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PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/plugins/taco2ProjectionPlugin | taco2ProjectionPlugin | taco2ProjectionLayerPlugin | /*
* 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_PROJECTIONLAYERPLUGIN_H
#define TT2I_PROJECTIONLAYERPLUGIN_H
#include "NvInfer.h"
#include <memory>
#include <string>
#include <vector>
namespace nvinfer1
{
namespace plugin
{
class Taco2ProjectionKernel;
class Taco2ProjectionLayerPlugin : public nvinfer1::IPluginV2DynamicExt
{
public:
using value_type = float;
/**
* @brief Get the name of this plugin.
*
* @return The name.
*/
static const char* getName();
/**
* @brief Get the version of this plugin.
*
* @return The version.
*/
static const char* getVersion();
/**
* @brief Create a new Taco2ProjectionLayerPlugin from serialized data.
*
* @param data The data.
* @param length The length of the data in bytes.
*
* @return The instantiated plugin.
*/
static Taco2ProjectionLayerPlugin deserialize(const void* data, size_t length);
/**
* @brief Create a new Taco2ProjectionLayerPlugin.
*
* @param weightsChannel The weights for channel projection.
* @param weightsGate The weights for gate projection.
* @param biasChannel The bias for channels.
* @param biasGate The bias for the gate.
* @param hiddenIntputLength The hidden input lentgh.
* @param contextIntputLength The context input length.
* @param channelDimension The number of channels.
* @param gateDimension The number of gates.
*/
Taco2ProjectionLayerPlugin(const nvinfer1::Weights& weightsChannel, const nvinfer1::Weights& weightsGate,
const nvinfer1::Weights& biasChannel, const nvinfer1::Weights& biasGate, int hiddenIntputLength,
int contextIntputLength, int channelDimension, int gateDimension);
/**
* @brief The move constructor.
*
* @param other The Taco2ProjectionLayerPlugin to move.
*/
Taco2ProjectionLayerPlugin(Taco2ProjectionLayerPlugin&& other);
/**
* @brief The move assignment operator.
*
* @param other The Taco2ProjectionLayerPlugin to move.
*
* @return This object.
*/
Taco2ProjectionLayerPlugin& operator=(Taco2ProjectionLayerPlugin&& other);
/**
* @brief Destructor.
*/
~Taco2ProjectionLayerPlugin();
// disable copying
Taco2ProjectionLayerPlugin(const Taco2ProjectionLayerPlugin& other) = delete;
Taco2ProjectionLayerPlugin& operator=(const Taco2ProjectionLayerPlugin& other) = delete;
/**
* @brief Return the data type of the plugin output at the requested index.
*
* @param index The output index.
* @param inputTypes The input data types.
* @param nbInputs The number of inputs.
*
* @return The type of output.
*/
nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override;
/**
* @brief Get the plugin type.
*
* @return The plugin type.
*/
const char* getPluginType() const override;
/**
* @brief Get the plugin version.
*
* @return The plugin version.
*/
const char* getPluginVersion() const override;
/**
* @brief Get the number of outputs.
*
* @return The number of outputs.
*/
int getNbOutputs() const override;
/**
* @brief Get the dimensions of an output tensor.
*
* @param outputIndex The index of the output tensor.
* @param inputs Expressions for dimensions of the input tensors.
* @param nbInputs The number of input tensors.
* @param expBuilder Object for generating new expressions.
*
* @return The resulting dimensions.
*/
DimsExprs getOutputDimensions(
int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& expBuilder) override;
/**
* @brief Check if the given plugin format is supported.
*
* @param pos The format position/index in inOut.format[].
* @param inOut The input and output formats.
* @param nbInputs The number of inputs.
* @param nbOutputs The number of outputs.
*
* @return True if it is supported.
*/
bool supportsFormatCombination(int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override;
/**
* @brief Configure this plugin with the given inputs, outputs, and datat
* types.
*
* @param in The input tensor attributes that used for configuration.
* @param nbInputs The number of inputs.
* @param out The output tensor attributes that are used for configuration.
* @param nbOutputs The number of outputs.
*/
void configurePlugin(
const DynamicPluginTensorDesc* in, int nbInputs, const DynamicPluginTensorDesc* out, int nbOutputs) override;
/**
* @brief Initialize the plugin.
*
* @return 0 if initialization was successful. Non-zero otherwise.
*/
int initialize() override;
/**
* @brief Terminate the plugin (deinitialize).
*/
void terminate() override;
/**
* @brief Get workspace size required by this plugin for up to the given
* batch size.
*
* @param in The input tensor descriptors.
* @param nbInputs The number of inputs.
* @param out The output tensor descriptors.
* @param nbOutputs The number of outputs.
*
* @return The workspace size in bytes.
*/
size_t getWorkspaceSize(
const PluginTensorDesc* in, int nbInputs, const PluginTensorDesc* out, int nbOutputs) const override;
/**
* @brief Set this plugin for execution on the stream.
*
* @param inputDesc The input tensor descriptors.
* @param outputDesc The output tensor descriptors.
* @param inputs The input tensors.
* @param outputs The output tensors.
* @param workspace The workspace.
* @param stream The stream to operate on.
*
* @return 0 if successfully queued, non-zero otherwise.
*/
int enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc, const void* const* inputs,
void* const* outputs, void* workspace, cudaStream_t stream);
/**
* @brief Get the number of bytes occupied by this plugin if serialized.
*
* @return The size in bytes.
*/
size_t getSerializationSize() const override;
/**
* @brief Serialize this plugin.
*
* @param buffer The buffer to write to.
*/
void serialize(void* buffer) const override;
/**
* @brief Destroy this plugin instance.
*/
void destroy() override;
/**
* @brief Clone this pulgin instance.
*
* @return The cloned plugin.
*/
IPluginV2DynamicExt* clone() const override;
/**
* @brief Set the namespace of this plugin.
*
* @param pluginNamespace The namespace.
*/
void setPluginNamespace(const char* pluginNamespace) override;
/**
* @brief Get the namespace of this plugin.
*
* @return The namespace.
*/
const char* getPluginNamespace() const override;
private:
static const float ONE;
static const float ZERO;
int mHiddenInputLength;
int mContextInputLength;
int mNumChannelDimension;
int mNumGateDimension;
std::vector<value_type> mWeightsChannel;
std::vector<value_type> mWeightsGate;
std::vector<value_type> mBiasChannel;
std::vector<value_type> mBiasGate;
std::unique_ptr<Taco2ProjectionKernel> mKernel;
std::string mNamespace;
int getTotalInputLength() const;
int getTotalDimensions() const;
};
} // namespace plugin
} // namespace nvinfer1
#endif
|
TensorFlow/Classification/ConvNets/dataprep | dataprep | build_imagenet_data | #!/usr/bin/python
# 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.
# ==============================================================================
"""Converts ImageNet data to TFRecords file format with Example protos.
The raw ImageNet data set is expected to reside in JPEG files located in the
following directory structure.
data_dir/n01440764/ILSVRC2012_val_00000293.JPEG
data_dir/n01440764/ILSVRC2012_val_00000543.JPEG
...
where 'n01440764' is the unique synset label associated with
these images.
The training data set consists of 1000 sub-directories (i.e. labels)
each containing 1200 JPEG images for a total of 1.2M JPEG images.
The evaluation data set consists of 1000 sub-directories (i.e. labels)
each containing 50 JPEG images for a total of 50K JPEG images.
This TensorFlow script converts the training and evaluation data into
a sharded data set consisting of 1024 and 128 TFRecord files, respectively.
train_directory/train-00000-of-01024
train_directory/train-00001-of-01024
...
train_directory/train-01023-of-01024
and
validation_directory/validation-00000-of-00128
validation_directory/validation-00001-of-00128
...
validation_directory/validation-00127-of-00128
Each validation TFRecord file contains ~390 records. Each training TFREcord
file contains ~1250 records. Each record within the TFRecord file is a
serialized Example proto. The Example proto contains the following fields:
image/encoded: string containing JPEG encoded image in RGB colorspace
image/height: integer, image height in pixels
image/width: integer, image width in pixels
image/colorspace: string, specifying the colorspace, always 'RGB'
image/channels: integer, specifying the number of channels, always 3
image/format: string, specifying the format, always 'JPEG'
image/filename: string containing the basename of the image file
e.g. 'n01440764_10026.JPEG' or 'ILSVRC2012_val_00000293.JPEG'
image/class/label: integer specifying the index in a classification layer.
The label ranges from [1, 1000] where 0 is not used.
image/class/synset: string specifying the unique ID of the label,
e.g. 'n01440764'
image/class/text: string specifying the human-readable version of the label
e.g. 'red fox, Vulpes vulpes'
image/object/bbox/xmin: list of integers specifying the 0+ human annotated
bounding boxes
image/object/bbox/xmax: list of integers specifying the 0+ human annotated
bounding boxes
image/object/bbox/ymin: list of integers specifying the 0+ human annotated
bounding boxes
image/object/bbox/ymax: list of integers specifying the 0+ human annotated
bounding boxes
image/object/bbox/label: integer specifying the index in a classification
layer. The label ranges from [1, 1000] where 0 is not used. Note this is
always identical to the image label.
Note that the length of xmin is identical to the length of xmax, ymin and ymax
for each example.
Running this script using 16 threads may take around ~2.5 hours on an HP Z420.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os
import random
import sys
import threading
import numpy as np
import six
import tensorflow as tf
tf.app.flags.DEFINE_string('train_directory', '/tmp/',
'Training data directory')
tf.app.flags.DEFINE_string('validation_directory', '/tmp/',
'Validation data directory')
tf.app.flags.DEFINE_string('output_directory', '/tmp/',
'Output data directory')
tf.app.flags.DEFINE_integer('train_shards', 1024,
'Number of shards in training TFRecord files.')
tf.app.flags.DEFINE_integer('validation_shards', 128,
'Number of shards in validation TFRecord files.')
tf.app.flags.DEFINE_integer('num_threads', 8,
'Number of threads to preprocess the images.')
# The labels file contains a list of valid labels are held in this file.
# Assumes that the file contains entries as such:
# n01440764
# n01443537
# n01484850
# where each line corresponds to a label expressed as a synset. We map
# each synset contained in the file to an integer (based on the alphabetical
# ordering). See below for details.
tf.app.flags.DEFINE_string('labels_file',
'imagenet_lsvrc_2015_synsets.txt',
'Labels file')
# This file containing mapping from synset to human-readable label.
# Assumes each line of the file looks like:
#
# n02119247 black fox
# n02119359 silver fox
# n02119477 red fox, Vulpes fulva
#
# where each line corresponds to a unique mapping. Note that each line is
# formatted as <synset>\t<human readable label>.
tf.app.flags.DEFINE_string('imagenet_metadata_file',
'imagenet_metadata.txt',
'ImageNet metadata file')
# This file is the output of process_bounding_box.py
# Assumes each line of the file looks like:
#
# n00007846_64193.JPEG,0.0060,0.2620,0.7545,0.9940
#
# where each line corresponds to one bounding box annotation associated
# with an image. Each line can be parsed as:
#
# <JPEG file name>, <xmin>, <ymin>, <xmax>, <ymax>
#
# Note that there might exist mulitple bounding box annotations associated
# with an image file.
tf.app.flags.DEFINE_string('bounding_box_file',
'./imagenet_2012_bounding_boxes.csv',
'Bounding box file')
FLAGS = tf.app.flags.FLAGS
def _int64_feature(value):
"""Wrapper for inserting int64 features into Example proto."""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _float_feature(value):
"""Wrapper for inserting float features into Example proto."""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def _bytes_feature(value):
"""Wrapper for inserting bytes features into Example proto."""
if six.PY3 and isinstance(value, six.text_type):
value = six.binary_type(value, encoding='utf-8')
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _convert_to_example(filename, image_buffer, label, synset, human, bbox,
height, width):
"""Build an Example proto for an example.
Args:
filename: string, path to an image file, e.g., '/path/to/example.JPG'
image_buffer: string, JPEG encoding of RGB image
label: integer, identifier for the ground truth for the network
synset: string, unique WordNet ID specifying the label, e.g., 'n02323233'
human: string, human-readable label, e.g., 'red fox, Vulpes vulpes'
bbox: list of bounding boxes; each box is a list of integers
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong to
the same label as the image label.
height: integer, image height in pixels
width: integer, image width in pixels
Returns:
Example proto
"""
xmin = []
ymin = []
xmax = []
ymax = []
for b in bbox:
assert len(b) == 4
# pylint: disable=expression-not-assigned
[l.append(point) for l, point in zip([xmin, ymin, xmax, ymax], b)]
# pylint: enable=expression-not-assigned
colorspace = 'RGB'
channels = 3
image_format = 'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': _int64_feature(height),
'image/width': _int64_feature(width),
'image/colorspace': _bytes_feature(colorspace),
'image/channels': _int64_feature(channels),
'image/class/label': _int64_feature(label),
'image/class/synset': _bytes_feature(synset),
'image/class/text': _bytes_feature(human),
'image/object/bbox/xmin': _float_feature(xmin),
'image/object/bbox/xmax': _float_feature(xmax),
'image/object/bbox/ymin': _float_feature(ymin),
'image/object/bbox/ymax': _float_feature(ymax),
'image/object/bbox/label': _int64_feature([label] * len(xmin)),
'image/format': _bytes_feature(image_format),
'image/filename': _bytes_feature(os.path.basename(filename)),
'image/encoded': _bytes_feature(image_buffer)}))
return example
class ImageCoder(object):
"""Helper class that provides TensorFlow image coding utilities."""
def __init__(self):
# Create a single Session to run all image coding calls.
self._sess = tf.Session()
# Initializes function that converts PNG to JPEG data.
self._png_data = tf.placeholder(dtype=tf.string)
image = tf.image.decode_png(self._png_data, channels=3)
self._png_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100)
# Initializes function that converts CMYK JPEG data to RGB JPEG data.
self._cmyk_data = tf.placeholder(dtype=tf.string)
image = tf.image.decode_jpeg(self._cmyk_data, channels=0)
self._cmyk_to_rgb = tf.image.encode_jpeg(image, format='rgb', quality=100)
# Initializes function that decodes RGB JPEG data.
self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
def png_to_jpeg(self, image_data):
return self._sess.run(self._png_to_jpeg,
feed_dict={self._png_data: image_data})
def cmyk_to_rgb(self, image_data):
return self._sess.run(self._cmyk_to_rgb,
feed_dict={self._cmyk_data: image_data})
def decode_jpeg(self, image_data):
image = self._sess.run(self._decode_jpeg,
feed_dict={self._decode_jpeg_data: image_data})
assert len(image.shape) == 3
assert image.shape[2] == 3
return image
def _is_png(filename):
"""Determine if a file contains a PNG format image.
