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PyTorch/Forecasting/TFT/triton | triton | prepare_input_data | # 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 numpy as np
import torch
from torch.utils.data import DataLoader
from configuration import ElectricityConfig
from data_utils import TFTDataset
import argparse
from deployment_toolkit.dump import JsonDumpWriter
def _verify_and_format_dump(**x):
data = {}
for k, v in x.items():
temp_data = {}
for i in range(v.shape[1]):
temp_data["INPUT" + str(i)] = v[:,i]
data[k] = temp_data
return data
def main():
args = _parse_args()
state_dict = torch.load(os.path.join(args.checkpoint, "checkpoint.pt"))
config = state_dict['config']
test_split = TFTDataset(os.path.join(args.dataset, "test.csv"), config)
data_loader = DataLoader(test_split, batch_size=args.batch_size, num_workers=2)
input_names_dict = {'s_cat': 's_cat__0', 's_cont':'s_cont__1', 'k_cat':'k_cat__2', 'k_cont':'k_cont__3', 'o_cat':'o_cat__4', 'o_cont':'o_cont__5', 'target':'target__6', 'id':'id__7'}
reshaper = [-1] + [1]
for step, batch in enumerate(data_loader):
bs = batch['target'].shape[0]
x = {input_names_dict[key]: tensor.numpy() if tensor.numel() else np.ones([bs]).reshape(reshaper) for key, tensor in batch.items()}
ids = batch['id'][:,0,:].numpy()
y_real = {'target__0':batch['target'][:,config.encoder_length:,:].numpy()}
break
import json
data = {"data": [{k: {"content": v[i].flatten().tolist(), "shape": list(v[i].shape), "dtype": str(v[i].dtype)} for k, v in x.items()} for i in range(args.batch_size)]}
with open(os.path.join(args.input_data_dir, "data.json"), "w") as f:
f.write(json.dumps(data))
f.close()
# d = json.load(f)
# print(d)
def _parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", required=True)
parser.add_argument("--batch-size", required=False, default=1)
parser.add_argument("--dataset", help="Path to dataset", required=True)
parser.add_argument("--input-data-dir", help="Path to output folder", required=True)
args, *_ = parser.parse_known_args()
args = parser.parse_args()
return args
if __name__ == "__main__":
main() |
TensorFlow2/Segmentation/Contrib/UNet3P/data_preparation | data_preparation | delete_zip_data | rm data/Training_Batch1.zip
rm data/Training_Batch2.zip |
PyTorch/SpeechRecognition/wav2vec2/common/fairseq | fairseq | incremental_decoding_utils | # 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.
import uuid
from typing import Dict, Optional
from torch import Tensor
class FairseqIncrementalState(object):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.init_incremental_state()
def init_incremental_state(self):
self._incremental_state_id = str(uuid.uuid4())
def _get_full_incremental_state_key(self, key: str) -> str:
return "{}.{}".format(self._incremental_state_id, key)
def get_incremental_state(
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
) -> Optional[Dict[str, Optional[Tensor]]]:
"""Helper for getting incremental state for an nn.Module."""
full_key = self._get_full_incremental_state_key(key)
if incremental_state is None or full_key not in incremental_state:
return None
return incremental_state[full_key]
def set_incremental_state(
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
key: str,
value: Dict[str, Optional[Tensor]],
) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]:
"""Helper for setting incremental state for an nn.Module."""
if incremental_state is not None:
full_key = self._get_full_incremental_state_key(key)
incremental_state[full_key] = value
return incremental_state
def with_incremental_state(cls):
cls.__bases__ = (FairseqIncrementalState,) + tuple(
b for b in cls.__bases__ if b != FairseqIncrementalState
)
return cls
|
TensorFlow2/Detection/Efficientdet/object_detection | object_detection | box_list | # 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.
# ==============================================================================
"""Bounding Box List definition.
BoxList represents a list of bounding boxes as tensorflow
tensors, where each bounding box is represented as a row of 4 numbers,
[y_min, x_min, y_max, x_max]. It is assumed that all bounding boxes
within a given list correspond to a single image. See also
box_list_ops.py for common box related operations (such as area, iou, etc).
Optionally, users can add additional related fields (such as weights).
We assume the following things to be true about fields:
* they correspond to boxes in the box_list along the 0th dimension
* they have inferable rank at graph construction time
* all dimensions except for possibly the 0th can be inferred
(i.e., not None) at graph construction time.
Some other notes:
* Following tensorflow conventions, we use height, width ordering,
and correspondingly, y,x (or ymin, xmin, ymax, xmax) ordering
* Tensors are always provided as (flat) [N, 4] tensors.
"""
import tensorflow.compat.v1 as tf
class BoxList(object):
"""Box collection."""
def __init__(self, boxes):
"""Constructs box collection.
Args:
boxes: a tensor of shape [N, 4] representing box corners
Raises:
ValueError: if invalid dimensions for bbox data or if bbox data is not in
float32 format.
"""
if len(boxes.get_shape()) != 2 or boxes.get_shape()[-1] != 4:
raise ValueError('Invalid dimensions for box data.')
if boxes.dtype != tf.float32:
raise ValueError('Invalid tensor type: should be tf.float32')
self.data = {'boxes': boxes}
def num_boxes(self):
"""Returns number of boxes held in collection.
Returns:
a tensor representing the number of boxes held in the collection.
"""
return tf.shape(self.data['boxes'])[0]
def num_boxes_static(self):
"""Returns number of boxes held in collection.
This number is inferred at graph construction time rather than run-time.
Returns:
Number of boxes held in collection (integer) or None if this is not
inferable at graph construction time.
"""
return self.data['boxes'].get_shape().as_list()[0]
def get_all_fields(self):
"""Returns all fields."""
return self.data.keys()
def get_extra_fields(self):
"""Returns all non-box fields (i.e., everything not named 'boxes')."""
return [k for k in self.data.keys() if k != 'boxes']
def add_field(self, field, field_data):
"""Add field to box list.
This method can be used to add related box data such as
weights/labels, etc.
Args:
field: a string key to access the data via `get`
field_data: a tensor containing the data to store in the BoxList
"""
self.data[field] = field_data
def has_field(self, field):
return field in self.data
def get(self):
"""Convenience function for accessing box coordinates.
Returns:
a tensor with shape [N, 4] representing box coordinates.
"""
return self.get_field('boxes')
def set(self, boxes):
"""Convenience function for setting box coordinates.
Args:
boxes: a tensor of shape [N, 4] representing box corners
Raises:
ValueError: if invalid dimensions for bbox data
"""
if len(boxes.get_shape()) != 2 or boxes.get_shape()[-1] != 4:
raise ValueError('Invalid dimensions for box data.')
self.data['boxes'] = boxes
def get_field(self, field):
"""Accesses a box collection and associated fields.
This function returns specified field with object; if no field is specified,
it returns the box coordinates.
Args:
field: this optional string parameter can be used to specify
a related field to be accessed.
Returns:
a tensor representing the box collection or an associated field.
Raises:
ValueError: if invalid field
"""
if not self.has_field(field):
raise ValueError('field ' + str(field) + ' does not exist')
return self.data[field]
def set_field(self, field, value):
"""Sets the value of a field.
Updates the field of a box_list with a given value.
Args:
field: (string) name of the field to set value.
value: the value to assign to the field.
Raises:
ValueError: if the box_list does not have specified field.
"""
if not self.has_field(field):
raise ValueError('field %s does not exist' % field)
self.data[field] = value
def get_center_coordinates_and_sizes(self, scope=None):
"""Computes the center coordinates, height and width of the boxes.
Args:
scope: name scope of the function.
Returns:
a list of 4 1-D tensors [ycenter, xcenter, height, width].
"""
with tf.name_scope(scope, 'get_center_coordinates_and_sizes'):
box_corners = self.get()
ymin, xmin, ymax, xmax = tf.unstack(tf.transpose(box_corners))
width = xmax - xmin
height = ymax - ymin
ycenter = ymin + height / 2.
xcenter = xmin + width / 2.
return [ycenter, xcenter, height, width]
def transpose_coordinates(self, scope=None):
"""Transpose the coordinate representation in a boxlist.
Args:
scope: name scope of the function.
"""
with tf.name_scope(scope, 'transpose_coordinates'):
y_min, x_min, y_max, x_max = tf.split(
value=self.get(), num_or_size_splits=4, axis=1)
self.set(tf.concat([x_min, y_min, x_max, y_max], 1))
def as_tensor_dict(self, fields=None):
"""Retrieves specified fields as a dictionary of tensors.
Args:
fields: (optional) list of fields to return in the dictionary.
If None (default), all fields are returned.
Returns:
tensor_dict: A dictionary of tensors specified by fields.
Raises:
ValueError: if specified field is not contained in boxlist.
"""
tensor_dict = {}
if fields is None:
fields = self.get_all_fields()
for field in fields:
if not self.has_field(field):
raise ValueError('boxlist must contain all specified fields')
tensor_dict[field] = self.get_field(field)
return tensor_dict
|
TensorFlow2/LanguageModeling/ELECTRA | ELECTRA | postprocess_pretrained_ckpt | # 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 argparse
import collections
import json
import os
import tensorflow as tf
from utils import log, heading
from run_pretraining import PretrainingConfig
from modeling import PretrainingModel
def from_pretrained_ckpt(args):
config = PretrainingConfig(
model_name='postprocessing',
data_dir='postprocessing',
generator_hidden_size=0.3333333,
)
# Padding for divisibility by 8
if config.vocab_size % 8 != 0:
config.vocab_size += 8 - (config.vocab_size % 8)
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
# Set up model
model = PretrainingModel(config)
# Load checkpoint
checkpoint = tf.train.Checkpoint(step=tf.Variable(1), model=model)
checkpoint.restore(args.pretrained_checkpoint).expect_partial()
log(" ** Restored from {} at step {}".format(args.pretrained_checkpoint, int(checkpoint.step) - 1))
disc_dir = os.path.join(args.output_dir, 'discriminator')
gen_dir = os.path.join(args.output_dir, 'generator')
heading(" ** Saving discriminator")
model.discriminator(model.discriminator.dummy_inputs)
model.discriminator.save_pretrained(disc_dir)
heading(" ** Saving generator")
model.generator(model.generator.dummy_inputs)
model.generator.save_pretrained(gen_dir)
if __name__ == '__main__':
# Parse essential args
parser = argparse.ArgumentParser()
parser.add_argument('--pretrained_checkpoint')
parser.add_argument('--output_dir')
parser.add_argument('--amp', action='store_true', default=False)
args = parser.parse_args()
from_pretrained_ckpt(args)
|
PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils | utils | miscellaneous | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import errno
import os
def mkdir(path):
try:
os.makedirs(path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
|
PyTorch/Classification/GPUNet/triton/runner | runner | runner_proxy | # 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 List, Type
# 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 .config import Config
from .executor import Executor
from .finalizer import Finalizer
from .maintainer import Maintainer
from .pipeline import Pipeline
from .preparer import Preparer
from .runner import Runner
class RunnerProxy:
"""
Runner proxy to configure original runner
"""
maintainer_cls: Type[Maintainer] = None
executor_cls: Type[Executor] = None
preparer_cls: Type[Preparer] = None
finalizer_cls: Type[Finalizer] = None
def __init__(self, config: Config, pipeline: Pipeline, devices: List[str]):
"""
RunnerProxy constructor
Args:
config: Config object
pipeline: Pipeline to evaluate
devices: List of devices to use for tests
"""
self._runner = Runner(
config=config,
pipeline=pipeline,
devices=devices,
maintainer_cls=self.maintainer_cls,
executor_cls=self.executor_cls,
preparer_cls=self.preparer_cls,
finalizer_cls=self.finalizer_cls,
)
def start(self) -> None:
"""
Runner interface
"""
self._runner.start()
|
TensorFlow/Detection/SSD/models/research/object_detection/models | models | embedded_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 embedded_ssd_mobilenet_v1_feature_extractor."""
import numpy as np
import tensorflow as tf
from object_detection.models import embedded_ssd_mobilenet_v1_feature_extractor
from object_detection.models import ssd_feature_extractor_test
class EmbeddedSSDMobileNetV1FeatureExtractorTest(
ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
is_training=True):
"""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.
Returns:
an ssd_meta_arch.SSDFeatureExtractor object.
"""
min_depth = 32
return (embedded_ssd_mobilenet_v1_feature_extractor.
EmbeddedSSDMobileNetV1FeatureExtractor(
is_training, depth_multiplier, min_depth, pad_to_multiple,
self.conv_hyperparams_fn,
override_base_feature_extractor_hyperparams=True))
def test_extract_features_returns_correct_shapes_256(self):
image_height = 256
image_width = 256
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 16, 16, 512), (2, 8, 8, 1024),
(2, 4, 4, 512), (2, 2, 2, 256),
(2, 1, 1, 256)]
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self):
image_height = 256
image_width = 256
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 16, 16, 512), (2, 8, 8, 1024),
(2, 4, 4, 512), (2, 2, 2, 256),
(2, 1, 1, 256)]
self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape)
def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
image_height = 256
image_width = 256
depth_multiplier = 0.5**12
pad_to_multiple = 1
expected_feature_map_shape = [(2, 16, 16, 32), (2, 8, 8, 32), (2, 4, 4, 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)
def test_extract_features_returns_correct_shapes_with_pad_to_multiple_of_1(
self):
image_height = 256
image_width = 256
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 16, 16, 512), (2, 8, 8, 1024),
(2, 4, 4, 512), (2, 2, 2, 256),
(2, 1, 1, 256)]
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape)
def test_extract_features_raises_error_with_pad_to_multiple_not_1(self):
depth_multiplier = 1.0
pad_to_multiple = 2
with self.assertRaises(ValueError):
_ = self._create_feature_extractor(depth_multiplier, pad_to_multiple)
def test_extract_features_raises_error_with_invalid_image_size(self):
image_height = 128
image_width = 128
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 = 256
image_width = 256
depth_multiplier = 1
pad_to_multiple = 1
test_image = np.random.rand(4, 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)
if __name__ == '__main__':
tf.test.main()
|
PyTorch/Forecasting/TFT/triton/runner | runner | core | # 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 enum import Enum
from typing import Any, Dict, List
import yaml
class CustomDumper(yaml.Dumper):
"""
Custom YAML dumper to avoid craeting aliases
"""
def ignore_aliases(self, data: Dict) -> bool:
return True
class Paths:
"""
Paths mapping inside Triton Container
"""
MODEL_REPOSITORY_PATH = "/mnt/triton-models"
LIBRARIES_PATH = "/mnt/libs"
class Framework(Enum):
"""
Supported frameworks
"""
TensorFlow1 = "TensorFlow1"
TensorFlow2 = "TensorFlow2"
PyTorch = "PyTorch"
class Command:
"""Represents wrapper of raw string command"""
def __init__(self, data: str):
"""
Store command data
Args:
data: string with bash commands to execute
"""
self._data = data
def __str__(self) -> str:
"""
String object representation
Returns:
String
"""
return self._data
class DataObject(object):
"""
Data object representation handling recursive transformation from object to dict
"""
READ_ONLY = set()
def to_dict(self) -> Dict:
"""
Represent object as dictionary
Returns:
Dict
"""
data = dict()
filtered_data = {key: value for key, value in self.__dict__.items() if key not in self.READ_ONLY}
for key, value in filtered_data.items():
data[key] = self._convert_value(value)
return data
def _convert_value(self, value: Any) -> Any:
"""
Convert value based on its type
Args:
value: variable to convert
Returns:
Converted object
"""
if isinstance(value, DataObject):
value = value.to_dict()
elif isinstance(value, dict):
value = self._from_dict(value)
elif isinstance(value, list):
value = self._from_list(value)
elif isinstance(value, Enum):
value = value.value
elif isinstance(value, pathlib.Path):
value = value.as_posix()
return value
def _from_dict(self, values: Dict) -> Any:
"""
Convert dictionary values
Args:
values: dictionary with values
Returns:
Any
"""
data = dict()
for key, value in values.items():
data[key] = self._convert_value(value)
return data
def _from_list(self, values: List) -> Any:
"""
Convert list of values
Args:
values: list with values
Returns:
Any
"""
items = list()
for value in values:
item = self._convert_value(value)
items.append(item)
return items
AVAILABLE_FRAMEWORKS = [f.value for f in Framework]
|
PyTorch/SpeechSynthesis/Tacotron2/notebooks/conversationalai | conversationalai | export_tacotron2_ts | # *****************************************************************************
# 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 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.
#
# *****************************************************************************
import torch
import argparse
import sys
sys.path.append('./')
from inference import checkpoint_from_distributed, unwrap_distributed, load_and_setup_model
def parse_args(parser):
"""
Parse commandline arguments.
"""
parser.add_argument('--tacotron2', type=str, required=True,
help='full path to the Tacotron2 model checkpoint file')
parser.add_argument('-o', '--output', type=str, default="trtis_repo/tacotron/1/model.pt",
help='filename for the Tacotron 2 TorchScript model')
parser.add_argument('--fp16', action='store_true',
help='inference with mixed precision')
return parser
def main():
parser = argparse.ArgumentParser(
description='PyTorch Tacotron 2 Inference')
parser = parse_args(parser)
args = parser.parse_args()
tacotron2 = load_and_setup_model('Tacotron2', parser, args.tacotron2,
fp16_run=args.fp16, cpu_run=False,
forward_is_infer=True)
jitted_tacotron2 = torch.jit.script(tacotron2)
torch.jit.save(jitted_tacotron2, args.output)
if __name__ == '__main__':
main()
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | vrd_evaluation | # 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.
# ==============================================================================
"""Evaluator class for Visual Relations Detection.
VRDDetectionEvaluator is a class which manages ground truth information of a
visual relations detection (vrd) dataset, and computes frequently used detection
metrics such as Precision, Recall, Recall@k, of the provided vrd detection
results.
It supports the following operations:
1) Adding ground truth information of images sequentially.
2) Adding detection results of images sequentially.
3) Evaluating detection metrics on already inserted detection results.
Note1: groundtruth should be inserted before evaluation.
Note2: This module operates on numpy boxes and box lists.
"""
from abc import abstractmethod
import collections
import logging
import numpy as np
from object_detection.core import standard_fields
from object_detection.utils import metrics
from object_detection.utils import object_detection_evaluation
from object_detection.utils import per_image_vrd_evaluation
# Below standard input numpy datatypes are defined:
# box_data_type - datatype of the groundtruth visual relations box annotations;
# this datatype consists of two named boxes: subject bounding box and object
# bounding box. Each box is of the format [y_min, x_min, y_max, x_max], each
# coordinate being of type float32.
# label_data_type - corresponding datatype of the visual relations label
# annotaions; it consists of three numerical class labels: subject class label,
# object class label and relation class label, each class label being of type
# int32.
vrd_box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))])
single_box_data_type = np.dtype([('box', 'f4', (4,))])
label_data_type = np.dtype([('subject', 'i4'), ('object', 'i4'), ('relation',
'i4')])
class VRDDetectionEvaluator(object_detection_evaluation.DetectionEvaluator):
"""A class to evaluate VRD detections.
This class serves as a base class for VRD evaluation in two settings:
- phrase detection
- relation detection.
"""
def __init__(self, matching_iou_threshold=0.5, metric_prefix=None):
"""Constructor.
Args:
matching_iou_threshold: IOU threshold to use for matching groundtruth
boxes to detection boxes.
metric_prefix: (optional) string prefix for metric name; if None, no
prefix is used.
"""
super(VRDDetectionEvaluator, self).__init__([])
self._matching_iou_threshold = matching_iou_threshold
self._evaluation = _VRDDetectionEvaluation(
matching_iou_threshold=self._matching_iou_threshold)
self._image_ids = set([])
self._metric_prefix = (metric_prefix + '_') if metric_prefix else ''
self._evaluatable_labels = {}
self._negative_labels = {}
@abstractmethod
def _process_groundtruth_boxes(self, groundtruth_box_tuples):
"""Pre-processes boxes before adding them to the VRDDetectionEvaluation.
Phrase detection and Relation detection subclasses re-implement this method
depending on the task.
Args:
groundtruth_box_tuples: A numpy array of structures with the shape
[M, 1], each structure containing the same number of named bounding
boxes. Each box is of the format [y_min, x_min, y_max, x_max] (see
datatype vrd_box_data_type, single_box_data_type above).
"""
raise NotImplementedError(
'_process_groundtruth_boxes method should be implemented in subclasses'
'of VRDDetectionEvaluator.')
@abstractmethod
def _process_detection_boxes(self, detections_box_tuples):
"""Pre-processes boxes before adding them to the VRDDetectionEvaluation.
Phrase detection and Relation detection subclasses re-implement this method
depending on the task.
Args:
detections_box_tuples: A numpy array of structures with the shape
[M, 1], each structure containing the same number of named bounding
boxes. Each box is of the format [y_min, x_min, y_max, x_max] (see
datatype vrd_box_data_type, single_box_data_type above).
"""
raise NotImplementedError(
'_process_detection_boxes method should be implemented in subclasses'
'of VRDDetectionEvaluator.')
def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
"""Adds groundtruth for a single image to be used for evaluation.
Args:
image_id: A unique string/integer identifier for the image.
groundtruth_dict: A dictionary containing -
standard_fields.InputDataFields.groundtruth_boxes: A numpy array
of structures with the shape [M, 1], representing M tuples, each tuple
containing the same number of named bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max] (see
datatype vrd_box_data_type, single_box_data_type above).
standard_fields.InputDataFields.groundtruth_classes: A numpy array of
structures shape [M, 1], representing the class labels of the
corresponding bounding boxes and possibly additional classes (see
datatype label_data_type above).
standard_fields.InputDataFields.groundtruth_image_classes: numpy array
of shape [K] containing verified labels.
Raises:
ValueError: On adding groundtruth for an image more than once.
"""
if image_id in self._image_ids:
raise ValueError('Image with id {} already added.'.format(image_id))
groundtruth_class_tuples = (
groundtruth_dict[standard_fields.InputDataFields.groundtruth_classes])
groundtruth_box_tuples = (
groundtruth_dict[standard_fields.InputDataFields.groundtruth_boxes])
self._evaluation.add_single_ground_truth_image_info(
image_key=image_id,
groundtruth_box_tuples=self._process_groundtruth_boxes(
groundtruth_box_tuples),
groundtruth_class_tuples=groundtruth_class_tuples)
self._image_ids.update([image_id])
all_classes = []
for field in groundtruth_box_tuples.dtype.fields:
all_classes.append(groundtruth_class_tuples[field])
groudtruth_positive_classes = np.unique(np.concatenate(all_classes))
verified_labels = groundtruth_dict.get(
standard_fields.InputDataFields.groundtruth_image_classes,
np.array([], dtype=int))
self._evaluatable_labels[image_id] = np.unique(
np.concatenate((verified_labels, groudtruth_positive_classes)))
self._negative_labels[image_id] = np.setdiff1d(verified_labels,
groudtruth_positive_classes)
def add_single_detected_image_info(self, image_id, detections_dict):
"""Adds detections for a single image to be used for evaluation.
Args:
image_id: A unique string/integer identifier for the image.
detections_dict: A dictionary containing -
standard_fields.DetectionResultFields.detection_boxes: A numpy array of
structures with shape [N, 1], representing N tuples, each tuple
containing the same number of named bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max] (as an example
see datatype vrd_box_data_type, single_box_data_type above).
standard_fields.DetectionResultFields.detection_scores: float32 numpy
array of shape [N] containing detection scores for the boxes.
standard_fields.DetectionResultFields.detection_classes: A numpy array
of structures shape [N, 1], representing the class labels of the
corresponding bounding boxes and possibly additional classes (see
datatype label_data_type above).
"""
if image_id not in self._image_ids:
logging.warn('No groundtruth for the image with id %s.', image_id)
# Since for the correct work of evaluator it is assumed that groundtruth
# is inserted first we make sure to break the code if is it not the case.
self._image_ids.update([image_id])
self._negative_labels[image_id] = np.array([])
self._evaluatable_labels[image_id] = np.array([])
num_detections = detections_dict[
standard_fields.DetectionResultFields.detection_boxes].shape[0]
detection_class_tuples = detections_dict[
standard_fields.DetectionResultFields.detection_classes]
detection_box_tuples = detections_dict[
standard_fields.DetectionResultFields.detection_boxes]
negative_selector = np.zeros(num_detections, dtype=bool)
selector = np.ones(num_detections, dtype=bool)
# Only check boxable labels
for field in detection_box_tuples.dtype.fields:
# Verify if one of the labels is negative (this is sure FP)
negative_selector |= np.isin(detection_class_tuples[field],
self._negative_labels[image_id])
# Verify if all labels are verified
selector &= np.isin(detection_class_tuples[field],
self._evaluatable_labels[image_id])
selector |= negative_selector
self._evaluation.add_single_detected_image_info(
image_key=image_id,
detected_box_tuples=self._process_detection_boxes(
detection_box_tuples[selector]),
detected_scores=detections_dict[
standard_fields.DetectionResultFields.detection_scores][selector],
detected_class_tuples=detection_class_tuples[selector])
def evaluate(self, relationships=None):
"""Compute evaluation result.
Args:
relationships: A dictionary of numerical label-text label mapping; if
specified, returns per-relationship AP.
Returns:
A dictionary of metrics with the following fields -
summary_metrics:
'weightedAP@<matching_iou_threshold>IOU' : weighted average precision
at the specified IOU threshold.
'AP@<matching_iou_threshold>IOU/<relationship>' : AP per relationship.
'mAP@<matching_iou_threshold>IOU': mean average precision at the
specified IOU threshold.
'Recall@50@<matching_iou_threshold>IOU': recall@50 at the specified IOU
threshold.
'Recall@100@<matching_iou_threshold>IOU': recall@100 at the specified
IOU threshold.
if relationships is specified, returns <relationship> in AP metrics as
readable names, otherwise the names correspond to class numbers.
"""
(weighted_average_precision, mean_average_precision, average_precisions, _,
_, recall_50, recall_100, _, _) = (
self._evaluation.evaluate())
vrd_metrics = {
(self._metric_prefix + 'weightedAP@{}IOU'.format(
self._matching_iou_threshold)):
weighted_average_precision,
self._metric_prefix + 'mAP@{}IOU'.format(self._matching_iou_threshold):
mean_average_precision,
self._metric_prefix + 'Recall@50@{}IOU'.format(
self._matching_iou_threshold):
recall_50,
self._metric_prefix + 'Recall@100@{}IOU'.format(
self._matching_iou_threshold):
recall_100,
}
if relationships:
for key, average_precision in average_precisions.iteritems():
vrd_metrics[self._metric_prefix + 'AP@{}IOU/{}'.format(
self._matching_iou_threshold,
relationships[key])] = average_precision
else:
for key, average_precision in average_precisions.iteritems():
vrd_metrics[self._metric_prefix + 'AP@{}IOU/{}'.format(
self._matching_iou_threshold, key)] = average_precision
return vrd_metrics
def clear(self):
"""Clears the state to prepare for a fresh evaluation."""
self._evaluation = _VRDDetectionEvaluation(
matching_iou_threshold=self._matching_iou_threshold)
self._image_ids.clear()
self._negative_labels.clear()
self._evaluatable_labels.clear()
class VRDRelationDetectionEvaluator(VRDDetectionEvaluator):
"""A class to evaluate VRD detections in relations setting.
Expected groundtruth box datatype is vrd_box_data_type, expected groudtruth
labels datatype is label_data_type.
Expected detection box datatype is vrd_box_data_type, expected detection
labels
datatype is label_data_type.
"""
def __init__(self, matching_iou_threshold=0.5):
super(VRDRelationDetectionEvaluator, self).__init__(
matching_iou_threshold=matching_iou_threshold,
metric_prefix='VRDMetric_Relationships')
def _process_groundtruth_boxes(self, groundtruth_box_tuples):
"""Pre-processes boxes before adding them to the VRDDetectionEvaluation.
Args:
groundtruth_box_tuples: A numpy array of structures with the shape
[M, 1], each structure containing the same number of named bounding
boxes. Each box is of the format [y_min, x_min, y_max, x_max].
Returns:
Unchanged input.
"""
return groundtruth_box_tuples
def _process_detection_boxes(self, detections_box_tuples):
"""Pre-processes boxes before adding them to the VRDDetectionEvaluation.
Phrase detection and Relation detection subclasses re-implement this method
depending on the task.
Args:
detections_box_tuples: A numpy array of structures with the shape
[M, 1], each structure containing the same number of named bounding
boxes. Each box is of the format [y_min, x_min, y_max, x_max] (see
datatype vrd_box_data_type, single_box_data_type above).
Returns:
Unchanged input.
"""
return detections_box_tuples
class VRDPhraseDetectionEvaluator(VRDDetectionEvaluator):
"""A class to evaluate VRD detections in phrase setting.
Expected groundtruth box datatype is vrd_box_data_type, expected groudtruth
labels datatype is label_data_type.
Expected detection box datatype is single_box_data_type, expected detection
labels datatype is label_data_type.
"""
def __init__(self, matching_iou_threshold=0.5):
super(VRDPhraseDetectionEvaluator, self).__init__(
matching_iou_threshold=matching_iou_threshold,
metric_prefix='VRDMetric_Phrases')
def _process_groundtruth_boxes(self, groundtruth_box_tuples):
"""Pre-processes boxes before adding them to the VRDDetectionEvaluation.
In case of phrase evaluation task, evaluation expects exactly one bounding
box containing all objects in the phrase. This bounding box is computed
as an enclosing box of all groundtruth boxes of a phrase.
Args:
groundtruth_box_tuples: A numpy array of structures with the shape
[M, 1], each structure containing the same number of named bounding
boxes. Each box is of the format [y_min, x_min, y_max, x_max]. See
vrd_box_data_type for an example of structure.
Returns:
result: A numpy array of structures with the shape [M, 1], each
structure containing exactly one named bounding box. i-th output
structure corresponds to the result of processing i-th input structure,
where the named bounding box is computed as an enclosing bounding box
of all bounding boxes of the i-th input structure.
"""
first_box_key = groundtruth_box_tuples.dtype.fields.keys()[0]
miny = groundtruth_box_tuples[first_box_key][:, 0]
minx = groundtruth_box_tuples[first_box_key][:, 1]
maxy = groundtruth_box_tuples[first_box_key][:, 2]
maxx = groundtruth_box_tuples[first_box_key][:, 3]
for fields in groundtruth_box_tuples.dtype.fields:
miny = np.minimum(groundtruth_box_tuples[fields][:, 0], miny)
minx = np.minimum(groundtruth_box_tuples[fields][:, 1], minx)
maxy = np.maximum(groundtruth_box_tuples[fields][:, 2], maxy)
maxx = np.maximum(groundtruth_box_tuples[fields][:, 3], maxx)
data_result = []
for i in range(groundtruth_box_tuples.shape[0]):
data_result.append(([miny[i], minx[i], maxy[i], maxx[i]],))
result = np.array(data_result, dtype=[('box', 'f4', (4,))])
return result
def _process_detection_boxes(self, detections_box_tuples):
"""Pre-processes boxes before adding them to the VRDDetectionEvaluation.
In case of phrase evaluation task, evaluation expects exactly one bounding
box containing all objects in the phrase. This bounding box is computed
as an enclosing box of all groundtruth boxes of a phrase.
Args:
detections_box_tuples: A numpy array of structures with the shape
[M, 1], each structure containing the same number of named bounding
boxes. Each box is of the format [y_min, x_min, y_max, x_max]. See
vrd_box_data_type for an example of this structure.
Returns:
result: A numpy array of structures with the shape [M, 1], each
structure containing exactly one named bounding box. i-th output
structure corresponds to the result of processing i-th input structure,
where the named bounding box is computed as an enclosing bounding box
of all bounding boxes of the i-th input structure.
"""
first_box_key = detections_box_tuples.dtype.fields.keys()[0]
miny = detections_box_tuples[first_box_key][:, 0]
minx = detections_box_tuples[first_box_key][:, 1]
maxy = detections_box_tuples[first_box_key][:, 2]
maxx = detections_box_tuples[first_box_key][:, 3]
for fields in detections_box_tuples.dtype.fields:
miny = np.minimum(detections_box_tuples[fields][:, 0], miny)
minx = np.minimum(detections_box_tuples[fields][:, 1], minx)
maxy = np.maximum(detections_box_tuples[fields][:, 2], maxy)
maxx = np.maximum(detections_box_tuples[fields][:, 3], maxx)
data_result = []
for i in range(detections_box_tuples.shape[0]):
data_result.append(([miny[i], minx[i], maxy[i], maxx[i]],))
result = np.array(data_result, dtype=[('box', 'f4', (4,))])
return result
VRDDetectionEvalMetrics = collections.namedtuple('VRDDetectionEvalMetrics', [
'weighted_average_precision', 'mean_average_precision',
'average_precisions', 'precisions', 'recalls', 'recall_50', 'recall_100',
'median_rank_50', 'median_rank_100'
])
class _VRDDetectionEvaluation(object):
"""Performs metric computation for the VRD task. This class is internal.
"""
def __init__(self, matching_iou_threshold=0.5):
"""Constructor.
Args:
matching_iou_threshold: IOU threshold to use for matching groundtruth
boxes to detection boxes.
"""
self._per_image_eval = per_image_vrd_evaluation.PerImageVRDEvaluation(
matching_iou_threshold=matching_iou_threshold)
self._groundtruth_box_tuples = {}
self._groundtruth_class_tuples = {}
self._num_gt_instances = 0
self._num_gt_imgs = 0
self._num_gt_instances_per_relationship = {}
self.clear_detections()
def clear_detections(self):
"""Clears detections."""
self._detection_keys = set()
self._scores = []
self._relation_field_values = []
self._tp_fp_labels = []
self._average_precisions = {}
self._precisions = []
self._recalls = []
def add_single_ground_truth_image_info(
self, image_key, groundtruth_box_tuples, groundtruth_class_tuples):
"""Adds groundtruth for a single image to be used for evaluation.
Args:
image_key: A unique string/integer identifier for the image.
groundtruth_box_tuples: A numpy array of structures with the shape
[M, 1], representing M tuples, each tuple containing the same number
of named bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max].
groundtruth_class_tuples: A numpy array of structures shape [M, 1],
representing the class labels of the corresponding bounding boxes and
possibly additional classes.
"""
if image_key in self._groundtruth_box_tuples:
logging.warn(
'image %s has already been added to the ground truth database.',
image_key)
return
self._groundtruth_box_tuples[image_key] = groundtruth_box_tuples
self._groundtruth_class_tuples[image_key] = groundtruth_class_tuples
self._update_groundtruth_statistics(groundtruth_class_tuples)
def add_single_detected_image_info(self, image_key, detected_box_tuples,
detected_scores, detected_class_tuples):
"""Adds detections for a single image to be used for evaluation.
Args:
image_key: A unique string/integer identifier for the image.
detected_box_tuples: A numpy array of structures with shape [N, 1],
representing N tuples, each tuple containing the same number of named
bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max].
detected_scores: A float numpy array of shape [N, 1], representing
the confidence scores of the detected N object instances.
detected_class_tuples: A numpy array of structures shape [N, 1],
representing the class labels of the corresponding bounding boxes and
possibly additional classes.
"""
self._detection_keys.add(image_key)
if image_key in self._groundtruth_box_tuples:
groundtruth_box_tuples = self._groundtruth_box_tuples[image_key]
groundtruth_class_tuples = self._groundtruth_class_tuples[image_key]
else:
groundtruth_box_tuples = np.empty(
shape=[0, 4], dtype=detected_box_tuples.dtype)
groundtruth_class_tuples = np.array([], dtype=detected_class_tuples.dtype)
scores, tp_fp_labels, mapping = (
self._per_image_eval.compute_detection_tp_fp(
detected_box_tuples=detected_box_tuples,
detected_scores=detected_scores,
detected_class_tuples=detected_class_tuples,
groundtruth_box_tuples=groundtruth_box_tuples,
groundtruth_class_tuples=groundtruth_class_tuples))
self._scores += [scores]
self._tp_fp_labels += [tp_fp_labels]
self._relation_field_values += [detected_class_tuples[mapping]['relation']]
def _update_groundtruth_statistics(self, groundtruth_class_tuples):
"""Updates grouth truth statistics.
Args:
groundtruth_class_tuples: A numpy array of structures shape [M, 1],
representing the class labels of the corresponding bounding boxes and
possibly additional classes.
"""
self._num_gt_instances += groundtruth_class_tuples.shape[0]
self._num_gt_imgs += 1
for relation_field_value in np.unique(groundtruth_class_tuples['relation']):
if relation_field_value not in self._num_gt_instances_per_relationship:
self._num_gt_instances_per_relationship[relation_field_value] = 0
self._num_gt_instances_per_relationship[relation_field_value] += np.sum(
groundtruth_class_tuples['relation'] == relation_field_value)
def evaluate(self):
"""Computes evaluation result.
Returns:
A named tuple with the following fields -
average_precision: a float number corresponding to average precision.
precisions: an array of precisions.
recalls: an array of recalls.
recall@50: recall computed on 50 top-scoring samples.
recall@100: recall computed on 100 top-scoring samples.
median_rank@50: median rank computed on 50 top-scoring samples.
median_rank@100: median rank computed on 100 top-scoring samples.
"""
if self._num_gt_instances == 0:
logging.warn('No ground truth instances')
if not self._scores:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self._scores)
tp_fp_labels = np.concatenate(self._tp_fp_labels)
relation_field_values = np.concatenate(self._relation_field_values)
for relation_field_value, _ in (
self._num_gt_instances_per_relationship.iteritems()):
precisions, recalls = metrics.compute_precision_recall(
scores[relation_field_values == relation_field_value],
tp_fp_labels[relation_field_values == relation_field_value],
self._num_gt_instances_per_relationship[relation_field_value])
self._average_precisions[
relation_field_value] = metrics.compute_average_precision(
precisions, recalls)
self._mean_average_precision = np.mean(self._average_precisions.values())
self._precisions, self._recalls = metrics.compute_precision_recall(
scores, tp_fp_labels, self._num_gt_instances)
self._weighted_average_precision = metrics.compute_average_precision(
self._precisions, self._recalls)
self._recall_50 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
50))
self._median_rank_50 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 50))
self._recall_100 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
100))
self._median_rank_100 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 100))
return VRDDetectionEvalMetrics(
self._weighted_average_precision, self._mean_average_precision,
self._average_precisions, self._precisions, self._recalls,
self._recall_50, self._recall_100, self._median_rank_50,
self._median_rank_100)
|
TensorFlow2/Detection/Efficientdet/model | model | activation_builder | # 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.
# ==============================================================================
"""Common utils."""
from typing import Text
import tensorflow as tf
def srelu_fn(x):
"""Smooth relu: a smooth version of relu."""
with tf.name_scope('srelu'):
beta = tf.Variable(20.0, name='srelu_beta', dtype=tf.float32)**2
beta = tf.cast(beta**2, x.dtype)
safe_log = tf.math.log(tf.where(x > 0., beta * x + 1., tf.ones_like(x)))
return tf.where((x > 0.), x - (1. / beta) * safe_log, tf.zeros_like(x))
def activation_fn(features: tf.Tensor, act_type: Text):
"""Customized non-linear activation type."""
if act_type in ('silu', 'swish'):
return tf.nn.swish(features)
elif act_type == 'swish_native':
return features * tf.sigmoid(features)
elif act_type == 'hswish':
return features * tf.nn.relu6(features + 3) / 6
elif act_type == 'relu':
return tf.nn.relu(features)
elif act_type == 'relu6':
return tf.nn.relu6(features)
elif act_type == 'mish':
return features * tf.math.tanh(tf.math.softplus(features))
elif act_type == 'identity':
return tf.identity(features)
elif act_type == 'srelu':
return srelu_fn(features)
else:
raise ValueError('Unsupported act_type {}'.format(act_type)) |
TensorFlow/LanguageModeling/BERT/biobert/scripts | scripts | rel_chemprot | #!/bin/bash
echo "Container nvidia build = " $NVIDIA_BUILD_ID
init_checkpoint=${1:-"/results/biobert_tf_uncased_base/model.ckpt"}
train_batch_size=${2:-8}
learning_rate=${3:-1.5e-6}
cased=${4:-false}
precision=${5:-"fp16"}
use_xla=${6:-"true"}
num_gpu=${7:-"16"}
seq_length=${8:-512}
bert_model=${9:-"base"}
eval_batch_size=${10:-16} #Eval and Predict BS is assumed to be same
epochs=${11:-"3.0"}
if [ "$cased" = "true" ] ; then
DO_LOWER_CASE=0
CASING_DIR_PREFIX="cased"
case_flag="--do_lower_case=False"
else
DO_LOWER_CASE=1
CASING_DIR_PREFIX="uncased"
case_flag="--do_lower_case=True"
fi
if [ "$bert_model" = "large" ] ; then
export BERT_DIR=/workspace/bert/data/download/google_pretrained_weights/${CASING_DIR_PREFIX}_L-24_H-1024_A-16
else
export BERT_DIR=/workspace/bert/data/download/google_pretrained_weights/${CASING_DIR_PREFIX}_L-12_H-768_A-12
fi
export GBS=$(expr $train_batch_size \* $num_gpu)
printf -v TAG "tf_bert_biobert_rel_chemprot_%s_%s_gbs%d" "$bert_model" "$precision" $GBS
DATESTAMP=`date +'%y%m%d%H%M%S'`
DATASET_DIR=/workspace/bert/data/biobert/chemprot-data_treeLSTM
OUTPUT_DIR=/results/${TAG}_${DATESTAMP}
mkdir -p ${OUTPUT_DIR}
use_fp16=""
if [ "$precision" = "fp16" ] ; then
echo "fp16 activated!"
use_fp16="--amp"
else
echo "fp32/tf32 activated!"
use_fp16="--noamp"
fi
if [ "$use_xla" = "true" ] ; then
use_xla_tag="--use_xla"
echo "XLA activated"
else
use_xla_tag="--nouse_xla"
fi
if [ $num_gpu -gt 1 ] ; then
mpi_command="mpirun -np $num_gpu -H localhost:$num_gpu \
--allow-run-as-root -bind-to none -map-by slot \
-x NCCL_DEBUG=INFO \
-x LD_LIBRARY_PATH \
-x PATH -mca pml ob1 -mca btl ^openib"
use_hvd="--horovod"
else
mpi_command=""
use_hvd=""
fi
$mpi_command python3 /workspace/bert/run_re.py \
--do_prepare=true \
--do_train=true \
--do_eval=true \
--do_predict=true \
--task_name="chemprot" \
--vocab_file=$BERT_DIR/vocab.txt \
--bert_config_file=$BERT_DIR/bert_config.json \
--init_checkpoint=$init_checkpoint \
--num_train_epochs=$epochs \
--data_dir=$DATASET_DIR \
--output_dir=$OUTPUT_DIR \
--learning_rate=$learning_rate \
--train_batch_size=$train_batch_size \
--eval_batch_size=$eval_batch_size \
--predict_batch_size=$eval_batch_size \
--max_seq_length=$seq_length \
"$use_hvd" "$use_fp16" $use_xla_tag $case_flag
python3 /workspace/bert/biobert/re_eval.py --task=chemprot --output_path=$OUTPUT_DIR/test_results.tsv \
--answer_path=$DATASET_DIR/test.tsv |& tee $OUTPUT_DIR/test_results.txt
|
TensorFlow/LanguageModeling/BERT/data | data | __init__ | # 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. |
PyTorch/Segmentation/MaskRCNN/pytorch/configs/caffe2 | caffe2 | e2e_faster_rcnn_R_101_FPN_1x_caffe2 | MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://Caffe2Detectron/COCO/35857890/e2e_faster_rcnn_R-101-FPN_1x"
BACKBONE:
CONV_BODY: "R-101-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
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"
DATASETS:
TEST: ("coco_2014_minival",)
DATALOADER:
SIZE_DIVISIBILITY: 32
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/generator/tabular | tabular | random | # 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 Optional, List
import cupy as cp
import pickle
import numpy as np
import pandas as pd
from syngen.generator.tabular.chunked_tabular_generator import ChunkedBaseTabularGenerator
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)
class RandomMVGenerator(ChunkedBaseTabularGenerator):
"""Random Multivariate Gaussian generator
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.ndims = None
self.column_order = None
def fit(self, data: Optional[pd.DataFrame] = None, ndims: Optional[int] = None,
columns: Optional[List[str]] = None,
categorical_columns=(), verbose=False):
"""
random ignores categorical columns at the moment
"""
assert ndims is not None or data is not None or self.ndims is not None or columns is not None
if data is not None:
ndims = len(data.columns)
self.column_order = list(data.columns)
if columns is not None:
self.column_order = columns
ndims = len(columns)
if ndims is None:
ndims = self.ndims
self.mu = np.random.randn(ndims).astype(np.float32)
self.cov = np.eye(ndims) * np.abs(
np.random.randn(ndims).reshape(-1, 1)
).astype(np.float32)
self.ndims = ndims
def _space_complexity_factor(self):
return 2.0
def sample(self, n, gpu=False, memmap_kwargs=None, start_idx=0, end_idx=None, **kwargs):
use_memmap = memmap_kwargs is not None
if use_memmap:
memmap_outfile = np.load(memmap_kwargs['filename'], mmap_mode='r+')
if gpu:
samples = cp.random.multivariate_normal(self.mu, self.cov, size=n, dtype=cp.float32)
samples = cp.asnumpy(samples)
else:
samples = np.random.multivariate_normal(self.mu, self.cov, size=n).astype(np.float32)
if use_memmap:
memmap_outfile[start_idx:end_idx] = samples
return None
else:
df = pd.DataFrame(samples)
if self.column_order is None:
df.columns = df.columns.astype(str)
else:
df.columns = self.column_order
return df
def save(self, path):
with open(path, 'wb') as file_handler:
pickle.dump(self, file_handler, protocol=pickle.HIGHEST_PROTOCOL)
@classmethod
def load(cls, path):
with open(path, 'rb') as file_handler:
model = pickle.load(file_handler)
return model
|
PyTorch/LanguageModeling/BERT/triton | triton | README | # Deploying the BERT model on Triton Inference Server
This folder contains instructions for deployment to run inference
on Triton Inference Server, as well as detailed performance analysis.
The purpose of this document is to help you with achieving
the best inference performance.
## Table of contents
- [Solution overview](#solution-overview)
- [Introduction](#introduction)
- [Deployment process](#deployment-process)
- [Setup](#setup)
- [Quick Start Guide](#quick-start-guide)
- [Release notes](#release-notes)
- [Changelog](#changelog)
- [Known issues](#known-issues)
## Solution overview
### Introduction
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.
This README provides step-by-step deployment instructions for models generated
during training (as described in the [model README](../readme.md)).
Additionally, this README provides the corresponding deployment scripts that
ensure optimal GPU utilization during inferencing on the Triton Inference Server.
### Deployment process
The deployment process consists of two steps:
1. Conversion.
The purpose of conversion is to find the best performing model
format supported by the Triton Inference Server.
Triton Inference Server uses a number of runtime backends such as
[TensorRT](https://developer.nvidia.com/tensorrt),
[LibTorch](https://github.com/triton-inference-server/pytorch_backend) and
[ONNX Runtime](https://github.com/triton-inference-server/onnxruntime_backend)
to support various model types. Refer to the
[Triton documentation](https://github.com/triton-inference-server/backend#where-can-i-find-all-the-backends-that-are-available-for-triton)
for a list of available backends.
2. Configuration.
Model configuration on the Triton Inference Server, which generates
necessary [configuration files](https://github.com/triton-inference-server/server/blob/master/docs/model_configuration.md).
After deployment, the Triton inference server is used for evaluation of the converted model in two steps:
1. Accuracy tests.
Produce results that are tested against given accuracy thresholds.
2. Performance tests.
Produce latency and throughput results for offline (static batching)
and online (dynamic batching) scenarios.
All steps are executed by the provided runner script. Refer to [Quick Start Guide](#quick-start-guide)
## Setup
Ensure you have the following components:
* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
* [PyTorch NGC container 21.10](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch)
* [Triton Inference Server NGC container 21.10](https://ngc.nvidia.com/catalog/containers/nvidia:tritonserver)
* [NVIDIA CUDA](https://docs.nvidia.com/cuda/archive//index.html)
* [NVIDIA Ampere](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/), [Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) or [Turing](https://www.nvidia.com/en-us/geforce/turing/) based GPU
## Quick Start Guide
Deployment is supported for the following architectures. For the deployment steps, refer to the appropriate readme file:
* [BERT-large](./large/README.md)
* [BERT-distilled-4l](./dist4l/README.md)
* [BERT-distilled-6l](./dist6l/README.md)
## Release Notes
We’re constantly refining and improving our performance on AI
and HPC workloads with frequent updates
to our software stack. For our latest performance data refer
to these pages for
[AI](https://developer.nvidia.com/deep-learning-performance-training-inference)
and [HPC](https://developer.nvidia.com/hpc-application-performance) benchmarks.
