class DecoderLayer(nn.Module):
def __init__(self, n_head, d_model, d_head, d_inner, dropout, **kwargs):
super(DecoderLayer, self).__init__()
self.dec_attn = MultiHeadAttn(n_head, d_model, d_head, dropout, **kwargs)
self.pos_ff = PositionwiseFF(d_model, d_inner, dropout,
pre_lnorm=kwargs.get('pre_lnorm'))
def forward(self, dec_inp, dec_attn_mask=None, mems=None):
output = self.dec_attn(dec_inp, attn_mask=dec_attn_mask,
mems=mems)
output = self.pos_ff(output)
return output
class RelLearnableDecoderLayer(nn.Module):
def __init__(self, n_head, d_model, d_head, d_inner, dropout,
**kwargs):
super(RelLearnableDecoderLayer, self).__init__()
self.dec_attn = RelLearnableMultiHeadAttn(n_head, d_model, d_head,
dropout, **kwargs)
self.pos_ff = PositionwiseFF(d_model, d_inner, dropout,
pre_lnorm=kwargs.get('pre_lnorm'))
def forward(self, dec_inp, r_emb, r_w_bias, r_bias, dec_attn_mask=None, mems=None):
output = self.dec_attn(dec_inp, r_emb, r_w_bias, r_bias,
attn_mask=dec_attn_mask,
mems=mems)
output = self.pos_ff(output)
return output
class RelPartialLearnableDecoderLayer(nn.Module):
def __init__(self, n_head, d_model, d_head, d_inner, dropout,
**kwargs):
super(RelPartialLearnableDecoderLayer, self).__init__()
self.dec_attn = RelPartialLearnableMultiHeadAttn(n_head, d_model,
d_head, dropout,
**kwargs)
self.pos_ff = PositionwiseFF(d_model, d_inner, dropout,
pre_lnorm=kwargs.get('pre_lnorm'))
def forward(self, dec_inp, r, r_w_bias, r_r_bias, dec_attn_mask=None, mems=None):
output = self.dec_attn(dec_inp, r, r_w_bias, r_r_bias,
attn_mask=dec_attn_mask,
mems=mems)
output = self.pos_ff(output)
return output
class AdaptiveEmbedding(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1,
sample_softmax=False):
super(AdaptiveEmbedding, self).__init__()
self.n_token = n_token
self.d_embed = d_embed
self.cutoffs = cutoffs + [n_token]
self.div_val = div_val
self.d_proj = d_proj
self.emb_scale = d_proj ** 0.5
self.cutoff_ends = [0] + self.cutoffs
self.emb_layers = nn.ModuleList()
self.emb_projs = nn.ParameterList()
if div_val == 1:
self.emb_layers.append(
nn.Embedding(n_token, d_embed, sparse=(sample_softmax > 0))
)
if d_proj != d_embed:
self.emb_projs.append(nn.Parameter(torch.Tensor(d_proj, d_embed).zero_()))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i+1]
d_emb_i = d_embed // (div_val ** i)
self.emb_layers.append(nn.Embedding(r_idx-l_idx, d_emb_i))
self.emb_projs.append(nn.Parameter(torch.Tensor(d_proj, d_emb_i).zero_()))
def forward(self, inp):
if self.div_val == 1:
embed = self.emb_layers[0](inp)
if self.d_proj != self.d_embed:
embed = F.linear(embed, self.emb_projs[0])
else:
param = next(self.parameters())
inp_flat = inp.view(-1)
emb_flat = torch.zeros([inp_flat.size(0), self.d_proj],
dtype=param.dtype, device=param.device)
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
mask_i = (inp_flat >= l_idx) & (inp_flat < r_idx)
indices_i = mask_i.nonzero(as_tuple=False).squeeze()
if indices_i.numel() == 0:
continue
inp_i = inp_flat.index_select(0, indices_i) - l_idx
emb_i = self.emb_layers[i](inp_i)
emb_i = F.linear(emb_i, self.emb_projs[i]).to(emb_flat.dtype)
emb_flat.index_copy_(0, indices_i, emb_i)
embed = emb_flat.view(*inp.size(), self.d_proj)
embed.mul_(self.emb_scale)
return embed
class MemTransformerLM(nn.Module):
def __init__(self, n_token, n_layer, n_head, d_model, d_head, d_inner,
dropout, dropatt, dtype, tie_weight=True, d_embed=None,
div_val=1, tie_projs=[False], pre_lnorm=False,
tgt_len=None, ext_len=None, mem_len=None,
cutoffs=[], adapt_inp=False,
same_length=False, attn_type=0, clamp_len=-1,
sample_softmax=-1):
super(MemTransformerLM, self).__init__()
self.n_token = n_token
d_embed = d_model if d_embed is None else d_embed
self.d_embed = d_embed
self.d_model = d_model
self.n_head = n_head
self.d_head = d_head
self.word_emb = AdaptiveEmbedding(n_token, d_embed, d_model, cutoffs,
div_val=div_val)
self.drop = nn.Dropout(dropout)
self.tie_weight = tie_weight
self.tie_projs = tie_projs
self.div_val = div_val
self.n_layer = n_layer
self.tgt_len = tgt_len
self.mem_len = mem_len
self.ext_len = ext_len
self.max_klen = tgt_len + ext_len + mem_len
self.attn_type = attn_type
self.layers = nn.ModuleList()
# the default attention
if attn_type == 0:
for i in range(n_layer):
self.layers.append(
RelPartialLearnableDecoderLayer(
n_head, d_model, d_head, d_inner, dropout,
tgt_len=tgt_len, ext_len=ext_len, mem_len=mem_len,
dropatt=dropatt, pre_lnorm=pre_lnorm)
)
# learnable embeddings
elif attn_type == 1:
for i in range(n_layer):
self.layers.append(
RelLearnableDecoderLayer(
n_head, d_model, d_head, d_inner, dropout,
tgt_len=tgt_len, ext_len=ext_len, mem_len=mem_len,
dropatt=dropatt, pre_lnorm=pre_lnorm)
)
# absolute embeddings
elif attn_type in [2, 3]:
for i in range(n_layer):
self.layers.append(
DecoderLayer(
n_head, d_model, d_head, d_inner, dropout,
dropatt=dropatt, pre_lnorm=pre_lnorm)
)
self.sample_softmax = sample_softmax
# use sampled softmax
if sample_softmax > 0:
self.out_layer = nn.Linear(d_model, n_token)
self.tie_weight = tie_weight
self.sampler = LogUniformSampler(n_token, sample_softmax)
# use adaptive softmax (including standard softmax)
else:
if tie_weight:
emb_layers = [i.weight for i in self.word_emb.emb_layers]
else:
emb_layers = None
emb_projs = self.word_emb.emb_projs
self.crit = ProjectedAdaptiveLogSoftmax(n_token, d_embed, d_model,
cutoffs, div_val=div_val,
tie_projs=tie_projs,
out_projs=emb_projs,
out_layers_weights=emb_layers)
self.same_length = same_length
self.clamp_len = clamp_len
self._create_params()
def backward_compatible(self):
self.sample_softmax = -1
def _create_params(self):
# default attention
if self.attn_type == 0:
self.pos_emb = PositionalEmbedding(self.d_model)
self.r_w_bias = nn.Parameter(torch.Tensor(self.n_head, self.d_head).zero_())
self.r_r_bias = nn.Parameter(torch.Tensor(self.n_head, self.d_head).zero_())
# learnable
elif self.attn_type == 1:
self.r_emb = nn.Parameter(torch.Tensor(
self.n_layer, self.max_klen, self.n_head, self.d_head).zero_())
self.r_w_bias = nn.Parameter(torch.Tensor(
self.n_layer, self.n_head, self.d_head).zero_())
self.r_bias = nn.Parameter(torch.Tensor(
self.n_layer, self.max_klen, self.n_head).zero_())
# absolute standard
elif self.attn_type == 2:
self.pos_emb = PositionalEmbedding(self.d_model)
# absolute deeper SA
elif self.attn_type == 3:
self.r_emb = nn.Parameter(torch.Tensor(
self.n_layer, self.max_klen, self.d_model).zero_())
def reset_length(self, tgt_len, ext_len, mem_len):
if tgt_len < 1:
raise RuntimeError(f'tgt_len should be >= 1, but got {tgt_len}')
if ext_len < 0:
raise RuntimeError(f'ext_len should be >= 0, but got {ext_len}')
if mem_len < 0:
raise RuntimeError(f'mem_len should be >= 0, but got {mem_len}')
self.tgt_len = tgt_len
self.mem_len = mem_len
self.ext_len = ext_len
def init_mems(self):
if self.mem_len > 0:
param = next(self.parameters())
mems = torch.empty(self.n_layer, 0, dtype=param.dtype,
device=param.device)
return mems
else:
return None
def _update_mems(self, hids, mems, qlen, mlen):
# does not deal with None
if mems is None:
return None
# mems is not None
assert len(hids) == len(mems), 'len(hids) != len(mems)'
# There are `mlen + qlen` steps that can be cached into mems
# For the next step, the last `ext_len` of the `qlen` tokens
# will be used as the extended context. Hence, we only cache
# the tokens from `mlen + qlen - self.ext_len - self.mem_len`
# to `mlen + qlen - self.ext_len`.
with torch.no_grad():
stacked = torch.stack(hids)
if (
self.mem_len == self.tgt_len
and self.ext_len == 0
and stacked.size(1) == self.mem_len
):
new_mems = stacked.detach()
else:
end_idx = mlen + max(0, qlen - self.ext_len)
beg_idx = max(0, end_idx - self.mem_len)
if mems.numel():
cat = torch.cat([mems, stacked], dim=1)
else:
cat = stacked
new_mems = cat[:, beg_idx:end_idx].detach()
return new_mems
def _forward(self, dec_inp, mems=None):
qlen, bsz = dec_inp.size()
word_emb = self.word_emb(dec_inp)
mlen = mems[0].size(0) if mems is not None else 0
klen = mlen + qlen
if self.same_length:
all_ones = word_emb.new_ones(qlen, klen)
mask_len = klen - self.mem_len - 1
if mask_len > 0:
mask_shift_len = qlen - mask_len
else:
mask_shift_len = qlen
dec_attn_mask = (torch.triu(all_ones, 1+mlen)
+ torch.tril(all_ones, -mask_shift_len)).bool()
else:
dec_attn_mask = torch.triu(
word_emb.new_ones(qlen, klen), diagonal=1+mlen).bool()
hids = []
# default
if self.attn_type == 0:
pos_seq = torch.arange(klen-1, -1, -1.0, device=word_emb.device,
dtype=word_emb.dtype)
if self.clamp_len > 0:
pos_seq.clamp_(max=self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
core_out = self.drop(word_emb)
pos_emb = self.drop(pos_emb)
for i, layer in enumerate(self.layers):
hids.append(core_out.detach())
mems_i = None if mems is None else mems[i]
core_out = layer(core_out, pos_emb, self.r_w_bias,
self.r_r_bias, dec_attn_mask=dec_attn_mask,
mems=mems_i)
# learnable
elif self.attn_type == 1:
core_out = self.drop(word_emb)
for i, layer in enumerate(self.layers):
hids.append(core_out.detach())
if self.clamp_len > 0:
r_emb = self.r_emb[i][-self.clamp_len:]
r_bias = self.r_bias[i][-self.clamp_len:]
else:
r_emb, r_bias = self.r_emb[i], self.r_bias[i]
mems_i = None if mems is None else mems[i]
core_out = layer(core_out, r_emb, self.r_w_bias[i],
r_bias, dec_attn_mask=dec_attn_mask, mems=mems_i)
# absolute
elif self.attn_type == 2:
pos_seq = torch.arange(klen - 1, -1, -1.0, device=word_emb.device,
dtype=word_emb.dtype)
if self.clamp_len > 0:
pos_seq.clamp_(max=self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
core_out = self.drop(word_emb + pos_emb[-qlen:])
for i, layer in enumerate(self.layers):
hids.append(core_out.detach())
mems_i = None if mems is None else mems[i]
if mems_i is not None and len(mems_i) and i == 0:
mems_i += pos_emb[:mlen]
core_out = layer(core_out, dec_attn_mask=dec_attn_mask,
mems=mems_i)
elif self.attn_type == 3:
core_out = self.drop(word_emb)
for i, layer in enumerate(self.layers):
hids.append(core_out.detach())
mems_i = None if mems is None else mems[i]
if mems_i is not None and len(mems_i) and mlen > 0:
cur_emb = self.r_emb[i][:-qlen]
cur_size = cur_emb.size(0)
if cur_size < mlen:
cur_emb_pad = cur_emb[0:1].expand(mlen-cur_size, -1, -1)
cur_emb = torch.cat([cur_emb_pad, cur_emb], 0)
else:
cur_emb = cur_emb[-mlen:]
mems_i += cur_emb.view(mlen, 1, -1)
core_out += self.r_emb[i][-qlen:].view(qlen, 1, -1)
core_out = layer(core_out, dec_attn_mask=dec_attn_mask,
mems=mems_i)
core_out = self.drop(core_out)
new_mems = self._update_mems(hids, mems, qlen, mlen)
return core_out, new_mems
def forward(self, data, target, mems):
# nn.DataParallel does not allow size(0) tensors to be broadcasted.
# So, have to initialize size(0) mems inside the model forward.
# Moreover, have to return new_mems to allow nn.DataParallel to piece
# them together.
if mems is None:
mems = self.init_mems()
tgt_len = target.size(0)
hidden, new_mems = self._forward(data, mems=mems)
pred_hid = hidden[-tgt_len:]
if self.sample_softmax > 0 and self.training:
assert self.tie_weight
logit = sample_logits(self.word_emb, self.out_layer.bias, target,
pred_hid, self.sampler)
loss = -F.log_softmax(logit, -1)[:, :, 0]
else:
loss = self.crit(pred_hid.view(-1, pred_hid.size(-1)), target.view(-1))
loss = loss.view(tgt_len, -1)
return (loss, new_mems)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='unit test')
parser.add_argument('--n_layer', type=int, default=4, help='')
parser.add_argument('--n_rel_layer', type=int, default=4, help='')
parser.add_argument('--n_head', type=int, default=2, help='')
parser.add_argument('--d_head', type=int, default=2, help='')
parser.add_argument('--d_model', type=int, default=200, help='')
parser.add_argument('--d_embed', type=int, default=200, help='')
parser.add_argument('--d_inner', type=int, default=200, help='')
parser.add_argument('--dropout', type=float, default=0.0, help='')
parser.add_argument('--cuda', action='store_true', help='')
parser.add_argument('--seed', type=int, default=1111, help='')
parser.add_argument('--multi_gpu', action='store_true', help='')
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
B = 4
tgt_len, mem_len, ext_len = 36, 36, 0
data_len = tgt_len * 20
args.n_token = 10000
import data_utils
data = torch.LongTensor(data_len*B).random_(0, args.n_token).to(device)
diter = data_utils.LMOrderedIterator(data, B, tgt_len, device=device, ext_len=ext_len)
cutoffs = [args.n_token // 2]
tie_projs = [False] + [True] * len(cutoffs)
for div_val in [1, 2]:
for d_embed in [200, 100]:
model = MemTransformerLM(args.n_token, args.n_layer, args.n_head,
args.d_model, args.d_head, args.d_inner,
args.dropout, dropatt=args.dropout,
tie_weight=True, d_embed=d_embed,
div_val=div_val, tie_projs=tie_projs,
pre_lnorm=True, tgt_len=tgt_len,
ext_len=ext_len, mem_len=mem_len,
cutoffs=cutoffs, attn_type=0,
dtype=None).to(device)
print(sum(p.numel() for p in model.parameters()))
mems = None
for idx, (inp, tgt, seqlen, _) in enumerate(diter):
print('batch {}'.format(idx))
_, mems = model(inp, tgt, mems)
|
PyTorch/Recommendation/DLRM/tests/feature_specs | feature_specs | wider_dtypes | channel_spec:
categorical:
- cat_0.bin
- cat_1.bin
- cat_2.bin
- cat_3.bin
- cat_4.bin
- cat_5.bin
- cat_6.bin
- cat_7.bin
- cat_8.bin
- cat_9.bin
- cat_10.bin
- cat_11.bin
- cat_12.bin
- cat_13.bin
- cat_14.bin
- cat_15.bin
- cat_16.bin
- cat_17.bin
- cat_18.bin
- cat_19.bin
- cat_20.bin
- cat_21.bin
- cat_22.bin
- cat_23.bin
- cat_24.bin
- cat_25.bin
label:
- label
numerical: &id001
- num_0
- num_1
- num_2
- num_3
- num_4
- num_5
- num_6
- num_7
- num_8
- num_9
- num_10
- num_11
- num_12
feature_spec:
cat_0.bin:
cardinality: 100000
dtype: int64
cat_1.bin:
cardinality: 100000
dtype: int32
cat_10.bin:
cardinality: 100000
dtype: int32
cat_11.bin:
cardinality: 100000
dtype: int32
cat_12.bin:
cardinality: 100000
dtype: int32
cat_13.bin:
cardinality: 100000
dtype: int32
cat_14.bin:
cardinality: 100000
dtype: int32
cat_15.bin:
cardinality: 100000
dtype: int64
cat_16.bin:
cardinality: 100000
dtype: int32
cat_17.bin:
cardinality: 100000
dtype: int32
cat_18.bin:
cardinality: 100000
dtype: int32
cat_19.bin:
cardinality: 100000
dtype: int32
cat_2.bin:
cardinality: 100000
dtype: int32
cat_20.bin:
cardinality: 100000
dtype: int64
cat_21.bin:
cardinality: 100000
dtype: int32
cat_22.bin:
cardinality: 100000
dtype: int32
cat_23.bin:
cardinality: 100000
dtype: int32
cat_24.bin:
cardinality: 100000
dtype: int32
cat_25.bin:
cardinality: 100000
dtype: int32
cat_3.bin:
cardinality: 100000
dtype: int64
cat_4.bin:
cardinality: 100000
dtype: int32
cat_5.bin:
cardinality: 100000
dtype: int32
cat_6.bin:
cardinality: 100000
dtype: int32
cat_7.bin:
cardinality: 100000
dtype: int64
cat_8.bin:
cardinality: 100000
dtype: int32
cat_9.bin:
cardinality: 100000
dtype: int64
label:
dtype: bool
num_0:
dtype: float16
num_1:
dtype: float16
num_10:
dtype: float16
num_11:
dtype: float16
num_12:
dtype: float16
num_2:
dtype: float16
num_3:
dtype: float16
num_4:
dtype: float16
num_5:
dtype: float16
num_6:
dtype: float16
num_7:
dtype: float16
num_8:
dtype: float16
num_9:
dtype: float16
metadata: {}
source_spec:
test:
- features: *id001
files:
- test/numerical.bin
type: split_binary
- features:
- label
files:
- test/label.bin
type: split_binary
- features:
- cat_0.bin
files:
- test/cat_0.bin
type: split_binary
- features:
- cat_1.bin
files:
- test/cat_1.bin
type: split_binary
- features:
- cat_2.bin
files:
- test/cat_2.bin
type: split_binary
- features:
- cat_3.bin
files:
- test/cat_3.bin
type: split_binary
- features:
- cat_4.bin
files:
- test/cat_4.bin
type: split_binary
- features:
- cat_5.bin
files:
- test/cat_5.bin
type: split_binary
- features:
- cat_6.bin
files:
- test/cat_6.bin
type: split_binary
- features:
- cat_7.bin
files:
- test/cat_7.bin
type: split_binary
- features:
- cat_8.bin
files:
- test/cat_8.bin
type: split_binary
- features:
- cat_9.bin
files:
- test/cat_9.bin
type: split_binary
- features:
- cat_10.bin
files:
- test/cat_10.bin
type: split_binary
- features:
- cat_11.bin
files:
- test/cat_11.bin
type: split_binary
- features:
- cat_12.bin
files:
- test/cat_12.bin
type: split_binary
- features:
- cat_13.bin
files:
- test/cat_13.bin
type: split_binary
- features:
- cat_14.bin
files:
- test/cat_14.bin
type: split_binary
- features:
- cat_15.bin
files:
- test/cat_15.bin
type: split_binary
- features:
- cat_16.bin
files:
- test/cat_16.bin
type: split_binary
- features:
- cat_17.bin
files:
- test/cat_17.bin
type: split_binary
- features:
- cat_18.bin
files:
- test/cat_18.bin
type: split_binary
- features:
- cat_19.bin
files:
- test/cat_19.bin
type: split_binary
- features:
- cat_20.bin
files:
- test/cat_20.bin
type: split_binary
- features:
- cat_21.bin
files:
- test/cat_21.bin
type: split_binary
- features:
- cat_22.bin
files:
- test/cat_22.bin
type: split_binary
- features:
- cat_23.bin
files:
- test/cat_23.bin
type: split_binary
- features:
- cat_24.bin
files:
- test/cat_24.bin
type: split_binary
- features:
- cat_25.bin
files:
- test/cat_25.bin
type: split_binary
train:
- features: *id001
files:
- train/numerical.bin
type: split_binary
- features:
- label
files:
- train/label.bin
type: split_binary
- features:
- cat_0.bin
files:
- train/cat_0.bin
type: split_binary
- features:
- cat_1.bin
files:
- train/cat_1.bin
type: split_binary
- features:
- cat_2.bin
files:
- train/cat_2.bin
type: split_binary
- features:
- cat_3.bin
files:
- train/cat_3.bin
type: split_binary
- features:
- cat_4.bin
files:
- train/cat_4.bin
type: split_binary
- features:
- cat_5.bin
files:
- train/cat_5.bin
type: split_binary
- features:
- cat_6.bin
files:
- train/cat_6.bin
type: split_binary
- features:
- cat_7.bin
files:
- train/cat_7.bin
type: split_binary
- features:
- cat_8.bin
files:
- train/cat_8.bin
type: split_binary
- features:
- cat_9.bin
files:
- train/cat_9.bin
type: split_binary
- features:
- cat_10.bin
files:
- train/cat_10.bin
type: split_binary
- features:
- cat_11.bin
files:
- train/cat_11.bin
type: split_binary
- features:
- cat_12.bin
files:
- train/cat_12.bin
type: split_binary
- features:
- cat_13.bin
files:
- train/cat_13.bin
type: split_binary
- features:
- cat_14.bin
files:
- train/cat_14.bin
type: split_binary
- features:
- cat_15.bin
files:
- train/cat_15.bin
type: split_binary
- features:
- cat_16.bin
files:
- train/cat_16.bin
type: split_binary
- features:
- cat_17.bin
files:
- train/cat_17.bin
type: split_binary
- features:
- cat_18.bin
files:
- train/cat_18.bin
type: split_binary
- features:
- cat_19.bin
files:
- train/cat_19.bin
type: split_binary
- features:
- cat_20.bin
files:
- train/cat_20.bin
type: split_binary
- features:
- cat_21.bin
files:
- train/cat_21.bin
type: split_binary
- features:
- cat_22.bin
files:
- train/cat_22.bin
type: split_binary
- features:
- cat_23.bin
files:
- train/cat_23.bin
type: split_binary
- features:
- cat_24.bin
files:
- train/cat_24.bin
type: split_binary
- features:
- cat_25.bin
files:
- train/cat_25.bin
type: split_binary
|
TensorFlow2/Segmentation/Contrib/UNet3P | UNet3P | benchmark_inference | """
Script to benchmark model throughput and latency
"""
import os
import numpy as np
from tqdm import tqdm
from timeit import default_timer as timer
import hydra
from omegaconf import DictConfig
import tensorflow as tf
from tensorflow.keras import mixed_precision
from data_generators import tf_data_generator
from utils.general_utils import join_paths, suppress_warnings
from utils.images_utils import postprocess_mask
from models.model import prepare_model
def benchmark_time(cfg: DictConfig):
"""
Output throughput and latency
"""
# suppress TensorFlow and DALI warnings
suppress_warnings()
if cfg.OPTIMIZATION.AMP:
print("Enabling Automatic Mixed Precision(AMP)")
policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_global_policy(policy)
if cfg.OPTIMIZATION.XLA:
print("Enabling Accelerated Linear Algebra(XLA)")
tf.config.optimizer.set_jit(True)
# data generator
val_generator = tf_data_generator.DataGenerator(cfg, mode="VAL")
validation_steps = val_generator.__len__()
warmup_steps, bench_steps = 50, 100
if "warmup_steps" in cfg.keys():
warmup_steps = cfg.warmup_steps
if "bench_steps" in cfg.keys():
bench_steps = cfg.bench_steps
validation_steps = min(validation_steps, (warmup_steps + bench_steps))
progress_bar = tqdm(total=validation_steps)
# create model
model = prepare_model(cfg)
# weights model path
checkpoint_path = join_paths(
cfg.WORK_DIR,
cfg.CALLBACKS.MODEL_CHECKPOINT.PATH,
f"{cfg.MODEL.WEIGHTS_FILE_NAME}.hdf5"
)
assert os.path.exists(checkpoint_path), \
f"Model weight's file does not exist at \n{checkpoint_path}"
# load model weights
model.load_weights(checkpoint_path, by_name=True, skip_mismatch=True)
# model.summary()
time_taken = []
# for each batch
for i, (batch_images, batch_mask) in enumerate(val_generator):
start_time = timer()
# make prediction on batch
batch_predictions = model.predict_on_batch(batch_images)
if len(model.outputs) > 1:
batch_predictions = batch_predictions[0]
# do postprocessing on predicted mask
batch_predictions = postprocess_mask(batch_predictions, cfg.OUTPUT.CLASSES)
time_taken.append(timer() - start_time)
progress_bar.update(1)
if i >= validation_steps:
break
progress_bar.close()
mean_time = np.mean(time_taken[warmup_steps:]) # skipping warmup_steps
throughput = (cfg.HYPER_PARAMETERS.BATCH_SIZE / mean_time)
print(f"Latency: {round(mean_time * 1e3, 2)} msec")
print(f"Throughput/FPS: {round(throughput, 2)} samples/sec")
@hydra.main(version_base=None, config_path="configs", config_name="config")
def main(cfg: DictConfig):
"""
Read config file and pass to benchmark_time method
"""
benchmark_time(cfg)
if __name__ == "__main__":
main()
|
TensorFlow2/Segmentation/UNet_Medical | UNet_Medical | download_dataset | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
PARSER = argparse.ArgumentParser(description="U-Net medical")
PARSER.add_argument('--data_dir',
type=str,
default='./data',
help="""Directory where to download the dataset""")
def main():
FLAGS = PARSER.parse_args()
if not os.path.exists(FLAGS.data_dir):
os.makedirs(FLAGS.data_dir)
os.system('wget http://brainiac2.mit.edu/isbi_challenge/sites/default/files/train-volume.tif -P {}'.format(FLAGS.data_dir))
os.system('wget http://brainiac2.mit.edu/isbi_challenge/sites/default/files/train-labels.tif -P {}'.format(FLAGS.data_dir))
os.system('wget http://brainiac2.mit.edu/isbi_challenge/sites/default/files/test-volume.tif -P {}'.format(FLAGS.data_dir))
print("Finished downloading files for U-Net medical to {}".format(FLAGS.data_dir))
if __name__ == '__main__':
main()
|
TensorFlow2/Detection/Efficientdet/object_detection | object_detection | matcher | # Copyright 2020 Google Research. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Matcher interface and Match class.