Args:
filename: string, path of the image file.
Returns:
boolean indicating if the image is a PNG.
"""
# File list from:
# https://groups.google.com/forum/embed/?place=forum/torch7#!topic/torch7/fOSTXHIESSU
return 'n02105855_2933.JPEG' in filename
def _is_cmyk(filename):
"""Determine if file contains a CMYK JPEG format image.
Args:
filename: string, path of the image file.
Returns:
boolean indicating if the image is a JPEG encoded with CMYK color space.
"""
# File list from:
# https://github.com/cytsai/ilsvrc-cmyk-image-list
blacklist = ['n01739381_1309.JPEG', 'n02077923_14822.JPEG',
'n02447366_23489.JPEG', 'n02492035_15739.JPEG',
'n02747177_10752.JPEG', 'n03018349_4028.JPEG',
'n03062245_4620.JPEG', 'n03347037_9675.JPEG',
'n03467068_12171.JPEG', 'n03529860_11437.JPEG',
'n03544143_17228.JPEG', 'n03633091_5218.JPEG',
'n03710637_5125.JPEG', 'n03961711_5286.JPEG',
'n04033995_2932.JPEG', 'n04258138_17003.JPEG',
'n04264628_27969.JPEG', 'n04336792_7448.JPEG',
'n04371774_5854.JPEG', 'n04596742_4225.JPEG',
'n07583066_647.JPEG', 'n13037406_4650.JPEG']
return filename.split('/')[-1] in blacklist
def _process_image(filename, coder):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels.
"""
# Read the image file.
with tf.gfile.FastGFile(filename, 'rb') as f:
image_data = f.read()
# Clean the dirty data.
if _is_png(filename):
# 1 image is a PNG.
print('Converting PNG to JPEG for %s' % filename)
image_data = coder.png_to_jpeg(image_data)
elif _is_cmyk(filename):
# 22 JPEG images are in CMYK colorspace.
print('Converting CMYK to RGB for %s' % filename)
image_data = coder.cmyk_to_rgb(image_data)
# Decode the RGB JPEG.
image = coder.decode_jpeg(image_data)
# Check that image converted to RGB
assert len(image.shape) == 3
height = image.shape[0]
width = image.shape[1]
assert image.shape[2] == 3
return image_data, height, width
def _process_image_files_batch(coder, thread_index, ranges, name, filenames,
synsets, labels, humans, bboxes, num_shards):
"""Processes and saves list of images as TFRecord in 1 thread.
Args:
coder: instance of ImageCoder to provide TensorFlow image coding utils.
thread_index: integer, unique batch to run index is within [0, len(ranges)).
ranges: list of pairs of integers specifying ranges of each batches to
analyze in parallel.
name: string, unique identifier specifying the data set
filenames: list of strings; each string is a path to an image file
synsets: list of strings; each string is a unique WordNet ID
labels: list of integer; each integer identifies the ground truth
humans: list of strings; each string is a human-readable label
bboxes: list of bounding boxes for each image. Note that each entry in this
list might contain from 0+ entries corresponding to the number of bounding
box annotations for the image.
num_shards: integer number of shards for this data set.
"""
# Each thread produces N shards where N = int(num_shards / num_threads).
# For instance, if num_shards = 128, and the num_threads = 2, then the first
# thread would produce shards [0, 64).
num_threads = len(ranges)
assert not num_shards % num_threads
num_shards_per_batch = int(num_shards / num_threads)
shard_ranges = np.linspace(ranges[thread_index][0],
ranges[thread_index][1],
num_shards_per_batch + 1).astype(int)
num_files_in_thread = ranges[thread_index][1] - ranges[thread_index][0]
counter = 0
for s in range(num_shards_per_batch):
# Generate a sharded version of the file name, e.g. 'train-00002-of-00010'
shard = thread_index * num_shards_per_batch + s
output_filename = '%s-%.5d-of-%.5d' % (name, shard, num_shards)
output_file = os.path.join(FLAGS.output_directory, output_filename)
writer = tf.python_io.TFRecordWriter(output_file)
shard_counter = 0
files_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int)
for i in files_in_shard:
filename = filenames[i]
label = labels[i]
synset = synsets[i]
human = humans[i]
bbox = bboxes[i]
image_buffer, height, width = _process_image(filename, coder)
example = _convert_to_example(filename, image_buffer, label,
synset, human, bbox,
height, width)
writer.write(example.SerializeToString())
shard_counter += 1
counter += 1
if not counter % 1000:
print('%s [thread %d]: Processed %d of %d images in thread batch.' %
(datetime.now(), thread_index, counter, num_files_in_thread))
sys.stdout.flush()
writer.close()
print('%s [thread %d]: Wrote %d images to %s' %
(datetime.now(), thread_index, shard_counter, output_file))
sys.stdout.flush()
shard_counter = 0
print('%s [thread %d]: Wrote %d images to %d shards.' %
(datetime.now(), thread_index, counter, num_files_in_thread))
sys.stdout.flush()
def _process_image_files(name, filenames, synsets, labels, humans,
bboxes, num_shards):
"""Process and save list of images as TFRecord of Example protos.
Args:
name: string, unique identifier specifying the data set
filenames: list of strings; each string is a path to an image file
synsets: list of strings; each string is a unique WordNet ID
labels: list of integer; each integer identifies the ground truth
humans: list of strings; each string is a human-readable label
bboxes: list of bounding boxes for each image. Note that each entry in this
list might contain from 0+ entries corresponding to the number of bounding
box annotations for the image.
num_shards: integer number of shards for this data set.
"""
assert len(filenames) == len(synsets)
assert len(filenames) == len(labels)
assert len(filenames) == len(humans)
assert len(filenames) == len(bboxes)
# Break all images into batches with a [ranges[i][0], ranges[i][1]].
spacing = np.linspace(0, len(filenames), FLAGS.num_threads + 1).astype(np.int)
ranges = []
threads = []
for i in range(len(spacing) - 1):
ranges.append([spacing[i], spacing[i + 1]])
# Launch a thread for each batch.
print('Launching %d threads for spacings: %s' % (FLAGS.num_threads, ranges))
sys.stdout.flush()
# Create a mechanism for monitoring when all threads are finished.
coord = tf.train.Coordinator()
# Create a generic TensorFlow-based utility for converting all image codings.
coder = ImageCoder()
threads = []
for thread_index in range(len(ranges)):
args = (coder, thread_index, ranges, name, filenames,
synsets, labels, humans, bboxes, num_shards)
t = threading.Thread(target=_process_image_files_batch, args=args)
t.start()
threads.append(t)
# Wait for all the threads to terminate.
coord.join(threads)
print('%s: Finished writing all %d images in data set.' %
(datetime.now(), len(filenames)))
sys.stdout.flush()
def _find_image_files(data_dir, labels_file):
"""Build a list of all images files and labels in the data set.
Args:
data_dir: string, path to the root directory of images.
Assumes that the ImageNet data set resides in JPEG files located in
the following directory structure.
data_dir/n01440764/ILSVRC2012_val_00000293.JPEG
data_dir/n01440764/ILSVRC2012_val_00000543.JPEG
where 'n01440764' is the unique synset label associated with these images.
labels_file: string, path to the labels file.
The list of valid labels are held in this file. Assumes that the file
contains entries as such:
n01440764
n01443537
n01484850
where each line corresponds to a label expressed as a synset. We map
each synset contained in the file to an integer (based on the alphabetical
ordering) starting with the integer 1 corresponding to the synset
contained in the first line.
The reason we start the integer labels at 1 is to reserve label 0 as an
unused background class.
Returns:
filenames: list of strings; each string is a path to an image file.
synsets: list of strings; each string is a unique WordNet ID.
labels: list of integer; each integer identifies the ground truth.
"""
print('Determining list of input files and labels from %s.' % data_dir)
challenge_synsets = [l.strip() for l in
tf.gfile.FastGFile(labels_file, 'r').readlines()]
labels = []
filenames = []
synsets = []
# Leave label index 0 empty as a background class.
label_index = 1
# Construct the list of JPEG files and labels.
for synset in challenge_synsets:
jpeg_file_path = '%s/%s/*.JPEG' % (data_dir, synset)
matching_files = tf.gfile.Glob(jpeg_file_path)
labels.extend([label_index] * len(matching_files))
synsets.extend([synset] * len(matching_files))
filenames.extend(matching_files)
if not label_index % 100:
print('Finished finding files in %d of %d classes.' % (
label_index, len(challenge_synsets)))
label_index += 1
# Shuffle the ordering of all image files in order to guarantee
# random ordering of the images with respect to label in the
# saved TFRecord files. Make the randomization repeatable.
shuffled_index = list(range(len(filenames)))
random.seed(12345)
random.shuffle(shuffled_index)
filenames = [filenames[i] for i in shuffled_index]
synsets = [synsets[i] for i in shuffled_index]
labels = [labels[i] for i in shuffled_index]
print('Found %d JPEG files across %d labels inside %s.' %
(len(filenames), len(challenge_synsets), data_dir))
return filenames, synsets, labels
def _find_human_readable_labels(synsets, synset_to_human):
"""Build a list of human-readable labels.
Args:
synsets: list of strings; each string is a unique WordNet ID.
synset_to_human: dict of synset to human labels, e.g.,
'n02119022' --> 'red fox, Vulpes vulpes'
Returns:
List of human-readable strings corresponding to each synset.
"""
humans = []
for s in synsets:
assert s in synset_to_human, ('Failed to find: %s' % s)
humans.append(synset_to_human[s])
return humans
def _find_image_bounding_boxes(filenames, image_to_bboxes):
"""Find the bounding boxes for a given image file.
Args:
filenames: list of strings; each string is a path to an image file.
image_to_bboxes: dictionary mapping image file names to a list of
bounding boxes. This list contains 0+ bounding boxes.
Returns:
List of bounding boxes for each image. Note that each entry in this
list might contain from 0+ entries corresponding to the number of bounding
box annotations for the image.
"""
num_image_bbox = 0
bboxes = []
for f in filenames:
basename = os.path.basename(f)
if basename in image_to_bboxes:
bboxes.append(image_to_bboxes[basename])
num_image_bbox += 1
else:
bboxes.append([])
print('Found %d images with bboxes out of %d images' % (
num_image_bbox, len(filenames)))
return bboxes
def _process_dataset(name, directory, num_shards, synset_to_human,
image_to_bboxes):
"""Process a complete data set and save it as a TFRecord.
Args:
name: string, unique identifier specifying the data set.
directory: string, root path to the data set.
num_shards: integer number of shards for this data set.
synset_to_human: dict of synset to human labels, e.g.,
'n02119022' --> 'red fox, Vulpes vulpes'
image_to_bboxes: dictionary mapping image file names to a list of
bounding boxes. This list contains 0+ bounding boxes.
"""
filenames, synsets, labels = _find_image_files(directory, FLAGS.labels_file)
humans = _find_human_readable_labels(synsets, synset_to_human)
bboxes = _find_image_bounding_boxes(filenames, image_to_bboxes)
_process_image_files(name, filenames, synsets, labels,
humans, bboxes, num_shards)
def _build_synset_lookup(imagenet_metadata_file):
"""Build lookup for synset to human-readable label.
Args:
imagenet_metadata_file: string, path to file containing mapping from
synset to human-readable label.
Assumes each line of the file looks like:
n02119247 black fox
n02119359 silver fox
n02119477 red fox, Vulpes fulva
where each line corresponds to a unique mapping. Note that each line is
formatted as <synset>\t<human readable label>.
Returns:
Dictionary of synset to human labels, such as:
'n02119022' --> 'red fox, Vulpes vulpes'
"""
lines = tf.gfile.FastGFile(imagenet_metadata_file, 'r').readlines()
synset_to_human = {}
for l in lines:
if l:
parts = l.strip().split('\t')
assert len(parts) == 2
synset = parts[0]
human = parts[1]
synset_to_human[synset] = human
return synset_to_human
def _build_bounding_box_lookup(bounding_box_file):
"""Build a lookup from image file to bounding boxes.
Args:
bounding_box_file: string, path to file with bounding boxes annotations.
Assumes each line of the file looks like:
n00007846_64193.JPEG,0.0060,0.2620,0.7545,0.9940
where each line corresponds to one bounding box annotation associated
with an image. Each line can be parsed as:
<JPEG file name>, <xmin>, <ymin>, <xmax>, <ymax>
Note that there might exist mulitple bounding box annotations associated
with an image file. This file is the output of process_bounding_boxes.py.
Returns:
Dictionary mapping image file names to a list of bounding boxes. This list
contains 0+ bounding boxes.