### Changelog
### Known issues
- There are no known issues with this model.
|
TensorFlow/LanguageModeling/BERT/scripts | scripts | run_pretraining_lamb_phase2 | #! /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.
echo "Container nvidia build = " $NVIDIA_BUILD_ID
train_batch_size_phase1=${1:-64}
train_batch_size_phase2=${2:-8}
eval_batch_size=${3:-8}
learning_rate_phase1=${4:-"7.5e-4"}
learning_rate_phase2=${5:-"5e-4"}
precision=${6:-"fp16"}
use_xla=${7:-"true"}
num_gpus=${8:-8}
warmup_steps_phase1=${9:-"2000"}
warmup_steps_phase2=${10:-"200"}
train_steps=${11:-7820}
save_checkpoints_steps=${12:-100}
num_accumulation_steps_phase1=${13:-128}
num_accumulation_steps_phase2=${14:-512}
bert_model=${15:-"large"}
DATA_DIR=${DATA_DIR:-data}
#Edit to save logs & checkpoints in a different directory
RESULTS_DIR=${RESULTS_DIR:-/results}
if [ "$bert_model" = "large" ] ; then
export BERT_CONFIG=data/download/nvidia_pretrained/bert_tf_pretraining_large_lamb/bert_config.json
else
export BERT_CONFIG=data/download/nvidia_pretrained/bert_tf_squad11_base_128/bert_config.json
fi
echo "Container nvidia build = " $NVIDIA_BUILD_ID
PREC=""
if [ "$precision" = "fp16" ] ; then
PREC="--amp"
elif [ "$precision" = "fp32" ] ; then
PREC="--noamp"
elif [ "$precision" = "tf32" ] ; then
PREC="--noamp"
elif [ "$precision" = "manual_fp16" ] ; then
PREC="--noamp --manual_fp16"
else
echo "Unknown <precision> argument"
exit -2
fi
if [ "$use_xla" = "true" ] ; then
PREC="$PREC --use_xla"
echo "XLA activated"
else
PREC="$PREC --nouse_xla"
fi
mpi=""
horovod_str=""
if [ $num_gpus -gt 1 ] ; then
mpi="mpiexec --allow-run-as-root -np $num_gpus --bind-to socket"
horovod_str="--horovod"
fi
#PHASE 1 Config
train_steps_phase1=$(expr $train_steps \* 9 \/ 10) #Phase 1 is 10% of training
gbs_phase1=$(expr $train_batch_size_phase1 \* $num_accumulation_steps_phase1)
PHASE1_CKPT=${RESULTS_DIR}/phase_1/model.ckpt-${train_steps_phase1}
#PHASE 2
seq_len=512
max_pred_per_seq=80
train_steps_phase2=$(expr $train_steps \* 1 \/ 10) #Phase 2 is 10% of training
gbs_phase2=$(expr $train_batch_size_phase2 \* $num_accumulation_steps_phase2)
train_steps_phase2=$(expr $train_steps_phase2 \* $gbs_phase1 \/ $gbs_phase2) # Adjust for batch size
RESULTS_DIR_PHASE2=${RESULTS_DIR}/phase_2
mkdir -m 777 -p $RESULTS_DIR_PHASE2
INPUT_FILES="$DATA_DIR/tfrecord/lower_case_1_seq_len_${seq_len}_max_pred_${max_pred_per_seq}_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus/training"
EVAL_FILES="$DATA_DIR/tfrecord/lower_case_1_seq_len_${seq_len}_max_pred_${max_pred_per_seq}_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus/test"
#Check if all necessary files are available before training
for DIR_or_file in $DATA_DIR $RESULTS_DIR $BERT_CONFIG ${PHASE1_CKPT}.meta; do
if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then
echo "Error! $DIR_or_file directory missing. Please mount correctly"
exit -1
fi
done
$mpi python /workspace/bert/run_pretraining.py \
--input_files_dir=$INPUT_FILES \
--init_checkpoint=$PHASE1_CKPT \
--eval_files_dir=$EVAL_FILES \
--output_dir=$RESULTS_DIR_PHASE2 \
--bert_config_file=$BERT_CONFIG \
--do_train=True \
--do_eval=True \
--train_batch_size=$train_batch_size_phase2 \
--eval_batch_size=$eval_batch_size \
--max_seq_length=$seq_len \
--max_predictions_per_seq=$max_pred_per_seq \
--num_train_steps=$train_steps_phase2 \
--num_accumulation_steps=$num_accumulation_steps_phase2 \
--num_warmup_steps=$warmup_steps_phase2 \
--save_checkpoints_steps=$save_checkpoints_steps \
--learning_rate=$learning_rate_phase2 \
$horovod_str $PREC \
--allreduce_post_accumulation=True
|
TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking | benchmarking | UNet_evalbench_AMP | #!/usr/bin/env bash
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script launches UNet evaluation benchmark in TF-AMP on 1 GPUs using 16 batch size
# Usage ./UNet_evalbench_AMP.sh <path to dataset> <dagm classID (1-10)>
BASEDIR="$( 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='inference_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
|
TensorFlow/Translation/GNMT/utils | utils | iterator_utils | # Copyright 2017 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.
# ==============================================================================
#
# 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.
"""For loading data into NMT models."""
from __future__ import print_function
import os
import tensorflow as tf
from utils import vocab_utils
def get_effective_epoch_size(hparams, train=True):
"""Get training epoch size after filtering."""
if train:
src_file = "%s.%s" % (hparams.train_prefix, hparams.src)
tgt_file = "%s.%s" % (hparams.train_prefix, hparams.tgt)
src_max_len = hparams.src_max_len
tgt_max_len = hparams.tgt_max_len
else:
src_file = "%s.%s" % (hparams.test_prefix, hparams.src)
tgt_file = "%s.%s" % (hparams.test_prefix, hparams.tgt)
src_max_len = hparams.src_max_len_infer
tgt_max_len = None
if src_max_len is None:
src_max_len = float('inf')
if tgt_max_len is None:
tgt_max_len = float('inf')
srcf = tf.gfile.GFile(src_file, "r")
tgtf = tf.gfile.GFile(tgt_file, "r")
epoch_size = 0
src_tokens = 0
tgt_tokens = 0
for srcline, tgtline in zip(srcf, tgtf):
len_srcline = len(srcline.split())
len_tgtline = len(tgtline.split())
if (
len_srcline < src_max_len and
len_tgtline < tgt_max_len):
epoch_size += 1
src_tokens += len_srcline
tgt_tokens += len_tgtline
srcf.close()
tgtf.close()
return epoch_size, src_tokens, tgt_tokens
# pylint: disable=g-long-lambda,line-too-long
def get_iterator(src_dataset,
tgt_dataset,
src_vocab_table,
tgt_vocab_table,
batch_size,
sos,
eos,
random_seed,
num_buckets,
src_max_len=None,
tgt_max_len=None,
num_parallel_calls=4,
output_buffer_size=None,
skip_count=None,
num_shards=1,
shard_index=0,
reshuffle_each_iteration=True,
use_char_encode=False,
num_repeat=1,
filter_oversized_sequences=False):
"""Function that returns input dataset."""
if not output_buffer_size:
output_buffer_size = batch_size * 1000
if use_char_encode:
src_eos_id = vocab_utils.EOS_CHAR_ID
else:
src_eos_id = tf.cast(src_vocab_table.lookup(tf.constant(eos)), tf.int32)
tgt_sos_id = tf.cast(tgt_vocab_table.lookup(tf.constant(sos)), tf.int32)
tgt_eos_id = tf.cast(tgt_vocab_table.lookup(tf.constant(eos)), tf.int32)
src_tgt_dataset = tf.data.Dataset.zip((src_dataset, tgt_dataset))
src_tgt_dataset = src_tgt_dataset.shard(num_shards, shard_index)
if skip_count is not None:
src_tgt_dataset = src_tgt_dataset.skip(skip_count)
src_tgt_dataset = src_tgt_dataset.shuffle(
output_buffer_size, random_seed,
reshuffle_each_iteration).repeat(num_repeat)
src_tgt_dataset = src_tgt_dataset.map(
lambda src, tgt: (tf.string_split([src]).values, tf.string_split([tgt]).values),
num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
# Filter zero length input sequences.
src_tgt_dataset = src_tgt_dataset.filter(
lambda src, tgt: tf.logical_and(tf.size(src) > 0, tf.size(tgt) > 0))
# Filter oversized input sequences.
if filter_oversized_sequences:
src_tgt_dataset = src_tgt_dataset.filter(
lambda src, tgt: tf.logical_and(tf.size(src) < src_max_len,
tf.size(tgt) < tgt_max_len))
if src_max_len:
src_tgt_dataset = src_tgt_dataset.map(
lambda src, tgt: (src[:src_max_len], tgt),
num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
if tgt_max_len:
src_tgt_dataset = src_tgt_dataset.map(
lambda src, tgt: (src, tgt[:tgt_max_len]),
num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
# Convert the word strings to ids. Word strings that are not in the
# vocab get the lookup table's default_value integer.
if use_char_encode:
src_tgt_dataset = src_tgt_dataset.map(
lambda src, tgt: (tf.reshape(vocab_utils.tokens_to_bytes(src), [-1]),
tf.cast(tgt_vocab_table.lookup(tgt), tf.int32)),
num_parallel_calls=num_parallel_calls)
else:
src_tgt_dataset = src_tgt_dataset.map(
lambda src, tgt: (tf.cast(src_vocab_table.lookup(src), tf.int32),
tf.cast(tgt_vocab_table.lookup(tgt), tf.int32)),
num_parallel_calls=num_parallel_calls)
src_tgt_dataset = src_tgt_dataset.prefetch(output_buffer_size)
# Create a tgt_input prefixed with <sos> and a tgt_output suffixed with <eos>.
src_tgt_dataset = src_tgt_dataset.map(
lambda src, tgt: (src,
tf.concat(([tgt_sos_id], tgt), 0),
tf.concat((tgt, [tgt_eos_id]), 0)),
num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
# Add in sequence lengths.
if use_char_encode:
src_tgt_dataset = src_tgt_dataset.map(
lambda src, tgt_in, tgt_out: (
src, tgt_in, tgt_out,
tf.to_int32(tf.size(src) / vocab_utils.DEFAULT_CHAR_MAXLEN),
tf.size(tgt_in)),
num_parallel_calls=num_parallel_calls)
else:
src_tgt_dataset = src_tgt_dataset.map(
lambda src, tgt_in, tgt_out: (
src, tgt_in, tgt_out, tf.size(src), tf.size(tgt_in)),
num_parallel_calls=num_parallel_calls)
src_tgt_dataset = src_tgt_dataset.prefetch(output_buffer_size)
use_xla_compile = os.environ["xla_compile"] == "true"
force_inputs_padding = os.environ["force_inputs_padding"] == "true"
use_static_input_shape = use_xla_compile or force_inputs_padding
# Bucket by source sequence length (buckets for lengths 0-9, 10-19, ...)
def batching_func(x):
return x.padded_batch(
batch_size,
# The first three entries are the source and target line rows;
# these have unknown-length vectors. The last two entries are
# the source and target row sizes; these are scalars.
padded_shapes=(
tf.TensorShape(
[src_max_len if use_static_input_shape else None]), # src
tf.TensorShape(
[tgt_max_len if use_static_input_shape else None]), # tgt_input
tf.TensorShape([tgt_max_len if use_static_input_shape else None
]), # tgt_output
tf.TensorShape([]), # src_len
tf.TensorShape([])), # tgt_len
# Pad the source and target sequences with eos tokens.
# (Though notice we don't generally need to do this since
# later on we will be masking out calculations past the true sequence.
padding_values=(
src_eos_id, # src
tgt_eos_id, # tgt_input
tgt_eos_id, # tgt_output
0, # src_len -- unused
0),
drop_remainder=True)
if num_buckets > 1:
def key_func(unused_1, unused_2, unused_3, src_len, tgt_len):
"""Calculate bucket_width by maximum source sequence length."""
# Pairs with length [0, bucket_width) go to bucket 0, length
# [bucket_width, 2 * bucket_width) go to bucket 1, etc. Pairs with length
# over ((num_bucket-1) * bucket_width) words all go into the last bucket.
if src_max_len:
bucket_width = (src_max_len + num_buckets - 1) // num_buckets
else:
bucket_width = 10
# Bucket sentence pairs by the length of their source sentence and target
# sentence.
bucket_id = tf.maximum(src_len // bucket_width, tgt_len // bucket_width)
return tf.to_int64(tf.minimum(num_buckets, bucket_id))
def reduce_func(unused_key, windowed_data):
return batching_func(windowed_data)
batched_dataset = src_tgt_dataset.apply(
tf.contrib.data.group_by_window(
key_func=key_func, reduce_func=reduce_func, window_size=batch_size))
else:
batched_dataset = batching_func(src_tgt_dataset)
# Make_one_shot_iterator is not applicable here since we have lookup table.
# Instead return a tf.data.dataset and let TpuEstimator to initialize and make
# iterator out of it.
batched_dataset = batched_dataset.map(
lambda src, tgt_in, tgt_out, source_size, tgt_in_size: (
{"source": src,
"target_input": tgt_in,
"target_output": tgt_out,
"source_sequence_length": source_size,
"target_sequence_length": tgt_in_size}))
return batched_dataset
def get_infer_iterator(src_dataset,
src_vocab_table,
batch_size,
eos,
src_max_len=None,
use_char_encode=False):
"""Get dataset for inference."""
if use_char_encode:
src_eos_id = vocab_utils.EOS_CHAR_ID
else:
src_eos_id = tf.cast(src_vocab_table.lookup(tf.constant(eos)), tf.int32)
src_dataset = src_dataset.map(lambda src: tf.string_split([src]).values)
if src_max_len:
src_dataset = src_dataset.map(lambda src: src[:src_max_len])
if use_char_encode:
# Convert the word strings to character ids
src_dataset = src_dataset.map(
lambda src: tf.reshape(vocab_utils.tokens_to_bytes(src), [-1]))
else:
# Convert the word strings to ids
src_dataset = src_dataset.map(
lambda src: tf.cast(src_vocab_table.lookup(src), tf.int32))
# Add in the word counts.
if use_char_encode:
src_dataset = src_dataset.map(
lambda src: (src,
tf.to_int32(
tf.size(src) / vocab_utils.DEFAULT_CHAR_MAXLEN)))
else:
src_dataset = src_dataset.map(lambda src: (src, tf.size(src)))
def batching_func(x):
return x.padded_batch(
batch_size,
# The entry is the source line rows;
# this has unknown-length vectors. The last entry is
# the source row size; this is a scalar.
padded_shapes=(
tf.TensorShape([None]), # src
tf.TensorShape([])), # src_len
# Pad the source sequences with eos tokens.
# (Though notice we don't generally need to do this since
# later on we will be masking out calculations past the true sequence.
padding_values=(
src_eos_id, # src
0)) # src_len -- unused
batched_dataset = batching_func(src_dataset)
batched_dataset = batched_dataset.map(
lambda src_ids, src_seq_len: (
{"source": src_ids,
"source_sequence_length": src_seq_len}))
return batched_dataset
|
TensorFlow/Translation/GNMT/utils | utils | vocab_utils | # Copyright 2017 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.
# ==============================================================================
"""Utility to handle vocabularies."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import codecs
import os
import tensorflow as tf
from tensorflow.python.ops import lookup_ops
from utils import misc_utils as utils
# word level special token
UNK = "<unk>"
SOS = "<s>"
EOS = "</s>"
UNK_ID = 0
# char ids 0-255 come from utf-8 encoding bytes
# assign 256-300 to special chars
BOS_CHAR_ID = 256 # <begin sentence>
EOS_CHAR_ID = 257 # <end sentence>
BOW_CHAR_ID = 258 # <begin word>
EOW_CHAR_ID = 259 # <end word>
PAD_CHAR_ID = 260 # <padding>
DEFAULT_CHAR_MAXLEN = 50 # max number of chars for each word.
def _string_to_bytes(text, max_length):
"""Given string and length, convert to byte seq of at most max_length.
This process mimics docqa/elmo's preprocessing:
https://github.com/allenai/document-qa/blob/master/docqa/elmo/data.py
Note that we make use of BOS_CHAR_ID and EOS_CHAR_ID in iterator_utils.py &
our usage differs from docqa/elmo.
Args:
text: tf.string tensor of shape []
max_length: max number of chars for each word.
Returns:
A tf.int32 tensor of the byte encoded text.
"""
byte_ids = tf.to_int32(tf.decode_raw(text, tf.uint8))
byte_ids = byte_ids[:max_length - 2]
padding = tf.fill([max_length - tf.shape(byte_ids)[0] - 2], PAD_CHAR_ID)
byte_ids = tf.concat(
[[BOW_CHAR_ID], byte_ids, [EOW_CHAR_ID], padding], axis=0)
tf.logging.info(byte_ids)
byte_ids = tf.reshape(byte_ids, [max_length])
tf.logging.info(byte_ids.get_shape().as_list())
return byte_ids + 1
def tokens_to_bytes(tokens):
"""Given a sequence of strings, map to sequence of bytes.
Args:
tokens: A tf.string tensor
Returns:
A tensor of shape words.shape + [bytes_per_word] containing byte versions
of each word.
"""
bytes_per_word = DEFAULT_CHAR_MAXLEN
with tf.device("/cpu:0"):
tf.assert_rank(tokens, 1)
shape = tf.shape(tokens)
tf.logging.info(tokens)
tokens_flat = tf.reshape(tokens, [-1])
as_bytes_flat = tf.map_fn(
fn=lambda x: _string_to_bytes(x, max_length=bytes_per_word),
elems=tokens_flat,
dtype=tf.int32,
back_prop=False)
tf.logging.info(as_bytes_flat)
as_bytes = tf.reshape(as_bytes_flat, [shape[0], bytes_per_word])
return as_bytes
def load_vocab(vocab_file):
vocab = []
with codecs.getreader("utf-8")(tf.gfile.GFile(vocab_file, "rb")) as f:
vocab_size = 0
for word in f:
vocab_size += 1
vocab.append(word.strip())
return vocab, vocab_size
def check_vocab(vocab_file, output_dir, check_special_token=True, sos=None,
eos=None, unk=None, pad_vocab=False):
"""Check if vocab_file doesn't exist, create from corpus_file."""
if tf.gfile.Exists(vocab_file):
utils.print_out("# Vocab file %s exists" % vocab_file)
vocab, vocab_size = load_vocab(vocab_file)
if check_special_token:
# Verify if the vocab starts with unk, sos, eos
# If not, prepend those tokens & generate a new vocab file
if not unk: unk = UNK
if not sos: sos = SOS
if not eos: eos = EOS
assert len(vocab) >= 3
if vocab[0] != unk or vocab[1] != sos or vocab[2] != eos:
utils.print_out("The first 3 vocab words [%s, %s, %s]"
" are not [%s, %s, %s]" %
(vocab[0], vocab[1], vocab[2], unk, sos, eos))
vocab = [unk, sos, eos] + vocab
vocab_size += 3
new_vocab_file = os.path.join(output_dir, os.path.basename(vocab_file))
with codecs.getwriter("utf-8")(
tf.gfile.GFile(new_vocab_file, "wb")) as f:
for word in vocab:
f.write("%s\n" % word)
vocab_file = new_vocab_file
if pad_vocab == True and vocab_size % 8 != 0:
new_vocab_file = os.path.join(output_dir, os.path.basename(vocab_file))
padded_vocab_size = ((vocab_size + 8 - 1)// 8) * 8
for i in range(0, padded_vocab_size - vocab_size):
token = "<madeupword" + str(i) + ">"
vocab.append(token)
with codecs.getwriter("utf-8")(
tf.gfile.GFile(new_vocab_file, "wb")) as f:
for word in vocab:
f.write("%s\n" % word)
vocab_file = new_vocab_file
else:
raise ValueError("vocab_file '%s' does not exist." % vocab_file)
vocab_size = len(vocab)
return vocab_size, vocab_file
def create_vocab_tables(src_vocab_file, tgt_vocab_file, share_vocab):
"""Creates vocab tables for src_vocab_file and tgt_vocab_file."""
src_vocab_table = lookup_ops.index_table_from_file(
src_vocab_file, default_value=UNK_ID)
if share_vocab:
tgt_vocab_table = src_vocab_table
else:
tgt_vocab_table = lookup_ops.index_table_from_file(
tgt_vocab_file, default_value=UNK_ID)
return src_vocab_table, tgt_vocab_table
def load_embed_txt(embed_file):
"""Load embed_file into a python dictionary.
Note: the embed_file should be a Glove/word2vec formatted txt file. Assuming
Here is an exampe assuming embed_size=5:
the -0.071549 0.093459 0.023738 -0.090339 0.056123
to 0.57346 0.5417 -0.23477 -0.3624 0.4037
and 0.20327 0.47348 0.050877 0.002103 0.060547
For word2vec format, the first line will be: <num_words> <emb_size>.
Args:
embed_file: file path to the embedding file.
Returns:
a dictionary that maps word to vector, and the size of embedding dimensions.
"""
emb_dict = dict()
emb_size = None
is_first_line = True
with codecs.getreader("utf-8")(tf.gfile.GFile(embed_file, "rb")) as f:
for line in f:
tokens = line.rstrip().split(" ")
if is_first_line:
is_first_line = False
if len(tokens) == 2: # header line
emb_size = int(tokens[1])
continue
word = tokens[0]
vec = list(map(float, tokens[1:]))
emb_dict[word] = vec
if emb_size:
if emb_size != len(vec):
utils.print_out(
"Ignoring %s since embeding size is inconsistent." % word)
del emb_dict[word]
else:
emb_size = len(vec)
return emb_dict, emb_size
|
TensorFlow2/Classification/ConvNets/model/blocks | blocks | __init__ | # 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 model.blocks.conv2d_block import conv2d_block
from model.blocks.mb_conv_block import mb_conv_block
from model.blocks.fused_mb_conv_block import fused_mb_conv_block
__all__ = ['conv2d_block', 'mb_conv_block','fused_mb_conv_block'] |
Tools/PyTorch/TimeSeriesPredictionPlatform/conf/trainer/optimizer | optimizer | SGD | # 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_: torch.optim.SGD
lr: 0.01
momentum: 0.0
weight_decay: 0.0
dampening: 0.0
nesterov: False
|
TensorFlow2/LanguageModeling/BERT/official/nlp/transformer | transformer | model_params | # 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.
# ==============================================================================
"""Defines Transformer model parameters."""
from collections import defaultdict
BASE_PARAMS = defaultdict(
lambda: None, # Set default value to None.
# Input params
default_batch_size=2048, # Maximum number of tokens per batch of examples.
default_batch_size_tpu=32768,
max_length=256, # Maximum number of tokens per example.
# Model params
initializer_gain=1.0, # Used in trainable variable initialization.
vocab_size=33708, # Number of tokens defined in the vocabulary file.
hidden_size=512, # Model dimension in the hidden layers.
num_hidden_layers=6, # Number of layers in the encoder and decoder stacks.
num_heads=8, # Number of heads to use in multi-headed attention.
filter_size=2048, # Inner layer dimension in the feedforward network.
# Dropout values (only used when training)
layer_postprocess_dropout=0.1,
attention_dropout=0.1,
relu_dropout=0.1,
# Training params
label_smoothing=0.1,
learning_rate=2.0,
learning_rate_decay_rate=1.0,
learning_rate_warmup_steps=16000,
# Optimizer params
optimizer_adam_beta1=0.9,
optimizer_adam_beta2=0.997,
optimizer_adam_epsilon=1e-09,
# Default prediction params
extra_decode_length=50,
beam_size=4,
alpha=0.6, # used to calculate length normalization in beam search
# TPU specific parameters
use_tpu=False,
static_batch=False,
allow_ffn_pad=True,
)
BIG_PARAMS = BASE_PARAMS.copy()
BIG_PARAMS.update(
default_batch_size=4096,
# default batch size is smaller than for BASE_PARAMS due to memory limits.
default_batch_size_tpu=16384,
hidden_size=1024,
filter_size=4096,
num_heads=16,
)
# Parameters for running the model in multi gpu. These should not change the
# params that modify the model shape (such as the hidden_size or num_heads).
BASE_MULTI_GPU_PARAMS = BASE_PARAMS.copy()
BASE_MULTI_GPU_PARAMS.update(
learning_rate_warmup_steps=8000
)
BIG_MULTI_GPU_PARAMS = BIG_PARAMS.copy()
BIG_MULTI_GPU_PARAMS.update(
layer_postprocess_dropout=0.3,
learning_rate_warmup_steps=8000
)
# Parameters for testing the model
TINY_PARAMS = BASE_PARAMS.copy()
TINY_PARAMS.update(
default_batch_size=1024,
default_batch_size_tpu=1024,
hidden_size=32,
num_heads=4,
filter_size=256,
)
|
PyTorch/Classification/GPUNet/triton/scripts | scripts | setup_environment | #!/usr/bin/env bash
# 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.
WORKDIR="${WORKDIR:=$(pwd)}"
export DATASETS_DIR=${WORKDIR}/datasets
export WORKSPACE_DIR=${WORKDIR}/runner_workspace
export CHECKPOINTS_DIR=${WORKSPACE_DIR}/checkpoints
export MODEL_REPOSITORY_PATH=${WORKSPACE_DIR}/model_store
export SHARED_DIR=${WORKSPACE_DIR}/shared_dir
echo "Preparing directories"
mkdir -p ${WORKSPACE_DIR}
mkdir -p ${DATASETS_DIR}
mkdir -p ${CHECKPOINTS_DIR}
mkdir -p ${MODEL_REPOSITORY_PATH}
mkdir -p ${SHARED_DIR}
echo "Setting up environment"
export MODEL_NAME=GPUnet
export ENSEMBLE_MODEL_NAME=
export TRITON_LOAD_MODEL_METHOD=explicit
export TRITON_INSTANCES=1 |
TensorFlow2/Segmentation/MaskRCNN/mrcnn_tf2/model | model | losses | # 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.
"""Losses used for Mask-RCNN."""
import tensorflow as tf
class MaskRCNNLoss(tf.keras.layers.Layer):
"""
Layer that computes the mask loss of Mask-RCNN.
This layer implements the mask loss of Mask-RCNN. As the `mask_outputs`
produces `num_classes` masks for each RoI, the reference model expands
`mask_targets` to match the shape of `mask_outputs` and selects only the
target that the RoI has a maximum overlap.
(Reference: https://github.com/facebookresearch/Detectron/blob/master/detectron/roi_data/mask_rcnn.py)
Instead, this implementation selects the `mask_outputs` by the `class_targets`
so that it doesn't expand `mask_targets`.
"""
def __init__(self):
super().__init__(trainable=False, dtype=tf.float32)
def call(self, inputs, **kwargs):
"""
Args:
inputs: dictionary with model outputs, which has to include:
mask_outputs: a float tensor representing the prediction for each mask,
with a shape of [batch_size, num_masks, mask_height, mask_width].
mask_targets: a float tensor representing the binary mask of ground truth
labels for each mask with a shape of [batch_size, num_masks, mask_height, mask_width].
select_class_targets: a tensor with a shape of [batch_size, num_masks],
representing the foreground mask targets.
Returns:
mask_loss: a float tensor representing total mask loss.
"""
mask_outputs = inputs['mask_outputs']
mask_targets = inputs['mask_targets']
select_class_targets = inputs['selected_class_targets']
batch_size, num_masks, mask_height, mask_width = mask_outputs.get_shape().as_list()
weights = tf.tile(
tf.reshape(tf.greater(select_class_targets, 0), [batch_size, num_masks, 1, 1]),
[1, 1, mask_height, mask_width]
)
weights = tf.cast(weights, tf.float32)
return _sigmoid_cross_entropy(
multi_class_labels=mask_targets,
logits=mask_outputs,
weights=weights,
sum_by_non_zeros_weights=True
)
class FastRCNNLoss(tf.keras.layers.Layer):
"""
Layer that computes the box and class loss (Fast-RCNN branch) of Mask-RCNN.
This layer implements the classification and box regression loss of the
Fast-RCNN branch in Mask-RCNN. As the `box_outputs` produces `num_classes`
boxes for each RoI, the reference model expands `box_targets` to match the
shape of `box_outputs` and selects only the target that the RoI has a maximum
overlap.
(Reference: https://github.com/facebookresearch/Detectron/blob/master/detectron/roi_data/fast_rcnn.py)
Instead, this function selects the `box_outputs` by the `class_targets` so
that it doesn't expand `box_targets`.
The loss computation has two parts: (1) classification loss is softmax on all
RoIs. (2) box loss is smooth L1-loss on only positive samples of RoIs.
Reference: https://github.com/facebookresearch/Detectron/blob/master/detectron/modeling/fast_rcnn_heads.py
"""
def __init__(self, num_classes):
super().__init__(trainable=False, dtype=tf.float32)
self._num_classes = num_classes
def call(self, inputs, **kwargs):
"""
Args:
inputs: dictionary with model outputs, which has to include:
class_outputs: a float tensor representing the class prediction for each box
with a shape of [batch_size, num_boxes, num_classes].
box_outputs: a float tensor representing the box prediction for each box
with a shape of [batch_size, num_boxes, num_classes * 4].
class_targets: a float tensor representing the class label for each box
with a shape of [batch_size, num_boxes].
box_targets: a float tensor representing the box label for each box
with a shape of [batch_size, num_boxes, 4].
Returns:
cls_loss: a float tensor representing total class loss.
box_loss: a float tensor representing total box regression loss.
"""
class_outputs = inputs['class_outputs']
box_outputs = inputs['box_outputs']
class_targets = inputs['class_targets']
box_targets = inputs['box_targets']
class_targets = tf.cast(class_targets, dtype=tf.int32)
# Selects the box from `box_outputs` based on `class_targets`, with which
# the box has the maximum overlap.
batch_size, num_rois, _ = box_outputs.get_shape().as_list()
box_outputs = tf.reshape(box_outputs, [batch_size, num_rois, self._num_classes, 4])
box_indices = tf.reshape(
class_targets +
tf.tile(tf.expand_dims(tf.range(batch_size) * num_rois * self._num_classes, 1), [1, num_rois]) +
tf.tile(tf.expand_dims(tf.range(num_rois) * self._num_classes, 0), [batch_size, 1]),
[-1]
)
box_outputs = tf.matmul(
tf.one_hot(
box_indices,
batch_size * num_rois * self._num_classes,
dtype=box_outputs.dtype
),
tf.reshape(box_outputs, [-1, 4])
)
box_outputs = tf.reshape(box_outputs, [batch_size, -1, 4])
box_loss = _fast_rcnn_box_loss(
box_outputs=box_outputs,
box_targets=box_targets,
class_targets=class_targets,
normalizer=1.0
)
class_targets = tf.one_hot(class_targets, self._num_classes)
class_loss = _fast_rcnn_class_loss(
class_outputs=class_outputs,
class_targets_one_hot=class_targets,
normalizer=1.0
)
return class_loss, box_loss
class RPNLoss(tf.keras.layers.Layer):
"""
Layer that computes total RPN detection loss.
Computes total RPN detection loss including box and score from all levels.
"""
def __init__(self, batch_size, rpn_batch_size_per_im, min_level, max_level):
super().__init__(trainable=False, dtype=tf.float32)
self._batch_size = batch_size
self._rpn_batch_size_per_im = rpn_batch_size_per_im
self._min_level = min_level
self._max_level = max_level
def call(self, inputs, **kwargs):
"""
Args:
inputs: dictionary with model outputs, which has to include:
score_outputs: an OrderDict with keys representing levels and values
representing scores in [batch_size, height, width, num_anchors].
box_outputs: an OrderDict with keys representing levels and values
representing box regression targets in [batch_size, height, width, num_anchors * 4].
score_targets_*: ground truth score targets
box_targets_*: ground truth box targets
Returns:
rpn_score_loss: a float tensor representing total score loss.
rpn_box_loss: a float tensor representing total box regression loss.
"""
score_outputs = inputs['rpn_score_outputs']
box_outputs = inputs['rpn_box_outputs']
score_losses = []
box_losses = []
for level in range(int(self._min_level), int(self._max_level + 1)):
score_targets_at_level = inputs['score_targets_%d' % level]
box_targets_at_level = inputs['box_targets_%d' % level]
score_losses.append(
_rpn_score_loss(
score_outputs=score_outputs[level],
score_targets=score_targets_at_level,
normalizer=tf.cast(self._batch_size * self._rpn_batch_size_per_im, dtype=tf.float32)
)
)
box_losses.append(_rpn_box_loss(
box_outputs=box_outputs[level],
box_targets=box_targets_at_level,
normalizer=1.0
))
# Sum per level losses to total loss.
rpn_score_loss = tf.add_n(score_losses)
rpn_box_loss = tf.add_n(box_losses)
return rpn_score_loss, rpn_box_loss
def _huber_loss(y_true, y_pred, weights, delta):
num_non_zeros = tf.math.count_nonzero(weights, dtype=tf.float32)
huber_keras_loss = tf.keras.losses.Huber(
delta=delta,
reduction=tf.keras.losses.Reduction.SUM,
name='huber_loss'
)
y_true = tf.expand_dims(y_true, axis=-1)
y_pred = tf.expand_dims(y_pred, axis=-1)
huber_loss = huber_keras_loss(
y_true,
y_pred,
sample_weight=weights
)
assert huber_loss.dtype == tf.float32
huber_loss = tf.math.divide_no_nan(huber_loss, num_non_zeros, name="huber_loss")
assert huber_loss.dtype == tf.float32
return huber_loss
def _sigmoid_cross_entropy(multi_class_labels, logits, weights, sum_by_non_zeros_weights=False):
assert weights.dtype == tf.float32
sigmoid_cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(
labels=multi_class_labels,
logits=logits,
name="x-entropy"
)
assert sigmoid_cross_entropy.dtype == tf.float32
sigmoid_cross_entropy = tf.math.multiply(sigmoid_cross_entropy, weights)
sigmoid_cross_entropy = tf.math.reduce_sum(input_tensor=sigmoid_cross_entropy)
assert sigmoid_cross_entropy.dtype == tf.float32
if sum_by_non_zeros_weights:
num_non_zeros = tf.math.count_nonzero(weights, dtype=tf.float32)
sigmoid_cross_entropy = tf.math.divide_no_nan(
sigmoid_cross_entropy,
num_non_zeros,
name="sum_by_non_zeros_weights"
)
assert sigmoid_cross_entropy.dtype == tf.float32
return sigmoid_cross_entropy
def _softmax_cross_entropy(onehot_labels, logits):
num_non_zeros = tf.math.count_nonzero(onehot_labels, dtype=tf.float32)
softmax_cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=tf.stop_gradient(onehot_labels),
logits=logits
)
assert softmax_cross_entropy.dtype == tf.float32
softmax_cross_entropy = tf.math.reduce_sum(input_tensor=softmax_cross_entropy)
softmax_cross_entropy = tf.math.divide_no_nan(
softmax_cross_entropy,
num_non_zeros,
name="softmax_cross_entropy"
)
assert softmax_cross_entropy.dtype == tf.float32
return softmax_cross_entropy
def _rpn_score_loss(score_outputs, score_targets, normalizer=1.0):
"""Computes score loss."""
with tf.name_scope('rpn_score_loss'):
# score_targets has three values:
# * (1) score_targets[i]=1, the anchor is a positive sample.
# * (2) score_targets[i]=0, negative.
# * (3) score_targets[i]=-1, the anchor is don't care (ignore).
mask = tf.math.greater_equal(score_targets, 0)
mask = tf.cast(mask, dtype=tf.float32)
score_targets = tf.maximum(score_targets, tf.zeros_like(score_targets))
score_targets = tf.cast(score_targets, dtype=tf.float32)
assert score_outputs.dtype == tf.float32
assert score_targets.dtype == tf.float32
score_loss = _sigmoid_cross_entropy(
multi_class_labels=score_targets,
logits=score_outputs,
weights=mask,
sum_by_non_zeros_weights=False
)
assert score_loss.dtype == tf.float32
if isinstance(normalizer, tf.Tensor) or normalizer != 1.0:
score_loss /= normalizer
assert score_loss.dtype == tf.float32
return score_loss
def _rpn_box_loss(box_outputs, box_targets, normalizer=1.0, delta=1. / 9):
"""Computes box regression loss."""
# delta is typically around the mean value of regression target.
# for instances, the regression targets of 512x512 input with 6 anchors on
# P2-P6 pyramid is about [0.1, 0.1, 0.2, 0.2].
with tf.name_scope('rpn_box_loss'):
mask = tf.not_equal(box_targets, 0.0)
mask = tf.cast(mask, tf.float32)
assert mask.dtype == tf.float32
# The loss is normalized by the sum of non-zero weights before additional
# normalizer provided by the function caller.
box_loss = _huber_loss(y_true=box_targets, y_pred=box_outputs, weights=mask, delta=delta)
assert box_loss.dtype == tf.float32
if isinstance(normalizer, tf.Tensor) or normalizer != 1.0:
box_loss /= normalizer
assert box_loss.dtype == tf.float32
return box_loss
def _fast_rcnn_class_loss(class_outputs, class_targets_one_hot, normalizer=1.0):
"""Computes classification loss."""
with tf.name_scope('fast_rcnn_class_loss'):
# The loss is normalized by the sum of non-zero weights before additional
# normalizer provided by the function caller.
class_loss = _softmax_cross_entropy(onehot_labels=class_targets_one_hot, logits=class_outputs)
if isinstance(normalizer, tf.Tensor) or normalizer != 1.0:
class_loss /= normalizer
return class_loss
def _fast_rcnn_box_loss(box_outputs, box_targets, class_targets, normalizer=1.0, delta=1.):
"""Computes box regression loss."""
# delta is typically around the mean value of regression target.
# for instances, the regression targets of 512x512 input with 6 anchors on
# P2-P6 pyramid is about [0.1, 0.1, 0.2, 0.2].
with tf.name_scope('fast_rcnn_box_loss'):
mask = tf.tile(tf.expand_dims(tf.greater(class_targets, 0), axis=2), [1, 1, 4])
# The loss is normalized by the sum of non-zero weights before additional
# normalizer provided by the function caller.
box_loss = _huber_loss(y_true=box_targets, y_pred=box_outputs, weights=mask, delta=delta)
if isinstance(normalizer, tf.Tensor) or normalizer != 1.0:
box_loss /= normalizer
return box_loss
|
TensorFlow/Recommendation/NCF | NCF | neumf | # Copyright (c) 2018. 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.
#
# -----------------------------------------------------------------------
#
# 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.
import tensorflow as tf
import horovod.tensorflow as hvd
def float32_variable_storage_getter(getter, name, shape=None, dtype=None,
initializer=None, regularizer=None,
trainable=True,
*args, **kwargs):
"""
Custom variable getter that forces trainable variables to be stored in
float32 precision and then casts them to the half-precision
"""
storage_dtype = tf.float32 if trainable else dtype
variable = getter(name, shape, dtype=storage_dtype,
initializer=initializer, regularizer=regularizer,
trainable=trainable,
*args, **kwargs)
if trainable and dtype != tf.float32:
variable = tf.cast(variable, dtype)
return variable
def neural_mf(users,
items,
model_dtype,
nb_users,
nb_items,
mf_dim,
mf_reg,
mlp_layer_sizes,
mlp_layer_regs,
dropout_rate,
sigmoid=False):
"""
Constructs the model graph
"""
# Check params
if len(mlp_layer_sizes) != len(mlp_layer_regs):
raise RuntimeError('u dummy, layer_sized != layer_regs')
if mlp_layer_sizes[0] % 2 != 0:
raise RuntimeError('u dummy, mlp_layer_sizes[0] % 2 != 0')
nb_mlp_layers = len(mlp_layer_sizes)
# Embeddings
user_embed = tf.get_variable(
"user_embeddings",
shape=[nb_users, mf_dim + mlp_layer_sizes[0] // 2],
initializer=tf.initializers.random_normal(mean=0.0, stddev=0.01))
item_embed = tf.get_variable(
"item_embeddings",
shape=[nb_items, mf_dim + mlp_layer_sizes[0] // 2],
initializer=tf.initializers.random_normal(mean=0.0, stddev=0.01))
# Matrix Factorization Embeddings
xmfu = tf.nn.embedding_lookup(user_embed[:, :mf_dim], users, partition_strategy='div')
xmfi = tf.nn.embedding_lookup(item_embed[:, :mf_dim], items, partition_strategy='div')
# MLP Network Embeddings
xmlpu = tf.nn.embedding_lookup(user_embed[:, mf_dim:], users, partition_strategy='div')
xmlpi = tf.nn.embedding_lookup(item_embed[:, mf_dim:], items, partition_strategy='div')
# Enforce model to use fp16 data types when manually enabling mixed precision
# (Tensorfow ops will use automatically use the data type of the first input)
if model_dtype == tf.float16:
xmfu = tf.cast(xmfu, model_dtype)
xmfi = tf.cast(xmfi, model_dtype)
xmlpu = tf.cast(xmlpu, model_dtype)
xmlpi = tf.cast(xmlpi, model_dtype)
# Matrix Factorization
xmf = tf.math.multiply(xmfu, xmfi)
# MLP Layers
xmlp = tf.concat((xmlpu, xmlpi), 1)
for i in range(1, nb_mlp_layers):
xmlp = tf.layers.Dense(
mlp_layer_sizes[i],
activation=tf.nn.relu,
kernel_initializer=tf.glorot_uniform_initializer()
).apply(xmlp)
xmlp = tf.layers.Dropout(rate=dropout_rate).apply(xmlp)
# Final fully-connected layer
logits = tf.concat((xmf, xmlp), 1)
logits = tf.layers.Dense(
1,
kernel_initializer=tf.keras.initializers.lecun_uniform()
).apply(logits)
if sigmoid:
logits = tf.math.sigmoid(logits)
# Cast model outputs back to float32 if manually enabling mixed precision for loss calculation
if model_dtype == tf.float16:
logits = tf.cast(logits, tf.float32)
return logits
def compute_eval_metrics(logits, dup_mask, val_batch_size, K):
"""
Constructs the graph to compute Hit Rate and NDCG
"""
# Replace duplicate (uid, iid) pairs with -inf
logits = logits * (1. - dup_mask)
logits = logits + (dup_mask * logits.dtype.min)
# Reshape tensors so that each row corresponds with a user
logits_by_user = tf.reshape(logits, [-1, val_batch_size])
dup_mask_by_user = tf.cast(tf.reshape(logits, [-1, val_batch_size]), tf.bool)
# Get the topk items for each user
top_item_indices = tf.math.top_k(logits_by_user, K)[1]
# Check that the positive sample (last index) is in the top K
is_positive = tf.cast(tf.equal(top_item_indices, val_batch_size-1), tf.int32)
found_positive = tf.reduce_sum(is_positive, axis=1)
# Extract the rankings of the positive samples
positive_ranks = tf.reduce_sum(is_positive * tf.expand_dims(tf.range(K), 0), axis=1)
dcg = tf.log(2.) / tf.log(tf.cast(positive_ranks, tf.float32) + 2)
dcg *= tf.cast(found_positive, dcg.dtype)
return found_positive, dcg
def ncf_model_ops(users,
items,
labels,
dup_mask,
params,
mode='TRAIN'):
"""
Constructs the training and evaluation graphs
"""
# Validation params
val_batch_size = params['val_batch_size']
K = params['top_k']
# Training params
learning_rate = params['learning_rate']
beta_1 = params['beta_1']
beta_2 = params['beta_2']
epsilon = params['epsilon']
# Model params
fp16 = False
nb_users = params['num_users']
nb_items = params['num_items']
mf_dim = params['num_factors']
mf_reg = params['mf_reg']
mlp_layer_sizes = params['layer_sizes']
mlp_layer_regs = params['layer_regs']
dropout = params['dropout']
sigmoid = False #params['sigmoid']
loss_scale = params['loss_scale']
model_dtype = tf.float16 if fp16 else tf.float32
# If manually enabling mixed precision, use the custom variable getter
custom_getter = None if not fp16 else float32_variable_storage_getter
# Allow soft device placement
with tf.device(None), \
tf.variable_scope('neumf', custom_getter=custom_getter):
# Model graph
logits = neural_mf(
users,
items,
model_dtype,
nb_users,
nb_items,
mf_dim,
mf_reg,
mlp_layer_sizes,
mlp_layer_regs,
dropout,
sigmoid
)
logits = tf.squeeze(logits)
if mode == 'INFERENCE':
return logits
# Evaluation Ops
found_positive, dcg = compute_eval_metrics(logits, dup_mask, val_batch_size, K)
# Metrics
hit_rate = tf.metrics.mean(found_positive, name='hit_rate')
ndcg = tf.metrics.mean(dcg, name='ndcg')
eval_op = tf.group(hit_rate[1], ndcg[1])
if mode == 'EVAL':
return hit_rate[0], ndcg[0], eval_op, None
# Labels
labels = tf.reshape(labels, [-1, 1])
logits = tf.reshape(logits, [-1, 1])
# Use adaptive momentum optimizer
optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate,
beta1=beta_1, beta2=beta_2,
epsilon=epsilon)
loss = tf.losses.sigmoid_cross_entropy(
labels,
logits,
reduction=tf.losses.Reduction.MEAN)
# Apply loss scaling if manually enabling mixed precision
if fp16:
if loss_scale is None:
loss_scale_manager = tf.contrib.mixed_precision.ExponentialUpdateLossScaleManager(2**32, 1000)
else:
loss_scale_manager = tf.contrib.mixed_precision.FixedLossScaleManager(loss_scale)
optimizer = tf.contrib.mixed_precision.LossScaleOptimizer(optimizer, loss_scale_manager)
# Horovod wrapper for distributed training
optimizer = hvd.DistributedOptimizer(optimizer)
# Update ops
global_step = tf.train.get_global_step()
train_op = optimizer.minimize(loss, global_step=global_step)
return hit_rate[0], ndcg[0], eval_op, train_op
|
Tools/PyTorch/TimeSeriesPredictionPlatform/conf/trainer/callbacks/callbacks | callbacks | logging | # 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_: callbacks.ctl_callbacks.LoggingCallback
|
TensorFlow2/Segmentation/MaskRCNN/mrcnn_tf2/ops | ops | preprocess_ops | # 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.
"""Preprocessing ops."""
import math
import tensorflow as tf
from mrcnn_tf2.object_detection import preprocessor
def normalize_image(image):
"""Normalize the image.
Args:
image: a tensor of shape [height, width, 3] in dtype=tf.float32.
Returns:
normalized_image: a tensor which has the same shape and dtype as image,
with pixel values normalized.
"""
offset = tf.constant([0.485, 0.456, 0.406])
offset = tf.reshape(offset, shape=(1, 1, 3))
scale = tf.constant([0.229, 0.224, 0.225])
scale = tf.reshape(scale, shape=(1, 1, 3))
normalized_image = (image - offset) / scale
return normalized_image
def random_horizontal_flip(image, boxes=None, masks=None, seed=None):
"""Random horizontal flip the image, boxes, and masks.
Args:
image: a tensor of shape [height, width, 3] representing the image.
boxes: (Optional) a tensor of shape [num_boxes, 4] represneting the box
corners in normalized coordinates.
masks: (Optional) a tensor of shape [num_masks, height, width]
representing the object masks. Note that the size of the mask is the
same as the image.
Returns:
image: the processed image tensor after being randomly flipped.
boxes: None or the processed box tensor after being randomly flipped.
masks: None or the processed mask tensor after being randomly flipped.
"""
return preprocessor.random_horizontal_flip(image, boxes, masks, seed=seed)
def resize_and_pad(image, target_size, stride, boxes=None, masks=None):
"""Resize and pad images, boxes and masks.
Resize and pad images, (optionally boxes and masks) given the desired output
size of the image and stride size.
Here are the preprocessing steps.
1. For a given image, keep its aspect ratio and rescale the image to make it
the largest rectangle to be bounded by the rectangle specified by the
`target_size`.
2. Pad the rescaled image such that the height and width of the image become
the smallest multiple of the stride that is larger or equal to the desired
output diemension.
Args:
image: an image tensor of shape [original_height, original_width, 3].
target_size: a tuple of two integers indicating the desired output
image size. Note that the actual output size could be different from this.
stride: the stride of the backbone network. Each of the output image sides
must be the multiple of this.
boxes: (Optional) a tensor of shape [num_boxes, 4] represneting the box
corners in normalized coordinates.
masks: (Optional) a tensor of shape [num_masks, height, width]
representing the object masks. Note that the size of the mask is the
same as the image.
Returns:
image: the processed image tensor after being resized and padded.
image_info: a tensor of shape [5] which encodes the height, width before
and after resizing and the scaling factor.
boxes: None or the processed box tensor after being resized and padded.
After the processing, boxes will be in the absolute coordinates w.r.t.
the scaled image.
masks: None or the processed mask tensor after being resized and padded.
"""
input_height, input_width, _ = tf.unstack(
tf.cast(tf.shape(input=image), dtype=tf.float32),
axis=0
)
target_height, target_width = target_size
scale_if_resize_height = target_height / input_height
scale_if_resize_width = target_width / input_width
scale = tf.minimum(scale_if_resize_height, scale_if_resize_width)
scaled_height = tf.cast(scale * input_height, dtype=tf.int32)
scaled_width = tf.cast(scale * input_width, dtype=tf.int32)
image = tf.image.resize(image, [scaled_height, scaled_width], method=tf.image.ResizeMethod.BILINEAR)
padded_height = int(math.ceil(target_height * 1.0 / stride) * stride)
padded_width = int(math.ceil(target_width * 1.0 / stride) * stride)
image = tf.image.pad_to_bounding_box(image, 0, 0, padded_height, padded_width)
image.set_shape([padded_height, padded_width, 3])
image_info = tf.stack([
tf.cast(scaled_height, dtype=tf.float32),
tf.cast(scaled_width, dtype=tf.float32),
1.0 / scale,
input_height,
input_width]
)
if boxes is not None:
normalized_box_list = preprocessor.box_list.BoxList(boxes)
scaled_boxes = preprocessor.box_list_scale(normalized_box_list, scaled_height, scaled_width).get()
else:
scaled_boxes = None
if masks is not None:
scaled_masks = tf.image.resize(
tf.expand_dims(masks, -1),
[scaled_height, scaled_width],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR
)
# Check if there is any instance in this image or not.
num_masks = tf.shape(input=scaled_masks)[0]
scaled_masks = tf.cond(
pred=tf.greater(num_masks, 0),
true_fn=lambda: tf.image.pad_to_bounding_box(scaled_masks, 0, 0, padded_height, padded_width),
false_fn=lambda: tf.zeros([0, padded_height, padded_width, 1])
)
else:
scaled_masks = None
return image, image_info, scaled_boxes, scaled_masks
def crop_gt_masks(instance_masks, boxes, gt_mask_size, image_size):
"""Crops the ground truth binary masks and resize to fixed-size masks."""
num_masks = tf.shape(input=instance_masks)[0]
scale_sizes = tf.convert_to_tensor(value=[image_size[0], image_size[1]] * 2, dtype=tf.float32)
boxes = boxes / scale_sizes
cropped_gt_masks = tf.image.crop_and_resize(
image=instance_masks,
boxes=boxes,
box_indices=tf.range(num_masks, dtype=tf.int32),
crop_size=[gt_mask_size, gt_mask_size],
method='bilinear')[:, :, :, 0]
cropped_gt_masks = tf.pad(
tensor=cropped_gt_masks,
paddings=tf.constant([[0, 0], [2, 2], [2, 2]]),
mode='CONSTANT',
constant_values=0.