This module defines the Matcher interface and the Match object. The job of the
matcher is to match row and column indices based on the similarity matrix and
other optional parameters. Each column is matched to at most one row. There
are three possibilities for the matching:
1) match: A column matches a row.
2) no_match: A column does not match any row.
3) ignore: A column that is neither 'match' nor no_match.
The ignore case is regularly encountered in object detection: when an anchor has
a relatively small overlap with a ground-truth box, one neither wants to
consider this box a positive example (match) nor a negative example (no match).
The Match class is used to store the match results and it provides simple apis
to query the results.
"""
import abc
import tensorflow.compat.v1 as tf
class Match(object):
"""Class to store results from the matcher.
This class is used to store the results from the matcher. It provides
convenient methods to query the matching results.
"""
def __init__(self, match_results):
"""Constructs a Match object.
Args:
match_results: Integer tensor of shape [N] with (1) match_results[i]>=0,
meaning that column i is matched with row match_results[i].
(2) match_results[i]=-1, meaning that column i is not matched.
(3) match_results[i]=-2, meaning that column i is ignored.
Raises:
ValueError: if match_results does not have rank 1 or is not an
integer int32 scalar tensor
"""
if match_results.shape.ndims != 1:
raise ValueError('match_results should have rank 1')
if match_results.dtype != tf.int32:
raise ValueError('match_results should be an int32 or int64 scalar '
'tensor')
self._match_results = match_results
@property
def match_results(self):
"""The accessor for match results.
Returns:
the tensor which encodes the match results.
"""
return self._match_results
def matched_column_indices(self):
"""Returns column indices that match to some row.
The indices returned by this op are always sorted in increasing order.
Returns:
column_indices: int32 tensor of shape [K] with column indices.
"""
return self._reshape_and_cast(tf.where(tf.greater(self._match_results, -1)))
def matched_column_indicator(self):
"""Returns column indices that are matched.
Returns:
column_indices: int32 tensor of shape [K] with column indices.
"""
return tf.greater_equal(self._match_results, 0)
def num_matched_columns(self):
"""Returns number (int32 scalar tensor) of matched columns."""
return tf.shape(self.matched_column_indices())[0]
def unmatched_column_indices(self):
"""Returns column indices that do not match any row.
The indices returned by this op are always sorted in increasing order.
Returns:
column_indices: int32 tensor of shape [K] with column indices.
"""
return self._reshape_and_cast(tf.where(tf.equal(self._match_results, -1)))
def unmatched_column_indicator(self):
"""Returns column indices that are unmatched.
Returns:
column_indices: int32 tensor of shape [K] with column indices.
"""
return tf.equal(self._match_results, -1)
def num_unmatched_columns(self):
"""Returns number (int32 scalar tensor) of unmatched columns."""
return tf.shape(self.unmatched_column_indices())[0]
def ignored_column_indices(self):
"""Returns column indices that are ignored (neither Matched nor Unmatched).
The indices returned by this op are always sorted in increasing order.
Returns:
column_indices: int32 tensor of shape [K] with column indices.
"""
return self._reshape_and_cast(tf.where(self.ignored_column_indicator()))
def ignored_column_indicator(self):
"""Returns boolean column indicator where True means the column is ignored.
Returns:
column_indicator: boolean vector which is True for all ignored column
indices.
"""
return tf.equal(self._match_results, -2)
def num_ignored_columns(self):
"""Returns number (int32 scalar tensor) of matched columns."""
return tf.shape(self.ignored_column_indices())[0]
def unmatched_or_ignored_column_indices(self):
"""Returns column indices that are unmatched or ignored.
The indices returned by this op are always sorted in increasing order.
Returns:
column_indices: int32 tensor of shape [K] with column indices.
"""
return self._reshape_and_cast(tf.where(tf.greater(0, self._match_results)))
def matched_row_indices(self):
"""Returns row indices that match some column.
The indices returned by this op are ordered so as to be in correspondence
with the output of matched_column_indicator(). For example if
self.matched_column_indicator() is [0,2], and self.matched_row_indices() is
[7, 3], then we know that column 0 was matched to row 7 and column 2 was
matched to row 3.
Returns:
row_indices: int32 tensor of shape [K] with row indices.
"""
return self._reshape_and_cast(
tf.gather(self._match_results, self.matched_column_indices()))
def _reshape_and_cast(self, t):
return tf.cast(tf.reshape(t, [-1]), tf.int32)
def gather_based_on_match(self, input_tensor, unmatched_value,
ignored_value):
"""Gathers elements from `input_tensor` based on match results.
For columns that are matched to a row, gathered_tensor[col] is set to
input_tensor[match_results[col]]. For columns that are unmatched,
gathered_tensor[col] is set to unmatched_value. Finally, for columns that
are ignored gathered_tensor[col] is set to ignored_value.
Note that the input_tensor.shape[1:] must match with unmatched_value.shape
and ignored_value.shape
Args:
input_tensor: Tensor to gather values from.
unmatched_value: Constant tensor value for unmatched columns.
ignored_value: Constant tensor value for ignored columns.
Returns:
gathered_tensor: A tensor containing values gathered from input_tensor.
The shape of the gathered tensor is [match_results.shape[0]] +
input_tensor.shape[1:].
"""
input_tensor = tf.concat([tf.stack([ignored_value, unmatched_value]),
input_tensor], axis=0)
gather_indices = tf.maximum(self.match_results + 2, 0)
gathered_tensor = tf.gather(input_tensor, gather_indices)
return gathered_tensor
class Matcher(object):
"""Abstract base class for matcher.
"""
__metaclass__ = abc.ABCMeta
def match(self, similarity_matrix, scope=None, **params):
"""Computes matches among row and column indices and returns the result.
Computes matches among the row and column indices based on the similarity
matrix and optional arguments.
Args:
similarity_matrix: Float tensor of shape [N, M] with pairwise similarity
where higher value means more similar.
scope: Op scope name. Defaults to 'Match' if None.
**params: Additional keyword arguments for specific implementations of
the Matcher.
Returns:
A Match object with the results of matching.
"""
with tf.name_scope(scope, 'Match', [similarity_matrix, params]) as scope:
return Match(self._match(similarity_matrix, **params))
@abc.abstractmethod
def _match(self, similarity_matrix, **params):
"""Method to be overridden by implementations.
Args:
similarity_matrix: Float tensor of shape [N, M] with pairwise similarity
where higher value means more similar.
**params: Additional keyword arguments for specific implementations of
the Matcher.
Returns:
match_results: Integer tensor of shape [M]: match_results[i]>=0 means
that column i is matched to row match_results[i], match_results[i]=-1
means that the column is not matched. match_results[i]=-2 means that
the column is ignored (usually this happens when there is a very weak
match which one neither wants as positive nor negative example).
"""
pass
|
TensorFlow/Detection/SSD/models/research/slim/nets | nets | pix2pix_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
"""Tests for pix2pix."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import pix2pix
class GeneratorTest(tf.test.TestCase):
def _reduced_default_blocks(self):
"""Returns the default blocks, scaled down to make test run faster."""
return [pix2pix.Block(b.num_filters // 32, b.decoder_keep_prob)
for b in pix2pix._default_generator_blocks()]
def test_output_size_nn_upsample_conv(self):
batch_size = 2
height, width = 256, 256
num_outputs = 4
images = tf.ones((batch_size, height, width, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits, _ = pix2pix.pix2pix_generator(
images, num_outputs, blocks=self._reduced_default_blocks(),
upsample_method='nn_upsample_conv')
with self.test_session() as session:
session.run(tf.global_variables_initializer())
np_outputs = session.run(logits)
self.assertListEqual([batch_size, height, width, num_outputs],
list(np_outputs.shape))
def test_output_size_conv2d_transpose(self):
batch_size = 2
height, width = 256, 256
num_outputs = 4
images = tf.ones((batch_size, height, width, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits, _ = pix2pix.pix2pix_generator(
images, num_outputs, blocks=self._reduced_default_blocks(),
upsample_method='conv2d_transpose')
with self.test_session() as session:
session.run(tf.global_variables_initializer())
np_outputs = session.run(logits)
self.assertListEqual([batch_size, height, width, num_outputs],
list(np_outputs.shape))
def test_block_number_dictates_number_of_layers(self):
batch_size = 2
height, width = 256, 256
num_outputs = 4
images = tf.ones((batch_size, height, width, 3))
blocks = [
pix2pix.Block(64, 0.5),
pix2pix.Block(128, 0),
]
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
_, end_points = pix2pix.pix2pix_generator(
images, num_outputs, blocks)
num_encoder_layers = 0
num_decoder_layers = 0
for end_point in end_points:
if end_point.startswith('encoder'):
num_encoder_layers += 1
elif end_point.startswith('decoder'):
num_decoder_layers += 1
self.assertEqual(num_encoder_layers, len(blocks))
self.assertEqual(num_decoder_layers, len(blocks))
class DiscriminatorTest(tf.test.TestCase):
def _layer_output_size(self, input_size, kernel_size=4, stride=2, pad=2):
return (input_size + pad * 2 - kernel_size) // stride + 1
def test_four_layers(self):
batch_size = 2
input_size = 256
output_size = self._layer_output_size(input_size)
output_size = self._layer_output_size(output_size)
output_size = self._layer_output_size(output_size)
output_size = self._layer_output_size(output_size, stride=1)
output_size = self._layer_output_size(output_size, stride=1)
images = tf.ones((batch_size, input_size, input_size, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits, end_points = pix2pix.pix2pix_discriminator(
images, num_filters=[64, 128, 256, 512])
self.assertListEqual([batch_size, output_size, output_size, 1],
logits.shape.as_list())
self.assertListEqual([batch_size, output_size, output_size, 1],
end_points['predictions'].shape.as_list())
def test_four_layers_no_padding(self):
batch_size = 2
input_size = 256
output_size = self._layer_output_size(input_size, pad=0)
output_size = self._layer_output_size(output_size, pad=0)
output_size = self._layer_output_size(output_size, pad=0)
output_size = self._layer_output_size(output_size, stride=1, pad=0)
output_size = self._layer_output_size(output_size, stride=1, pad=0)
images = tf.ones((batch_size, input_size, input_size, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
logits, end_points = pix2pix.pix2pix_discriminator(
images, num_filters=[64, 128, 256, 512], padding=0)
self.assertListEqual([batch_size, output_size, output_size, 1],
logits.shape.as_list())
self.assertListEqual([batch_size, output_size, output_size, 1],
end_points['predictions'].shape.as_list())
def test_four_layers_wrog_paddig(self):
batch_size = 2
input_size = 256
images = tf.ones((batch_size, input_size, input_size, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
with self.assertRaises(TypeError):
pix2pix.pix2pix_discriminator(
images, num_filters=[64, 128, 256, 512], padding=1.5)
def test_four_layers_negative_padding(self):
batch_size = 2
input_size = 256
images = tf.ones((batch_size, input_size, input_size, 3))
with tf.contrib.framework.arg_scope(pix2pix.pix2pix_arg_scope()):
with self.assertRaises(ValueError):
pix2pix.pix2pix_discriminator(
images, num_filters=[64, 128, 256, 512], padding=-1)
if __name__ == '__main__':
tf.test.main()
|
PyTorch/SpeechRecognition/wav2vec2/scripts | scripts | inference | #!/usr/bin/env bash
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
: ${DATASET_DIR:="/datasets/LibriSpeech"}
: ${VALID_SUBSET:="test-other"}
: ${OUTPUT_DIR:="results/inference"}
: ${NUM_GPUS:=1}
: ${BATCH_SIZE:=8}
: ${AMP:=false}
: ${BF16:=false}
: ${FP16:=false}
: ${EMA:=0.0}
: ${SEED:=1}
: ${FINETUNED_MODEL:=results/finetune_base_960h/wav2vec2_update320000.pt}
: ${MASK_PROB:=0.5}
: ${MASK_CHANNEL_PROB:=0.25}
: ${DISTRIBUTED:="-m torch.distributed.launch --nproc_per_node=$NUM_GPUS"}
# inference
: ${MAX_DURATION:=""}
: ${NUM_STEPS:=0}
: ${NUM_WARMUP_STEPS:=0}
: ${CPU:=false}
: ${LOGITS_FILE:=}
: ${PREDICTION_FILE:="${OUTPUT_DIR}/${DATASET}.predictions"}
: ${TORCHSCRIPT:=false}
: ${TORCHSCRIPT_SAVE:=false}
: ${LOG_FILE:=$OUTPUT_DIR/nvlog.json}
mkdir -p "$OUTPUT_DIR"
ARGS+=" --w2v_path $FINETUNED_MODEL"
ARGS+=" --data $DATASET_DIR"
ARGS+=" --valid_subset $VALID_SUBSET"
ARGS+=" --output_dir $OUTPUT_DIR"
ARGS+=" --ema $EMA"
ARGS+=" --seed $SEED"
ARGS+=" --skip_invalid_size_inputs_valid_test"
ARGS+=" --apply_mask"
ARGS+=" --mask_prob $MASK_PROB"
ARGS+=" --mask_channel_prob $MASK_CHANNEL_PROB"
ARGS+=" --mask_channel_length 64"
ARGS+=" --encoder_layerdrop 0.1" # NOTE This is called `layerdrop` in fairseq finetuning yamls
ARGS+=" --activation_dropout 0.1"
ARGS+=" --feature_grad_mult 0.0"
ARGS+=" --batch_size=$BATCH_SIZE"
ARGS+=" --steps $NUM_STEPS"
ARGS+=" --warmup_steps $NUM_WARMUP_STEPS"
[ "$AMP" = true ] && ARGS+=" --amp --fp16"
[ "$BF16" = true ] && ARGS+=" --bf16"
[ "$TORCHSCRIPT" = true ] && ARGS+=" --torchscript"
[ "$TORCHSCRIPT_SAVE" = true ] && ARGS+=" --torchscript_export"
[ -n "$LOG_FILE" ] && ARGS+=" --log_file $LOG_FILE"
[ "$CPU" == "true" ] && ARGS+=" --cpu"
[ -n "$MAX_DURATION" ] && ARGS+=" --max_duration ${MAX_DURATION}"
set -x
if [ $NUM_GPUS -gt 1 ]; then
python3 -m torch.distributed.launch --nproc_per_node=$NUM_GPUS inference.py $ARGS $@
else
python3 inference.py $ARGS $@
fi
|
Tools/PyTorch/TimeSeriesPredictionPlatform/examples | examples | seed_sweep | /# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
python launch_training.py \
-m \
seed='range(1,9)' \
model=tft \
dataset=electricity \
trainer/criterion=quantile \
trainer.config.num_epochs=3 \
hydra/callbacks=[merge_logs] \
hydra/launcher=joblib \
hydra.launcher.n_jobs=8
|
PyTorch/SpeechRecognition/Jasper/triton/model_repo_configs/fp16/jasper-onnx | jasper-onnx | config | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
name: "jasper-onnx"
platform: "onnxruntime_onnx"
default_model_filename: "model.onnx"
max_batch_size: 8#MAX_BATCH
input [
{
name: "input__0"
data_type: TYPE_FP16
dims: [64, -1]
}
]
output [
{
name: "output__0"
data_type: TYPE_FP16
dims: [-1, 29 ]
}
]
instance_group {
count: 1#NUM_ENGINES
gpus: 0
kind: KIND_GPU
}
#db#dynamic_batching {
#db# preferred_batch_size: 8#MAX_BATCH
#db# max_queue_delay_microseconds: #MAX_QUEUE
#db#}
|
TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools | dataset_tools | create_coco_tf_record_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test for create_coco_tf_record.py."""
import io
import json
import os
import numpy as np
import PIL.Image
import tensorflow as tf
from object_detection.dataset_tools import create_coco_tf_record
class CreateCocoTFRecordTest(tf.test.TestCase):
def _assertProtoEqual(self, proto_field, expectation):
"""Helper function to assert if a proto field equals some value.
Args:
proto_field: The protobuf field to compare.
expectation: The expected value of the protobuf field.
"""
proto_list = [p for p in proto_field]
self.assertListEqual(proto_list, expectation)
def test_create_tf_example(self):
image_file_name = 'tmp_image.jpg'
image_data = np.random.rand(256, 256, 3)
tmp_dir = self.get_temp_dir()
save_path = os.path.join(tmp_dir, image_file_name)
image = PIL.Image.fromarray(image_data, 'RGB')
image.save(save_path)
image = {
'file_name': image_file_name,
'height': 256,
'width': 256,
'id': 11,
}
annotations_list = [{
'area': .5,
'iscrowd': False,
'image_id': 11,
'bbox': [64, 64, 128, 128],
'category_id': 2,
'id': 1000,
}]
image_dir = tmp_dir
category_index = {
1: {
'name': 'dog',
'id': 1
},
2: {
'name': 'cat',
'id': 2
},
3: {
'name': 'human',
'id': 3
}
}
(_, example,
num_annotations_skipped) = create_coco_tf_record.create_tf_example(
image, annotations_list, image_dir, category_index)
self.assertEqual(num_annotations_skipped, 0)
self._assertProtoEqual(
example.features.feature['image/height'].int64_list.value, [256])
self._assertProtoEqual(
example.features.feature['image/width'].int64_list.value, [256])
self._assertProtoEqual(
example.features.feature['image/filename'].bytes_list.value,
[image_file_name])
self._assertProtoEqual(
example.features.feature['image/source_id'].bytes_list.value,
[str(image['id'])])
self._assertProtoEqual(
example.features.feature['image/format'].bytes_list.value, ['jpeg'])
self._assertProtoEqual(
example.features.feature['image/object/bbox/xmin'].float_list.value,
[0.25])
self._assertProtoEqual(
example.features.feature['image/object/bbox/ymin'].float_list.value,
[0.25])
self._assertProtoEqual(
example.features.feature['image/object/bbox/xmax'].float_list.value,
[0.75])
self._assertProtoEqual(
example.features.feature['image/object/bbox/ymax'].float_list.value,
[0.75])
self._assertProtoEqual(
example.features.feature['image/object/class/text'].bytes_list.value,
['cat'])
def test_create_tf_example_with_instance_masks(self):
image_file_name = 'tmp_image.jpg'
image_data = np.random.rand(8, 8, 3)
tmp_dir = self.get_temp_dir()
save_path = os.path.join(tmp_dir, image_file_name)
image = PIL.Image.fromarray(image_data, 'RGB')
image.save(save_path)
image = {
'file_name': image_file_name,
'height': 8,
'width': 8,
'id': 11,
}
annotations_list = [{
'area': .5,
'iscrowd': False,
'image_id': 11,
'bbox': [0, 0, 8, 8],
'segmentation': [[4, 0, 0, 0, 0, 4], [8, 4, 4, 8, 8, 8]],
'category_id': 1,
'id': 1000,
}]
image_dir = tmp_dir
category_index = {
1: {
'name': 'dog',
'id': 1
},
}
(_, example,
num_annotations_skipped) = create_coco_tf_record.create_tf_example(
image, annotations_list, image_dir, category_index, include_masks=True)
self.assertEqual(num_annotations_skipped, 0)
self._assertProtoEqual(
example.features.feature['image/height'].int64_list.value, [8])
self._assertProtoEqual(
example.features.feature['image/width'].int64_list.value, [8])
self._assertProtoEqual(
example.features.feature['image/filename'].bytes_list.value,
[image_file_name])
self._assertProtoEqual(
example.features.feature['image/source_id'].bytes_list.value,
[str(image['id'])])
self._assertProtoEqual(
example.features.feature['image/format'].bytes_list.value, ['jpeg'])
self._assertProtoEqual(
example.features.feature['image/object/bbox/xmin'].float_list.value,
[0])
self._assertProtoEqual(
example.features.feature['image/object/bbox/ymin'].float_list.value,
[0])
self._assertProtoEqual(
example.features.feature['image/object/bbox/xmax'].float_list.value,
[1])
self._assertProtoEqual(
example.features.feature['image/object/bbox/ymax'].float_list.value,
[1])
self._assertProtoEqual(
example.features.feature['image/object/class/text'].bytes_list.value,
['dog'])
encoded_mask_pngs = [
io.BytesIO(encoded_masks) for encoded_masks in example.features.feature[
'image/object/mask'].bytes_list.value
]
pil_masks = [
np.array(PIL.Image.open(encoded_mask_png))
for encoded_mask_png in encoded_mask_pngs
]
self.assertTrue(len(pil_masks) == 1)
self.assertAllEqual(pil_masks[0],
[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1, 1]])
def test_create_sharded_tf_record(self):
tmp_dir = self.get_temp_dir()
image_paths = ['tmp1_image.jpg', 'tmp2_image.jpg']
for image_path in image_paths:
image_data = np.random.rand(256, 256, 3)
save_path = os.path.join(tmp_dir, image_path)
image = PIL.Image.fromarray(image_data, 'RGB')
image.save(save_path)
images = [{
'file_name': image_paths[0],
'height': 256,
'width': 256,
'id': 11,
}, {
'file_name': image_paths[1],
'height': 256,
'width': 256,
'id': 12,
}]
annotations = [{
'area': .5,
'iscrowd': False,
'image_id': 11,
'bbox': [64, 64, 128, 128],
'category_id': 2,
'id': 1000,
}]
category_index = [{
'name': 'dog',
'id': 1
}, {
'name': 'cat',
'id': 2
}, {
'name': 'human',
'id': 3
}]
groundtruth_data = {'images': images, 'annotations': annotations,
'categories': category_index}
annotation_file = os.path.join(tmp_dir, 'annotation.json')
with open(annotation_file, 'w') as annotation_fid:
json.dump(groundtruth_data, annotation_fid)
output_path = os.path.join(tmp_dir, 'out.record')
create_coco_tf_record._create_tf_record_from_coco_annotations(
annotation_file,
tmp_dir,
output_path,
False,
2)
self.assertTrue(os.path.exists(output_path + '-00000-of-00002'))
self.assertTrue(os.path.exists(output_path + '-00001-of-00002'))
if __name__ == '__main__':
tf.test.main()
|
PyTorch/SpeechRecognition/wav2vec2/common/fairseq/modules | modules | same_pad | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from torch import nn
class SamePad(nn.Module):
def __init__(self, kernel_size, causal=False):
super().__init__()
if causal:
self.remove = kernel_size - 1
else:
self.remove = 1 if kernel_size % 2 == 0 else 0
def forward(self, x):
if self.remove > 0:
x = x[:, :, : -self.remove]
return x
|
Tools/PyTorch/TimeSeriesPredictionPlatform/conf/model | model | lstm | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
_target_: models.lstm.LSTM
config:
hidden_size: 128
dropout: 0.1
missing_data_strategy: 'mask'
defaults:
- _self_
- /trainer@_global_/trainer: ctltrainer
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/plugins/taco2AttentionPlugin | taco2AttentionPlugin | taco2AttentionLayerPlugin | /*
* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "taco2AttentionLayerPlugin.h"
#include "taco2AttentionLayerKernel.h"
#include "taco2Utils.h"
#include <cassert>
#include <cstdlib>
#include <cstring>
#include <cuda_runtime.h> // cudaError_t
#include <iostream>
#include <sstream>
#include <stdexcept>
#include <string>
namespace nvinfer1
{
namespace plugin
{
using value_type = Taco2AttentionLayerPlugin::value_type;
/******************************************************************************
* CONSTANTS ******************************************************************
*****************************************************************************/
namespace
{
constexpr const char* const PLUGIN_NAME = "Taco2Attention";
constexpr const char* const PLUGIN_VERSION = "0.1.0";
} // namespace
/******************************************************************************
* HELPER FUNCTIONS ***********************************************************
*****************************************************************************/
namespace
{
std::vector<value_type> toVector(const Weights& weights)
{
if (weights.type != DataType::kFLOAT)
{
throw std::runtime_error(
"Invalid data type for Attention weights: " + std::to_string(static_cast<int>(weights.type)));
}
const value_type* const valuesBegin = static_cast<const value_type*>(weights.values);
const value_type* const valuesEnd = valuesBegin + weights.count;
return std::vector<value_type>(valuesBegin, valuesEnd);
}
const void* offset(const void* ptr, const size_t offset)
{
return reinterpret_cast<const void*>(static_cast<const uint8_t*>(ptr) + offset);
}
} // namespace
/******************************************************************************
* STATIC METHODS *************************************************************
*****************************************************************************/
const char* Taco2AttentionLayerPlugin::getName()
{
return PLUGIN_NAME;
}
const char* Taco2AttentionLayerPlugin::getVersion()
{
return PLUGIN_VERSION;
}
Taco2AttentionLayerPlugin Taco2AttentionLayerPlugin::deserialize(const void* const data, const size_t length)
{
static constexpr const size_t numDims = 5;
if (length < numDims * sizeof(int32_t))
{
throw std::runtime_error("Invalid serialized size: " + std::to_string(length));
}
const int numEncodingDimension = static_cast<const int32_t*>(data)[0];
const int numQueryDimension = static_cast<const int32_t*>(data)[1];
const int numFilters = static_cast<const int32_t*>(data)[2];
const int convKernelSize = static_cast<const int32_t*>(data)[3];
const int numAttentionDimension = static_cast<const int32_t*>(data)[4];
const int numQueryWeights = numQueryDimension * numAttentionDimension;
const int numConvWeights = numFilters * 2 * convKernelSize;
const int numLocationWeights = numFilters * numAttentionDimension;