"""
lines = tf.gfile.FastGFile(bounding_box_file, 'r').readlines()
images_to_bboxes = {}
num_bbox = 0
num_image = 0
for l in lines:
if l:
parts = l.split(',')
assert len(parts) == 5, ('Failed to parse: %s' % l)
filename = parts[0]
xmin = float(parts[1])
ymin = float(parts[2])
xmax = float(parts[3])
ymax = float(parts[4])
box = [xmin, ymin, xmax, ymax]
if filename not in images_to_bboxes:
images_to_bboxes[filename] = []
num_image += 1
images_to_bboxes[filename].append(box)
num_bbox += 1
print('Successfully read %d bounding boxes '
'across %d images.' % (num_bbox, num_image))
return images_to_bboxes
def main(unused_argv):
assert not FLAGS.train_shards % FLAGS.num_threads, (
'Please make the FLAGS.num_threads commensurate with FLAGS.train_shards')
assert not FLAGS.validation_shards % FLAGS.num_threads, (
'Please make the FLAGS.num_threads commensurate with '
'FLAGS.validation_shards')
print('Saving results to %s' % FLAGS.output_directory)
# Build a map from synset to human-readable label.
synset_to_human = _build_synset_lookup(FLAGS.imagenet_metadata_file)
image_to_bboxes = _build_bounding_box_lookup(FLAGS.bounding_box_file)
# Run it!
_process_dataset('validation', FLAGS.validation_directory,
FLAGS.validation_shards, synset_to_human, image_to_bboxes)
_process_dataset('train', FLAGS.train_directory, FLAGS.train_shards,
synset_to_human, image_to_bboxes)
if __name__ == '__main__':
tf.app.run()
|
TensorFlow/Translation/GNMT | GNMT | gnmt_model | # 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.
"""GNMT attention sequence-to-sequence model with dynamic RNN support."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib.cudnn_rnn.python.layers import cudnn_rnn
import attention_wrapper
import block_lstm
import model
import model_helper
from utils import misc_utils as utils
class GNMTModel(model.BaseModel):
"""Sequence-to-sequence dynamic model with GNMT attention architecture.
"""
def __init__(self,
hparams,
mode,
features,
scope=None,
extra_args=None):
self.is_gnmt_attention = (
hparams.attention_architecture in ["gnmt", "gnmt_v2"])
super(GNMTModel, self).__init__(
hparams=hparams,
mode=mode,
features=features,
scope=scope,
extra_args=extra_args)
def _prepare_beam_search_decoder_inputs(
self, beam_width, memory, source_sequence_length, encoder_state):
memory = tf.contrib.seq2seq.tile_batch(
memory, multiplier=beam_width)
source_sequence_length = tf.contrib.seq2seq.tile_batch(
source_sequence_length, multiplier=beam_width)
encoder_state = tf.contrib.seq2seq.tile_batch(
encoder_state, multiplier=beam_width)
batch_size = self.batch_size * beam_width
return memory, source_sequence_length, encoder_state, batch_size
def _build_encoder(self, hparams):
"""Build a GNMT encoder."""
assert hparams.encoder_type == "gnmt"
# Build GNMT encoder.
num_bi_layers = 1
num_uni_layers = self.num_encoder_layers - num_bi_layers
utils.print_out("# Build a GNMT encoder")
utils.print_out(" num_bi_layers = %d" % num_bi_layers)
utils.print_out(" num_uni_layers = %d" % num_uni_layers)
# source is batch-majored
source = self.features["source"]
import sys
print('source.shape: %s' % source.shape, file=sys.stderr)
if self.time_major:
# Later rnn would use time-majored inputs
source = tf.transpose(source)
with tf.variable_scope("encoder"):
dtype = self.dtype
encoder_emb_inp = tf.cast(
self.encoder_emb_lookup_fn(self.embedding_encoder, source), dtype)
# Build 1st bidi layer.
bi_encoder_outputs, bi_encoder_state = self._build_encoder_layers_bidi(
encoder_emb_inp, self.features["source_sequence_length"], hparams,
dtype)
# Build all the rest unidi layers
encoder_state, encoder_outputs = self._build_encoder_layers_unidi(
bi_encoder_outputs, self.features["source_sequence_length"],
num_uni_layers, hparams, dtype)
# Pass all encoder states to the decoder
# except the first bi-directional layer
encoder_state = (bi_encoder_state[1],) + (
(encoder_state,) if num_uni_layers == 1 else encoder_state)
return encoder_outputs, encoder_state
def _build_encoder_layers_bidi(self, inputs, sequence_length, hparams, dtype):
"""docstring."""
if hparams.use_fused_lstm:
fn = self._build_bidi_rnn_fused
elif hparams.use_cudnn_lstm:
fn = self._build_bidi_rnn_cudnn
else:
fn = self._build_bidi_rnn_base
return fn(inputs, sequence_length, hparams, dtype)
def _build_bidi_rnn_fused(self, inputs, sequence_length, hparams, dtype):
if (not np.isclose(hparams.dropout, 0.) and
self.mode == tf.contrib.learn.ModeKeys.TRAIN):
inputs = tf.nn.dropout(inputs, keep_prob=1-hparams.dropout)
fwd_cell = block_lstm.LSTMBlockFusedCell(
hparams.num_units, hparams.forget_bias, dtype=dtype)
fwd_encoder_outputs, (fwd_final_c, fwd_final_h) = fwd_cell(
inputs,
dtype=dtype,
sequence_length=sequence_length)
inputs_r = tf.reverse_sequence(
inputs, sequence_length, batch_axis=1, seq_axis=0)
bak_cell = block_lstm.LSTMBlockFusedCell(
hparams.num_units, hparams.forget_bias, dtype=dtype)
bak_encoder_outputs, (bak_final_c, bak_final_h) = bak_cell(
inputs_r,
dtype=dtype,
sequence_length=sequence_length)
bak_encoder_outputs = tf.reverse_sequence(
bak_encoder_outputs, sequence_length, batch_axis=1, seq_axis=0)
bi_encoder_outputs = tf.concat(
[fwd_encoder_outputs, bak_encoder_outputs], axis=-1)
fwd_state = tf.nn.rnn_cell.LSTMStateTuple(fwd_final_c, fwd_final_h)
bak_state = tf.nn.rnn_cell.LSTMStateTuple(bak_final_c, bak_final_h)
bi_encoder_state = (fwd_state, bak_state)
# mask aren't applied on outputs, but final states are post-masking.
return bi_encoder_outputs, bi_encoder_state
def _build_unidi_rnn_fused(self, inputs, state,
sequence_length, hparams, dtype):
if (not np.isclose(hparams.dropout, 0.) and
self.mode == tf.contrib.learn.ModeKeys.TRAIN):
inputs = tf.nn.dropout(inputs, keep_prob=1-hparams.dropout)
cell = block_lstm.LSTMBlockFusedCell(
hparams.num_units, hparams.forget_bias, dtype=dtype)
outputs, (final_c, final_h) = cell(
inputs,
state,
dtype=dtype,
sequence_length=sequence_length)
# mask aren't applied on outputs, but final states are post-masking.
return outputs, tf.nn.rnn_cell.LSTMStateTuple(final_c, final_h)
def _build_unidi_rnn_cudnn(self, inputs, state, sequence_length, dtype,
hparams, num_layers, is_fwd):
# cudnn inputs only support time-major
if not self.time_major:
inputs = tf.transpose(inputs, axis=[1, 0, 2])
if num_layers == 1 and not np.isclose(hparams.dropout, 0.):
# Special case when drop is used and only one layer
dropout = 0.
inputs = tf.nn.dropout(inputs, keep_prob=1-dropout)
else:
dropout = hparams.dropout
# the outputs would be in time-majored
sequence_length = tf.transpose(sequence_length)
if not is_fwd:
inputs = tf.reverse_sequence(
inputs, sequence_length, batch_axis=1, seq_axis=0)
cell = tf.contrib.cudnn_rnn.CudnnLSTM(
num_layers=num_layers,
num_units=hparams.num_units,
direction=cudnn_rnn.CUDNN_RNN_UNIDIRECTION,
dtype=self.dtype,
dropout=dropout)
outputs, (h, c) = cell(inputs, initial_state=state)
"""
# Mask outputs
# [batch, time]
mask = tf.sequence_mask(sequence_length, dtype=self.dtype)
# [time, batch]
mask = tf.transpose(mask)
outputs *= mask
"""
if not is_fwd:
outputs = tf.reverse_sequence(
inputs, sequence_length, batch_axis=1, seq_axis=0)
# NOTICE! There's no way to get the "correct" masked cell state in cudnn
# rnn.
if num_layers == 1:
h = tf.squeeze(h, axis=0)
c = tf.squeeze(c, axis=0)
return outputs, tf.nn.rnn_cell.LSTMStateTuple(c=c, h=h)
# Split h and c to form a
h.set_shape((num_layers, None, hparams.num_units))
c.set_shape((num_layers, None, hparams.num_units))
hs = tf.unstack(h)
cs = tf.unstack(c)
# The cell passed to bidi-dyanmic-rnn is a MultiRNNCell consisting 2 regular
# LSTM, the state of each is a simple LSTMStateTuple. Thus the state of the
# MultiRNNCell is a tuple of LSTMStateTuple.
states = tuple(
tf.nn.rnn_cell.LSTMStateTuple(c=c, h=h) for h, c in zip(hs, cs))
# No need to transpose back
return outputs, states
def _build_encoder_cell(self, hparams, num_layers, num_residual_layers,
dtype=None):
"""Build a multi-layer RNN cell that can be used by encoder."""
return model_helper.create_rnn_cell(
unit_type=hparams.unit_type,
num_units=self.num_units,
num_layers=num_layers,
num_residual_layers=num_residual_layers,
forget_bias=hparams.forget_bias,
dropout=hparams.dropout,
mode=self.mode,
dtype=dtype,
single_cell_fn=self.single_cell_fn,
use_block_lstm=hparams.use_block_lstm)
def _build_bidi_rnn_base(self, inputs, sequence_length, hparams, dtype):
"""Create and call biddirectional RNN cells."""
# num_residual_layers: Number of residual layers from top to bottom. For
# example, if `num_bi_layers=4` and `num_residual_layers=2`, the last 2
# RNN layers in each RNN cell will be wrapped with `ResidualWrapper`.
# Construct forward and backward cells
fw_cell = self._build_encoder_cell(hparams,
1, # num_bi_layers,
0, # num_bi_residual_layers,
dtype)
bw_cell = self._build_encoder_cell(hparams,
1, # num_bi_layers,
0, # num_bi_residual_layers,
dtype)
if hparams.use_dynamic_rnn:
bi_outputs, bi_state = tf.nn.bidirectional_dynamic_rnn(
fw_cell,
bw_cell,
inputs,
dtype=dtype,
sequence_length=sequence_length,
time_major=self.time_major,
swap_memory=True)
else:
bi_outputs, bi_state = tf.contrib.recurrent.bidirectional_functional_rnn(
fw_cell,
bw_cell,
inputs,
dtype=dtype,
sequence_length=sequence_length,
time_major=self.time_major,
use_tpu=False)
return tf.concat(bi_outputs, -1), bi_state
def _build_bidi_rnn_cudnn(self, inputs, sequence_length, hparams, dtype):
# Notice cudnn rnn dropout is applied between layers. (if 1 layer only then
# no dropout).
if not np.isclose(hparams.dropout, 0.):
inputs = tf.nn.dropout(inputs, keep_prob=1-hparams.dropout)
if not hparams.use_loose_bidi_cudnn_lstm:
fwd_outputs, fwd_states = self._build_unidi_rnn_cudnn(
inputs, None, # initial_state
sequence_length, dtype, hparams,
1, # num_layer
is_fwd=True)
bak_outputs, bak_states = self._build_unidi_rnn_cudnn(
inputs, None, # initial_state
sequence_length, dtype, hparams,
1, # num_layer
is_fwd=False)
bi_outputs = tf.concat([fwd_outputs, bak_outputs], axis=-1)
return bi_outputs, (fwd_states, bak_states)
else:
# Cudnn only accept time-majored inputs
if not self.time_major:
inputs = tf.transpose(inputs, axis=[1, 0, 2])
bi_outputs, (bi_h, bi_c) = tf.contrib.cudnn_rnn.CudnnLSTM(
num_layers=1, # num_bi_layers,
num_units=hparams.num_units,
direction=cudnn_rnn.CUDNN_RNN_BIDIRECTION,
dropout=0., # one layer, dropout isn't applied anyway,
seed=hparams.random_seed,
dtype=self.dtype,
kernel_initializer=tf.get_variable_scope().initializer,
bias_initializer=tf.zeros_initializer())(inputs)
# state shape is [num_layers * num_dir, batch, dim]
bi_h.set_shape((2, None, hparams.num_units))
bi_c.set_shape((2, None, hparams.num_units))
fwd_h, bak_h = tf.unstack(bi_h)
fwd_c, bak_c = tf.unstack(bi_c)
# No need to transpose back
return bi_outputs, (tf.nn.rnn_cell.LSTMStateTuple(c=fwd_c, h=fwd_h),
tf.nn.rnn_cell.LSTMStateTuple(c=bak_c, h=bak_h))
def _build_encoder_layers_unidi(self, inputs, sequence_length,
num_uni_layers, hparams, dtype):
"""Build encoder layers all at once."""