)
return cropped_gt_masks
def pad_to_fixed_size(data, pad_value, output_shape):
"""Pad data to a fixed length at the first dimension.
Args:
data: Tensor to be padded to output_shape.
pad_value: A constant value assigned to the paddings.
output_shape: The output shape of a 2D tensor.
Returns:
The Padded tensor with output_shape [max_num_instances, dimension].
"""
max_num_instances = output_shape[0]
dimension = output_shape[1]
data = tf.reshape(data, [-1, dimension])
num_instances = tf.shape(input=data)[0]
pad_length = max_num_instances - num_instances
paddings = pad_value * tf.ones([pad_length, dimension])
padded_data = tf.reshape(tf.concat([data, paddings], axis=0), output_shape)
return padded_data
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/tacotron2 | tacotron2 | tacotron2StreamingInstance | /*
* 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_TACOTRON2STREAMINGINSTANCE_H
#define TT2I_TACOTRON2STREAMINGINSTANCE_H
#include "cudaMemory.h"
#include "timedObject.h"
#include "trtPtr.h"
#include <memory>
namespace nvinfer1
{
class ICudaEngine;
}
namespace tts
{
class EncoderInstance;
class DecoderInstance;
class DecoderInstancePlain;
class DecoderInstancePlugins;
class PostNetInstance;
class Tacotron2StreamingInstance : public virtual TimedObject
{
public:
/**
* @brief Create a new Tacotron2 instance.
*
* @param encoder The built encoder network.
* @param decoder The built decoder network without plugins.
* @param decoder The built decoder network with plugins.
* @param postnet The built postnet network.
*/
Tacotron2StreamingInstance(
TRTPtr<nvinfer1::ICudaEngine> encoder,
TRTPtr<nvinfer1::ICudaEngine> decoderPlain,
TRTPtr<nvinfer1::ICudaEngine> decoderPlugins,
TRTPtr<nvinfer1::ICudaEngine> postnet);
// deleted copy constructor and assignment operator
Tacotron2StreamingInstance(const Tacotron2StreamingInstance& other) = delete;
Tacotron2StreamingInstance& operator=(const Tacotron2StreamingInstance& other)
= delete;
/**
* @brief Setup inference for a given input tensor.
*
* @param batchSize The number of sequences in the batch.
* @param inputDevice The input for each item in the batch.
* @param inputSpacing The spacing between the start of each item in the
* batch.
* @param inputLength The length of each input.
* @param stream The stream to operate on.
*/
void startInference(
int batchSize,
const int* inputDevice,
int inputSpacing,
const int* inputLength,
cudaStream_t stream);
/**
* @brief Generate the next chunk of output.
*
* @param outputDevice The location to write the output tensor in batch,
* frame, channel order.
* @param outputLength The length of each output sequence.
* @param stream The stream to operate on.
*
* @return True if not all sequences have finished.
*/
bool inferNext(float* outputDevice, int* outputLength, cudaStream_t stream);
/**
* @brief The random seed to use for dropouts. This resets the
* inference state, and `startInference()` must be called afterwards.
*
* @param seed The seed value.
*/
void setSeed(unsigned int seed);
/**
* @brief Get the number of mels produced at once.
*
* @return The number of mels.
*/
int getChunkSize() const;
/**
* @brief Get the number of channels each frame will have.
*
* @return The number of channels.
*/
int getNumMelChannels() const;
/**
* @brief Get the maximum length of an input sequence.
*
* @return The maximum length of the sequence.
*/
int getMaximumInputLength() const;
/**
* @brief Get the maximum batch size supported by this Tacotron2 instance.
*
* @return The maximum batch size.
*/
int getMaxBatchSize() const;
/**
* @brief Set whether or not to use plugins when possible. This resets the
* inference state, and `startInference()` must be called afterwards.
*
* @param usePlugins True to use plugins, false to not.
*/
void usePlugins(bool usePlugins);
/**
* @brief Check whether or not plugins will be used for the given batch size.
*
* @param batchSize The batch size.
*
* @return True if plugins would be used.
*/
bool willUsePlugins(int batchSize) const;
/**
* @brief Set the number of decoder loops to execute for subsequent calls to
* nextInfer. The number must be less than or equal to the return of
* `getMaxChunkSize()`.
*
* @param chunkSize The number of frames to generate.
*/
void setNextChunkSize(int chunkSize);
private:
// TRT network components
std::shared_ptr<EncoderInstance> mEncoder;
std::shared_ptr<DecoderInstancePlain> mDecoderPlain;
std::shared_ptr<DecoderInstancePlugins> mDecoderPlugins;
std::shared_ptr<PostNetInstance> mPostnet;
int mMaxInputLength;
int mNumMelChannels;
int mNumMelChunks;
int mMaxBatchSize;
int mBatchSize;
bool mUsePlugins;
DecoderInstance* mInUseDecoder;
CudaMemory<int32_t> mPaddedInputDevice;
CudaMemory<float> mInputMaskDevice;
CudaMemory<int32_t> mInputLengthsDevice;
CudaMemory<float> mEncodingDevice;
CudaMemory<float> mProcessedEncodingDevice;
CudaMemory<float> mMelChunkDevice;
const int* mInputLengthHost;
/**
* @brief Reset internal variables to prevent `inferNext()` from being
* called until `startInference()` is called.
*/
void resetInference();
};
} // namespace tts
#endif
|
PyTorch/Classification/ConvNets/efficientnet/training/TF32 | TF32 | DGXA100_efficientnet-widese-b4_TF32 | python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision TF32 --mode convergence --platform DGXA100 /imagenet --workspace ${1:-./} --raport-file raport.json
|
TensorFlow2/Segmentation/UNet_Medical/model | model | layers | # 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.
# -*- coding: utf-8 -*-
""" Contains a set of utilities that allow building the UNet model
"""
import tensorflow as tf
def _crop_and_concat(inputs, residual_input):
""" Perform a central crop of ``residual_input`` and concatenate to ``inputs``
Args:
inputs (tf.Tensor): Tensor with input
residual_input (tf.Tensor): Residual input
Return:
Concatenated tf.Tensor with the size of ``inputs``
"""
factor = inputs.shape[1] / residual_input.shape[1]
return tf.concat([inputs, tf.image.central_crop(residual_input, factor)], axis=-1)
class InputBlock(tf.keras.Model):
def __init__(self, filters):
""" UNet input block
Perform two unpadded convolutions with a specified number of filters and downsample
through max-pooling. First convolution
Args:
filters (int): Number of filters in convolution
"""
super().__init__(self)
with tf.name_scope('input_block'):
self.conv1 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 3),
activation=tf.nn.relu)
self.conv2 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 3),
activation=tf.nn.relu)
self.maxpool = tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=2)
def call(self, inputs):
out = self.conv1(inputs)
out = self.conv2(out)
mp = self.maxpool(out)
return mp, out
class DownsampleBlock(tf.keras.Model):
def __init__(self, filters, idx):
""" UNet downsample block
Perform two unpadded convolutions with a specified number of filters and downsample
through max-pooling
Args:
filters (int): Number of filters in convolution
idx (int): Index of block
Return:
Tuple of convolved ``inputs`` after and before downsampling
"""
super().__init__(self)
with tf.name_scope('downsample_block_{}'.format(idx)):
self.conv1 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 3),
activation=tf.nn.relu)
self.conv2 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 3),
activation=tf.nn.relu)
self.maxpool = tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=2)
def call(self, inputs):
out = self.conv1(inputs)
out = self.conv2(out)
mp = self.maxpool(out)
return mp, out
class BottleneckBlock(tf.keras.Model):
def __init__(self, filters):
""" UNet central block
Perform two unpadded convolutions with a specified number of filters and upsample
including dropout before upsampling for training
Args:
filters (int): Number of filters in convolution
"""
super().__init__(self)
with tf.name_scope('bottleneck_block'):
self.conv1 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 3),
activation=tf.nn.relu)
self.conv2 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 3),
activation=tf.nn.relu)
self.dropout = tf.keras.layers.Dropout(rate=0.5)
self.conv_transpose = tf.keras.layers.Conv2DTranspose(filters=filters // 2,
kernel_size=(3, 3),
strides=(2, 2),
padding='same',
activation=tf.nn.relu)
def call(self, inputs, training):
out = self.conv1(inputs)
out = self.conv2(out)
out = self.dropout(out, training=training)
out = self.conv_transpose(out)
return out
class UpsampleBlock(tf.keras.Model):
def __init__(self, filters, idx):
""" UNet upsample block
Perform two unpadded convolutions with a specified number of filters and upsample
Args:
filters (int): Number of filters in convolution
idx (int): Index of block
"""
super().__init__(self)
with tf.name_scope('upsample_block_{}'.format(idx)):
self.conv1 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 3),
activation=tf.nn.relu)
self.conv2 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 3),
activation=tf.nn.relu)
self.conv_transpose = tf.keras.layers.Conv2DTranspose(filters=filters // 2,
kernel_size=(3, 3),
strides=(2, 2),
padding='same',
activation=tf.nn.relu)
def call(self, inputs, residual_input):
out = _crop_and_concat(inputs, residual_input)
out = self.conv1(out)
out = self.conv2(out)
out = self.conv_transpose(out)
return out
class OutputBlock(tf.keras.Model):
def __init__(self, filters, n_classes):
""" UNet output block
Perform three unpadded convolutions, the last one with the same number
of channels as classes we want to classify
Args:
filters (int): Number of filters in convolution
n_classes (int): Number of output classes
"""
super().__init__(self)
with tf.name_scope('output_block'):
self.conv1 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 3),
activation=tf.nn.relu)
self.conv2 = tf.keras.layers.Conv2D(filters=filters,
kernel_size=(3, 3),
activation=tf.nn.relu)
self.conv3 = tf.keras.layers.Conv2D(filters=n_classes,
kernel_size=(1, 1),
activation=None)
def call(self, inputs, residual_input):
out = _crop_and_concat(inputs, residual_input)
out = self.conv1(out)
out = self.conv2(out)
out = self.conv3(out)
return out
|
PyTorch/LanguageModeling/BART/utils | utils | make_datafiles | # 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 sys
import os
import hashlib
dm_single_close_quote = u'\u2019' # unicode
dm_double_close_quote = u'\u201d'
END_TOKENS = ['.', '!', '?', '...', "'", "`", '"', dm_single_close_quote, dm_double_close_quote, ")"] # acceptable ways to end a sentence
all_train_urls = "url_lists/all_train.txt"
all_val_urls = "url_lists/all_val.txt"
all_test_urls = "url_lists/all_test.txt"
finished_files_dir = "cnn_dm"
# These are the number of .story files we expect there to be in cnn_stories_dir and dm_stories_dir
num_expected_cnn_stories = 92579
num_expected_dm_stories = 219506
def read_text_file(text_file):
lines = []
with open(text_file, "r") as f:
for line in f:
lines.append(line.strip())
return lines
def hashhex(s):
"""Returns a heximal formated SHA1 hash of the input string."""
h = hashlib.sha1()
h.update(s.encode())
return h.hexdigest()
def get_url_hashes(url_list):
return [hashhex(url) for url in url_list]
def fix_missing_period(line):
"""Adds a period to a line that is missing a period"""
if "@highlight" in line: return line
if line=="": return line
if line[-1] in END_TOKENS: return line
# print line[-1]
return line + " ."
def get_art_abs(story_file):
lines = read_text_file(story_file)
# Put periods on the ends of lines that are missing them (this is a problem in the dataset because many image captions don't end in periods; consequently they end up in the body of the article as run-on sentences)
lines = [fix_missing_period(line) for line in lines]
# Separate out article and abstract sentences
article_lines = []
highlights = []
next_is_highlight = False
for idx,line in enumerate(lines):
if line == "":
continue # empty line
elif line.startswith("@highlight"):
next_is_highlight = True
elif next_is_highlight:
highlights.append(line)
else:
article_lines.append(line)
# Make article into a single string
article = ' '.join(article_lines)
# Make abstract into a signle string
abstract = ' '.join(highlights)
return article, abstract
def write_to_bin(url_file, out_name):
"""Reads the tokenized .story files corresponding to the urls listed in the url_file and writes them to a out_file."""
print("Making bin file for URLs listed in %s..." % url_file)
url_list = read_text_file(url_file)
url_hashes = get_url_hashes(url_list)
story_fnames = [s+".story" for s in url_hashes]
num_stories = len(story_fnames)
article_out = out_name + '.source'
abstract_out = out_name + '.target'
with open(article_out, 'w') as article_writer, open(abstract_out, 'w') as abstract_writer:
for idx,s in enumerate(story_fnames):
if idx % 1000 == 0:
print("Writing story %i of %i; %.2f percent done" % (idx, num_stories, float(idx)*100.0/float(num_stories)))
# Look in the tokenized story dirs to find the .story file corresponding to this url
if os.path.isfile(os.path.join(cnn_stories_dir, s)):
story_file = os.path.join(cnn_stories_dir, s)
elif os.path.isfile(os.path.join(dm_stories_dir, s)):
story_file = os.path.join(dm_stories_dir, s)
else:
print("Error: Couldn't find story file %s in story directories %s and %s." % (s, cnn_stories_dir, dm_stories_dir))
# Check again if stories directories contain correct number of files
print("Checking that the stories directories %s and %s contain correct number of files..." % (cnn_stories_dir, dm_stories_dir))
check_num_stories(cnn_stories_dir, num_expected_cnn_stories)
check_num_stories(dm_stories_dir, num_expected_dm_stories)
raise Exception("Stories directories %s and %s contain correct number of files but story file %s found in neither." % (cnn_stories_dir, dm_stories_dir, s))
# Get the strings to write to .bin file
article, abstract = get_art_abs(story_file)
article_writer.write(article + '\n')
abstract_writer.write(abstract + '\n')
print("Finished writing file %s and %s\n" % (article_out, abstract_out))
def check_num_stories(stories_dir, num_expected):
num_stories = len(os.listdir(stories_dir))
if num_stories != num_expected:
raise Exception("stories directory %s contains %i files but should contain %i" % (stories_dir, num_stories, num_expected))
if __name__ == '__main__':
if len(sys.argv) != 3:
print("USAGE: python make_datafiles.py <cnn_stories_dir> <dailymail_stories_dir>")
sys.exit()
cnn_stories_dir = sys.argv[1]
dm_stories_dir = sys.argv[2]
# Check the stories directories contain the correct number of .story files
check_num_stories(cnn_stories_dir, num_expected_cnn_stories)
check_num_stories(dm_stories_dir, num_expected_dm_stories)
# Create some new directories
if not os.path.exists(finished_files_dir): os.makedirs(finished_files_dir)
# Read the tokenized stories, do a little postprocessing then write to bin files
write_to_bin(all_test_urls, os.path.join(finished_files_dir, "test"))
write_to_bin(all_val_urls, os.path.join(finished_files_dir, "val"))
write_to_bin(all_train_urls, os.path.join(finished_files_dir, "train"))
|
PyTorch/SpeechSynthesis/Tacotron2/platform | platform | DGXA100_tacotron2_TF32_4NGPU_train | mkdir -p output
python -m multiproc train.py -m Tacotron2 -o output/ -lr 1e-3 --epochs 1501 -bs 128 --weight-decay 1e-6 --grad-clip-thresh 1.0 --cudnn-enabled --load-mel-from-disk --training-files=filelists/ljs_mel_text_train_filelist.txt --validation-files=filelists/ljs_mel_text_val_filelist.txt --log-file nvlog.json --anneal-steps 500 1000 1500 --anneal-factor 0.1
|
PaddlePaddle/LanguageModeling/BERT/data | data | BooksDownloader | # 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 subprocess
class BooksDownloader:
def __init__(self, save_path):
self.save_path = save_path
pass
def download(self):
bookscorpus_download_command = 'python3 /workspace/bookcorpus/download_files.py --list /workspace/bookcorpus/url_list.jsonl --out'
bookscorpus_download_command += ' ' + self.save_path + '/bookscorpus'
bookscorpus_download_command += ' --trash-bad-count'
subprocess.run(bookscorpus_download_command, shell=True, check=True)
|
TensorFlow/Segmentation/UNet_3D_Medical | UNet_3D_Medical | README | # 3D-UNet Medical Image Segmentation for TensorFlow 1.x
This repository provides a script and recipe to train the 3D-UNet model to achieve state-of-the-art accuracy.
The content of this repository is tested and maintained by NVIDIA.
3D-UNet model for TensorFlow1 is no longer maintained and will soon become unavailable, please consider other PyTorch or TensorFlow2 models as a substitute for your requirements.
## 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 80G)](#training-accuracy-nvidia-dgx-a100-8x-a100-80g)
* [Training accuracy: NVIDIA DGX-1 (8x V100 16G)](#training-accuracy-nvidia-dgx-1-8x-v100-16g)
* [Training performance results](#training-performance-results)
* [Training performance: NVIDIA DGX A100 (8x A100 80G)](#training-performance-nvidia-dgx-a100-8x-a100-80g)
* [Training performance: NVIDIA DGX-1 (8x V100 16G)](#training-performance-nvidia-dgx-1-8x-v100-16g)
* [Inference performance results](#inference-performance-results)
* [Inference performance: NVIDIA DGX A100 (1x A100 80G)](#inference-performance-nvidia-dgx-a100-1x-a100-80g)
* [Inference performance: NVIDIA DGX-1 (1x V100 16G)](#inference-performance-nvidia-dgx-1-1x-v100-16g)
- [Release notes](#release-notes)
* [Changelog](#changelog)
* [Known issues](#known-issues)
## Model overview
The U-Net model is a convolutional neural network for 3D image segmentation. This repository contains a 3D-UNet implementation introduced in [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://arxiv.org/pdf/1606.06650), with modifications described in [No New-Net](https://arxiv.org/pdf/1809.10483).
This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results up to 2.3x 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
3D-UNet was first introduced by Olaf Ronneberger, Philip Fischer, and Thomas Brox in the paper: [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://arxiv.org/pdf/1606.06650). In this repository we host a 3D-UNet version adapted by Fabian Isensee et al. to brain tumor segmentation. 3D-UNet allows for seamless segmentation of 3D volumes, with high accuracy and performance, and can be adapted to solve many different segmentation problems.
The following figure shows the construction of the 3D-UNet model and its different components. 3D-UNet is composed of a contractive and an expanding path, that aims at building a bottleneck in its centermost part through a combination of convolution and pooling operations. After this bottleneck, the image is reconstructed through a combination of convolutions and upsampling. Skip connections are added with the goal of helping the backward flow of gradients in order to improve the training.
### Default configuration
3D-UNet consists of a contractive (left-side) and expanding (right-side) path. It repeatedly applies unpadded convolutions followed by max pooling for downsampling. Every step in the expanding path consists of an upsampling of the feature maps and a concatenation with the correspondingly cropped feature map from the contractive path.
### Feature support matrix
The following features are supported by this model.
| **Feature** | **3D-UNet** |
|---------------------------------|-----|
| Automatic mixed precision (AMP) | Yes |
| Horovod Multi-GPU (NCCL) | Yes |
| Accelerated Linear Algebra (XLA)| Yes |
#### Features
**Automatic Mixed Precision (AMP)**
This implementation of 3D-UNet uses AMP to implement mixed precision training. Computation graphs can be modified by TensorFlow on runtime to support mixed precision training. Detailed explanation of mixed precision can be found in the next section.
**Horovod**
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).
Multi-GPU training with Horovod
Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the [TensorFlow tutorial](https://github.com/horovod/horovod/#usage).
**XLA support (experimental)**
XLA is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. The results are improvements in speed and memory usage: most internal benchmarks run ~1.1-1.5x faster after XLA is enabled.
### 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](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) previously required two steps:
1. Porting the model to use the FP16 data type where appropriate.
2. Adding loss scaling to preserve small gradient values.
This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally.
In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.
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.
- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.
#### Enabling mixed precision
Mixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which 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 TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.
To enable mixed precision, you can simply add the values to the environmental variable inside your training script:
```
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
```
Exporting these variables ensures that loss scaling is performed correctly and automatically.
By supplying the `--amp` flag to the `main.py` script while training in FP32/TF32, the following variables are set to their correct value for mixed precision training:
```
if params.amp:
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
```
#### 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 3D-UNet model.
### Requirements
This repository contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:
- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
- TensorFlow 21.10-tf1-py3 [NGC container](https://ngc.nvidia.com/registry/nvidia-tensorflow)
- GPU-based architecture:
- [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
- [NVIDIA Turing](https://www.nvidia.com/en-us/geforce/turing/)
- [NVIDIA Ampere](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/dgx/user-guide/index.html#accessing_registry)
- [Running TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running)
For those unable to use the TensorFlow 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 3D-UNet model on the [Brain Tumor Segmentation 2019](https://www.med.upenn.edu/cbica/brats-2019/) dataset. These steps enable you to build the 3D-UNet TensorFlow NGC container, train and evaluate your model, and generate predictions on the test data. For the specifics concerning training and inference, see the [Advanced](#advanced) section.
1. Clone the repository.
Executing this command will create your local repository with all the code to run 3D-UNet.
```bash
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/TensorFlow/Segmentation/U-Net3D_TF
2. Build the U-Net TensorFlow NGC container.
This command will use the `Dockerfile` to create a Docker image named `unet3d_tf`, downloading all the required components automatically.
```bash
docker build -t unet3d_tf .
```
The NGC container contains all the components optimized for usage on NVIDIA hardware.
3. Start an interactive session in the NGC container to run preprocessing/training/inference.
The following command will launch the container and mount the `./data` directory as a volume to the `/data` directory inside the container, and `./results` directory to the `/results` directory in the container.
```bash
mkdir data
mkdir results
docker run --runtime=nvidia -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --rm --ipc=host -v ${PWD}/data:/data -v ${PWD}/results:/results unet3d_tf:latest /bin/bash
```
Any datasets and experiment results (logs, checkpoints, etc.) saved to `/data` or `/results` will be accessible
in the `./data` or `./results` directory on the host, respectively.
4. Download and pre-process the data.
Data can be obtained by registering on [Brain Tumor Segmentation 2019 dataset](https://www.med.upenn.edu/cbica/brats-2019/) website. The data should be downloaded and placed where `/data` in the container is mounted. The `dataset/preprocess_data.py` script will convert the raw data into tfrecord format used for training and evaluation.
The script can be launched as
```python
python dataset/preprocess_data.py -i /data/<name/of/the/raw/data/folder> -o /data/<name/of/the/preprocessed/data/folder> -v
```
5. Start training.
After the Docker container is launched, the training of a single fold (fold 0) with the [default hyperparameters](#default-parameters) (for example 1/8 GPUs TF-AMP/FP32/TF32) can be started with:
```bash
bash scripts/unet3d_train_single{_TF-AMP}.sh <number/of/gpus> <path/to/dataset> <path/to/checkpoint> <batch/size>
```
For example, to run with 32-bit precision (FP32 or TF32) with batch size 2 on 1 GPU, simply use:
```bash
bash scripts/unet3d_train_single.sh 1 /data/preprocessed /results 2
```
to train a single fold with mixed precision (TF-AMP) with on 8 GPUs batch size 2 per GPU, use:
```bash
bash scripts/unet3d_train_single_TF-AMP.sh 8 /data/preprocessed /results 2
```
The obtained dice scores will be reported after the training has finished.
6. Start benchmarking.
The training performance can be evaluated by using benchmarking scripts, such as:
```bash
bash scripts/unet3d_{train,infer}_benchmark{_TF-AMP}.sh <number/of/gpus/for/training> <path/to/dataset> <path/to/checkpoint> <batch/size>
```
which will make the model run and report the performance. For example, to benchmark training with TF-AMP with batch size 2 on 4 GPUs, use:
```bash
bash scripts/unet3d_train_benchmark_TF-AMP.sh 4 /data/preprocessed /results 2
```
to obtain inference performance with 32-bit precision (FP32 or TF32) with batch size 1, use:
```bash
bash scripts/unet3d_infer_benchmark.sh /data/preprocessed /results 1
```
## 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:
* `main.py`: Serves as the entry point to the application. Encapsulates the training routine.
* `Dockerfile`: Container with the basic set of dependencies to run U-Net.
* `requirements.txt`: Set of extra requirements for running U-Net.
* `preprocess_data.py`: Converts the dataset to tfrecord format for training.
The `dataset/` folder contains the necessary tools to train and perform inference using U-Net. Its main components are:
* `data_loader.py`: Implements the data loading and augmentation.
* `transforms.py`: Implements the data augmentation functions.
* `preprocess_data.py`: Implements the data conversion and pre-processing functionality.
The `runtime/` folder contains scripts with training and inference logic. Its contents are:
* `arguments.py`: Implements the command-line arguments parsing.
* `hooks.py`: Collects different metrics to be used for benchmarking and testing.
* `parse_results.py`: Defines a set of functions used for parsing the partial results.
* `setup.py`: Defines a set of functions to set the environment up.
The `model/` folder contains information about the building blocks of 3D-UNet and the way they are assembled. Its contents are:
* `layers.py`: Defines the different blocks that are used to assemble 3D-UNet.
* `losses.py`: Defines the different losses used during training and evaluation.
* `model_fn.py`: Defines the computational graph to optimize.
* `unet3d.py`: Defines the model architecture using the blocks from the `layers.py` file.
Other folders included in the root directory are:
* `scripts/`: Provides examples for training and benchmarking U-Net
* `images/`: Contains the model diagram
### Parameters
The complete list of the available parameters for the main.py script contains:
* `--exec_mode`: Select the execution mode to run the model (default: `train`). Modes available:
* `train` - trains a model and stores checkpoints in the directory passed using `--model_dir`
* `evaluate` - loads checkpoint (if available) and performs evaluation on validation subset (requires `--fold` other than `None`).
* `train_and_evaluate` - trains model from scratch and performs validation at the end (requires `--fold` other than `None`).
* `predict` - loads checkpoint (if available) and runs inference on the test set. Stores the results in the `--model_dir` directory.
* `train_and_predict` - trains model from scratch and performs inference.
* `--model_dir`: Set the output directory for information related to the model (default: `/results`).
* `--log_dir`: Set the output directory for logs (default: None).
* `--data_dir`: Set the input directory containing the dataset (default: `None`).
* `--batch_size`: Size of each minibatch per GPU (default: `1`).
* `--fold`: Selected fold for cross-validation (default: `None`).
* `--max_steps`: Maximum number of steps (batches) for training (default: `16000`).
* `--seed`: Set random seed for reproducibility (default: `0`).
* `--log_every`: Log performance every n steps (default: `100`).
* `--learning_rate`: Model’s learning rate (default: `0.0002`).
* `--augment`: Enable data augmentation (disabled by default).
* `--benchmark`: Enable performance benchmarking (disabled by default). If the flag is set, the script runs in a benchmark mode - each iteration is timed and the performance result (in images per second) is printed at the end. Works for both `train` and `predict` execution modes.
* `--warmup_steps`: Used during benchmarking - the number of steps to skip (default: `200`). First iterations are usually much slower since the graph is being constructed. Skipping the initial iterations is required for a fair performance assessment.
* `--resume_training`: Whether to resume training from a checkpoint, if there is one (disabled by default)
* `--xla`: Enable accelerated linear algebra optimization (disabled by default).
* `--amp`: Enable automatic mixed precision (disabled by default).
### Command line options
To see the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:
```bash
python main.py --help
```
The following example output is printed when running the model:
```python main.py --help
usage: main.py [-h] --data_dir DATA_DIR --model_dir MODEL_DIR
[--exec_mode {train,evaluate,train_and_evaluate,predict}]
[--benchmark] [--max_steps MAX_STEPS]
[--learning_rate LEARNING_RATE] [--log_every LOG_EVERY]
[--log_dir LOG_DIR] [--loss {dice,ce,dice+ce}]
[--warmup_steps WARMUP_STEPS][--resume_training]
[--augment] [--batch_size BATCH_SIZE] [--fold FOLD]
[--amp] [--xla]
UNet-3D
optional arguments:
-h, --help show this help message and exit
--model_dir MODEL_DIR
Output directory for information related to the model
--data_dir DATA_DIR Input directory containing the dataset for training
the model
--exec_mode {train,train_and_predict,predict,evaluate,train_and_evaluate}
Execution mode of running the model
--log_dir LOG_DIR Output directory for training logs
--batch_size BATCH_SIZE
Size of each minibatch per GPU
--learning_rate LEARNING_RATE
Learning rate coefficient for AdamOptimizer
--fold Fold number
Chosen fold for cross-validation. Use None to disable
cross-validation
--max_steps MAX_STEPS
Maximum number of steps (batches) used for training
--log_every LOG_EVERY
Log performance every n steps
--warmup_steps WARMUP_STEPS
Number of warmup steps
--resume_training Whether to resume training from the checkpoint
--seed SEED Random seed
--augment Perform data augmentation during training
--benchmark Collect performance metrics during training
--amp Train using TF-AMP
--xla Train using XLA
```
### Getting the data
The 3D-UNet model was trained in the [Brain Tumor Segmentation 2019 dataset](https://www.med.upenn.edu/cbica/brats-2019/). Test images provided by the organization were used to produce the resulting masks for submission. Upon registration, the challenge's data is made available through the https//ipp.cbica.upenn.edu service.
The dataset consists of 335 240x240x155 `nifti` volumes. Each volume is represented by 4 modalities and a corresponding segmentation mask.
The modalities are:
* Native T1-weighted (T1),
* Post-contrast T1-weighted (T1Gd),
* Native T2-weighted (T2),
* T2 Fluid Attenuated Inversion Recovery (FLAIR).
Each voxel in a segmentation mask belongs to one of four classes:
* 0 corresponds to healthy tissue or background,
* 1 indicates the presence of the necrotic and non-enhancing tumor core (TC),
* 2 indicates the presence of the peritumoral edema (ED),
* 4 indicates the presence of the GD-enhancing tumor (ET).
The objective is to produce a set of masks that segment the data as accurately as possible. The results are expected to be submitted as a 12-bit `nifti` 3D image, with values corresponding to the underlying class.
#### Dataset guidelines
The training and test datasets are given as 3D `nifti` volumes that can be read using the Nibabel library and NumPy (both packages are installed by the `Dockerfile`).
Initially, all modalities are loaded, stacked and converted into 240x240x155x4 NumPy arrays using Nibabel. To decrease the size of the dataset, each volume is clipped to 85% of the maximal value, normalized to 255 for each modality separately, casted to 8-bit, grouped by 4 volumes, and saved as a `tfrecord` file. The process of converting from `nifti` to `tfrecord` can be found in the `preprocess_data.py` script.
The `tfrecord` files are fed to the model through `tf.data.TFRecordDataset()` to achieve high performance.
The foreground voxel intensities then z-score normalized, whereas labels are one-hot encoded for their later use in dice or pixel-wise cross-entropy loss, becoming 240x240x155x4 tensors.
If augmentation is enabled, the following set of augmentation techniques are applied:
* Random horizontal flipping
* Random 128x128x128x4 crop
* Random brightness shifting
In addition, random vertical flip and random gamma correction augmentations were implemented, but are not used. The process of loading, normalizing and augmenting the data contained in the dataset can be found in the `data_loader.py` script.
#### Multi-dataset
This implementation is tuned for the Brain Tumor Segmentation 2019 dataset. Using other datasets is possible, but might require changes to the code (data loader) and tuning some hyperparameters (e.g. learning rate, number of iterations).
In the current implementation, the data loader works with tfrecord files. It should work seamlessly with any dataset containing 3D data stored in tfrecord format, as long as features (with corresponding mean and standard deviation) and labels are stored as bytestream in the same file as `X`, `Y`, `mean`, and `stdev`. See the data pre-processing script for details. If your data is stored in a different format, you will have to modify the parsing function in the `dataset/data_loader.py` file. For a walk-through, check the [TensorFlow tf.data API guide](https://www.tensorflow.org/guide/data_performance)
## Training process
The model trains for a total 16,000 (16,000 / number of GPUs) iterations for each fold, with the default 3D-UNet setup:
* Adam optimizer with learning rate of 0.0002.
* Training and evaluation batch size of 2.
This default parametrization is applied when running scripts from the `./examples` directory and when running `main.py` without explicitly overriding these parameters. By default, the training is in full precision. To enable AMP, pass the `--amp` flag. AMP can be enabled for every mode of execution.
The default configuration minimizes a function _L = 1 - DICE + cross entropy_ during training and reports achieved convergence as dice score per class, mean dice score, and dice score for whole tumor vs background. The training with a combination of dice and cross entropy has been proven to achieve better convergence than a training using only dice.
The training can be run directly without using the predefined scripts. The name of the training script is `main.py`. Because of the multi-GPU support, training should always be run with the Horovod distributed launcher like this:
```bash
horovodrun -np <number/of/gpus> python main.py --data_dir /data/preprocessed --exec_mode train [other parameters]
```
*Note:* When calling the `main.py` script manually, data augmentation is disabled. In order to enable data augmentation, use the `--augment` flag in your invocation.
The main result of the training are checkpoints stored by default in `./results/` directory on the host machine, and in the `/results` directory in the container. This location can be controlled
by the `--model_dir` command-line argument, if a different location was mounted while starting the container. In the case when the training is run in `train_and_predict` mode, the inference will take place after the training is finished, and inference results will be stored to the `/results` directory.
If the `--exec_mode train_and_evaluate` parameter was used, and if `--fold` parameter is set to an integer value of {0, 1, 2, 3, 4}, the evaluation of the validation set takes place after the training is completed. The results of the evaluation will be printed to the console.
### Inference process
Inference can be launched with the same script used for training by passing the `--exec_mode predict` flag:
```bash
python main.py --exec_mode predict --data_dir /data/preprocessed --model_dir <path/to/checkpoint> [other parameters]
```
The script will then:
* Load the checkpoint from the directory specified by the `<path/to/checkpoint>` directory (`/results`),
* Run inference on the test dataset,
* Save the resulting masks in the `numpy` format in the `--model_dir` 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
The following section shows how to run benchmarks measuring the model performance in training and inference modes.
#### Training performance benchmark
To benchmark training, run one of the `train_benchmark` scripts in `./scripts/`:
```bash
bash scripts/unet3d_train_benchmark{_TF-AMP}.sh <num/of/gpus> <path/to/dataset> <path/to/checkpoints> <batch/size>
```
For example, to benchmark training using mixed-precision on 4 GPUs with batch size of 2 use:
```bash
bash scripts/unet3d_train_benchmark_TF-AMP.sh 4 <path/to/dataset> <path/to/checkpoints> 2
```
Each of these scripts will by default run 40 warm-up iterations and benchmark the performance during training in the next 40 iterations.
To have more control, you can run the script by directly providing all relevant run parameters. For example:
```bash
horovodrun -np <num/of/gpus> python main.py --exec_mode train --benchmark --augment --data_dir <path/to/dataset> --model_dir <path/to/checkpoints> --batch_size <batch/size> --warmup_steps <warm-up/steps> --max_steps <max/steps>
```
At the end of the script, a line reporting the best train throughput will be printed.
#### Inference performance benchmark
To benchmark inference, run one of the scripts in `./scripts/`:
```bash
bash scripts/unet3d_infer_benchmark{_TF-AMP}.sh <path/to/dataset> <path/to/checkpoints> <batch/size>
```
For example, to benchmark inference using mixed-precision with batch size 4:
```bash
bash scripts/unet3d_infer_benchmark_TF-AMP.sh <path/to/dataset> <path/to/checkpoints> 4
```
Each of these scripts will by default run 20 warm-up iterations and benchmark the performance during inference in the next 20 iterations.
To have more control, you can run the script by directly providing all relevant run parameters. For example:
```bash
python main.py --exec_mode predict --benchmark --data_dir <path/to/dataset> --model_dir <optional, path/to/checkpoint> --batch_size <batch/size> --warmup_steps <warm-up/steps> --max_steps <max/steps>
```
At the end of the script, a line reporting the best inference throughput will be printed.
### Results
The following sections provide details on how we achieved our performance and accuracy of training and inference.
#### Training accuracy results
To reproduce this result, start the Docker container interactively and run one of the train scripts:
```bash
bash scripts/unet3d_train_full{_TF-AMP}.sh <num/of/gpus> <path/to/dataset> <path/to/checkpoint> <batch/size>
```
for example to train using 8 GPUs and batch size of 2:
```bash
bash scripts/unet3d_train_full_TF-AMP.sh 8 /data/preprocessed /results 2
```
This command will launch a script which will run 5-fold cross-validation training for 16,000 iterations on each fold and print:
* the validation DICE scores for each class: Tumor Core (TC), Peritumoral Edema (ED), Enhancing Tumor (ET),
* the mean DICE score,
* the whole tumor (WT) which represents a binary classification case (tumor vs background).
The time reported is for one fold, which means that the training of 5 folds will take 5 times longer. The default batch size is 2, however if you have less than 16 GB memory card and you encounter GPU memory issues you should decrease the batch size. The logs of the runs can be found in the `/results` directory once the script is finished.
##### Training accuracy: NVIDIA DGX A100 (8x A100 80G)
The following table lists the average DICE score across 5-fold cross-validation. Our results were obtained by running the `scripts/unet3d_train_full{_TF-AMP}.sh` training script in the `tensorflow:21.10-tf1-py3` NGC container on NVIDIA DGX A100 (8x A100 80G) GPUs.
| GPUs | Batch size / GPU | DICE - TF32 | DICE - mixed precision | Time to train - FP32 | Time to train - mixed precision | Time to train speedup (FP32 to mixed precision) |
|---|---|--------|--------|--------|--------|------|
| 8 | 2 | 0.8818 | 0.8819 | 8 min | 7 min | 1.14 |
##### Training accuracy: NVIDIA DGX-1 (8x V100 16G)
The following table lists the average DICE score across 5-fold cross-validation. Our results were obtained by running the `scripts/unet3d_train_full{_TF-AMP}.sh` training script in the `tensorflow:21.10-tf1-py3` NGC container on NVIDIA DGX-1 (8x V100 16G) GPUs.
| GPUs | Batch size / GPU | DICE - FP32 | DICE - mixed precision | Time to train - FP32 | Time to train - mixed precision | Time to train speedup (FP32 to mixed precision) |
|---|---|--------|--------|--------|--------|------|
| 8 | 2 | 0.8818 | 0.8819 | 33 min | 13 min | 2.54 |
#### Training performance results
##### Training performance: NVIDIA DGX A100 (8x A100 80G)
Our results were obtained by running the `scripts/unet3d_train_benchmark{_TF-AMP}.sh` training script in the `tensorflow:21.10-tf1-py3` NGC container on NVIDIA DGX A100 with (8x A100 80G) GPUs. Performance numbers (in volumes per second) were averaged over 80 iterations, excluding the first 40 warm-up steps.
| GPUs | Batch size / GPU | Throughput - TF32 [img/s] | Throughput - mixed precision [img/s] | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision |
|---|---|--------|--------|------|------|------|
| 1 | 2 | 10.40 | 17.91 | 1.72 | N/A | N/A |
| 1 | 4 | 10.66 | 19.88 | 1.86 | N/A | N/A |
| 1 | 8 | 3.99 | 20.89 | 5.23 | N/A | N/A |
| 8 | 2 | 81.71 | 100.24 | 1.23 | 7.85 | 5.60 |
| 8 | 4 | 80.65 | 140.44 | 1.74 | 7.56 | 7.06 |
| 8 | 8 | 29.79 | 137.61 | 4.62 | 7.47 | 6.59 |
##### Training performance: NVIDIA DGX-1 (8x V100 16G)
Our results were obtained by running the `scripts/unet3d_train_benchmark{_TF-AMP}.sh` training script in the `tensorflow:21.10-tf1-py3` NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. Performance numbers (in volumes per second) were averaged over 80 iterations, excluding the first 40 warm-up steps.
| GPUs | Batch size / GPU | Throughput - FP32 [img/s] | Throughput - mixed precision [img/s] | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision |
|---|---|-------|-------|------|------|------|
| 1 | 1 | 1.87 | 7.45 | 3.98 | N/A | N/A |
| 1 | 2 | 2.32 | 8.79 | 3.79 | N/A | N/A |
| 8 | 1 | 14.49 | 46.88 | 3.23 | 7.75 | 6.29 |
| 8 | 2 | 18.06 | 58.30 | 3.23 | 7.78 | 6.63 |
To achieve these same results, follow the steps in the [Training performance benchmark](#training-performance-benchmark) section.
#### Inference performance results
##### Inference performance: NVIDIA DGX A100 (1x A100 80G)
Our results were obtained by running the `scripts/unet3d_infer_benchmark{_TF-AMP}.sh` inference benchmarking script in the `tensorflow:21.10-tf1-py3` NGC container on NVIDIA DGX A100 with (1x A100 80G) GPU. Performance numbers (in volumes per second) were averaged over 40 iterations, excluding the first 20 warm-up steps.
FP16
| Batch size | Resolution | Throughput Avg [img/s] | Latency Avg [ms] | Latency 90% [ms] | Latency 95% [ms] | Latency 99% [ms] |
|---|---------------|-------|--------|--------|--------|--------|
| 1 | 224x224x160x4 | 15.58 | 67.32 | 68.63 | 78.00 | 109.42 |
| 2 | 224x224x160x4 | 15.81 | 129.06 | 129.93 | 135.31 | 166.62 |
| 4 | 224x224x160x4 | 8.34 | 479.47 | 482.55 | 487.68 | 494.80 |
TF32
| Batch size | Resolution | Throughput Avg [img/s] | Latency Avg [ms] | Latency 90% [ms] | Latency 95% [ms] | Latency 99% [ms] |
|---|---------------|-------|---------|---------|---------|---------|
| 1 | 224x224x160x4 | 9.42 | 106.22 | 106.68 | 107.67 | 122.73 |
| 2 | 224x224x160x4 | 4.69 | 427.13 | 428.33 | 428.76 | 429.19 |
| 4 | 224x224x160x4 | 2.32 | 1723.79 | 1725.77 | 1726.30 | 1728.23 |
To achieve these same results, follow the steps in the [Inference performance benchmark](#inference-performance-benchmark) section.
##### Inference performance: NVIDIA DGX-1 (1x V100 16G)
Our results were obtained by running the `scripts/unet3d_infer_benchmark{_TF-AMP}.sh` inference benchmarking script in the `tensorflow:21.10-tf1-py3` NGC container on NVIDIA DGX-1 with (1x V100 16G) GPU. Performance numbers (in volumes per second) were averaged over 40 iterations, excluding the first 20 warm-up steps.
FP16
| Batch size | Resolution | Throughput Avg [img/s] | Latency Avg [ms] | Latency 90% [ms] | Latency 95% [ms] | Latency 99% [ms] |
|---|---------------|------|--------|--------|--------|--------|
| 1 | 224x224x160x4 | 7.64 | 136.81 | 138.94 | 143.59 | 152.74 |
| 2 | 224x224x160x4 | 7.75 | 260.66 | 267.07 | 270.88 | 274.44 |
| 4 | 224x224x160x4 | 4.78 | 838.52 | 842.88 | 843.30 | 844.62 |
FP32
| Batch size | Resolution | Throughput Avg [img/s] | Latency Avg [ms] | Latency 90% [ms] | Latency 95% [ms] | Latency 99% [ms] |
|---|---------------|------|--------|--------|--------|--------|
| 1 | 224x224x160x4 | 2.30 | 434.95 | 436.82 | 437.40 | 438.48 |
| 2 | 224x224x160x4 | 2.40 | 834.99 | 837.22 | 837.51 | 838.18 |
| 4 | 224x224x160x4 | OOM | | | | |
To achieve these same results, follow the steps in the [Inference performance benchmark](#inference-performance-benchmark) section.
## Release notes
### Changelog
April 2023
* Ceased maintenance of this model in TensorFlow1
November 2021
* Updated README tables
June 2020
* Initial release
### Known issues
There are no known issues in this release.
|
PyTorch/SpeechRecognition/Jasper/triton/model_repo_configs/fp16/jasper-tensorrt-ensemble | jasper-tensorrt-ensemble | config | name: "jasper-tensorrt-ensemble"
platform: "ensemble"
max_batch_size: 8#MAX_BATCH
input {
name: "AUDIO_SIGNAL"
data_type: TYPE_FP16
dims: -1#AUDIO_LENGTH
}
input {
name: "NUM_SAMPLES"
data_type: TYPE_INT32
dims: [ 1 ]
}
output {
name: "TRANSCRIPT"
data_type: TYPE_INT32
dims: [-1]
}
ensemble_scheduling {
step {
model_name: "feature-extractor-ts-trace"
model_version: -1
input_map {
key: "input__0"
value: "AUDIO_SIGNAL"
}
input_map {
key: "input__1"
value: "NUM_SAMPLES"
}
output_map {
key: "output__0"
value: "AUDIO_FEATURES"
}
}
step {
model_name: "jasper-tensorrt"
model_version: -1
input_map {
key: "input__0"
value: "AUDIO_FEATURES"
}
output_map {
key: "output__0"
value: "CHARACTER_PROBABILITIES"
}
}
step {
model_name: "decoder-ts-script"
model_version: -1
input_map {
key: "input__0"
value: "CHARACTER_PROBABILITIES"
}
output_map {
key: "output__0"
value: "TRANSCRIPT"
}
}
}
|
TensorFlow2/Recommendation/DLRM_and_DCNv2/deployment/hps | hps | __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.