const int numEnergyWeights = numAttentionDimension;
const size_t reqSize = numDims * sizeof(int32_t)
+ sizeof(value_type) * (numQueryWeights + numConvWeights + numLocationWeights + numEnergyWeights);
if (reqSize != length)
{
throw std::runtime_error(
"Invalid serialized size: " + std::to_string(length) + " / " + std::to_string(reqSize));
}
const Weights queryWeights{DataType::kFLOAT, offset(data, numDims * sizeof(int32_t)), numQueryWeights};
const Weights convWeights{
DataType::kFLOAT, offset(queryWeights.values, sizeof(value_type) * numQueryWeights), numConvWeights};
const Weights locationWeights{
DataType::kFLOAT, offset(convWeights.values, sizeof(value_type) * numConvWeights), numLocationWeights};
const Weights energyWeights{
DataType::kFLOAT, offset(locationWeights.values, sizeof(value_type) * numLocationWeights), numEnergyWeights};
return Taco2AttentionLayerPlugin(numEncodingDimension, numQueryDimension, numFilters, convKernelSize,
numAttentionDimension, queryWeights, convWeights, locationWeights, energyWeights);
}
/******************************************************************************
* CONSTRUCTORS / DESTRUCTOR **************************************************
*****************************************************************************/
Taco2AttentionLayerPlugin::Taco2AttentionLayerPlugin(int encDimension, int queryDimension, int numFilters,
int convKernelSize, int attDimension, const nvinfer1::Weights& queryWeights, const nvinfer1::Weights& convWeights,
const nvinfer1::Weights& locationWeights, const nvinfer1::Weights& energyWeights)
: mNumEncodingDimension(encDimension)
, mNumQueryDimension(queryDimension)
, mNumFilters(numFilters)
, mConvKernelSize(convKernelSize)
, mNumAttentionDimension(attDimension)
, mQueryWeightsHost(toVector(queryWeights))
, mConvWeightsHost(toVector(convWeights))
, mLocationWeightsHost(toVector(locationWeights))
, mEnergyWeightsHost(toVector(energyWeights))
, mKernel(nullptr)
, mNamespace()
{
const size_t expectedQueryWeights = mNumQueryDimension * mNumAttentionDimension;
const size_t expectedConvWeights = mNumFilters * mConvKernelSize * 2;
const size_t expectedLocationWeights = mNumFilters * mNumAttentionDimension;
const size_t expectedEnergyWeights = mNumAttentionDimension;
if (mQueryWeightsHost.size() != expectedQueryWeights)
{
throw std::runtime_error("Attention expected " + std::to_string(expectedQueryWeights)
+ " query weights but given " + std::to_string(mQueryWeightsHost.size()));
}
if (mConvWeightsHost.size() != expectedConvWeights)
{
throw std::runtime_error("Attention expected " + std::to_string(expectedConvWeights)
+ " conv weights but given " + std::to_string(mConvWeightsHost.size()));
}
if (mLocationWeightsHost.size() != expectedLocationWeights)
{
throw std::runtime_error("Attention expected " + std::to_string(expectedLocationWeights)
+ " location weights but given " + std::to_string(mLocationWeightsHost.size()));
}
if (mEnergyWeightsHost.size() != expectedEnergyWeights)
{
throw std::runtime_error("Attention expected " + std::to_string(expectedEnergyWeights)
+ " energy weights but given " + std::to_string(mEnergyWeightsHost.size()));
}
}
Taco2AttentionLayerPlugin::Taco2AttentionLayerPlugin(Taco2AttentionLayerPlugin&& other)
: mNumEncodingDimension(other.mNumEncodingDimension)
, mNumQueryDimension(other.mNumQueryDimension)
, mNumFilters(other.mNumFilters)
, mConvKernelSize(other.mConvKernelSize)
, mNumAttentionDimension(other.mNumAttentionDimension)
, mQueryWeightsHost(std::move(other.mQueryWeightsHost))
, mConvWeightsHost(std::move(other.mConvWeightsHost))
, mLocationWeightsHost(std::move(other.mLocationWeightsHost))
, mEnergyWeightsHost(std::move(other.mEnergyWeightsHost))
, mKernel(std::move(other.mKernel))
, mNamespace(std::move(other.mNamespace))
{
other.mNumEncodingDimension = 0;
other.mNumQueryDimension = 0;
other.mNumFilters = 0;
other.mConvKernelSize = 0;
other.mNumAttentionDimension = 0;
}
Taco2AttentionLayerPlugin::~Taco2AttentionLayerPlugin()
{
destroy();
}
/******************************************************************************
* PUBLIC METHODS *************************************************************
*****************************************************************************/
Taco2AttentionLayerPlugin& Taco2AttentionLayerPlugin::operator=(Taco2AttentionLayerPlugin&& other)
{
// defere to constructor
*this = Taco2AttentionLayerPlugin(std::move(other));
return *this;
}
DataType Taco2AttentionLayerPlugin::getOutputDataType(
const int /* index */, const DataType* const /* inputTypes */, const int /* nbInputs */) const
{
return DataType::kFLOAT;
}
const char* Taco2AttentionLayerPlugin::getPluginType() const
{
return getName();
}
const char* Taco2AttentionLayerPlugin::getPluginVersion() const
{
return getVersion();
}
int Taco2AttentionLayerPlugin::getNbOutputs() const
{
return 2;
}
DimsExprs Taco2AttentionLayerPlugin::getOutputDimensions(
const int outputIndex, const DimsExprs* inputs, const int nbInputs, IExprBuilder& exprBuilder)
{
if (outputIndex >= getNbOutputs())
{
throw std::runtime_error(
"Invalid output index: " + std::to_string(outputIndex) + " / " + std::to_string(getNbOutputs()) + ".");
}
if (nbInputs != NUM_INPUTS)
{
throw std::runtime_error(
"Can only handle " + std::to_string(NUM_INPUTS) + " input tensors: " + std::to_string(nbInputs));
}
if (outputIndex == CONTEXT_OUTPUT)
{
return DimsExprs{
3, {inputs[MEMORY_INDEX].d[0], exprBuilder.constant(1), exprBuilder.constant(mNumEncodingDimension)}};
}
else if (outputIndex == WEIGHT_OUTPUT)
{
return DimsExprs{3, {inputs[MEMORY_INDEX].d[0], exprBuilder.constant(2), inputs[MEMORY_INDEX].d[1]}};
}
else
{
throw std::runtime_error("Unknown output index: " + std::to_string(outputIndex));
}
}
bool Taco2AttentionLayerPlugin::supportsFormatCombination(
const int pos, const PluginTensorDesc* const inOut, const int /* nbInputs */, const int /* nbOutputs */)
{
return inOut[pos].format == TensorFormat::kLINEAR && inOut[pos].type == DataType::kFLOAT;
}
void Taco2AttentionLayerPlugin::configurePlugin(const DynamicPluginTensorDesc* const in, const int nbInputs,
const DynamicPluginTensorDesc* const out, const int nbOutputs)
{
if (nbInputs != NUM_INPUTS)
{
throw std::runtime_error(
"Can only handle " + std::to_string(NUM_INPUTS) + " input tensors: " + std::to_string(nbInputs));
}
for (int i = 0; i < nbInputs; ++i)
{
if (in[i].desc.type != DataType::kFLOAT)
{
throw std::runtime_error("Only FLOAT supported as input " + std::to_string(i) + " : "
+ std::to_string(static_cast<int>(in[i].desc.type)));
}
}
// assert dimensions
if (in[MEMORY_INDEX].desc.dims.d[2] != mNumEncodingDimension)
{
throw std::runtime_error("Memory input must be L x " + std::to_string(mNumEncodingDimension) + " but got "
+ taco2::Taco2Utils::dimsToString(in[MEMORY_INDEX].desc.dims));
}
if (in[PROCESSED_MEMORY_INDEX].desc.dims.d[2] != mNumAttentionDimension)
{
throw std::runtime_error("Processed Memory input must be L x " + std::to_string(mNumAttentionDimension)
+ " but got " + taco2::Taco2Utils::dimsToString(in[PROCESSED_MEMORY_INDEX].desc.dims));
}
if (in[WEIGHT_INDEX].desc.dims.d[1] != 2)
{
throw std::runtime_error(
"Weights input must be 2 x L but got " + taco2::Taco2Utils::dimsToString(in[WEIGHT_INDEX].desc.dims));
}
if (taco2::Taco2Utils::getDimensionsSize(in[ATTENTION_HIDDEN_INDEX].desc.dims)
!= static_cast<size_t>(mNumQueryDimension))
{
throw std::runtime_error("Attention hidden input must be " + std::to_string(mNumQueryDimension) + " but got "
+ taco2::Taco2Utils::dimsToString(in[ATTENTION_HIDDEN_INDEX].desc.dims) + " ("
+ std::to_string(taco2::Taco2Utils::getDimensionsSize(in[ATTENTION_HIDDEN_INDEX].desc.dims)) + ").");
}
if (nbOutputs != NUM_OUTPUTS)
{
throw std::runtime_error("Only two outputs is implemented: " + std::to_string(nbOutputs));
}
for (int i = 0; i < nbOutputs; ++i)
{
if (out[i].desc.type != DataType::kFLOAT)
{
throw std::runtime_error("Only FLOAT supported as output: " + std::to_string(i) + " : "
+ std::to_string(static_cast<int>(out[i].desc.type)));
}
}
}
int Taco2AttentionLayerPlugin::initialize()
{
try
{
mKernel.reset(
new Taco2AttentionLayerKernel(mQueryWeightsHost, mConvWeightsHost, mLocationWeightsHost, mEnergyWeightsHost,
mNumEncodingDimension, mNumQueryDimension, mNumFilters, mConvKernelSize, mNumAttentionDimension));
}
catch (const std::exception& e)
{
std::cerr << "Taco2AttentionLayerPlugin initialization failed: " << e.what() << std::endl;
return 1;
}
return 0;
}
void Taco2AttentionLayerPlugin::terminate()
{
mKernel.reset();
}
size_t Taco2AttentionLayerPlugin::getWorkspaceSize(
const PluginTensorDesc* const in, const int nbInputs, const PluginTensorDesc* const /* out */, const int /* nbOutputs */) const
{
if (nbInputs != NUM_INPUTS) {
throw std::runtime_error("Invalid number of inputs: " +
std::to_string(nbInputs) + ", but expected " + std::to_string(NUM_INPUTS));
}
const int inputLength = in[MEMORY_INDEX].dims.d[1];
const int batchSize = in[MEMORY_INDEX].dims.d[0];
// space for queryOutput (num attention dimensions),
// convOutput (input length*num filters), elemSum (input length), and
// energyScratch (inputLength).
const size_t numWorkspaceElements
= mNumAttentionDimension + // query output
(inputLength * mNumFilters) + // conv output
(mNumAttentionDimension * inputLength) + // elem sum
inputLength; // enery scratch
return numWorkspaceElements * sizeof(value_type) * batchSize;
}
int Taco2AttentionLayerPlugin::enqueue(const PluginTensorDesc* const inputDesc,
const PluginTensorDesc* /* outputDesc */,
const void* const* const inputs, void* const* const outputs, void* const workspace, cudaStream_t stream)
{
const int inputLength = inputDesc[MEMORY_INDEX].dims.d[1];
const int batchSize = inputDesc[MEMORY_INDEX].dims.d[0];
if (batchSize != 1)
{
// we only support batch size of 1 right now
std::cerr << "Taco2AttentionLayerPlugin plugin does not support batch size other than "
"1: got "
<< batchSize << std::endl;
std::cerr << "Recompile without plugins to use a larger batch size." << std::endl;
return 1;
}
// name inputs and outputs
const value_type* const memoryDevice = static_cast<const value_type*>(inputs[MEMORY_INDEX]);
const value_type* const processedMemoryDevice = static_cast<const value_type*>(inputs[PROCESSED_MEMORY_INDEX]);
const value_type* const weightsDevice = static_cast<const value_type*>(inputs[WEIGHT_INDEX]);
const value_type* const attentionHiddenDevice = static_cast<const value_type*>(inputs[ATTENTION_HIDDEN_INDEX]);
value_type* const outputContextDevice = static_cast<value_type*>(outputs[CONTEXT_OUTPUT]);
value_type* const outputWeightsDevice = static_cast<value_type*>(outputs[WEIGHT_OUTPUT]);
try
{
mKernel->execute(memoryDevice, processedMemoryDevice, weightsDevice, attentionHiddenDevice, outputContextDevice,
outputWeightsDevice, inputLength, static_cast<value_type*>(workspace), stream);
}
catch (const std::exception& e)
{
std::cerr << "Taco2AttentionLayerPlugin failed: " << e.what() << std::endl;
return 1;
}
return 0;
}
size_t Taco2AttentionLayerPlugin::getSerializationSize() const
{
const int numQueryWeights = mNumQueryDimension * mNumAttentionDimension;
const int numConvWeights = mNumFilters * 2 * mConvKernelSize;
const int numLocationWeights = mNumFilters * mNumAttentionDimension;
const int numEnergyWeights = mNumAttentionDimension;
return 5 * sizeof(int32_t)
+ sizeof(value_type) * (numQueryWeights + numConvWeights + numLocationWeights + numEnergyWeights);
}
void Taco2AttentionLayerPlugin::serialize(void* const buffer) const
{
static_cast<int32_t*>(buffer)[0] = mNumEncodingDimension;
static_cast<int32_t*>(buffer)[1] = mNumQueryDimension;
static_cast<int32_t*>(buffer)[2] = mNumFilters;
static_cast<int32_t*>(buffer)[3] = mConvKernelSize;
static_cast<int32_t*>(buffer)[4] = mNumAttentionDimension;
float* const queryWeights = reinterpret_cast<float*>(static_cast<int32_t*>(buffer) + 5);
float* const convWeights = queryWeights + mQueryWeightsHost.size();
float* const locationWeights = convWeights + mConvWeightsHost.size();
float* const energyWeights = locationWeights + mLocationWeightsHost.size();
memcpy(queryWeights, mQueryWeightsHost.data(), sizeof(value_type) * mQueryWeightsHost.size());
memcpy(convWeights, mConvWeightsHost.data(), sizeof(value_type) * mConvWeightsHost.size());
memcpy(locationWeights, mLocationWeightsHost.data(), sizeof(value_type) * mLocationWeightsHost.size());
memcpy(energyWeights, mEnergyWeightsHost.data(), sizeof(value_type) * mEnergyWeightsHost.size());
}
void Taco2AttentionLayerPlugin::destroy()
{
terminate();
}
IPluginV2DynamicExt* Taco2AttentionLayerPlugin::clone() const
{
// call constructor which copy's data
Taco2AttentionLayerPlugin clone(mNumEncodingDimension, mNumQueryDimension, mNumFilters, mConvKernelSize,
mNumAttentionDimension,
Weights{DataType::kFLOAT, mQueryWeightsHost.data(), static_cast<int64_t>(mQueryWeightsHost.size())},
Weights{DataType::kFLOAT, mConvWeightsHost.data(), static_cast<int64_t>(mConvWeightsHost.size())},
Weights{DataType::kFLOAT, mLocationWeightsHost.data(), static_cast<int64_t>(mLocationWeightsHost.size())},
Weights{DataType::kFLOAT, mEnergyWeightsHost.data(), static_cast<int64_t>(mEnergyWeightsHost.size())});
if (mKernel)
{
// initialize the clone too
clone.initialize();
}
// move it to the heap last to avoid exceptions causing memory leaks
return new Taco2AttentionLayerPlugin(std::move(clone));
}
void Taco2AttentionLayerPlugin::setPluginNamespace(const char* pluginNamespace)
{
mNamespace = pluginNamespace;
}
const char* Taco2AttentionLayerPlugin::getPluginNamespace() const
{
return mNamespace.c_str();
}
} // namespace plugin
} // namespace nvinfer1
|
PyTorch/Recommendation/DLRM/dlrm/data | data | samplers | # Copyright (c) 2021 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from torch.utils.data import RandomSampler
from dlrm.utils.distributed import get_local_rank
class RandomDistributedSampler(RandomSampler):
_SAMPLE_FILE = "/tmp/dlrm_training_sample.npy"
def __iter__(self):
"""
To guarantee all ranks have the same same permutation, generating it from rank 0 and sync
to other rank by writing to disk
"""
if get_local_rank() == 0:
np.save(self._SAMPLE_FILE, np.array(list(super().__iter__())))
torch.distributed.barrier()
sample = np.load(self._SAMPLE_FILE)
return iter(sample)
|
TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads | heads | class_head | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Class Head.
Contains Class prediction head classes for different meta architectures.
All the class prediction heads have a predict function that receives the
`features` as the first argument and returns class predictions with background.
"""
import functools
import tensorflow as tf
from object_detection.predictors.heads import head
slim = tf.contrib.slim
class MaskRCNNClassHead(head.Head):
"""Mask RCNN class prediction head.
Please refer to Mask RCNN paper:
https://arxiv.org/abs/1703.06870
"""
def __init__(self,
is_training,
num_class_slots,
fc_hyperparams_fn,
use_dropout,
dropout_keep_prob):
"""Constructor.
Args:
is_training: Indicates whether the BoxPredictor is in training mode.
num_class_slots: number of class slots. Note that num_class_slots may or
may not include an implicit background category.
fc_hyperparams_fn: A function to generate tf-slim arg_scope with
hyperparameters for fully connected ops.
use_dropout: Option to use dropout or not. Note that a single dropout
op is applied here prior to both box and class predictions, which stands
in contrast to the ConvolutionalBoxPredictor below.
dropout_keep_prob: Keep probability for dropout.
This is only used if use_dropout is True.
"""
super(MaskRCNNClassHead, self).__init__()
self._is_training = is_training
self._num_class_slots = num_class_slots
self._fc_hyperparams_fn = fc_hyperparams_fn
self._use_dropout = use_dropout
self._dropout_keep_prob = dropout_keep_prob
def predict(self, features, num_predictions_per_location=1):
"""Predicts boxes and class scores.
Args:
features: A float tensor of shape [batch_size, height, width, channels]
containing features for a batch of images.
num_predictions_per_location: Int containing number of predictions per
location.
Returns:
class_predictions_with_background: A float tensor of shape
[batch_size, 1, num_class_slots] representing the class predictions for
the proposals.
Raises:
ValueError: If num_predictions_per_location is not 1.
"""
if num_predictions_per_location != 1:
raise ValueError('Only num_predictions_per_location=1 is supported')
spatial_averaged_roi_pooled_features = tf.reduce_mean(
features, [1, 2], keep_dims=True, name='AvgPool')
flattened_roi_pooled_features = slim.flatten(
spatial_averaged_roi_pooled_features)
if self._use_dropout:
flattened_roi_pooled_features = slim.dropout(
flattened_roi_pooled_features,
keep_prob=self._dropout_keep_prob,
is_training=self._is_training)
with slim.arg_scope(self._fc_hyperparams_fn()):
class_predictions_with_background = slim.fully_connected(
flattened_roi_pooled_features,
self._num_class_slots,
activation_fn=None,
scope='ClassPredictor')
class_predictions_with_background = tf.reshape(
class_predictions_with_background,
[-1, 1, self._num_class_slots])
return class_predictions_with_background
class ConvolutionalClassHead(head.Head):
"""Convolutional class prediction head."""
def __init__(self,
is_training,
num_class_slots,
use_dropout,
dropout_keep_prob,
kernel_size,
apply_sigmoid_to_scores=False,
class_prediction_bias_init=0.0,
use_depthwise=False):
"""Constructor.
Args:
is_training: Indicates whether the BoxPredictor is in training mode.
num_class_slots: number of class slots. Note that num_class_slots may or
may not include an implicit background category.
use_dropout: Option to use dropout or not. Note that a single dropout
op is applied here prior to both box and class predictions, which stands
in contrast to the ConvolutionalBoxPredictor below.
dropout_keep_prob: Keep probability for dropout.
This is only used if use_dropout is True.
kernel_size: Size of final convolution kernel. If the
spatial resolution of the feature map is smaller than the kernel size,
then the kernel size is automatically set to be
min(feature_width, feature_height).
apply_sigmoid_to_scores: if True, apply the sigmoid on the output
class_predictions.
class_prediction_bias_init: constant value to initialize bias of the last
conv2d layer before class prediction.
use_depthwise: Whether to use depthwise convolutions for prediction
steps. Default is False.
Raises:
ValueError: if min_depth > max_depth.
"""
super(ConvolutionalClassHead, self).__init__()
self._is_training = is_training
self._num_class_slots = num_class_slots
self._use_dropout = use_dropout
self._dropout_keep_prob = dropout_keep_prob
self._kernel_size = kernel_size
self._apply_sigmoid_to_scores = apply_sigmoid_to_scores
self._class_prediction_bias_init = class_prediction_bias_init
self._use_depthwise = use_depthwise
def predict(self, features, num_predictions_per_location):
"""Predicts boxes.
Args:
features: A float tensor of shape [batch_size, height, width, channels]
containing image features.
num_predictions_per_location: Number of box predictions to be made per
spatial location.
Returns:
class_predictions_with_background: A float tensors of shape
[batch_size, num_anchors, num_class_slots] representing the class
predictions for the proposals.
"""
net = features
if self._use_dropout:
net = slim.dropout(net, keep_prob=self._dropout_keep_prob)
if self._use_depthwise:
class_predictions_with_background = slim.separable_conv2d(
net, None, [self._kernel_size, self._kernel_size],
padding='SAME', depth_multiplier=1, stride=1,
rate=1, scope='ClassPredictor_depthwise')
class_predictions_with_background = slim.conv2d(
class_predictions_with_background,
num_predictions_per_location * self._num_class_slots, [1, 1],
activation_fn=None,
normalizer_fn=None,
normalizer_params=None,
scope='ClassPredictor')
else:
class_predictions_with_background = slim.conv2d(
net,
num_predictions_per_location * self._num_class_slots,
[self._kernel_size, self._kernel_size],
activation_fn=None,
normalizer_fn=None,
normalizer_params=None,
scope='ClassPredictor',
biases_initializer=tf.constant_initializer(
self._class_prediction_bias_init))
if self._apply_sigmoid_to_scores:
class_predictions_with_background = tf.sigmoid(
class_predictions_with_background)
batch_size = features.get_shape().as_list()[0]
if batch_size is None:
batch_size = tf.shape(features)[0]
class_predictions_with_background = tf.reshape(
class_predictions_with_background,
[batch_size, -1, self._num_class_slots])
return class_predictions_with_background
# TODO(alirezafathi): See if possible to unify Weight Shared with regular
# convolutional class head.
class WeightSharedConvolutionalClassHead(head.Head):
"""Weight shared convolutional class prediction head.
This head allows sharing the same set of parameters (weights) when called more
then once on different feature maps.
"""
def __init__(self,
num_class_slots,
kernel_size=3,
class_prediction_bias_init=0.0,
use_dropout=False,
dropout_keep_prob=0.8,
use_depthwise=False,
score_converter_fn=tf.identity):
"""Constructor.
Args:
num_class_slots: number of class slots. Note that num_class_slots may or
may not include an implicit background category.
kernel_size: Size of final convolution kernel.
class_prediction_bias_init: constant value to initialize bias of the last
conv2d layer before class prediction.
use_dropout: Whether to apply dropout to class prediction head.
dropout_keep_prob: Probability of keeping activiations.
use_depthwise: Whether to use depthwise convolutions for prediction
steps. Default is False.
score_converter_fn: Callable elementwise nonlinearity (that takes tensors
as inputs and returns tensors).
"""
super(WeightSharedConvolutionalClassHead, self).__init__()
self._num_class_slots = num_class_slots
self._kernel_size = kernel_size
self._class_prediction_bias_init = class_prediction_bias_init
self._use_dropout = use_dropout
self._dropout_keep_prob = dropout_keep_prob
self._use_depthwise = use_depthwise
self._score_converter_fn = score_converter_fn
def predict(self, features, num_predictions_per_location):
"""Predicts boxes.
Args:
features: A float tensor of shape [batch_size, height, width, channels]
containing image features.
num_predictions_per_location: Number of box predictions to be made per
spatial location.
Returns:
class_predictions_with_background: A tensor of shape
[batch_size, num_anchors, num_class_slots] representing the class
predictions for the proposals.
"""
class_predictions_net = features
if self._use_dropout:
class_predictions_net = slim.dropout(
class_predictions_net, keep_prob=self._dropout_keep_prob)
if self._use_depthwise:
conv_op = functools.partial(slim.separable_conv2d, depth_multiplier=1)
else:
conv_op = slim.conv2d
class_predictions_with_background = conv_op(
class_predictions_net,
num_predictions_per_location * self._num_class_slots,
[self._kernel_size, self._kernel_size],
activation_fn=None, stride=1, padding='SAME',
normalizer_fn=None,
biases_initializer=tf.constant_initializer(
self._class_prediction_bias_init),
scope='ClassPredictor')
batch_size = features.get_shape().as_list()[0]
if batch_size is None:
batch_size = tf.shape(features)[0]
class_predictions_with_background = self._score_converter_fn(
class_predictions_with_background)
class_predictions_with_background = tf.reshape(
class_predictions_with_background,
[batch_size, -1, self._num_class_slots])
return class_predictions_with_background
|
PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head | box_head | box_head | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from .roi_box_feature_extractors import make_roi_box_feature_extractor
from .roi_box_predictors import make_roi_box_predictor
from .inference import make_roi_box_post_processor
from .loss import make_roi_box_loss_evaluator
class ROIBoxHead(torch.nn.Module):
"""
Generic Box Head class.
"""
def __init__(self, cfg):
super(ROIBoxHead, self).__init__()
self.feature_extractor = make_roi_box_feature_extractor(cfg)
self.predictor = make_roi_box_predictor(cfg)
self.post_processor = make_roi_box_post_processor(cfg)
self.loss_evaluator = make_roi_box_loss_evaluator(cfg)
def forward(self, features, proposals, targets=None):
"""
Arguments:
features (list[Tensor]): feature-maps from possibly several levels
proposals (list[BoxList]): proposal boxes
targets (list[BoxList], optional): the ground-truth targets.