encoder_outputs = None
encoder_state = tuple()
if hparams.use_fused_lstm:
for i in range(num_uni_layers):
if (not np.isclose(hparams.dropout, 0.) and
self.mode == tf.contrib.learn.ModeKeys.TRAIN):
cell_inputs = tf.nn.dropout(inputs, keep_prob=1-hparams.dropout)
else:
cell_inputs = inputs
cell = block_lstm.LSTMBlockFusedCell(
hparams.num_units, hparams.forget_bias, dtype=dtype)
encoder_outputs, (final_c, final_h) = cell(
cell_inputs,
dtype=dtype,
sequence_length=sequence_length)
encoder_state += (tf.nn.rnn_cell.LSTMStateTuple(final_c, final_h),)
if i >= num_uni_layers - self.num_encoder_residual_layers:
# Add the pre-dropout inputs. Residual wrapper is applied after
# dropout wrapper.
encoder_outputs += inputs
inputs = encoder_outputs
elif hparams.use_cudnn_lstm:
# Single layer cudnn rnn, dropout isnt applied in the kernel
for i in range(num_uni_layers):
if (not np.isclose(hparams.dropout, 0.) and
self.mode == tf.contrib.learn.ModeKeys.TRAIN):
inputs = tf.nn.dropout(inputs, keep_prob=1-hparams.dropout)
encoder_outputs, encoder_states = self._build_unidi_rnn_cudnn(
inputs,
None, # initial_state
sequence_length,
dtype,
hparams,
1, # num_layer
is_fwd=True)
encoder_state += (tf.nn.rnn_cell.LSTMStateTuple(encoder_states.c,
encoder_states.h),)
if i >= num_uni_layers - self.num_encoder_residual_layers:
encoder_outputs += inputs
inputs = encoder_outputs
else:
uni_cell = model_helper.create_rnn_cell(
unit_type=hparams.unit_type,
num_units=hparams.num_units,
num_layers=num_uni_layers,
num_residual_layers=self.num_encoder_residual_layers,
forget_bias=hparams.forget_bias,
dropout=hparams.dropout,
dtype=dtype,
mode=self.mode,
single_cell_fn=self.single_cell_fn,
use_block_lstm=hparams.use_block_lstm)
if hparams.use_dynamic_rnn:
encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
uni_cell,
inputs,
dtype=dtype,
sequence_length=sequence_length,
time_major=self.time_major)
else:
encoder_outputs, encoder_state = tf.contrib.recurrent.functional_rnn(
uni_cell,
inputs,
dtype=dtype,
sequence_length=sequence_length,
time_major=self.time_major,
use_tpu=False)
return encoder_state, encoder_outputs
def _build_decoder_cell(self, hparams, encoder_outputs, encoder_state,
source_sequence_length):
"""Build a RNN cell with GNMT attention architecture."""
# GNMT attention
assert self.is_gnmt_attention
attention_option = hparams.attention
attention_architecture = hparams.attention_architecture
assert attention_option == "normed_bahdanau"
assert attention_architecture == "gnmt_v2"
num_units = hparams.num_units
infer_mode = hparams.infer_mode
dtype = tf.float16 if hparams.use_fp16 else tf.float32
if self.time_major:
memory = tf.transpose(encoder_outputs, [1, 0, 2])
else:
memory = encoder_outputs
if (self.mode == tf.contrib.learn.ModeKeys.INFER and
infer_mode == "beam_search"):
memory, source_sequence_length, encoder_state, batch_size = (
self._prepare_beam_search_decoder_inputs(
hparams.beam_width, memory, source_sequence_length,
encoder_state))
else:
batch_size = self.batch_size
attention_mechanism = model.create_attention_mechanism(
num_units, memory, source_sequence_length, dtype=dtype)
cell_list = model_helper._cell_list( # pylint: disable=protected-access
unit_type=hparams.unit_type,
num_units=num_units,
num_layers=self.num_decoder_layers,
num_residual_layers=self.num_decoder_residual_layers,
forget_bias=hparams.forget_bias,
dropout=hparams.dropout,
mode=self.mode,
dtype=dtype,
single_cell_fn=self.single_cell_fn,
residual_fn=gnmt_residual_fn,
use_block_lstm=hparams.use_block_lstm)
# Only wrap the bottom layer with the attention mechanism.
attention_cell = cell_list.pop(0)
# Only generate alignment in greedy INFER mode.
alignment_history = (self.mode == tf.contrib.learn.ModeKeys.INFER and
infer_mode != "beam_search")
attention_cell = attention_wrapper.AttentionWrapper(
attention_cell,
attention_mechanism,
attention_layer_size=None, # don't use attention layer.
output_attention=False,
alignment_history=alignment_history,
name="attention")
cell = GNMTAttentionMultiCell(attention_cell, cell_list)
if hparams.pass_hidden_state:
decoder_initial_state = tuple(
zs.clone(cell_state=es)
if isinstance(zs, attention_wrapper.AttentionWrapperState) else es
for zs, es in zip(
cell.zero_state(batch_size, dtype), encoder_state))
else:
decoder_initial_state = cell.zero_state(batch_size, dtype)
return cell, decoder_initial_state
def _build_decoder_cudnn(self, encoder_outputs, encoder_state, hparams):
pass
"""
# Training
# Use dynamic_rnn to compute the 1st layer outputs and attention
# GNMT attention
with tf.variable_scope("decoder") as decoder_scope:
assert self.is_gnmt_attention
attention_option = hparams.attention
attention_architecture = hparams.attention_architecture
assert attention_option == "normed_bahdanau"
assert attention_architecture == "gnmt_v2"
num_units = hparams.num_units
infer_mode = hparams.infer_mode
dtype = tf.float16 if hparams.use_fp16 else tf.float32
if self.time_major:
memory = tf.transpose(encoder_outputs, [1, 0, 2])
else:
memory = encoder_outputs
source_sequence_length = self.features["source_sequence_length"]
if (self.mode == tf.contrib.learn.ModeKeys.INFER and
infer_mode == "beam_search"):
memory, source_sequence_length, encoder_state, batch_size = (
self._prepare_beam_search_decoder_inputs(
hparams.beam_width, memory, source_sequence_length,
encoder_state))
else:
batch_size = self.batch_size
attention_mechanism = model.create_attention_mechanism(
num_units, memory, source_sequence_length, dtype=dtype)
attention_cell = model_helper._cell_list( # pylint: disable=protected-access
unit_type=hparams.unit_type,
num_units=num_units,
num_layers=1, # just one layer
num_residual_layers=0, # 1st layer has no residual connection.
forget_bias=hparams.forget_bias,
dropout=hparams.dropout,
mode=self.mode,
dtype=dtype,
single_cell_fn=self.single_cell_fn,
residual_fn=gnmt_residual_fn,
use_block_lstm=False)[0]
# Only generate alignment in greedy INFER mode.
alignment_history = (self.mode == tf.contrib.learn.ModeKeys.INFER and
infer_mode != "beam_search")
attention_cell = attention_wrapper.AttentionWrapper(
attention_cell,
attention_mechanism,
attention_layer_size=None, # don't use attention layer.
output_attention=False,
alignment_history=alignment_history,
name="attention")
decoder_attention_cell_initial_state = attention_cell.zero_state(
batch_size, dtype).clone(cell_state=encoder_state[0])
# TODO(jamesqin): support frnn
# [batch, time]
target_input = self.features["target_input"]
if self.time_major:
# If using time_major mode, then target_input should be [time, batch]
# then the decoder_emb_inp would be [time, batch, dim]
target_input = tf.transpose(target_input)
decoder_emb_inp = tf.cast(
tf.nn.embedding_lookup(self.embedding_decoder, target_input),
self.dtype)
attention_cell_outputs, attention_cell_state = tf.nn.dynamic_rnn(
attention_cell,
decoder_emb_inp,
sequence_length=self.features["target_sequence_length"],
initial_state=decoder_attention_cell_initial_state,
dtype=self.dtype,
scope=decoder_scope,
parallel_iterations=hparams.parallel_iterations,
time_major=self.time_major)
attention = None
inputs = tf.concat([target_input, attention_cell_outputs], axis=-1)
initial_state = encoder_state[1:]
num_bi_layers = 1
num_unidi_decoder_layers = self.num_decoder_layers = num_bi_layers
# 3 layers of uni cudnn
for i in range(num_unidi_decoder_layers):
# Concat input with attention
if (not np.isclose(hparams.dropout, 0.) and
self.mode == tf.contrib.learn.ModeKeys.TRAIN):
inputs = tf.nn.dropout(inputs, keep_prob=1 - hparams.dropout)
outputs, states = self._build_unidi_rnn_cudnn(
inputs,
initial_state[i],
self.features["target_sequence_length"],
self.dtype,
hparams,
1, # num_layer
is_fwd=True)
if i >= num_unidi_decoder_layers - self.num_decoder_residual_layers:
outputs += inputs
inputs = outputs
pass
"""
def _build_decoder_fused_for_training(self, encoder_outputs, initial_state,
decoder_emb_inp, hparams):
assert self.mode == tf.contrib.learn.ModeKeys.TRAIN
num_bi_layers = 1
num_unidi_decoder_layers = self.num_decoder_layers - num_bi_layers
assert num_unidi_decoder_layers == 3
# The 1st LSTM layer
if self.time_major:
batch = tf.shape(encoder_outputs)[1]
tgt_max_len = tf.shape(decoder_emb_inp)[0]
# [batch_size] -> scalar
initial_attention = tf.zeros(
shape=[tgt_max_len, batch, hparams.num_units], dtype=self.dtype)
else:
batch = tf.shape(encoder_outputs)[0]
tgt_max_len = tf.shape(decoder_emb_inp)[1]
initial_attention = tf.zeros(
shape=[batch, tgt_max_len, hparams.num_units], dtype=self.dtype)
# Concat with initial attention
dec_inp = tf.concat([decoder_emb_inp, initial_attention], axis=-1)
# [tgt_time, batch, units]
# var_scope naming chosen to agree with inference graph.
with tf.variable_scope("multi_rnn_cell/cell_0_attention/attention"):
outputs, _ = self._build_unidi_rnn_fused(
dec_inp,
initial_state[0],
self.features["target_sequence_length"],
hparams,
self.dtype)
# Get attention
# Fused attention layer has memory of shape [batch, src_time, ...]
if self.time_major:
memory = tf.transpose(encoder_outputs, [1, 0, 2])
else:
memory = encoder_outputs
fused_attention_layer = attention_wrapper.BahdanauAttentionFusedLayer(
hparams.num_units, memory,
memory_sequence_length=self.features["source_sequence_length"],
dtype=self.dtype)
# [batch, tgt_time, units]
if self.time_major:
queries = tf.transpose(outputs, [1, 0, 2])
else:
queries = outputs
fused_attention = fused_attention_layer(queries)
if self.time_major:
# [tgt_time, batch, units]
fused_attention = tf.transpose(fused_attention, [1, 0, 2])
# 2-4th layer
inputs = outputs
for i in range(num_unidi_decoder_layers):
# [tgt_time, batch, 2 * units]
concat_inputs = tf.concat([inputs, fused_attention], axis=-1)
# var_scope naming chosen to agree with inference graph.
with tf.variable_scope("multi_rnn_cell/cell_%d" % (i+1)):
outputs, _ = self._build_unidi_rnn_fused(
concat_inputs, initial_state[i + 1],
self.features["target_sequence_length"], hparams, self.dtype)
if i >= num_unidi_decoder_layers - self.num_decoder_residual_layers:
# gnmt_v2 attention adds the original inputs.
outputs += inputs
inputs = outputs
return outputs
class GNMTAttentionMultiCell(tf.nn.rnn_cell.MultiRNNCell):
"""A MultiCell with GNMT attention style."""
def __init__(self, attention_cell, cells):
"""Creates a GNMTAttentionMultiCell.
Args:
attention_cell: An instance of AttentionWrapper.
cells: A list of RNNCell wrapped with AttentionInputWrapper.
"""
cells = [attention_cell] + cells
super(GNMTAttentionMultiCell, self).__init__(cells, state_is_tuple=True)
def __call__(self, inputs, state, scope=None):
"""Run the cell with bottom layer's attention copied to all upper layers."""
if not tf.contrib.framework.nest.is_sequence(state):
raise ValueError(
"Expected state to be a tuple of length %d, but received: %s"
% (len(self.state_size), state))
with tf.variable_scope(scope or "multi_rnn_cell"):
new_states = []
with tf.variable_scope("cell_0_attention"):
attention_cell = self._cells[0]
attention_state = state[0]
cur_inp, new_attention_state = attention_cell(inputs, attention_state)
new_states.append(new_attention_state)
for i in range(1, len(self._cells)):
with tf.variable_scope("cell_%d" % i):
cell = self._cells[i]
cur_state = state[i]
cur_inp = tf.concat([cur_inp, new_attention_state.attention], -1)
cur_inp, new_state = cell(cur_inp, cur_state)
new_states.append(new_state)
return cur_inp, tuple(new_states)
def gnmt_residual_fn(inputs, outputs):
"""Residual function that handles different inputs and outputs inner dims.