#
# author: Tomasz Grel (tgrel@nvidia.com)
from deployment.hps.constants import dense_model_name, hps_model_name
from deployment.hps.deploy_dense import deploy_dense
from deployment.hps.deploy_ensemble import deploy_ensemble
from deployment.hps.deploy_sparse import deploy_sparse
|
PyTorch/Translation/Transformer/fairseq/optim | optim | adagrad | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import torch.optim
from . import FairseqOptimizer, register_optimizer
@register_optimizer('adagrad')
class Adagrad(FairseqOptimizer):
def __init__(self, args, params):
super().__init__(args, params)
self._optimizer = torch.optim.Adagrad(params, **self.optimizer_config)
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
'lr': self.args.lr[0],
'weight_decay': self.args.weight_decay,
}
|
TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures | meta_architectures | faster_rcnn_meta_arch_test_lib | # 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.meta_architectures.faster_rcnn_meta_arch."""
import functools
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from google.protobuf import text_format
from object_detection.anchor_generators import grid_anchor_generator
from object_detection.builders import box_predictor_builder
from object_detection.builders import hyperparams_builder
from object_detection.builders import post_processing_builder
from object_detection.core import balanced_positive_negative_sampler as sampler
from object_detection.core import losses
from object_detection.core import post_processing
from object_detection.core import target_assigner
from object_detection.meta_architectures import faster_rcnn_meta_arch
from object_detection.protos import box_predictor_pb2
from object_detection.protos import hyperparams_pb2
from object_detection.protos import post_processing_pb2
from object_detection.utils import ops
from object_detection.utils import test_case
from object_detection.utils import test_utils
slim = tf.contrib.slim
BOX_CODE_SIZE = 4
class FakeFasterRCNNFeatureExtractor(
faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
"""Fake feature extracture to use in tests."""
def __init__(self):
super(FakeFasterRCNNFeatureExtractor, self).__init__(
is_training=False,
first_stage_features_stride=32,
reuse_weights=None,
weight_decay=0.0)
def preprocess(self, resized_inputs):
return tf.identity(resized_inputs)
def _extract_proposal_features(self, preprocessed_inputs, scope):
with tf.variable_scope('mock_model'):
proposal_features = 0 * slim.conv2d(
preprocessed_inputs, num_outputs=3, kernel_size=1, scope='layer1')
return proposal_features, {}
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
with tf.variable_scope('mock_model'):
return 0 * slim.conv2d(proposal_feature_maps,
num_outputs=3, kernel_size=1, scope='layer2')
class FasterRCNNMetaArchTestBase(test_case.TestCase, parameterized.TestCase):
"""Base class to test Faster R-CNN and R-FCN meta architectures."""
def _build_arg_scope_with_hyperparams(self,
hyperparams_text_proto,
is_training):
hyperparams = hyperparams_pb2.Hyperparams()
text_format.Merge(hyperparams_text_proto, hyperparams)
return hyperparams_builder.build(hyperparams, is_training=is_training)
def _get_second_stage_box_predictor_text_proto(self):
box_predictor_text_proto = """
mask_rcnn_box_predictor {
fc_hyperparams {
op: FC
activation: NONE
regularizer {
l2_regularizer {
weight: 0.0005
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
"""
return box_predictor_text_proto
def _add_mask_to_second_stage_box_predictor_text_proto(
self, masks_are_class_agnostic=False):
agnostic = 'true' if masks_are_class_agnostic else 'false'
box_predictor_text_proto = """
mask_rcnn_box_predictor {
predict_instance_masks: true
masks_are_class_agnostic: """ + agnostic + """
mask_height: 14
mask_width: 14
conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
}
"""
return box_predictor_text_proto
def _get_second_stage_box_predictor(self, num_classes, is_training,
predict_masks, masks_are_class_agnostic):
box_predictor_proto = box_predictor_pb2.BoxPredictor()
text_format.Merge(self._get_second_stage_box_predictor_text_proto(),
box_predictor_proto)
if predict_masks:
text_format.Merge(
self._add_mask_to_second_stage_box_predictor_text_proto(
masks_are_class_agnostic),
box_predictor_proto)
return box_predictor_builder.build(
hyperparams_builder.build,
box_predictor_proto,
num_classes=num_classes,
is_training=is_training)
def _get_model(self, box_predictor, **common_kwargs):
return faster_rcnn_meta_arch.FasterRCNNMetaArch(
initial_crop_size=3,
maxpool_kernel_size=1,
maxpool_stride=1,
second_stage_mask_rcnn_box_predictor=box_predictor,
**common_kwargs)
def _build_model(self,
is_training,
number_of_stages,
second_stage_batch_size,
first_stage_max_proposals=8,
num_classes=2,
hard_mining=False,
softmax_second_stage_classification_loss=True,
predict_masks=False,
pad_to_max_dimension=None,
masks_are_class_agnostic=False,
use_matmul_crop_and_resize=False,
clip_anchors_to_image=False,
use_matmul_gather_in_matcher=False,
use_static_shapes=False):
def image_resizer_fn(image, masks=None):
"""Fake image resizer function."""
resized_inputs = []
resized_image = tf.identity(image)
if pad_to_max_dimension is not None:
resized_image = tf.image.pad_to_bounding_box(image, 0, 0,
pad_to_max_dimension,
pad_to_max_dimension)
resized_inputs.append(resized_image)
if masks is not None:
resized_masks = tf.identity(masks)
if pad_to_max_dimension is not None:
resized_masks = tf.image.pad_to_bounding_box(tf.transpose(masks,
[1, 2, 0]),
0, 0,
pad_to_max_dimension,
pad_to_max_dimension)
resized_masks = tf.transpose(resized_masks, [2, 0, 1])
resized_inputs.append(resized_masks)
resized_inputs.append(tf.shape(image))
return resized_inputs
# anchors in this test are designed so that a subset of anchors are inside
# the image and a subset of anchors are outside.
first_stage_anchor_scales = (0.001, 0.005, 0.1)
first_stage_anchor_aspect_ratios = (0.5, 1.0, 2.0)
first_stage_anchor_strides = (1, 1)
first_stage_anchor_generator = grid_anchor_generator.GridAnchorGenerator(
first_stage_anchor_scales,
first_stage_anchor_aspect_ratios,
anchor_stride=first_stage_anchor_strides)
first_stage_target_assigner = target_assigner.create_target_assigner(
'FasterRCNN',
'proposal',
use_matmul_gather=use_matmul_gather_in_matcher)
fake_feature_extractor = FakeFasterRCNNFeatureExtractor()
first_stage_box_predictor_hyperparams_text_proto = """
op: CONV
activation: RELU
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
}
}
"""
first_stage_box_predictor_arg_scope_fn = (
self._build_arg_scope_with_hyperparams(
first_stage_box_predictor_hyperparams_text_proto, is_training))
first_stage_box_predictor_kernel_size = 3
first_stage_atrous_rate = 1
first_stage_box_predictor_depth = 512
first_stage_minibatch_size = 3
first_stage_sampler = sampler.BalancedPositiveNegativeSampler(
positive_fraction=0.5, is_static=use_static_shapes)
first_stage_nms_score_threshold = -1.0
first_stage_nms_iou_threshold = 1.0
first_stage_max_proposals = first_stage_max_proposals
first_stage_non_max_suppression_fn = functools.partial(
post_processing.batch_multiclass_non_max_suppression,
score_thresh=first_stage_nms_score_threshold,
iou_thresh=first_stage_nms_iou_threshold,
max_size_per_class=first_stage_max_proposals,
max_total_size=first_stage_max_proposals,
use_static_shapes=use_static_shapes)
first_stage_localization_loss_weight = 1.0
first_stage_objectness_loss_weight = 1.0
post_processing_text_proto = """
batch_non_max_suppression {
score_threshold: -20.0
iou_threshold: 1.0
max_detections_per_class: 5
max_total_detections: 5
use_static_shapes: """ +'{}'.format(use_static_shapes) + """
}
"""
post_processing_config = post_processing_pb2.PostProcessing()
text_format.Merge(post_processing_text_proto, post_processing_config)
second_stage_target_assigner = target_assigner.create_target_assigner(
'FasterRCNN', 'detection',
use_matmul_gather=use_matmul_gather_in_matcher)
second_stage_non_max_suppression_fn, _ = post_processing_builder.build(
post_processing_config)
second_stage_sampler = sampler.BalancedPositiveNegativeSampler(
positive_fraction=1.0, is_static=use_static_shapes)
second_stage_score_conversion_fn = tf.identity
second_stage_localization_loss_weight = 1.0
second_stage_classification_loss_weight = 1.0
if softmax_second_stage_classification_loss:
second_stage_classification_loss = (
losses.WeightedSoftmaxClassificationLoss())
else:
second_stage_classification_loss = (
losses.WeightedSigmoidClassificationLoss())
hard_example_miner = None
if hard_mining:
hard_example_miner = losses.HardExampleMiner(
num_hard_examples=1,
iou_threshold=0.99,
loss_type='both',
cls_loss_weight=second_stage_classification_loss_weight,
loc_loss_weight=second_stage_localization_loss_weight,
max_negatives_per_positive=None)
crop_and_resize_fn = (
ops.matmul_crop_and_resize
if use_matmul_crop_and_resize else ops.native_crop_and_resize)
common_kwargs = {
'is_training': is_training,
'num_classes': num_classes,
'image_resizer_fn': image_resizer_fn,
'feature_extractor': fake_feature_extractor,
'number_of_stages': number_of_stages,
'first_stage_anchor_generator': first_stage_anchor_generator,
'first_stage_target_assigner': first_stage_target_assigner,
'first_stage_atrous_rate': first_stage_atrous_rate,
'first_stage_box_predictor_arg_scope_fn':
first_stage_box_predictor_arg_scope_fn,
'first_stage_box_predictor_kernel_size':
first_stage_box_predictor_kernel_size,
'first_stage_box_predictor_depth': first_stage_box_predictor_depth,
'first_stage_minibatch_size': first_stage_minibatch_size,
'first_stage_sampler': first_stage_sampler,
'first_stage_non_max_suppression_fn':
first_stage_non_max_suppression_fn,
'first_stage_max_proposals': first_stage_max_proposals,
'first_stage_localization_loss_weight':
first_stage_localization_loss_weight,
'first_stage_objectness_loss_weight':
first_stage_objectness_loss_weight,
'second_stage_target_assigner': second_stage_target_assigner,
'second_stage_batch_size': second_stage_batch_size,
'second_stage_sampler': second_stage_sampler,
'second_stage_non_max_suppression_fn':
second_stage_non_max_suppression_fn,
'second_stage_score_conversion_fn': second_stage_score_conversion_fn,
'second_stage_localization_loss_weight':
second_stage_localization_loss_weight,
'second_stage_classification_loss_weight':
second_stage_classification_loss_weight,
'second_stage_classification_loss':
second_stage_classification_loss,
'hard_example_miner': hard_example_miner,
'crop_and_resize_fn': crop_and_resize_fn,
'clip_anchors_to_image': clip_anchors_to_image,
'use_static_shapes': use_static_shapes,
'resize_masks': True,
}
return self._get_model(
self._get_second_stage_box_predictor(
num_classes=num_classes,
is_training=is_training,
predict_masks=predict_masks,
masks_are_class_agnostic=masks_are_class_agnostic), **common_kwargs)
def test_predict_gives_correct_shapes_in_inference_mode_first_stage_only(
self, use_static_shapes=False):
batch_size = 2
height = 10
width = 12
input_image_shape = (batch_size, height, width, 3)
def graph_fn(images):
"""Function to construct tf graph for the test."""
model = self._build_model(
is_training=False,
number_of_stages=1,
second_stage_batch_size=2,
clip_anchors_to_image=use_static_shapes,
use_static_shapes=use_static_shapes)
preprocessed_inputs, true_image_shapes = model.preprocess(images)
prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
return (prediction_dict['rpn_box_predictor_features'],
prediction_dict['rpn_features_to_crop'],
prediction_dict['image_shape'],
prediction_dict['rpn_box_encodings'],
prediction_dict['rpn_objectness_predictions_with_background'],
prediction_dict['anchors'])
images = np.zeros(input_image_shape, dtype=np.float32)
# In inference mode, anchors are clipped to the image window, but not
# pruned. Since MockFasterRCNN.extract_proposal_features returns a
# tensor with the same shape as its input, the expected number of anchors
# is height * width * the number of anchors per location (i.e. 3x3).
expected_num_anchors = height * width * 3 * 3
expected_output_shapes = {
'rpn_box_predictor_features': (batch_size, height, width, 512),
'rpn_features_to_crop': (batch_size, height, width, 3),
'rpn_box_encodings': (batch_size, expected_num_anchors, 4),
'rpn_objectness_predictions_with_background':
(batch_size, expected_num_anchors, 2),
'anchors': (expected_num_anchors, 4)
}
if use_static_shapes:
results = self.execute(graph_fn, [images])
else:
results = self.execute_cpu(graph_fn, [images])
self.assertAllEqual(results[0].shape,
expected_output_shapes['rpn_box_predictor_features'])
self.assertAllEqual(results[1].shape,
expected_output_shapes['rpn_features_to_crop'])
self.assertAllEqual(results[2],
input_image_shape)
self.assertAllEqual(results[3].shape,
expected_output_shapes['rpn_box_encodings'])
self.assertAllEqual(
results[4].shape,
expected_output_shapes['rpn_objectness_predictions_with_background'])
self.assertAllEqual(results[5].shape,
expected_output_shapes['anchors'])
# Check that anchors are clipped to window.
anchors = results[5]
self.assertTrue(np.all(np.greater_equal(anchors, 0)))
self.assertTrue(np.all(np.less_equal(anchors[:, 0], height)))
self.assertTrue(np.all(np.less_equal(anchors[:, 1], width)))
self.assertTrue(np.all(np.less_equal(anchors[:, 2], height)))
self.assertTrue(np.all(np.less_equal(anchors[:, 3], width)))
def test_predict_gives_valid_anchors_in_training_mode_first_stage_only(self):
test_graph = tf.Graph()
with test_graph.as_default():
model = self._build_model(
is_training=True, number_of_stages=1, second_stage_batch_size=2)
batch_size = 2
height = 10
width = 12
input_image_shape = (batch_size, height, width, 3)
_, true_image_shapes = model.preprocess(tf.zeros(input_image_shape))
preprocessed_inputs = tf.placeholder(
dtype=tf.float32, shape=(batch_size, None, None, 3))
prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
expected_output_keys = set([
'rpn_box_predictor_features', 'rpn_features_to_crop', 'image_shape',
'rpn_box_encodings', 'rpn_objectness_predictions_with_background',
'anchors'])
# At training time, anchors that exceed image bounds are pruned. Thus
# the `expected_num_anchors` in the above inference mode test is now
# a strict upper bound on the number of anchors.
num_anchors_strict_upper_bound = height * width * 3 * 3
init_op = tf.global_variables_initializer()
with self.test_session(graph=test_graph) as sess:
sess.run(init_op)
prediction_out = sess.run(prediction_dict,
feed_dict={
preprocessed_inputs:
np.zeros(input_image_shape)
})
self.assertEqual(set(prediction_out.keys()), expected_output_keys)
self.assertAllEqual(prediction_out['image_shape'], input_image_shape)
# Check that anchors have less than the upper bound and
# are clipped to window.
anchors = prediction_out['anchors']
self.assertTrue(len(anchors.shape) == 2 and anchors.shape[1] == 4)
num_anchors_out = anchors.shape[0]
self.assertLess(num_anchors_out, num_anchors_strict_upper_bound)
self.assertTrue(np.all(np.greater_equal(anchors, 0)))
self.assertTrue(np.all(np.less_equal(anchors[:, 0], height)))
self.assertTrue(np.all(np.less_equal(anchors[:, 1], width)))
self.assertTrue(np.all(np.less_equal(anchors[:, 2], height)))
self.assertTrue(np.all(np.less_equal(anchors[:, 3], width)))
self.assertAllEqual(prediction_out['rpn_box_encodings'].shape,
(batch_size, num_anchors_out, 4))
self.assertAllEqual(
prediction_out['rpn_objectness_predictions_with_background'].shape,
(batch_size, num_anchors_out, 2))
def test_predict_correct_shapes_in_inference_mode_two_stages(
self, use_static_shapes=False):
def compare_results(results, expected_output_shapes):
"""Checks if the shape of the predictions are as expected."""
self.assertAllEqual(results[0].shape,
expected_output_shapes['rpn_box_predictor_features'])
self.assertAllEqual(results[1].shape,
expected_output_shapes['rpn_features_to_crop'])
self.assertAllEqual(results[2].shape,
expected_output_shapes['image_shape'])
self.assertAllEqual(results[3].shape,
expected_output_shapes['rpn_box_encodings'])
self.assertAllEqual(
results[4].shape,
expected_output_shapes['rpn_objectness_predictions_with_background'])
self.assertAllEqual(results[5].shape,
expected_output_shapes['anchors'])
self.assertAllEqual(results[6].shape,
expected_output_shapes['refined_box_encodings'])
self.assertAllEqual(
results[7].shape,
expected_output_shapes['class_predictions_with_background'])
self.assertAllEqual(results[8].shape,
expected_output_shapes['num_proposals'])
self.assertAllEqual(results[9].shape,
expected_output_shapes['proposal_boxes'])
self.assertAllEqual(results[10].shape,
expected_output_shapes['proposal_boxes_normalized'])
self.assertAllEqual(results[11].shape,
expected_output_shapes['box_classifier_features'])
batch_size = 2
image_size = 10
max_num_proposals = 8
initial_crop_size = 3
maxpool_stride = 1
input_shapes = [(batch_size, image_size, image_size, 3),
(None, image_size, image_size, 3),
(batch_size, None, None, 3),
(None, None, None, 3)]
def graph_fn_tpu(images):
"""Function to construct tf graph for the test."""
model = self._build_model(
is_training=False,
number_of_stages=2,
second_stage_batch_size=2,
predict_masks=False,
use_matmul_crop_and_resize=use_static_shapes,
clip_anchors_to_image=use_static_shapes,
use_static_shapes=use_static_shapes)
preprocessed_inputs, true_image_shapes = model.preprocess(images)
prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
return (prediction_dict['rpn_box_predictor_features'],
prediction_dict['rpn_features_to_crop'],
prediction_dict['image_shape'],
prediction_dict['rpn_box_encodings'],
prediction_dict['rpn_objectness_predictions_with_background'],
prediction_dict['anchors'],
prediction_dict['refined_box_encodings'],
prediction_dict['class_predictions_with_background'],
prediction_dict['num_proposals'],
prediction_dict['proposal_boxes'],
prediction_dict['proposal_boxes_normalized'],
prediction_dict['box_classifier_features'])
expected_num_anchors = image_size * image_size * 3 * 3
expected_shapes = {
'rpn_box_predictor_features':
(2, image_size, image_size, 512),
'rpn_features_to_crop': (2, image_size, image_size, 3),
'image_shape': (4,),
'rpn_box_encodings': (2, expected_num_anchors, 4),
'rpn_objectness_predictions_with_background':
(2, expected_num_anchors, 2),
'anchors': (expected_num_anchors, 4),
'refined_box_encodings': (2 * max_num_proposals, 2, 4),
'class_predictions_with_background': (2 * max_num_proposals, 2 + 1),
'num_proposals': (2,),
'proposal_boxes': (2, max_num_proposals, 4),
'proposal_boxes_normalized': (2, max_num_proposals, 4),
'box_classifier_features':
self._get_box_classifier_features_shape(image_size,
batch_size,
max_num_proposals,
initial_crop_size,
maxpool_stride,
3)
}
if use_static_shapes:
input_shape = (batch_size, image_size, image_size, 3)
images = np.zeros(input_shape, dtype=np.float32)
results = self.execute(graph_fn_tpu, [images])
compare_results(results, expected_shapes)
else:
for input_shape in input_shapes:
test_graph = tf.Graph()
with test_graph.as_default():
model = self._build_model(
is_training=False,
number_of_stages=2,
second_stage_batch_size=2,
predict_masks=False)
preprocessed_inputs = tf.placeholder(tf.float32, shape=input_shape)
_, true_image_shapes = model.preprocess(preprocessed_inputs)
result_tensor_dict = model.predict(
preprocessed_inputs, true_image_shapes)
init_op = tf.global_variables_initializer()
with self.test_session(graph=test_graph) as sess:
sess.run(init_op)
tensor_dict_out = sess.run(result_tensor_dict, feed_dict={
preprocessed_inputs:
np.zeros((batch_size, image_size, image_size, 3))})
self.assertEqual(set(tensor_dict_out.keys()),
set(expected_shapes.keys()))
for key in expected_shapes:
self.assertAllEqual(tensor_dict_out[key].shape, expected_shapes[key])
def test_predict_gives_correct_shapes_in_train_mode_both_stages(
self,
use_static_shapes=False):
batch_size = 2
image_size = 10
max_num_proposals = 7
initial_crop_size = 3
maxpool_stride = 1
def graph_fn(images, gt_boxes, gt_classes, gt_weights):
"""Function to construct tf graph for the test."""
model = self._build_model(
is_training=True,
number_of_stages=2,
second_stage_batch_size=7,
predict_masks=False,
use_matmul_crop_and_resize=use_static_shapes,
clip_anchors_to_image=use_static_shapes,
use_static_shapes=use_static_shapes)
preprocessed_inputs, true_image_shapes = model.preprocess(images)
model.provide_groundtruth(
groundtruth_boxes_list=tf.unstack(gt_boxes),
groundtruth_classes_list=tf.unstack(gt_classes),
groundtruth_weights_list=tf.unstack(gt_weights))
result_tensor_dict = model.predict(preprocessed_inputs, true_image_shapes)
return (result_tensor_dict['refined_box_encodings'],
result_tensor_dict['class_predictions_with_background'],
result_tensor_dict['proposal_boxes'],
result_tensor_dict['proposal_boxes_normalized'],
result_tensor_dict['anchors'],
result_tensor_dict['rpn_box_encodings'],
result_tensor_dict['rpn_objectness_predictions_with_background'],
result_tensor_dict['rpn_features_to_crop'],
result_tensor_dict['rpn_box_predictor_features'],
)
image_shape = (batch_size, image_size, image_size, 3)
images = np.zeros(image_shape, dtype=np.float32)
gt_boxes = np.stack([
np.array([[0, 0, .5, .5], [.5, .5, 1, 1]], dtype=np.float32),
np.array([[0, .5, .5, 1], [.5, 0, 1, .5]], dtype=np.float32)
])
gt_classes = np.stack([
np.array([[1, 0], [0, 1]], dtype=np.float32),
np.array([[1, 0], [1, 0]], dtype=np.float32)
])
gt_weights = np.stack([
np.array([1, 1], dtype=np.float32),
np.array([1, 1], dtype=np.float32)
])
if use_static_shapes:
results = self.execute(graph_fn,
[images, gt_boxes, gt_classes, gt_weights])
else:
results = self.execute_cpu(graph_fn,
[images, gt_boxes, gt_classes, gt_weights])
expected_shapes = {
'rpn_box_predictor_features': (2, image_size, image_size, 512),
'rpn_features_to_crop': (2, image_size, image_size, 3),
'refined_box_encodings': (2 * max_num_proposals, 2, 4),
'class_predictions_with_background': (2 * max_num_proposals, 2 + 1),
'proposal_boxes': (2, max_num_proposals, 4),
'rpn_box_encodings': (2, image_size * image_size * 9, 4),
'proposal_boxes_normalized': (2, max_num_proposals, 4),
'box_classifier_features':
self._get_box_classifier_features_shape(
image_size, batch_size, max_num_proposals, initial_crop_size,
maxpool_stride, 3),
'rpn_objectness_predictions_with_background':
(2, image_size * image_size * 9, 2)
}
# TODO(rathodv): Possibly change utils/test_case.py to accept dictionaries
# and return dicionaries so don't have to rely on the order of tensors.
self.assertAllEqual(results[0].shape,
expected_shapes['refined_box_encodings'])
self.assertAllEqual(results[1].shape,
expected_shapes['class_predictions_with_background'])
self.assertAllEqual(results[2].shape, expected_shapes['proposal_boxes'])
self.assertAllEqual(results[3].shape,
expected_shapes['proposal_boxes_normalized'])
anchors_shape = results[4].shape
self.assertAllEqual(results[5].shape,
[batch_size, anchors_shape[0], 4])
self.assertAllEqual(results[6].shape,
[batch_size, anchors_shape[0], 2])
self.assertAllEqual(results[7].shape,
expected_shapes['rpn_features_to_crop'])
self.assertAllEqual(results[8].shape,
expected_shapes['rpn_box_predictor_features'])
def test_postprocess_first_stage_only_inference_mode(
self, use_static_shapes=False, pad_to_max_dimension=None):
batch_size = 2
first_stage_max_proposals = 4 if use_static_shapes else 8
def graph_fn(images,
rpn_box_encodings,
rpn_objectness_predictions_with_background,
rpn_features_to_crop,
anchors):
"""Function to construct tf graph for the test."""
model = self._build_model(
is_training=False, number_of_stages=1, second_stage_batch_size=6,
use_matmul_crop_and_resize=use_static_shapes,
clip_anchors_to_image=use_static_shapes,
use_static_shapes=use_static_shapes,
use_matmul_gather_in_matcher=use_static_shapes,
first_stage_max_proposals=first_stage_max_proposals,
pad_to_max_dimension=pad_to_max_dimension)
_, true_image_shapes = model.preprocess(images)
proposals = model.postprocess({
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'rpn_features_to_crop': rpn_features_to_crop,
'anchors': anchors}, true_image_shapes)
return (proposals['num_detections'],
proposals['detection_boxes'],
proposals['detection_scores'])
anchors = np.array(
[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[16, 16, 32, 32]], dtype=np.float32)
rpn_box_encodings = np.zeros(
(batch_size, anchors.shape[0], BOX_CODE_SIZE), dtype=np.float32)
# use different numbers for the objectness category to break ties in
# order of boxes returned by NMS
rpn_objectness_predictions_with_background = np.array([
[[-10, 13],
[10, -10],
[10, -11],
[-10, 12]],
[[10, -10],
[-10, 13],
[-10, 12],
[10, -11]]], dtype=np.float32)
rpn_features_to_crop = np.ones((batch_size, 8, 8, 10), dtype=np.float32)
image_shape = (batch_size, 32, 32, 3)
images = np.zeros(image_shape, dtype=np.float32)
if use_static_shapes:
results = self.execute(graph_fn,
[images, rpn_box_encodings,
rpn_objectness_predictions_with_background,
rpn_features_to_crop, anchors])
else:
results = self.execute_cpu(graph_fn,
[images, rpn_box_encodings,
rpn_objectness_predictions_with_background,
rpn_features_to_crop, anchors])
expected_proposal_boxes = [
[[0, 0, .5, .5], [.5, .5, 1, 1], [0, .5, .5, 1], [.5, 0, 1.0, .5]]
+ 4 * [4 * [0]],
[[0, .5, .5, 1], [.5, 0, 1.0, .5], [0, 0, .5, .5], [.5, .5, 1, 1]]
+ 4 * [4 * [0]]]
expected_proposal_scores = [[1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0]]
expected_num_proposals = [4, 4]
self.assertAllClose(results[0], expected_num_proposals)
for indx, num_proposals in enumerate(expected_num_proposals):
self.assertAllClose(results[1][indx][0:num_proposals],
expected_proposal_boxes[indx][0:num_proposals])
self.assertAllClose(results[2][indx][0:num_proposals],
expected_proposal_scores[indx][0:num_proposals])
def _test_postprocess_first_stage_only_train_mode(self,
pad_to_max_dimension=None):
model = self._build_model(
is_training=True, number_of_stages=1, second_stage_batch_size=2,
pad_to_max_dimension=pad_to_max_dimension)
batch_size = 2
anchors = tf.constant(
[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[16, 16, 32, 32]], dtype=tf.float32)
rpn_box_encodings = tf.zeros(
[batch_size, anchors.get_shape().as_list()[0],
BOX_CODE_SIZE], dtype=tf.float32)
# use different numbers for the objectness category to break ties in
# order of boxes returned by NMS
rpn_objectness_predictions_with_background = tf.constant([
[[-10, 13],
[-10, 12],
[-10, 11],
[-10, 10]],
[[-10, 13],
[-10, 12],
[-10, 11],
[-10, 10]]], dtype=tf.float32)
rpn_features_to_crop = tf.ones((batch_size, 8, 8, 10), dtype=tf.float32)
image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
groundtruth_boxes_list = [
tf.constant([[0, 0, .5, .5], [.5, .5, 1, 1]], dtype=tf.float32),
tf.constant([[0, .5, .5, 1], [.5, 0, 1, .5]], dtype=tf.float32)]
groundtruth_classes_list = [tf.constant([[1, 0], [0, 1]], dtype=tf.float32),
tf.constant([[1, 0], [1, 0]], dtype=tf.float32)]
groundtruth_weights_list = [
tf.constant([1, 1], dtype=tf.float32),
tf.constant([1, 1], dtype=tf.float32)
]
_, true_image_shapes = model.preprocess(tf.zeros(image_shape))
model.provide_groundtruth(
groundtruth_boxes_list,
groundtruth_classes_list,
groundtruth_weights_list=groundtruth_weights_list)
proposals = model.postprocess({
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'rpn_features_to_crop': rpn_features_to_crop,
'anchors': anchors}, true_image_shapes)
expected_proposal_boxes = [
[[0, 0, .5, .5], [.5, .5, 1, 1]], [[0, .5, .5, 1], [.5, 0, 1, .5]]]
expected_proposal_scores = [[1, 1],
[1, 1]]
expected_num_proposals = [2, 2]
expected_output_keys = set(['detection_boxes', 'detection_scores',
'num_detections'])
self.assertEqual(set(proposals.keys()), expected_output_keys)
with self.test_session() as sess:
proposals_out = sess.run(proposals)
for image_idx in range(batch_size):
self.assertTrue(
test_utils.first_rows_close_as_set(
proposals_out['detection_boxes'][image_idx].tolist(),
expected_proposal_boxes[image_idx]))
self.assertAllClose(proposals_out['detection_scores'],
expected_proposal_scores)
self.assertAllEqual(proposals_out['num_detections'],
expected_num_proposals)
def test_postprocess_first_stage_only_train_mode(self):
self._test_postprocess_first_stage_only_train_mode()
def test_postprocess_first_stage_only_train_mode_padded_image(self):
self._test_postprocess_first_stage_only_train_mode(pad_to_max_dimension=56)
def test_postprocess_second_stage_only_inference_mode(
self, use_static_shapes=False, pad_to_max_dimension=None):
batch_size = 2
num_classes = 2
image_shape = np.array((2, 36, 48, 3), dtype=np.int32)
first_stage_max_proposals = 8
total_num_padded_proposals = batch_size * first_stage_max_proposals
def graph_fn(images,
refined_box_encodings,
class_predictions_with_background,
num_proposals,
proposal_boxes):
"""Function to construct tf graph for the test."""
model = self._build_model(
is_training=False, number_of_stages=2,
second_stage_batch_size=6,
use_matmul_crop_and_resize=use_static_shapes,
clip_anchors_to_image=use_static_shapes,
use_static_shapes=use_static_shapes,
use_matmul_gather_in_matcher=use_static_shapes,
pad_to_max_dimension=pad_to_max_dimension)
_, true_image_shapes = model.preprocess(images)
detections = model.postprocess({
'refined_box_encodings': refined_box_encodings,
'class_predictions_with_background':
class_predictions_with_background,
'num_proposals': num_proposals,
'proposal_boxes': proposal_boxes,
}, true_image_shapes)
return (detections['num_detections'],
detections['detection_boxes'],
detections['detection_scores'],
detections['detection_classes'])
proposal_boxes = np.array(
[[[1, 1, 2, 3],
[0, 0, 1, 1],
[.5, .5, .6, .6],
4*[0], 4*[0], 4*[0], 4*[0], 4*[0]],
[[2, 3, 6, 8],
[1, 2, 5, 3],
4*[0], 4*[0], 4*[0], 4*[0], 4*[0], 4*[0]]], dtype=np.float32)
num_proposals = np.array([3, 2], dtype=np.int32)
refined_box_encodings = np.zeros(
[total_num_padded_proposals, num_classes, 4], dtype=np.float32)
class_predictions_with_background = np.ones(
[total_num_padded_proposals, num_classes+1], dtype=np.float32)
images = np.zeros(image_shape, dtype=np.float32)
if use_static_shapes:
results = self.execute(graph_fn,
[images, refined_box_encodings,
class_predictions_with_background,
num_proposals, proposal_boxes])
else:
results = self.execute_cpu(graph_fn,
[images, refined_box_encodings,
class_predictions_with_background,
num_proposals, proposal_boxes])
expected_num_detections = [5, 4]
expected_detection_classes = [[0, 0, 0, 1, 1], [0, 0, 1, 1, 0]]
expected_detection_scores = [[1, 1, 1, 1, 1], [1, 1, 1, 1, 0]]
self.assertAllClose(results[0], expected_num_detections)
for indx, num_proposals in enumerate(expected_num_detections):
self.assertAllClose(results[2][indx][0:num_proposals],
expected_detection_scores[indx][0:num_proposals])
self.assertAllClose(results[3][indx][0:num_proposals],
expected_detection_classes[indx][0:num_proposals])
if not use_static_shapes:
self.assertAllEqual(results[1].shape, [2, 5, 4])
def test_preprocess_preserves_input_shapes(self):
image_shapes = [(3, None, None, 3),
(None, 10, 10, 3),
(None, None, None, 3)]
for image_shape in image_shapes:
model = self._build_model(
is_training=False, number_of_stages=2, second_stage_batch_size=6)
image_placeholder = tf.placeholder(tf.float32, shape=image_shape)
preprocessed_inputs, _ = model.preprocess(image_placeholder)
self.assertAllEqual(preprocessed_inputs.shape.as_list(), image_shape)
# TODO(rathodv): Split test into two - with and without masks.
def test_loss_first_stage_only_mode(self):
model = self._build_model(
is_training=True, number_of_stages=1, second_stage_batch_size=6)
batch_size = 2
anchors = tf.constant(
[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[16, 16, 32, 32]], dtype=tf.float32)
rpn_box_encodings = tf.zeros(
[batch_size,
anchors.get_shape().as_list()[0],
BOX_CODE_SIZE], dtype=tf.float32)
# use different numbers for the objectness category to break ties in
# order of boxes returned by NMS
rpn_objectness_predictions_with_background = tf.constant([
[[-10, 13],
[10, -10],
[10, -11],
[-10, 12]],
[[10, -10],
[-10, 13],
[-10, 12],
[10, -11]]], dtype=tf.float32)
image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
groundtruth_boxes_list = [
tf.constant([[0, 0, .5, .5], [.5, .5, 1, 1]], dtype=tf.float32),
tf.constant([[0, .5, .5, 1], [.5, 0, 1, .5]], dtype=tf.float32)]
groundtruth_classes_list = [tf.constant([[1, 0], [0, 1]], dtype=tf.float32),
tf.constant([[1, 0], [1, 0]], dtype=tf.float32)]
prediction_dict = {
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'image_shape': image_shape,
'anchors': anchors
}
_, true_image_shapes = model.preprocess(tf.zeros(image_shape))
model.provide_groundtruth(groundtruth_boxes_list,
groundtruth_classes_list)
loss_dict = model.loss(prediction_dict, true_image_shapes)
with self.test_session() as sess:
loss_dict_out = sess.run(loss_dict)
self.assertAllClose(loss_dict_out['Loss/RPNLoss/localization_loss'], 0)
self.assertAllClose(loss_dict_out['Loss/RPNLoss/objectness_loss'], 0)
self.assertNotIn('Loss/BoxClassifierLoss/localization_loss',
loss_dict_out)
self.assertNotIn('Loss/BoxClassifierLoss/classification_loss',
loss_dict_out)
# TODO(rathodv): Split test into two - with and without masks.
def test_loss_full(self):
model = self._build_model(
is_training=True, number_of_stages=2, second_stage_batch_size=6)
batch_size = 3
anchors = tf.constant(
[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[16, 16, 32, 32]], dtype=tf.float32)
rpn_box_encodings = tf.zeros(
[batch_size,
anchors.get_shape().as_list()[0],
BOX_CODE_SIZE], dtype=tf.float32)
# use different numbers for the objectness category to break ties in
# order of boxes returned by NMS
rpn_objectness_predictions_with_background = tf.constant(
[[[-10, 13], [10, -10], [10, -11], [-10, 12]], [[10, -10], [-10, 13], [
-10, 12
], [10, -11]], [[10, -10], [-10, 13], [-10, 12], [10, -11]]],
dtype=tf.float32)
image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
num_proposals = tf.constant([6, 6, 6], dtype=tf.int32)
proposal_boxes = tf.constant(
3 * [[[0, 0, 16, 16], [0, 16, 16, 32], [16, 0, 32, 16],
[16, 16, 32, 32], [0, 0, 16, 16], [0, 16, 16, 32]]],
dtype=tf.float32)
refined_box_encodings = tf.zeros(
(batch_size * model.max_num_proposals,
model.num_classes,
BOX_CODE_SIZE), dtype=tf.float32)
class_predictions_with_background = tf.constant(
[
[-10, 10, -10], # first image
[10, -10, -10],
[10, -10, -10],
[-10, -10, 10],
[-10, 10, -10],
[10, -10, -10],
[10, -10, -10], # second image
[-10, 10, -10],
[-10, 10, -10],
[10, -10, -10],
[10, -10, -10],
[-10, 10, -10],
[10, -10, -10], # third image
[-10, 10, -10],
[-10, 10, -10],
[10, -10, -10],
[10, -10, -10],
[-10, 10, -10]
],
dtype=tf.float32)
mask_predictions_logits = 20 * tf.ones((batch_size *
model.max_num_proposals,
model.num_classes,
14, 14),
dtype=tf.float32)
groundtruth_boxes_list = [
tf.constant([[0, 0, .5, .5], [.5, .5, 1, 1]], dtype=tf.float32),
tf.constant([[0, .5, .5, 1], [.5, 0, 1, .5]], dtype=tf.float32),
tf.constant([[0, .5, .5, 1], [.5, 0, 1, 1]], dtype=tf.float32)
]
groundtruth_classes_list = [
tf.constant([[1, 0], [0, 1]], dtype=tf.float32),
tf.constant([[1, 0], [1, 0]], dtype=tf.float32),
tf.constant([[1, 0], [0, 1]], dtype=tf.float32)
]
# Set all elements of groundtruth mask to 1.0. In this case all proposal
# crops of the groundtruth masks should return a mask that covers the entire
# proposal. Thus, if mask_predictions_logits element values are all greater
# than 20, the loss should be zero.
groundtruth_masks_list = [
tf.convert_to_tensor(np.ones((2, 32, 32)), dtype=tf.float32),
tf.convert_to_tensor(np.ones((2, 32, 32)), dtype=tf.float32),
tf.convert_to_tensor(np.ones((2, 32, 32)), dtype=tf.float32)
]
groundtruth_weights_list = [
tf.constant([1, 1], dtype=tf.float32),
tf.constant([1, 1], dtype=tf.float32),
tf.constant([1, 0], dtype=tf.float32)
]
prediction_dict = {
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'image_shape': image_shape,
'anchors': anchors,
'refined_box_encodings': refined_box_encodings,
'class_predictions_with_background': class_predictions_with_background,
'proposal_boxes': proposal_boxes,
'num_proposals': num_proposals,
'mask_predictions': mask_predictions_logits
}
_, true_image_shapes = model.preprocess(tf.zeros(image_shape))
model.provide_groundtruth(
groundtruth_boxes_list,
groundtruth_classes_list,
groundtruth_masks_list,
groundtruth_weights_list=groundtruth_weights_list)
loss_dict = model.loss(prediction_dict, true_image_shapes)
with self.test_session() as sess:
loss_dict_out = sess.run(loss_dict)
self.assertAllClose(loss_dict_out['Loss/RPNLoss/localization_loss'], 0)
self.assertAllClose(loss_dict_out['Loss/RPNLoss/objectness_loss'], 0)
self.assertAllClose(loss_dict_out[
'Loss/BoxClassifierLoss/localization_loss'], 0)
self.assertAllClose(loss_dict_out[
'Loss/BoxClassifierLoss/classification_loss'], 0)
self.assertAllClose(loss_dict_out['Loss/BoxClassifierLoss/mask_loss'], 0)
def test_loss_full_zero_padded_proposals(self):
model = self._build_model(
is_training=True, number_of_stages=2, second_stage_batch_size=6)
batch_size = 1
anchors = tf.constant(
[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[16, 16, 32, 32]], dtype=tf.float32)
rpn_box_encodings = tf.zeros(
[batch_size,
anchors.get_shape().as_list()[0],
BOX_CODE_SIZE], dtype=tf.float32)
# use different numbers for the objectness category to break ties in
# order of boxes returned by NMS
rpn_objectness_predictions_with_background = tf.constant([
[[-10, 13],
[10, -10],
[10, -11],
[10, -12]],], dtype=tf.float32)
image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
# box_classifier_batch_size is 6, but here we assume that the number of
# actual proposals (not counting zero paddings) is fewer (3).
num_proposals = tf.constant([3], dtype=tf.int32)
proposal_boxes = tf.constant(
[[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[0, 0, 0, 0], # begin paddings
[0, 0, 0, 0],
[0, 0, 0, 0]]], dtype=tf.float32)
refined_box_encodings = tf.zeros(
(batch_size * model.max_num_proposals,
model.num_classes,
BOX_CODE_SIZE), dtype=tf.float32)
class_predictions_with_background = tf.constant(
[[-10, 10, -10],
[10, -10, -10],
[10, -10, -10],
[0, 0, 0], # begin paddings
[0, 0, 0],
[0, 0, 0]], dtype=tf.float32)
mask_predictions_logits = 20 * tf.ones((batch_size *
model.max_num_proposals,
model.num_classes,
14, 14),
dtype=tf.float32)
groundtruth_boxes_list = [
tf.constant([[0, 0, .5, .5]], dtype=tf.float32)]
groundtruth_classes_list = [tf.constant([[1, 0]], dtype=tf.float32)]
# Set all elements of groundtruth mask to 1.0. In this case all proposal
# crops of the groundtruth masks should return a mask that covers the entire
# proposal. Thus, if mask_predictions_logits element values are all greater
# than 20, the loss should be zero.
groundtruth_masks_list = [tf.convert_to_tensor(np.ones((1, 32, 32)),
dtype=tf.float32)]
prediction_dict = {
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'image_shape': image_shape,
'anchors': anchors,
'refined_box_encodings': refined_box_encodings,
'class_predictions_with_background': class_predictions_with_background,
'proposal_boxes': proposal_boxes,
'num_proposals': num_proposals,
'mask_predictions': mask_predictions_logits
}
_, true_image_shapes = model.preprocess(tf.zeros(image_shape))
model.provide_groundtruth(groundtruth_boxes_list,
groundtruth_classes_list,
groundtruth_masks_list)
loss_dict = model.loss(prediction_dict, true_image_shapes)
with self.test_session() as sess:
loss_dict_out = sess.run(loss_dict)
self.assertAllClose(loss_dict_out['Loss/RPNLoss/localization_loss'], 0)
self.assertAllClose(loss_dict_out['Loss/RPNLoss/objectness_loss'], 0)
self.assertAllClose(loss_dict_out[
'Loss/BoxClassifierLoss/localization_loss'], 0)
self.assertAllClose(loss_dict_out[
'Loss/BoxClassifierLoss/classification_loss'], 0)
self.assertAllClose(loss_dict_out['Loss/BoxClassifierLoss/mask_loss'], 0)
def test_loss_full_multiple_label_groundtruth(self):
model = self._build_model(
is_training=True, number_of_stages=2, second_stage_batch_size=6,
softmax_second_stage_classification_loss=False)
batch_size = 1
anchors = tf.constant(
[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[16, 16, 32, 32]], dtype=tf.float32)
rpn_box_encodings = tf.zeros(
[batch_size,
anchors.get_shape().as_list()[0],
BOX_CODE_SIZE], dtype=tf.float32)
# use different numbers for the objectness category to break ties in
# order of boxes returned by NMS
rpn_objectness_predictions_with_background = tf.constant([
[[-10, 13],
[10, -10],
[10, -11],
[10, -12]],], dtype=tf.float32)
image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
# box_classifier_batch_size is 6, but here we assume that the number of
# actual proposals (not counting zero paddings) is fewer (3).
num_proposals = tf.constant([3], dtype=tf.int32)
proposal_boxes = tf.constant(
[[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[0, 0, 0, 0], # begin paddings
[0, 0, 0, 0],
[0, 0, 0, 0]]], dtype=tf.float32)
# second_stage_localization_loss should only be computed for predictions
# that match groundtruth. For multiple label groundtruth boxes, the loss
# should only be computed once for the label with the smaller index.
refined_box_encodings = tf.constant(
[[[0, 0, 0, 0], [1, 1, -1, -1]],
[[1, 1, -1, -1], [1, 1, 1, 1]],
[[1, 1, -1, -1], [1, 1, 1, 1]],
[[1, 1, -1, -1], [1, 1, 1, 1]],
[[1, 1, -1, -1], [1, 1, 1, 1]],
[[1, 1, -1, -1], [1, 1, 1, 1]]], dtype=tf.float32)
class_predictions_with_background = tf.constant(
[[-100, 100, 100],
[100, -100, -100],
[100, -100, -100],
[0, 0, 0], # begin paddings
[0, 0, 0],
[0, 0, 0]], dtype=tf.float32)
mask_predictions_logits = 20 * tf.ones((batch_size *
model.max_num_proposals,
model.num_classes,
14, 14),
dtype=tf.float32)
groundtruth_boxes_list = [
tf.constant([[0, 0, .5, .5]], dtype=tf.float32)]
# Box contains two ground truth labels.
groundtruth_classes_list = [tf.constant([[1, 1]], dtype=tf.float32)]
# Set all elements of groundtruth mask to 1.0. In this case all proposal
# crops of the groundtruth masks should return a mask that covers the entire
# proposal. Thus, if mask_predictions_logits element values are all greater
# than 20, the loss should be zero.
groundtruth_masks_list = [tf.convert_to_tensor(np.ones((1, 32, 32)),
dtype=tf.float32)]
prediction_dict = {
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'image_shape': image_shape,
'anchors': anchors,
'refined_box_encodings': refined_box_encodings,
'class_predictions_with_background': class_predictions_with_background,
'proposal_boxes': proposal_boxes,
'num_proposals': num_proposals,
'mask_predictions': mask_predictions_logits
}
_, true_image_shapes = model.preprocess(tf.zeros(image_shape))
model.provide_groundtruth(groundtruth_boxes_list,
groundtruth_classes_list,
groundtruth_masks_list)
loss_dict = model.loss(prediction_dict, true_image_shapes)
with self.test_session() as sess:
loss_dict_out = sess.run(loss_dict)
self.assertAllClose(loss_dict_out['Loss/RPNLoss/localization_loss'], 0)
self.assertAllClose(loss_dict_out['Loss/RPNLoss/objectness_loss'], 0)
self.assertAllClose(loss_dict_out[
'Loss/BoxClassifierLoss/localization_loss'], 0)
self.assertAllClose(loss_dict_out[
'Loss/BoxClassifierLoss/classification_loss'], 0)
self.assertAllClose(loss_dict_out['Loss/BoxClassifierLoss/mask_loss'], 0)
def test_loss_full_zero_padded_proposals_nonzero_loss_with_two_images(
self, use_static_shapes=False, shared_boxes=False):
batch_size = 2
first_stage_max_proposals = 8
second_stage_batch_size = 6
num_classes = 2
def graph_fn(anchors, rpn_box_encodings,
rpn_objectness_predictions_with_background, images,
num_proposals, proposal_boxes, refined_box_encodings,
class_predictions_with_background, groundtruth_boxes,
groundtruth_classes):
"""Function to construct tf graph for the test."""