Returns:
x (Tensor): the result of the feature extractor
proposals (list[BoxList]): during training, the subsampled proposals
are returned. During testing, the predicted boxlists are returned
losses (dict[Tensor]): During training, returns the losses for the
head. During testing, returns an empty dict.
"""
if self.training:
# Faster R-CNN subsamples during training the proposals with a fixed
# positive / negative ratio
with torch.no_grad():
proposals = self.loss_evaluator.subsample(proposals, targets)
# extract features that will be fed to the final classifier. The
# feature_extractor generally corresponds to the pooler + heads
x = self.feature_extractor(features, proposals)
# final classifier that converts the features into predictions
class_logits, box_regression = self.predictor(x)
if not self.training:
result = self.post_processor((class_logits, box_regression), proposals)
return x, result, {}
loss_classifier, loss_box_reg = self.loss_evaluator(
[class_logits], [box_regression]
)
return (
x,
proposals,
dict(loss_classifier=loss_classifier, loss_box_reg=loss_box_reg),
)
def build_roi_box_head(cfg):
"""
Constructs a new box head.
By default, uses ROIBoxHead, but if it turns out not to be enough, just register a new class
and make it a parameter in the config
"""
return ROIBoxHead(cfg)
|
TensorFlow2/LanguageModeling/BERT/official/nlp/modeling/networks | networks | transformer_encoder | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Transformer-based text encoder network."""
from __future__ import absolute_import
from __future__ import division
# from __future__ import google_type_annotations
from __future__ import print_function
import tensorflow as tf
from official.modeling import activations
from official.nlp.modeling import layers
@tf.keras.utils.register_keras_serializable(package='Text')
class TransformerEncoder(tf.keras.Model):
"""Bi-directional Transformer-based encoder network.
This network implements a bi-directional Transformer-based encoder as
described in "BERT: Pre-training of Deep Bidirectional Transformers for
Language Understanding" (https://arxiv.org/abs/1810.04805). It includes the
embedding lookups and transformer layers, but not the masked language model
or classification task networks.
The default values for this object are taken from the BERT-Base implementation
in "BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding".
Attributes:
vocab_size: The size of the token vocabulary.
hidden_size: The size of the transformer hidden layers.
num_layers: The number of transformer layers.
num_attention_heads: The number of attention heads for each transformer. The
hidden size must be divisible by the number of attention heads.
sequence_length: The sequence length that this encoder expects. If None, the
sequence length is dynamic; if an integer, the encoder will require
sequences padded to this length.
max_sequence_length: The maximum sequence length that this encoder can
consume. If None, max_sequence_length uses the value from sequence length.
This determines the variable shape for positional embeddings.
type_vocab_size: The number of types that the 'type_ids' input can take.
intermediate_size: The intermediate size for the transformer layers.
activation: The activation to use for the transformer layers.
dropout_rate: The dropout rate to use for the transformer layers.
attention_dropout_rate: The dropout rate to use for the attention layers
within the transformer layers.
initializer: The initialzer to use for all weights in this encoder.
float_dtype: The dtype of this encoder. Can be 'float32' or 'float16'.
"""
def __init__(self,
vocab_size,
hidden_size=768,
num_layers=12,
num_attention_heads=12,
sequence_length=512,
max_sequence_length=None,
type_vocab_size=16,
intermediate_size=3072,
activation=activations.gelu,
dropout_rate=0.1,
attention_dropout_rate=0.1,
initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),
float_dtype='float32',
**kwargs):
activation = tf.keras.activations.get(activation)
initializer = tf.keras.initializers.get(initializer)
if not max_sequence_length:
max_sequence_length = sequence_length
self._self_setattr_tracking = False
self._config_dict = {
'vocab_size': vocab_size,
'hidden_size': hidden_size,
'num_layers': num_layers,
'num_attention_heads': num_attention_heads,
'sequence_length': sequence_length,
'max_sequence_length': max_sequence_length,
'type_vocab_size': type_vocab_size,
'intermediate_size': intermediate_size,
'activation': tf.keras.activations.serialize(activation),
'dropout_rate': dropout_rate,
'attention_dropout_rate': attention_dropout_rate,
'initializer': tf.keras.initializers.serialize(initializer),
'float_dtype': float_dtype,
}
word_ids = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name='input_word_ids')
mask = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name='input_mask')
type_ids = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name='input_type_ids')
self._embedding_layer = layers.OnDeviceEmbedding(
vocab_size=vocab_size,
embedding_width=hidden_size,
initializer=initializer,
name='word_embeddings')
word_embeddings = self._embedding_layer(word_ids)
# Always uses dynamic slicing for simplicity.
self._position_embedding_layer = layers.PositionEmbedding(
initializer=initializer,
use_dynamic_slicing=True,
max_sequence_length=max_sequence_length)
position_embeddings = self._position_embedding_layer(word_embeddings)
type_embeddings = (
layers.OnDeviceEmbedding(
vocab_size=type_vocab_size,
embedding_width=hidden_size,
initializer=initializer,
use_one_hot=True,
name='type_embeddings')(type_ids))
embeddings = tf.keras.layers.Add()(
[word_embeddings, position_embeddings, type_embeddings])
embeddings = (
tf.keras.layers.LayerNormalization(
name='embeddings/layer_norm',
axis=-1,
epsilon=1e-12,
dtype=tf.float32)(embeddings))
embeddings = (
tf.keras.layers.Dropout(rate=dropout_rate,
dtype=tf.float32)(embeddings))
if float_dtype == 'float16':
embeddings = tf.cast(embeddings, tf.float16)
data = embeddings
attention_mask = layers.SelfAttentionMask()([data, mask])
for i in range(num_layers):
layer = layers.Transformer(
num_attention_heads=num_attention_heads,
intermediate_size=intermediate_size,
intermediate_activation=activation,
dropout_rate=dropout_rate,
attention_dropout_rate=attention_dropout_rate,
kernel_initializer=initializer,
dtype=float_dtype,
name='transformer/layer_%d' % i)
data = layer([data, attention_mask])
first_token_tensor = (
tf.keras.layers.Lambda(lambda x: tf.squeeze(x[:, 0:1, :], axis=1))(data)
)
cls_output = tf.keras.layers.Dense(
units=hidden_size,
activation='tanh',
kernel_initializer=initializer,
name='pooler_transform')(
first_token_tensor)
super(TransformerEncoder, self).__init__(
inputs=[word_ids, mask, type_ids],
outputs=[data, cls_output],
**kwargs)
def get_embedding_table(self):
return self._embedding_layer.embeddings
def get_config(self):
return self._config_dict
@classmethod
def from_config(cls, config, custom_objects=None):
return cls(**config)
|
PyTorch/SpeechSynthesis/FastPitch/filelists | filelists | ljs_audio_pitch_text_test | wavs/LJ045-0096.wav|pitch/LJ045-0096.pt|Mrs. De Mohrenschildt thought that Oswald,
wavs/LJ049-0022.wav|pitch/LJ049-0022.pt|The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.
wavs/LJ033-0042.wav|pitch/LJ033-0042.pt|Between the hours of eight and nine p.m. they were occupied with the children in the bedrooms located at the extreme east end of the house.
wavs/LJ016-0117.wav|pitch/LJ016-0117.pt|The prisoner had nothing to deal with but wooden panels, and by dint of cutting and chopping he got both the lower panels out.
wavs/LJ025-0157.wav|pitch/LJ025-0157.pt|Under these circumstances, unnatural as they are, with proper management, the bean will thrust forth its radicle and its plumule;
wavs/LJ042-0219.wav|pitch/LJ042-0219.pt|Oswald demonstrated his thinking in connection with his return to the United States by preparing two sets of identical questions of the type which he might have thought
wavs/LJ032-0164.wav|pitch/LJ032-0164.pt|it is not possible to state with scientific certainty that a particular small group of fibers come from a certain piece of clothing
wavs/LJ046-0092.wav|pitch/LJ046-0092.pt|has confidence in the dedicated Secret Service men who are ready to lay down their lives for him
wavs/LJ050-0118.wav|pitch/LJ050-0118.pt|Since these agencies are already obliged constantly to evaluate the activities of such groups,
wavs/LJ043-0016.wav|pitch/LJ043-0016.pt|Jeanne De Mohrenschildt said, quote,
wavs/LJ021-0078.wav|pitch/LJ021-0078.pt|no economic panacea, which could simply revive over-night the heavy industries and the trades dependent upon them.
wavs/LJ039-0148.wav|pitch/LJ039-0148.pt|Examination of the cartridge cases found on the sixth floor of the Depository Building
wavs/LJ047-0202.wav|pitch/LJ047-0202.pt|testified that the information available to the Federal Government about Oswald before the assassination would, if known to PRS,
wavs/LJ023-0056.wav|pitch/LJ023-0056.pt|It is an easy document to understand when you remember that it was called into being
wavs/LJ021-0025.wav|pitch/LJ021-0025.pt|And in many directions, the intervention of that organized control which we call government
wavs/LJ030-0105.wav|pitch/LJ030-0105.pt|Communications in the motorcade.
wavs/LJ021-0012.wav|pitch/LJ021-0012.pt|with respect to industry and business, but nearly all are agreed that private enterprise in times such as these
wavs/LJ019-0169.wav|pitch/LJ019-0169.pt|and one or two men were allowed to mend clothes and make shoes. The rules made by the Secretary of State were hung up in conspicuous parts of the prison;
wavs/LJ039-0088.wav|pitch/LJ039-0088.pt|It just is an aid in seeing in the fact that you only have the one element, the crosshair,
wavs/LJ016-0192.wav|pitch/LJ016-0192.pt|"I think I could do that sort of job," said Calcraft, on the spur of the moment.
wavs/LJ014-0142.wav|pitch/LJ014-0142.pt|was strewn in front of the dock, and sprinkled it towards the bench with a contemptuous gesture.
wavs/LJ012-0015.wav|pitch/LJ012-0015.pt|Weedon and Lecasser to twelve and six months respectively in Coldbath Fields.
wavs/LJ048-0033.wav|pitch/LJ048-0033.pt|Prior to November twenty-two, nineteen sixty-three
wavs/LJ028-0349.wav|pitch/LJ028-0349.pt|who were each required to send so large a number to Babylon, that in all there were collected no fewer than fifty thousand.
wavs/LJ030-0197.wav|pitch/LJ030-0197.pt|At first Mrs. Connally thought that her husband had been killed,
wavs/LJ017-0133.wav|pitch/LJ017-0133.pt|Palmer speedily found imitators.
wavs/LJ034-0123.wav|pitch/LJ034-0123.pt|Although Brennan testified that the man in the window was standing when he fired the shots, most probably he was either sitting or kneeling.
wavs/LJ003-0282.wav|pitch/LJ003-0282.pt|Many years were to elapse before these objections should be fairly met and universally overcome.
wavs/LJ032-0204.wav|pitch/LJ032-0204.pt|Special Agent Lyndal L. Shaneyfelt, a photography expert with the FBI,
wavs/LJ016-0241.wav|pitch/LJ016-0241.pt|Calcraft served the city of London till eighteen seventy-four, when he was pensioned at the rate of twenty-five shillings per week.
wavs/LJ023-0033.wav|pitch/LJ023-0033.pt|we will not allow ourselves to run around in new circles of futile discussion and debate, always postponing the day of decision.
wavs/LJ009-0286.wav|pitch/LJ009-0286.pt|There has never been much science in the system of carrying out the extreme penalty in this country; the "finisher of the law"
wavs/LJ008-0181.wav|pitch/LJ008-0181.pt|he had his pockets filled with bread and cheese, and it was generally supposed that he had come a long distance to see the fatal show.
wavs/LJ015-0052.wav|pitch/LJ015-0052.pt|to the value of twenty thousand pounds.
wavs/LJ016-0314.wav|pitch/LJ016-0314.pt|Sir George Grey thought there was a growing feeling in favor of executions within the prison precincts.
wavs/LJ047-0056.wav|pitch/LJ047-0056.pt|From August nineteen sixty-two
wavs/LJ010-0027.wav|pitch/LJ010-0027.pt|Nor did the methods by which they were perpetrated greatly vary from those in times past.
wavs/LJ010-0065.wav|pitch/LJ010-0065.pt|At the former the "Provisional Government" was to be established,
wavs/LJ046-0113.wav|pitch/LJ046-0113.pt|The Commission has concluded that at the time of the assassination
wavs/LJ028-0410.wav|pitch/LJ028-0410.pt|There among the ruins they still live in the same kind of houses,
wavs/LJ044-0137.wav|pitch/LJ044-0137.pt|More seriously, the facts of his defection had become known, leaving him open to almost unanswerable attack by those who opposed his views.
wavs/LJ008-0215.wav|pitch/LJ008-0215.pt|One by one the huge uprights of black timber were fitted together,
wavs/LJ030-0084.wav|pitch/LJ030-0084.pt|or when the press of the crowd made it impossible for the escort motorcycles to stay in position on the car's rear flanks.
wavs/LJ020-0092.wav|pitch/LJ020-0092.pt|Have yourself called on biscuit mornings an hour earlier than usual.
wavs/LJ029-0096.wav|pitch/LJ029-0096.pt|On November fourteen, Lawson and Sorrels attended a meeting at Love Field
wavs/LJ015-0308.wav|pitch/LJ015-0308.pt|and others who swore to the meetings of the conspirators and their movements. Saward was found guilty,
wavs/LJ012-0067.wav|pitch/LJ012-0067.pt|But Mrs. Solomons could not resist the temptation to dabble in stolen goods, and she was found shipping watches of the wrong category to New York.
wavs/LJ018-0231.wav|pitch/LJ018-0231.pt|namely, to suppress it and substitute another.
wavs/LJ014-0265.wav|pitch/LJ014-0265.pt|and later he became manager of the newly rebuilt Olympic at Wych Street.
wavs/LJ024-0102.wav|pitch/LJ024-0102.pt|would be the first to exclaim as soon as an amendment was proposed
wavs/LJ007-0233.wav|pitch/LJ007-0233.pt|it consists of several circular perforations, about two inches in diameter,
wavs/LJ013-0213.wav|pitch/LJ013-0213.pt|This seems to have decided Courvoisier,
wavs/LJ032-0045.wav|pitch/LJ032-0045.pt|This price included nineteen dollars, ninety-five cents for the rifle and the scope, and one dollar, fifty cents for postage and handling.
wavs/LJ011-0048.wav|pitch/LJ011-0048.pt|Wherefore let him that thinketh he standeth take heed lest he fall," and was full of the most pointed allusions to the culprit.
wavs/LJ005-0294.wav|pitch/LJ005-0294.pt|It was frequently stated in evidence that the jail of the borough was in so unfit a state for the reception of prisoners,
wavs/LJ016-0007.wav|pitch/LJ016-0007.pt|There were others less successful.
wavs/LJ028-0138.wav|pitch/LJ028-0138.pt|perhaps the tales that travelers told him were exaggerated as travelers' tales are likely to be,
wavs/LJ050-0029.wav|pitch/LJ050-0029.pt|that is reflected in definite and comprehensive operating procedures.
wavs/LJ014-0121.wav|pitch/LJ014-0121.pt|The prisoners were in due course transferred to Newgate, to be put upon their trial at the Central Criminal Court.
wavs/LJ014-0146.wav|pitch/LJ014-0146.pt|They had to handcuff her by force against the most violent resistance, and still she raged and stormed,
wavs/LJ046-0111.wav|pitch/LJ046-0111.pt|The Secret Service has attempted to perform this function through the activities of its Protective Research Section
wavs/LJ012-0257.wav|pitch/LJ012-0257.pt|But the affair still remained a profound mystery. No light was thrown upon it till, towards the end of March,
wavs/LJ002-0260.wav|pitch/LJ002-0260.pt|Yet the public opinion of the whole body seems to have checked dissipation.
wavs/LJ031-0014.wav|pitch/LJ031-0014.pt|the Presidential limousine arrived at the emergency entrance of the Parkland Hospital at about twelve:thirty-five p.m.
wavs/LJ047-0093.wav|pitch/LJ047-0093.pt|Oswald was arrested and jailed by the New Orleans Police Department for disturbing the peace, in connection with a street fight which broke out when he was accosted
wavs/LJ003-0324.wav|pitch/LJ003-0324.pt|gaming of all sorts should be peremptorily forbidden under heavy pains and penalties.
wavs/LJ021-0115.wav|pitch/LJ021-0115.pt|we have reached into the heart of the problem which is to provide such annual earnings for the lowest paid worker as will meet his minimum needs.
wavs/LJ046-0191.wav|pitch/LJ046-0191.pt|it had established periodic regular review of the status of four hundred individuals;
wavs/LJ034-0197.wav|pitch/LJ034-0197.pt|who was one of the first witnesses to alert the police to the Depository as the source of the shots, as has been discussed in chapter three.
wavs/LJ002-0253.wav|pitch/LJ002-0253.pt|were governed by rules which they themselves had framed, and under which subscriptions were levied
wavs/LJ048-0288.wav|pitch/LJ048-0288.pt|might have been more alert in the Dallas motorcade if they had retired promptly in Fort Worth.
wavs/LJ007-0112.wav|pitch/LJ007-0112.pt|Many of the old customs once prevalent in the State Side, so properly condemned and abolished,
wavs/LJ017-0189.wav|pitch/LJ017-0189.pt|who was presently attacked in the same way as the others, but, but, thanks to the prompt administration of remedies, he recovered.
wavs/LJ042-0230.wav|pitch/LJ042-0230.pt|basically, although I hate the USSR and socialist system I still think marxism can work under different circumstances, end quote.
wavs/LJ050-0161.wav|pitch/LJ050-0161.pt|The Secret Service should not and does not plan to develop its own intelligence gathering facilities to duplicate the existing facilities of other Federal agencies.
wavs/LJ003-0011.wav|pitch/LJ003-0011.pt|that not more than one bottle of wine or one quart of beer could be issued at one time. No account was taken of the amount of liquors admitted in one day,
wavs/LJ008-0206.wav|pitch/LJ008-0206.pt|and caused a number of stout additional barriers to be erected in front of the scaffold,
wavs/LJ002-0261.wav|pitch/LJ002-0261.pt|The poorer prisoners were not in abject want, as in other prisons,
wavs/LJ012-0189.wav|pitch/LJ012-0189.pt|Hunt, in consideration of the information he had given, escaped death, and was sentenced to transportation for life.
wavs/LJ019-0317.wav|pitch/LJ019-0317.pt|The former, which consisted principally of the tread-wheel, cranks, capstans, shot-drill,
wavs/LJ011-0041.wav|pitch/LJ011-0041.pt|Visited Mr. Fauntleroy. My application for books for him not having been attended, I had no prayer-book to give him.
wavs/LJ023-0089.wav|pitch/LJ023-0089.pt|That is not only my accusation.
wavs/LJ044-0224.wav|pitch/LJ044-0224.pt|would not agree with that particular wording, end quote.
wavs/LJ013-0104.wav|pitch/LJ013-0104.pt|He found them at length residing at the latter place, one as a landed proprietor, the other as a publican.
wavs/LJ013-0055.wav|pitch/LJ013-0055.pt|The jury did not believe him, and the verdict was for the defendants.
wavs/LJ014-0306.wav|pitch/LJ014-0306.pt|These had been attributed to political action; some thought that the large purchases in foreign grains, effected at losing prices,
wavs/LJ029-0052.wav|pitch/LJ029-0052.pt|To supplement the PRS files, the Secret Service depends largely on local police departments and local offices of other Federal agencies
wavs/LJ028-0459.wav|pitch/LJ028-0459.pt|Its bricks, measuring about thirteen inches square and three inches in thickness, were burned and stamped with the usual short inscription:
wavs/LJ017-0183.wav|pitch/LJ017-0183.pt|Soon afterwards Dixon died, showing all the symptoms already described.
wavs/LJ009-0084.wav|pitch/LJ009-0084.pt|At length the ordinary pauses, and then, in a deep tone, which, though hardly above a whisper, is audible to all, says,
wavs/LJ007-0170.wav|pitch/LJ007-0170.pt|That in this vast metropolis, the center of wealth, civilization, and information;
wavs/LJ016-0277.wav|pitch/LJ016-0277.pt|This is proved by contemporary accounts, especially one graphic and realistic article which appeared in the 'Times,'
wavs/LJ009-0061.wav|pitch/LJ009-0061.pt|He staggers towards the pew, reels into it, stumbles forward, flings himself on the ground, and, by a curious twist of the spine,
wavs/LJ019-0201.wav|pitch/LJ019-0201.pt|to select a sufficiently spacious piece of ground, and erect a prison which from foundations to roofs should be in conformity with the newest ideas.
wavs/LJ030-0063.wav|pitch/LJ030-0063.pt|He had repeated this wish only a few days before, during his visit to Tampa, Florida.
wavs/LJ010-0257.wav|pitch/LJ010-0257.pt|a third miscreant made a similar but far less serious attempt in the month of July following.
wavs/LJ009-0106.wav|pitch/LJ009-0106.pt|The keeper tries to appear unmoved, but his eye wanders anxiously over the combustible assembly.
wavs/LJ008-0121.wav|pitch/LJ008-0121.pt|After the construction and action of the machine had been explained, the doctor asked the governor what kind of men he had commanded at Goree,
wavs/LJ050-0069.wav|pitch/LJ050-0069.pt|the Secret Service had received from the FBI some nine thousand reports on members of the Communist Party.
wavs/LJ006-0202.wav|pitch/LJ006-0202.pt|The news-vendor was also a tobacconist,
wavs/LJ012-0230.wav|pitch/LJ012-0230.pt|Shortly before the day fixed for execution, Bishop made a full confession, the bulk of which bore the impress of truth,
wavs/LJ005-0248.wav|pitch/LJ005-0248.pt|and stated that in his opinion Newgate, as the common jail of Middlesex, was wholly inadequate to the proper confinement of its prisoners.
wavs/LJ037-0053.wav|pitch/LJ037-0053.pt|who had been greatly upset by her experience, was able to view a lineup of four men handcuffed together at the police station.
wavs/LJ045-0177.wav|pitch/LJ045-0177.pt|For the first time
wavs/LJ004-0036.wav|pitch/LJ004-0036.pt|it was hoped that their rulers would hire accommodation in the county prisons, and that the inferior establishments would in course of time disappear.
wavs/LJ026-0054.wav|pitch/LJ026-0054.pt|carbohydrates (starch, cellulose) and fats.
wavs/LJ020-0085.wav|pitch/LJ020-0085.pt|Break apart from one another and pile on a plate, throwing a clean doily or a small napkin over them. Break open at table.
wavs/LJ046-0226.wav|pitch/LJ046-0226.pt|The several military intelligence agencies reported crank mail and similar threats involving the President.
wavs/LJ014-0233.wav|pitch/LJ014-0233.pt|he shot an old soldier who had attempted to detain him. He was convicted and executed.
wavs/LJ033-0152.wav|pitch/LJ033-0152.pt|The portion of the palm which was identified was the heel of the right palm, i.e., the area near the wrist, on the little finger side.
wavs/LJ004-0009.wav|pitch/LJ004-0009.pt|as indefatigable and self-sacrificing, found by personal visitation that the condition of jails throughout the kingdom was,
wavs/LJ017-0134.wav|pitch/LJ017-0134.pt|Within a few weeks occurred the Leeds poisoning case, in which the murderer undoubtedly was inspired by the facts made public at Palmer's trial.
wavs/LJ019-0318.wav|pitch/LJ019-0318.pt|was to be the rule for all convicted prisoners throughout the early stages of their detention;
wavs/LJ020-0093.wav|pitch/LJ020-0093.pt|Rise, wash face and hands, rinse the mouth out and brush back the hair.
wavs/LJ012-0188.wav|pitch/LJ012-0188.pt|Probert was then admitted as a witness, and the case was fully proved against Thurtell, who was hanged in front of Hertford Jail.
wavs/LJ019-0202.wav|pitch/LJ019-0202.pt|The preference given to the Pentonville system destroyed all hopes of a complete reformation of Newgate.
wavs/LJ039-0027.wav|pitch/LJ039-0027.pt|Oswald's revolver
wavs/LJ040-0176.wav|pitch/LJ040-0176.pt|He admitted to fantasies about being powerful and sometimes hurting and killing people, but refused to elaborate on them.
wavs/LJ018-0354.wav|pitch/LJ018-0354.pt|Doubts were long entertained whether Thomas Wainwright,
wavs/LJ031-0185.wav|pitch/LJ031-0185.pt|From the Presidential airplane, the Vice President telephoned Attorney General Robert F. Kennedy,
wavs/LJ006-0137.wav|pitch/LJ006-0137.pt|They were not obliged to attend chapel, and seldom if ever went; "prisoners," said one of them under examination, "did not like the trouble of going to chapel."
wavs/LJ032-0085.wav|pitch/LJ032-0085.pt|The Hidell signature on the notice of classification was in the handwriting of Oswald.
wavs/LJ009-0037.wav|pitch/LJ009-0037.pt|the schoolmaster and the juvenile prisoners being seated round the communion-table, opposite the pulpit.
wavs/LJ006-0021.wav|pitch/LJ006-0021.pt|Later on he had devoted himself to the personal investigation of the prisons of the United States.
wavs/LJ006-0082.wav|pitch/LJ006-0082.pt|and this particular official took excellent care to select as residents for his own ward those most suitable from his own point of view.
wavs/LJ016-0380.wav|pitch/LJ016-0380.pt|with hope to the last. There is always the chance of a flaw in the indictment, of a missing witness, or extenuating circumstances.
wavs/LJ019-0344.wav|pitch/LJ019-0344.pt|monitor, or schoolmaster, nor to be engaged in the service of any officer of the prison.
wavs/LJ019-0161.wav|pitch/LJ019-0161.pt|These disciplinary improvements were, however, only slowly and gradually introduced.
wavs/LJ028-0145.wav|pitch/LJ028-0145.pt|And here I may not omit to tell the use to which the mould dug out of the great moat was turned, nor the manner wherein the wall was wrought.
wavs/LJ018-0349.wav|pitch/LJ018-0349.pt|His disclaimer, distinct and detailed on every point, was intended simply for effect.