Args:
inputs: cell inputs, this is actual inputs concatenated with the attention
vector.
outputs: cell outputs
Returns:
outputs + actual inputs
"""
def split_input(inp, out):
inp_dim = inp.get_shape().as_list()[-1]
out_dim = out.get_shape().as_list()[-1]
return tf.split(inp, [out_dim, inp_dim - out_dim], axis=-1)
actual_inputs, _ = tf.contrib.framework.nest.map_structure(
split_input, inputs, outputs)
def assert_shape_match(inp, out):
inp.get_shape().assert_is_compatible_with(out.get_shape())
tf.contrib.framework.nest.assert_same_structure(actual_inputs, outputs)
tf.contrib.framework.nest.map_structure(
assert_shape_match, actual_inputs, outputs)
return tf.contrib.framework.nest.map_structure(
lambda inp, out: inp + out, actual_inputs, outputs)
|
TensorFlow/Segmentation/UNet_Medical/utils/hooks | hooks | training_hook | # 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
import horovod.tensorflow as hvd
class TrainingHook(tf.estimator.SessionRunHook):
def __init__(self, logger, max_steps, log_every=1):
self._log_every = log_every
self._iter_idx = 0
self.logger = logger
self.max_steps = max_steps
def before_run(self, run_context):
run_args = tf.estimator.SessionRunArgs(
fetches=[
'cross_loss_ref:0',
'dice_loss_ref:0',
'total_loss_ref:0',
]
)
return run_args
def after_run(self,
run_context,
run_values):
cross_loss, dice_loss, total_loss = run_values.results
if (self._iter_idx % self._log_every == 0) and (hvd.rank() == 0):
self.logger.log(step=(self._iter_idx, self.max_steps),
data={'train_ce_loss': float(cross_loss),
'train_dice_loss': float(dice_loss),
'train_total_loss': float(total_loss)})
self._iter_idx += 1
|
TensorFlow2/LanguageModeling/BERT/scripts | scripts | run_inference_benchmark | #!/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.
# ==============================================================================
echo "Container nvidia build = " $NVIDIA_BUILD_ID
bert_model=${1:-"large"}
batch_size=${2:-"8"}
precision=${3:-"fp16"}
use_xla=${4:-"true"}
squad_version="1.1"
if [ "$bert_model" = "large" ] ; then
export BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16
else
export BERT_DIR=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12
fi
init_checkpoint=$BERT_DIR/bert_model.ckpt
export SQUAD_DIR=data/download/squad/v${squad_version}
export SQUAD_VERSION=v$squad_version
if [ "$squad_version" = "1.1" ] ; then
version_2_with_negative="False"
else
version_2_with_negative="True"
fi
echo "Squad directory set as " $SQUAD_DIR " BERT directory set as " $BERT_DIR
echo "Results directory set as " $RESULTS_DIR
use_fp16=""
if [ "$precision" = "fp16" ] ; then
echo "fp16 activated!"
use_fp16="--use_fp16"
fi
if [ "$use_xla" = "true" ] ; then
use_xla_tag="--enable_xla"
echo "XLA activated"
else
use_xla_tag=""
fi
ckpt_str=${init_checkpoint//\//-}
printf -v TAG "squad_inference_benchmark_%s_%s_bs%d" "$bert_model" "$precision" $batch_size
DATESTAMP=`date +'%y%m%d%H%M%S'`
#Edit to save logs & checkpoints in a different directory
RESULTS_DIR=/tmp/bert_inference_benchmark_${DATESTAMP}
LOGFILE=/results/$TAG.log
printf "Logs written to %s\n" "$LOGFILE"
mkdir -p $RESULTS_DIR
mkdir -p /results
#Check if all necessary files are available before training
for DIR_or_file in $SQUAD_DIR $RESULTS_DIR $BERT_DIR/vocab.txt $BERT_DIR/bert_config.json; do
if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then
echo "Error! $DIR_or_file directory missing. Please mount correctly"
exit -1
fi
done
python run_squad.py \
--mode=predict \
--input_meta_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_meta_data \
--vocab_file=$BERT_DIR/vocab.txt \
--bert_config_file=$BERT_DIR/bert_config.json \
--init_checkpoint=$init_checkpoint \
--predict_file=$SQUAD_DIR/dev-v${squad_version}.json \
--predict_batch_size=$batch_size \
--model_dir=$RESULTS_DIR \
--benchmark \
$use_fp16 $use_xla_tag |& tee $LOGFILE
rm $RESULTS_DIR -r |
PyTorch/DrugDiscovery/SE3Transformer | SE3Transformer | README | # [SE(3)-Transformers For PyTorch [migrated, click here]](https://github.com/NVIDIA/DeepLearningExamples/blob/master/DGLPyTorch/DrugDiscovery/SE3Transformer)
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | per_image_evaluation_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for object_detection.utils.per_image_evaluation."""
import numpy as np
import tensorflow as tf
from object_detection.utils import per_image_evaluation
class SingleClassTpFpWithDifficultBoxesTest(tf.test.TestCase):
def setUp(self):
num_groundtruth_classes = 1
matching_iou_threshold = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
self.eval = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
nms_max_output_boxes)
self.detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
dtype=float)
self.detected_scores = np.array([0.6, 0.8, 0.5], dtype=float)
detected_masks_0 = np.array([[0, 1, 1, 0],
[0, 0, 1, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_1 = np.array([[1, 0, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_2 = np.array([[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 0, 0]], dtype=np.uint8)
self.detected_masks = np.stack(
[detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
self.groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 10, 10]],
dtype=float)
groundtruth_masks_0 = np.array([[1, 1, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]], dtype=np.uint8)
groundtruth_masks_1 = np.array([[0, 0, 0, 1],
[0, 0, 0, 1],
[0, 0, 0, 1]], dtype=np.uint8)
self.groundtruth_masks = np.stack(
[groundtruth_masks_0, groundtruth_masks_1], axis=0)
def test_match_to_gt_box_0(self):
groundtruth_groundtruth_is_difficult_list = np.array([False, True],
dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, True, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_to_gt_mask_0(self):
groundtruth_groundtruth_is_difficult_list = np.array([False, True],
dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_match_to_gt_box_1(self):
groundtruth_groundtruth_is_difficult_list = np.array([True, False],
dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_to_gt_mask_1(self):
groundtruth_groundtruth_is_difficult_list = np.array([True, False],
dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
expected_scores = np.array([0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
class SingleClassTpFpWithGroupOfBoxesTest(tf.test.TestCase):
def setUp(self):
num_groundtruth_classes = 1
matching_iou_threshold = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
self.eval = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
nms_max_output_boxes)
self.detected_boxes = np.array(
[[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float)
self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
detected_masks_0 = np.array([[0, 1, 1, 0],
[0, 0, 1, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_1 = np.array([[1, 0, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_2 = np.array([[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 0, 0]], dtype=np.uint8)
self.detected_masks = np.stack(
[detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
self.groundtruth_boxes = np.array(
[[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float)
groundtruth_masks_0 = np.array([[1, 0, 0, 0],
[1, 0, 0, 0],
[1, 0, 0, 0]], dtype=np.uint8)
groundtruth_masks_1 = np.array([[0, 0, 1, 0],
[0, 0, 1, 0],
[0, 0, 1, 0]], dtype=np.uint8)
groundtruth_masks_2 = np.array([[0, 1, 0, 0],
[0, 1, 0, 0],
[0, 1, 0, 0]], dtype=np.uint8)
self.groundtruth_masks = np.stack(
[groundtruth_masks_0, groundtruth_masks_1, groundtruth_masks_2], axis=0)
def test_match_to_non_group_of_and_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, True, True], dtype=bool)
expected_scores = np.array([0.8], dtype=float)
expected_tp_fp_labels = np.array([True], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_to_non_group_of_and_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, True, True], dtype=bool)
expected_scores = np.array([0.6], dtype=float)
expected_tp_fp_labels = np.array([True], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_match_two_to_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[True, False, True], dtype=bool)
expected_scores = np.array([0.5], dtype=float)
expected_tp_fp_labels = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_two_to_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[True, False, True], dtype=bool)
expected_scores = np.array([0.8], dtype=float)
expected_tp_fp_labels = np.array([True], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
class SingleClassTpFpWithGroupOfBoxesTestWeighted(tf.test.TestCase):
def setUp(self):
num_groundtruth_classes = 1
matching_iou_threshold = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
self.group_of_weight = 0.5
self.eval = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
nms_max_output_boxes, self.group_of_weight)
self.detected_boxes = np.array(
[[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float)
self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
detected_masks_0 = np.array(
[[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], dtype=np.uint8)
detected_masks_1 = np.array(
[[1, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0]], dtype=np.uint8)
detected_masks_2 = np.array(
[[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0]], dtype=np.uint8)
self.detected_masks = np.stack(
[detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
self.groundtruth_boxes = np.array(
[[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float)
groundtruth_masks_0 = np.array(
[[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]], dtype=np.uint8)
groundtruth_masks_1 = np.array(
[[0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0]], dtype=np.uint8)
groundtruth_masks_2 = np.array(
[[0, 1, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0]], dtype=np.uint8)
self.groundtruth_masks = np.stack(
[groundtruth_masks_0, groundtruth_masks_1, groundtruth_masks_2], axis=0)
def test_match_to_non_group_of_and_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, True, True], dtype=bool)
expected_scores = np.array([0.8, 0.6], dtype=float)
expected_tp_fp_labels = np.array([1.0, self.group_of_weight], dtype=float)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_to_non_group_of_and_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, True, True], dtype=bool)
expected_scores = np.array([0.6, 0.8, 0.5], dtype=float)
expected_tp_fp_labels = np.array(
[1.0, self.group_of_weight, self.group_of_weight], dtype=float)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
tf.logging.info(
"test_mask_match_to_non_group_of_and_group_of_box {} {}".format(
tp_fp_labels, expected_tp_fp_labels))
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_match_two_to_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[True, False, True], dtype=bool)
expected_scores = np.array([0.5, 0.8], dtype=float)
expected_tp_fp_labels = np.array([0.0, self.group_of_weight], dtype=float)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
tf.logging.info("test_match_two_to_group_of_box {} {}".format(
tp_fp_labels, expected_tp_fp_labels))
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_two_to_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[True, False, True], dtype=bool)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array(
[1.0, self.group_of_weight, self.group_of_weight], dtype=float)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
tf.logging.info("test_mask_match_two_to_group_of_box {} {}".format(
tp_fp_labels, expected_tp_fp_labels))
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
class SingleClassTpFpNoDifficultBoxesTest(tf.test.TestCase):
def setUp(self):
num_groundtruth_classes = 1
matching_iou_threshold_high_iou = 0.5
matching_iou_threshold_low_iou = 0.1
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
self.eval_high_iou = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold_high_iou,
nms_iou_threshold, nms_max_output_boxes)
self.eval_low_iou = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold_low_iou,
nms_iou_threshold, nms_max_output_boxes)
self.detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
dtype=float)
self.detected_scores = np.array([0.6, 0.8, 0.5], dtype=float)
detected_masks_0 = np.array([[0, 1, 1, 0],
[0, 0, 1, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_1 = np.array([[1, 0, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_2 = np.array([[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 0, 0]], dtype=np.uint8)
self.detected_masks = np.stack(
[detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
def test_no_true_positives(self):
groundtruth_boxes = np.array([[100, 100, 105, 105]], dtype=float)
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_no_true_positives(self):
groundtruth_boxes = np.array([[100, 100, 105, 105]], dtype=float)
groundtruth_masks_0 = np.array([[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]], dtype=np.uint8)
groundtruth_masks = np.stack([groundtruth_masks_0], axis=0)
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=groundtruth_masks)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_one_true_positives_with_large_iou_threshold(self):
groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, True, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_one_true_positives_with_large_iou_threshold(self):
groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
groundtruth_masks_0 = np.array([[1, 0, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]], dtype=np.uint8)
groundtruth_masks = np.stack([groundtruth_masks_0], axis=0)
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=groundtruth_masks)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_one_true_positives_with_very_small_iou_threshold(self):
groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval_low_iou._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_two_true_positives_with_large_iou_threshold(self):
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3.5, 3.5]], dtype=float)
groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, True, True], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
class MultiClassesTpFpTest(tf.test.TestCase):
def test_tp_fp(self):
num_groundtruth_classes = 3
matching_iou_threshold = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes,
matching_iou_threshold,
nms_iou_threshold,
nms_max_output_boxes)
detected_boxes = np.array([[0, 0, 1, 1], [10, 10, 5, 5], [0, 0, 2, 2],
[5, 10, 10, 5], [10, 5, 5, 10], [0, 0, 3, 3]],
dtype=float)
detected_scores = np.array([0.8, 0.1, 0.8, 0.9, 0.7, 0.8], dtype=float)
detected_class_labels = np.array([0, 1, 1, 2, 0, 2], dtype=int)
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3.5, 3.5]], dtype=float)
groundtruth_class_labels = np.array([0, 2], dtype=int)
groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=float)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels, _ = eval1.compute_object_detection_metrics(
detected_boxes, detected_scores, detected_class_labels,
groundtruth_boxes, groundtruth_class_labels,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = [np.array([0.8], dtype=float)] * 3
expected_tp_fp_labels = [np.array([True]), np.array([False]), np.array([True
])]
for i in range(len(expected_scores)):
self.assertTrue(np.allclose(expected_scores[i], scores[i]))
self.assertTrue(np.array_equal(expected_tp_fp_labels[i], tp_fp_labels[i]))
class CorLocTest(tf.test.TestCase):
def test_compute_corloc_with_normal_iou_threshold(self):
num_groundtruth_classes = 3
matching_iou_threshold = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes,
matching_iou_threshold,
nms_iou_threshold,
nms_max_output_boxes)
detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3],
[0, 0, 5, 5]], dtype=float)
detected_scores = np.array([0.9, 0.9, 0.1, 0.9], dtype=float)
detected_class_labels = np.array([0, 1, 0, 2], dtype=int)
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3, 3], [0, 0, 6, 6]],
dtype=float)
groundtruth_class_labels = np.array([0, 0, 2], dtype=int)
is_class_correctly_detected_in_image = eval1._compute_cor_loc(
detected_boxes, detected_scores, detected_class_labels,
groundtruth_boxes, groundtruth_class_labels)
expected_result = np.array([1, 0, 1], dtype=int)
self.assertTrue(np.array_equal(expected_result,
is_class_correctly_detected_in_image))
def test_compute_corloc_with_very_large_iou_threshold(self):
num_groundtruth_classes = 3
matching_iou_threshold = 0.9
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes,
matching_iou_threshold,
nms_iou_threshold,
nms_max_output_boxes)
detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3],
[0, 0, 5, 5]], dtype=float)
detected_scores = np.array([0.9, 0.9, 0.1, 0.9], dtype=float)
detected_class_labels = np.array([0, 1, 0, 2], dtype=int)
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3, 3], [0, 0, 6, 6]],
dtype=float)
groundtruth_class_labels = np.array([0, 0, 2], dtype=int)
is_class_correctly_detected_in_image = eval1._compute_cor_loc(
detected_boxes, detected_scores, detected_class_labels,
groundtruth_boxes, groundtruth_class_labels)
expected_result = np.array([1, 0, 0], dtype=int)
self.assertTrue(np.array_equal(expected_result,
is_class_correctly_detected_in_image))
if __name__ == "__main__":
tf.test.main()
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | metrics | # 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.