model = self._build_model(
is_training=True, number_of_stages=2,
second_stage_batch_size=second_stage_batch_size,
first_stage_max_proposals=first_stage_max_proposals,
num_classes=num_classes,
use_matmul_crop_and_resize=use_static_shapes,
clip_anchors_to_image=use_static_shapes,
use_static_shapes=use_static_shapes)
prediction_dict = {
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'image_shape': tf.shape(images),
'anchors': anchors,
'refined_box_encodings': refined_box_encodings,
'class_predictions_with_background':
class_predictions_with_background,
'proposal_boxes': proposal_boxes,
'num_proposals': num_proposals
}
_, true_image_shapes = model.preprocess(images)
model.provide_groundtruth(tf.unstack(groundtruth_boxes),
tf.unstack(groundtruth_classes))
loss_dict = model.loss(prediction_dict, true_image_shapes)
return (loss_dict['Loss/RPNLoss/localization_loss'],
loss_dict['Loss/RPNLoss/objectness_loss'],
loss_dict['Loss/BoxClassifierLoss/localization_loss'],
loss_dict['Loss/BoxClassifierLoss/classification_loss'])
anchors = np.array(
[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[16, 16, 32, 32]], dtype=np.float32)
rpn_box_encodings = np.zeros(
[batch_size, anchors.shape[1], BOX_CODE_SIZE], dtype=np.float32)
# use different numbers for the objectness category to break ties in
# order of boxes returned by NMS
rpn_objectness_predictions_with_background = np.array(
[[[-10, 13],
[10, -10],
[10, -11],
[10, -12]],
[[-10, 13],
[10, -10],
[10, -11],
[10, -12]]], dtype=np.float32)
images = np.zeros([batch_size, 32, 32, 3], dtype=np.float32)
# box_classifier_batch_size is 6, but here we assume that the number of
# actual proposals (not counting zero paddings) is fewer.
num_proposals = np.array([3, 2], dtype=np.int32)
proposal_boxes = np.array(
[[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[0, 0, 0, 0], # begin paddings
[0, 0, 0, 0],
[0, 0, 0, 0]],
[[0, 0, 16, 16],
[0, 16, 16, 32],
[0, 0, 0, 0], # begin paddings
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]]], dtype=np.float32)
refined_box_encodings = np.zeros(
(batch_size * second_stage_batch_size, 1
if shared_boxes else num_classes, BOX_CODE_SIZE),
dtype=np.float32)
class_predictions_with_background = np.array(
[[-10, 10, -10], # first image
[10, -10, -10],
[10, -10, -10],
[0, 0, 0], # begin paddings
[0, 0, 0],
[0, 0, 0],
[-10, -10, 10], # second image
[10, -10, -10],
[0, 0, 0], # begin paddings
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],], dtype=np.float32)
# The first groundtruth box is 4/5 of the anchor size in both directions
# experiencing a loss of:
# 2 * SmoothL1(5 * log(4/5)) / num_proposals
# = 2 * (abs(5 * log(1/2)) - .5) / 3
# The second groundtruth box is identical to the prediction and thus
# experiences zero loss.
# Total average loss is (abs(5 * log(1/2)) - .5) / 3.
groundtruth_boxes = np.stack([
np.array([[0.05, 0.05, 0.45, 0.45]], dtype=np.float32),
np.array([[0.0, 0.0, 0.5, 0.5]], dtype=np.float32)])
groundtruth_classes = np.stack([np.array([[1, 0]], dtype=np.float32),
np.array([[0, 1]], dtype=np.float32)])
execute_fn = self.execute_cpu
if use_static_shapes:
execute_fn = self.execute
results = execute_fn(graph_fn, [
anchors, rpn_box_encodings, rpn_objectness_predictions_with_background,
images, num_proposals, proposal_boxes, refined_box_encodings,
class_predictions_with_background, groundtruth_boxes,
groundtruth_classes
])
exp_loc_loss = (-5 * np.log(.8) - 0.5) / 3.0
self.assertAllClose(results[0], exp_loc_loss, rtol=1e-4, atol=1e-4)
self.assertAllClose(results[1], 0.0)
self.assertAllClose(results[2], exp_loc_loss, rtol=1e-4, atol=1e-4)
self.assertAllClose(results[3], 0.0)
def test_loss_with_hard_mining(self):
model = self._build_model(is_training=True,
number_of_stages=2,
second_stage_batch_size=None,
first_stage_max_proposals=6,
hard_mining=True)
batch_size = 1
anchors = tf.constant(
[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[16, 16, 32, 32]], dtype=tf.float32)
rpn_box_encodings = tf.zeros(
[batch_size,
anchors.get_shape().as_list()[0],
BOX_CODE_SIZE], dtype=tf.float32)
# use different numbers for the objectness category to break ties in
# order of boxes returned by NMS
rpn_objectness_predictions_with_background = tf.constant(
[[[-10, 13],
[-10, 12],
[10, -11],
[10, -12]]], dtype=tf.float32)
image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
# box_classifier_batch_size is 6, but here we assume that the number of
# actual proposals (not counting zero paddings) is fewer (3).
num_proposals = tf.constant([3], dtype=tf.int32)
proposal_boxes = tf.constant(
[[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[0, 0, 0, 0], # begin paddings
[0, 0, 0, 0],
[0, 0, 0, 0]]], dtype=tf.float32)
refined_box_encodings = tf.zeros(
(batch_size * model.max_num_proposals,
model.num_classes,
BOX_CODE_SIZE), dtype=tf.float32)
class_predictions_with_background = tf.constant(
[[-10, 10, -10], # first image
[-10, -10, 10],
[10, -10, -10],
[0, 0, 0], # begin paddings
[0, 0, 0],
[0, 0, 0]], dtype=tf.float32)
# The first groundtruth box is 4/5 of the anchor size in both directions
# experiencing a loss of:
# 2 * SmoothL1(5 * log(4/5)) / num_proposals
# = 2 * (abs(5 * log(1/2)) - .5) / 3
# The second groundtruth box is 46/50 of the anchor size in both directions
# experiencing a loss of:
# 2 * SmoothL1(5 * log(42/50)) / num_proposals
# = 2 * (.5(5 * log(.92))^2 - .5) / 3.
# Since the first groundtruth box experiences greater loss, and we have
# set num_hard_examples=1 in the HardMiner, the final localization loss
# corresponds to that of the first groundtruth box.
groundtruth_boxes_list = [
tf.constant([[0.05, 0.05, 0.45, 0.45],
[0.02, 0.52, 0.48, 0.98],], dtype=tf.float32)]
groundtruth_classes_list = [tf.constant([[1, 0], [0, 1]], dtype=tf.float32)]
exp_loc_loss = 2 * (-5 * np.log(.8) - 0.5) / 3.0
prediction_dict = {
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'image_shape': image_shape,
'anchors': anchors,
'refined_box_encodings': refined_box_encodings,
'class_predictions_with_background': class_predictions_with_background,
'proposal_boxes': proposal_boxes,
'num_proposals': num_proposals
}
_, true_image_shapes = model.preprocess(tf.zeros(image_shape))
model.provide_groundtruth(groundtruth_boxes_list,
groundtruth_classes_list)
loss_dict = model.loss(prediction_dict, true_image_shapes)
with self.test_session() as sess:
loss_dict_out = sess.run(loss_dict)
self.assertAllClose(loss_dict_out[
'Loss/BoxClassifierLoss/localization_loss'], exp_loc_loss)
self.assertAllClose(loss_dict_out[
'Loss/BoxClassifierLoss/classification_loss'], 0)
def test_loss_with_hard_mining_and_losses_mask(self):
model = self._build_model(is_training=True,
number_of_stages=2,
second_stage_batch_size=None,
first_stage_max_proposals=6,
hard_mining=True)
batch_size = 2
number_of_proposals = 3
anchors = tf.constant(
[[0, 0, 16, 16],
[0, 16, 16, 32],
[16, 0, 32, 16],
[16, 16, 32, 32]], dtype=tf.float32)
rpn_box_encodings = tf.zeros(
[batch_size,
anchors.get_shape().as_list()[0],
BOX_CODE_SIZE], dtype=tf.float32)
# use different numbers for the objectness category to break ties in
# order of boxes returned by NMS
rpn_objectness_predictions_with_background = tf.constant(
[[[-10, 13],
[-10, 12],
[10, -11],
[10, -12]],
[[-10, 13],
[-10, 12],
[10, -11],
[10, -12]]], dtype=tf.float32)
image_shape = tf.constant([batch_size, 32, 32, 3], dtype=tf.int32)
# box_classifier_batch_size is 6, but here we assume that the number of
# actual proposals (not counting zero paddings) is fewer (3).
num_proposals = tf.constant([number_of_proposals, number_of_proposals],
dtype=tf.int32)
proposal_boxes = tf.constant(
[[[0, 0, 16, 16], # first image
[0, 16, 16, 32],
[16, 0, 32, 16],
[0, 0, 0, 0], # begin paddings
[0, 0, 0, 0],
[0, 0, 0, 0]],
[[0, 0, 16, 16], # second image
[0, 16, 16, 32],
[16, 0, 32, 16],
[0, 0, 0, 0], # begin paddings
[0, 0, 0, 0],
[0, 0, 0, 0]]], dtype=tf.float32)
refined_box_encodings = tf.zeros(
(batch_size * model.max_num_proposals,
model.num_classes,
BOX_CODE_SIZE), dtype=tf.float32)
class_predictions_with_background = tf.constant(
[[-10, 10, -10], # first image
[-10, -10, 10],
[10, -10, -10],
[0, 0, 0], # begin paddings
[0, 0, 0],
[0, 0, 0],
[-10, 10, -10], # second image
[-10, -10, 10],
[10, -10, -10],
[0, 0, 0], # begin paddings
[0, 0, 0],
[0, 0, 0]], dtype=tf.float32)
# The first groundtruth box is 4/5 of the anchor size in both directions
# experiencing a loss of:
# 2 * SmoothL1(5 * log(4/5)) / (num_proposals * batch_size)
# = 2 * (abs(5 * log(1/2)) - .5) / 3
# The second groundtruth box is 46/50 of the anchor size in both directions
# experiencing a loss of:
# 2 * SmoothL1(5 * log(42/50)) / (num_proposals * batch_size)
# = 2 * (.5(5 * log(.92))^2 - .5) / 3.
# Since the first groundtruth box experiences greater loss, and we have
# set num_hard_examples=1 in the HardMiner, the final localization loss
# corresponds to that of the first groundtruth box.
groundtruth_boxes_list = [
tf.constant([[0.05, 0.05, 0.45, 0.45],
[0.02, 0.52, 0.48, 0.98]], dtype=tf.float32),
tf.constant([[0.05, 0.05, 0.45, 0.45],
[0.02, 0.52, 0.48, 0.98]], dtype=tf.float32)]
groundtruth_classes_list = [
tf.constant([[1, 0], [0, 1]], dtype=tf.float32),
tf.constant([[1, 0], [0, 1]], dtype=tf.float32)]
is_annotated_list = [tf.constant(True, dtype=tf.bool),
tf.constant(False, dtype=tf.bool)]
exp_loc_loss = (2 * (-5 * np.log(.8) - 0.5) /
(number_of_proposals * batch_size))
prediction_dict = {
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'image_shape': image_shape,
'anchors': anchors,
'refined_box_encodings': refined_box_encodings,
'class_predictions_with_background': class_predictions_with_background,
'proposal_boxes': proposal_boxes,
'num_proposals': num_proposals
}
_, true_image_shapes = model.preprocess(tf.zeros(image_shape))
model.provide_groundtruth(groundtruth_boxes_list,
groundtruth_classes_list,
is_annotated_list=is_annotated_list)
loss_dict = model.loss(prediction_dict, true_image_shapes)
with self.test_session() as sess:
loss_dict_out = sess.run(loss_dict)
self.assertAllClose(loss_dict_out[
'Loss/BoxClassifierLoss/localization_loss'], exp_loc_loss)
self.assertAllClose(loss_dict_out[
'Loss/BoxClassifierLoss/classification_loss'], 0)
def test_restore_map_for_classification_ckpt(self):
# Define mock tensorflow classification graph and save variables.
test_graph_classification = tf.Graph()
with test_graph_classification.as_default():
image = tf.placeholder(dtype=tf.float32, shape=[1, 20, 20, 3])
with tf.variable_scope('mock_model'):
net = slim.conv2d(image, num_outputs=3, kernel_size=1, scope='layer1')
slim.conv2d(net, num_outputs=3, kernel_size=1, scope='layer2')
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
save_path = self.get_temp_dir()
with self.test_session(graph=test_graph_classification) as sess:
sess.run(init_op)
saved_model_path = saver.save(sess, save_path)
# Create tensorflow detection graph and load variables from
# classification checkpoint.
test_graph_detection = tf.Graph()
with test_graph_detection.as_default():
model = self._build_model(
is_training=False, number_of_stages=2, second_stage_batch_size=6)
inputs_shape = (2, 20, 20, 3)
inputs = tf.to_float(tf.random_uniform(
inputs_shape, minval=0, maxval=255, dtype=tf.int32))
preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
model.postprocess(prediction_dict, true_image_shapes)
var_map = model.restore_map(fine_tune_checkpoint_type='classification')
self.assertIsInstance(var_map, dict)
saver = tf.train.Saver(var_map)
with self.test_session(graph=test_graph_classification) as sess:
saver.restore(sess, saved_model_path)
for var in sess.run(tf.report_uninitialized_variables()):
self.assertNotIn(model.first_stage_feature_extractor_scope, var)
self.assertNotIn(model.second_stage_feature_extractor_scope, var)
def test_restore_map_for_detection_ckpt(self):
# Define first detection graph and save variables.
test_graph_detection1 = tf.Graph()
with test_graph_detection1.as_default():
model = self._build_model(
is_training=False, number_of_stages=2, second_stage_batch_size=6)
inputs_shape = (2, 20, 20, 3)
inputs = tf.to_float(tf.random_uniform(
inputs_shape, minval=0, maxval=255, dtype=tf.int32))
preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
model.postprocess(prediction_dict, true_image_shapes)
another_variable = tf.Variable([17.0], name='another_variable') # pylint: disable=unused-variable
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
save_path = self.get_temp_dir()
with self.test_session(graph=test_graph_detection1) as sess:
sess.run(init_op)
saved_model_path = saver.save(sess, save_path)
# Define second detection graph and restore variables.
test_graph_detection2 = tf.Graph()
with test_graph_detection2.as_default():
model2 = self._build_model(is_training=False, number_of_stages=2,
second_stage_batch_size=6, num_classes=42)
inputs_shape2 = (2, 20, 20, 3)
inputs2 = tf.to_float(tf.random_uniform(
inputs_shape2, minval=0, maxval=255, dtype=tf.int32))
preprocessed_inputs2, true_image_shapes = model2.preprocess(inputs2)
prediction_dict2 = model2.predict(preprocessed_inputs2, true_image_shapes)
model2.postprocess(prediction_dict2, true_image_shapes)
another_variable = tf.Variable([17.0], name='another_variable') # pylint: disable=unused-variable
var_map = model2.restore_map(fine_tune_checkpoint_type='detection')
self.assertIsInstance(var_map, dict)
saver = tf.train.Saver(var_map)
with self.test_session(graph=test_graph_detection2) as sess:
saver.restore(sess, saved_model_path)
uninitialized_vars_list = sess.run(tf.report_uninitialized_variables())
self.assertIn('another_variable', uninitialized_vars_list)
for var in uninitialized_vars_list:
self.assertNotIn(model2.first_stage_feature_extractor_scope, var)
self.assertNotIn(model2.second_stage_feature_extractor_scope, var)
def test_load_all_det_checkpoint_vars(self):
test_graph_detection = tf.Graph()
with test_graph_detection.as_default():
model = self._build_model(
is_training=False,
number_of_stages=2,
second_stage_batch_size=6,
num_classes=42)
inputs_shape = (2, 20, 20, 3)
inputs = tf.to_float(
tf.random_uniform(inputs_shape, minval=0, maxval=255, dtype=tf.int32))
preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
model.postprocess(prediction_dict, true_image_shapes)
another_variable = tf.Variable([17.0], name='another_variable') # pylint: disable=unused-variable
var_map = model.restore_map(
fine_tune_checkpoint_type='detection',
load_all_detection_checkpoint_vars=True)
self.assertIsInstance(var_map, dict)
self.assertIn('another_variable', var_map)
if __name__ == '__main__':
tf.test.main()
|
TensorFlow/Detection/SSD/models/research/slim/scripts | scripts | finetune_inception_resnet_v2_on_flowers | #!/bin/bash
# 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.
# ==============================================================================
#
# This script performs the following operations:
# 1. Downloads the Flowers dataset
# 2. Fine-tunes an Inception Resnet V2 model on the Flowers training set.
# 3. Evaluates the model on the Flowers validation set.
#
# Usage:
# cd slim
# ./slim/scripts/finetune_inception_resnet_v2_on_flowers.sh
set -e
# Where the pre-trained Inception Resnet V2 checkpoint is saved to.
PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints
# Where the pre-trained Inception Resnet V2 checkpoint is saved to.
MODEL_NAME=inception_resnet_v2
# Where the training (fine-tuned) checkpoint and logs will be saved to.
TRAIN_DIR=/tmp/flowers-models/${MODEL_NAME}
# Where the dataset is saved to.
DATASET_DIR=/tmp/flowers
# Download the pre-trained checkpoint.
if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then
mkdir ${PRETRAINED_CHECKPOINT_DIR}
fi
if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/${MODEL_NAME}.ckpt ]; then
wget http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz
tar -xvf inception_resnet_v2_2016_08_30.tar.gz
mv inception_resnet_v2.ckpt ${PRETRAINED_CHECKPOINT_DIR}/${MODEL_NAME}.ckpt
rm inception_resnet_v2_2016_08_30.tar.gz
fi
# Download the dataset
python download_and_convert_data.py \
--dataset_name=flowers \
--dataset_dir=${DATASET_DIR}
# Fine-tune only the new layers for 1000 steps.
python train_image_classifier.py \
--train_dir=${TRAIN_DIR} \
--dataset_name=flowers \
--dataset_split_name=train \
--dataset_dir=${DATASET_DIR} \
--model_name=${MODEL_NAME} \
--checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/${MODEL_NAME}.ckpt \
--checkpoint_exclude_scopes=InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits \
--trainable_scopes=InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits \
--max_number_of_steps=1000 \
--batch_size=32 \
--learning_rate=0.01 \
--learning_rate_decay_type=fixed \
--save_interval_secs=60 \
--save_summaries_secs=60 \
--log_every_n_steps=10 \
--optimizer=rmsprop \
--weight_decay=0.00004
# Run evaluation.
python eval_image_classifier.py \
--checkpoint_path=${TRAIN_DIR} \
--eval_dir=${TRAIN_DIR} \
--dataset_name=flowers \
--dataset_split_name=validation \
--dataset_dir=${DATASET_DIR} \
--model_name=${MODEL_NAME}
# Fine-tune all the new layers for 500 steps.
python train_image_classifier.py \
--train_dir=${TRAIN_DIR}/all \
--dataset_name=flowers \
--dataset_split_name=train \
--dataset_dir=${DATASET_DIR} \
--model_name=${MODEL_NAME} \
--checkpoint_path=${TRAIN_DIR} \
--max_number_of_steps=500 \
--batch_size=32 \
--learning_rate=0.0001 \
--learning_rate_decay_type=fixed \
--save_interval_secs=60 \
--save_summaries_secs=60 \
--log_every_n_steps=10 \
--optimizer=rmsprop \
--weight_decay=0.00004
# Run evaluation.
python eval_image_classifier.py \
--checkpoint_path=${TRAIN_DIR}/all \
--eval_dir=${TRAIN_DIR}/all \
--dataset_name=flowers \
--dataset_split_name=validation \
--dataset_dir=${DATASET_DIR} \
--model_name=${MODEL_NAME}
|
TensorFlow/Classification/ConvNets | ConvNets | .gitignore | # Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
*.whl
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Python IDEs
.idea/
.vscode/
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
models/
# Testing
tests/
pytest.ini
requirements_test.txt
# Inference related
workspace/
navigator_workspace/ |
PyTorch/Detection/Efficientdet/scripts/D0 | D0 | inference_AMP_V100-32G | #!/bin/bash
rm -rf *.json
python -u -m bind_launch --nproc_per_node=${NUM_PROC:-1} validate.py '/workspace/object_detection/datasets/coco/' --model efficientdet_d0 -b ${BATCH_SIZE:-8} --torchscript --use-ema --amp --checkpoint ${CKPT_PATH:-/checkpoints/Effdet_B0.pth} --inference |
PyTorch/SpeechSynthesis/Tacotron2/phrases | phrases | phrase_4_256 | The forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves and the form of printed letters should be beautiful, and that their arrangement on pages.
The forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves and the form of printed letters should be beautiful, and that their arrangement on pages.
The forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves and the form of printed letters should be beautiful, and that their arrangement on pages.
The forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves and the form of printed letters should be beautiful, and that their arrangement on pages.
|
TensorFlow/Classification/ConvNets/se-resnext101-32x4d/training | training | DGX2_SE-RNxt101-32x4d_FP32_90E | #!/bin/bash
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
WORKSPACE=${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 ${BIND_TO_SOCKET} -np 16 python3 main.py --arch=se-resnext101-32x4d \
--mode=train_and_evaluate --iter_unit=epoch --num_iter=90 \
--batch_size=64 --warmup_steps=100 --cosine_lr --label_smoothing 0.1 \
--lr_init=0.256 --lr_warmup_epochs=8 --momentum=0.875 --weight_decay=6.103515625e-05 \
--data_dir=${DATA_DIR}/tfrecords --data_idx_dir=${DATA_DIR}/dali_idx \
--results_dir=${WORKSPACE}/results --weight_init=fan_in ${OTHER}
|
PyTorch/SpeechSynthesis/HiFiGAN/scripts | scripts | extract_fine_tune_mels | #!/usr/bin/env bash
# Runs a process which resembles FastPitch training and extracts mel-scale
# spectrograms generated with FastPitch for HiFi-GAN fine-tuning.
export OMP_NUM_THREADS=1
: ${NUM_GPUS:=8}
: ${BATCH_SIZE:=16}
: ${AMP:=false}
: ${DATASET_PATH:="data/LJSpeech-1.1"}
: ${OUTPUT_DIR:="data/mels-fastpitch-ljs22khz"}
: ${DATASET_FILELIST:=data/filelists/ljs_audio_pitch_text.txt} # train + val + test
: ${LOAD_PITCH_FROM_DISK:=true}
: ${LOAD_MEL_FROM_DISK:=false} # mel-spec of the original data
: ${SAMPLING_RATE:=22050}
: ${FASTPITCH:="pretrained_models/fastpitch/nvidia_fastpitch_210824.pt"}
mkdir -p "$OUTPUT_DIR"
# Pre-calculate pitch values and write to disk
# This step requires only CPU
if [[ "$LOAD_PITCH_FROM_DISK" = true && ! -d "$DATASET_PATH/pitch" ]]; then
echo "Pitch values needs for FastPitch not found in $DATASET_PATH/pitch."
echo "Calcluating..."
python prepare_dataset.py \
--wav-text-filelists data/filelists/ljs_audio_text.txt \
--n-workers 16 \
--batch-size 1 \
--dataset-path $DATASET_PATH \
--extract-pitch
fi
ARGS+=" --cuda"
ARGS+=" -o $OUTPUT_DIR"
ARGS+=" --dataset-path $DATASET_PATH"
ARGS+=" --dataset-files $DATASET_FILELIST"
ARGS+=" -bs $BATCH_SIZE"
[ -n "$FASTPITCH" ] && ARGS+=" --checkpoint-path $FASTPITCH"
[ -z "$FASTPITCH" ] && ARGS+=" --resume"
[ "$AMP" = "true" ] && ARGS+=" --amp"
[ "$LOAD_MEL_FROM_DISK" = true ] && ARGS+=" --load-mel-from-disk"
[ "$LOAD_PITCH_FROM_DISK" = true ] && ARGS+=" --load-pitch-from-disk"
[ "$PITCH_ONLINE_DIR" != "" ] && ARGS+=" --pitch-online-dir $PITCH_ONLINE_DIR" # e.g., /dev/shm/pitch
if [ "$SAMPLING_RATE" == "44100" ]; then
ARGS+=" --sampling-rate 44100"
ARGS+=" --filter-length 2048"
ARGS+=" --hop-length 512"
ARGS+=" --win-length 2048"
ARGS+=" --mel-fmin 0.0"
ARGS+=" --mel-fmax 22050.0"
elif [ "$SAMPLING_RATE" != "22050" ]; then
echo "Sampling rate $SAMPLING_RATE not supported. Edit $0 manually."
exit 1
fi
: ${DISTRIBUTED:="-m torch.distributed.launch --nproc_per_node $NUM_GPUS"}
python $DISTRIBUTED fastpitch/extract_mels.py $ARGS "$@"
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/cli/commands | commands | mimic_dataset | # 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.
import os
import argparse
import json
import logging
from collections import defaultdict
from syngen.cli.commands.base_command import BaseCommand
from syngen.configuration import SynGenDatasetFeatureSpec, SynGenConfiguration
from syngen.generator.tabular import tabular_generators_classes
from syngen.utils.types import MetaData
logger = logging.getLogger(__name__)
log = logger
class MimicDatasetCommand(BaseCommand):
def init_parser(self, base_parser):
mimic_parser = base_parser.add_parser(
"mimic-dataset",
help="Quickly creates a SynGen Configuration for the given dataset",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
mimic_parser.set_defaults(action=self.run)
mimic_parser.add_argument(
"-dp", "--dataset-path", type=str, required=True,
help="Path to the dataset in SynGen format"
)
mimic_parser.add_argument(
"-of", "--output-file", type=str, required=True,
help="Path to the generated SynGen Configuration"
)
mimic_parser.add_argument(
"-tg", "--tab-gen", type=str, choices=list(tabular_generators_classes.keys()), default='kde',
help="Tabular Generator to mimic all tabular features"
)
mimic_parser.add_argument(
"-rsg", "--random-struct-gen", action='store_true',
help="Generates random structure based on Erdos-Renyi model"
)
mimic_parser.add_argument(
"-es", "--edge-scale", type=float, default=None,
help="Multiples the number of edges to generate by the provided number"
)
mimic_parser.add_argument(
"-en", "--node-scale", type=float, default=None,
help="Multiples the number of nodes to generate by the provided number"
)
mimic_parser.add_argument(
"-gdp", "--gen-dump-path", type=str, default=None,
help="Path to store the fitted generators"
)
def run(self, args):
dict_args = vars(args)
feature_spec = SynGenDatasetFeatureSpec.instantiate_from_preprocessed(dict_args['dataset_path'])
scales = {
MetaData.EDGES: dict_args['edge_scale'],
MetaData.NODES: dict_args['node_scale'],
}
for part in [MetaData.NODES, MetaData.EDGES]:
for part_info in feature_spec[part]:
if scales[part]:
part_info[MetaData.COUNT] = int(part_info[MetaData.COUNT] * scales[part])
if MetaData.FEATURES in part_info and len(part_info[MetaData.FEATURES]) > 0:
feature_files_content = defaultdict(list)
for feature in part_info[MetaData.FEATURES]:
if MetaData.FEATURE_FILE in feature:
feature_files_content[feature[MetaData.FEATURE_FILE]].append(feature[MetaData.NAME])
if feature_files_content:
part_info[MetaData.TABULAR_GENERATORS] = [
{
MetaData.TYPE: dict_args['tab_gen'],
MetaData.FEATURES_LIST: feats_list,
MetaData.FEATURE_FILE: ff,
MetaData.DATA_SOURCE: {
MetaData.TYPE: 'rnd',
} if dict_args['tab_gen'] == 'random'
else
{
MetaData.TYPE: 'cfg',
MetaData.PATH: dict_args['dataset_path'],
MetaData.NAME: part_info[MetaData.NAME],
},
MetaData.PARAMS: {},
MetaData.DUMP_PATH: os.path.join(dict_args['gen_dump_path'],
f"{part}_{part_info[MetaData.NAME]}_tab_gen_{idx}.pkl")
if dict_args['gen_dump_path'] else None
}
for idx, (ff, feats_list) in enumerate(feature_files_content.items())
]
else:
part_info[MetaData.TABULAR_GENERATORS] = [
{
MetaData.TYPE: dict_args['tab_gen'],
MetaData.FEATURES_LIST: -1,
MetaData.DATA_SOURCE: {
MetaData.TYPE: 'rnd',
} if dict_args['tab_gen'] == 'random'
else
{
MetaData.TYPE: 'cfg',
MetaData.PATH: dict_args['dataset_path'],
MetaData.NAME: part_info[MetaData.NAME],
},
MetaData.PARAMS: {},
MetaData.DUMP_PATH: os.path.join(dict_args['gen_dump_path'],
f"{part}_{part_info[MetaData.NAME]}_tab_gen_{0}.pkl")
if dict_args['gen_dump_path'] else None
}
]
if part == MetaData.EDGES:
part_info[MetaData.STRUCTURE_GENERATOR] = {
MetaData.TYPE: 'RMAT',
MetaData.DATA_SOURCE: {
MetaData.TYPE: 'rnd',
} if dict_args['random_struct_gen']
else
{
MetaData.TYPE: 'cfg',
MetaData.PATH: dict_args['dataset_path'],
MetaData.NAME: part_info[MetaData.NAME],
},
MetaData.PARAMS: {},
MetaData.DUMP_PATH: os.path.join(dict_args['gen_dump_path'],
f"{part_info[MetaData.NAME]}_struct_gen.pkl")
if dict_args['gen_dump_path'] else None
}
config = SynGenConfiguration(feature_spec)
with open(dict_args['output_file'], 'w') as f:
json.dump(config, f, indent=4)
log.info(f"SynGen Configuration saved into {dict_args['output_file']}")
|
PyTorch/Detection/Efficientdet/effdet | effdet | model | """ PyTorch EfficientDet model
Based on official Tensorflow version at: https://github.com/google/automl/tree/master/efficientdet
Paper: https://arxiv.org/abs/1911.09070
Hacked together by Ross Wightman
"""
# 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 torch
import torch.nn as nn
import logging
import math
from collections import OrderedDict
from typing import List, Callable
from .layers import create_conv2d, drop_path, create_pool2d, Swish, get_act_layer
from .config import get_fpn_config, get_backbone_config
from .efficientnet import EfficientNet, efficientnet_configs
_DEBUG = False
_ACT_LAYER = Swish
class SequentialAppend(nn.Sequential):
def __init__(self, *args):
super(SequentialAppend, self).__init__(*args)
def forward(self, x: List[torch.Tensor]):
for module in self:
x.append(module(x))
return x
class SequentialAppendLast(nn.Sequential):
def __init__(self, *args):
super(SequentialAppendLast, self).__init__(*args)
def forward(self, x: List[torch.Tensor]):
for module in self:
x.append(module(x[-1]))
return x
class ConvBnAct2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, padding='', bias=False,
norm_layer=nn.BatchNorm2d, norm_kwargs=None, act_layer=_ACT_LAYER):
super(ConvBnAct2d, self).__init__()
norm_kwargs = norm_kwargs or {}
self.conv = create_conv2d(
in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, padding=padding, bias=bias)
self.bn = None if norm_layer is None else norm_layer(out_channels, **norm_kwargs) # here
self.act = None if act_layer is None else act_layer(inplace=True)
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
x = self.bn(x)
if self.act is not None:
x = self.act(x)
return x
class SeparableConv2d(nn.Module):
""" Separable Conv
"""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, padding='', bias=False,
channel_multiplier=1.0, pw_kernel_size=1, act_layer=_ACT_LAYER,
norm_layer=nn.BatchNorm2d, norm_kwargs=None):
super(SeparableConv2d, self).__init__()
norm_kwargs = norm_kwargs or {}
self.conv_dw = create_conv2d(
in_channels, int(in_channels * channel_multiplier), kernel_size,
stride=stride, dilation=dilation, padding=padding, depthwise=True)
self.conv_pw = create_conv2d(
int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias)
self.bn = None if norm_layer is None else norm_layer(out_channels, **norm_kwargs) # Here
self.act = None if act_layer is None else act_layer(inplace=True)
def forward(self, x):
x = self.conv_dw(x)
x = self.conv_pw(x)
if self.bn is not None:
x = self.bn(x)
if self.act is not None:
x = self.act(x)
return x
class ResampleFeatureMap(nn.Sequential):
def __init__(self, in_channels, out_channels, reduction_ratio=1., pad_type='', pooling_type='max',
norm_layer=nn.BatchNorm2d, norm_kwargs=None, apply_bn=False, conv_after_downsample=False,
redundant_bias=False):
super(ResampleFeatureMap, self).__init__()
pooling_type = pooling_type or 'max'
self.in_channels = in_channels
self.out_channels = out_channels
self.reduction_ratio = reduction_ratio
self.conv_after_downsample = conv_after_downsample
conv = None
if in_channels != out_channels:
conv = ConvBnAct2d(
in_channels, out_channels, kernel_size=1, padding=pad_type,
norm_layer=norm_layer if apply_bn else None, norm_kwargs=norm_kwargs,
bias=not apply_bn or redundant_bias, act_layer=None)
if reduction_ratio > 1:
stride_size = int(reduction_ratio)
if conv is not None and not self.conv_after_downsample:
self.add_module('conv', conv)
self.add_module(
'downsample',
create_pool2d(
pooling_type, kernel_size=stride_size + 1, stride=stride_size, padding=pad_type))
if conv is not None and self.conv_after_downsample:
self.add_module('conv', conv)
else:
if conv is not None:
self.add_module('conv', conv)
if reduction_ratio < 1:
scale = int(1 // reduction_ratio)
self.add_module('upsample', nn.UpsamplingNearest2d(scale_factor=scale))
# def forward(self, x):
# # here for debugging only
# assert x.shape[1] == self.in_channels
# if self.reduction_ratio > 1:
# if hasattr(self, 'conv') and not self.conv_after_downsample:
# x = self.conv(x)
# x = self.downsample(x)
# if hasattr(self, 'conv') and self.conv_after_downsample:
# x = self.conv(x)
# else:
# if hasattr(self, 'conv'):
# x = self.conv(x)
# if self.reduction_ratio < 1:
# x = self.upsample(x)
# return x
class FpnCombine(nn.Module):
def __init__(self, feature_info, fpn_config, fpn_channels, inputs_offsets, target_reduction, pad_type='',
pooling_type='max', norm_layer=nn.BatchNorm2d, norm_kwargs=None, apply_bn_for_resampling=False,
conv_after_downsample=False, redundant_bias=False, weight_method='attn'):
super(FpnCombine, self).__init__()
self.inputs_offsets = inputs_offsets
self.weight_method = weight_method
self.resample = nn.ModuleDict()
for idx, offset in enumerate(inputs_offsets):
in_channels = fpn_channels
if offset < len(feature_info):
in_channels = feature_info[offset]['num_chs']
input_reduction = feature_info[offset]['reduction']
else:
node_idx = offset - len(feature_info)
input_reduction = fpn_config.nodes[node_idx]['reduction']
reduction_ratio = target_reduction / input_reduction
self.resample[str(offset)] = ResampleFeatureMap(
in_channels, fpn_channels, reduction_ratio=reduction_ratio, pad_type=pad_type,
pooling_type=pooling_type, norm_layer=norm_layer, norm_kwargs=norm_kwargs,
apply_bn=apply_bn_for_resampling, conv_after_downsample=conv_after_downsample,
redundant_bias=redundant_bias)
if weight_method == 'attn' or weight_method == 'fastattn':
# WSM
self.edge_weights = nn.Parameter(torch.ones(len(inputs_offsets)), requires_grad=True)
else:
self.edge_weights = None
def forward(self, x):
dtype = x[0].dtype
nodes = []
for offset in self.inputs_offsets:
input_node = x[offset]
input_node = self.resample[str(offset)](input_node)
nodes.append(input_node)
if self.weight_method == 'attn':
normalized_weights = torch.softmax(self.edge_weights.type(dtype), dim=0)
x = torch.stack(nodes, dim=-1) * normalized_weights
elif self.weight_method == 'fastattn':
edge_weights = nn.functional.relu(self.edge_weights.type(dtype))
weights_sum = torch.sum(edge_weights)
x = torch.stack(
[(nodes[i] * edge_weights[i]) / (weights_sum + 0.0001) for i in range(len(nodes))], dim=-1)
elif self.weight_method == 'sum':
x = torch.stack(nodes, dim=-1)
else:
raise ValueError('unknown weight_method {}'.format(self.weight_method))
x = torch.sum(x, dim=-1)
return x
class BiFpnLayer(nn.Module):
def __init__(self, feature_info, fpn_config, fpn_channels, num_levels=5, pad_type='',
pooling_type='max', norm_layer=nn.BatchNorm2d, norm_kwargs=None, act_layer=_ACT_LAYER,
apply_bn_for_resampling=False, conv_after_downsample=True, conv_bn_relu_pattern=False,
separable_conv=True, redundant_bias=False):
super(BiFpnLayer, self).__init__()
self.fpn_config = fpn_config
self.num_levels = num_levels
self.conv_bn_relu_pattern = False
self.feature_info = []
self.fnode = SequentialAppend()
for i, fnode_cfg in enumerate(fpn_config.nodes):
logging.debug('fnode {} : {}'.format(i, fnode_cfg))
fnode_layers = OrderedDict()
# combine features
reduction = fnode_cfg['reduction']
fnode_layers['combine'] = FpnCombine(
feature_info, fpn_config, fpn_channels, fnode_cfg['inputs_offsets'], target_reduction=reduction,
pad_type=pad_type, pooling_type=pooling_type, norm_layer=norm_layer, norm_kwargs=norm_kwargs,
apply_bn_for_resampling=apply_bn_for_resampling, conv_after_downsample=conv_after_downsample,
redundant_bias=redundant_bias, weight_method=fpn_config.weight_method)
self.feature_info.append(dict(num_chs=fpn_channels, reduction=reduction))
# after combine ops
after_combine = OrderedDict()
if not conv_bn_relu_pattern:
after_combine['act'] = act_layer(inplace=True)
conv_bias = redundant_bias
conv_act = None
else:
conv_bias = False
conv_act = act_layer
conv_kwargs = dict(
in_channels=fpn_channels, out_channels=fpn_channels, kernel_size=3, padding=pad_type,
bias=conv_bias, norm_layer=norm_layer, norm_kwargs=norm_kwargs, act_layer=conv_act)
after_combine['conv'] = SeparableConv2d(**conv_kwargs) if separable_conv else ConvBnAct2d(**conv_kwargs)
fnode_layers['after_combine'] = nn.Sequential(after_combine)
self.fnode.add_module(str(i), nn.Sequential(fnode_layers))
self.feature_info = self.feature_info[-num_levels::]
def forward(self, x):
x = self.fnode(x)
return x[-self.num_levels::]
class BiFpn(nn.Module):
def __init__(self, config, feature_info, norm_layer=nn.BatchNorm2d, norm_kwargs=None, act_layer=_ACT_LAYER):
super(BiFpn, self).__init__()
self.config = config
fpn_config = config.fpn_config or get_fpn_config(config.fpn_name)
self.resample = SequentialAppendLast()
for level in range(config.num_levels):
if level < len(feature_info):
in_chs = feature_info[level]['num_chs']
reduction = feature_info[level]['reduction']
else:
# Adds a coarser level by downsampling the last feature map
reduction_ratio = 2
self.resample.add_module(str(level), ResampleFeatureMap(
in_channels=in_chs,
out_channels=config.fpn_channels,
pad_type=config.pad_type,
pooling_type=config.pooling_type,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
reduction_ratio=reduction_ratio,
apply_bn=config.apply_bn_for_resampling,
conv_after_downsample=config.conv_after_downsample,
redundant_bias=config.redundant_bias,
))
in_chs = config.fpn_channels
reduction = int(reduction * reduction_ratio)
feature_info.append(dict(num_chs=in_chs, reduction=reduction))
self.cell = nn.Sequential()
for rep in range(config.fpn_cell_repeats):
logging.debug('building cell {}'.format(rep))
fpn_layer = BiFpnLayer(
feature_info=feature_info,
fpn_config=fpn_config,
fpn_channels=config.fpn_channels,
num_levels=config.num_levels,
pad_type=config.pad_type,
pooling_type=config.pooling_type,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
act_layer=act_layer,
separable_conv=config.separable_conv,
apply_bn_for_resampling=config.apply_bn_for_resampling,
conv_after_downsample=config.conv_after_downsample,
conv_bn_relu_pattern=config.conv_bn_relu_pattern,
redundant_bias=config.redundant_bias,
)
self.cell.add_module(str(rep), fpn_layer)
feature_info = fpn_layer.feature_info
def forward(self, x):
assert len(self.resample) == self.config.num_levels - len(x)
x = self.resample(x)
x = self.cell(x)
return x
class HeadNet(nn.Module):
def __init__(self, config, num_outputs, norm_layer=nn.BatchNorm2d, norm_kwargs=None, act_layer=_ACT_LAYER, predict_nhwc=False):
super(HeadNet, self).__init__()
norm_kwargs = norm_kwargs or {}
self.config = config
self.predict_nhwc = predict_nhwc
num_anchors = len(config.aspect_ratios) * config.num_scales
self.conv_rep = nn.ModuleList()
self.bn_rep = nn.ModuleList()
conv_kwargs = dict(
in_channels=config.fpn_channels, out_channels=config.fpn_channels, kernel_size=3,
padding=self.config.pad_type, bias=config.redundant_bias, act_layer=None, norm_layer=None)
for i in range(config.box_class_repeats):
conv = SeparableConv2d(**conv_kwargs) if config.separable_conv else ConvBnAct2d(**conv_kwargs)
self.conv_rep.append(conv)
bn_levels = []
for _ in range(config.num_levels):
bn_seq = nn.Sequential()
bn_seq.add_module('bn', norm_layer(config.fpn_channels, **norm_kwargs)) # Here
bn_levels.append(bn_seq)
self.bn_rep.append(nn.ModuleList(bn_levels))
self.act = act_layer(inplace=True)
predict_kwargs = dict(
in_channels=config.fpn_channels, out_channels=num_outputs * num_anchors, kernel_size=3,
padding=self.config.pad_type, bias=True, norm_layer=None, act_layer=None)
if config.separable_conv:
self.predict = SeparableConv2d(**predict_kwargs)
else:
self.predict = ConvBnAct2d(**predict_kwargs)
if self.predict_nhwc:
self.predict = self.predict.to(memory_format=torch.channels_last)
def forward(self, x):
outputs = []
for level in range(self.config.num_levels):
x_level = x[level]
for i in range(self.config.box_class_repeats):
x_level_ident = x_level
x_level = self.conv_rep[i](x_level)
x_level = self.bn_rep[i][level](x_level)
x_level = self.act(x_level)
if i > 0 and self.config.fpn_drop_path_rate:
x_level = drop_path(x_level, self.config.fpn_drop_path_rate, self.training)
x_level += x_level_ident
if self.predict_nhwc:
x_level = x_level.contiguous(memory_format=torch.channels_last)
outputs.append(self.predict(x_level))
return outputs
def _init_weight(m, n='', ):
""" Weight initialization as per Tensorflow official implementations.
"""
def _fan_in_out(w, groups=1):
dimensions = w.dim()
if dimensions < 2:
raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions")
num_input_fmaps = w.size(1)
num_output_fmaps = w.size(0)
receptive_field_size = 1
if w.dim() > 2:
receptive_field_size = w[0][0].numel()
fan_in = num_input_fmaps * receptive_field_size
fan_out = num_output_fmaps * receptive_field_size
fan_out //= groups
return fan_in, fan_out
def _glorot_uniform(w, gain=1, groups=1):
fan_in, fan_out = _fan_in_out(w, groups)
gain /= max(1., (fan_in + fan_out) / 2.) # fan avg
limit = math.sqrt(3.0 * gain)
w.data.uniform_(-limit, limit)
def _variance_scaling(w, gain=1, groups=1):
fan_in, fan_out = _fan_in_out(w, groups)
# gain /= max(1., fan_in) # fan in
gain /= max(1., (fan_in + fan_out) / 2.) # fan
# should it be normal or trunc normal? using normal for now since no good trunc in PT
# constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
# std = math.sqrt(gain) / .87962566103423978
# w.data.trunc_normal(std=std)
std = math.sqrt(gain)
w.data.normal_(std=std)
if isinstance(m, SeparableConv2d):
if 'box_net' in n or 'class_net' in n:
_variance_scaling(m.conv_dw.weight, groups=m.conv_dw.groups)
_variance_scaling(m.conv_pw.weight)
if m.conv_pw.bias is not None:
if 'class_net.predict' in n:
m.conv_pw.bias.data.fill_(-math.log((1 - 0.01) / 0.01))
else:
m.conv_pw.bias.data.zero_()
else:
_glorot_uniform(m.conv_dw.weight, groups=m.conv_dw.groups)
_glorot_uniform(m.conv_pw.weight)
if m.conv_pw.bias is not None:
m.conv_pw.bias.data.zero_()
elif isinstance(m, ConvBnAct2d):
if 'box_net' in n or 'class_net' in n:
m.conv.weight.data.normal_(std=.01)
if m.conv.bias is not None:
if 'class_net.predict' in n:
m.conv.bias.data.fill_(-math.log((1 - 0.01) / 0.01))
else:
m.conv.bias.data.zero_()
else:
_glorot_uniform(m.conv.weight)
if m.conv.bias is not None:
m.conv.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
# looks like all bn init the same?
m.weight.data.fill_(1.0)
m.bias.data.zero_()
def _init_weight_alt(m, n='', ):
""" Weight initialization alternative, based on EfficientNet bacbkone init w/ class bias addition
NOTE: this will likely be removed after some experimentation
"""
if isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
if 'class_net.predict' in n:
m.bias.data.fill_(-math.log((1 - 0.01) / 0.01))
else:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1.0)
m.bias.data.zero_()
class EfficientDet(nn.Module):
def __init__(self, config, norm_kwargs=None, pretrained_backbone_path='', alternate_init=False):
super(EfficientDet, self).__init__()
norm_kwargs = norm_kwargs or dict(eps=.001, momentum=.01)
## Replacing backbone
global_config = efficientnet_configs['fanout']
backbone_config = get_backbone_config(config.backbone_name)
self.backbone = EfficientNet(width_coeff=backbone_config['width_coeff'], depth_coeff=backbone_config['depth_coeff'], \
dropout=backbone_config['dropout'], num_classes=1000, global_config=global_config, features_only=True, out_indices=[2,3,4])
feature_info = self.backbone.feature_info
if pretrained_backbone_path != '':
ckpt_state_dict = torch.load(pretrained_backbone_path, map_location=lambda storage, loc: storage)
print("Backbone being loaded from checkpoint {}".format(pretrained_backbone_path))
self.backbone.load_state_dict(ckpt_state_dict, strict=False)
del ckpt_state_dict
# Pad to multiple of 8 for better performance
if config.fused_focal_loss:
num_classes = (config.num_classes + 7) // 8 * 8
else:
num_classes = config.num_classes
# TODO: predict_nhwc=config.fused_focal_loss for class_net
act_layer = get_act_layer(config.act_type)
self.fpn = BiFpn(config, feature_info, norm_kwargs=norm_kwargs, act_layer=act_layer)
self.class_net = HeadNet(config, num_outputs=num_classes, norm_kwargs=norm_kwargs,
act_layer=act_layer)
self.box_net = HeadNet(config, num_outputs=4, norm_kwargs=norm_kwargs, act_layer=act_layer)
for n, m in self.named_modules():
if 'backbone' not in n:
if alternate_init:
_init_weight_alt(m, n)
else:
_init_weight(m, n)
def forward(self, x):
_, x = self.backbone(x)
x = self.fpn(x)
x_class = self.class_net(x)
x_box = self.box_net(x)
return x_class, x_box
|
Tools/PyTorch/TimeSeriesPredictionPlatform/models/tft_pyt/scripts | scripts | run_traffic_DGX1-16G | # 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.
: ${SEED:=1}
: ${LR:=1e-3}
: ${NGPU:=8}
: ${BATCH_SIZE:=1024}
: ${EPOCHS:=20}
python -m torch.distributed.run --nproc_per_node=${NGPU} train.py \
--dataset traffic \
--data_path /data/processed/traffic_bin \
--batch_size=${BATCH_SIZE} \
--sample 450000 50000 \
--lr ${LR} \
--epochs ${EPOCHS} \
--seed ${SEED} \
--use_amp \
--results /results/TFT_traffic_bs${NGPU}x${BATCH_SIZE}_lr${LR}/seed_${SEED}
|
PyTorch/Classification/GPUNet/triton/runner | runner | stages | # 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 List, Optional, Tuple, Union
# 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
class ResultsType:
"""
Results types generated by runner
"""
TRITON_PERFORMANCE_OFFLINE = "triton_performance_offline"
TRITON_PERFORMANCE_ONLINE = "triton_performance_online"
class Stage:
"""
Stage definition
"""
label: str
commands: List[Command]
result_path: Optional[str]
result_type: Optional[str]
def __init__(
self,
commands: Union[Tuple[str, ...], List[str]],
result_path: Optional[str] = None,
result_type: Optional[str] = None,
):
"""
Args:
commands: List or Tuple of commands provided as raw string
result_path: Path to results file generated by stage
result_type: Type of results generated by stage
"""
if type(commands) not in [tuple, list]:
raise ValueError("""Incorrect type of commands list. Please, provide list of commands as tuple.""")
self.commands = list(map(lambda command: Command(data=command), commands))
self.result_path = result_path
self.result_type = result_type
class ExportStage(Stage):
label = "Export Model"
class ConversionStage(Stage):
label = "Convert Model"
class DeployStage(Stage):
label = "Deploy Model"
class CorrectnessStage(Stage):
label = "Model Correctness"
class TritonPreparePerformanceProfilingDataStage(Stage):
label = "Prepare Triton Profiling Data"
class TritonPerformanceOfflineStage(Stage):
label = "Triton Performance Offline"
class TritonPerformanceOnlineStage(Stage):
label = "Triton Performance Online"
|
PyTorch/LanguageModeling/Transformer-XL | Transformer-XL | requirements | pytorch-transformers==1.1.0
sacremoses==0.0.35
pynvml==8.0.4
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/cli/commands | commands | pretrain | # 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.
import argparse
import logging
from syngen.cli.commands.base_command import BaseCommand
from syngen.benchmark.tasks import train_ec
from syngen.configuration import SynGenDatasetFeatureSpec, SynGenConfiguration
from syngen.generator.tabular import tabular_generators_classes
from syngen.utils.types import MetaData
from syngen.benchmark.models import MODELS
logging.basicConfig()
logging.root.setLevel(logging.NOTSET)
logger = logging.getLogger(__name__)
log = logger
class PretrainCommand(BaseCommand):
def init_parser(self, base_parser):
pretrain_parser = base_parser.add_parser(
"pretrain",
help="Run Synthetic Graph Data Pre-training Tool",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
pretrain_parser.set_defaults(action=self.run)
# global
pretrain_parser.add_argument(
"--task",
type=str,
default="ec",
help=f"now the only available option is ec (edge-classification)",
)
pretrain_parser.add_argument(
"--seed",
type=int,
default=777,
help="Set a seed globally"
)
pretrain_parser.add_argument(
"--timeit",
action="store_true",
help="Measures average training time",
)
pretrain_parser.add_argument(
"--data-path",
type=str,
required=True,
help="Path to dataset in SynGen format to train/finetune on",
)
pretrain_parser.add_argument(
"--edge-name",
type=str,
required=True,
help="Name of the edge to be used during train/finetune",
)
pretrain_parser.add_argument(
"--pretraining-data-path",
type=str,
default=None,
help="Path to dataset in SynGen format to pretrain on",
)
pretrain_parser.add_argument(
"--pretraining-edge-name",
type=str,
default=None,
help="Name of the edge to be used during pretraining",
)
# model
pretrain_parser.add_argument(
"--model",
type=str,
default="gat_ec",
help=f"List of available models: {list(MODELS.keys())}",
)
pretrain_parser.add_argument(
"--hidden-dim",
type=int,
default=128,
help="Hidden feature dimension"
)
pretrain_parser.add_argument(
"--out-dim",
type=int,
default=32,
help="Output feature dimension",
)
pretrain_parser.add_argument(
"--num-classes",
type=int,
required=True,
help="Number of classes in the target column",
)
pretrain_parser.add_argument(
"--n-layers",
type=int,
default=1,
help="Multi-layer full neighborhood sampler layers",
)
for key in MODELS.keys():
MODELS[key].add_args(pretrain_parser)
# dataset
pretrain_parser.add_argument(
"--target-col",
type=str,
required=True,
help="Target column for downstream prediction",
)
pretrain_parser.add_argument(
"--train-ratio",
type=float,
default=0.8,
help="Ratio of data to use as train",
)
pretrain_parser.add_argument(
"--val-ratio",
type=float,
default=0.1,
help="Ratio of data to use as val",
)
pretrain_parser.add_argument(
"--test-ratio",
type=float,
default=0.1,
help="Ratio of data to use as test",
)
# training
pretrain_parser.add_argument(
"--learning-rate",
"--lr",
dest="learning_rate",
type=float,
default=1e-3,
help=f"Initial learning rate for optimizer",
)
pretrain_parser.add_argument(
"--weight-decay",
type=float,
default=0.1,
help=f"Weight decay for optimizer",
)
pretrain_parser.add_argument(
"--batch-size",
type=int,
default=128,
help="Pre-training and Fine-tuning dataloader batch size",
)
pretrain_parser.add_argument(
"--num-workers",
type=int,
default=8,
help="Number of dataloading workers",
)
pretrain_parser.add_argument(
"--shuffle",
action="store_true",
default=False,
help="Shuffles data each epoch"
)
pretrain_parser.add_argument(
"--pretrain-epochs",
type=int,
default=0,
help="Number of pre-training epochs",
)
pretrain_parser.add_argument(
"--finetune-epochs",
type=int,
default=1,
help="Number of finetuning epochs",
)
pretrain_parser.add_argument(
"--log-interval",
type=int,
default=1,
help="logging interval"
)
def run(self, args):
dict_args = vars(args)
finetune_feature_spec = SynGenDatasetFeatureSpec.instantiate_from_preprocessed(
dict_args['data_path']
)
pretrain_feature_spec = None
if dict_args['pretraining_data_path']:
pretrain_feature_spec = SynGenDatasetFeatureSpec.instantiate_from_preprocessed(
dict_args['pretraining_data_path']
)
if args.task == "ec":
out = train_ec(
args,
finetune_feature_spec=finetune_feature_spec,
pretrain_feature_spec=pretrain_feature_spec,
)
else:
raise ValueError("benchmark not supported")
log.info(out)
return out
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/util | util | layerData | /*
* 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 "layerData.h"
#include <sstream>
#include <stdexcept>
using namespace nvinfer1;
namespace tts
{
/******************************************************************************
* CONSTRUCTORS / DESTRUCTOR **************************************************
*****************************************************************************/
LayerData::LayerData()
: mKeys()
, mPrefix{0}
, mData{}
{
}
/******************************************************************************
* PUBLIC METHODS *************************************************************
*****************************************************************************/
Weights LayerData::get(const std::string& name) const
{
auto pos = mKeys.find(name);
if (pos == mKeys.end())
{
std::ostringstream ss;
ss << "Unable to find '" << name << "' in : {";
for (auto pair : mKeys)
{
ss << "'" << pair.first << "', ";
}
ss << "}";
throw std::runtime_error(ss.str());
}
const size_t idx = pos->second;
return Weights{DataType::kFLOAT, (const void*) (mData.data() + mPrefix[idx]),
static_cast<int64_t>(mPrefix[idx + 1] - mPrefix[idx])};
}
bool LayerData::has(const std::string& name) const
{
return mKeys.count(name) > 0;
}
/******************************************************************************
* OUTPUT FUNCTIONS ***********************************************************
*****************************************************************************/
std::ostream& operator<<(std::ostream& stream, const LayerData& data)
{
stream << "LayerData: {";
for (auto pair : data.mKeys)
{
stream << pair.first << ":" << (data.mPrefix[pair.second + 1] - data.mPrefix[pair.second]) << ", ";
}
stream << "}";
return stream;
}
} // namespace tts
|
DGLPyTorch/DrugDiscovery/SE3Transformer/se3_transformer/model | model | basis | # Copyright (c) 2021-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES
# SPDX-License-Identifier: MIT
from functools import lru_cache
from typing import Dict, List
import e3nn.o3 as o3
import torch
import torch.nn.functional as F
from torch import Tensor
from torch.cuda.nvtx import range as nvtx_range
from se3_transformer.runtime.utils import degree_to_dim
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
@lru_cache(maxsize=None)
def get_clebsch_gordon(J: int, d_in: int, d_out: int, device) -> Tensor:
""" Get the (cached) Q^{d_out,d_in}_J matrices from equation (8) """
return o3.wigner_3j(J, d_in, d_out, dtype=torch.float64, device=device).permute(2, 1, 0)
@lru_cache(maxsize=None)
def get_all_clebsch_gordon(max_degree: int, device) -> List[List[Tensor]]:
all_cb = []
for d_in in range(max_degree + 1):
for d_out in range(max_degree + 1):
K_Js = []
for J in range(abs(d_in - d_out), d_in + d_out + 1):
K_Js.append(get_clebsch_gordon(J, d_in, d_out, device))
all_cb.append(K_Js)
return all_cb
def get_spherical_harmonics(relative_pos: Tensor, max_degree: int) -> List[Tensor]:
all_degrees = list(range(2 * max_degree + 1))
sh = o3.spherical_harmonics(all_degrees, relative_pos, normalize=True)
return torch.split(sh, [degree_to_dim(d) for d in all_degrees], dim=1)
@torch.jit.script
def get_basis_script(max_degree: int,
use_pad_trick: bool,
spherical_harmonics: List[Tensor],
clebsch_gordon: List[List[Tensor]],
amp: bool) -> Dict[str, Tensor]:
"""
Compute pairwise bases matrices for degrees up to max_degree
:param max_degree: Maximum input or output degree
:param use_pad_trick: Pad some of the odd dimensions for a better use of Tensor Cores
:param spherical_harmonics: List of computed spherical harmonics
:param clebsch_gordon: List of computed CB-coefficients
:param amp: When true, return bases in FP16 precision
"""
basis = {}
idx = 0
# Double for loop instead of product() because of JIT script
for d_in in range(max_degree + 1):
for d_out in range(max_degree + 1):
key = f'{d_in},{d_out}'
K_Js = []
for freq_idx, J in enumerate(range(abs(d_in - d_out), d_in + d_out + 1)):
Q_J = clebsch_gordon[idx][freq_idx]
K_Js.append(torch.einsum('n f, k l f -> n l k', spherical_harmonics[J].float(), Q_J.float()))
basis[key] = torch.stack(K_Js, 2) # Stack on second dim so order is n l f k
if amp:
basis[key] = basis[key].half()
if use_pad_trick:
basis[key] = F.pad(basis[key], (0, 1)) # Pad the k dimension, that can be sliced later
idx += 1
return basis
@torch.jit.script
def update_basis_with_fused(basis: Dict[str, Tensor],
max_degree: int,
use_pad_trick: bool,
fully_fused: bool) -> Dict[str, Tensor]:
""" Update the basis dict with partially and optionally fully fused bases """
num_edges = basis['0,0'].shape[0]
device = basis['0,0'].device
dtype = basis['0,0'].dtype
sum_dim = sum([degree_to_dim(d) for d in range(max_degree + 1)])
# Fused per output degree
for d_out in range(max_degree + 1):
sum_freq = sum([degree_to_dim(min(d, d_out)) for d in range(max_degree + 1)])
basis_fused = torch.zeros(num_edges, sum_dim, sum_freq, degree_to_dim(d_out) + int(use_pad_trick),
device=device, dtype=dtype)
acc_d, acc_f = 0, 0
for d_in in range(max_degree + 1):
basis_fused[:, acc_d:acc_d + degree_to_dim(d_in), acc_f:acc_f + degree_to_dim(min(d_out, d_in)),
:degree_to_dim(d_out)] = basis[f'{d_in},{d_out}'][:, :, :, :degree_to_dim(d_out)]
acc_d += degree_to_dim(d_in)
acc_f += degree_to_dim(min(d_out, d_in))
basis[f'out{d_out}_fused'] = basis_fused
# Fused per input degree
for d_in in range(max_degree + 1):
sum_freq = sum([degree_to_dim(min(d, d_in)) for d in range(max_degree + 1)])
basis_fused = torch.zeros(num_edges, degree_to_dim(d_in), sum_freq, sum_dim,
device=device, dtype=dtype)
acc_d, acc_f = 0, 0
for d_out in range(max_degree + 1):
basis_fused[:, :, acc_f:acc_f + degree_to_dim(min(d_out, d_in)), acc_d:acc_d + degree_to_dim(d_out)] \
= basis[f'{d_in},{d_out}'][:, :, :, :degree_to_dim(d_out)]
acc_d += degree_to_dim(d_out)
acc_f += degree_to_dim(min(d_out, d_in))
basis[f'in{d_in}_fused'] = basis_fused
if fully_fused:
# Fully fused
# Double sum this way because of JIT script
sum_freq = sum([
sum([degree_to_dim(min(d_in, d_out)) for d_in in range(max_degree + 1)]) for d_out in range(max_degree + 1)
])
basis_fused = torch.zeros(num_edges, sum_dim, sum_freq, sum_dim, device=device, dtype=dtype)
acc_d, acc_f = 0, 0
for d_out in range(max_degree + 1):
b = basis[f'out{d_out}_fused']
basis_fused[:, :, acc_f:acc_f + b.shape[2], acc_d:acc_d + degree_to_dim(d_out)] = b[:, :, :,
:degree_to_dim(d_out)]
acc_f += b.shape[2]
acc_d += degree_to_dim(d_out)
basis['fully_fused'] = basis_fused
del basis['0,0'] # We know that the basis for l = k = 0 is filled with a constant
return basis
def get_basis(relative_pos: Tensor,
max_degree: int = 4,
compute_gradients: bool = False,
use_pad_trick: bool = False,
amp: bool = False) -> Dict[str, Tensor]:
with nvtx_range('spherical harmonics'):
spherical_harmonics = get_spherical_harmonics(relative_pos, max_degree)
with nvtx_range('CB coefficients'):
clebsch_gordon = get_all_clebsch_gordon(max_degree, relative_pos.device)
with torch.autograd.set_grad_enabled(compute_gradients):
with nvtx_range('bases'):
basis = get_basis_script(max_degree=max_degree,
use_pad_trick=use_pad_trick,
spherical_harmonics=spherical_harmonics,
clebsch_gordon=clebsch_gordon,
amp=amp)
return basis
|
PyTorch/Classification/ConvNets | ConvNets | README | # Convolutional Network for Image Classification in PyTorch
In this repository you will find implementations of various image classification models.