wavs/LJ043-0010.wav|pitch/LJ043-0010.pt|Some of the members of that group saw a good deal of the Oswalds through the fall of nineteen sixty-three,
wavs/LJ027-0178.wav|pitch/LJ027-0178.pt|These were undoubtedly perennibranchs. In the Permian and Triassic higher forms appeared, which were certainly caducibranch.
wavs/LJ041-0070.wav|pitch/LJ041-0070.pt|He did not rise above the rank of private first class, even though he had passed a qualifying examination for the rank of corporal.
wavs/LJ008-0266.wav|pitch/LJ008-0266.pt|Thus in the years between May first, eighteen twenty-seven, and thirtieth April, eighteen thirty-one,
wavs/LJ021-0091.wav|pitch/LJ021-0091.pt|In this recent reorganization we have recognized three distinct functions:
wavs/LJ019-0129.wav|pitch/LJ019-0129.pt|which marked the growth of public interest in prison affairs, and which was the germ of the new system
wavs/LJ018-0215.wav|pitch/LJ018-0215.pt|William Roupell was the eldest but illegitimate son of a wealthy man who subsequently married Roupell's mother, and had further legitimate issue.
wavs/LJ015-0194.wav|pitch/LJ015-0194.pt|and behaved so as to justify a belief that he had been a jail-bird all his life.
wavs/LJ016-0137.wav|pitch/LJ016-0137.pt|that numbers of men, "lifers," and others with ten, fourteen, or twenty years to do, can be trusted to work out of doors without bolts and bars
wavs/LJ002-0289.wav|pitch/LJ002-0289.pt|the latter raised eighteen pence among them to pay for a truss of straw for the poor woman to lie on.
wavs/LJ023-0016.wav|pitch/LJ023-0016.pt|In nineteen thirty-three you and I knew that we must never let our economic system get completely out of joint again
wavs/LJ011-0141.wav|pitch/LJ011-0141.pt|There were at the moment in Newgate six convicts sentenced to death for forging wills.
wavs/LJ016-0283.wav|pitch/LJ016-0283.pt|to do them mere justice, there was at least till then a half-drunken ribald gaiety among the crowd that made them all akin."
wavs/LJ035-0082.wav|pitch/LJ035-0082.pt|The only interval was the time necessary to ride in the elevator from the second to the sixth floor and walk back to the southeast corner.
wavs/LJ045-0194.wav|pitch/LJ045-0194.pt|Anyone who was familiar with that area of Dallas would have known that the motorcade would probably pass the Texas School Book Depository to get from Main Street
wavs/LJ009-0124.wav|pitch/LJ009-0124.pt|occupied when they saw it last, but a few hours ago, by their comrades who are now dead;
wavs/LJ030-0162.wav|pitch/LJ030-0162.pt|In the Presidential Limousine
wavs/LJ050-0223.wav|pitch/LJ050-0223.pt|The plan provides for an additional two hundred five agents for the Secret Service. Seventeen of this number are proposed for the Protective Research Section;
wavs/LJ008-0228.wav|pitch/LJ008-0228.pt|their harsh and half-cracked voices full of maudlin, besotted sympathy for those about to die.
wavs/LJ002-0096.wav|pitch/LJ002-0096.pt|The eight courts above enumerated were well supplied with water;
wavs/LJ018-0288.wav|pitch/LJ018-0288.pt|After this the other conspirators traveled to obtain genuine bills and master the system of the leading houses at home and abroad.
wavs/LJ002-0106.wav|pitch/LJ002-0106.pt|in which latterly a copper had been fixed for the cooking of provisions sent in by charitable persons.
wavs/LJ025-0129.wav|pitch/LJ025-0129.pt|On each lobe of the bi-lobed leaf of Venus flytrap are three delicate filaments which stand out at right angles from the surface of the leaf.
wavs/LJ044-0013.wav|pitch/LJ044-0013.pt|Hands Off Cuba, end quote, an application form for, and a membership card in,
wavs/LJ049-0115.wav|pitch/LJ049-0115.pt|of the person who is actually in the exercise of the executive power, or
wavs/LJ019-0145.wav|pitch/LJ019-0145.pt|But reformation was only skin deep. Below the surface many of the old evils still rankled.
wavs/LJ019-0355.wav|pitch/LJ019-0355.pt|came up in all respects to modern requirements.
wavs/LJ019-0289.wav|pitch/LJ019-0289.pt|There was unrestrained association of untried and convicted, juvenile with adult prisoners, vagrants, misdemeanants, felons.
wavs/LJ048-0222.wav|pitch/LJ048-0222.pt|in Fort Worth, there occurred a breach of discipline by some members of the Secret Service who were officially traveling with the President.
wavs/LJ016-0367.wav|pitch/LJ016-0367.pt|Under the new system the whole of the arrangements from first to last fell upon the officers.
wavs/LJ047-0097.wav|pitch/LJ047-0097.pt|Agent Quigley did not know of Oswald's prior FBI record when he interviewed him,
wavs/LJ007-0075.wav|pitch/LJ007-0075.pt|as effectually to rebuke and abash the profane spirit of the more insolent and daring of the criminals.
wavs/LJ047-0022.wav|pitch/LJ047-0022.pt|provided by other agencies.
wavs/LJ007-0085.wav|pitch/LJ007-0085.pt|at Newgate and York Castle as long as five years; "at Ilchester and Morpeth for seven years; at Warwick for eight years,
wavs/LJ047-0075.wav|pitch/LJ047-0075.pt|Hosty had inquired earlier and found no evidence that it was functioning in the Dallas area.
wavs/LJ008-0098.wav|pitch/LJ008-0098.pt|One was the "yeoman of the halter," a Newgate official, the executioner's assistant, whom Mr. J. T. Smith, who was present at the execution,
wavs/LJ017-0102.wav|pitch/LJ017-0102.pt|The second attack was fatal, and ended in Cook's death from tetanus.
wavs/LJ046-0105.wav|pitch/LJ046-0105.pt|Second, the adequacy of other advance preparations for the security of the President, during his visit to Dallas,
wavs/LJ018-0206.wav|pitch/LJ018-0206.pt|He was a tall, slender man, with a long face and iron-gray hair.
wavs/LJ012-0271.wav|pitch/LJ012-0271.pt|Whether it was greed or a quarrel that drove Greenacre to the desperate deed remains obscure.
wavs/LJ005-0086.wav|pitch/LJ005-0086.pt|with such further separation as the justices should deem conducive to good order and discipline.
wavs/LJ042-0097.wav|pitch/LJ042-0097.pt|and considerably better living quarters than those accorded to Soviet citizens of equal age and station.
wavs/LJ047-0126.wav|pitch/LJ047-0126.pt|we would handle it in due course, in accord with the whole context of the investigation. End quote.
wavs/LJ041-0022.wav|pitch/LJ041-0022.pt|Oswald first wrote, quote, Edward Vogel, end quote, an obvious misspelling of Voebel's name,
wavs/LJ015-0025.wav|pitch/LJ015-0025.pt|The bank enjoyed an excellent reputation, it had a good connection, and was supposed to be perfectly sound.
wavs/LJ012-0194.wav|pitch/LJ012-0194.pt|But Burke and Hare had their imitators further south,
wavs/LJ028-0416.wav|pitch/LJ028-0416.pt|(if man may speak so confidently of His great impenetrable counsels), for an eternal Testimony of His great work in the confusion of Man's pride,
wavs/LJ007-0130.wav|pitch/LJ007-0130.pt|are all huddled together without discrimination, oversight, or control."
wavs/LJ015-0005.wav|pitch/LJ015-0005.pt|About this time Davidson and Gordon, the people above-mentioned,
wavs/LJ016-0125.wav|pitch/LJ016-0125.pt|with this, placed against the wall near the chevaux-de-frise, he made an escalade.
wavs/LJ014-0224.wav|pitch/LJ014-0224.pt|As Dwyer survived, Cannon escaped the death sentence, which was commuted to penal servitude for life.
wavs/LJ005-0019.wav|pitch/LJ005-0019.pt|refuted by abundant evidence, and having no foundation whatever in truth.
wavs/LJ042-0221.wav|pitch/LJ042-0221.pt|With either great ambivalence, or cold calculation he prepared completely different answers to the same questions.
wavs/LJ001-0063.wav|pitch/LJ001-0063.pt|which was generally more formally Gothic than the printing of the German workmen,
wavs/LJ030-0006.wav|pitch/LJ030-0006.pt|They took off in the Presidential plane, Air Force One, at eleven a.m., arriving at San Antonio at one:thirty p.m., Eastern Standard Time.
wavs/LJ024-0054.wav|pitch/LJ024-0054.pt|democracy will have failed far beyond the importance to it of any king of precedent concerning the judiciary.
wavs/LJ006-0044.wav|pitch/LJ006-0044.pt|the same callous indifference to the moral well-being of the prisoners, the same want of employment and of all disciplinary control.
wavs/LJ039-0154.wav|pitch/LJ039-0154.pt|four point eight to five point six seconds if the second shot missed,
wavs/LJ050-0090.wav|pitch/LJ050-0090.pt|they seem unduly restrictive in continuing to require some manifestation of animus against a Government official.
wavs/LJ028-0421.wav|pitch/LJ028-0421.pt|it was the beginning of the great collections of Babylonian antiquities in the museums of the Western world.
wavs/LJ033-0205.wav|pitch/LJ033-0205.pt|then I would say the possibility exists, these fibers could have come from this blanket, end quote.
wavs/LJ019-0335.wav|pitch/LJ019-0335.pt|The books and journals he was to keep were minutely specified, and his constant presence in or near the jail was insisted upon.
wavs/LJ013-0045.wav|pitch/LJ013-0045.pt|Wallace's relations warned him against his Liverpool friend,
wavs/LJ037-0002.wav|pitch/LJ037-0002.pt|Chapter four. The Assassin: Part six.
wavs/LJ018-0159.wav|pitch/LJ018-0159.pt|This was all the police wanted to know.
wavs/LJ026-0140.wav|pitch/LJ026-0140.pt|In the plant as in the animal metabolism must consist of anabolic and catabolic processes.
wavs/LJ014-0171.wav|pitch/LJ014-0171.pt|I will briefly describe one or two of the more remarkable murders in the years immediately following, then pass on to another branch of crime.
wavs/LJ037-0007.wav|pitch/LJ037-0007.pt|Three others subsequently identified Oswald from a photograph.
wavs/LJ033-0174.wav|pitch/LJ033-0174.pt|microscopic and UV (ultra violet) characteristics, end quote.
wavs/LJ040-0110.wav|pitch/LJ040-0110.pt|he apparently adjusted well enough there to have had an average, although gradually deteriorating, school record
wavs/LJ039-0192.wav|pitch/LJ039-0192.pt|he had a total of between four point eight and five point six seconds between the two shots which hit
wavs/LJ032-0261.wav|pitch/LJ032-0261.pt|When he appeared before the Commission, Michael Paine lifted the blanket
wavs/LJ040-0097.wav|pitch/LJ040-0097.pt|Lee was brought up in this atmosphere of constant money problems, and I am sure it had quite an effect on him, and also Robert, end quote.
wavs/LJ037-0249.wav|pitch/LJ037-0249.pt|Mrs. Earlene Roberts, the housekeeper at Oswald's roominghouse and the last person known to have seen him before he reached tenth Street and Patton Avenue,
wavs/LJ016-0248.wav|pitch/LJ016-0248.pt|Marwood was proud of his calling, and when questioned as to whether his process was satisfactory, replied that he heard "no complaints."
wavs/LJ004-0083.wav|pitch/LJ004-0083.pt|As Mr. Buxton pointed out, many old acts of parliament designed to protect the prisoner were still in full force.
wavs/LJ014-0029.wav|pitch/LJ014-0029.pt|This was Delarue's watch, fully identified as such, which Hocker told his brother Delarue had given him the morning of the murder.
wavs/LJ021-0110.wav|pitch/LJ021-0110.pt|have been best calculated to promote industrial recovery and a permanent improvement of business and labor conditions.
wavs/LJ003-0107.wav|pitch/LJ003-0107.pt|he slept in the same bed with a highwayman on one side, and a man charged with murder on the other.
wavs/LJ039-0076.wav|pitch/LJ039-0076.pt|Ronald Simmons, chief of the U.S. Army Infantry Weapons Evaluation Branch of the Ballistics Research Laboratory, said, quote,
wavs/LJ016-0347.wav|pitch/LJ016-0347.pt|had undoubtedly a solemn, impressive effect upon those outside.
wavs/LJ001-0072.wav|pitch/LJ001-0072.pt|After the end of the fifteenth century the degradation of printing, especially in Germany and Italy,
wavs/LJ024-0018.wav|pitch/LJ024-0018.pt|Consequently, although there never can be more than fifteen, there may be only fourteen, or thirteen, or twelve.
wavs/LJ032-0180.wav|pitch/LJ032-0180.pt|that the fibers were caught in the crevice of the rifle's butt plate, quote, in the recent past, end quote,
wavs/LJ010-0083.wav|pitch/LJ010-0083.pt|and measures taken to arrest them when their plans were so far developed that no doubt could remain as to their guilt.
wavs/LJ002-0299.wav|pitch/LJ002-0299.pt|and gave the garnish for the common side at that sum, which is five shillings more than Mr. Neild says was extorted on the common side.
wavs/LJ048-0143.wav|pitch/LJ048-0143.pt|the Secret Service did not at the time of the assassination have any established procedure governing its relationships with them.
wavs/LJ012-0054.wav|pitch/LJ012-0054.pt|Solomons, while waiting to appear in court, persuaded the turnkeys to take him to a public-house, where all might "refresh."
wavs/LJ019-0270.wav|pitch/LJ019-0270.pt|Vegetables, especially the potato, that most valuable anti-scorbutic, was too often omitted.
wavs/LJ035-0164.wav|pitch/LJ035-0164.pt|three minutes after the shooting.
wavs/LJ014-0326.wav|pitch/LJ014-0326.pt|Maltby and Co. would issue warrants on them deliverable to the importer, and the goods were then passed to be stored in neighboring warehouses.
wavs/LJ001-0173.wav|pitch/LJ001-0173.pt|The essential point to be remembered is that the ornament, whatever it is, whether picture or pattern-work, should form part of the page,
wavs/LJ050-0056.wav|pitch/LJ050-0056.pt|On December twenty-six, nineteen sixty-three, the FBI circulated additional instructions to all its agents,
wavs/LJ003-0319.wav|pitch/LJ003-0319.pt|provided only that their security was not jeopardized, and dependent upon the enforcement of another new rule,
wavs/LJ006-0040.wav|pitch/LJ006-0040.pt|The fact was that the years as they passed, nearly twenty in all, had worked but little permanent improvement in this detestable prison.
wavs/LJ017-0231.wav|pitch/LJ017-0231.pt|His body was found lying in a pool of blood in a night-dress, stabbed over and over again in the left side.
wavs/LJ017-0226.wav|pitch/LJ017-0226.pt|One half of the mutineers fell upon him unawares with handspikes and capstan-bars.
wavs/LJ004-0239.wav|pitch/LJ004-0239.pt|He had been committed for an offense for which he was acquitted.
wavs/LJ048-0112.wav|pitch/LJ048-0112.pt|The Commission also regards the security arrangements worked out by Lawson and Sorrels at Love Field as entirely adequate.
wavs/LJ039-0125.wav|pitch/LJ039-0125.pt|that Oswald was a good shot, somewhat better than or equal to -- better than the average let us say.
wavs/LJ030-0196.wav|pitch/LJ030-0196.pt|He cried out, quote, Oh, no, no, no. My God, they are going to kill us all, end quote,
wavs/LJ010-0228.wav|pitch/LJ010-0228.pt|He was released from Broadmoor in eighteen seventy-eight, and went abroad.
wavs/LJ045-0228.wav|pitch/LJ045-0228.pt|On the other hand, he could have traveled some distance with the money he did have and he did return to his room where he obtained his revolver.
wavs/LJ028-0168.wav|pitch/LJ028-0168.pt|in the other was the sacred precinct of Jupiter Belus,
wavs/LJ021-0140.wav|pitch/LJ021-0140.pt|and in such an effort we should be able to secure for employers and employees and consumers
wavs/LJ009-0280.wav|pitch/LJ009-0280.pt|Again the wretched creature succeeded in obtaining foothold, but this time on the left side of the drop.
wavs/LJ003-0159.wav|pitch/LJ003-0159.pt|To constitute this the aristocratic quarter, unwarrantable demands were made upon the space properly allotted to the female felons,
wavs/LJ016-0274.wav|pitch/LJ016-0274.pt|and the windows of the opposite houses, which commanded a good view, as usual fetched high prices.
wavs/LJ035-0014.wav|pitch/LJ035-0014.pt|it sounded high and I immediately kind of looked up,
wavs/LJ033-0120.wav|pitch/LJ033-0120.pt|which he believed was where the bag reached when it was laid on the seat with one edge against the door.
wavs/LJ045-0015.wav|pitch/LJ045-0015.pt|which Johnson said he did not receive until after the assassination. The letter said in part, quote,
wavs/LJ003-0299.wav|pitch/LJ003-0299.pt|the latter end of the nineteenth century, several of which still fall far short of our English ideal,
wavs/LJ032-0206.wav|pitch/LJ032-0206.pt|After comparing the rifle in the simulated photograph with the rifle in Exhibit Number one thirty-three A, Shaneyfelt testified, quote,
wavs/LJ028-0494.wav|pitch/LJ028-0494.pt|Between the several sections were wide spaces where foot soldiers and charioteers might fight.
wavs/LJ005-0099.wav|pitch/LJ005-0099.pt|and report at length upon the condition of the prisons of the country.
wavs/LJ015-0144.wav|pitch/LJ015-0144.pt|developed to a colossal extent the frauds he had already practiced as a subordinate.
wavs/LJ019-0221.wav|pitch/LJ019-0221.pt|It was intended as far as possible that, except awaiting trial, no prisoner should find himself relegated to Newgate.
wavs/LJ003-0088.wav|pitch/LJ003-0088.pt|in one, for seven years -- that of a man sentenced to death, for whom great interest had been made, but whom it was not thought right to pardon.
wavs/LJ045-0216.wav|pitch/LJ045-0216.pt|nineteen sixty-three, merely to disarm her and to provide a justification of sorts,
wavs/LJ042-0135.wav|pitch/LJ042-0135.pt|that he was not yet twenty years old when he went to the Soviet Union with such high hopes and not quite twenty-three when he returned bitterly disappointed.
wavs/LJ049-0196.wav|pitch/LJ049-0196.pt|On the other hand, it is urged that all features of the protection of the President and his family should be committed to an elite and independent corps.
wavs/LJ018-0278.wav|pitch/LJ018-0278.pt|This was the well and astutely devised plot of the brothers Bidwell,
wavs/LJ030-0238.wav|pitch/LJ030-0238.pt|and then looked around again and saw more of this movement, and so I proceeded to go to the back seat and get on top of him.
wavs/LJ018-0309.wav|pitch/LJ018-0309.pt|where probably the money still remains.
wavs/LJ041-0199.wav|pitch/LJ041-0199.pt|is shown most clearly by his employment relations after his return from the Soviet Union. Of course, he made his real problems worse to the extent
wavs/LJ007-0076.wav|pitch/LJ007-0076.pt|The lax discipline maintained in Newgate was still further deteriorated by the presence of two other classes of prisoners who ought never to have been inmates of such a jail.
wavs/LJ039-0118.wav|pitch/LJ039-0118.pt|He had high motivation. He had presumably a good to excellent rifle and good ammunition.
wavs/LJ024-0019.wav|pitch/LJ024-0019.pt|And there may be only nine.
wavs/LJ008-0085.wav|pitch/LJ008-0085.pt|The fire had not quite burnt out at twelve, in nearly four hours, that is to say.
wavs/LJ018-0031.wav|pitch/LJ018-0031.pt|This fixed the crime pretty certainly upon Müller, who had already left the country, thus increasing suspicion under which he lay.
wavs/LJ030-0032.wav|pitch/LJ030-0032.pt|Dallas police stood at intervals along the fence and Dallas plain clothes men mixed in the crowd.
wavs/LJ050-0004.wav|pitch/LJ050-0004.pt|General Supervision of the Secret Service
wavs/LJ039-0096.wav|pitch/LJ039-0096.pt|This is a definite advantage to the shooter, the vehicle moving directly away from him and the downgrade of the street, and he being in an elevated position
wavs/LJ041-0195.wav|pitch/LJ041-0195.pt|Oswald's interest in Marxism led some people to avoid him,
wavs/LJ047-0158.wav|pitch/LJ047-0158.pt|After a moment's hesitation, she told me that he worked at the Texas School Book Depository near the downtown area of Dallas.
wavs/LJ050-0162.wav|pitch/LJ050-0162.pt|In planning its data processing techniques,
wavs/LJ001-0051.wav|pitch/LJ001-0051.pt|and paying great attention to the "press work" or actual process of printing,
wavs/LJ028-0136.wav|pitch/LJ028-0136.pt|Of all the ancient descriptions of the famous walls and the city they protected, that of Herodotus is the fullest.
wavs/LJ034-0134.wav|pitch/LJ034-0134.pt|Shortly after the assassination Brennan noticed
wavs/LJ019-0348.wav|pitch/LJ019-0348.pt|Every facility was promised. The sanction of the Secretary of State would not be withheld if plans and estimates were duly submitted,
wavs/LJ010-0219.wav|pitch/LJ010-0219.pt|While one stood over the fire with the papers, another stood with lighted torch to fire the house.
wavs/LJ011-0245.wav|pitch/LJ011-0245.pt|Mr. Mullay called again, taking with him five hundred pounds in cash. Howard discovered this, and his manner was very suspicious;
wavs/LJ030-0035.wav|pitch/LJ030-0035.pt|Organization of the Motorcade
wavs/LJ044-0135.wav|pitch/LJ044-0135.pt|While he had drawn some attention to himself and had actually appeared on two radio programs, he had been attacked by Cuban exiles and arrested,
wavs/LJ045-0090.wav|pitch/LJ045-0090.pt|He was very much interested in autobiographical works of outstanding statesmen of the United States, to whom his wife thought he compared himself.
wavs/LJ026-0034.wav|pitch/LJ026-0034.pt|When any given "protist" has to be classified the case must be decided on its individual merits;
wavs/LJ045-0092.wav|pitch/LJ045-0092.pt|as to the fact that he was an outstanding man, end quote.
wavs/LJ017-0050.wav|pitch/LJ017-0050.pt|Palmer, who was only thirty-one at the time of his trial, was in appearance short and stout, with a round head
wavs/LJ036-0104.wav|pitch/LJ036-0104.pt|Whaley picked Oswald.
wavs/LJ019-0055.wav|pitch/LJ019-0055.pt|High authorities were in favor of continuous separation.
wavs/LJ010-0030.wav|pitch/LJ010-0030.pt|The brutal ferocity of the wild beast once aroused, the same means, the same weapons were employed to do the dreadful deed,
wavs/LJ038-0047.wav|pitch/LJ038-0047.pt|Some of the officers saw Oswald strike McDonald with his fist. Most of them heard a click which they assumed to be a click of the hammer of the revolver.
wavs/LJ009-0074.wav|pitch/LJ009-0074.pt|Let us pass on.
wavs/LJ048-0069.wav|pitch/LJ048-0069.pt|Efforts made by the Bureau since the assassination, on the other hand,
wavs/LJ003-0211.wav|pitch/LJ003-0211.pt|They were never left quite alone for fear of suicide, and for the same reason they were searched for weapons or poisons.
wavs/LJ048-0053.wav|pitch/LJ048-0053.pt|It is the conclusion of the Commission that, even in the absence of Secret Service criteria
wavs/LJ033-0093.wav|pitch/LJ033-0093.pt|Frazier estimated that the bag was two feet long, quote, give and take a few inches, end quote, and about five or six inches wide.
wavs/LJ006-0149.wav|pitch/LJ006-0149.pt|The turnkeys left the prisoners very much to themselves, never entering the wards after locking-up time, at dusk, till unlocking next morning,
wavs/LJ018-0211.wav|pitch/LJ018-0211.pt|The false coin was bought by an agent from an agent, and dealings were carried on secretly at the "Clock House" in Seven Dials.
wavs/LJ008-0054.wav|pitch/LJ008-0054.pt|This contrivance appears to have been copied with improvements from that which had been used in Dublin at a still earlier date,
wavs/LJ040-0052.wav|pitch/LJ040-0052.pt|that his commitment to Marxism was an important factor influencing his conduct during his adult years.
wavs/LJ028-0023.wav|pitch/LJ028-0023.pt|Two weeks pass, and at last you stand on the eastern edge of the plateau
wavs/LJ009-0184.wav|pitch/LJ009-0184.pt|Lord Ferrers' body was brought to Surgeons' Hall after execution in his own carriage and six;
wavs/LJ005-0252.wav|pitch/LJ005-0252.pt|A committee was appointed, under the presidency of the Duke of Richmond
wavs/LJ015-0266.wav|pitch/LJ015-0266.pt|has probably no parallel in the annals of crime. Saward himself is a striking and in some respects an unique figure in criminal history.
wavs/LJ017-0059.wav|pitch/LJ017-0059.pt|even after sentence, and until within a few hours of execution, he was buoyed up with the hope of reprieve.
wavs/LJ024-0034.wav|pitch/LJ024-0034.pt|What do they mean by the words "packing the Court"?
wavs/LJ016-0089.wav|pitch/LJ016-0089.pt|He was engaged in whitewashing and cleaning; the officer who had him in charge left him on the stairs leading to the gallery.
wavs/LJ039-0227.wav|pitch/LJ039-0227.pt|with two hits, within four point eight and five point six seconds.
wavs/LJ001-0096.wav|pitch/LJ001-0096.pt|have now come into general use and are obviously a great improvement on the ordinary "modern style" in use in England, which is in fact the Bodoni type
wavs/LJ018-0129.wav|pitch/LJ018-0129.pt|who threatened to betray the theft. But Brewer, either before or after this, succumbed to temptation,
wavs/LJ010-0157.wav|pitch/LJ010-0157.pt|and that, as he was starving, he had resolved on this desperate deed,
wavs/LJ038-0264.wav|pitch/LJ038-0264.pt|He concluded that, quote, the general rifling characteristics of the rifle are of the same type as those found on the bullet
wavs/LJ031-0165.wav|pitch/LJ031-0165.pt|When security arrangements at the airport were complete, the Secret Service made the necessary arrangements for the Vice President to leave the hospital.