# ==============================================================================
"""Functions for computing metrics like precision, recall, CorLoc and etc."""
from __future__ import division
import numpy as np
def compute_precision_recall(scores, labels, num_gt):
"""Compute precision and recall.
Args:
scores: A float numpy array representing detection score
labels: A float numpy array representing weighted true/false positive labels
num_gt: Number of ground truth instances
Raises:
ValueError: if the input is not of the correct format
Returns:
precision: Fraction of positive instances over detected ones. This value is
None if no ground truth labels are present.
recall: Fraction of detected positive instance over all positive instances.
This value is None if no ground truth labels are present.
"""
if not isinstance(labels, np.ndarray) or len(labels.shape) != 1:
raise ValueError("labels must be single dimension numpy array")
if labels.dtype != np.float and labels.dtype != np.bool:
raise ValueError("labels type must be either bool or float")
if not isinstance(scores, np.ndarray) or len(scores.shape) != 1:
raise ValueError("scores must be single dimension numpy array")
if num_gt < np.sum(labels):
raise ValueError("Number of true positives must be smaller than num_gt.")
if len(scores) != len(labels):
raise ValueError("scores and labels must be of the same size.")
if num_gt == 0:
return None, None
sorted_indices = np.argsort(scores)
sorted_indices = sorted_indices[::-1]
true_positive_labels = labels[sorted_indices]
false_positive_labels = (true_positive_labels <= 0).astype(float)
cum_true_positives = np.cumsum(true_positive_labels)
cum_false_positives = np.cumsum(false_positive_labels)
precision = cum_true_positives.astype(float) / (
cum_true_positives + cum_false_positives)
recall = cum_true_positives.astype(float) / num_gt
return precision, recall
def compute_average_precision(precision, recall):
"""Compute Average Precision according to the definition in VOCdevkit.
Precision is modified to ensure that it does not decrease as recall
decrease.
Args:
precision: A float [N, 1] numpy array of precisions
recall: A float [N, 1] numpy array of recalls
Raises:
ValueError: if the input is not of the correct format
Returns:
average_precison: The area under the precision recall curve. NaN if
precision and recall are None.
"""
if precision is None:
if recall is not None:
raise ValueError("If precision is None, recall must also be None")
return np.NAN
if not isinstance(precision, np.ndarray) or not isinstance(
recall, np.ndarray):
raise ValueError("precision and recall must be numpy array")
if precision.dtype != np.float or recall.dtype != np.float:
raise ValueError("input must be float numpy array.")
if len(precision) != len(recall):
raise ValueError("precision and recall must be of the same size.")
if not precision.size:
return 0.0
if np.amin(precision) < 0 or np.amax(precision) > 1:
raise ValueError("Precision must be in the range of [0, 1].")
if np.amin(recall) < 0 or np.amax(recall) > 1:
raise ValueError("recall must be in the range of [0, 1].")
if not all(recall[i] <= recall[i + 1] for i in range(len(recall) - 1)):
raise ValueError("recall must be a non-decreasing array")
recall = np.concatenate([[0], recall, [1]])
precision = np.concatenate([[0], precision, [0]])
# Preprocess precision to be a non-decreasing array
for i in range(len(precision) - 2, -1, -1):
precision[i] = np.maximum(precision[i], precision[i + 1])
indices = np.where(recall[1:] != recall[:-1])[0] + 1
average_precision = np.sum(
(recall[indices] - recall[indices - 1]) * precision[indices])
return average_precision
def compute_cor_loc(num_gt_imgs_per_class,
num_images_correctly_detected_per_class):
"""Compute CorLoc according to the definition in the following paper.
https://www.robots.ox.ac.uk/~vgg/rg/papers/deselaers-eccv10.pdf
Returns nans if there are no ground truth images for a class.
Args:
num_gt_imgs_per_class: 1D array, representing number of images containing
at least one object instance of a particular class
num_images_correctly_detected_per_class: 1D array, representing number of
images that are correctly detected at least one object instance of a
particular class
Returns:
corloc_per_class: A float numpy array represents the corloc score of each
class
"""
return np.where(
num_gt_imgs_per_class == 0, np.nan,
num_images_correctly_detected_per_class / num_gt_imgs_per_class)
def compute_median_rank_at_k(tp_fp_list, k):
"""Computes MedianRank@k, where k is the top-scoring labels.
Args:
tp_fp_list: a list of numpy arrays; each numpy array corresponds to the all
detection on a single image, where the detections are sorted by score in
descending order. Further, each numpy array element can have boolean or
float values. True positive elements have either value >0.0 or True;
any other value is considered false positive.
k: number of top-scoring proposals to take.
Returns:
median_rank: median rank of all true positive proposals among top k by
score.
"""
ranks = []
for i in range(len(tp_fp_list)):
ranks.append(
np.where(tp_fp_list[i][0:min(k, tp_fp_list[i].shape[0])] > 0)[0])
concatenated_ranks = np.concatenate(ranks)
return np.median(concatenated_ranks)
def compute_recall_at_k(tp_fp_list, num_gt, k):
"""Computes Recall@k, MedianRank@k, where k is the top-scoring labels.
Args:
tp_fp_list: a list of numpy arrays; each numpy array corresponds to the all
detection on a single image, where the detections are sorted by score in
descending order. Further, each numpy array element can have boolean or
float values. True positive elements have either value >0.0 or True;
any other value is considered false positive.
num_gt: number of groundtruth anotations.
k: number of top-scoring proposals to take.
Returns:
recall: recall evaluated on the top k by score detections.
"""
tp_fp_eval = []
for i in range(len(tp_fp_list)):
tp_fp_eval.append(tp_fp_list[i][0:min(k, tp_fp_list[i].shape[0])])
tp_fp_eval = np.concatenate(tp_fp_eval)
return np.sum(tp_fp_eval) / num_gt
|
TensorFlow2/Recommendation/WideAndDeep/scripts | scripts | preproc | #!/bin/bash
# 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.
set -e
function usage() {
echo "Usage: bash scripts/preproc.sh"
}
if [ ! -d "scripts" ] || [ ! "$(ls -A 'scripts')" ]; then
echo "You are probably calling this script from wrong directory"
usage
exit 1
fi
time python -m data.outbrain.nvtabular.preproc "$@"
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | dataset_util_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for object_detection.utils.dataset_util."""
import os
import tensorflow as tf
from object_detection.utils import dataset_util
class DatasetUtilTest(tf.test.TestCase):
def test_read_examples_list(self):
example_list_data = """example1 1\nexample2 2"""
example_list_path = os.path.join(self.get_temp_dir(), 'examples.txt')
with tf.gfile.Open(example_list_path, 'wb') as f:
f.write(example_list_data)
examples = dataset_util.read_examples_list(example_list_path)
self.assertListEqual(['example1', 'example2'], examples)
if __name__ == '__main__':
tf.test.main()
|
PyTorch/LanguageModeling/Transformer-XL/pytorch/scripts/docker | docker | build | #!/bin/bash
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
docker build . --network=host --rm -t transformer-xl:latest
|
TensorFlow/Segmentation/UNet_3D_Medical/scripts | scripts | unet3d_train_full | # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script launches 3D-UNet run 5-fold cross-validation FP32 training for 16000 iterations each.
# Usage:
# bash examples/unet3d_train_full.sh <number/of/gpus> <path/to/dataset> <path/to/results/directory> <batch/size>
horovodrun -np $1 python main.py --data_dir $2 --model_dir $3 --log_dir $3/log.json --exec_mode train_and_evaluate --max_steps 16000 --augment --batch_size $4 --fold 0 --use_xla > $3/log_FP32_$1GPU_fold0.txt
horovodrun -np $1 python main.py --data_dir $2 --model_dir $3 --log_dir $3/log.json --exec_mode train_and_evaluate --max_steps 16000 --augment --batch_size $4 --fold 1 --use_xla > $3/log_FP32_$1GPU_fold1.txt
horovodrun -np $1 python main.py --data_dir $2 --model_dir $3 --log_dir $3/log.json --exec_mode train_and_evaluate --max_steps 16000 --augment --batch_size $4 --fold 2 --use_xla > $3/log_FP32_$1GPU_fold2.txt
horovodrun -np $1 python main.py --data_dir $2 --model_dir $3 --log_dir $3/log.json --exec_mode train_and_evaluate --max_steps 16000 --augment --batch_size $4 --fold 3 --use_xla > $3/log_FP32_$1GPU_fold3.txt
horovodrun -np $1 python main.py --data_dir $2 --model_dir $3 --log_dir $3/log.json --exec_mode train_and_evaluate --max_steps 16000 --augment --batch_size $4 --fold 4 --use_xla > $3/log_FP32_$1GPU_fold4.txt
python runtime/parse_results.py --model_dir $3 --env FP32_$1GPU
|
PyTorch/SpeechSynthesis/HiFiGAN | HiFiGAN | inference | # 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 argparse
import itertools
import sys
import time
import warnings
from pathlib import Path
from tqdm import tqdm
import torch
import numpy as np
from scipy.stats import norm
from scipy.io.wavfile import write
from torch.nn.functional import l1_loss
from torch.nn.utils.rnn import pad_sequence
import dllogger as DLLogger
from dllogger import StdOutBackend, JSONStreamBackend, Verbosity
import models
from common import gpu_affinity
from common.tb_dllogger import stdout_metric_format, unique_log_fpath
from common.text import cmudict
from common.text.text_processing import TextProcessing
from common.utils import l2_promote
from fastpitch.pitch_transform import pitch_transform_custom
from hifigan.data_function import MAX_WAV_VALUE, mel_spectrogram
from hifigan.logging import init_inference_metadata
from hifigan.models import Denoiser
CHECKPOINT_SPECIFIC_ARGS = [
'sampling_rate', 'hop_length', 'win_length', 'p_arpabet', 'text_cleaners',
'symbol_set', 'max_wav_value', 'prepend_space_to_text',
'append_space_to_text']
def parse_args(parser):
"""
Parse commandline arguments.
"""
parser.add_argument('-i', '--input', type=str, required=True,
help='Full path to the input text '
'(phareses separated by newlines)')
parser.add_argument('-o', '--output', default=None,
help='Output folder to save audio (file per phrase)')
parser.add_argument('--log-file', type=str, default=None,
help='Path to a DLLogger log file')
parser.add_argument('--save-mels', action='store_true',
help='Save generator outputs to disk')
parser.add_argument('--cuda', action='store_true',
help='Run inference on a GPU using CUDA')
parser.add_argument('--cudnn-benchmark', action='store_true',
help='Enable cudnn benchmark mode')
parser.add_argument('--l2-promote', action='store_true',
help='Increase max fetch granularity of GPU L2 cache')
parser.add_argument('--fastpitch', type=str, default=None, required=False,
help='Full path to the spectrogram generator .pt file '
'(skip to synthesize from ground truth mels)')
parser.add_argument('--waveglow', type=str, default=None, required=False,
help='Full path to a WaveGlow model .pt file')
parser.add_argument('-s', '--waveglow-sigma-infer', default=0.9, type=float,
help='WaveGlow sigma')
parser.add_argument('--hifigan', type=str, default=None, required=False,
help='Full path to a HiFi-GAN model .pt file')
parser.add_argument('-d', '--denoising-strength', default=0.0, type=float,
help='Capture and subtract model bias to enhance audio')
parser.add_argument('--hop-length', type=int, default=256,
help='STFT hop length for estimating audio length from mel size')
parser.add_argument('--win-length', type=int, default=1024,
help='STFT win length for denoiser and mel loss')
parser.add_argument('-sr', '--sampling-rate', default=22050, type=int,
choices=[22050, 44100], help='Sampling rate')
parser.add_argument('--max_wav_value', default=32768.0, type=float,
help='Maximum audiowave value')
parser.add_argument('--amp', action='store_true',
help='Inference with AMP')
parser.add_argument('-bs', '--batch-size', type=int, default=64)
parser.add_argument('--warmup-steps', type=int, default=0,
help='Warmup iterations before measuring performance')
parser.add_argument('--repeats', type=int, default=1,
help='Repeat inference for benchmarking')
parser.add_argument('--torchscript', action='store_true',
help='Run inference with TorchScript model (convert to TS if needed)')
parser.add_argument('--checkpoint-format', type=str,
choices=['pyt', 'ts'], default='pyt',
help='Input checkpoint format (PyT or TorchScript)')
parser.add_argument('--torch-tensorrt', action='store_true',
help='Run inference with Torch-TensorRT model (compile beforehand)')
parser.add_argument('--report-mel-loss', action='store_true',
help='Report mel loss in metrics')
parser.add_argument('--ema', action='store_true',
help='Use EMA averaged model (if saved in checkpoints)')
parser.add_argument('--dataset-path', type=str,
help='Path to dataset (for loading extra data fields)')
parser.add_argument('--speaker', type=int, default=0,
help='Speaker ID for a multi-speaker model')
parser.add_argument('--affinity', type=str, default='single',
choices=['socket', 'single', 'single_unique',
'socket_unique_interleaved',
'socket_unique_continuous',
'disabled'],
help='type of CPU affinity')
transf = parser.add_argument_group('transform')
transf.add_argument('--fade-out', type=int, default=6,
help='Number of fadeout frames at the end')
transf.add_argument('--pace', type=float, default=1.0,
help='Adjust the pace of speech')
transf.add_argument('--pitch-transform-flatten', action='store_true',
help='Flatten the pitch')
transf.add_argument('--pitch-transform-invert', action='store_true',
help='Invert the pitch wrt mean value')
transf.add_argument('--pitch-transform-amplify', type=float, default=1.0,
help='Multiplicative amplification of pitch variability. '
'Typical values are in the range (1.0, 3.0).')