Detailed information on each model can be found here:
## Table Of Contents
* [Models](#models)
* [Validation accuracy results](#validation-accuracy-results)
* [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-2 (16x V100 32GB)](#training-performance-nvidia-dgx-2-16x-v100-32gb)
* [Model comparison](#model-comparison)
* [Accuracy vs FLOPS](#accuracy-vs-flops)
* [Latency vs Throughput on different batch sizes](#latency-vs-throughput-on-different-batch-sizes)
## Models
The following table provides links to where you can find additional information on each model:
| **Model** | **Link**|
|:-:|:-:|
| resnet50 | [README](./resnet50v1.5/README.md) |
| resnext101-32x4d | [README](./resnext101-32x4d/README.md) |
| se-resnext101-32x4d | [README](./se-resnext101-32x4d/README.md) |
| EfficientNet | [README](./efficientnet/README.md) |
## Validation accuracy results
Our results were obtained by running the applicable
training scripts in the 20.12 PyTorch NGC container
on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
The specific training script that was run is documented
in the corresponding model's README.
The following table shows the validation accuracy results of the
three classification models side-by-side.
| **Model** | **Mixed Precision Top1** | **Mixed Precision Top5** | **32 bit Top1** | **32 bit Top5** |
|:----------------------:|:------------------------:|:------------------------:|:---------------:|:---------------:|
| efficientnet-b0 | 77.63 | 93.82 | 77.31 | 93.76 |
| efficientnet-b4 | 82.98 | 96.44 | 82.92 | 96.43 |
| efficientnet-widese-b0 | 77.89 | 94.00 | 77.97 | 94.05 |
| efficientnet-widese-b4 | 83.28 | 96.45 | 83.30 | 96.47 |
| resnet50 | 78.60 | 94.19 | 78.69 | 94.16 |
| resnext101-32x4d | 80.43 | 95.06 | 80.40 | 95.04 |
| se-resnext101-32x4d | 81.00 | 95.48 | 81.09 | 95.45 |
## Training performance results
### Training performance: NVIDIA DGX A100 (8x A100 80GB)
Our results were obtained by running the applicable
training scripts in the 21.03 PyTorch NGC container
on NVIDIA DGX A100 with (8x A100 80GB) GPUs.
Performance numbers (in images per second)
were averaged over an entire training epoch.
The specific training script that was run is documented
in the corresponding model's README.
The following table shows the training accuracy results of
all the classification models side-by-side.
| **Model** | **Mixed Precision** | **TF32** | **Mixed Precision Speedup** |
|:----------------------:|:-------------------:|:----------:|:---------------------------:|
| efficientnet-b0 | 16652 img/s | 8193 img/s | 2.03 x |
| efficientnet-b4 | 2570 img/s | 1223 img/s | 2.1 x |
| efficientnet-widese-b0 | 16368 img/s | 8244 img/s | 1.98 x |
| efficientnet-widese-b4 | 2585 img/s | 1223 img/s | 2.11 x |
| resnet50 | 16621 img/s | 7248 img/s | 2.29 x |
| resnext101-32x4d | 7925 img/s | 3471 img/s | 2.28 x |
| se-resnext101-32x4d | 5779 img/s | 2991 img/s | 1.93 x |
### Training performance: NVIDIA DGX-1 16G (8x V100 16GB)
Our results were obtained by running the applicable
training scripts in the 21.03 PyTorch NGC container
on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
Performance numbers (in images per second)
were averaged over an entire training epoch.
The specific training script that was run is documented
in the corresponding model's README.
The following table shows the training accuracy results of all the
classification models side-by-side.
| **Model** | **Mixed Precision** | **FP32** | **Mixed Precision Speedup** |
|:----------------------:|:-------------------:|:----------:|:---------------------------:|
| efficientnet-b0 | 7789 img/s | 4672 img/s | 1.66 x |
| efficientnet-b4 | 1366 img/s | 616 img/s | 2.21 x |
| efficientnet-widese-b0 | 7875 img/s | 4592 img/s | 1.71 x |
| efficientnet-widese-b4 | 1356 img/s | 612 img/s | 2.21 x |
| resnet50 | 8322 img/s | 2855 img/s | 2.91 x |
| resnext101-32x4d | 4065 img/s | 1133 img/s | 3.58 x |
| se-resnext101-32x4d | 2971 img/s | 1004 img/s | 2.95 x |
## Model Comparison
### Accuracy vs FLOPS
Plot describes relationship between floating point operations
needed for computing forward pass on a 224px x 224px image,
for the implemented models.
Dot size indicates number of trainable parameters.
### Latency vs Throughput on different batch sizes
Plot describes relationship between
inference latency, throughput and batch size
for the implemented models.
|
TensorFlow/Segmentation/UNet_Industrial/model/layers | layers | conv2d | #!/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__ = ['conv2d']
def conv2d(
inputs,
n_channels=8,
kernel_size=(3, 3),
strides=(1, 1),
padding='VALID',
data_format='NHWC',
dilation_rate=(1, 1),
use_bias=True,
kernel_initializer=tf.variance_scaling_initializer(),
bias_initializer=tf.zeros_initializer(),
trainable=True
):
if data_format not in ['NHWC', 'NCHW']:
raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
if padding.upper() not in ['SAME', 'VALID']:
raise ValueError("Unknown padding: `%s` (accepted: ['SAME', 'VALID'])" % padding.upper())
net = tf.layers.conv2d(
inputs,
filters=n_channels,
kernel_size=kernel_size,
strides=strides,
padding=padding,
dilation_rate=dilation_rate,
data_format='channels_last' if data_format == 'NHWC' else 'channels_first',
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
trainable=trainable,
activation=None
)
_log_hparams(
classname='Conv2D',
layername=net.name,
n_channels=n_channels,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
use_bias=use_bias,
trainable=trainable,
out_shape=str(net.get_shape()),
out_dtype=net.dtype
)
return net |
TensorFlow/Detection/SSD/models | models | README | # TensorFlow Models
This repository contains a number of different models implemented in [TensorFlow](https://www.tensorflow.org):
The [official models](https://github.com/tensorflow/models/tree/master/official) are a collection of example models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read. We especially recommend newer TensorFlow users to start here.
The [research models](https://github.com/tensorflow/models/tree/master/research) are a large collection of models implemented in TensorFlow by researchers. They are not officially supported or available in release branches; it is up to the individual researchers to maintain the models and/or provide support on issues and pull requests.
The [samples folder](https://github.com/tensorflow/models/tree/master/samples) contains code snippets and smaller models that demonstrate features of TensorFlow, including code presented in various blog posts.
The [tutorials folder](https://github.com/tensorflow/models/tree/master/tutorials) is a collection of models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
## Contribution guidelines
If you want to contribute to models, be sure to review the [contribution guidelines](CONTRIBUTING.md).
## License
[Apache License 2.0](LICENSE)
|
TensorFlow/Detection/SSD/models/research/object_detection/core | core | standard_fields | # 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.
# ==============================================================================
"""Contains classes specifying naming conventions used for object detection.
Specifies:
InputDataFields: standard fields used by reader/preprocessor/batcher.
DetectionResultFields: standard fields returned by object detector.
BoxListFields: standard field used by BoxList
TfExampleFields: standard fields for tf-example data format (go/tf-example).
"""
class InputDataFields(object):
"""Names for the input tensors.
Holds the standard data field names to use for identifying input tensors. This
should be used by the decoder to identify keys for the returned tensor_dict
containing input tensors. And it should be used by the model to identify the
tensors it needs.
Attributes:
image: image.
image_additional_channels: additional channels.
original_image: image in the original input size.
original_image_spatial_shape: image in the original input size.
key: unique key corresponding to image.
source_id: source of the original image.
filename: original filename of the dataset (without common path).
groundtruth_image_classes: image-level class labels.
groundtruth_image_confidences: image-level class confidences.
groundtruth_boxes: coordinates of the ground truth boxes in the image.
groundtruth_classes: box-level class labels.
groundtruth_confidences: box-level class confidences. The shape should be
the same as the shape of groundtruth_classes.
groundtruth_label_types: box-level label types (e.g. explicit negative).
groundtruth_is_crowd: [DEPRECATED, use groundtruth_group_of instead]
is the groundtruth a single object or a crowd.
groundtruth_area: area of a groundtruth segment.
groundtruth_difficult: is a `difficult` object
groundtruth_group_of: is a `group_of` objects, e.g. multiple objects of the
same class, forming a connected group, where instances are heavily
occluding each other.
proposal_boxes: coordinates of object proposal boxes.
proposal_objectness: objectness score of each proposal.
groundtruth_instance_masks: ground truth instance masks.
groundtruth_instance_boundaries: ground truth instance boundaries.
groundtruth_instance_classes: instance mask-level class labels.
groundtruth_keypoints: ground truth keypoints.
groundtruth_keypoint_visibilities: ground truth keypoint visibilities.
groundtruth_label_weights: groundtruth label weights.
groundtruth_weights: groundtruth weight factor for bounding boxes.
num_groundtruth_boxes: number of groundtruth boxes.
is_annotated: whether an image has been labeled or not.
true_image_shapes: true shapes of images in the resized images, as resized
images can be padded with zeros.
multiclass_scores: the label score per class for each box.
"""
image = 'image'
image_additional_channels = 'image_additional_channels'
original_image = 'original_image'
original_image_spatial_shape = 'original_image_spatial_shape'
key = 'key'
source_id = 'source_id'
filename = 'filename'
groundtruth_image_classes = 'groundtruth_image_classes'
groundtruth_image_confidences = 'groundtruth_image_confidences'
groundtruth_boxes = 'groundtruth_boxes'
groundtruth_classes = 'groundtruth_classes'
groundtruth_confidences = 'groundtruth_confidences'
groundtruth_label_types = 'groundtruth_label_types'
groundtruth_is_crowd = 'groundtruth_is_crowd'
groundtruth_area = 'groundtruth_area'
groundtruth_difficult = 'groundtruth_difficult'
groundtruth_group_of = 'groundtruth_group_of'
proposal_boxes = 'proposal_boxes'
proposal_objectness = 'proposal_objectness'
groundtruth_instance_masks = 'groundtruth_instance_masks'
groundtruth_instance_boundaries = 'groundtruth_instance_boundaries'
groundtruth_instance_classes = 'groundtruth_instance_classes'
groundtruth_keypoints = 'groundtruth_keypoints'
groundtruth_keypoint_visibilities = 'groundtruth_keypoint_visibilities'
groundtruth_label_weights = 'groundtruth_label_weights'
groundtruth_weights = 'groundtruth_weights'
num_groundtruth_boxes = 'num_groundtruth_boxes'
is_annotated = 'is_annotated'
true_image_shape = 'true_image_shape'
multiclass_scores = 'multiclass_scores'
class DetectionResultFields(object):
"""Naming conventions for storing the output of the detector.
Attributes:
source_id: source of the original image.
key: unique key corresponding to image.
detection_boxes: coordinates of the detection boxes in the image.
detection_scores: detection scores for the detection boxes in the image.
detection_classes: detection-level class labels.
detection_masks: contains a segmentation mask for each detection box.
detection_boundaries: contains an object boundary for each detection box.
detection_keypoints: contains detection keypoints for each detection box.
num_detections: number of detections in the batch.
"""
source_id = 'source_id'
key = 'key'
detection_boxes = 'detection_boxes'
detection_scores = 'detection_scores'
detection_classes = 'detection_classes'
detection_masks = 'detection_masks'
detection_boundaries = 'detection_boundaries'
detection_keypoints = 'detection_keypoints'
num_detections = 'num_detections'
class BoxListFields(object):
"""Naming conventions for BoxLists.
Attributes:
boxes: bounding box coordinates.
classes: classes per bounding box.
scores: scores per bounding box.
weights: sample weights per bounding box.
objectness: objectness score per bounding box.
masks: masks per bounding box.
boundaries: boundaries per bounding box.
keypoints: keypoints per bounding box.
keypoint_heatmaps: keypoint heatmaps per bounding box.
is_crowd: is_crowd annotation per bounding box.
"""
boxes = 'boxes'
classes = 'classes'
scores = 'scores'
weights = 'weights'
confidences = 'confidences'
objectness = 'objectness'
masks = 'masks'
boundaries = 'boundaries'
keypoints = 'keypoints'
keypoint_heatmaps = 'keypoint_heatmaps'
is_crowd = 'is_crowd'
class TfExampleFields(object):
"""TF-example proto feature names for object detection.
Holds the standard feature names to load from an Example proto for object
detection.
Attributes:
image_encoded: JPEG encoded string
image_format: image format, e.g. "JPEG"
filename: filename
channels: number of channels of image
colorspace: colorspace, e.g. "RGB"
height: height of image in pixels, e.g. 462
width: width of image in pixels, e.g. 581
source_id: original source of the image
image_class_text: image-level label in text format
image_class_label: image-level label in numerical format
object_class_text: labels in text format, e.g. ["person", "cat"]
object_class_label: labels in numbers, e.g. [16, 8]
object_bbox_xmin: xmin coordinates of groundtruth box, e.g. 10, 30
object_bbox_xmax: xmax coordinates of groundtruth box, e.g. 50, 40
object_bbox_ymin: ymin coordinates of groundtruth box, e.g. 40, 50
object_bbox_ymax: ymax coordinates of groundtruth box, e.g. 80, 70
object_view: viewpoint of object, e.g. ["frontal", "left"]
object_truncated: is object truncated, e.g. [true, false]
object_occluded: is object occluded, e.g. [true, false]
object_difficult: is object difficult, e.g. [true, false]
object_group_of: is object a single object or a group of objects
object_depiction: is object a depiction
object_is_crowd: [DEPRECATED, use object_group_of instead]
is the object a single object or a crowd
object_segment_area: the area of the segment.
object_weight: a weight factor for the object's bounding box.
instance_masks: instance segmentation masks.
instance_boundaries: instance boundaries.
instance_classes: Classes for each instance segmentation mask.
detection_class_label: class label in numbers.
detection_bbox_ymin: ymin coordinates of a detection box.
detection_bbox_xmin: xmin coordinates of a detection box.
detection_bbox_ymax: ymax coordinates of a detection box.
detection_bbox_xmax: xmax coordinates of a detection box.
detection_score: detection score for the class label and box.
"""
image_encoded = 'image/encoded'
image_format = 'image/format' # format is reserved keyword
filename = 'image/filename'
channels = 'image/channels'
colorspace = 'image/colorspace'
height = 'image/height'
width = 'image/width'
source_id = 'image/source_id'
image_class_text = 'image/class/text'
image_class_label = 'image/class/label'
object_class_text = 'image/object/class/text'
object_class_label = 'image/object/class/label'
object_bbox_ymin = 'image/object/bbox/ymin'
object_bbox_xmin = 'image/object/bbox/xmin'
object_bbox_ymax = 'image/object/bbox/ymax'
object_bbox_xmax = 'image/object/bbox/xmax'
object_view = 'image/object/view'
object_truncated = 'image/object/truncated'
object_occluded = 'image/object/occluded'
object_difficult = 'image/object/difficult'
object_group_of = 'image/object/group_of'
object_depiction = 'image/object/depiction'
object_is_crowd = 'image/object/is_crowd'
object_segment_area = 'image/object/segment/area'
object_weight = 'image/object/weight'
instance_masks = 'image/segmentation/object'
instance_boundaries = 'image/boundaries/object'
instance_classes = 'image/segmentation/object/class'
detection_class_label = 'image/detection/label'
detection_bbox_ymin = 'image/detection/bbox/ymin'
detection_bbox_xmin = 'image/detection/bbox/xmin'
detection_bbox_ymax = 'image/detection/bbox/ymax'
detection_bbox_xmax = 'image/detection/bbox/xmax'
detection_score = 'image/detection/score'
|
TensorFlow2/Recommendation/DLRM_and_DCNv2/tensorflow-dot-based-interact/tensorflow_dot_based_interact/python/ops | ops | __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.
#
|
TensorFlow/Segmentation/UNet_Industrial/model/layers | layers | array_ops | #!/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__ = ['concat', 'flatten', 'reshape', 'squeeze', 'upscale_2d']
def concat(values, axis, name='concat'):
net = tf.concat(values=values, axis=axis, name=name)
_log_hparams(classname='Concat', layername=net.name, axis=axis, out_shape=str(net.get_shape()), out_dtype=net.dtype)
return net
def flatten(inputs, name='flatten'):
net = tf.layers.flatten(inputs, name=name)
_log_hparams(classname='Flatten', layername=net.name, out_shape=str(net.get_shape()), out_dtype=net.dtype)
return net
def reshape(tensor, shape, name='reshape'):
net = tf.reshape(tensor, shape=shape, name=name)
_log_hparams(
classname='Reshape', layername=net.name, shape=shape, out_shape=str(net.get_shape()), out_dtype=net.dtype
)
return net
def squeeze(tensor, axis, name='squeeze'):
net = tf.squeeze(tensor, axis=axis, name=name)
_log_hparams(
classname='Squeeze', layername=net.name, axis=axis, out_shape=str(net.get_shape()), out_dtype=net.dtype
)
return net
def upscale_2d(inputs, size, is_scale=True, method=0, align_corners=True, data_format='NHWC', name='upsample2d_layer'):
if not isinstance(size, (list, tuple)) and len(size) == 2:
raise AssertionError()
if data_format not in ['NHWC', 'NCHW']:
raise ValueError("Unknown data format received: `%s` (allowed: `NHWC`, `NCHW`)" % data_format)
input_shape = inputs.get_shape()
if len(inputs.get_shape()) == 3:
if is_scale:
size_h = size[0] * int(inputs.get_shape()[0])
size_w = size[1] * int(inputs.get_shape()[1])
_size = [size_h, size_w]
else:
_size = size
elif len(inputs.get_shape()) == 4:
if data_format == 'NCHW':
inputs = tf.transpose(inputs, [0, 2, 3, 1]) # NCHW => NHWC
if is_scale:
size_h = size[0] * int(inputs.get_shape()[1])
size_w = size[1] * int(inputs.get_shape()[2])
_size = [size_h, size_w]
else:
_size = size
else:
raise Exception("Do not support shape %s" % str(inputs.get_shape()))
with tf.variable_scope(name):
net = tf.image.resize_images(inputs, size=_size, method=method, align_corners=align_corners)
if data_format == 'NCHW' and len(inputs.get_shape()) == 4:
net = tf.transpose(net, [0, 3, 1, 2]) # NHWC => NCHW
_log_hparams(
classname='Upscale2D',
layername=net.name,
size=size,
is_scale=is_scale,
method=method,
align_corners=align_corners,
data_format=data_format,
input_shape=str(input_shape),
out_shape=str(net.get_shape()),
out_dtype=net.dtype
)
return net
|
DGLPyTorch/DrugDiscovery/SE3Transformer/se3_transformer/data_loading | data_loading | data_module | # Copyright (c) 2021-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES
# SPDX-License-Identifier: MIT
import torch.distributed as dist
from abc import ABC
from torch.utils.data import DataLoader, DistributedSampler, Dataset
from se3_transformer.runtime.utils import get_local_rank
def _get_dataloader(dataset: Dataset, shuffle: bool, **kwargs) -> DataLoader:
# Classic or distributed dataloader depending on the context
sampler = DistributedSampler(dataset, shuffle=shuffle) if dist.is_initialized() else None
return DataLoader(dataset, shuffle=(shuffle and sampler is None), sampler=sampler, **kwargs)
class DataModule(ABC):
""" Abstract DataModule. Children must define self.ds_{train | val | test}. """
def __init__(self, **dataloader_kwargs):
super().__init__()
if get_local_rank() == 0:
self.prepare_data()
# Wait until rank zero has prepared the data (download, preprocessing, ...)
if dist.is_initialized():
dist.barrier(device_ids=[get_local_rank()])
self.dataloader_kwargs = {'pin_memory': True, 'persistent_workers': dataloader_kwargs.get('num_workers', 0) > 0,
**dataloader_kwargs}
self.ds_train, self.ds_val, self.ds_test = None, None, None
def prepare_data(self):
""" Method called only once per node. Put here any downloading or preprocessing """
pass
def train_dataloader(self) -> DataLoader:
return _get_dataloader(self.ds_train, shuffle=True, **self.dataloader_kwargs)
def val_dataloader(self) -> DataLoader:
return _get_dataloader(self.ds_val, shuffle=False, **self.dataloader_kwargs)
def test_dataloader(self) -> DataLoader:
return _get_dataloader(self.ds_test, shuffle=False, **self.dataloader_kwargs)
|
TensorFlow2/Segmentation/MaskRCNN/mrcnn_tf2/runtime | runtime | run | import logging
import os
import tensorflow as tf
import dllogger
from mrcnn_tf2.model.mask_rcnn import MaskRCNN
from mrcnn_tf2.runtime.callbacks import DLLoggerMetricsCallback, DLLoggerPerfCallback, PretrainedWeightsLoadingCallback
from mrcnn_tf2.runtime.evaluation import evaluate
from mrcnn_tf2.runtime.learning_rate import PiecewiseConstantWithWarmupSchedule
from mrcnn_tf2.runtime.weights_mapping import WEIGHTS_MAPPING
def run_training(dataset, params):
setup(params)
strategy = tf.distribute.MirroredStrategy()
params.replicas = strategy.num_replicas_in_sync
params.global_train_batch_size = params.train_batch_size * params.replicas
logging.info(f'Distributed Strategy is activated for {params.replicas} device(s)')
with strategy.scope():
learning_rate = PiecewiseConstantWithWarmupSchedule(
init_value=params.init_learning_rate,
# scale boundaries from epochs to steps
boundaries=[
int(b * dataset.train_size / params.global_train_batch_size)
for b in params.learning_rate_boundaries
],
values=params.learning_rate_values,
# scale only by local BS as distributed strategy later scales it by number of replicas
scale=params.train_batch_size
)
optimizer = tf.keras.optimizers.SGD(
learning_rate=learning_rate,
momentum=params.momentum
)
mask_rcnn_model = create_model(params)
mask_rcnn_model.compile(
optimizer=optimizer
)
# distributed strategy splits data between instances so we need global BS
train_data = dataset.train_fn(batch_size=params.global_train_batch_size)
if params.eagerly:
mask_rcnn_model.run_eagerly = True
logging.warning('Model is running in eager mode which might reduce performance')
mask_rcnn_model.fit(
x=train_data,
epochs=params.epochs,
steps_per_epoch=params.steps_per_epoch or (dataset.train_size // params.global_train_batch_size),
callbacks=list(create_callbacks(params)),
verbose=0
)
def run_evaluation(dataset, params):
setup(params)
mask_rcnn_model = create_model(params)
if params.eagerly:
mask_rcnn_model.run_eagerly = True
logging.warning('Model is running in eager mode which might reduce performance')
predictions = mask_rcnn_model.predict(
x=dataset.eval_fn(params.eval_batch_size),
callbacks=list(create_callbacks(params))
)
eval_results = evaluate(
predictions=predictions,
eval_file=params.eval_file,
include_mask=params.include_mask
)
dllogger.log(
step=tuple(),
data={k: float(v) for k, v in eval_results.items()}
)
def run_inference(dataset, params):
setup(params)
mask_rcnn_model = create_model(params)
if params.eagerly:
mask_rcnn_model.run_eagerly = True
logging.warning('Model is running in eager mode which might reduce performance')
mask_rcnn_model.predict(
x=dataset.eval_fn(params.eval_batch_size),
callbacks=list(create_callbacks(params))
)
def setup(params):
# enforces that AMP is enabled using --amp and not env var
# mainly for NGC where it is enabled by default
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '0'
if params.xla:
tf.config.optimizer.set_jit(True)
logging.info('XLA is activated')
if params.amp:
policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16", loss_scale="dynamic")
tf.keras.mixed_precision.experimental.set_policy(policy)
logging.info('AMP is activated')
def create_model(params):
model = MaskRCNN(
params=params,
trainable='train' in params.mode
)
checkpoint_path = tf.train.latest_checkpoint(params.model_dir)
# if there is no checkpoint we are done
if checkpoint_path is None:
logging.info(f"No checkpoint was found in: {params.model_dir}")
return model
model.load_weights(checkpoint_path).expect_partial()
logging.info(f"Loaded weights from checkpoint: {checkpoint_path}")
# don't load backbone weights to do not override the checkpoint
if params.backbone_checkpoint:
params.backbone_checkpoint = None
logging.info("Pretrained backbone weights will not be loaded")
return model
def create_callbacks(params):
yield DLLoggerMetricsCallback(
dllogger=dllogger,
log_every=params.log_every
)
yield DLLoggerPerfCallback(
dllogger=dllogger,
batch_sizes={
'train': params.train_batch_size * getattr(params, 'replicas', 1),
'test': params.eval_batch_size * getattr(params, 'replicas', 1),
'predict': params.eval_batch_size * getattr(params, 'replicas', 1)
},
warmup_steps=params.log_warmup_steps,
log_every=params.log_every
)
if params.backbone_checkpoint:
yield PretrainedWeightsLoadingCallback(
checkpoint_path=params.backbone_checkpoint,
mapping=lambda name: WEIGHTS_MAPPING.get(name.replace(':0', ''), name)
)
yield tf.keras.callbacks.ModelCheckpoint(
filepath=os.path.join(params.model_dir, params.checkpoint_name_format),
verbose=1
)
if params.log_tensorboard:
yield tf.keras.callbacks.TensorBoard(
log_dir=params.log_tensorboard,
update_freq='batch'
)
|
Tools/PyTorch/TimeSeriesPredictionPlatform/distributed_launcher | distributed_launcher | setup | # 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.
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from setuptools import find_namespace_packages, setup
setup(
name="hydra-torch-dist-launcher",
version="0.1",
author="Jan Baczek",
author_email="jbaczek@nvidia.com",
description="Torch distributed launcher plugin",
packages=find_namespace_packages(include=["hydra_plugins.*"]),
classifiers=[
"License :: OSI Approved :: MIT Apache License, Version 2.0",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
],
install_requires=["hydra-core==1.1.1" ],
include_package_data=True,
)
|
TensorFlow2/Recommendation/DLRM_and_DCNv2 | DLRM_and_DCNv2 | main | # 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.
#
# author: Tomasz Grel (tgrel@nvidia.com)
from absl import app, flags
import os
import sys
import json
from distributed_embeddings.python.layers import dist_model_parallel as dmp
# Define the flags first before importing TensorFlow.
# Otherwise, enabling XLA-Lite would be impossible with a command-line flag
def define_common_flags():
flags.DEFINE_enum("mode", default="train", enum_values=['inference', 'eval', 'train'],
help='Choose "train" to train the model, "inference" to benchmark inference'
' and "eval" to run validation')
# Debug parameters
flags.DEFINE_bool("run_eagerly", default=False, help="Disable all tf.function decorators for debugging")
flags.DEFINE_bool("tfdata_debug", default=False, help="Run tf.data operations eagerly (experimental)")
flags.DEFINE_integer("seed", default=None, help="Random seed")
flags.DEFINE_bool("embedding_zeros_initializer", default=False,
help="Initialize the embeddings to zeros. This takes much less time so it's useful"
" for benchmarking and debugging.")
flags.DEFINE_bool("embedding_trainable", default=True, help="If True the embeddings will be trainable, otherwise frozen")
# Hardware and performance features
flags.DEFINE_bool("amp", default=False, help="Enable automatic mixed precision")
flags.DEFINE_bool("use_mde_embeddings", default=True,
help="Use the embedding implementation from the TensorFlow Distributed Embeddings package")
flags.DEFINE_bool("concat_embedding", default=False,
help="Concatenate embeddings with the same dimension. Only supported for singleGPU.")
flags.DEFINE_string("dist_strategy", default='memory_balanced',
help="Strategy for the Distributed Embeddings to use. Supported options are"
"'memory_balanced', 'basic' and 'memory_optimized'")
flags.DEFINE_integer("column_slice_threshold", default=5*1000*1000*1000,
help='Number of elements above which a distributed embedding will be sliced across'
'multiple devices')
flags.DEFINE_integer("row_slice_threshold", default=10*1000*1000*1000,
help='Number of elements above which a distributed embedding will be sliced across'
'multiple devices')
flags.DEFINE_integer("data_parallel_threshold", default=None,
help='Number of elements above which a distributed embedding will be sliced across'
'multiple devices')
flags.DEFINE_integer("cpu_offloading_threshold_gb", default=75,
help='Size of the embedding tables in GB above which '
'offloading to CPU memory should be employed.'
'Applies only to singleGPU at the moment.')
flags.DEFINE_bool('cpu', default=False, help='Place the entire model on CPU')
flags.DEFINE_bool("xla", default=False, help="Enable XLA")
flags.DEFINE_integer("loss_scale", default=65536, help="Static loss scale to use with mixed precision training")
flags.DEFINE_integer("inter_op_parallelism", default=None, help='Number of inter op threads')
flags.DEFINE_integer("intra_op_parallelism", default=None, help='Number of intra op threads')
# Checkpointing
flags.DEFINE_string("save_checkpoint_path", default=None,
help="Path to which to save a checkpoint file at the end of the training")
flags.DEFINE_string("restore_checkpoint_path", default=None,
help="Path from which to restore a checkpoint before training")
# Evaluation, logging, profiling
flags.DEFINE_integer("auc_thresholds", default=8000,
help="Number of thresholds for the AUC computation")
flags.DEFINE_integer("epochs", default=1, help="Number of epochs to train for")
flags.DEFINE_integer("max_steps", default=-1, help="Stop the training/inference after this many optimiation steps")
flags.DEFINE_integer("evals_per_epoch", default=1, help='Number of evaluations per epoch')
flags.DEFINE_float("print_freq", default=100, help='Number of steps between debug prints')
flags.DEFINE_integer("profiler_start_step", default=None, help='Step at which to start profiling')
flags.DEFINE_integer("profiled_rank", default=1, help='Rank to profile')
flags.DEFINE_string("log_path", default='dlrm_tf_log.json', help="Path to JSON file for storing benchmark results")
# dataset and dataloading settings
flags.DEFINE_string("dataset_path", default=None,
help="Path to dataset directory")
flags.DEFINE_string("feature_spec", default="feature_spec.yaml",
help="Name of the feature spec file in the dataset directory")
flags.DEFINE_enum("dataset_type", default="tf_raw",
enum_values=['tf_raw', 'synthetic', 'split_tfrecords'],
help='The type of the dataset to use')
flags.DEFINE_boolean("data_parallel_input", default=False, help="Use a data-parallel dataloader,"
" i.e., load a local batch of of data for all input features")
# Synthetic dataset settings
flags.DEFINE_boolean("synthetic_dataset_use_feature_spec", default=False,
help="Create a temporary synthetic dataset based on a real one. "
"Uses --dataset_path and --feature_spec"
"Overrides synthetic dataset dimension flags, except the number of batches")
flags.DEFINE_integer('synthetic_dataset_train_batches', default=64008,
help='Number of training batches in the synthetic dataset')
flags.DEFINE_integer('synthetic_dataset_valid_batches', default=1350,
help='Number of validation batches in the synthetic dataset')
flags.DEFINE_list('synthetic_dataset_cardinalities', default=26*[1000],
help='Number of categories for each embedding table of the synthetic dataset')
flags.DEFINE_list('synthetic_dataset_hotness', default=26*[20],
help='Number of categories for each embedding table of the synthetic dataset')
flags.DEFINE_integer('synthetic_dataset_num_numerical_features', default=13,
help='Number of numerical features of the synthetic dataset')
define_common_flags()
FLAGS = flags.FLAGS
app.define_help_flags()
app.parse_flags_with_usage(sys.argv)
if FLAGS.xla:
if FLAGS.cpu:
os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible --tf_xla_cpu_global_jit'
else:
os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible'
import time
import tensorflow as tf
import tensorflow_addons as tfa
import numpy as np
import horovod.tensorflow as hvd
from tensorflow.keras.mixed_precision import LossScaleOptimizer
import dllogger
from utils.logging import IterTimer, init_logging
from utils.distributed import dist_print
from dataloading.dataloader import create_input_pipelines, get_dataset_metadata
from nn.lr_scheduler import LearningRateScheduler
from nn.model import Model
from nn.evaluator import Evaluator
from nn.trainer import Trainer
def init_tf(FLAGS):
"""
Set global options for TensorFlow
"""
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
visible_gpus = []
if gpus and not FLAGS.cpu:
visible_gpus = gpus[hvd.local_rank()]
tf.config.experimental.set_visible_devices(visible_gpus, 'GPU')
if FLAGS.amp:
policy = tf.keras.mixed_precision.Policy("mixed_float16")
tf.keras.mixed_precision.set_global_policy(policy)
tf.config.run_functions_eagerly(FLAGS.run_eagerly)
if FLAGS.tfdata_debug:
tf.data.experimental.enable_debug_mode()
if FLAGS.inter_op_parallelism:
tf.config.threading.set_inter_op_parallelism_threads(FLAGS.inter_op_parallelism)
if FLAGS.intra_op_parallelism:
tf.config.threading.set_intra_op_parallelism_threads(FLAGS.intra_op_parallelism)
tf.random.set_seed(hash((FLAGS.seed, hvd.rank())))
def parse_embedding_dimension(embedding_dim, num_embeddings):
try:
embedding_dim = int(embedding_dim)
embedding_dim = [embedding_dim] * num_embeddings
return embedding_dim
except:
pass
if not isinstance(embedding_dim, str):
return ValueError(f'Unsupported embedding_dimension type: f{type(embedding_dim)}')
if os.path.exists(embedding_dim):
# json file with a list of dimensions for each feature
with open(embedding_dim) as f:
edim = json.load(f)
else:
edim = embedding_dim.split(',')
edim = [int(d) for d in edim]
if len(edim) != num_embeddings:
raise ValueError(f'Length of specified embedding dimensions ({len(edim)}) does not match'
f' the number of embedding layers in the neural network ({num_embeddings})')
return edim
def compute_eval_points(train_batches, evals_per_epoch):
eval_points = np.linspace(0, train_batches - 1, evals_per_epoch + 1)[1:]
eval_points = np.round(eval_points).tolist()
return eval_points
def inference_benchmark(validation_pipeline, dlrm, timer, FLAGS):
if FLAGS.max_steps == -1:
FLAGS.max_steps = 1000
evaluator = Evaluator(model=dlrm, timer=timer, auc_thresholds=FLAGS.auc_thresholds,
max_steps=FLAGS.max_steps, cast_dtype=None)
auc, test_loss, latencies = evaluator(validation_pipeline)
# don't benchmark the first few warmup steps
latencies = latencies[10:]
result_data = {
'mean_inference_throughput': FLAGS.valid_batch_size / np.mean(latencies),
'mean_inference_latency': np.mean(latencies)
}
for percentile in [90, 95, 99]:
result_data[f'p{percentile}_inference_latency'] = np.percentile(latencies, percentile)
result_data['auc'] = auc
if hvd.rank() == 0:
dllogger.log(data=result_data, step=tuple())
def validate_cmd_line_flags():
if FLAGS.cpu and hvd.size() > 1:
raise ValueError('MultiGPU mode is not supported when training on CPU')
if FLAGS.cpu and FLAGS.interaction == 'custom_cuda':
raise ValueError('"custom_cuda" dot interaction not supported for CPU. '
'Please specify "--dot_interaction tensorflow" if you want to run on CPU')
if FLAGS.concat_embedding and hvd.size() != 1:
raise ValueError('Concat embedding is currently unsupported in multiGPU mode.')
if FLAGS.concat_embedding and FLAGS.dataset_type != 'tf_raw':
raise ValueError('Concat embedding is only supported for dataset_type="tf_raw",'
f'got dataset_type={FLAGS.dataset_type}')
all_embedding_dims_equal = all(dim == FLAGS.embedding_dim[0] for dim in FLAGS.embedding_dim)
if FLAGS.concat_embedding and not all_embedding_dims_equal:
raise ValueError('Concat embedding is only supported when all embeddings have the same output dimension,'
f'got embedding_dim={FLAGS.embedding_dim}')
def create_optimizers(flags):
if flags.optimizer == 'sgd':
embedding_optimizer = tf.keras.optimizers.legacy.SGD(learning_rate=flags.learning_rate, momentum=0)
if flags.amp:
embedding_optimizer = LossScaleOptimizer(embedding_optimizer,
initial_scale=flags.loss_scale,
dynamic=False)
mlp_optimizer = embedding_optimizer
elif flags.optimizer == 'adam':
embedding_optimizer = tfa.optimizers.LazyAdam(learning_rate=flags.learning_rate,
beta_1=flags.beta1, beta_2=flags.beta2)
mlp_optimizer = tf.keras.optimizers.legacy.Adam(learning_rate=flags.learning_rate,
beta_1=flags.beta1, beta_2=flags.beta2)
if flags.amp:
# only wrap the mlp optimizer and not the embedding optimizer because the embeddings are not run in FP16
mlp_optimizer = LossScaleOptimizer(mlp_optimizer, initial_scale=flags.loss_scale, dynamic=False)
return mlp_optimizer, embedding_optimizer
def main():
hvd.init()
init_logging(log_path=FLAGS.log_path, params_dict=FLAGS.flag_values_dict(), enabled=hvd.rank()==0)
init_tf(FLAGS)
dataset_metadata = get_dataset_metadata(FLAGS.dataset_path, FLAGS.feature_spec)
FLAGS.embedding_dim = parse_embedding_dimension(FLAGS.embedding_dim,
num_embeddings=len(dataset_metadata.categorical_cardinalities))
validate_cmd_line_flags()
if FLAGS.restore_checkpoint_path is not None:
model = Model.create_from_checkpoint(FLAGS.restore_checkpoint_path)
else:
model = Model(**FLAGS.flag_values_dict(), num_numerical_features=dataset_metadata.num_numerical_features,
categorical_cardinalities=dataset_metadata.categorical_cardinalities,
transpose=False)
table_ids = model.sparse_model.get_local_table_ids(hvd.rank())
print(f'local feature ids={table_ids}')
train_pipeline, validation_pipeline = create_input_pipelines(dataset_type=FLAGS.dataset_type,
dataset_path=FLAGS.dataset_path,
train_batch_size=FLAGS.batch_size,
test_batch_size=FLAGS.valid_batch_size,
table_ids=table_ids,
feature_spec=FLAGS.feature_spec,
rank=hvd.rank(), world_size=hvd.size(),
concat_features=FLAGS.concat_embedding,
data_parallel_input=FLAGS.data_parallel_input)
mlp_optimizer, embedding_optimizer = create_optimizers(FLAGS)
scheduler = LearningRateScheduler([mlp_optimizer, embedding_optimizer],
warmup_steps=FLAGS.warmup_steps,
base_lr=FLAGS.learning_rate,
decay_start_step=FLAGS.decay_start_step,
decay_steps=FLAGS.decay_steps)
timer = IterTimer(train_batch_size=FLAGS.batch_size, test_batch_size=FLAGS.batch_size,
optimizer=embedding_optimizer, print_freq=FLAGS.print_freq, enabled=hvd.rank() == 0)
if FLAGS.mode == 'inference':
inference_benchmark(validation_pipeline, model, timer, FLAGS)
return
elif FLAGS.mode == 'eval':
evaluator = Evaluator(model=model, timer=timer, auc_thresholds=FLAGS.auc_thresholds, max_steps=FLAGS.max_steps)
test_auc, test_loss, _ = evaluator(validation_pipeline)
if hvd.rank() == 0:
dllogger.log(data=dict(auc=test_auc, test_loss=test_loss), step=tuple())
return
eval_points = compute_eval_points(train_batches=len(train_pipeline),
evals_per_epoch=FLAGS.evals_per_epoch)
trainer = Trainer(model, embedding_optimizer=embedding_optimizer, mlp_optimizer=mlp_optimizer, amp=FLAGS.amp,
lr_scheduler=scheduler, tf_dataset_op=train_pipeline.op, cpu=FLAGS.cpu)
evaluator = Evaluator(model=model, timer=timer, auc_thresholds=FLAGS.auc_thresholds, distributed=hvd.size() > 1)
best_auc = 0
best_loss = 1e6
train_begin = time.time()
for epoch in range(FLAGS.epochs):
print('Starting epoch: ', epoch)
for step in range(len(train_pipeline)):
if step == FLAGS.profiler_start_step and hvd.rank() == FLAGS.profiled_rank:
tf.profiler.experimental.start('logdir')
if FLAGS.profiler_start_step and step == FLAGS.profiler_start_step + 100 and hvd.rank() == FLAGS.profiled_rank:
tf.profiler.experimental.stop()
loss = trainer.train_step()
if step == 0 and hvd.size() > 1:
dmp.broadcast_variables(model.variables, root_rank=0)
if step % FLAGS.print_freq == 0:
if tf.math.is_nan(loss):
print('NaN loss encountered in training. Aborting.')