wavs/LJ018-0244.wav|pitch/LJ018-0244.pt|The effect of establishing the forgeries would be to restore to the Roupell family lands for which a price had already been paid
wavs/LJ007-0071.wav|pitch/LJ007-0071.pt|in the face of impediments confessedly discouraging
wavs/LJ028-0340.wav|pitch/LJ028-0340.pt|Such of the Babylonians as witnessed the treachery took refuge in the temple of Jupiter Belus;
wavs/LJ017-0164.wav|pitch/LJ017-0164.pt|with the idea of subjecting her to the irritant poison slowly but surely until the desired effect, death, was achieved.
wavs/LJ048-0197.wav|pitch/LJ048-0197.pt|I then told the officers that their primary duty was traffic and crowd control and that they should be alert for any persons who might attempt to throw anything
wavs/LJ013-0098.wav|pitch/LJ013-0098.pt|Mr. Oxenford having denied that he had made any transfer of stock, the matter was at once put into the hands of the police.
wavs/LJ012-0049.wav|pitch/LJ012-0049.pt|led him to think seriously of trying his fortunes in another land.
wavs/LJ030-0014.wav|pitch/LJ030-0014.pt|quote, that the crowd was about the same as the one which came to see him before but there were one hundred thousand extra people on hand who came to see Mrs. Kennedy.
wavs/LJ014-0186.wav|pitch/LJ014-0186.pt|A milliner's porter,
wavs/LJ015-0027.wav|pitch/LJ015-0027.pt|Yet even so early as the death of the first Sir John Paul,
wavs/LJ047-0049.wav|pitch/LJ047-0049.pt|Marina Oswald, however, recalled that her husband was upset by this interview.
wavs/LJ012-0021.wav|pitch/LJ012-0021.pt|at fourteen he was a pickpocket and a "duffer," or a seller of sham goods.
wavs/LJ003-0140.wav|pitch/LJ003-0140.pt|otherwise he would have been stripped of his clothes. End quote.
wavs/LJ042-0130.wav|pitch/LJ042-0130.pt|Shortly thereafter, less than eighteen months after his defection, about six weeks before he met Marina Prusakova,
wavs/LJ019-0180.wav|pitch/LJ019-0180.pt|His letter to the Corporation, under date fourth June,
wavs/LJ017-0108.wav|pitch/LJ017-0108.pt|He was struck with the appearance of the corpse, which was not emaciated, as after a long disease ending in death;
wavs/LJ006-0268.wav|pitch/LJ006-0268.pt|Women saw men if they merely pretended to be wives; even boys were visited by their sweethearts.
wavs/LJ044-0125.wav|pitch/LJ044-0125.pt|of residence in the U.S.S.R. against any cause which I join, by association,
wavs/LJ015-0231.wav|pitch/LJ015-0231.pt|It was Tester's business, who had access to the railway company's books, to watch for this.
wavs/LJ002-0225.wav|pitch/LJ002-0225.pt|The rentals of rooms and fees went to the warden, whose income was two thousand three hundred seventy-two pounds.
wavs/LJ034-0072.wav|pitch/LJ034-0072.pt|The employees raced the elevators to the first floor. Givens saw Oswald standing at the gate on the fifth floor as the elevator went by.
wavs/LJ045-0033.wav|pitch/LJ045-0033.pt|He began to treat me better. He helped me more -- although he always did help. But he was more attentive, end quote.
wavs/LJ031-0058.wav|pitch/LJ031-0058.pt|to infuse blood and fluids into the circulatory system.
wavs/LJ029-0197.wav|pitch/LJ029-0197.pt|During November the Dallas papers reported frequently on the plans for protecting the President, stressing the thoroughness of the preparations.
wavs/LJ043-0047.wav|pitch/LJ043-0047.pt|Oswald and his family lived for a brief period with his mother at her urging, but Oswald soon decided to move out.
wavs/LJ021-0026.wav|pitch/LJ021-0026.pt|seems necessary to produce the same result of justice and right conduct
wavs/LJ003-0230.wav|pitch/LJ003-0230.pt|The prison allowances were eked out by the broken victuals generously given by several eating-house keepers in the city,
wavs/LJ037-0252.wav|pitch/LJ037-0252.pt|Ted Callaway, who saw the gunman moments after the shooting, testified that Commission Exhibit Number one sixty-two
wavs/LJ031-0008.wav|pitch/LJ031-0008.pt|Meanwhile, Chief Curry ordered the police base station to notify Parkland Hospital that the wounded President was en route.
wavs/LJ030-0021.wav|pitch/LJ030-0021.pt|all one had to do was get a high building someday with a telescopic rifle, and there was nothing anybody could do to defend against such an attempt.
wavs/LJ046-0179.wav|pitch/LJ046-0179.pt|being reviewed regularly.
wavs/LJ025-0118.wav|pitch/LJ025-0118.pt|and that, however diverse may be the fabrics or tissues of which their bodies are composed, all these varied structures result
wavs/LJ028-0278.wav|pitch/LJ028-0278.pt|Zopyrus, when they told him, not thinking that it could be true, went and saw the colt with his own eyes;
wavs/LJ007-0090.wav|pitch/LJ007-0090.pt|Not only did their presence tend greatly to interfere with the discipline of the prison, but their condition was deplorable in the extreme.
wavs/LJ045-0045.wav|pitch/LJ045-0045.pt|that she would be able to leave the Soviet Union. Marina Oswald has denied this.
wavs/LJ028-0289.wav|pitch/LJ028-0289.pt|For he cut off his own nose and ears, and then, clipping his hair close and flogging himself with a scourge,
wavs/LJ009-0276.wav|pitch/LJ009-0276.pt|Calcraft, the moment he had adjusted the cap and rope, ran down the steps, drew the bolt, and disappeared.
wavs/LJ031-0122.wav|pitch/LJ031-0122.pt|treated the gunshot wound in the left thigh.
wavs/LJ016-0205.wav|pitch/LJ016-0205.pt|he received a retaining fee of five pounds, five shillings, with the usual guinea for each job;
wavs/LJ019-0248.wav|pitch/LJ019-0248.pt|leading to an inequality, uncertainty, and inefficiency of punishment productive of the most prejudicial results.
wavs/LJ033-0183.wav|pitch/LJ033-0183.pt|it was not surprising that the replica sack made on December one, nineteen sixty-three,
wavs/LJ037-0001.wav|pitch/LJ037-0001.pt|Report of the President's Commission on the Assassination of President Kennedy. The Warren Commission Report. By The President's Commission on the Assassination of President Kennedy.
wavs/LJ018-0218.wav|pitch/LJ018-0218.pt|In eighteen fifty-five
wavs/LJ001-0102.wav|pitch/LJ001-0102.pt|Here and there a book is printed in France or Germany with some pretension to good taste,
wavs/LJ007-0125.wav|pitch/LJ007-0125.pt|It was diverted from its proper uses, and, as the "place of the greatest comfort," was allotted to persons who should not have been sent to Newgate at all.
wavs/LJ050-0022.wav|pitch/LJ050-0022.pt|A formal and thorough description of the responsibilities of the advance agent is now in preparation by the Service.
wavs/LJ028-0212.wav|pitch/LJ028-0212.pt|On the night of the eleventh day Gobrias killed the son of the King.
wavs/LJ028-0357.wav|pitch/LJ028-0357.pt|yet we may be sure that Babylon was taken by Darius only by use of stratagem. Its walls were impregnable.
wavs/LJ014-0199.wav|pitch/LJ014-0199.pt|there was no case to make out; why waste money on lawyers for the defense? His demeanor was cool and collected throughout;
wavs/LJ016-0077.wav|pitch/LJ016-0077.pt|A man named Lears, under sentence of transportation for an attempt at murder on board ship, got up part of the way,
wavs/LJ009-0194.wav|pitch/LJ009-0194.pt|and that executors or persons having lawful possession of the bodies
wavs/LJ014-0094.wav|pitch/LJ014-0094.pt|Discovery of the murder came in this wise. O'Connor, a punctual and well-conducted official, was at once missed at the London Docks.
wavs/LJ001-0079.wav|pitch/LJ001-0079.pt|Caslon's type is clear and neat, and fairly well designed;
wavs/LJ026-0052.wav|pitch/LJ026-0052.pt|In the nutrition of the animal the most essential and characteristic part of the food supply is derived from vegetable
wavs/LJ013-0005.wav|pitch/LJ013-0005.pt|One of the earliest of the big operators in fraudulent finance was Edward Beaumont Smith,
wavs/LJ033-0072.wav|pitch/LJ033-0072.pt|I then stepped off of it and the officer picked it up in the middle and it bent so.
wavs/LJ036-0067.wav|pitch/LJ036-0067.pt|According to McWatters, the Beckley bus was behind the Marsalis bus, but he did not actually see it.
wavs/LJ025-0098.wav|pitch/LJ025-0098.pt|and it is probable that amyloid substances are universally present in the animal organism, though not in the precise form of starch.
wavs/LJ005-0257.wav|pitch/LJ005-0257.pt|during which time a host of witnesses were examined, and the committee presented three separate reports,
wavs/LJ004-0024.wav|pitch/LJ004-0024.pt|Thus in eighteen thirteen the exaction of jail fees had been forbidden by law,
wavs/LJ049-0154.wav|pitch/LJ049-0154.pt|In eighteen ninety-four,
wavs/LJ039-0059.wav|pitch/LJ039-0059.pt|(three) his experience and practice after leaving the Marine Corps, and (four) the accuracy of the weapon and the quality of the ammunition.
wavs/LJ007-0150.wav|pitch/LJ007-0150.pt|He is allowed intercourse with prostitutes who, in nine cases out of ten, have originally conduced to his ruin;
wavs/LJ015-0001.wav|pitch/LJ015-0001.pt|Chronicles of Newgate, Volume two. By Arthur Griffiths. Section eighteen: Newgate notorieties continued, part three.
wavs/LJ010-0158.wav|pitch/LJ010-0158.pt|feeling, as he said, that he might as well be shot or hanged as remain in such a state.
wavs/LJ010-0281.wav|pitch/LJ010-0281.pt|who had borne the Queen's commission, first as cornet, and then lieutenant, in the tenth Hussars.
wavs/LJ033-0055.wav|pitch/LJ033-0055.pt|and he could disassemble it more rapidly.
wavs/LJ015-0218.wav|pitch/LJ015-0218.pt|A new accomplice was now needed within the company's establishment, and Pierce looked about long before he found the right person.
wavs/LJ027-0006.wav|pitch/LJ027-0006.pt|In all these lines the facts are drawn together by a strong thread of unity.
wavs/LJ016-0049.wav|pitch/LJ016-0049.pt|He had here completed his ascent.
wavs/LJ006-0088.wav|pitch/LJ006-0088.pt|It was not likely that a system which left innocent men -- for the great bulk of new arrivals were still untried
wavs/LJ042-0133.wav|pitch/LJ042-0133.pt|a great change must have occurred in Oswald's thinking to induce him to return to the United States.
wavs/LJ045-0234.wav|pitch/LJ045-0234.pt|While he did become enraged at at least one point in his interrogation,
wavs/LJ046-0033.wav|pitch/LJ046-0033.pt|The adequacy of existing procedures can fairly be assessed only after full consideration of the difficulty of the protective assignment,
wavs/LJ037-0061.wav|pitch/LJ037-0061.pt|and having, quote, somewhat bushy, end quote, hair.
wavs/LJ032-0025.wav|pitch/LJ032-0025.pt|the officers of Klein's discovered that a rifle bearing serial number C two seven six six had been shipped to one A. Hidell,
wavs/LJ047-0197.wav|pitch/LJ047-0197.pt|in view of all the information concerning Oswald in its files, should have alerted the Secret Service to Oswald's presence in Dallas
wavs/LJ018-0130.wav|pitch/LJ018-0130.pt|and stole paper on a much larger scale than Brown.
wavs/LJ005-0265.wav|pitch/LJ005-0265.pt|It was recommended that the dietaries should be submitted and approved like the rules; that convicted prisoners should not receive any food but the jail allowance;
wavs/LJ044-0105.wav|pitch/LJ044-0105.pt|He presented Arnold Johnson, Gus Hall,
wavs/LJ015-0043.wav|pitch/LJ015-0043.pt|This went on for some time, and might never have been discovered had some good stroke of luck provided any of the partners
wavs/LJ030-0125.wav|pitch/LJ030-0125.pt|On several occasions when the Vice President's car was slowed down by the throng, Special Agent Youngblood stepped out to hold the crowd back.
wavs/LJ043-0140.wav|pitch/LJ043-0140.pt|He also studied Dallas bus schedules to prepare for his later use of buses to travel to and from General Walker's house.
wavs/LJ002-0220.wav|pitch/LJ002-0220.pt|In consequence of these disclosures, both Bambridge and Huggin, his predecessor in the office, were committed to Newgate,
wavs/LJ034-0117.wav|pitch/LJ034-0117.pt|At one:twenty-nine p.m. the police radio reported
wavs/LJ018-0276.wav|pitch/LJ018-0276.pt|The first plot was against Mr. Harry Emmanuel, but he escaped, and the attempt was made upon Loudon and Ryder.
wavs/LJ004-0077.wav|pitch/LJ004-0077.pt|nor has he a right to poison or starve his fellow-creatures."
wavs/LJ042-0194.wav|pitch/LJ042-0194.pt|they should not be confused with slowness, indecision or fear. Only the intellectually fearless could even be remotely attracted to our doctrine,
wavs/LJ029-0114.wav|pitch/LJ029-0114.pt|The route chosen from the airport to Main Street was the normal one, except where Harwood Street was selected as the means of access to Main Street
wavs/LJ014-0194.wav|pitch/LJ014-0194.pt|The policemen were now in possession;
wavs/LJ032-0027.wav|pitch/LJ032-0027.pt|According to its microfilm records, Klein's received an order for a rifle on March thirteen, nineteen sixty-three,
wavs/LJ048-0289.wav|pitch/LJ048-0289.pt|However, there is no evidence that these men failed to take any action in Dallas within their power that would have averted the tragedy.
wavs/LJ043-0188.wav|pitch/LJ043-0188.pt|that he was the leader of a fascist organization, and when I said that even though all of that might be true, just the same he had no right to take his life,
wavs/LJ011-0118.wav|pitch/LJ011-0118.pt|In eighteen twenty-nine the gallows claimed two more victims for this offense.
wavs/LJ040-0201.wav|pitch/LJ040-0201.pt|After her interview with Mrs. Oswald,
wavs/LJ033-0056.wav|pitch/LJ033-0056.pt|While the rifle may have already been disassembled when Oswald arrived home on Thursday, he had ample time that evening to disassemble the rifle
wavs/LJ047-0073.wav|pitch/LJ047-0073.pt|Hosty considered the information to be, quote, stale, unquote, by that time, and did not attempt to verify Oswald's reported statement.
wavs/LJ001-0153.wav|pitch/LJ001-0153.pt|only nominally so, however, in many cases, since when he uses a headline he counts that in,
wavs/LJ007-0158.wav|pitch/LJ007-0158.pt|or any kind of moral improvement was impossible; the prisoner's career was inevitably downward, till he struck the lowest depths.
wavs/LJ028-0502.wav|pitch/LJ028-0502.pt|The Ishtar gateway leading to the palace was encased with beautiful blue glazed bricks,
wavs/LJ028-0226.wav|pitch/LJ028-0226.pt|Though Herodotus wrote nearly a hundred years after Babylon fell, his story seems to bear the stamp of truth.
wavs/LJ010-0038.wav|pitch/LJ010-0038.pt|as there had been before; as in the year eighteen forty-nine, a year memorable for the Rush murders at Norwich,
wavs/LJ019-0241.wav|pitch/LJ019-0241.pt|But in the interval very comprehensive and, I think it must be admitted, salutary changes were successively introduced into the management of prisons.
wavs/LJ001-0094.wav|pitch/LJ001-0094.pt|were induced to cut punches for a series of "old style" letters.
wavs/LJ001-0015.wav|pitch/LJ001-0015.pt|the forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves.
wavs/LJ047-0015.wav|pitch/LJ047-0015.pt|From defection to return to Fort Worth.
wavs/LJ044-0139.wav|pitch/LJ044-0139.pt|since there was no background to the New Orleans FPCC, quote, organization, end quote, which consisted solely of Oswald.
wavs/LJ050-0031.wav|pitch/LJ050-0031.pt|that the Secret Service consciously set about the task of inculcating and maintaining the highest standard of excellence and esprit, for all of its personnel.
wavs/LJ050-0235.wav|pitch/LJ050-0235.pt|It has also used other Federal law enforcement agents during Presidential visits to cities in which such agents are stationed.
wavs/LJ050-0137.wav|pitch/LJ050-0137.pt|FBI, and the Secret Service.
wavs/LJ031-0109.wav|pitch/LJ031-0109.pt|At one:thirty-five p.m., after Governor Connally had been moved to the operating room, Dr. Shaw started the first operation
wavs/LJ031-0041.wav|pitch/LJ031-0041.pt|He noted that the President was blue-white or ashen in color; had slow, spasmodic, agonal respiration without any coordination;
wavs/LJ021-0139.wav|pitch/LJ021-0139.pt|There should be at least a full and fair trial given to these means of ending industrial warfare;
wavs/LJ029-0004.wav|pitch/LJ029-0004.pt|The narrative of these events is based largely on the recollections of the participants,
wavs/LJ023-0122.wav|pitch/LJ023-0122.pt|It was said in last year's Democratic platform,
wavs/LJ005-0264.wav|pitch/LJ005-0264.pt|inspectors of prisons should be appointed, who should visit all the prisons from time to time and report to the Secretary of State.
wavs/LJ002-0105.wav|pitch/LJ002-0105.pt|and beyond it was a room called the "wine room," because formerly used for the sale of wine, but
wavs/LJ017-0035.wav|pitch/LJ017-0035.pt|in the interests and for the due protection of the public, that the fullest and fairest inquiry should be made,
wavs/LJ048-0252.wav|pitch/LJ048-0252.pt|Three of these agents occupied positions on the running boards of the car, and the fourth was seated in the car.
wavs/LJ013-0109.wav|pitch/LJ013-0109.pt|The proceeds of the robbery were lodged in a Boston bank,
wavs/LJ039-0139.wav|pitch/LJ039-0139.pt|Oswald obtained a hunting license, joined a hunting club and went hunting about six times, as discussed more fully in chapter six.
wavs/LJ044-0047.wav|pitch/LJ044-0047.pt|that anyone ever attacked any street demonstration in which Oswald was involved, except for the Bringuier incident mentioned above,
wavs/LJ016-0417.wav|pitch/LJ016-0417.pt|Catherine Wilson, the poisoner, was reserved and reticent to the last, expressing no contrition, but also no fear --
wavs/LJ045-0178.wav|pitch/LJ045-0178.pt|he left his wedding ring in a cup on the dresser in his room. He also left one hundred seventy dollars in a wallet in one of the dresser drawers.
wavs/LJ009-0172.wav|pitch/LJ009-0172.pt|While in London, for instance, in eighteen twenty-nine, twenty-four persons had been executed for crimes other than murder,
wavs/LJ049-0202.wav|pitch/LJ049-0202.pt|incident to its responsibilities.
wavs/LJ032-0103.wav|pitch/LJ032-0103.pt|The name "Hidell" was stamped on some of the "Chapter's" printed literature and on the membership application blanks.
wavs/LJ013-0091.wav|pitch/LJ013-0091.pt|and Elder had to be assisted by two bank porters, who carried it for him to a carriage waiting near the Mansion House.
wavs/LJ037-0208.wav|pitch/LJ037-0208.pt|nineteen dollars, ninety-five cents, plus one dollar, twenty-seven cents shipping charge, had been collected from the consignee, Hidell.
wavs/LJ014-0128.wav|pitch/LJ014-0128.pt|her hair was dressed in long crepe bands. She had lace ruffles at her wrist, and wore primrose-colored kid gloves.
wavs/LJ015-0007.wav|pitch/LJ015-0007.pt|This affected Cole's credit, and ugly reports were in circulation charging him with the issue of simulated warrants.
wavs/LJ036-0169.wav|pitch/LJ036-0169.pt|he would have reached his destination at approximately twelve:fifty-four p.m.
wavs/LJ021-0040.wav|pitch/LJ021-0040.pt|The second step we have taken in the restoration of normal business enterprise
wavs/LJ015-0036.wav|pitch/LJ015-0036.pt|The bank was already insolvent,
wavs/LJ034-0041.wav|pitch/LJ034-0041.pt|Although Bureau experiments had shown that twenty-four hours was a likely maximum time, Latona stated
wavs/LJ009-0192.wav|pitch/LJ009-0192.pt|The dissection of executed criminals was abolished soon after the discovery of the crime of burking,
wavs/LJ037-0248.wav|pitch/LJ037-0248.pt|The eyewitnesses vary in their identification of the jacket.
wavs/LJ015-0289.wav|pitch/LJ015-0289.pt|As each transaction was carried out from a different address, and a different messenger always employed,
wavs/LJ005-0072.wav|pitch/LJ005-0072.pt|After a few years of active exertion the Society was rewarded by fresh legislation.
wavs/LJ023-0047.wav|pitch/LJ023-0047.pt|The three horses are, of course, the three branches of government -- the Congress, the Executive and the courts.
wavs/LJ009-0126.wav|pitch/LJ009-0126.pt|Hardly any one.
wavs/LJ034-0097.wav|pitch/LJ034-0097.pt|The window was approximately one hundred twenty feet away.
wavs/LJ028-0462.wav|pitch/LJ028-0462.pt|They were laid in bitumen.
wavs/LJ046-0055.wav|pitch/LJ046-0055.pt|It is now possible for Presidents to travel the length and breadth of a land far larger than the United States
wavs/LJ019-0371.wav|pitch/LJ019-0371.pt|Yet the law was seldom if ever enforced.
wavs/LJ039-0207.wav|pitch/LJ039-0207.pt|Although all of the shots were a few inches high and to the right of the target,
wavs/LJ002-0174.wav|pitch/LJ002-0174.pt|Mr. Buxton's friends at once paid the forty shillings, and the boy was released.
wavs/LJ016-0233.wav|pitch/LJ016-0233.pt|In his own profession
wavs/LJ026-0108.wav|pitch/LJ026-0108.pt|It is clear that there are upward and downward currents of water containing food (comparable to blood of an animal),
wavs/LJ038-0035.wav|pitch/LJ038-0035.pt|Oswald rose from his seat, bringing up both hands.
wavs/LJ026-0148.wav|pitch/LJ026-0148.pt|water which is lost by evaporation, especially from the leaf surface through the stomata;
wavs/LJ001-0186.wav|pitch/LJ001-0186.pt|the position of our Society that a work of utility might be also a work of art, if we cared to make it so.
wavs/LJ016-0264.wav|pitch/LJ016-0264.pt|The upturned faces of the eager spectators resembled those of the 'gods' at Drury Lane on Boxing Night;
wavs/LJ009-0041.wav|pitch/LJ009-0041.pt|The occupants of this terrible black pew were the last always to enter the chapel.
wavs/LJ010-0297.wav|pitch/LJ010-0297.pt|But there were other notorious cases of forgery.
wavs/LJ040-0018.wav|pitch/LJ040-0018.pt|the Commission is not able to reach any definite conclusions as to whether or not he was, quote, sane, unquote, under prevailing legal standards.
wavs/LJ005-0253.wav|pitch/LJ005-0253.pt|"to inquire into and report upon the several jails and houses of correction in the counties, cities, and corporate towns within England and Wales
wavs/LJ027-0176.wav|pitch/LJ027-0176.pt|Fishes first appeared in the Devonian and Upper Silurian in very reptilian or rather amphibian forms.
wavs/LJ034-0035.wav|pitch/LJ034-0035.pt|The position of this palmprint on the carton was parallel with the long axis of the box, and at right angles with the short axis;
wavs/LJ016-0054.wav|pitch/LJ016-0054.pt|But he did not like the risk of entering a room by the fireplace, and the chances of detection it offered.
wavs/LJ018-0262.wav|pitch/LJ018-0262.pt|Roupell received the announcement with a cheerful countenance,
wavs/LJ044-0237.wav|pitch/LJ044-0237.pt|with thirteen dollars, eighty-seven cents when considerably greater resources were available to him.
wavs/LJ034-0166.wav|pitch/LJ034-0166.pt|Two other witnesses were able to offer partial descriptions of a man they saw in the southeast corner window
wavs/LJ016-0238.wav|pitch/LJ016-0238.pt|"just to steady their legs a little;" in other words, to add his weight to that of the hanging bodies.
wavs/LJ042-0198.wav|pitch/LJ042-0198.pt|The discussion above has already set forth examples of his expression of hatred for the United States.
wavs/LJ031-0189.wav|pitch/LJ031-0189.pt|At two:thirty-eight p.m., Eastern Standard Time, Lyndon Baines Johnson took the oath of office as the thirty-sixth President of the United States.
wavs/LJ050-0084.wav|pitch/LJ050-0084.pt|or, quote, other high government officials in the nature of a complaint coupled with an expressed or implied determination to use a means,
wavs/LJ044-0158.wav|pitch/LJ044-0158.pt|As for my return entrance visa please consider it separately. End quote.
wavs/LJ045-0082.wav|pitch/LJ045-0082.pt|it appears that Marina Oswald also complained that her husband was not able to provide more material things for her.
wavs/LJ045-0190.wav|pitch/LJ045-0190.pt|appeared in The Dallas Times Herald on November fifteen, nineteen sixty-three.
wavs/LJ035-0155.wav|pitch/LJ035-0155.pt|The only exit from the office in the direction Oswald was moving was through the door to the front stairway.
wavs/LJ044-0004.wav|pitch/LJ044-0004.pt|Political Activities
wavs/LJ046-0016.wav|pitch/LJ046-0016.pt|The Commission has not undertaken a comprehensive examination of all facets of this subject;
wavs/LJ019-0368.wav|pitch/LJ019-0368.pt|The latter too was to be laid before the House of Commons.