transf.add_argument('--pitch-transform-shift', type=float, default=0.0,
help='Raise/lower the pitch by <hz>')
transf.add_argument('--pitch-transform-custom', action='store_true',
help='Apply the transform from pitch_transform.py')
txt = parser.add_argument_group('Text processing parameters')
txt.add_argument('--text-cleaners', type=str, nargs='*',
default=['english_cleaners_v2'],
help='Type of text cleaners for input text')
txt.add_argument('--symbol-set', type=str, default='english_basic',
help='Define symbol set for input text')
txt.add_argument('--p-arpabet', type=float, default=0.0, help='')
txt.add_argument('--heteronyms-path', type=str,
default='data/cmudict/heteronyms', help='')
txt.add_argument('--cmudict-path', type=str,
default='data/cmudict/cmudict-0.7b', help='')
return parser
def load_fields(fpath):
lines = [l.strip() for l in open(fpath, encoding='utf-8')]
if fpath.endswith('.tsv'):
columns = lines[0].split('\t')
fields = list(zip(*[t.split('\t') for t in lines[1:]]))
else:
columns = ['text']
fields = [lines]
return {c: f for c, f in zip(columns, fields)}
def prepare_input_sequence(fields, device, symbol_set, text_cleaners,
batch_size=128, dataset=None, load_mels=False,
load_pitch=False, p_arpabet=0.0):
tp = TextProcessing(symbol_set, text_cleaners, p_arpabet=p_arpabet)
fields['text'] = [torch.LongTensor(tp.encode_text(text))
for text in fields['text']]
order = np.argsort([-t.size(0) for t in fields['text']])
fields['text'] = [fields['text'][i] for i in order]
fields['text_lens'] = torch.LongTensor([t.size(0) for t in fields['text']])
for t in fields['text']:
print(tp.sequence_to_text(t.numpy()))
if load_mels:
assert 'mel' in fields
assert dataset is not None
fields['mel'] = [
torch.load(Path(dataset, fields['mel'][i])).t() for i in order]
fields['mel_lens'] = torch.LongTensor([t.size(0) for t in fields['mel']])
if load_pitch:
assert 'pitch' in fields
fields['pitch'] = [
torch.load(Path(dataset, fields['pitch'][i])) for i in order]
fields['pitch_lens'] = torch.LongTensor([t.size(0) for t in fields['pitch']])
if 'output' in fields:
fields['output'] = [fields['output'][i] for i in order]
# cut into batches & pad
batches = []
for b in range(0, len(order), batch_size):
batch = {f: values[b:b+batch_size] for f, values in fields.items()}
for f in batch:
if f == 'text':
batch[f] = pad_sequence(batch[f], batch_first=True)
elif f == 'mel' and load_mels:
batch[f] = pad_sequence(batch[f], batch_first=True).permute(0, 2, 1)
elif f == 'pitch' and load_pitch:
batch[f] = pad_sequence(batch[f], batch_first=True)
if type(batch[f]) is torch.Tensor:
batch[f] = batch[f].to(device)
batches.append(batch)
return batches
def build_pitch_transformation(args):
if args.pitch_transform_custom:
def custom_(pitch, pitch_lens, mean, std):
return (pitch_transform_custom(pitch * std + mean, pitch_lens)
- mean) / std
return custom_
fun = 'pitch'
if args.pitch_transform_flatten:
fun = f'({fun}) * 0.0'
if args.pitch_transform_invert:
fun = f'({fun}) * -1.0'
if args.pitch_transform_amplify != 1.0:
ampl = args.pitch_transform_amplify
fun = f'({fun}) * {ampl}'
if args.pitch_transform_shift != 0.0:
hz = args.pitch_transform_shift
fun = f'({fun}) + {hz} / std'
if fun == 'pitch':
return None
return eval(f'lambda pitch, pitch_lens, mean, std: {fun}')
def setup_mel_loss_reporting(args, voc_train_setup):
if args.denoising_strength > 0.0:
print('WARNING: denoising will be included in vocoder mel loss')
num_mels = voc_train_setup.get('num_mels', 80)
fmin = voc_train_setup.get('mel_fmin', 0)
fmax = voc_train_setup.get('mel_fmax', 8000) # not mel_fmax_loss
def compute_audio_mel_loss(gen_audios, gt_mels, mel_lens):
gen_audios /= MAX_WAV_VALUE
total_loss = 0
for gen_audio, gt_mel, mel_len in zip(gen_audios, gt_mels, mel_lens):
mel_len = mel_len.item()
gen_audio = gen_audio[None, :mel_len * args.hop_length]
gen_mel = mel_spectrogram(gen_audio, args.win_length, num_mels,
args.sampling_rate, args.hop_length,
args.win_length, fmin, fmax)[0]
total_loss += l1_loss(gen_mel, gt_mel[:, :mel_len])
return total_loss.item()
return compute_audio_mel_loss
def compute_mel_loss(mels, lens, gt_mels, gt_lens):
total_loss = 0
for mel, len_, gt_mel, gt_len in zip(mels, lens, gt_mels, gt_lens):
min_len = min(len_, gt_len)
total_loss += l1_loss(gt_mel[:, :min_len], mel[:, :min_len])
return total_loss.item()
class MeasureTime(list):
def __init__(self, *args, cuda=True, **kwargs):
super(MeasureTime, self).__init__(*args, **kwargs)
self.cuda = cuda
def __enter__(self):
if self.cuda:
torch.cuda.synchronize()
self.t0 = time.time()
def __exit__(self, exc_type, exc_value, exc_traceback):
if self.cuda:
torch.cuda.synchronize()
self.append(time.time() - self.t0)
def __add__(self, other):
assert len(self) == len(other)
return MeasureTime((sum(ab) for ab in zip(self, other)), cuda=self.cuda)
def main():
"""
Launches text-to-speech inference on a single GPU.
"""
parser = argparse.ArgumentParser(description='PyTorch FastPitch Inference',
allow_abbrev=False)
parser = parse_args(parser)
args, unk_args = parser.parse_known_args()
if args.affinity != 'disabled':
nproc_per_node = torch.cuda.device_count()
# print(nproc_per_node)
affinity = gpu_affinity.set_affinity(
0,
nproc_per_node,
args.affinity
)
print(f'Thread affinity: {affinity}')
if args.l2_promote:
l2_promote()
torch.backends.cudnn.benchmark = args.cudnn_benchmark
if args.output is not None:
Path(args.output).mkdir(parents=False, exist_ok=True)
log_fpath = args.log_file or str(Path(args.output, 'nvlog_infer.json'))
DLLogger.init(backends=[
JSONStreamBackend(Verbosity.DEFAULT, log_fpath, append=True),
JSONStreamBackend(Verbosity.DEFAULT, unique_log_fpath(log_fpath)),
StdOutBackend(Verbosity.VERBOSE, metric_format=stdout_metric_format)
])
init_inference_metadata(args.batch_size)
[DLLogger.log("PARAMETER", {k: v}) for k, v in vars(args).items()]
device = torch.device('cuda' if args.cuda else 'cpu')
gen_train_setup = {}
voc_train_setup = {}
generator = None
vocoder = None
denoiser = None
is_ts_based_infer = args.torch_tensorrt or args.torchscript
assert args.checkpoint_format == 'pyt' or is_ts_based_infer, \
'TorchScript checkpoint can be used only for TS or Torch-TRT' \
' inference. Please set --torchscript or --torch-tensorrt flag.'
assert args.waveglow is None or args.hifigan is None, \
"Specify a single vocoder model"
def _load_pyt_or_ts_model(model_name, ckpt_path):
if args.checkpoint_format == 'ts':
model = models.load_and_setup_ts_model(model_name, ckpt_path,
args.amp, device)
model_train_setup = {}
return model, model_train_setup
model, _, model_train_setup = models.load_and_setup_model(
model_name, parser, ckpt_path, args.amp, device,
unk_args=unk_args, forward_is_infer=True, jitable=is_ts_based_infer)
if is_ts_based_infer:
model = torch.jit.script(model)
return model, model_train_setup
if args.fastpitch is not None:
gen_name = 'fastpitch'
generator, gen_train_setup = _load_pyt_or_ts_model('FastPitch',
args.fastpitch)
if args.waveglow is not None:
voc_name = 'waveglow'
with warnings.catch_warnings():
warnings.simplefilter("ignore")
vocoder, _, voc_train_setup = models.load_and_setup_model(
'WaveGlow', parser, args.waveglow, args.amp, device,
unk_args=unk_args, forward_is_infer=True, jitable=False)
if args.denoising_strength > 0.0:
denoiser = Denoiser(vocoder, sigma=0.0,
win_length=args.win_length).to(device)
# if args.torchscript:
# vocoder = torch.jit.script(vocoder)
def generate_audio(mel):
audios = vocoder(mel, sigma=args.waveglow_sigma_infer)
if denoiser is not None:
audios = denoiser(audios.float(), args.denoising_strength).squeeze(1)
return audios
elif args.hifigan is not None:
voc_name = 'hifigan'
vocoder, voc_train_setup = _load_pyt_or_ts_model('HiFi-GAN',
args.hifigan)
if args.denoising_strength > 0.0:
denoiser = Denoiser(vocoder, win_length=args.win_length).to(device)
if args.torch_tensorrt:
vocoder = models.convert_ts_to_trt('HiFi-GAN', vocoder, parser,
args.amp, unk_args)
def generate_audio(mel):
audios = vocoder(mel).float()
if denoiser is not None:
audios = denoiser(audios.squeeze(1), args.denoising_strength)
return audios.squeeze(1) * args.max_wav_value
if len(unk_args) > 0:
raise ValueError(f'Invalid options {unk_args}')
for k in CHECKPOINT_SPECIFIC_ARGS:
v1 = gen_train_setup.get(k, None)
v2 = voc_train_setup.get(k, None)
assert v1 is None or v2 is None or v1 == v2, \
f'{k} mismatch in spectrogram generator and vocoder'
val = v1 or v2
if val and getattr(args, k) != val:
src = 'generator' if v2 is None else 'vocoder'
print(f'Overwriting args.{k}={getattr(args, k)} with {val} '
f'from {src} checkpoint.')
setattr(args, k, val)
gen_kw = {'pace': args.pace,
'speaker': args.speaker,
'pitch_tgt': None,
'pitch_transform': build_pitch_transformation(args)}
if is_ts_based_infer and generator is not None:
gen_kw.pop('pitch_transform')
print('Note: --pitch-transform-* args are disabled with TorchScript. '
'To condition on pitch, pass pitch_tgt as input.')