break
timer.step_train(loss=loss)
if FLAGS.max_steps != -1 and step > FLAGS.max_steps:
dist_print(f'Max steps of {FLAGS.max_steps} reached, exiting')
break
if step in eval_points:
test_auc, test_loss, _ = evaluator(validation_pipeline)
dist_print(f'Evaluation completed, AUC: {test_auc:.6f}, test_loss: {test_loss:.6f}')
timer.test_idx = 0
best_auc = max(best_auc, test_auc)
best_loss = min(best_loss, test_loss)
elapsed = time.time() - train_begin
if FLAGS.save_checkpoint_path is not None:
model.save_checkpoint(FLAGS.save_checkpoint_path)
if hvd.rank() == 0:
dist_print(f'Training run completed, elapsed: {elapsed:.0f} [s]')
results = {
'throughput': FLAGS.batch_size / timer.mean_train_time(),
'mean_step_time_ms': timer.mean_train_time() * 1000,
'auc': best_auc,
'validation_loss': best_loss
}
dllogger.log(data=results, step=tuple())
|
PyTorch/Translation/Transformer | Transformer | preprocess | #!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
#
import argparse
from itertools import zip_longest
import os
import shutil
from fairseq.data import indexed_dataset, dictionary
from fairseq.tokenizer import Tokenizer, tokenize_line
def get_parser():
parser = argparse.ArgumentParser(
description='Data pre-processing: Create dictionary and store data in binary format')
parser.add_argument('-s', '--source-lang', default=None, metavar='SRC',
help='source language')
parser.add_argument('-t', '--target-lang', default=None, metavar='TARGET',
help='target language')
parser.add_argument('--trainpref', metavar='FP', default=None,
help='train file prefix')
parser.add_argument('--validpref', metavar='FP', default=None,
help='comma separated, valid file prefixes')
parser.add_argument('--testpref', metavar='FP', default=None,
help='comma separated, test file prefixes')
parser.add_argument('--destdir', metavar='DIR', default='data-bin',
help='destination dir')
parser.add_argument('--thresholdtgt', metavar='N', default=0, type=int,
help='map words appearing less than threshold times to unknown')
parser.add_argument('--thresholdsrc', metavar='N', default=0, type=int,
help='map words appearing less than threshold times to unknown')
parser.add_argument('--tgtdict', metavar='FP', help='reuse given target dictionary')
parser.add_argument('--srcdict', metavar='FP', help='reuse given source dictionary')
parser.add_argument('--nwordstgt', metavar='N', default=-1, type=int,
help='number of target words to retain')
parser.add_argument('--nwordssrc', metavar='N', default=-1, type=int,
help='number of source words to retain')
parser.add_argument('--alignfile', metavar='ALIGN', default=None,
help='an alignment file (optional)')
parser.add_argument('--output-format', metavar='FORMAT', default='binary', choices=['binary', 'raw'],
help='output format (optional)')
parser.add_argument('--joined-dictionary', action='store_true', help='Generate joined dictionary')
parser.add_argument('--only-source', action='store_true', help='Only process the source language')
parser.add_argument('--padding-factor', metavar='N', default=8, type=int,
help='Pad dictionary size to be multiple of N')
return parser
def main(args):
print(args)
os.makedirs(args.destdir, exist_ok=True)
target = not args.only_source
def build_dictionary(filenames):
d = dictionary.Dictionary()
for filename in filenames:
Tokenizer.add_file_to_dictionary(filename, d, tokenize_line)
return d
def train_path(lang):
return '{}{}'.format(args.trainpref, ('.' + lang) if lang else '')
def file_name(prefix, lang):
fname = prefix
if lang is not None:
fname += f'.{lang}'
return fname
def dest_path(prefix, lang):
return os.path.join(args.destdir, file_name(prefix, lang))
def dict_path(lang):
return dest_path('dict', lang) + '.txt'
def dataset_dest_path(output_prefix, lang, extension):
base = f'{args.destdir}/{output_prefix}'
lang_part = f'.{args.source_lang}-{args.target_lang}.{lang}' if lang is not None else ''
return f'{base}{lang_part}.{extension}'
if args.joined_dictionary:
assert not args.srcdict, 'cannot combine --srcdict and --joined-dictionary'
assert not args.tgtdict, 'cannot combine --tgtdict and --joined-dictionary'
src_dict = build_dictionary({
train_path(lang)
for lang in [args.source_lang, args.target_lang]
})
tgt_dict = src_dict
else:
if args.srcdict:
src_dict = dictionary.Dictionary.load(args.srcdict)
else:
assert args.trainpref, "--trainpref must be set if --srcdict is not specified"
src_dict = build_dictionary([train_path(args.source_lang)])
if target:
if args.tgtdict:
tgt_dict = dictionary.Dictionary.load(args.tgtdict)
else:
assert args.trainpref, "--trainpref must be set if --tgtdict is not specified"
tgt_dict = build_dictionary([train_path(args.target_lang)])
src_dict.finalize(
threshold=args.thresholdsrc,
nwords=args.nwordssrc,
padding_factor=args.padding_factor,
)
src_dict.save(dict_path(args.source_lang))
if target:
if not args.joined_dictionary:
tgt_dict.finalize(
threshold=args.thresholdtgt,
nwords=args.nwordstgt,
padding_factor=args.padding_factor,
)
tgt_dict.save(dict_path(args.target_lang))
def make_binary_dataset(input_prefix, output_prefix, lang):
_dict = dictionary.Dictionary.load(dict_path(lang))
print('| [{}] Dictionary: {} types'.format(lang, len(_dict) - 1))
ds = indexed_dataset.IndexedDatasetBuilder(dataset_dest_path(output_prefix, lang, 'bin'))
def consumer(tensor):
ds.add_item(tensor)
input_file = '{}{}'.format(input_prefix, ('.' + lang) if lang is not None else '')
res = Tokenizer.binarize(input_file, _dict, consumer)
print('| [{}] {}: {} sents, {} tokens, {:.3}% replaced by {}'.format(
lang, input_file, res['nseq'], res['ntok'],
100 * res['nunk'] / res['ntok'], _dict.unk_word))
ds.finalize(dataset_dest_path(output_prefix, lang, 'idx'))
def make_dataset(input_prefix, output_prefix, lang):
if args.output_format == 'binary':
make_binary_dataset(input_prefix, output_prefix, lang)
elif args.output_format == 'raw':
# Copy original text file to destination folder
output_text_file = dest_path(
output_prefix + '.{}-{}'.format(args.source_lang, args.target_lang),
lang,
)
shutil.copyfile(file_name(input_prefix, lang), output_text_file)
def make_all(lang):
if args.trainpref:
make_dataset(args.trainpref, 'train', lang)
if args.validpref:
for k, validpref in enumerate(args.validpref.split(',')):
outprefix = 'valid{}'.format(k) if k > 0 else 'valid'
make_dataset(validpref, outprefix, lang)
if args.testpref:
for k, testpref in enumerate(args.testpref.split(',')):
outprefix = 'test{}'.format(k) if k > 0 else 'test'
make_dataset(testpref, outprefix, lang)
make_all(args.source_lang)
if target:
make_all(args.target_lang)
print('| Wrote preprocessed data to {}'.format(args.destdir))
if args.alignfile:
assert args.trainpref, "--trainpref must be set if --alignfile is specified"
src_file_name = train_path(args.source_lang)
tgt_file_name = train_path(args.target_lang)
src_dict = dictionary.Dictionary.load(dict_path(args.source_lang))
tgt_dict = dictionary.Dictionary.load(dict_path(args.target_lang))
freq_map = {}
with open(args.alignfile, 'r') as align_file:
with open(src_file_name, 'r') as src_file:
with open(tgt_file_name, 'r') as tgt_file:
for a, s, t in zip_longest(align_file, src_file, tgt_file):
si = Tokenizer.tokenize(s, src_dict, add_if_not_exist=False)
ti = Tokenizer.tokenize(t, tgt_dict, add_if_not_exist=False)
ai = list(map(lambda x: tuple(x.split('-')), a.split()))
for sai, tai in ai:
srcidx = si[int(sai)]
tgtidx = ti[int(tai)]
if srcidx != src_dict.unk() and tgtidx != tgt_dict.unk():
assert srcidx != src_dict.pad()
assert srcidx != src_dict.eos()
assert tgtidx != tgt_dict.pad()
assert tgtidx != tgt_dict.eos()
if srcidx not in freq_map:
freq_map[srcidx] = {}
if tgtidx not in freq_map[srcidx]:
freq_map[srcidx][tgtidx] = 1
else:
freq_map[srcidx][tgtidx] += 1
align_dict = {}
for srcidx in freq_map:
align_dict[srcidx] = max(freq_map[srcidx], key=freq_map[srcidx].get)
with open(os.path.join(args.destdir, 'alignment.{}-{}.txt'.format(
args.source_lang, args.target_lang)), 'w') as f:
for k, v in align_dict.items():
print('{} {}'.format(src_dict[k], tgt_dict[v]), file=f)
if __name__ == '__main__':
parser = get_parser()
ARGS = parser.parse_args()
main(ARGS)
|
PyTorch/Forecasting/TFT/triton/deployment_toolkit | deployment_toolkit | core | # 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 abc
import importlib
import logging
import os
from enum import Enum
from pathlib import Path
from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
import numpy as np
LOGGER = logging.getLogger(__name__)
DATALOADER_FN_NAME = "get_dataloader_fn"
GET_MODEL_FN_NAME = "get_model"
GET_SERVING_INPUT_RECEIVER_FN = "get_serving_input_receiver_fn"
GET_ARGPARSER_FN_NAME = "update_argparser"
class TensorSpec(NamedTuple):
name: str
dtype: str
shape: Tuple
class Parameter(Enum):
def __lt__(self, other: "Parameter") -> bool:
return self.value < other.value
def __str__(self):
return self.value
class Accelerator(Parameter):
NONE = "none"
AMP = "amp"
TRT = "trt"
CUDA = NONE # backward compatibility
class Precision(Parameter):
INT8 = "int8"
FP16 = "fp16"
FP32 = "fp32"
TF32 = "tf32" # Deprecated
class Format(Parameter):
TF_GRAPHDEF = "tf-graphdef"
TF_SAVEDMODEL = "tf-savedmodel"
TF_TRT = "tf-trt"
TF_ESTIMATOR = "tf-estimator"
TF_KERAS = "tf-keras"
ONNX = "onnx"
TRT = "trt"
TS_SCRIPT = "ts-script"
TS_TRACE = "ts-trace"
PYT = "pyt"
FASTERTRANSFORMER = "fastertransformer"
class Model(NamedTuple):
handle: object
# TODO: precision should be removed
precision: Optional[Precision]
inputs: Dict[str, TensorSpec]
outputs: Dict[str, TensorSpec]
def load_from_file(file_path, label, target):
spec = importlib.util.spec_from_file_location(name=label, location=file_path)
my_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(my_module) # pytype: disable=attribute-error
return getattr(my_module, target, None)
class BaseLoader(abc.ABC):
required_fn_name_for_signature_parsing: Optional[str] = None
@abc.abstractmethod
def load(self, model_path: Union[str, Path], **kwargs) -> Model:
"""
Loads and process model from file based on given set of args
"""
pass
class BaseSaver(abc.ABC):
required_fn_name_for_signature_parsing: Optional[str] = None
@abc.abstractmethod
def save(self, model: Model, model_path: Union[str, Path], dataloader_fn) -> None:
"""
Save model to file
"""
pass
class BaseRunner(abc.ABC):
required_fn_name_for_signature_parsing: Optional[str] = None
@abc.abstractmethod
def init_inference(self, model: Model):
raise NotImplementedError
class BaseRunnerSession(abc.ABC):
def __init__(self, model: Model):
self._model = model
@abc.abstractmethod
def __enter__(self):
raise NotImplementedError()
@abc.abstractmethod
def __exit__(self, exc_type, exc_value, traceback):
raise NotImplementedError()
@abc.abstractmethod
def __call__(self, x: Dict[str, object]):
raise NotImplementedError()
def _set_env_variables(self) -> Dict[str, object]:
"""this method not remove values; fix it if needed"""
to_set = {}
old_values = {k: os.environ.pop(k, None) for k in to_set}
os.environ.update(to_set)
return old_values
def _recover_env_variables(self, old_envs: Dict[str, object]):
for name, value in old_envs.items():
if value is None:
del os.environ[name]
else:
os.environ[name] = str(value)
class BaseConverter(abc.ABC):
required_fn_name_for_signature_parsing: Optional[str] = None
@abc.abstractmethod
def convert(self, model: Model, dataloader_fn) -> Model:
raise NotImplementedError()
@staticmethod
def required_source_model_precision(requested_model_precision: Precision) -> Precision:
return requested_model_precision
class BaseMetricsCalculator(abc.ABC):
required_fn_name_for_signature_parsing: Optional[str] = None
def calc(
self,
*,
ids: List[Any],
y_pred: Dict[str, np.ndarray],
x: Optional[Dict[str, np.ndarray]],
y_real: Optional[Dict[str, np.ndarray]],
) -> Dict[str, float]:
"""
Calculates error/accuracy metrics
Args:
ids: List of ids identifying each sample in the batch
y_pred: model output as dict where key is output name and value is output value
x: model input as dict where key is input name and value is input value
y_real: input ground truth as dict where key is output name and value is output value
Returns:
dictionary where key is metric name and value is its value
"""
pass
@abc.abstractmethod
def update(
self,
ids: List[Any],
y_pred: Dict[str, np.ndarray],
x: Optional[Dict[str, np.ndarray]],
y_real: Optional[Dict[str, np.ndarray]],
):
pass
@property
@abc.abstractmethod
def metrics(self) -> Dict[str, Any]:
pass
class ShapeSpec(NamedTuple):
min: Tuple
opt: Tuple
max: Tuple
class MeasurementMode(Enum):
COUNT_WINDOWS = "count_windows"
TIME_WINDOWS = "time_windows"
class PerformanceTool(Enum):
"""
Available performance evaluation tools
"""
MODEL_ANALYZER = "model_analyzer"
PERF_ANALYZER = "perf_analyzer"
class BatchingMode(Enum):
"""
Available batching modes
"""
STATIC = "static"
DYNAMIC = "dynamic"
class EvaluationMode(Enum):
"""
Available evaluation modes
"""
OFFLINE = "offline"
ONLINE = "online"
class OfflineMode(Enum):
SYSTEM = "system"
CUDA = "cuda"
|
TensorFlow/Detection/SSD/models/research/slim/datasets | datasets | cifar10 | # Copyright 2016 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.
# ==============================================================================
"""Provides data for the Cifar10 dataset.
The dataset scripts used to create the dataset can be found at:
tensorflow/models/research/slim/datasets/download_and_convert_cifar10.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
from datasets import dataset_utils
slim = tf.contrib.slim
_FILE_PATTERN = 'cifar10_%s.tfrecord'
SPLITS_TO_SIZES = {'train': 50000, 'test': 10000}
_NUM_CLASSES = 10
_ITEMS_TO_DESCRIPTIONS = {
'image': 'A [32 x 32 x 3] color image.',
'label': 'A single integer between 0 and 9',
}
def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
"""Gets a dataset tuple with instructions for reading cifar10.
Args:
split_name: A train/test split name.
dataset_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the dataset sources.
It is assumed that the pattern contains a '%s' string so that the split
name can be inserted.
reader: The TensorFlow reader type.
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/test split.
"""
if split_name not in SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
if not file_pattern:
file_pattern = _FILE_PATTERN
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
# Allowing None in the signature so that dataset_factory can use the default.
if not reader:
reader = tf.TFRecordReader
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
'image/class/label': tf.FixedLenFeature(
[], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
}
items_to_handlers = {
'image': slim.tfexample_decoder.Image(shape=[32, 32, 3]),
'label': slim.tfexample_decoder.Tensor('image/class/label'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
labels_to_names = None
if dataset_utils.has_labels(dataset_dir):
labels_to_names = dataset_utils.read_label_file(dataset_dir)
return slim.dataset.Dataset(
data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=SPLITS_TO_SIZES[split_name],
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=_NUM_CLASSES,
labels_to_names=labels_to_names)
|
TensorFlow2/LanguageModeling/BERT | BERT | create_finetuning_data | # 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.
# ==============================================================================
"""BERT finetuning task dataset generator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import json
from absl import app
from absl import flags
import tensorflow as tf
import classifier_data_lib
# word-piece tokenizer based squad_lib
import squad_lib as squad_lib_wp
# sentence-piece tokenizer based squad_lib
import squad_lib_sp
import tokenization
FLAGS = flags.FLAGS
flags.DEFINE_enum(
"fine_tuning_task_type", "classification", ["classification", "squad"],
"The name of the BERT fine tuning task for which data "
"will be generated..")
# BERT classification specific flags.
flags.DEFINE_string(
"input_data_dir", None,
"The input data dir. Should contain the .tsv files (or other data files) "
"for the task.")
flags.DEFINE_enum("classification_task_name", "MNLI",
["COLA", "MNLI", "MRPC", "QNLI", "SST-2", "XNLI"],
"The name of the task to train BERT classifier.")
# BERT Squad task specific flags.
flags.DEFINE_string(
"squad_data_file", None,
"The input data file in for generating training data for BERT squad task.")
flags.DEFINE_integer(
"doc_stride", 128,
"When splitting up a long document into chunks, how much stride to "
"take between chunks.")
flags.DEFINE_integer(
"max_query_length", 64,
"The maximum number of tokens for the question. Questions longer than "
"this will be truncated to this length.")
flags.DEFINE_bool(
"version_2_with_negative", False,
"If true, the SQuAD examples contain some that do not have an answer.")
# Shared flags across BERT fine-tuning tasks.
flags.DEFINE_string("vocab_file", None,
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_string(
"train_data_output_path", None,
"The path in which generated training input data will be written as tf"
" records.")
flags.DEFINE_string(
"eval_data_output_path", None,
"The path in which generated training input data will be written as tf"
" records.")
flags.DEFINE_string("meta_data_file_path", None,
"The path in which input meta data will be written.")
flags.DEFINE_bool(
"do_lower_case", True,
"Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
flags.DEFINE_integer(
"max_seq_length", 128,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded.")
flags.DEFINE_string("sp_model_file", "",
"The path to the model used by sentence piece tokenizer.")
flags.DEFINE_enum(
"tokenizer_impl", "word_piece", ["word_piece", "sentence_piece"],
"Specifies the tokenizer implementation, i.e., whehter to use word_piece "
"or sentence_piece tokenizer. Canonical BERT uses word_piece tokenizer, "
"while ALBERT uses sentence_piece tokenizer.")
def generate_classifier_dataset():
"""Generates classifier dataset and returns input meta data."""
assert FLAGS.input_data_dir and FLAGS.classification_task_name
processors = {
"cola": classifier_data_lib.ColaProcessor,
"mnli": classifier_data_lib.MnliProcessor,
"mrpc": classifier_data_lib.MrpcProcessor,
"qnli": classifier_data_lib.QnliProcessor,
"sst-2": classifier_data_lib.SstProcessor,
"xnli": classifier_data_lib.XnliProcessor,
}
task_name = FLAGS.classification_task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
if FLAGS.tokenizer_impl == "word_piece":
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
processor_text_fn = tokenization.convert_to_unicode
else:
assert FLAGS.tokenizer_impl == "sentence_piece"
tokenizer = tokenization.FullSentencePieceTokenizer(FLAGS.sp_model_file)
processor_text_fn = functools.partial(
tokenization.preprocess_text, lower=FLAGS.do_lower_case)
processor = processors[task_name](processor_text_fn)
return classifier_data_lib.generate_tf_record_from_data_file(
processor,
FLAGS.input_data_dir,
tokenizer,
train_data_output_path=FLAGS.train_data_output_path,
eval_data_output_path=FLAGS.eval_data_output_path,
max_seq_length=FLAGS.max_seq_length)
def generate_squad_dataset():
"""Generates squad training dataset and returns input meta data."""
assert FLAGS.squad_data_file
if FLAGS.tokenizer_impl == "word_piece":
return squad_lib_wp.generate_tf_record_from_json_file(
FLAGS.squad_data_file, FLAGS.vocab_file, FLAGS.train_data_output_path,
FLAGS.max_seq_length, FLAGS.do_lower_case, FLAGS.max_query_length,
FLAGS.doc_stride, FLAGS.version_2_with_negative)
else:
assert FLAGS.tokenizer_impl == "sentence_piece"
return squad_lib_sp.generate_tf_record_from_json_file(
FLAGS.squad_data_file, FLAGS.sp_model_file,
FLAGS.train_data_output_path, FLAGS.max_seq_length, FLAGS.do_lower_case,
FLAGS.max_query_length, FLAGS.doc_stride, FLAGS.version_2_with_negative)
def main(_):
if FLAGS.tokenizer_impl == "word_piece":
if not FLAGS.vocab_file:
raise ValueError(
"FLAG vocab_file for word-piece tokenizer is not specified.")
else:
assert FLAGS.tokenizer_impl == "sentence_piece"
if not FLAGS.sp_model_file:
raise ValueError(
"FLAG sp_model_file for sentence-piece tokenizer is not specified.")
if FLAGS.fine_tuning_task_type == "classification":
input_meta_data = generate_classifier_dataset()
else:
input_meta_data = generate_squad_dataset()
with tf.io.gfile.GFile(FLAGS.meta_data_file_path, "w") as writer:
writer.write(json.dumps(input_meta_data, indent=4) + "\n")
if __name__ == "__main__":
flags.mark_flag_as_required("train_data_output_path")
flags.mark_flag_as_required("meta_data_file_path")
app.run(main)
|
PyTorch/Classification/ConvNets/resnet50v1.5/training/FP32 | FP32 | DGX2V_resnet50_FP32_250E | python ./multiproc.py --nproc_per_node 8 ./launch.py --model resnet50 --precision FP32 --mode convergence --platform DGX2V /imagenet --workspace ${1:-./} --raport-file raport.json
|
TensorFlow/Recommendation/NCF | NCF | prepare_dataset | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#!/bin/bash
set -e
DATASET_NAME=${1:-'ml-20m'}
RAW_DATADIR=${2:-'/data'}
CACHED_DATADIR=${3:-"${RAW_DATADIR}/cache/${DATASET_NAME}"}
# you can add another option to this case in order to support other datasets
case ${DATASET_NAME} in
'ml-20m')
ZIP_PATH=${RAW_DATADIR}/'ml-20m.zip'
RATINGS_PATH=${RAW_DATADIR}'/ml-20m/ratings.csv'
;;
'ml-1m')
ZIP_PATH=${RAW_DATADIR}/'ml-1m.zip'
RATINGS_PATH=${RAW_DATADIR}'/ml-1m/ratings.dat'
;;
*)
echo "Unsupported dataset name: $DATASET_NAME"
exit 1
esac
if [ ! -d ${RAW_DATADIR} ]; then
mkdir -p ${RAW_DATADIR}
fi
if [ ! -d ${CACHED_DATADIR} ]; then
mkdir -p ${CACHED_DATADIR}
fi
rm -f log
if [ ! -f ${ZIP_PATH} ]; then
echo "Dataset not found!"
echo "To continue please download the dataset from http://files.grouplens.org/datasets/movielens/ml-20m.zip \\
put it in ${ZIP_PATH} and rerun this script"
exit 1
fi
if [ ! -f ${RATINGS_PATH} ]; then
unzip -u ${ZIP_PATH} -d ${RAW_DATADIR}
fi
if [ ! -f ${CACHED_DATADIR}/train_ratings.pickle ]; then
echo "preprocessing ${RATINGS_PATH} and save to disk"
t0=$(date +%s)
python convert.py --path ${RATINGS_PATH} --output ${CACHED_DATADIR}
t1=$(date +%s)
delta=$(( $t1 - $t0 ))
echo "Finish preprocessing in $delta seconds"
else
echo 'Using cached preprocessed data'
fi
echo "Dataset $DATASET_NAME successfully prepared at: $CACHED_DATADIR"
|
TensorFlow/Detection/SSD/models/research/object_detection/predictors | predictors | convolutional_keras_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_keras_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_keras_box_predictor as box_predictor
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def _build_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.KerasLayerHyperparams(conv_hyperparams)
def test_get_boxes_for_five_aspect_ratios_per_location(self):
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_convolutional_keras_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
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([image_features])
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_keras_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[1],
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([image_features])
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_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
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([image_features])
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_keras_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
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([image_features])
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/SharedConvolutions_0/Conv2d_0_1x1_32/bias',
'BoxPredictor/SharedConvolutions_0/Conv2d_0_1x1_32/kernel',
'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor/bias',
'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor/kernel',
'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/bias',
'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/kernel'])
self.assertEqual(expected_variable_set, actual_variable_set)
self.assertEqual(conv_box_predictor._sorted_head_names,
['box_encodings', 'class_predictions_with_background'])
# TODO(kaftan): Remove conditional after CMLE moves to TF 1.10
if __name__ == '__main__':
tf.test.main()
|
TensorFlow/Detection/SSD/models/research/object_detection/models | models | ssd_mobilenet_v1_ppn_feature_extractor_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 ssd_mobilenet_v1_ppn_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_ppn_feature_extractor
slim = tf.contrib.slim
class SsdMobilenetV1PpnFeatureExtractorTest(
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_ppn_feature_extractor.
SSDMobileNetV1PpnFeatureExtractor(
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_320(self):
image_height = 320
image_width = 320
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 20, 20, 512), (2, 10, 10, 512),
(2, 5, 5, 512), (2, 3, 3, 512),
(2, 2, 2, 512), (2, 1, 1, 512)]
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_300(self):
image_height = 300
image_width = 300
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 19, 19, 512), (2, 10, 10, 512),
(2, 5, 5, 512), (2, 3, 3, 512),
(2, 2, 2, 512), (2, 1, 1, 512)]
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_640(self):
image_height = 640
image_width = 640
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 40, 40, 512), (2, 20, 20, 512),
(2, 10, 10, 512), (2, 5, 5, 512),
(2, 3, 3, 512), (2, 2, 2, 512)]
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 = 320
image_width = 320
depth_multiplier = 1.0
pad_to_multiple = 1
expected_feature_map_shape = [(2, 20, 20, 512), (2, 10, 10, 512),
(2, 5, 5, 512), (2, 3, 3, 512),
(2, 2, 2, 512), (2, 1, 1, 512)]
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_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, 512),
(2, 5, 5, 512), (2, 3, 3, 512),
(2, 2, 2, 512)]
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_enforcing_min_depth(self):
image_height = 256
image_width = 256
depth_multiplier = 0.5**12
pad_to_multiple = 1
expected_feature_map_shape = [(2, 16, 16, 32), (2, 8, 8, 32),
(2, 4, 4, 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_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 = 320
image_width = 320
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()
|
TensorFlow/Detection/SSD | SSD | README | # SSD320 v1.2 For TensorFlow
This repository provides a script and recipe to train SSD320 v1.2 to achieve state of the art accuracy, and is tested and maintained by NVIDIA.
SSD model for TensorFlow1 is no longer maintained and will soon become unavailable, please consider a PyTorch version or EfficientDet TensorFlow2 model as a substitute for your requirements.
## 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)
* [Glossary](#glossary)
* [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)
* [Training process](#training-process)
* [Data preprocessing](#data-preprocessing)
* [Data augmentation](#data-augmentation)
* [Enabling mixed precision](#enabling-mixed-precision)
* [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 40GB)](#training-accuracy-nvidia-dgx-a100-8x-a100-40gb)
* [Training accuracy: NVIDIA DGX-1 (8x V100 16GB)](#training-accuracy-nvidia-dgx-1-8x-v100-16gb)
* [Training performance results](#training-performance-results)
* [Training performance: NVIDIA DGX A100 (8x A100 40GB)](#training-performance-nvidia-dgx-a100-8x-a100-40gb)
* [Training performance: NVIDIA DGX-1 (8x V100 16GB)](#training-performance-nvidia-dgx-1-8x-v100-16gb)
* [Inference performance results](#inference-performance-results)
* [Inference performance: NVIDIA DGX A100 (1x A100 40GB)](#inference-performance-nvidia-dgx-a100-1x-a100-40gb)
* [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 SSD320 v1.2 model is based on the [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper, which describes SSD as "a method for detecting objects in images using a single deep neural network".
This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.5x 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
Our implementation is based on the existing [model from the TensorFlow models repository](https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config).
The network was altered in order to improve accuracy and increase throughput. Changes include:
- Replacing the VGG backbone with the more popular ResNet50.
- Adding multi-scale detection to the backbone using [Feature Pyramid Networks](https://arxiv.org/pdf/1612.03144.pdf).
- Replacing the original hard negative mining loss function with [Focal Loss](https://arxiv.org/pdf/1708.02002.pdf).
- Decreasing the input size to 320 x 320.
### Default configuration
We trained the model for 12500 steps (27 epochs) with the following setup:
- [SGDR](https://arxiv.org/pdf/1608.03983.pdf) with cosine decay learning rate
- Learning rate base = 0.16
- Momentum = 0.9
- Warm-up learning rate = 0.0693312
- Warm-up steps = 1000
- Batch size per GPU = 32
- Number of GPUs = 8
### Feature support matrix
The following features are supported by this model:
| **Feature** | **Transformer-XL** |
|:------------|-------------------:|
|[Automatic mixed precision (AMP)](https://nvidia.github.io/apex/amp.html) | Yes |
|[Horovod Multi-GPU (NCCL)](https://github.com/horovod/horovod) | Yes |
#### Features
[TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) - a
tool that enables Tensor Core-accelerated training. Refer to the [Enabling
mixed precision](#enabling-mixed-precision) section for more details.
[Horovod](https://github.com/horovod/horovod) - 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).
[Multi-GPU training with Horovod](https://github.com/horovod/horovod/#usage) - our model
uses Horovod to implement efficient multi-GPU training with NCCL. For details,
see example sources in this repository or see the [TensorFlow
tutorial](https://github.com/horovod/horovod/#usage).
### 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 previously required two
steps:
1. Porting the model to use the FP16 data type where appropriate.
2. Adding loss scaling to preserve small gradient values.
This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enablethe full
[mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow)
in your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically.
The TensorFlow framework code makes all necessary model changes internally.
In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16,
and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work
with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment
variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically
rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.
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.
* How to access and enable AMP for TensorFlow, see [Using
TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp)
from the TensorFlow User Guide.
#### Enabling mixed precision
Mixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP)
extension which 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 TensorFlow, loss scaling can be applied statically by
using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed
precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations.
First, programmers need not modify network model code, reducing development and maintenance effort.
Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.
To enable mixed precision, you can simply add the values to the environmental variables inside your training script:
- Enable TF-AMP graph rewrite:
```
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
```
- Enable Automated Mixed Precision:
```
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
```
#### 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 list the requirements in order to start training the SSD320 v1.2 model.
### Requirements
This repository contains `Dockerfile` which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following software:
* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
* [TensorFlow 20.06-py3](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) (or later) NGC container
* GPU-based architecture:
* [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
* [NVIDIA Turing](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
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 TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running)
## Quick Start Guide
To train your model using mixed precision or TF32 with tensor cores or using TF32, FP32,
perform the following steps using the default parameters of the SSD320 v1.2 model on the
[COCO 2017](http://cocodataset.org/#download) dataset.
### 1. Clone the repository.
```
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/TensorFlow/Detection/SSD
```
### 2. Build the SSD320 v1.2 TensorFlow NGC container.
```
docker build . -t nvidia_ssd
```
### 3. Download and preprocess the dataset.
Extract the COCO 2017 dataset with:
```
download_all.sh nvidia_ssd <data_dir_path> <checkpoint_dir_path>
```
Data will be downloaded, preprocessed to tfrecords format and saved in the `<data_dir_path>` directory (on the host).
Moreover the script will download pre-trained RN50 checkpoint in the `<checkpoint_dir_path>` directory
### 4. Launch the NGC container to run training/inference.
```
nvidia-docker run --rm -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -v <data_dir_path>:/data/coco2017_tfrecords -v <checkpoint_dir_path>:/checkpoints --ipc=host nvidia_ssd
```
### 5. Start training.
The `./examples` directory provides several sample scripts for various GPU settings and act as wrappers around
`object_detection/model_main.py` script. The example scripts can be modified by arguments:
- A path to directory for checkpoints
- A path to directory for configs
- Additional arguments to `object_detection/model_main.py`
If you want to run 8 GPUs, training with tensor cores acceleration and save checkpoints in `/checkpoints` directory, run:
```
bash ./examples/SSD320_FP16_8GPU.sh /checkpoints
```
### 6. Start validation/evaluation.
The `model_main.py` training script automatically runs validation during training.
The results from the validation are printed to `stdout`.
Pycocotools’ open-sourced scripts provides a consistent way to evaluate models on the COCO dataset.
We are using these scripts during validation to measure models performance in AP metric.
Metrics below are evaluated using pycocotools’ methodology,
in the following format:during validation to measure models performance in AP metric.
Metrics below are evaluated using pycocotools’ methodology, in the following format:
```
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.273
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.423
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.291
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.024
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.218
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.451
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.257
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.398
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.070
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.645
```
The metric reported in our results is present in the first row.
To evaluate a checkpointed model saved in the previous step, you can use script from examples directory.
If you want to run inference with tensor cores acceleration, run:
```
bash examples/SSD320_evaluate.sh <path to checkpoint>
```
## Advanced
The following sections provide greater details of the dataset, running training and inference, and the training results.
### Scripts and sample code
* `Dockerfile`: a container with the basic set of dependencies to run SSD
In the `model/research/object_detection` directory, the most important files are:
* `model_main.py`: serves as the entry point to launch the training and inference
* `models/ssd_resnet_v1_fpn_feature_extractor.py`: implementation of the model
* `metrics/coco_tools.py`: implementation of mAP metric
* `utils/exp_utils.py`: utility functions for running training and benchmarking
### Parameters
The complete list of available parameters for the `model/research/object_detection/model_main.py` script contains:
```
./object_detection/model_main.py:
--[no]allow_xla: Enable XLA compilation
(default: 'false')
--checkpoint_dir: Path to directory holding a checkpoint. If `checkpoint_dir` is provided, this binary operates in
eval-only mode, writing resulting metrics to `model_dir`.
--eval_count: How many times the evaluation should be run
(default: '1')
(an integer)
--[no]eval_training_data: If training data should be evaluated for this job. Note that one call only use this in eval-
only mode, and `checkpoint_dir` must be supplied.
(default: 'false')
--hparams_overrides: Hyperparameter overrides, represented as a string containing comma-separated hparam_name=value
pairs.
--model_dir: Path to output model directory where event and checkpoint files will be written.
--num_train_steps: Number of train steps.
(an integer)
--pipeline_config_path: Path to pipeline config file.
--raport_file: Path to dlloger json
(default: 'summary.json')
--[no]run_once: If running in eval-only mode, whether to run just one round of eval vs running continuously (default).
(default: 'false')
--sample_1_of_n_eval_examples: Will sample one of every n eval input examples, where n is provided.
(default: '1')
(an integer)
--sample_1_of_n_eval_on_train_examples: Will sample one of every n train input examples for evaluation, where n is
provided. This is only used if `eval_training_data` is True.
(default: '5')
(an integer)
```
### Command line options
The SSD model training is conducted by the script from the object_detection library, `model_main.py`.
Our experiments were done with settings described in the `examples` directory.
If you would like to get more details about available arguments, please run:
```
python object_detection/model_main.py --help
```
### Getting the data
The SSD320 v1.2 model was trained on the COCO 2017 dataset. The val2017 validation set was used as a validation dataset.
The `download_data.sh` script will preprocess the data to tfrecords format.
This repository contains the `download_dataset.sh` script which will automatically download and preprocess the training,
validation and test datasets. By default, data will be downloaded to the `/data/coco2017_tfrecords` directory.
### Training process
Training the SSD model is implemented in the `object_detection/model_main.py` script.
All training parameters are set in the config files. Because evaluation is relatively time consuming,
it does not run every epoch. By default, evaluation is executed only once at the end of the training.
The model is evaluated using pycocotools distributed with the COCO dataset.
The number of evaluations can be changed using the `eval_count` parameter.
To run training with tensor cores, use `./examples/SSD320_FP16_{1,4,8}GPU.sh` scripts. For more details see [Enabling mixed precision](#enabling-mixed-precision) section below.
#### Data preprocessing
Before we feed data to the model, both during training and inference, we perform:
* Normalization
* Encoding bounding boxes
* Resize to 320x320
#### Data augmentation
During training we perform the following augmentation techniques:
* Random crop
* Random horizontal flip
* Color jitter
### Enabling mixed precision
[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 the Volta and Turing 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](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) training previously required two steps:
1. Porting the model to use the FP16 data type where appropriate.
2. Manually adding loss scaling to preserve small gradient values.
This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code.
AMP enables mixed precision training on Volta and Turing GPUs automatically.
The TensorFlow framework code makes all necessary model changes internally.
In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16,
and the loss scaling is automatically applied inside of supported optimizers.
AMP can be configured to work with the existing `tf.contrib` loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization.
It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.
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.
- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.
- 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.
## 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
Training benchmark was run in various scenarios on V100 16G GPU. For each scenario, batch size was set to 32.
To benchmark training, run:
```
bash examples/SSD320_{PREC}_{NGPU}GPU_BENCHMARK.sh
```
Where the `{NGPU}` defines number of GPUs used in benchmark, and the `{PREC}` defines precision.
The benchmark runs training with only 1200 steps and computes average training speed of last 300 steps.
#### Inference performance benchmark
Inference benchmark was run with various batch-sizes on V100 16G GPU.
For inference we are using single GPU setting. Examples are taken from the validation dataset.
To benchmark inference, run:
```
bash examples/SSD320_FP{16,32}_inference.sh --batch_size <batch size> --checkpoint_dir <path to checkpoint>
```
Batch size for the inference benchmark is controlled by the `--batch_size` argument,
while the checkpoint is provided to the script with the `--checkpoint_dir` argument.
The benchmark script provides extra arguments for extra control over the experiment.
We were using default values for the extra arguments during the experiments. For more details about them, please run:
```
bash examples/SSD320_FP16_inference.sh --help
```
### 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 40GB)
Our results were obtained by running the `./examples/SSD320_FP{16,32}_{1,4,8}GPU.sh` script in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX A100 (8x A100 40GB) GPUs.
All the results are obtained with batch size set to 32.
| **Number of GPUs** | **Mixed precision mAP** | **Training time with mixed precision** | **TF32 mAP** | **Training time with TF32** |
|:------------------:|:-----------------------:|:--------------------------------------:|:------------:|:---------------------------:|
| 1 | 0.279 | 4h 48min | 0.280 | 6h 40min |
| 4 | 0.280 | 1h 20min | 0.279 | 1h 53min |
| 8 | 0.281 | 0h 53min | 0.282 | 1h 05min |
##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)
Our results were obtained by running the `./examples/SSD320_FP{16,32}_{1,4,8}GPU.sh` script in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.
All the results are obtained with batch size set to 32.
| **Number of GPUs** | **Mixed precision mAP** | **Training time with mixed precision** | **FP32 mAP** | **Training time with FP32** |
|:------------------:|:-----------------------:|:--------------------------------------:|:------------:|:---------------------------:|
| 1 | 0.279 | 7h 36min | 0.278 | 10h 38min |
| 4 | 0.277 | 2h 18min | 0.279 | 2h 58min |
| 8 | 0.280 | 1h 28min | 0.282 | 1h 55min |
Here are example graphs of TF32, FP32 and FP16 training on 8 GPU configuration:
#### Training performance results
##### Training performance: NVIDIA DGX A100 (8x A100 40GB)
Our results were obtained by running:
```
python bash examples/SSD320_FP*GPU_BENCHMARK.sh
```
scripts in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX A100 (8x A100 40GB) GPUs.
| **Number of GPUs** | **Batch size per GPU** | **Mixed precision img/s** | **TF32 img/s** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with mixed precision** | **Multi-gpu weak scaling with TF32** |
|:------------------:|:----------------------:|:-------------------------:|:--------------:|:---------------------------------:|:-----------------------------------------------:|:------------------------------------:|
| 1 | 32 | 180.55 | 123.48 | 1.46 | 1.00 | 1.00 |
| 4 | 32 | 624.35 | 449.17 | 1.39 | 3.46 | 3.64 |
| 8 | 32 | 1008.46 | 779.96 | 1.29 | 5.59 | 6.32 |
To achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.
Those results can be improved when [XLA](https://www.tensorflow.org/xla) is used
in conjunction with mixed precision, delivering up to 2x speedup over FP32 on a single GPU (~179 img/s).
However XLA is still considered experimental.
##### Training performance: NVIDIA DGX-1 (8x V100 16GB)
Our results were obtained by running:
```
python bash examples/SSD320_FP*GPU_BENCHMARK.sh
```
scripts in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX-1 with V100 16G GPUs.
| **Number of GPUs** | **Batch size per GPU** | **Mixed precision img/s** | **FP32 img/s** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with mixed precision** | **Multi-gpu weak scaling with FP32** |
|:------------------:|:----------------------:|:-------------------------:|:--------------:|:---------------------------------:|:-----------------------------------------------:|:------------------------------------:|
| 1 | 32 | 127.96 | 84.96 | 1.51 | 1.00 | 1.00 |
| 4 | 32 | 396.38 | 283.30 | 1.40 | 3.10 | 3.33 |
| 8 | 32 | 676.83 | 501.30 | 1.35 | 5.29 | 5.90 |
To achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.
Those results can be improved when [XLA](https://www.tensorflow.org/xla) is used
in conjunction with mixed precision, delivering up to 2x speedup over FP32 on a single GPU (~179 img/s).
However XLA is still considered experimental.
#### Inference performance results
##### Inference performance: NVIDIA DGX A100 (1x A100 40GB)
Our results were obtained by running the `examples/SSD320_FP{16,32}_inference.sh` script in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX A100 (1x A100 40GB) GPU.
FP16
| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |
|------------|----------------|-------|-------|-------|-------|
| 1 | 40.88 | 24.46 | 25.76 | 26.47 | 27.91 |
| 2 | 49.26 | 40.60 | 42.09 | 42.61 | 45.26 |
| 4 | 58.81 | 68.01 | 73.12 | 76.02 | 80.38 |
| 8 | 69.13 |115.73 |121.58 |123.87 |129.00 |
| 16 | 78.10 |204.85 |212.40 |216.38 |225.80 |
| 32 | 76.19 |420.00 |437.24 |443.21 |479.80 |
| 64 | 77.92 |821.37 |840.82 |867.62 |1204.64|
TF32
| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |
|------------|----------------|-------|-------|-------|-------|
| 1 | 36.93 | 27.08 | 29.10 | 29.89 | 32.24 |
| 2 | 44.03 | 45.42 | 48.67 | 49.56 | 51.12 |
| 4 | 54.65 | 73.20 | 77.50 | 78.89 | 85.81 |
| 8 | 62.96 |127.06 |137.04 |141.64 |152.92 |
| 16 | 71.48 |223.83 |231.36 |233.35 |247.51 |
| 32 | 73.11 |437.71 |450.86 |455.14 |467.11 |
| 64 | 73.74 |867.88 |898.99 |912.07 |1077.13|
To achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.
##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)
Our results were obtained by running the `examples/SSD320_FP{16,32}_inference.sh` script in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPU.
FP16
| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |
|------------|----------------|-------|-------|-------|-------|
| 1 | 28.34 | 35.29 | 38.09 | 39.06 | 41.07 |
| 2 | 41.21 | 48.54 | 52.77 | 54.45 | 57.10 |
| 4 | 55.41 | 72.19 | 75.44 | 76.99 | 84.15 |
| 8 | 61.83 |129.39 |133.37 |136.89 |145.69 |
| 16 | 66.36 |241.12 |246.05 |249.47 |259.79 |
| 32 | 65.01 |492.21 |510.01 |516.45 |526.83 |
| 64 | 64.75 |988.47 |1012.11|1026.19|1290.54|
FP32
| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |
|------------|----------------|-------|-------|-------|-------|
| 1 | 29.15 | 34.31 | 36.26 | 37.63 | 39.95 |
| 2 | 41.20 | 48.54 | 53.08 | 54.47 | 57.32 |
| 4 | 50.72 | 78.86 | 82.49 | 84.08 | 92.15 |
| 8 | 55.72 |143.57 |147.20 |148.92 |152.44 |
| 16 | 59.41 |269.32 |278.30 |281.06 |286.54 |
| 32 | 59.81 |534.99 |542.49 |551.58 |572.16 |
| 64 | 58.93 |1085.96|1111.20|1118.21|1253.74|
To achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.
##### Inference performance: NVIDIA T4
Our results were obtained by running the `examples/SSD320_FP{16,32}_inference.sh` script in the TensorFlow-20.06-py3 NGC container on NVIDIA T4.
FP16
| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |
|------------|----------------|-------|-------|-------|-------|
| 1 | 19.29 | 51.90 | 53.77 | 54.95 | 59.21 |
| 2 | 30.36 | 66.04 | 70.13 | 71.49 | 73.97 |
| 4 | 37.71 |106.21 |111.32 |113.04 |118.03 |
| 8 | 40.95 |195.49 |201.66 |204.00 |210.32 |
| 16 | 41.04 |390.05 |399.73 |402.88 |410.02 |
| 32 | 40.36 |794.48 |815.81 |825.39 |841.45 |
| 64 | 40.27 |1590.98|1631.00|1642.22|1838.95|
FP32
| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |
|------------|----------------|-------|-------|-------|-------|
| 1 | 14.30 | 69.99 | 72.30 | 73.29 | 76.35 |
| 2 | 20.04 | 99.87 |104.50 |106.03 |108.15 |
| 4 | 25.01 |159.99 |163.00 |164.13 |168.63 |
| 8 | 28.42 |281.58 |286.57 |289.01 |294.37 |
| 16 | 32.56 |492.08 |501.98 |505.29 |509.95 |
| 32 | 34.14 |939.11 |961.35 |968.26 |983.77 |
| 64 | 33.47 |1915.36|1971.90|1992.24|2030.54|
To achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.
## Release notes
### Changelog
April 2023
* Ceased maintenance of this model in TensorFlow1
June 2020
* Updated performance tables to include A100 results
March 2019
* Initial release
May 2019
* Test scripts updated
## Known issues
There are no known issues with this model.
|
TensorFlow/Detection/SSD/models/research/object_detection/legacy | legacy | eval | # 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.
# ==============================================================================
r"""Evaluation executable for detection models.
This executable is used to evaluate DetectionModels. There are two ways of
configuring the eval job.
1) A single pipeline_pb2.TrainEvalPipelineConfig file maybe specified instead.
In this mode, the --eval_training_data flag may be given to force the pipeline
to evaluate on training data instead.
Example usage:
./eval \
--logtostderr \
--checkpoint_dir=path/to/checkpoint_dir \
--eval_dir=path/to/eval_dir \
--pipeline_config_path=pipeline_config.pbtxt
2) Three configuration files may be provided: a model_pb2.DetectionModel
configuration file to define what type of DetectionModel is being evaluated, an
input_reader_pb2.InputReader file to specify what data the model is evaluating
and an eval_pb2.EvalConfig file to configure evaluation parameters.
Example usage:
./eval \
--logtostderr \
--checkpoint_dir=path/to/checkpoint_dir \
--eval_dir=path/to/eval_dir \
--eval_config_path=eval_config.pbtxt \
--model_config_path=model_config.pbtxt \
--input_config_path=eval_input_config.pbtxt
"""
import functools
import os
import tensorflow as tf
from object_detection.builders import dataset_builder
from object_detection.builders import graph_rewriter_builder
from object_detection.builders import model_builder
from object_detection.legacy import evaluator
from object_detection.utils import config_util
from object_detection.utils import label_map_util
tf.logging.set_verbosity(tf.logging.INFO)
flags = tf.app.flags
flags.DEFINE_boolean('eval_training_data', False,
'If training data should be evaluated for this job.')
flags.DEFINE_string(
'checkpoint_dir', '',
'Directory containing checkpoints to evaluate, typically '
'set to `train_dir` used in the training job.')
flags.DEFINE_string('eval_dir', '', 'Directory to write eval summaries to.')
flags.DEFINE_string(
'pipeline_config_path', '',
'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
'file. If provided, other configs are ignored')
flags.DEFINE_string('eval_config_path', '',
'Path to an eval_pb2.EvalConfig config file.')
flags.DEFINE_string('input_config_path', '',
'Path to an input_reader_pb2.InputReader config file.')
flags.DEFINE_string('model_config_path', '',
'Path to a model_pb2.DetectionModel config file.')
flags.DEFINE_boolean(
'run_once', False, 'Option to only run a single pass of '
'evaluation. Overrides the `max_evals` parameter in the '
'provided config.')