wavs/LJ010-0062.wav|pitch/LJ010-0062.pt|But they proceeded in all seriousness, and would have shrunk from no outrage or atrocity in furtherance of their foolhardy enterprise.
wavs/LJ033-0159.wav|pitch/LJ033-0159.pt|It was from Oswald's right hand, in which he carried the long package as he walked from Frazier's car to the building.
wavs/LJ002-0171.wav|pitch/LJ002-0171.pt|The boy declared he saw no one, and accordingly passed through without paying the toll of a penny.
wavs/LJ002-0298.wav|pitch/LJ002-0298.pt|in his evidence in eighteen fourteen, said it was more,
wavs/LJ012-0219.wav|pitch/LJ012-0219.pt|and in one corner, at some depth, a bundle of clothes were unearthed, which, with a hairy cap,
wavs/LJ017-0190.wav|pitch/LJ017-0190.pt|After this came the charge of administering oil of vitriol, which failed, as has been described.
wavs/LJ019-0179.wav|pitch/LJ019-0179.pt|This, with a scheme for limiting the jail to untried prisoners, had been urgently recommended by Lord John Russell in eighteen thirty.
wavs/LJ050-0188.wav|pitch/LJ050-0188.pt|each patrolman might be given a prepared booklet of instructions explaining what is expected of him. The Secret Service has expressed concern
wavs/LJ006-0043.wav|pitch/LJ006-0043.pt|The disgraceful overcrowding had been partially ended, but the same evils of indiscriminate association were still present; there was the old neglect of decency,
wavs/LJ029-0060.wav|pitch/LJ029-0060.pt|A number of people who resembled some of those in the photographs were placed under surveillance at the Trade Mart.
wavs/LJ019-0052.wav|pitch/LJ019-0052.pt|Both systems came to us from the United States. The difference was really more in degree than in principle,
wavs/LJ037-0081.wav|pitch/LJ037-0081.pt|Later in the day each woman found an empty shell on the ground near the house. These two shells were delivered to the police.
wavs/LJ048-0200.wav|pitch/LJ048-0200.pt|paying particular attention to the crowd for any unusual activity.
wavs/LJ016-0426.wav|pitch/LJ016-0426.pt|come along, gallows.
wavs/LJ008-0182.wav|pitch/LJ008-0182.pt|A tremendous crowd assembled when Bellingham was executed in eighteen twelve for the murder of Spencer Percival, at that time prime minister;
wavs/LJ043-0107.wav|pitch/LJ043-0107.pt|Upon moving to New Orleans on April twenty-four, nineteen sixty-three,
wavs/LJ006-0084.wav|pitch/LJ006-0084.pt|and so numerous were his opportunities of showing favoritism, that all the prisoners may be said to be in his power.
wavs/LJ025-0081.wav|pitch/LJ025-0081.pt|has no permanent digestive cavity or mouth, but takes in its food anywhere and digests, so to speak, all over its body.
wavs/LJ019-0042.wav|pitch/LJ019-0042.pt|These were either satisfied with a makeshift, and modified existing buildings, without close regard to their suitability, or for a long time did nothing at all.
wavs/LJ047-0240.wav|pitch/LJ047-0240.pt|They agree that Hosty told Revill
wavs/LJ032-0012.wav|pitch/LJ032-0012.pt|the resistance to arrest and the attempted shooting of another police officer by the man (Lee Harvey Oswald) subsequently accused of assassinating President Kennedy
wavs/LJ050-0209.wav|pitch/LJ050-0209.pt|The assistant to the Director of the FBI testified that
|
TensorFlow2/LanguageModeling/ELECTRA | ELECTRA | run_tf_squad | # Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import subprocess
import time
import argparse
import json
import logging
import tensorflow as tf
import horovod.tensorflow as hvd
from horovod.tensorflow.compression import Compression
from gpu_affinity import set_affinity
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
from tqdm import tqdm
import dllogger
from utils import is_main_process, format_step, get_rank, get_world_size, log
from configuration import ElectraConfig
from modeling import TFElectraForQuestionAnswering
from tokenization import ElectraTokenizer
from optimization import create_optimizer
from squad_utils import SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features, \
SquadResult, RawResult, get_answers
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/electra-small-generator",
"google/electra-base-generator",
"google/electra-large-generator",
"google/electra-small-discriminator",
"google/electra-base-discriminator",
"google/electra-large-discriminator",
# See all ELECTRA models at https://huggingface.co/models?filter=electra
]
def parse_args():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--electra_model", default=None, type=str, required=True,
help="Model selected in the list: " + ", ".join(TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST))
parser.add_argument("--data_dir", default=None, type=str, required=True,
help="Path to dataset.")
parser.add_argument("--output_dir", default=".", type=str, required=True,
help="The output directory where the model checkpoints and predictions will be written.")
parser.add_argument("--init_checkpoint",
default=None,
type=str,
help="The checkpoint file from pretraining")
# Other parameters
parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
parser.add_argument("--do_predict", action='store_true', help="Whether to run eval on the dev set.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to use evaluate accuracy of predictions")
parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json")
parser.add_argument("--predict_file", default=None, type=str,
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.")
parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.")
parser.add_argument("--learning_rate", default=1e-4, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay_rate", default=0.01, type=float, help="Weight decay if we apply some.")
parser.add_argument("--layerwise_lr_decay", default=0.8, type=float,
help="The layerwise learning rate decay. Shallower layers have lower learning rates.")
parser.add_argument("--num_train_epochs", default=3, type=int,
help="Total number of training epochs to perform.")
parser.add_argument("--max_steps", default=-1.0, type=float,
help="Total number of training steps to perform.")
parser.add_argument("--warmup_proportion", default=0.1, type=float,
help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% "
"of training.")
parser.add_argument("--max_seq_length", default=384, type=int,
help="The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded.")
parser.add_argument("--doc_stride", default=128, type=int,
help="When splitting up a long document into chunks, how much stride to take between chunks.")
parser.add_argument("--max_query_length", default=64, type=int,
help="The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument("--vocab_file", default=None, type=str,
help="Path to vocabulary file use for tokenization")
parser.add_argument("--ci", action="store_true", help="true if running on CI")
parser.add_argument(
"--joint_head",
default=True,
type=bool,
help="Jointly predict the start and end positions",
)
parser.add_argument(
"--beam_size",
default=4,
type=int,
help="Beam size when doing joint predictions",
)
parser.add_argument("--n_best_size", default=20, type=int,
help="The total number of n-best predictions to generate in the nbest_predictions.json "
"output file.")
parser.add_argument("--max_answer_length", default=30, type=int,
help="The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument("--verbose_logging", action='store_true',
help="If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
"--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step."
)
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument("--do_lower_case",
action='store_true',
help="Whether to lower case the input text. True for uncased models, False for cased models.")
parser.add_argument("--local_rank",
type=int,
default=os.getenv('LOCAL_RANK', -1),
help="local_rank for distributed training on gpus")
parser.add_argument('--amp',
action='store_true',
help="Automatic mixed precision training")
parser.add_argument('--fp16_all_reduce',
action='store_true',
help="Whether to use 16-bit all reduce")
parser.add_argument('--xla',
action='store_true',
help="Whether to use XLA")
parser.add_argument('--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.')
parser.add_argument('--null_score_diff_threshold',
type=float, default=0.0,
help="If null_score - best_non_null is greater than the threshold predict null.")
parser.add_argument('--log_freq',
type=int, default=50,
help='frequency of logging loss.')
parser.add_argument('--json-summary', type=str, default="results/dllogger.json",
help='If provided, the json summary will be written to the specified file.')
parser.add_argument("--eval_script",
help="Script to evaluate squad predictions",
default="evaluate.py",
type=str)
parser.add_argument("--use_env",
action='store_true',
help="Whether to read local rank from ENVVAR")
parser.add_argument('--skip_checkpoint',
default=False,
action='store_true',
help="Whether to save checkpoints")
parser.add_argument('--disable-progress-bar',
default=False,
action='store_true',
help='Disable tqdm progress bar')
parser.add_argument("--skip_cache",
default=False,
action='store_true',
help="Whether to cache train features")
parser.add_argument("--cache_dir",
default=None,
type=str,
help="Location to cache train feaures. Will default to the dataset direct")
args = parser.parse_args()
if not args.do_train and (not args.init_checkpoint or args.init_checkpoint == 'None'):
raise ValueError("Checkpoint is required if do_train is not set")
return args
def get_dataset_from_features(features, batch_size, drop_remainder=True, ngpu=8, mode="train", v2=False):
"""Input function for training"""
all_input_ids = tf.convert_to_tensor([f.input_ids for f in features], dtype=tf.int64)
all_input_mask = tf.convert_to_tensor([f.attention_mask for f in features], dtype=tf.int64)
all_segment_ids = tf.convert_to_tensor([f.token_type_ids for f in features], dtype=tf.int64)
all_start_pos = tf.convert_to_tensor([f.start_position for f in features], dtype=tf.int64)
all_end_pos = tf.convert_to_tensor([f.end_position for f in features], dtype=tf.int64)
# if v2 else None:
all_cls_index = tf.convert_to_tensor([f.cls_index for f in features], dtype=tf.int64)
all_p_mask = tf.convert_to_tensor([f.p_mask for f in features], dtype=tf.float32)
all_is_impossible = tf.convert_to_tensor([f.is_impossible for f in features], dtype=tf.float32)
dataset = tf.data.Dataset.from_tensor_slices(
(all_input_ids, all_input_mask, all_segment_ids, all_start_pos, all_end_pos)
+ (all_cls_index, all_p_mask, all_is_impossible))
if ngpu > 1:
dataset = dataset.shard(get_world_size(), get_rank())
if mode == "train":
dataset = dataset.shuffle(batch_size * 3)
# dataset = dataset.map(self._preproc_samples,
# num_parallel_calls=multiprocessing.cpu_count()//self._num_gpus)
dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
dataset = dataset.prefetch(batch_size)
return dataset
@tf.function
def train_step(model, inputs, loss, amp, opt, init, v2=False, loss_class=None, fp16=False, clip_norm=1.0):
with tf.GradientTape() as tape:
[input_ids, input_mask, segment_ids, start_positions, end_positions, cls_index, p_mask, is_impossible] = inputs
if not v2:
is_impossible = None
start_logits, end_logits, cls_logits = model(input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
start_positions=start_positions,
end_positions=end_positions,
cls_index=cls_index,
p_mask=p_mask,
is_impossible=is_impossible,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=True,
)[0:3]
# If we are on multi-GPU, split add a dimension
if len(start_positions.shape) > 1:
start_positions = tf.squeeze(start_positions, axis=-1, name="squeeze_start_positions")
if len(end_positions.shape) > 1:
end_positions = tf.squeeze(end_positions, axis=-1, name="squeeze_end_positions")
if is_impossible is not None and len(is_impossible.shape) > 1 and v2 and cls_logits is not None:
is_impossible = tf.squeeze(is_impossible, axis=-1, name="squeeze_is_impossible")
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.shape[1]
start_positions = tf.clip_by_value(start_positions, 0, ignored_index, name="clip_start_positions")
end_positions = tf.clip_by_value(end_positions, 0, ignored_index, name="clip_end_positions")
start_loss = loss(y_true=start_positions, y_pred=tf.cast(start_logits, tf.float32))
end_loss = loss(y_true=end_positions, y_pred=tf.cast(end_logits, tf.float32))
loss_value = (start_loss + end_loss) / 2
if v2:
cls_loss_value = loss_class(y_true=is_impossible, y_pred=tf.cast(cls_logits, tf.float32))
loss_value += cls_loss_value * 0.5
unscaled_loss = tf.stop_gradient(loss_value)
if amp:
loss_value = opt.get_scaled_loss(loss_value)
tape = hvd.DistributedGradientTape(tape, sparse_as_dense=True,
compression=Compression.fp16 if fp16 else Compression.none)
gradients = tape.gradient(loss_value, model.trainable_variables)
if amp:
gradients = opt.get_unscaled_gradients(gradients)
(gradients, _) = tf.clip_by_global_norm(gradients, clip_norm=clip_norm)
opt.apply_gradients(zip(gradients, model.trainable_variables)) # , clip_norm=1.0)
if init:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
return unscaled_loss # , outputs#, tape.gradient(loss_value, model.trainable_variables)
@tf.function
def infer_step(model, input_ids,
attention_mask=None,
token_type_ids=None,
cls_index=None,
p_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
return model(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
cls_index=cls_index,
p_mask=p_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
training=training,
)
def main():
args = parse_args()
hvd.init()
set_affinity(hvd.local_rank())
if is_main_process():
log("Running total processes: {}".format(get_world_size()))
log("Starting process: {}".format(get_rank()))
if is_main_process():
dllogger.init(backends=[dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE,
filename=args.json_summary),
dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE, step_format=format_step)])
else:
dllogger.init(backends=[])
dllogger.metadata("exact_match", {"unit": None})
dllogger.metadata("F1", {"unit": None})
dllogger.metadata("inference_sequences_per_second", {"unit": "sequences/s"})
dllogger.metadata("training_sequences_per_second", {"unit": "sequences/s"})
tf.random.set_seed(args.seed)
dllogger.log(step="PARAMETER", data={"SEED": args.seed})
# script parameters
BATCH_SIZE = args.train_batch_size
EVAL_BATCH_SIZE = args.predict_batch_size
USE_XLA = args.xla
USE_AMP = args.amp
EPOCHS = args.num_train_epochs
if not args.do_train:
EPOCHS = args.num_train_epochs = 1
log("Since running inference only, setting args.num_train_epochs to 1")
if not os.path.exists(args.output_dir) and is_main_process():
os.makedirs(args.output_dir)
# TensorFlow configuration
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
tf.config.optimizer.set_jit(USE_XLA)
#tf.config.optimizer.set_experimental_options({"auto_mixed_precision": USE_AMP})
if args.amp:
policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16", loss_scale="dynamic")
tf.keras.mixed_precision.experimental.set_policy(policy)
print('Compute dtype: %s' % policy.compute_dtype) # Compute dtype: float16
print('Variable dtype: %s' % policy.variable_dtype) # Variable dtype: float32
if is_main_process():
log("***** Loading tokenizer and model *****")
# Load tokenizer and model from pretrained model/vocabulary. Specify the number of labels to classify (2+: classification, 1: regression)
electra_model = args.electra_model
config = ElectraConfig.from_pretrained(electra_model, cache_dir=args.cache_dir)
config.update({"amp": args.amp})
if args.vocab_file is None:
tokenizer = ElectraTokenizer.from_pretrained(electra_model, cache_dir=args.cache_dir)
else:
tokenizer = ElectraTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case)
model = TFElectraForQuestionAnswering.from_pretrained(electra_model, config=config, cache_dir=args.cache_dir, args=args)
if is_main_process():
log("***** Loading dataset *****")
# Load data
processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
train_examples = processor.get_train_examples(args.data_dir) if args.do_train else None
dev_examples = processor.get_dev_examples(args.data_dir) if args.do_predict else None
if is_main_process():
log("***** Loading features *****")
# Load cached features
squad_version = '2.0' if args.version_2_with_negative else '1.1'
if args.cache_dir is None:
args.cache_dir = args.data_dir
cached_train_features_file = args.cache_dir.rstrip('/') + '/' + 'TF2_train-v{4}.json_{1}_{2}_{3}'.format(
electra_model.split("/")[1], str(args.max_seq_length), str(args.doc_stride),
str(args.max_query_length), squad_version)
cached_dev_features_file = args.cache_dir.rstrip('/') + '/' + 'TF2_dev-v{4}.json_{1}_{2}_{3}'.format(
electra_model.split("/")[1], str(args.max_seq_length), str(args.doc_stride),
str(args.max_query_length), squad_version)
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader) if args.do_train else []
with open(cached_dev_features_file, "rb") as reader:
dev_features = pickle.load(reader) if args.do_predict else []
except:
train_features = ( # TODO: (yy) do on rank 0?
squad_convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=True,
return_dataset="",
)
if args.do_train
else []
)
dev_features = (
squad_convert_examples_to_features(
examples=dev_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False,
return_dataset="",
)
if args.do_predict
else []
)
# Dump Cached features
if not args.skip_cache and is_main_process():
if args.do_train:
log("***** Building Cache Files: {} *****".format(cached_train_features_file))
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
if args.do_predict:
log("***** Building Cache Files: {} *****".format(cached_dev_features_file))
with open(cached_dev_features_file, "wb") as writer:
pickle.dump(dev_features, writer)
len_train_features = len(train_features)
total_train_steps = int((len_train_features * EPOCHS / BATCH_SIZE) / get_world_size()) + 1
train_steps_per_epoch = int((len_train_features / BATCH_SIZE) / get_world_size()) + 1
len_dev_features = len(dev_features)
total_dev_steps = int((len_dev_features / EVAL_BATCH_SIZE)) + 1
train_dataset = get_dataset_from_features(train_features, BATCH_SIZE,
v2=args.version_2_with_negative) if args.do_train else []
dev_dataset = get_dataset_from_features(dev_features, EVAL_BATCH_SIZE, drop_remainder=False, ngpu=1, mode="dev",
v2=args.version_2_with_negative) if args.do_predict else []
opt = create_optimizer(init_lr=args.learning_rate, num_train_steps=total_train_steps,
num_warmup_steps=int(args.warmup_proportion * total_train_steps),
weight_decay_rate=args.weight_decay_rate,
layerwise_lr_decay=args.layerwise_lr_decay,
n_transformer_layers=model.num_hidden_layers)
if USE_AMP:
# loss scaling is currently required when using mixed precision
opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, "dynamic")
# Define loss function
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
loss_class = tf.keras.losses.BinaryCrossentropy(
from_logits=True,
name='binary_crossentropy'
)
metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
model.compile(optimizer=opt, loss=loss, metrics=[metric])
train_loss_results = []
if args.do_train and is_main_process():
log("***** Running training *****")
log(" Num examples = ", len_train_features)
log(" Num Epochs = ", args.num_train_epochs)
log(" Instantaneous batch size per GPU = ", args.train_batch_size)
log(
" Total train batch size (w. parallel, distributed & accumulation) = ",
args.train_batch_size
* get_world_size(),
)
log(" Total optimization steps =", total_train_steps)
total_train_time = 0
latency = []
for epoch in range(EPOCHS):
if args.do_train:
epoch_loss_avg = tf.keras.metrics.Mean()
epoch_perf_avg = tf.keras.metrics.Mean()
epoch_start = time.time()
epoch_iterator = tqdm(train_dataset, total=train_steps_per_epoch, desc="Iteration", mininterval=5,
disable=not is_main_process())
for iter, inputs in enumerate(epoch_iterator):
# breaking criterion if max_steps if > 1
if args.max_steps > 0 and (epoch * train_steps_per_epoch + iter) > args.max_steps:
break
iter_start = time.time()
# Optimize the model
loss_value = train_step(model, inputs, loss, USE_AMP, opt, (iter == 0 and epoch == 0),
v2=args.version_2_with_negative, loss_class=loss_class, fp16=USE_AMP)
#introduce CPU-GPU sync for training perf computation
loss_numpy = loss_value.numpy()
epoch_perf_avg.update_state(1. * BATCH_SIZE / (time.time() - iter_start))
if iter % args.log_freq == 0:
if is_main_process():
log("\nEpoch: {:03d}, Step:{:6d}, Loss:{:12.8f}, Perf:{:5.0f}, loss_scale:{}, opt_step:{}".format(epoch, iter, loss_value,
epoch_perf_avg.result() * get_world_size(), opt.loss_scale if config.amp else 1,
int(opt.iterations)))
dllogger.log(step=(epoch, iter,), data={"step_loss": float(loss_value.numpy()),
"train_perf": float( epoch_perf_avg.result().numpy() * get_world_size())})
# Track progress
epoch_loss_avg.update_state(loss_value) # Add current batch loss
# End epoch
train_loss_results.append(epoch_loss_avg.result())
total_train_time += float(time.time() - epoch_start)
# Summarize and save checkpoint at the end of each epoch
if is_main_process():
dllogger.log(step=tuple(), data={"e2e_train_time": total_train_time,
"training_sequences_per_second": float(
epoch_perf_avg.result().numpy() * get_world_size()),
"final_loss": float(epoch_loss_avg.result().numpy())})
if not args.skip_checkpoint:
if args.ci:
checkpoint_name = "{}/electra_base_qa_v2_{}_epoch_{}_ckpt".format(args.output_dir, args.version_2_with_negative, epoch + 1)
else:
checkpoint_name = "checkpoints/electra_base_qa_v2_{}_epoch_{}_ckpt".format(args.version_2_with_negative, epoch + 1)
if is_main_process():
model.save_weights(checkpoint_name)
if args.do_predict and (args.evaluate_during_training or epoch == args.num_train_epochs - 1):
if not args.do_train:
log("***** Loading checkpoint: {} *****".format(args.init_checkpoint))
model.load_weights(args.init_checkpoint).expect_partial()
current_feature_id = 0
all_results = []
if is_main_process():
log("***** Running evaluation *****")
log(" Num Batches = ", total_dev_steps)
log(" Batch size = ", args.predict_batch_size)
raw_infer_start = time.time()
if is_main_process():
infer_perf_avg = tf.keras.metrics.Mean()
dev_iterator = tqdm(dev_dataset, total=total_dev_steps, desc="Iteration", mininterval=5,
disable=not is_main_process())
for input_ids, input_mask, segment_ids, start_positions, end_positions, cls_index, p_mask, is_impossible in dev_iterator:
# training=False is needed only if there are layers with different
# behavior during training versus inference (e.g. Dropout).
iter_start = time.time()
if not args.joint_head:
batch_start_logits, batch_end_logits = infer_step(model, input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
)[:2]
#Synchronize with GPU to compute time
_ = batch_start_logits.numpy()
else:
outputs = infer_step(model, input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
cls_index=cls_index,
p_mask=p_mask,
)
#Synchronize with GPU to compute time
_ = outputs[0].numpy()
infer_time = (time.time() - iter_start)
infer_perf_avg.update_state(1. * EVAL_BATCH_SIZE / infer_time)
latency.append(infer_time)
for iter_ in range(input_ids.shape[0]):
if not args.joint_head:
start_logits = batch_start_logits[iter_].numpy().tolist()
end_logits = batch_end_logits[iter_].numpy().tolist()
dev_feature = dev_features[current_feature_id]
current_feature_id += 1
unique_id = int(dev_feature.unique_id)
all_results.append(RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
else:
dev_feature = dev_features[current_feature_id]
current_feature_id += 1
unique_id = int(dev_feature.unique_id)
output = [output[iter_].numpy().tolist() for output in outputs]
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
all_results.append(result)
# Compute and save predictions
answers, nbest_answers = get_answers(dev_examples, dev_features, all_results, args)
output_prediction_file = os.path.join(args.output_dir, "predictions.json")
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
e2e_infer_time = time.time() - raw_infer_start
# if args.version_2_with_negative:
# output_null_log_odds_file = os.path.join(args.output_dir, "null_odds.json")
# else:
# output_null_log_odds_file = None
with open(output_prediction_file, "w") as f:
f.write(json.dumps(answers, indent=4) + "\n")
with open(output_nbest_file, "w") as f:
f.write(json.dumps(nbest_answers, indent=4) + "\n")
if args.do_eval:
if args.version_2_with_negative:
dev_file = "dev-v2.0.json"
else:
dev_file = "dev-v1.1.json"
eval_out = subprocess.check_output([sys.executable, args.eval_script,
args.data_dir + "/" + dev_file, output_prediction_file])
log(eval_out.decode('UTF-8'))
scores = str(eval_out).strip()
exact_match = float(scores.split(":")[1].split(",")[0])
if args.version_2_with_negative:
f1 = float(scores.split(":")[2].split(",")[0])
else:
f1 = float(scores.split(":")[2].split("}")[0])
log("Epoch: {:03d} Results: {}".format(epoch, eval_out.decode('UTF-8')))
log("**EVAL SUMMARY** - Epoch: {:03d}, EM: {:6.3f}, F1: {:6.3f}, Infer_Perf: {:4.0f} seq/s"
.format(epoch, exact_match, f1, infer_perf_avg.result()))
latency_all = sorted(latency)[:-2]
log(
"**LATENCY SUMMARY** - Epoch: {:03d}, Ave: {:6.3f} ms, 90%: {:6.3f} ms, 95%: {:6.3f} ms, 99%: {:6.3f} ms"
.format(epoch, sum(latency_all) / len(latency_all) * 1000,
sum(latency_all[:int(len(latency_all) * 0.9)]) / int(len(latency_all) * 0.9) * 1000,
sum(latency_all[:int(len(latency_all) * 0.95)]) / int(len(latency_all) * 0.95) * 1000,
sum(latency_all[:int(len(latency_all) * 0.99)]) / int(len(latency_all) * 0.99) * 1000,
))
dllogger.log(step=tuple(),
data={"inference_sequences_per_second": float(infer_perf_avg.result().numpy()),
"e2e_inference_time": e2e_infer_time})
if is_main_process() and args.do_train and args.do_eval:
log(
"**RESULTS SUMMARY** - EM: {:6.3f}, F1: {:6.3f}, Train_Time: {:4.0f} s, Train_Perf: {:4.0f} seq/s, Infer_Perf: {:4.0f} seq/s"
.format(exact_match, f1, total_train_time, epoch_perf_avg.result() * get_world_size(),
infer_perf_avg.result()))
dllogger.log(step=tuple(), data={"exact_match": exact_match, "F1": f1})
if __name__ == "__main__":
main()
|
TensorFlow/LanguageModeling/BERT | BERT | multilingual | ## Models
There are two multilingual models currently available. We do not plan to release
more single-language models, but we may release `BERT-Large` versions of these
two in the future:
* **[`BERT-Base, Multilingual Cased (New, recommended)`](https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip)**:
104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
* **[`BERT-Base, Multilingual Uncased (Orig, not recommended)`](https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip)**:
102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
* **[`BERT-Base, Chinese`](https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip)**:
Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M
parameters
**The `Multilingual Cased (New)` model also fixes normalization issues in many
languages, so it is recommended in languages with non-Latin alphabets (and is
often better for most languages with Latin alphabets). When using this model,
make sure to pass `--do_lower_case=false` to `run_pretraining.py` and other
scripts.**
See the [list of languages](#list-of-languages) that the Multilingual model
supports. The Multilingual model does include Chinese (and English), but if your
fine-tuning data is Chinese-only, then the Chinese model will likely produce
better results.