FLAGS = flags.FLAGS
@tf.contrib.framework.deprecated(None, 'Use object_detection/model_main.py.')
def main(unused_argv):
assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
assert FLAGS.eval_dir, '`eval_dir` is missing.'
tf.gfile.MakeDirs(FLAGS.eval_dir)
if FLAGS.pipeline_config_path:
configs = config_util.get_configs_from_pipeline_file(
FLAGS.pipeline_config_path)
tf.gfile.Copy(
FLAGS.pipeline_config_path,
os.path.join(FLAGS.eval_dir, 'pipeline.config'),
overwrite=True)
else:
configs = config_util.get_configs_from_multiple_files(
model_config_path=FLAGS.model_config_path,
eval_config_path=FLAGS.eval_config_path,
eval_input_config_path=FLAGS.input_config_path)
for name, config in [('model.config', FLAGS.model_config_path),
('eval.config', FLAGS.eval_config_path),
('input.config', FLAGS.input_config_path)]:
tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True)
model_config = configs['model']
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
if FLAGS.eval_training_data:
input_config = configs['train_input_config']
model_fn = functools.partial(
model_builder.build, model_config=model_config, is_training=False)
def get_next(config):
return dataset_builder.make_initializable_iterator(
dataset_builder.build(config)).get_next()
create_input_dict_fn = functools.partial(get_next, input_config)
categories = label_map_util.create_categories_from_labelmap(
input_config.label_map_path)
if FLAGS.run_once:
eval_config.max_evals = 1
graph_rewriter_fn = None
if 'graph_rewriter_config' in configs:
graph_rewriter_fn = graph_rewriter_builder.build(
configs['graph_rewriter_config'], is_training=False)
evaluator.evaluate(
create_input_dict_fn,
model_fn,
eval_config,
categories,
FLAGS.checkpoint_dir,
FLAGS.eval_dir,
graph_hook_fn=graph_rewriter_fn)
if __name__ == '__main__':
tf.app.run()
|
CUDA-Optimized/FastSpeech/fastspeech/trt/plugins/repeat | repeat | RepeatPlugin | // Copyright (c) 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 "RepeatPlugin.h"
#include "cuda_fp16.h"
#include <thread>
#include <cub/cub.cuh>
#define ck(call) check(call, __LINE__, __FILE__)
inline bool check(cudaError_t e, int iLine, const char *szFile) {
if (e != cudaSuccess) {
cout << "CUDA runtime API error " << cudaGetErrorName(e) << " at line " << iLine << " in file " << szFile << endl;
return false;
}
return true;
}
cudaDeviceProp getCudaDeviceProp() {
cudaError_t error;
cudaDeviceProp dev;
int device;
cudaGetDevice(&device);
error = cudaGetDeviceProperties(&dev, device);
if(error != cudaSuccess)
{
printf("Error: %s\n", cudaGetErrorString(error));
exit(-1);
}
return dev;
}
__global__ void ComputeOffset(float *pRepeatCnt, int *pOffset) {
int x = blockIdx.x * blockDim.x + threadIdx.x;
const int maxInputLength = 1024;
cub::BlockScan<int, maxInputLength>().ExclusiveSum(static_cast<int>(pRepeatCnt[x]), pOffset[x]);
}
template<typename T>
__global__ void RepeatTensor(T *pOut, T *pIn, float *pRepeatCnt, int *pOffset, int maxOutputLength) {
int b = blockIdx.x;
int t = blockIdx.y;
int h = threadIdx.x;
int dTime = gridDim.y;
int dHid = blockDim.x;
int offset_time = pOffset[b * dTime + t];
int duration = static_cast<int>(pRepeatCnt[b * dTime + t]);
T in = pIn[(b * dTime + t) * dHid + h];
for (int i=offset_time; i < min(offset_time + duration, maxOutputLength); i++) {
int offset_batch = b * maxOutputLength;
pOut[(offset_batch + i) * dHid + h] = in;
}
}
int RepeatPlugin::enqueue(int nBatch, const void * const *inputs, void **outputs, void* workspace, cudaStream_t stream) {
int dTime = m.inputDim.d[0];
int dHid = m.inputDim.d[1];
int maxOutputLength = m.maxOutputLength;
#ifndef NDEBUG
cudaDeviceProp dev = getCudaDeviceProp();
assert (dHid <= dev.maxThreadsPerBlock);
#endif
float * pRepeatCnt = (float *)inputs[1];
// get output time dim offset
int * pOffset;
ck(cudaMalloc(&pOffset, nBatch * dTime * sizeof(int)));
ComputeOffset<<<nBatch, dTime>>>(pRepeatCnt, pOffset);
if (m.dataType == DataType::kFLOAT || m.dataType == DataType::kINT32) {
// std::cout << "[RepeatPlugin] Running kernel in fp32" << std::endl;
float * pIn = (float *)inputs[0];
float * pOut = (float *)outputs[0];
dim3 dimGrid(nBatch, dTime);
dim3 dimBlock(dHid);
RepeatTensor<<<dimGrid, dimBlock>>>(pOut, pIn, pRepeatCnt, pOffset, maxOutputLength);
} else if (m.dataType == DataType::kHALF) {
// std::cout << "[RepeatPlugin] Running kernel in fp16" << std::endl;
__half * pIn = (__half *)inputs[0];
__half * pOut = (__half *)outputs[0];
dim3 dimGrid(nBatch, dTime);
dim3 dimBlock(dHid);
RepeatTensor<<<dimGrid, dimBlock>>>(pOut, pIn, pRepeatCnt, pOffset, maxOutputLength);
}
return 0;
}
REGISTER_TENSORRT_PLUGIN(RepeatPluginCreator); |
TensorFlow/Detection/SSD/models/research/slim/datasets | datasets | imagenet_2012_validation_synset_labels | n01751748
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|
TensorFlow/LanguageModeling/BERT/biobert/scripts | scripts | biobert_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.
docker run --runtime=nvidia -v $PWD:/workspace/bert \
--rm --shm-size=1g --ulimit memlock=-1 \
--ulimit stack=67108864 --ipc=host -t -i \
bert bash -c "bash data/create_biobert_datasets_from_start.sh" |
PyTorch/LanguageModeling/BERT/triton/runner | runner | exporter | # 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 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:
stagefile.write(str(command))
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
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/plugins/taco2ModulationRemovalPlugin | taco2ModulationRemovalPlugin | taco2ModulationRemovalLayerPlugin | /*
* 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 "taco2ModulationRemovalLayerPlugin.h"
#include "taco2ModulationRemovalKernel.h"
#include "taco2Utils.h"
#include <cassert>
#include <cstdlib>
#include <cstring>
#include <cuda_runtime.h> // cudaError_t
#include <iostream>
#include <sstream>
#include <stdexcept>
#include <string>
using namespace tts;
namespace nvinfer1
{
namespace plugin
{
using value_type = Taco2ModulationRemovalLayerPlugin::value_type;
/******************************************************************************
* CONSTANTS ******************************************************************
*****************************************************************************/
namespace
{
constexpr const char* const PLUGIN_NAME = "Taco2ModulationRemoval";
constexpr const char* const PLUGIN_VERSION = "0.1.0";
constexpr const int NUM_INPUTS = 1;
constexpr const int NUM_OUTPUTS = 1;
} // namespace
/******************************************************************************
* STATIC METHODS *************************************************************
*****************************************************************************/
const char* Taco2ModulationRemovalLayerPlugin::getName()
{
return PLUGIN_NAME;
}
const char* Taco2ModulationRemovalLayerPlugin::getVersion()
{
return PLUGIN_VERSION;
}
Taco2ModulationRemovalLayerPlugin Taco2ModulationRemovalLayerPlugin::deserialize(
const void* const data, const size_t length)
{
if (length < sizeof(int32_t) * 3)
{
throw std::runtime_error("Invalid serialized size: " + std::to_string(length));
}
const int inputLength = static_cast<const int32_t*>(data)[0];
const int filterLength = static_cast<const int32_t*>(data)[1];
const int hopLength = static_cast<const int32_t*>(data)[2];
const size_t reqSize = 3 * sizeof(int32_t) + sizeof(value_type) * filterLength;
if (reqSize != length)
{
throw std::runtime_error(
"Invalid serialized size: " + std::to_string(length) + " / " + std::to_string(reqSize));
}
const Weights weights{DataType::kFLOAT, static_cast<const int32_t*>(data) + 3, filterLength};
Taco2ModulationRemovalLayerPlugin layer(weights, inputLength, filterLength, hopLength);
return layer;
}
/******************************************************************************
* CONSTRUCTORS / DESTRUCTOR **************************************************
*****************************************************************************/
Taco2ModulationRemovalLayerPlugin::Taco2ModulationRemovalLayerPlugin(
const Weights& weights,
const int inputLength,
const int filterLength,
const int hopLength) :
mInputLength(inputLength),
mFilterLength(filterLength),
mHopLength(hopLength),
mWeightsHost(taco2::Taco2Utils::toFloatVector(weights)),
mWeightsDevice(),
mNamespace()
{
if (mInputLength <= 0)
{
throw std::runtime_error("Invalid Taco2ModulationRemoval inputLength: " + std::to_string(mInputLength));
}
if (mFilterLength <= 0)
{
throw std::runtime_error("Invalid Taco2ModulationRemoval filterLength " + std::to_string(mFilterLength));
}
if (mHopLength <= 0)
{
throw std::runtime_error("Invalid Taco2ModulationRemoval hopLength: " + std::to_string(mHopLength));
}
const int expNumWeights = mFilterLength;
if (mWeightsHost.size() != static_cast<size_t>(expNumWeights))
{
throw std::runtime_error("Incorrect Taco2ModulationRemoval number of weights: "
+ std::to_string(mWeightsHost.size()) + " / " + std::to_string(expNumWeights));
}
}
Taco2ModulationRemovalLayerPlugin::Taco2ModulationRemovalLayerPlugin(Taco2ModulationRemovalLayerPlugin&& other)
: mInputLength(other.mInputLength)
, mFilterLength(other.mFilterLength)
, mHopLength(other.mHopLength)
, mWeightsHost(std::move(other.mWeightsHost))
, mWeightsDevice(std::move(other.mWeightsDevice))
, mNamespace(std::move(other.mNamespace))
{
other.mFilterLength = 0;
other.mInputLength = 0;
other.mHopLength = 0;
}
Taco2ModulationRemovalLayerPlugin::~Taco2ModulationRemovalLayerPlugin()
{
destroy();
}
/******************************************************************************
* PUBLIC METHODS *************************************************************
*****************************************************************************/
Taco2ModulationRemovalLayerPlugin& Taco2ModulationRemovalLayerPlugin::operator=(
Taco2ModulationRemovalLayerPlugin&& other)
{
// defere to constructor
*this = Taco2ModulationRemovalLayerPlugin(std::move(other));
return *this;
}
DataType Taco2ModulationRemovalLayerPlugin::getOutputDataType(
const int /* index */, const DataType* const /* inputTypes */, const int /* nbInputs */) const
{
return DataType::kFLOAT;
}
bool Taco2ModulationRemovalLayerPlugin::isOutputBroadcastAcrossBatch(
const int /* outputIndex */, const bool* const /* inputIsBroadCasted */, const int /* nbInputs */) const
{
return false;
}
bool Taco2ModulationRemovalLayerPlugin::canBroadcastInputAcrossBatch(const int /* inputIndex */) const
{
return false;
}
const char* Taco2ModulationRemovalLayerPlugin::getPluginType() const
{
return getName();
}
const char* Taco2ModulationRemovalLayerPlugin::getPluginVersion() const
{
return getVersion();
}
int Taco2ModulationRemovalLayerPlugin::getNbOutputs() const
{
return NUM_OUTPUTS;
}
Dims Taco2ModulationRemovalLayerPlugin::getOutputDimensions(
const int index, const Dims* const /*inputs*/, const int nbInputDims)
{
if (index >= getNbOutputs())
{
throw std::runtime_error("Only has one output.");
}
if (nbInputDims != NUM_INPUTS)
{
throw std::runtime_error(
"Can only handle " + std::to_string(NUM_INPUTS) + " input tensors: " + std::to_string(nbInputDims));
}
// magnitude and phase are of the same size
return Dims3(1, 1, mInputLength - mFilterLength);
}
bool Taco2ModulationRemovalLayerPlugin::supportsFormat(
const nvinfer1::DataType type, const nvinfer1::PluginFormat /* format */) const
{
return type == DataType::kFLOAT;
}
void Taco2ModulationRemovalLayerPlugin::configurePlugin(const nvinfer1::Dims* const inputDims, const int nbInputs,
const nvinfer1::Dims* const /* outputDims */, const int nbOutputs, const nvinfer1::DataType* const inputTypes,
const nvinfer1::DataType* const /*outputTypes*/, const bool* const inputIsBroadcast,
const bool* const /*outputIsBroadcast*/, const nvinfer1::PluginFormat /* format */, const int /* maxBatchSize */)
{
if (nbInputs != NUM_INPUTS)
{
throw std::runtime_error(
"Can only handle " + std::to_string(NUM_INPUTS) + " input tensors: " + std::to_string(nbInputs));
}
if (nbOutputs != NUM_OUTPUTS)
{
throw std::runtime_error(
"Can only handle " + std::to_string(NUM_OUTPUTS) + " output tensors: " + std::to_string(nbOutputs));
}
for (int i = 0; i < nbInputs; ++i)
{
if (inputTypes[i] != DataType::kFLOAT)
{
throw std::runtime_error("Only FLOAT supported as input " + std::to_string(i) + " : "
+ std::to_string(static_cast<int>(inputTypes[i])));
}
if (inputIsBroadcast[i])
{
throw std::runtime_error("Broadcasting input is not supported.");
}
}
// assert dimensions
{
const Dims dims = taco2::Taco2Utils::getCompactedDims(inputDims[0], 1);
if (dims.nbDims != 1 || dims.d[0] != mInputLength)
{
throw std::runtime_error("Taco2ModulationRemoval input must be 1* x inputLength ("
+ std::to_string(mInputLength) + ") : " + taco2::Taco2Utils::dimsToString(dims));
}
}
}
int Taco2ModulationRemovalLayerPlugin::initialize()
{
try
{
mWeightsDevice = CudaMemory<float>(mWeightsHost);
}
catch (const std::exception& e)
{
std::cerr << "Taco2ModulationRemoval initialization failed: " << e.what() << std::endl;
return 1;
}
return 0;
}
void Taco2ModulationRemovalLayerPlugin::terminate()
{
mWeightsDevice.clear();
}
size_t Taco2ModulationRemovalLayerPlugin::getWorkspaceSize(const int /*maxBatchSize*/) const
{
return 0;
}
int Taco2ModulationRemovalLayerPlugin::enqueue(const int batchSize, const void* const* const inputs,
void** const outputs, void* const /*workspace*/, cudaStream_t stream)
{
// name inputs and outputs
const value_type* const inputDevice = static_cast<const value_type*>(inputs[0]);
value_type* const outputDevice = static_cast<value_type*>(outputs[0]);
try
{
Taco2ModulationRemovalKernel::compute(batchSize, static_cast<const float*>(mWeightsDevice.data()), inputDevice,
outputDevice, mInputLength, mFilterLength, mHopLength, stream);
}
catch (const std::exception& e)
{
std::cerr << "Failed to launch Taco2ModulationRemoval kernel due to: " << e.what() << std::endl;
return 1;
}
return 0;
}
size_t Taco2ModulationRemovalLayerPlugin::getSerializationSize() const
{
return sizeof(int32_t) * 3 + sizeof(value_type) * mWeightsHost.size();
}
void Taco2ModulationRemovalLayerPlugin::serialize(void* const buffer) const
{
static_cast<int32_t*>(buffer)[0] = mInputLength;
static_cast<int32_t*>(buffer)[1] = mFilterLength;
static_cast<int32_t*>(buffer)[2] = mHopLength;
value_type* const weights = reinterpret_cast<value_type*>(static_cast<int32_t*>(buffer) + 3);
memcpy(weights, mWeightsHost.data(), sizeof(value_type) * mWeightsHost.size());
}
void Taco2ModulationRemovalLayerPlugin::destroy()
{
terminate();
}
IPluginV2Ext* Taco2ModulationRemovalLayerPlugin::clone() const
{
// call constructor which copy's data
Taco2ModulationRemovalLayerPlugin clone(
Weights{DataType::kFLOAT, mWeightsHost.data(), static_cast<int64_t>(mWeightsHost.size())}, mInputLength,
mFilterLength, mHopLength);
if (mWeightsDevice.size() > 0)
{
// initialize the clone too
clone.initialize();
}
// move it to the heap last to avoid exceptions causing memory leaks
return new Taco2ModulationRemovalLayerPlugin(std::move(clone));
}
void Taco2ModulationRemovalLayerPlugin::setPluginNamespace(const char* pluginNamespace)
{
mNamespace = pluginNamespace;
}
const char* Taco2ModulationRemovalLayerPlugin::getPluginNamespace() const
{
return mNamespace.c_str();
}
} // namespace plugin
} // namespace nvinfer1
|
PyTorch/Classification/ConvNets/triton | triton | client | # 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 argparse
import json
import os
import numpy as np
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from image_classification.dataloaders import get_pytorch_val_loader
from tqdm import tqdm
import tritongrpcclient
from tritonclientutils import InferenceServerException
def get_data_loader(batch_size, *, data_path):
valdir = os.path.join(data_path, "val-jpeg")
val_dataset = datasets.ImageFolder(
valdir,
transforms.Compose(
[transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()]
),
)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=batch_size, shuffle=False
)
return val_loader
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--triton-server-url",
type=str,
required=True,
help="URL adress of trtion server (with port)",
)
parser.add_argument(
"--triton-model-name",
type=str,
required=True,
help="Triton deployed model name",
)
parser.add_argument(
"-v", "--verbose", action="store_true", default=False, help="Verbose mode."
)
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(
"--fp16",
action="store_true",
default=False,
help="Use fp16 precision for input data",
)
FLAGS = parser.parse_args()
triton_client = tritongrpcclient.InferenceServerClient(
url=FLAGS.triton_server_url, verbose=FLAGS.verbose
)
dataloader = get_data_loader(FLAGS.batch_size, data_path=FLAGS.inference_data)
inputs = []
inputs.append(
tritongrpcclient.InferInput(
"input__0",
[FLAGS.batch_size, 3, 224, 224],
"FP16" if FLAGS.fp16 else "FP32",
)
)
outputs = []
outputs.append(tritongrpcclient.InferRequestedOutput("output__0"))
all_img = 0
cor_img = 0
result_prev = None
for image, target in tqdm(dataloader):
if FLAGS.fp16:
image = image.half()
inputs[0].set_data_from_numpy(image.numpy())
result = triton_client.infer(
FLAGS.triton_model_name, inputs, outputs=outputs, headers=None
)
result = result.as_numpy("output__0")
result = np.argmax(result, axis=1)
cor_img += np.sum(result == target.numpy())
all_img += result.shape[0]
acc = cor_img / all_img
print(f"Final accuracy {acc:.04f}")
|
PyTorch/SpeechSynthesis/Tacotron2 | Tacotron2 | main | # *****************************************************************************
# Copyright (c) 2018, 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.
#
# *****************************************************************************
import argparse
from train import main as main_train
from inference_perf import main as main_infer
def parse_args(parser):
"""
Parse commandline arguments.
"""
parser.add_argument('--bench-class', type=str, choices=['train', 'perf-infer', 'perf-train'], required=True, help='Choose test class')
return parser
def main():
parser = argparse.ArgumentParser(description='PyTorch Tacotron 2 Testing')
parser = parse_args(parser)
args, unknown_args = parser.parse_known_args()
if "train" in args.bench_class:
main_train()
else:
main_infer()
if __name__ == '__main__':
main()
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/cli/commands | commands | preprocess | # 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.
import argparse
import logging
from syngen.cli.commands.base_command import BaseCommand
from syngen.preprocessing.datasets import DATASETS
logger = logging.getLogger(__name__)
log = logger
class PreprocessingCommand(BaseCommand):
def init_parser(self, base_parser):
preprocessing_parser = base_parser.add_parser(
"preprocess",
help="Run Dataset Preprocessing",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
preprocessing_parser.set_defaults(action=self.run)
preprocessing_parser.add_argument(
"--dataset", type=str, default=None, required=True, choices=list(DATASETS.keys()),
help="Dataset to preprocess",
)
preprocessing_parser.add_argument(
"-sp", "--source-path", type=str, default=None, required=True,
help="Path to raw data",
)
preprocessing_parser.add_argument(
"-dp", "--destination-path", type=str, default=None, required=False,
help="Path to store the preprocessed data. Default is $source_path/syngen_preprocessed",
)
preprocessing_parser.add_argument(
"--download",
action='store_true',
help="Downloads the dataset if specified",
)
preprocessing_parser.add_argument(
"--cpu",
action='store_true',
help='Performs the preprocessing_parser without leveraging GPU'
)
preprocessing_parser.add_argument(
"--use-cache",
action='store_true',
help='Does nothing if the target preprocessed dataset exists'
)
for preprocessing_class in DATASETS.values():
preprocessing_class.add_cli_args(preprocessing_parser)
def run(self, args):
dict_args = vars(args)
dataset_name = dict_args.pop('dataset')
source_path = dict_args.pop('source_path')
destination_path = dict_args.pop('destination_path')
download = dict_args.pop('download')
gpu = not dict_args.pop('cpu')
use_cache = dict_args.pop('use_cache')
preprocessing_class = DATASETS[dataset_name]
if download:
try:
preprocessing_class(source_path=source_path,
destination_path=destination_path,
download=download,
**dict_args)
log.info(f"{dataset_name} successfully downloaded into {source_path}")
except NotImplementedError:
log.info(f"{dataset_name} does not support automatic downloading, please download the dataset manually")
else:
preprocessing = preprocessing_class(source_path=source_path,
destination_path=destination_path,
download=download,
**dict_args)
preprocessing.transform(gpu=gpu, use_cache=use_cache)
log.info(f"{dataset_name} successfully preprocessed into {preprocessing.destination_path}")
|
PyTorch/LanguageModeling/BERT/data | data | BookscorpusTextFormatting | # 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 glob
import os
class BookscorpusTextFormatting:
def __init__(self, books_path, output_filename, recursive = False):
self.books_path = books_path
self.recursive = recursive
self.output_filename = output_filename
# This puts one book per line
def merge(self):
with open(self.output_filename, mode='w', newline='\n') as ofile:
for filename in glob.glob(self.books_path + '/' + '*.txt', recursive=True):
with open(filename, mode='r', encoding='utf-8-sig', newline='\n') as file:
for line in file:
if line.strip() != '':
ofile.write(line.strip() + ' ')
ofile.write("\n\n") |
PyTorch/Classification/GPUNet/triton | triton | run_inference_on_triton | #!/usr/bin/env python3
# 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.
r"""
To infer the model deployed on Triton, you can use `run_inference_on_triton.py` script.
It sends a request with data obtained from pointed data loader and dumps received data into dump files.
Those files are stored in directory pointed by `--output-dir` argument.
Currently, the client communicates with the Triton server asynchronously using GRPC protocol.
Example call:
```shell script
python ./triton/run_inference_on_triton.py \
--server-url localhost:8001 \
--model-name ResNet50 \
--model-version 1 \
--dump-labels \
--output-dir /results/dump_triton
```
"""
import argparse
import logging
import time
import traceback
from pathlib import Path
from tqdm import tqdm
# method from PEP-366 to support relative import in executed modules
if __package__ is None:
__package__ = Path(__file__).parent.name
from .deployment_toolkit.args import ArgParserGenerator
from .deployment_toolkit.core import DATALOADER_FN_NAME, load_from_file
from .deployment_toolkit.dump import JsonDumpWriter
from .deployment_toolkit.triton_inference_runner import TritonInferenceRunner
LOGGER = logging.getLogger("run_inference_on_triton")
def _parse_args():
parser = argparse.ArgumentParser(description="Infer model on Triton server", allow_abbrev=False)
parser.add_argument(
"--server-url", type=str, default="localhost:8001", help="Inference server URL (default localhost:8001)"
)
parser.add_argument("--model-name", help="The name of the model used for inference.", required=True)
parser.add_argument("--model-version", help="The version of the model used for inference.", required=True)
parser.add_argument("--dataloader", help="Path to python file containing dataloader.", required=True)
parser.add_argument("--dump-labels", help="Dump labels to output dir", action="store_true", default=False)
parser.add_argument("--dump-inputs", help="Dump inputs to output dir", action="store_true", default=False)
parser.add_argument("-v", "--verbose", help="Verbose logs", action="store_true", default=True)
parser.add_argument("--output-dir", required=True, help="Path to directory where outputs will be saved")
parser.add_argument(
"--response-wait-time", required=False, help="Maximal time to wait for response", default=120, type=float
)
parser.add_argument(
"--max-unresponded-requests",
required=False,
help="Maximal number of unresponded requests",
default=128,
type=int,
)
parser.add_argument(
"--synchronous", help="Enable synchronous calls to Triton Server", action="store_true", default=False
)
args, *_ = parser.parse_known_args()
get_dataloader_fn = load_from_file(args.dataloader, label="dataloader", target=DATALOADER_FN_NAME)
ArgParserGenerator(get_dataloader_fn).update_argparser(parser)
args = parser.parse_args()
return args
def main():
args = _parse_args()
log_format = "%(asctime)s %(levelname)s %(name)s %(message)s"
log_level = logging.INFO if not args.verbose else logging.DEBUG
logging.basicConfig(level=log_level, format=log_format)
LOGGER.info("args:")
for key, value in vars(args).items():
LOGGER.info(f" {key} = {value}")
get_dataloader_fn = load_from_file(args.dataloader, label="dataloader", target=DATALOADER_FN_NAME)
dataloader_fn = ArgParserGenerator(get_dataloader_fn).from_args(args)
try:
runner = TritonInferenceRunner(
server_url=args.server_url,
model_name=args.model_name,
model_version=args.model_version,
dataloader_fn=dataloader_fn,
verbose=False,
response_wait_time=args.response_wait_time,
max_unresponded_requests=args.max_unresponded_requests,
synchronous=args.synchronous,
)
except Exception as e:
message = traceback.format_exc()
LOGGER.error(f"Encountered exception \n{message}")
raise e
with JsonDumpWriter(output_dir=args.output_dir) as writer:
start = time.time()
for ids, x, y_pred, y_real in tqdm(runner, unit="batch", mininterval=10):
data = _verify_and_format_dump(args, ids, x, y_pred, y_real)
writer.write(**data)
stop = time.time()
LOGGER.info(f"\nThe inference took {stop - start:0.3f}s")
def _verify_and_format_dump(args, ids, x, y_pred, y_real):
data = {"outputs": y_pred, "ids": {"ids": ids}}
if args.dump_inputs:
data["inputs"] = x
if args.dump_labels:
if not y_real:
raise ValueError(
"Found empty label values. Please provide labels in dataloader_fn or do not use --dump-labels argument"
)
data["labels"] = y_real
return data
if __name__ == "__main__":
main()
|
TensorFlow2/LanguageModeling/BERT/official/utils/logs | logs | mlperf_helper | # 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.
# ==============================================================================
"""Wrapper for the mlperf logging utils.
MLPerf compliance logging is only desired under a limited set of circumstances.
This module is intended to keep users from needing to consider logging (or
install the module) unless they are performing mlperf runs.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
import json
import os
import re
import subprocess
import sys
import typing
import tensorflow as tf
_MIN_VERSION = (0, 0, 10)
_STACK_OFFSET = 2
SUDO = "sudo" if os.geteuid() else ""
# This indirection is used in docker.
DROP_CACHE_LOC = os.getenv("DROP_CACHE_LOC", "/proc/sys/vm/drop_caches")
_NCF_PREFIX = "NCF_RAW_"
# TODO(robieta): move line parsing to mlperf util
_PREFIX = r"(?:{})?:::MLPv([0-9]+).([0-9]+).([0-9]+)".format(_NCF_PREFIX)
_BENCHMARK = r"([a-zA-Z0-9_]+)"
_TIMESTAMP = r"([0-9]+\.[0-9]+)"
_CALLSITE = r"\((.+):([0-9]+)\)"
_TAG = r"([a-zA-Z0-9_]+)"
_VALUE = r"(.*)"
ParsedLine = namedtuple("ParsedLine", ["version", "benchmark", "timestamp",
"callsite", "tag", "value"])
LINE_PATTERN = re.compile(
"^{prefix} {benchmark} {timestamp} {callsite} {tag}(: |$){value}?$".format(
prefix=_PREFIX, benchmark=_BENCHMARK, timestamp=_TIMESTAMP,
callsite=_CALLSITE, tag=_TAG, value=_VALUE))
def parse_line(line): # type: (str) -> typing.Optional[ParsedLine]
match = LINE_PATTERN.match(line.strip())
if not match:
return
major, minor, micro, benchmark, timestamp = match.groups()[:5]
call_file, call_line, tag, _, value = match.groups()[5:]
return ParsedLine(version=(int(major), int(minor), int(micro)),
benchmark=benchmark, timestamp=timestamp,
callsite=(call_file, call_line), tag=tag, value=value)
def unparse_line(parsed_line): # type: (ParsedLine) -> str
version_str = "{}.{}.{}".format(*parsed_line.version)
callsite_str = "({}:{})".format(*parsed_line.callsite)
value_str = ": {}".format(parsed_line.value) if parsed_line.value else ""
return ":::MLPv{} {} {} {} {} {}".format(
version_str, parsed_line.benchmark, parsed_line.timestamp, callsite_str,
parsed_line.tag, value_str)
def get_mlperf_log():
"""Shielded import of mlperf_log module."""
try:
import mlperf_compliance
def test_mlperf_log_pip_version():
"""Check that mlperf_compliance is up to date."""
import pkg_resources
version = pkg_resources.get_distribution("mlperf_compliance")
version = tuple(int(i) for i in version.version.split("."))
if version < _MIN_VERSION:
tf.compat.v1.logging.warning(
"mlperf_compliance is version {}, must be >= {}".format(
".".join([str(i) for i in version]),
".".join([str(i) for i in _MIN_VERSION])))
raise ImportError
return mlperf_compliance.mlperf_log
mlperf_log = test_mlperf_log_pip_version()
except ImportError:
mlperf_log = None
return mlperf_log
class Logger(object):
"""MLPerf logger indirection class.
This logger only logs for MLPerf runs, and prevents various errors associated
with not having the mlperf_compliance package installed.
"""
class Tags(object):
def __init__(self, mlperf_log):
self._enabled = False
self._mlperf_log = mlperf_log
def __getattr__(self, item):
if self._mlperf_log is None or not self._enabled:
return
return getattr(self._mlperf_log, item)
def __init__(self):
self._enabled = False
self._mlperf_log = get_mlperf_log()
self.tags = self.Tags(self._mlperf_log)
def __call__(self, enable=False):
if enable and self._mlperf_log is None:
raise ImportError("MLPerf logging was requested, but mlperf_compliance "
"module could not be loaded.")
self._enabled = enable
self.tags._enabled = enable
return self
def __enter__(self):
pass
def __exit__(self, exc_type, exc_val, exc_tb):
self._enabled = False
self.tags._enabled = False
@property
def log_file(self):
if self._mlperf_log is None:
return
return self._mlperf_log.LOG_FILE
@property
def enabled(self):
return self._enabled
def ncf_print(self, key, value=None, stack_offset=_STACK_OFFSET,
deferred=False, extra_print=False, prefix=_NCF_PREFIX):
if self._mlperf_log is None or not self.enabled:
return
self._mlperf_log.ncf_print(key=key, value=value, stack_offset=stack_offset,
deferred=deferred, extra_print=extra_print,
prefix=prefix)
def set_ncf_root(self, path):
if self._mlperf_log is None:
return
self._mlperf_log.ROOT_DIR_NCF = path
LOGGER = Logger()
ncf_print, set_ncf_root = LOGGER.ncf_print, LOGGER.set_ncf_root
TAGS = LOGGER.tags
def clear_system_caches():
if not LOGGER.enabled:
return
ret_code = subprocess.call(
["sync && echo 3 | {} tee {}".format(SUDO, DROP_CACHE_LOC)],
shell=True)
if ret_code:
raise ValueError("Failed to clear caches")
if __name__ == "__main__":
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
with LOGGER(True):
ncf_print(key=TAGS.RUN_START)
|
TensorFlow/Segmentation/UNet_Industrial/model/layers | layers | dense | #!/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__ = ['dense']
def dense(
inputs,
units,
use_bias=True,
trainable=True,
kernel_initializer=tf.variance_scaling_initializer(),
bias_initializer=tf.zeros_initializer()
):
net = tf.layers.dense(
inputs,
units=units,
activation=None,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
trainable=trainable
)
_log_hparams(
classname='Dense',
layername=net.name,
units=units,
use_bias=use_bias,
trainable=trainable,
out_shape=str(net.get_shape()),
out_dtype=net.dtype
)
return net
|
PyTorch/Recommendation/DLRM | DLRM | dgxa100_ccx | # 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.
#! /bin/bash
bind_cpu_cores=([0]="48-51,176-179" [1]="60-63,188-191" [2]="16-19,144-147" [3]="28-31,156-159"
[4]="112-115,240-243" [5]="124-127,252-255" [6]="80-83,208-211" [7]="92-95,220-223")
bind_mem=([0]="3" [1]="3" [2]="1" [3]="1"
[4]="7" [5]="7" [6]="5" [7]="5")
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/test | test | UnitTest | /*
* 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 <exception>
#include <iostream>
namespace
{
std::vector<UnitTest*>* s_tests = nullptr;
}
/******************************************************************************
* PUBLIC STATIC METHODS ******************************************************
*****************************************************************************/
bool UnitTest::runAll()
{
size_t numPassed = 0;
size_t numTests = 0;
if (s_tests) {
numTests = s_tests->size();
for (UnitTest* const test : *s_tests) {
try {
test->run();
if (test->passed()) {
std::cout << "Test: " << test->fullname() << " passed." << std::endl;
++numPassed;
continue;
}
} catch (const TestException&) {
// assertion failed
} catch (const std::exception& e) {
std::cout << "Unhandled excpetion: " << e.what() << std::endl;
}
std::cout << "Test: " << test->fullname() << " failed." << std::endl;
}
}
std::cout << numPassed << " / " << numTests << " passed." << std::endl;
return numPassed == numTests;
}
void UnitTest::registerTest(UnitTest* const test)
{
if (!s_tests) {
s_tests = new std::vector<UnitTest*>(0);
}
s_tests->emplace_back(test);
}
/******************************************************************************
* CONSTRUCTORS / DESTRUCTOR **************************************************
*****************************************************************************/
UnitTest::UnitTest(const std::string& filename, const std::string& name)
: m_nullStream(), m_passed(true), m_filename(filename), m_name(name)
{
registerTest(this);
}
/******************************************************************************
* PUBLIC METHODS *************************************************************
*****************************************************************************/
std::string UnitTest::fullname() const
{
return m_filename + "__" + m_name;
}
bool UnitTest::passed() const
{
return m_passed;
}
/******************************************************************************
* PROTECTED METHODS **********************************************************
*****************************************************************************/
void UnitTest::failure()
{
m_passed = false;
}
/******************************************************************************
* MAIN ***********************************************************************
*****************************************************************************/
int main(int /*argc*/, char** /*argv*/)
{
if (UnitTest::runAll()) {
return 0;
} else {
return 1;
}
}
|
PyTorch/Classification/ConvNets/triton/scripts/docker | docker | build | #!/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.
docker build -t resnet50 . -f triton/resnet50/Dockerfile
|
PyTorch/DrugDiscovery/MoFlow | MoFlow | setup | # 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.
from setuptools import setup
setup(
name='moflow_pyt',
packages=[
'moflow',
'moflow.data',
'moflow.model',
'moflow.runtime'
],
version='0.0.1',
description='MoFlow: an invertible flow model for generating molecular graphs',
)
|
TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking | benchmarking | UNet_trainbench_4GPU | #!/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 FP32/TF32 on 4 GPUs using 16 batch size (4 per GPU)
# Usage ./UNet_trainbench_4GPU.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}"
mpirun \
-np 4 \
-H localhost:4 \
-bind-to none \
-map-by slot \
-x NCCL_DEBUG=VERSION \
-x LD_LIBRARY_PATH \
-x PATH \
-mca pml ob1 -mca btl ^openib \
--allow-run-as-root \
python "${BASEDIR}/../../main.py" \
--unet_variant='tinyUNet' \
--activation_fn='relu' \
--exec_mode='training_benchmark' \
--iter_unit='batch' \
--num_iter=1500 \
--batch_size=4 \
--warmup_step=500 \
--results_dir="${RESULT_DIR}" \
--data_dir="${1}" \
--dataset_name='DAGM2007' \
--dataset_classID="${2}" \
--data_format='NCHW' \
--use_auto_loss_scaling \
--noamp \
--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
|
TensorFlow/Recommendation/WideAndDeep | WideAndDeep | setup | # 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.
# Copyright 2016 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.
import setuptools
NAME = 'trainer'
VERSION = '1.0'
TENSORFLOW_TRANSFORM = 'tensorflow-transform==0.1.8'
if __name__ == '__main__':
setuptools.setup(name=NAME, version=VERSION, packages=['trainer'],
install_requires=[TENSORFLOW_TRANSFORM])
|
TensorFlow/Detection/SSD/models/research/object_detection/g3doc | g3doc | installation | # Installation
## Dependencies
Tensorflow Object Detection API depends on the following libraries:
* Protobuf 3.0.0
* Python-tk
* Pillow 1.0
* lxml
* tf Slim (which is included in the "tensorflow/models/research/" checkout)
* Jupyter notebook
* Matplotlib
* Tensorflow (>=1.9.0)
* Cython
* contextlib2
* cocoapi
For detailed steps to install Tensorflow, follow the [Tensorflow installation
instructions](https://www.tensorflow.org/install/). A typical user can install
Tensorflow using one of the following commands:
``` bash
# For CPU
pip install tensorflow
# For GPU
pip install tensorflow-gpu
```
The remaining libraries can be installed on Ubuntu 16.04 using via apt-get:
``` bash
sudo apt-get install protobuf-compiler python-pil python-lxml python-tk
pip install --user Cython
pip install --user contextlib2
pip install --user jupyter
pip install --user matplotlib
```
Alternatively, users can install dependencies using pip:
``` bash
pip install --user Cython
pip install --user contextlib2
pip install --user pillow
pip install --user lxml
pip install --user jupyter
pip install --user matplotlib
```
<!-- common_typos_disable -->
**Note**: sometimes "sudo apt-get install protobuf-compiler" will install
Protobuf 3+ versions for you and some users have issues when using 3.5.
If that is your case, try the [manual](#Manual-protobuf-compiler-installation-and-usage) installation.
## COCO API installation
Download the
[cocoapi](https://github.com/cocodataset/cocoapi) and
copy the pycocotools subfolder to the tensorflow/models/research directory if
you are interested in using COCO evaluation metrics. The default metrics are
based on those used in Pascal VOC evaluation. To use the COCO object detection
metrics add `metrics_set: "coco_detection_metrics"` to the `eval_config` message
in the config file. To use the COCO instance segmentation metrics add
`metrics_set: "coco_mask_metrics"` to the `eval_config` message in the config
file.
```bash
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
make
cp -r pycocotools <path_to_tensorflow>/models/research/
```
## Protobuf Compilation
The Tensorflow Object Detection API uses Protobufs to configure model and
training parameters. Before the framework can be used, the Protobuf libraries
must be compiled. This should be done by running the following command from
the tensorflow/models/research/ directory:
``` bash
# From tensorflow/models/research/
protoc object_detection/protos/*.proto --python_out=.
```
**Note**: If you're getting errors while compiling, you might be using an incompatible protobuf compiler. If that's the case, use the following manual installation
## Manual protobuf-compiler installation and usage
**If you are on linux:**
Download and install the 3.0 release of protoc, then unzip the file.
```bash
# From tensorflow/models/research/
wget -O protobuf.zip https://github.com/google/protobuf/releases/download/v3.0.0/protoc-3.0.0-linux-x86_64.zip
unzip protobuf.zip
```
Run the compilation process again, but use the downloaded version of protoc
```bash
# From tensorflow/models/research/
./bin/protoc object_detection/protos/*.proto --python_out=.
```
**If you are on MacOS:**
If you have homebrew, download and install the protobuf with
```brew install protobuf```
Alternately, run:
```PROTOC_ZIP=protoc-3.3.0-osx-x86_64.zip
curl -OL https://github.com/google/protobuf/releases/download/v3.3.0/$PROTOC_ZIP
sudo unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
rm -f $PROTOC_ZIP
```
Run the compilation process again:
``` bash
# From tensorflow/models/research/
protoc object_detection/protos/*.proto --python_out=.
```
## Add Libraries to PYTHONPATH
When running locally, the tensorflow/models/research/ and slim directories
should be appended to PYTHONPATH. This can be done by running the following from
tensorflow/models/research/:
``` bash
# From tensorflow/models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
```
Note: This command needs to run from every new terminal you start. If you wish
to avoid running this manually, you can add it as a new line to the end of your
~/.bashrc file, replacing \`pwd\` with the absolute path of
tensorflow/models/research on your system.
# Testing the Installation
You can test that you have correctly installed the Tensorflow Object Detection\
API by running the following command:
```bash
python object_detection/builders/model_builder_test.py
```
|
PaddlePaddle/LanguageModeling/BERT/bert_configs | bert_configs | bert-large-cased | {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"max_position_embeddings": 512,
"num_attention_heads": 16,
"num_hidden_layers": 24,
"type_vocab_size": 2,
"vocab_size": 28996
}
|
PyTorch/Translation/Transformer/scripts | scripts | run_training | #! /bin/bash
#
# 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.
nvidia-smi
RESULTS_DIR='/results'
CHECKPOINTS_DIR='/results/checkpoints'
STAT_FILE=${RESULTS_DIR}/run_log.json
mkdir -p $CHECKPOINTS_DIR
: ${PREC:='amp'}
: ${SEED:=1}
: ${LR:=0.000846}
: ${WARMUP:=4000}
: ${NUM_EPOCHS:=30}
: ${BS:=5120}
: ${NUM_GPU:=8}
: ${USE_SLURM:=0}
: ${USE_DISTRIBUTED:=1}
DISTRIBUTED=""
[ ${USE_DISTRIBUTED} = 1 ] && DISTRIBUTED+="-m torch.distributed.run --nproc_per_node=${NUM_GPU}"
[ ${USE_DISTRIBUTED} = 1 ] && [ ${USE_SLURM} = 1 ] && DISTRIBUTED+=" --nnodes ${WORLD_SIZE} --node_rank ${SLURM_NODEID} \
--master_addr ${MASTER_ADDR} --master_port ${MASTER_PORT} "
if [ "$PREC" = "amp" ];
then
PREC='--amp '
else
PREC=''
fi
python ${DISTRIBUTED} /workspace/translation/train.py \
/data/ \
--arch transformer_wmt_en_de_big_t2t \
--share-all-embeddings \
--optimizer adam \
--adam-betas 0.9 0.997 \
--adam-eps 1e-9 \
--clip-norm 0.0 \
--lr-scheduler inverse_sqrt \
--warmup-init-lr 0.0 \
--warmup-updates ${WARMUP} \
--lr $LR \
--min-lr 0.0 \
--dropout 0.1 \
--weight-decay 0.0 \
--criterion label_smoothed_cross_entropy \
--label-smoothing 0.1 \
--max-tokens ${BS} \
--seed ${SEED} \
--max-epoch ${NUM_EPOCHS} \
--no-save \
--fuse-layer-norm \
--online-eval \
--log-interval 500 \
--save-dir ${RESULTS_DIR} \
--stat-file ${STAT_FILE} \
${PREC}
|
TensorFlow2/Recommendation/DLRM_and_DCNv2/deployment/hps | hps | deploy_ensemble | # 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.
#
# author: Tomasz Grel (tgrel@nvidia.com)
import os
from collections import namedtuple
Tensor = namedtuple("Tensor", ["name", "dtype", "dims"])
_config_template = r'''
name: "{model_name}"
platform: "ensemble"
max_batch_size: {max_batch_size}
input [
{{
name: "EMB_KEY"
data_type: TYPE_INT64
dims: [-1]
}},
{{
name: "EMB_N_KEY"
data_type: TYPE_INT32
dims: [-1]
}},
{{
name: "numerical_features"
data_type: TYPE_FP32
dims: [-1]
}}
]
output [
{{
name: "DENSE_OUTPUT"
data_type: TYPE_FP32
dims: [-1]
}}
]
ensemble_scheduling {{
step [
{{
model_name: "{sparse_model_name}"
model_version: -1
input_map {{
key: "KEYS"
value: "EMB_KEY"
}},
input_map {{
key: "NUMKEYS"
value: "EMB_N_KEY"
}},
output_map {{
key: "OUTPUT0"
value: "LOOKUP_VECTORS"
}}
}},
{{
model_name: "{dense_model_name}"
model_version: -1
input_map {{
key: "args_1"
value: "LOOKUP_VECTORS"
}},
input_map {{
key: "args_0"
value: "numerical_features"
}},
output_map {{
key: "output_1"
value: "DENSE_OUTPUT"
}}
}}
]
}}
'''
def deploy_ensemble(dst, model_name, sparse_model_name, dense_model_name,
num_cat_features, num_numerical_features, max_batch_size, version):
config_str = _config_template.format(model_name=model_name,
sparse_model_name=sparse_model_name,
dense_model_name=dense_model_name,
max_batch_size=max_batch_size)
with open(os.path.join(dst, "config.pbtxt"), "w") as f:
f.write(config_str)
os.mkdir(os.path.join(dst, str(version)))
print("Ensemble configuration:")
print(config_str)
|
PyTorch/Forecasting/TFT/triton/runner | runner | preparer | # 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 abc
import pathlib
from datetime import datetime
from typing import Dict, List
# 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 .config import Config
from .configuration import Configuration
from .downloader import download
from .experiment import Experiment, Stage
from .logger import LOGGER
from .maintainer import Maintainer
from .pipeline import Pipeline
from .stages import ResultsType, TritonPerformanceOfflineStage, TritonPerformanceOnlineStage
from .task import Checkpoint, Dataset, SystemInfo, Task
from .triton import Triton
from .utils import clean_directory
class Preparer(abc.ABC):
"""
Runner preparer object.
"""
@abc.abstractmethod
def exec(
self,
workspace: pathlib.Path,
config: Config,
pipeline: Pipeline,
maintainer: Maintainer,
triton: Triton,
logs_dir: pathlib.Path,
):
pass
class ExperimentPreparer(Preparer):
"""
Experiment runner preparer object.
"""
def exec(
self,
workspace: pathlib.Path,
config: Config,
pipeline: Pipeline,
maintainer: Maintainer,
triton: Triton,
logs_dir: pathlib.Path,
):
LOGGER.info("Preparing Triton container image")
triton_container_image = self._prepare_triton_container_image(config, maintainer, triton)
LOGGER.info("Initialize task")
task = self._initialize_task(
workspace=workspace,
config=config,
pipeline=pipeline,
triton_container_image=triton_container_image,
logs_dir=logs_dir,
)
LOGGER.info("Preparing directories")
self._create_dirs(workspace, task)
LOGGER.info("Clean previous run artifacts directories")
self._clean_previous_run_artifacts(workspace, task)
LOGGER.info("Downloading checkpoints")
self._download_checkpoints(task)
return task
def _create_dirs(self, workspace: pathlib.Path, task: Task) -> None:
"""
Create directories used to store artifacts and final results
Returns:
None
"""
for directory in [task.results_dir, task.logs_dir, task.checkpoints_dir]:
directory_path = workspace / directory
directory_path.mkdir(parents=True, exist_ok=True)
LOGGER.info(f"Directory {directory} created.")
def _clean_previous_run_artifacts(self, workspace: pathlib.Path, task: Task) -> None:
"""
Clean logs from previous run
Returns:
None
"""
for directory in [
task.logs_dir,
task.results_dir,
]:
directory_path = workspace / directory
clean_directory(directory_path)
LOGGER.info(f"Location {directory} cleaned.")
def _prepare_triton_container_image(self, config: Config, maintainer: Maintainer, triton: Triton) -> str:
"""
Prepare Triton Container Image based on provided configuration
Returns:
Name of container image to use in process
"""
if not config.triton_dockerfile:
image_name = triton.container_image(config.container_version)
LOGGER.info(f"Using official Triton container image: {image_name}.")
return image_name
if config.triton_container_image:
LOGGER.info(f"Using provided Triton Container Image: {config.triton_container_image}")
return config.triton_container_image
normalized_model_name = config.model_name.lower().replace("_", "-")
image_name = f"tritonserver-{normalized_model_name}:latest"
LOGGER.info(f"Building Triton Container Image: {image_name}")
maintainer.build_image(
image_name=image_name,
image_file_path=pathlib.Path(config.triton_dockerfile),
build_args={"FROM_IMAGE": triton.container_image(container_version=config.container_version)},
)
return image_name
def _download_checkpoints(self, task: Task) -> None:
"""
Download checkpoints
"""
for variant, checkpoint in task.checkpoints.items():
checkpoint_url = checkpoint.url
download_path = checkpoint.path
if download_path.is_dir():
LOGGER.info(f"Checkpoint {download_path.name} already downloaded.")
continue
if not checkpoint_url:
LOGGER.warning(
f"Checkpoint {variant} url is not provided."
"\nIf you want to use that checkpoint please train the model locally"
f"\nand copy to {download_path} directory"
)
continue
download(checkpoint_url, download_path)
def _initialize_task(
self,
workspace: pathlib.Path,
config: Config,
pipeline: Pipeline,
triton_container_image: str,
logs_dir: pathlib.Path,
) -> Task:
"""
Initialize task object
Args:
workspace: Path to workspace where artifacts are stored
config: Config object
pipeline: Pipeline object
triton_container_image: Triton Inference Server container image used for tests
Returns:
Task object
"""
datasets = {}
for dataset in config.datasets:
datasets[dataset.name] = Dataset(name=dataset.name)
checkpoints = {}
for checkpoint in config.checkpoints:
download_path = workspace / Task.checkpoints_dir / checkpoint.name
checkpoints[checkpoint.name] = Checkpoint(name=checkpoint.name, url=checkpoint.url, path=download_path)
results_types = self._task_results_types(pipeline=pipeline)
stages = dict()
for stage in pipeline.stages():
stages[stage.label] = {"result_path": stage.result_path, "result_type": stage.result_type}
experiments = list()
for idx, configuration in enumerate(config.configurations, start=1):
experiment = self._prepare_experiment(
idx=idx,
configuration=configuration,
results_types=results_types,
stages=stages,
)
experiments.append(experiment)
system_info = SystemInfo.from_host()
task = Task(
model_name=config.model_name,
framework=config.framework,
checkpoints=checkpoints,
datasets=datasets,
datasets_dir=config.datasets_dir,
experiments=experiments,
container_version=config.container_version,
system_info=system_info,
triton_container_image=triton_container_image,
triton_custom_operations=config.triton_custom_operations,
triton_load_model_method=config.triton_load_model_method,
started_at=int(datetime.utcnow().timestamp()),
logs_dir=logs_dir,
)
return task
def _task_results_types(self, pipeline: Pipeline) -> List[str]:
"""
Types of results generated as part of task
Returns:
List of result types
"""
results = list()
for stage in pipeline.stages():
if TritonPerformanceOfflineStage.label == stage.label:
results.append(ResultsType.TRITON_PERFORMANCE_OFFLINE)
continue
if TritonPerformanceOnlineStage.label == stage.label:
results.append(ResultsType.TRITON_PERFORMANCE_ONLINE)
continue
return results
def _prepare_experiment(
self,
idx: int,
configuration: Configuration,
results_types: List[str],
stages: Dict,
) -> Experiment:
"""
Prepare experiments data
Args:
idx: Experiment index
configuration: Configuration object
results_types: Results types stored in experiment
stages: Stages executed as part of experiment
Returns:
Experiment object
"""
parameters = {key.lower(): value for key, value in configuration.parameters.items()}
results_mapped = dict()
for result_type in results_types:
results_mapped[result_type] = result_type
stages_mapped = dict()
for name, stage_data in stages.items():
stages_mapped[name] = Stage(name=name, **stage_data)
experiment = Experiment(
experiment_id=idx,
parameters=parameters,
stages=stages_mapped,
results=results_mapped,
)
return experiment
|
PyTorch/Detection/Efficientdet/scripts/docker | docker | build | #!/bin/bash
docker build --rm -t nvcr.io/nvidia/effdet:21.06-py3-stage . -f Dockerfile
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/utils/types | types | metadata | # 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 __future__ import annotations
from .str_enum import StrEnum
class MetaData(StrEnum):
PATH = "path"
EDGES = "edges"
NODES = "nodes"
ALIGNERS = "[gen]aligners"
GRAPHS = "graphs"
NAME = "name"
COUNT = "count"
NODE_DATA = "node_data"
EDGE_DATA = "edge_data"
TYPE = "type"
DTYPE = "dtype"
SRC = "src"
SRC_NAME = "src_name"
SRC_NODE_TYPE = "src_node_type"
DST = "dst"
DST_NAME = "dst_name"
DST_NODE_TYPE = "dst_node_type"
NODE_NAME = "node_name"
NODE_COLUMNS = "node_columns"
EDGE_NAME = "edge_name"
LABELS = "labels"
FEATURES = "features"
FEATURES_PATH = "features_path"
FEATURES_DATA = "features_data"
FEATURE_TYPE = "feature_type"
FEATURE_FILE = "feature_file"
FILENAME_PREFIX = "filename_prefix"
STRUCTURE_PATH = "structure_path"
STRUCTURE_DATA = "structure_data"
NODE_FEAT = "node_feat"
EDGE_FEAT = "edge_feat"
TRAIN_MASK = "train_mask"
VAL_MASK = "val_mask"
TEST_MASK = "test_mask"
CONTINUOUS = "continuous"
CATEGORICAL = "categorical"
CONTINUOUS_COLUMNS = "continuous_columns"
CATEGORICAL_COLUMNS = "categorical_columns"
UNDIRECTED = "undirected"
DIRECTED = "directed"
# generation related keys
STRUCTURE_GENERATOR = "[gen]structure_generator"
TABULAR_GENERATORS = "[gen]tabular_generators"
DATA_SOURCE = "data_source"
FEATURES_LIST = "features_list"
PARAMS = "params"
DUMP_PATH = "dump_path"
|
Tools/PyTorch/TimeSeriesPredictionPlatform/models/tft_pyt/triton/runner | runner | start_NVIDIA-DGX-A100-(1x-A100-80GB) | # 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.
#!/bin/bash
# Install Docker
. /etc/os-release && \
curl -fsSL https://download.docker.com/linux/debian/gpg | apt-key add - && \
echo "deb [arch=amd64] https://download.docker.com/linux/debian buster stable" > /etc/apt/sources.list.d/docker.list && \
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey| apt-key add - && \
curl -s -L https://nvidia.github.io/nvidia-docker/$ID$VERSION_ID/nvidia-docker.list > /etc/apt/sources.list.d/nvidia-docker.list && \
apt-get update && \
apt-get install -y docker-ce docker-ce-cli containerd.io nvidia-docker2
# Install packages
pip install -r triton/runner/requirements.txt
# Evaluate Runner
python3 -m "triton.runner.__main__" \
--config-path "triton/runner/config_NVIDIA-DGX-A100-(1x-A100-80GB).yaml" \
--device 0 |