## Results
To evaluate these systems, we use the
[XNLI dataset](https://github.com/facebookresearch/XNLI) dataset, which is a
version of [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) where the
dev and test sets have been translated (by humans) into 15 languages. Note that
the training set was *machine* translated (we used the translations provided by
XNLI, not Google NMT). For clarity, we only report on 6 languages below:
<!-- mdformat off(no table) -->
| System | English | Chinese | Spanish | German | Arabic | Urdu |
| --------------------------------- | -------- | -------- | -------- | -------- | -------- | -------- |
| XNLI Baseline - Translate Train | 73.7 | 67.0 | 68.8 | 66.5 | 65.8 | 56.6 |
| XNLI Baseline - Translate Test | 73.7 | 68.3 | 70.7 | 68.7 | 66.8 | 59.3 |
| BERT - Translate Train Cased | **81.9** | **76.6** | **77.8** | **75.9** | **70.7** | 61.6 |
| BERT - Translate Train Uncased | 81.4 | 74.2 | 77.3 | 75.2 | 70.5 | 61.7 |
| BERT - Translate Test Uncased | 81.4 | 70.1 | 74.9 | 74.4 | 70.4 | **62.1** |
| BERT - Zero Shot Uncased | 81.4 | 63.8 | 74.3 | 70.5 | 62.1 | 58.3 |
<!-- mdformat on -->
The first two rows are baselines from the XNLI paper and the last three rows are
our results with BERT.
**Translate Train** means that the MultiNLI training set was machine translated
from English into the foreign language. So training and evaluation were both
done in the foreign language. Unfortunately, training was done on
machine-translated data, so it is impossible to quantify how much of the lower
accuracy (compared to English) is due to the quality of the machine translation
vs. the quality of the pre-trained model.
**Translate Test** means that the XNLI test set was machine translated from the
foreign language into English. So training and evaluation were both done on
English. However, test evaluation was done on machine-translated English, so the
accuracy depends on the quality of the machine translation system.
**Zero Shot** means that the Multilingual BERT system was fine-tuned on English
MultiNLI, and then evaluated on the foreign language XNLI test. In this case,
machine translation was not involved at all in either the pre-training or
fine-tuning.
Note that the English result is worse than the 84.2 MultiNLI baseline because
this training used Multilingual BERT rather than English-only BERT. This implies
that for high-resource languages, the Multilingual model is somewhat worse than
a single-language model. However, it is not feasible for us to train and
maintain dozens of single-language model. Therefore, if your goal is to maximize
performance with a language other than English or Chinese, you might find it
beneficial to run pre-training for additional steps starting from our
Multilingual model on data from your language of interest.
Here is a comparison of training Chinese models with the Multilingual
`BERT-Base` and Chinese-only `BERT-Base`:
System | Chinese
----------------------- | -------
XNLI Baseline | 67.0
BERT Multilingual Model | 74.2
BERT Chinese-only Model | 77.2
Similar to English, the single-language model does 3% better than the
Multilingual model.
## Fine-tuning Example
The multilingual model does **not** require any special consideration or API
changes. We did update the implementation of `BasicTokenizer` in
`tokenization.py` to support Chinese character tokenization, so please update if
you forked it. However, we did not change the tokenization API.
To test the new models, we did modify `run_classifier.py` to add support for the
[XNLI dataset](https://github.com/facebookresearch/XNLI). This is a 15-language
version of MultiNLI where the dev/test sets have been human-translated, and the
training set has been machine-translated.
To run the fine-tuning code, please download the
[XNLI dev/test set](https://s3.amazonaws.com/xnli/XNLI-1.0.zip) and the
[XNLI machine-translated training set](https://s3.amazonaws.com/xnli/XNLI-MT-1.0.zip)
and then unpack both .zip files into some directory `$XNLI_DIR`.
To run fine-tuning on XNLI. The language is hard-coded into `run_classifier.py`
(Chinese by default), so please modify `XnliProcessor` if you want to run on
another language.
This is a large dataset, so this will training will take a few hours on a GPU
(or about 30 minutes on a Cloud TPU). To run an experiment quickly for
debugging, just set `num_train_epochs` to a small value like `0.1`.
```shell
export BERT_BASE_DIR=/path/to/bert/chinese_L-12_H-768_A-12 # or multilingual_L-12_H-768_A-12
export XNLI_DIR=/path/to/xnli
python run_classifier.py \
--task_name=XNLI \
--do_train=true \
--do_eval=true \
--data_dir=$XNLI_DIR \
--vocab_file=$BERT_BASE_DIR/vocab.txt \
--bert_config_file=$BERT_BASE_DIR/bert_config.json \
--init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt \
--max_seq_length=128 \
--train_batch_size=32 \
--learning_rate=5e-5 \
--num_train_epochs=2.0 \
--output_dir=/tmp/xnli_output/
```
With the Chinese-only model, the results should look something like this:
```
***** Eval results *****
eval_accuracy = 0.774116
eval_loss = 0.83554
global_step = 24543
loss = 0.74603
```
## Details
### Data Source and Sampling
The languages chosen were the
[top 100 languages with the largest Wikipedias](https://meta.wikimedia.org/wiki/List_of_Wikipedias).
The entire Wikipedia dump for each language (excluding user and talk pages) was
taken as the training data for each language
However, the size of the Wikipedia for a given language varies greatly, and
therefore low-resource languages may be "under-represented" in terms of the
neural network model (under the assumption that languages are "competing" for
limited model capacity to some extent).
However, the size of a Wikipedia also correlates with the number of speakers of
a language, and we also don't want to overfit the model by performing thousands
of epochs over a tiny Wikipedia for a particular language.
To balance these two factors, we performed exponentially smoothed weighting of
the data during pre-training data creation (and WordPiece vocab creation). In
other words, let's say that the probability of a language is *P(L)*, e.g.,
*P(English) = 0.21* means that after concatenating all of the Wikipedias
together, 21% of our data is English. We exponentiate each probability by some
factor *S* and then re-normalize, and sample from that distribution. In our case
we use *S=0.7*. So, high-resource languages like English will be under-sampled,
and low-resource languages like Icelandic will be over-sampled. E.g., in the
original distribution English would be sampled 1000x more than Icelandic, but
after smoothing it's only sampled 100x more.
### Tokenization
For tokenization, we use a 110k shared WordPiece vocabulary. The word counts are
weighted the same way as the data, so low-resource languages are upweighted by
some factor. We intentionally do *not* use any marker to denote the input
language (so that zero-shot training can work).
Because Chinese (and Japanese Kanji and Korean Hanja) does not have whitespace
characters, we add spaces around every character in the
[CJK Unicode range](https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_\(Unicode_block\))
before applying WordPiece. This means that Chinese is effectively
character-tokenized. Note that the CJK Unicode block only includes
Chinese-origin characters and does *not* include Hangul Korean or
Katakana/Hiragana Japanese, which are tokenized with whitespace+WordPiece like
all other languages.
For all other languages, we apply the
[same recipe as English](https://github.com/google-research/bert#tokenization):
(a) lower casing+accent removal, (b) punctuation splitting, (c) whitespace
tokenization. We understand that accent markers have substantial meaning in some
languages, but felt that the benefits of reducing the effective vocabulary make
up for this. Generally the strong contextual models of BERT should make up for
any ambiguity introduced by stripping accent markers.
### List of Languages
The multilingual model supports the following languages. These languages were
chosen because they are the top 100 languages with the largest Wikipedias:
* Afrikaans
* Albanian
* Arabic
* Aragonese
* Armenian
* Asturian
* Azerbaijani
* Bashkir
* Basque
* Bavarian
* Belarusian
* Bengali
* Bishnupriya Manipuri
* Bosnian
* Breton
* Bulgarian
* Burmese
* Catalan
* Cebuano
* Chechen
* Chinese (Simplified)
* Chinese (Traditional)
* Chuvash
* Croatian
* Czech
* Danish
* Dutch
* English
* Estonian
* Finnish
* French
* Galician
* Georgian
* German
* Greek
* Gujarati
* Haitian
* Hebrew
* Hindi
* Hungarian
* Icelandic
* Ido
* Indonesian
* Irish
* Italian
* Japanese
* Javanese
* Kannada
* Kazakh
* Kirghiz
* Korean
* Latin
* Latvian
* Lithuanian
* Lombard
* Low Saxon
* Luxembourgish
* Macedonian
* Malagasy
* Malay
* Malayalam
* Marathi
* Minangkabau
* Nepali
* Newar
* Norwegian (Bokmal)
* Norwegian (Nynorsk)
* Occitan
* Persian (Farsi)
* Piedmontese
* Polish
* Portuguese
* Punjabi
* Romanian
* Russian
* Scots
* Serbian
* Serbo-Croatian
* Sicilian
* Slovak
* Slovenian
* South Azerbaijani
* Spanish
* Sundanese
* Swahili
* Swedish
* Tagalog
* Tajik
* Tamil
* Tatar
* Telugu
* Turkish
* Ukrainian
* Urdu
* Uzbek
* Vietnamese
* Volapük
* Waray-Waray
* Welsh
* West
* Western Punjabi
* Yoruba
The **Multilingual Cased (New)** release contains additionally **Thai** and
**Mongolian**, which were not included in the original release.
|
TensorFlow/Detection/SSD/models/research/object_detection/predictors | predictors | convolutional_box_predictor_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for object_detection.predictors.convolutional_box_predictor."""
import numpy as np
import tensorflow as tf
from google.protobuf import text_format
from object_detection.builders import box_predictor_builder
from object_detection.builders import hyperparams_builder
from object_detection.predictors import convolutional_box_predictor as box_predictor
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
class ConvolutionalBoxPredictorTest(test_case.TestCase):
def _build_arg_scope_with_conv_hyperparams(self):
conv_hyperparams = hyperparams_pb2.Hyperparams()
conv_hyperparams_text_proto = """
activation: RELU_6
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
"""
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.build(conv_hyperparams, is_training=True)
def test_get_boxes_for_five_aspect_ratios_per_location(self):
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, objectness_predictions)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, objectness_predictions) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
def test_get_boxes_for_one_aspect_ratio_per_location(self):
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
return (box_encodings, objectness_predictions)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, objectness_predictions) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 64, 1, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 64, 1])
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
self):
num_classes_without_background = 6
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features],
num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
(box_encodings,
class_predictions_with_background) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 320, num_classes_without_background+1])
def test_get_predictions_with_feature_maps_of_dynamic_shape(
self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
init_op = tf.global_variables_initializer()
resolution = 32
expected_num_anchors = resolution*resolution*5
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
objectness_predictions_shape) = sess.run(
[tf.shape(box_encodings), tf.shape(objectness_predictions)],
feed_dict={image_features:
np.random.rand(4, resolution, resolution, 64)})
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4])
self.assertAllEqual(objectness_predictions_shape,
[4, expected_num_anchors, 1])
expected_variable_set = set([
'BoxPredictor/Conv2d_0_1x1_32/biases',
'BoxPredictor/Conv2d_0_1x1_32/weights',
'BoxPredictor/BoxEncodingPredictor/biases',
'BoxPredictor/BoxEncodingPredictor/weights',
'BoxPredictor/ClassPredictor/biases',
'BoxPredictor/ClassPredictor/weights'])
self.assertEqual(expected_variable_set, actual_variable_set)
def test_use_depthwise_convolution(self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4,
use_dropout=True,
use_depthwise=True))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
init_op = tf.global_variables_initializer()
resolution = 32
expected_num_anchors = resolution*resolution*5
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
objectness_predictions_shape) = sess.run(
[tf.shape(box_encodings), tf.shape(objectness_predictions)],
feed_dict={image_features:
np.random.rand(4, resolution, resolution, 64)})
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4])
self.assertAllEqual(objectness_predictions_shape,
[4, expected_num_anchors, 1])
expected_variable_set = set([
'BoxPredictor/Conv2d_0_1x1_32/biases',
'BoxPredictor/Conv2d_0_1x1_32/weights',
'BoxPredictor/BoxEncodingPredictor_depthwise/biases',
'BoxPredictor/BoxEncodingPredictor_depthwise/depthwise_weights',
'BoxPredictor/BoxEncodingPredictor/biases',
'BoxPredictor/BoxEncodingPredictor/weights',
'BoxPredictor/ClassPredictor_depthwise/biases',
'BoxPredictor/ClassPredictor_depthwise/depthwise_weights',
'BoxPredictor/ClassPredictor/biases',
'BoxPredictor/ClassPredictor/weights'])
self.assertEqual(expected_variable_set, actual_variable_set)
def test_no_dangling_outputs(self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
conv_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4,
use_dropout=True,
use_depthwise=True))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
bad_dangling_ops = []
types_safe_to_dangle = set(['Assign', 'Mul', 'Const'])
for op in tf.get_default_graph().get_operations():
if (not op.outputs) or (not op.outputs[0].consumers()):
if 'BoxPredictor' in op.name:
if op.type not in types_safe_to_dangle:
bad_dangling_ops.append(op)
self.assertEqual(bad_dangling_ops, [])
class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
def _build_arg_scope_with_conv_hyperparams(self):
conv_hyperparams = hyperparams_pb2.Hyperparams()
conv_hyperparams_text_proto = """
activation: RELU_6
regularizer {
l2_regularizer {
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
train: true,
}
"""
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.build(conv_hyperparams, is_training=True)
def _build_conv_arg_scope_no_batch_norm(self):
conv_hyperparams = hyperparams_pb2.Hyperparams()
conv_hyperparams_text_proto = """
activation: RELU_6
regularizer {
l2_regularizer {
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
"""
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.build(conv_hyperparams, is_training=True)
def test_get_boxes_for_five_aspect_ratios_per_location(self):
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
return (box_encodings, objectness_predictions)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, objectness_predictions) = self.execute(
graph_fn, [image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
def test_bias_predictions_to_background_with_sigmoid_score_conversion(self):
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=True,
num_classes=2,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=1,
class_prediction_bias_init=-4.6,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
class_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
return (tf.nn.sigmoid(class_predictions),)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
class_predictions = self.execute(graph_fn, [image_features])
self.assertAlmostEqual(np.mean(class_predictions), 0.01, places=3)
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
self):
num_classes_without_background = 6
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features],
num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
return (box_encodings, class_predictions_with_background)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, class_predictions_with_background) = self.execute(
graph_fn, [image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 320, num_classes_without_background+1])
def test_get_multi_class_predictions_from_two_feature_maps(
self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2],
num_predictions_per_location=[5, 5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
image_features1 = np.random.rand(4, 8, 8, 64).astype(np.float32)
image_features2 = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, class_predictions_with_background) = self.execute(
graph_fn, [image_features1, image_features2])
self.assertAllEqual(box_encodings.shape, [4, 640, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 640, num_classes_without_background+1])
def test_get_multi_class_predictions_from_feature_maps_of_different_depth(
self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2, image_features3):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2, image_features3],
num_predictions_per_location=[5, 5, 5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
image_features1 = np.random.rand(4, 8, 8, 64).astype(np.float32)
image_features2 = np.random.rand(4, 8, 8, 64).astype(np.float32)
image_features3 = np.random.rand(4, 8, 8, 32).astype(np.float32)
(box_encodings, class_predictions_with_background) = self.execute(
graph_fn, [image_features1, image_features2, image_features3])
self.assertAllEqual(box_encodings.shape, [4, 960, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 960, num_classes_without_background+1])
def test_predictions_multiple_feature_maps_share_weights_separate_batchnorm(
self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=2,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2],
num_predictions_per_location=[5, 5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
expected_variable_set = set([
# Box prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
# Box prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/biases'),
# Class prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
# Class prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/biases')])
self.assertEqual(expected_variable_set, actual_variable_set)
def test_predictions_multiple_feature_maps_share_weights_without_batchnorm(
self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
apply_batch_norm=False))
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2],
num_predictions_per_location=[5, 5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
expected_variable_set = set([
# Box prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/biases'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/biases'),
# Box prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/biases'),
# Class prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/biases'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/biases'),
# Class prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/biases')])
self.assertEqual(expected_variable_set, actual_variable_set)
def test_predictions_multiple_feature_maps_share_weights_with_depthwise(
self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
apply_batch_norm=False,
use_depthwise=True))
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2],
num_predictions_per_location=[5, 5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
expected_variable_set = set([
# Box prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/depthwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/pointwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/biases'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/depthwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/pointwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/biases'),
# Box prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/depthwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/pointwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/biases'),
# Class prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/depthwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/pointwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/biases'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/depthwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/pointwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/biases'),
# Class prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/depthwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/pointwise_weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/biases')])
self.assertEqual(expected_variable_set, actual_variable_set)
def test_no_batchnorm_params_when_batchnorm_is_not_configured(self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_conv_arg_scope_no_batch_norm(),
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
apply_batch_norm=False))
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2],
num_predictions_per_location=[5, 5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
expected_variable_set = set([
# Box prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/biases'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/biases'),
# Box prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/biases'),
# Class prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/biases'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/biases'),
# Class prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/biases')])
self.assertEqual(expected_variable_set, actual_variable_set)
def test_predictions_share_weights_share_tower_separate_batchnorm(
self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
share_prediction_tower=True))
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2],
num_predictions_per_location=[5, 5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
expected_variable_set = set([
# Shared prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
# Box prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/biases'),
# Class prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/biases')])
self.assertEqual(expected_variable_set, actual_variable_set)
def test_predictions_share_weights_share_tower_without_batchnorm(
self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
share_prediction_tower=True,
apply_batch_norm=False))
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2],
num_predictions_per_location=[5, 5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
expected_variable_set = set([
# Shared prediction tower
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_0/biases'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/biases'),
# Box prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxPredictor/biases'),
# Class prediction head
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictor/biases')])
self.assertEqual(expected_variable_set, actual_variable_set)
def test_get_predictions_with_feature_maps_of_dynamic_shape(
self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(box_predictions[box_predictor.BOX_ENCODINGS],
axis=1)
objectness_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
init_op = tf.global_variables_initializer()
resolution = 32
expected_num_anchors = resolution*resolution*5
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
objectness_predictions_shape) = sess.run(
[tf.shape(box_encodings), tf.shape(objectness_predictions)],
feed_dict={image_features:
np.random.rand(4, resolution, resolution, 64)})
self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 4])
self.assertAllEqual(objectness_predictions_shape,
[4, expected_num_anchors, 1])
if __name__ == '__main__':
tf.test.main()
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/test | test | Taco2ModulationRemovalLayerPlugin_test | /*
* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "UnitTest.hpp"
#include "cudaMemory.h"
#include "taco2ModulationRemovalLayerPlugin.h"
#include "trtUtils.h"
#include "NvInfer.h"
#include <cfloat>
#include <random>
#include <vector>
using namespace nvinfer1;
using namespace nvinfer1::plugin;
using namespace tts;
/******************************************************************************
* HELPER FUNCTIONS ***********************************************************
*****************************************************************************/
namespace
{
template <typename RNG>
std::vector<float> genVec(const size_t size, RNG& rng)
{
std::uniform_real_distribution<float> dist(-1.0, 1.0);
std::vector<float> vec(size);
for (size_t i = 0; i < size; ++i) {
vec[i] = dist(rng);
}
return vec;
}
} // namespace
/******************************************************************************
* UNIT TEST ******************************************************************
*****************************************************************************/
TEST(CPUCompareTestBatch1)
{
std::mt19937 rng(0);
const int numFrames = 250;
const int filterLength = 1024;
const int hopLength = 256;
const int inputLength = numFrames * hopLength;
std::vector<float> weightsHost = genVec(filterLength, rng);
std::fill(weightsHost.begin(), weightsHost.end(), 1.0f);
Taco2ModulationRemovalLayerPlugin layer(
TRTUtils::toWeights(weightsHost), inputLength, filterLength, hopLength);
std::vector<float> inputHost = genVec(inputLength, rng);
std::fill(inputHost.begin(), inputHost.end(), 1.0f);
CudaMemory<float> inputDevice(inputHost);
std::vector<Dims> inputDims{Dims3(1, 1, inputLength)};
const std::vector<Dims> outputDims{Dims3(1, 1, inputLength)};
const std::vector<DataType> dataTypes{DataType::kFLOAT};
const bool broadcast[] = {false};
layer.configurePlugin(
inputDims.data(),
static_cast<int>(inputDims.size()),
outputDims.data(),
static_cast<int>(outputDims.size()),
dataTypes.data(),
dataTypes.data(),
broadcast,
broadcast,
#if NV_TENSORRT_MAJOR < 6
PluginFormat::kNCHW,
#else
PluginFormat::kLINEAR,
#endif
1);
layer.initialize();
std::vector<const float*> inputs{inputDevice.data()};
CudaMemory<float> outputDevice(inputLength - filterLength);
std::vector<float*> outputs{outputDevice.data()};
layer.enqueue(
1,
reinterpret_cast<const void* const*>(inputs.data()),
reinterpret_cast<void**>(outputs.data()),
nullptr,
0);
CudaUtils::sync(0);
// perform operations on cpu
std::vector<float> windowSum(inputLength, 0);
for (int i = 0; i < inputLength; i += hopLength) {
for (int j = 0; j < filterLength; ++j) {
const int idx = i + j;
if (idx < inputLength) {
windowSum[idx] += weightsHost[j];
}
}
}
std::vector<float> expOutput(inputLength, 0);
for (int x = 0; x < inputLength; ++x) {
float val = inputHost[x];
if (windowSum[x] > FLT_MIN) {
val /= windowSum[x];
}
val *= static_cast<float>(filterLength) / static_cast<float>(hopLength);
expOutput[x] = val;
}
expOutput.erase(expOutput.begin(), expOutput.begin() + (filterLength / 2));
expOutput.erase(expOutput.end() - (filterLength / 2), expOutput.end());
// match outputs
const std::vector<float> actOutput = outputDevice.toHost();
ASSERT_EQ(expOutput.size(), actOutput.size());
for (size_t i = 0; i < expOutput.size(); ++i) {
EXPECT_NEAR(expOutput[i], actOutput[i], 1e-6) << "i = " << i;
}
}
TEST(CPUCompareTestBatch4)
{
std::mt19937 rng(0);
const int batchSize = 2;
const int numFrames = 250;
const int filterLength = 1024;
const int hopLength = 256;
const int inputLength = numFrames * hopLength;
std::vector<float> weightsHost = genVec(filterLength, rng);
std::fill(weightsHost.begin(), weightsHost.end(), 1.0f);
Taco2ModulationRemovalLayerPlugin layer(
TRTUtils::toWeights(weightsHost), inputLength, filterLength, hopLength);
std::vector<float> inputHost = genVec(batchSize * inputLength, rng);
std::fill(inputHost.begin(), inputHost.end(), 1.0f);
CudaMemory<float> inputDevice(inputHost);
std::vector<Dims> inputDims{Dims3(1, 1, inputLength)};
const std::vector<Dims> outputDims{Dims3(1, 1, inputLength)};
const std::vector<DataType> dataTypes{DataType::kFLOAT};
const bool broadcast[] = {false};
layer.configurePlugin(
inputDims.data(),
static_cast<int>(inputDims.size()),
outputDims.data(),
static_cast<int>(outputDims.size()),
dataTypes.data(),
dataTypes.data(),
broadcast,
broadcast,
PluginFormat::kLINEAR,
batchSize);
layer.initialize();
std::vector<const float*> inputs{inputDevice.data()};
CudaMemory<float> outputDevice((inputLength - filterLength) * batchSize);
std::vector<float*> outputs{outputDevice.data()};
layer.enqueue(
batchSize,
reinterpret_cast<const void* const*>(inputs.data()),
reinterpret_cast<void**>(outputs.data()),
nullptr,
0);
CudaUtils::sync(0);
// perform operations on cpu
std::vector<float> windowSum(inputLength, 0);
for (int i = 0; i < inputLength; i += hopLength) {
for (int j = 0; j < filterLength; ++j) {
const int idx = i + j;
if (idx < inputLength) {
windowSum[idx] += weightsHost[j];
}
}
}
std::vector<float> expOutput(inputLength, 0);
for (int x = 0; x < inputLength; ++x) {
float val = inputHost[x];
if (windowSum[x] > FLT_MIN) {
val /= windowSum[x];
}
val *= static_cast<float>(filterLength) / static_cast<float>(hopLength);
expOutput[x] = val;
}
expOutput.erase(expOutput.begin(), expOutput.begin() + (filterLength / 2));
expOutput.erase(expOutput.end() - (filterLength / 2), expOutput.end());
// match outputs -- across entire batch
const std::vector<float> actOutput = outputDevice.toHost();
ASSERT_EQ(expOutput.size() * batchSize, actOutput.size());
for (int b = 0; b < batchSize; ++b) {
for (size_t i = 0; i < expOutput.size(); ++i) {
EXPECT_NEAR(expOutput[i], actOutput[i + b * expOutput.size()], 1e-6)
<< "i = " << i << ", b = " << b;
}
}
}
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/util | util | trtPtr | /*
* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef TT2I_TRTPTR_H
#define TT2I_TRTPTR_H
#include <memory>
namespace tts
{
template <typename T>
class Destroyer
{
public:
void operator()(T* const t)
{
t->destroy();
}
};
template <typename T>
using TRTPtr = std::unique_ptr<T, Destroyer<T>>;
} // namespace tts
#endif
|
TensorFlow/LanguageModeling/BERT/scripts | scripts | data_download | #!/usr/bin/env bash
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
NV_VISIBLE_DEVICES=${NVIDIA_VISIBLE_DEVICES:-"all"}
to_download=${1:-"wiki_only"} # By default, we don't download BooksCorpus dataset due to recent issues with the host server
docker run --gpus $NV_VISIBLE_DEVICES \
--rm -it \
--net=host \
--shm-size=1g \
--ulimit memlock=-1 \
--ulimit stack=67108864 \
-v $PWD:/workspace/bert \
bert bash -c "bash data/create_datasets_from_start.sh ${to_download}" |