if args.p_arpabet > 0.0:
cmudict.initialize(args.cmudict_path, args.heteronyms_path)
if args.report_mel_loss:
mel_loss_fn = setup_mel_loss_reporting(args, voc_train_setup)
fields = load_fields(args.input)
batches = prepare_input_sequence(
fields, device, args.symbol_set, args.text_cleaners, args.batch_size,
args.dataset_path, load_mels=(generator is None or args.report_mel_loss),
p_arpabet=args.p_arpabet)
cycle = itertools.cycle(batches)
# Use real data rather than synthetic - FastPitch predicts len
for _ in tqdm(range(args.warmup_steps), 'Warmup'):
with torch.no_grad():
b = next(cycle)
if generator is not None:
mel, *_ = generator(b['text'])
else:
mel, mel_lens = b['mel'], b['mel_lens']
if args.amp:
mel = mel.half()
if vocoder is not None:
audios = generate_audio(mel)
gen_measures = MeasureTime(cuda=args.cuda)
vocoder_measures = MeasureTime(cuda=args.cuda)
all_utterances = 0
all_samples = 0
all_batches = 0
all_letters = 0
all_frames = 0
gen_mel_loss_sum = 0
voc_mel_loss_sum = 0
reps = args.repeats
log_enabled = reps == 1
log = lambda s, d: DLLogger.log(step=s, data=d) if log_enabled else None
for rep in (tqdm(range(reps), 'Inference') if reps > 1 else range(reps)):
for b in batches:
if generator is None:
mel, mel_lens = b['mel'], b['mel_lens']
if args.amp:
mel = mel.half()
else:
with torch.no_grad(), gen_measures:
mel, mel_lens, *_ = generator(b['text'], **gen_kw)
if args.report_mel_loss:
gen_mel_loss_sum += compute_mel_loss(
mel, mel_lens, b['mel'], b['mel_lens'])
gen_infer_perf = mel.size(0) * mel.size(2) / gen_measures[-1]
all_letters += b['text_lens'].sum().item()
all_frames += mel.size(0) * mel.size(2)
log(rep, {f"{gen_name}_frames/s": gen_infer_perf})
log(rep, {f"{gen_name}_latency": gen_measures[-1]})
if args.save_mels:
for i, mel_ in enumerate(mel):
m = mel_[:, :mel_lens[i].item()].permute(1, 0)
fname = b['output'][i] if 'output' in b else f'mel_{i}.npy'
mel_path = Path(args.output, Path(fname).stem + '.npy')
np.save(mel_path, m.cpu().numpy())
if vocoder is not None:
with torch.no_grad(), vocoder_measures:
audios = generate_audio(mel)
vocoder_infer_perf = (
audios.size(0) * audios.size(1) / vocoder_measures[-1])
log(rep, {f"{voc_name}_samples/s": vocoder_infer_perf})
log(rep, {f"{voc_name}_latency": vocoder_measures[-1]})
if args.report_mel_loss:
voc_mel_loss_sum += mel_loss_fn(audios, mel, mel_lens)
if args.output is not None and reps == 1:
for i, audio in enumerate(audios):
audio = audio[:mel_lens[i].item() * args.hop_length]
if args.fade_out:
fade_len = args.fade_out * args.hop_length
fade_w = torch.linspace(1.0, 0.0, fade_len)
audio[-fade_len:] *= fade_w.to(audio.device)
audio = audio / torch.max(torch.abs(audio))
fname = b['output'][i] if 'output' in b else f'audio_{i}.wav'
audio_path = Path(args.output, fname)
write(audio_path, args.sampling_rate, audio.cpu().numpy())
if generator is not None:
log(rep, {"latency": (gen_measures[-1] + vocoder_measures[-1])})
all_utterances += mel.size(0)
all_samples += mel_lens.sum().item() * args.hop_length
all_batches += 1
log_enabled = True
if generator is not None:
gm = np.sort(np.asarray(gen_measures))
rtf = all_samples / (all_utterances * gm.mean() * args.sampling_rate)
rtf_at = all_samples / (all_batches * gm.mean() * args.sampling_rate)
log((), {f"avg_{gen_name}_tokens/s": all_letters / gm.sum()})
log((), {f"avg_{gen_name}_frames/s": all_frames / gm.sum()})
log((), {f"avg_{gen_name}_latency": gm.mean()})
log((), {f"avg_{gen_name}_RTF": rtf})
log((), {f"avg_{gen_name}_RTF@{args.batch_size}": rtf_at})
log((), {f"90%_{gen_name}_latency": gm.mean() + norm.ppf((1.0 + 0.90) / 2) * gm.std()})
log((), {f"95%_{gen_name}_latency": gm.mean() + norm.ppf((1.0 + 0.95) / 2) * gm.std()})
log((), {f"99%_{gen_name}_latency": gm.mean() + norm.ppf((1.0 + 0.99) / 2) * gm.std()})
if args.report_mel_loss:
log((), {f"avg_{gen_name}_mel-loss": gen_mel_loss_sum / all_utterances})
if vocoder is not None:
vm = np.sort(np.asarray(vocoder_measures))
rtf = all_samples / (all_utterances * vm.mean() * args.sampling_rate)
rtf_at = all_samples / (all_batches * vm.mean() * args.sampling_rate)
log((), {f"avg_{voc_name}_samples/s": all_samples / vm.sum()})
log((), {f"avg_{voc_name}_latency": vm.mean()})
log((), {f"avg_{voc_name}_RTF": rtf})
log((), {f"avg_{voc_name}_RTF@{args.batch_size}": rtf_at})
log((), {f"90%_{voc_name}_latency": vm.mean() + norm.ppf((1.0 + 0.90) / 2) * vm.std()})
log((), {f"95%_{voc_name}_latency": vm.mean() + norm.ppf((1.0 + 0.95) / 2) * vm.std()})
log((), {f"99%_{voc_name}_latency": vm.mean() + norm.ppf((1.0 + 0.99) / 2) * vm.std()})
if args.report_mel_loss:
log((), {f"avg_{voc_name}_mel-loss": voc_mel_loss_sum / all_utterances})
if generator is not None and vocoder is not None:
m = gm + vm
rtf = all_samples / (all_utterances * m.mean() * args.sampling_rate)
rtf_at = all_samples / (all_batches * m.mean() * args.sampling_rate)
log((), {"avg_samples/s": all_samples / m.sum()})
log((), {"avg_letters/s": all_letters / m.sum()})
log((), {"avg_latency": m.mean()})
log((), {"avg_RTF": rtf})
log((), {f"avg_RTF@{args.batch_size}": rtf_at})
log((), {"90%_latency": m.mean() + norm.ppf((1.0 + 0.90) / 2) * m.std()})
log((), {"95%_latency": m.mean() + norm.ppf((1.0 + 0.95) / 2) * m.std()})
log((), {"99%_latency": m.mean() + norm.ppf((1.0 + 0.99) / 2) * m.std()})
DLLogger.flush()
if __name__ == '__main__':
main()
|
PaddlePaddle/LanguageModeling/BERT/scripts/docker | docker | launch | #!/bin/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.
CMD=${1:-/bin/bash}
NV_VISIBLE_DEVICES=${2:-"all"}
DOCKER_BRIDGE=${3:-"host"}
docker run -it --rm \
--gpus device=$NV_VISIBLE_DEVICES \
--net=$DOCKER_BRIDGE \
--shm-size=1g \
--ulimit memlock=-1 \
--ulimit stack=67108864 \
-v $PWD:/workspace/bert \
-v $PWD/results:/results \
bert $CMD
|
PyTorch/SpeechSynthesis/FastPitch/waveglow | waveglow | model | # *****************************************************************************
# 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 torch
from torch.autograd import Variable
import torch.nn.functional as F
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
class Invertible1x1Conv(torch.nn.Module):
"""
The layer outputs both the convolution, and the log determinant
of its weight matrix. If reverse=True it does convolution with
inverse
"""
def __init__(self, c):
super(Invertible1x1Conv, self).__init__()
self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0,
bias=False)
# Sample a random orthonormal matrix to initialize weights
W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
# Ensure determinant is 1.0 not -1.0
if torch.det(W) < 0:
W[:, 0] = -1 * W[:, 0]
W = W.view(c, c, 1)
W = W.contiguous()
self.conv.weight.data = W
def forward(self, z):
# shape
batch_size, group_size, n_of_groups = z.size()
W = self.conv.weight.squeeze()
# Forward computation
log_det_W = batch_size * n_of_groups * torch.logdet(W.unsqueeze(0).float()).squeeze()
z = self.conv(z)
return z, log_det_W
def infer(self, z):
# shape
batch_size, group_size, n_of_groups = z.size()
W = self.conv.weight.squeeze()
if not hasattr(self, 'W_inverse'):
# Reverse computation
W_inverse = W.float().inverse()
W_inverse = Variable(W_inverse[..., None])
if z.type() == 'torch.cuda.HalfTensor' or z.type() == 'torch.HalfTensor':
W_inverse = W_inverse.half()
self.W_inverse = W_inverse
z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
return z
class WN(torch.nn.Module):
"""
This is the WaveNet like layer for the affine coupling. The primary
difference from WaveNet is the convolutions need not be causal. There is
also no dilation size reset. The dilation only doubles on each layer
"""
def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels,
kernel_size):
super(WN, self).__init__()
assert(kernel_size % 2 == 1)
assert(n_channels % 2 == 0)
self.n_layers = n_layers
self.n_channels = n_channels
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self.cond_layers = torch.nn.ModuleList()
start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
start = torch.nn.utils.weight_norm(start, name='weight')
self.start = start
# Initializing last layer to 0 makes the affine coupling layers
# do nothing at first. This helps with training stability
end = torch.nn.Conv1d(n_channels, 2 * n_in_channels, 1)
end.weight.data.zero_()
end.bias.data.zero_()
self.end = end
for i in range(n_layers):
dilation = 2 ** i
padding = int((kernel_size * dilation - dilation) / 2)
in_layer = torch.nn.Conv1d(n_channels, 2 * n_channels, kernel_size,
dilation=dilation, padding=padding)
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
self.in_layers.append(in_layer)
cond_layer = torch.nn.Conv1d(n_mel_channels, 2 * n_channels, 1)
cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
self.cond_layers.append(cond_layer)
# last one is not necessary
if i < n_layers - 1:
res_skip_channels = 2 * n_channels
else:
res_skip_channels = n_channels
res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
res_skip_layer = torch.nn.utils.weight_norm(
res_skip_layer, name='weight')
self.res_skip_layers.append(res_skip_layer)
def forward(self, forward_input):
audio, spect = forward_input
audio = self.start(audio)
for i in range(self.n_layers):
acts = fused_add_tanh_sigmoid_multiply(
self.in_layers[i](audio),
self.cond_layers[i](spect),
torch.IntTensor([self.n_channels]))
res_skip_acts = self.res_skip_layers[i](acts)
if i < self.n_layers - 1:
audio = res_skip_acts[:, :self.n_channels, :] + audio
skip_acts = res_skip_acts[:, self.n_channels:, :]
else:
skip_acts = res_skip_acts
if i == 0:
output = skip_acts
else:
output = skip_acts + output
return self.end(output)
class WaveGlow(torch.nn.Module):
def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
n_early_size, WN_config):
super(WaveGlow, self).__init__()
self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
n_mel_channels,
1024, stride=256)
assert(n_group % 2 == 0)
self.n_flows = n_flows
self.n_group = n_group
self.n_early_every = n_early_every
self.n_early_size = n_early_size
self.WN = torch.nn.ModuleList()
self.convinv = torch.nn.ModuleList()
n_half = int(n_group / 2)
# Set up layers with the right sizes based on how many dimensions
# have been output already
n_remaining_channels = n_group
for k in range(n_flows):
if k % self.n_early_every == 0 and k > 0:
n_half = n_half - int(self.n_early_size / 2)
n_remaining_channels = n_remaining_channels - self.n_early_size
self.convinv.append(Invertible1x1Conv(n_remaining_channels))
self.WN.append(WN(n_half, n_mel_channels * n_group, **WN_config))
self.n_remaining_channels = n_remaining_channels
def forward(self, forward_input):
"""
forward_input[0] = mel_spectrogram: batch x n_mel_channels x frames
forward_input[1] = audio: batch x time
"""
spect, audio = forward_input
# Upsample spectrogram to size of audio
spect = self.upsample(spect)
assert(spect.size(2) >= audio.size(1))
if spect.size(2) > audio.size(1):
spect = spect[:, :, :audio.size(1)]
spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
spect = spect.contiguous().view(spect.size(0), spect.size(1), -1)
spect = spect.permute(0, 2, 1)
audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
output_audio = []
log_s_list = []
log_det_W_list = []
for k in range(self.n_flows):
if k % self.n_early_every == 0 and k > 0:
output_audio.append(audio[:, :self.n_early_size, :])
audio = audio[:, self.n_early_size:, :]
audio, log_det_W = self.convinv[k](audio)
log_det_W_list.append(log_det_W)
n_half = int(audio.size(1) / 2)
audio_0 = audio[:, :n_half, :]
audio_1 = audio[:, n_half:, :]
output = self.WN[k]((audio_0, spect))
log_s = output[:, n_half:, :]
b = output[:, :n_half, :]
audio_1 = torch.exp(log_s) * audio_1 + b
log_s_list.append(log_s)
audio = torch.cat([audio_0, audio_1], 1)
output_audio.append(audio)
return torch.cat(output_audio, 1), log_s_list, log_det_W_list
def infer(self, spect, sigma=1.0):
spect = self.upsample(spect)
# trim conv artifacts. maybe pad spec to kernel multiple
time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
spect = spect[:, :, :-time_cutoff]
spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
spect = spect.contiguous().view(spect.size(0), spect.size(1), -1)
spect = spect.permute(0, 2, 1)
audio = torch.randn(spect.size(0),
self.n_remaining_channels,
spect.size(2), device=spect.device).to(spect.dtype)
audio = torch.autograd.Variable(sigma * audio)
for k in reversed(range(self.n_flows)):
n_half = int(audio.size(1) / 2)
audio_0 = audio[:, :n_half, :]
audio_1 = audio[:, n_half:, :]
output = self.WN[k]((audio_0, spect))
s = output[:, n_half:, :]
b = output[:, :n_half, :]
audio_1 = (audio_1 - b) / torch.exp(s)
audio = torch.cat([audio_0, audio_1], 1)
audio = self.convinv[k].infer(audio)
if k % self.n_early_every == 0 and k > 0:
z = torch.randn(spect.size(0), self.n_early_size, spect.size(
2), device=spect.device).to(spect.dtype)
audio = torch.cat((sigma * z, audio), 1)
audio = audio.permute(
0, 2, 1).contiguous().view(
audio.size(0), -1).data
return audio
def infer_onnx(self, spect, z, sigma=0.9):
spect = self.upsample(spect)
# trim conv artifacts. maybe pad spec to kernel multiple
time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
spect = spect[:, :, :-time_cutoff]
length_spect_group = spect.size(2)//8
mel_dim = 80
batch_size = spect.size(0)
spect = spect.view((batch_size, mel_dim, length_spect_group, self.n_group))
spect = spect.permute(0, 2, 1, 3)
spect = spect.contiguous()
spect = spect.view((batch_size, length_spect_group, self.n_group*mel_dim))
spect = spect.permute(0, 2, 1)
spect = spect.contiguous()
audio = z[:, :self.n_remaining_channels, :]
z = z[:, self.n_remaining_channels:self.n_group, :]
audio = sigma*audio
for k in reversed(range(self.n_flows)):
n_half = int(audio.size(1) // 2)
audio_0 = audio[:, :n_half, :]
audio_1 = audio[:, n_half:(n_half+n_half), :]
output = self.WN[k]((audio_0, spect))
s = output[:, n_half:(n_half+n_half), :]
b = output[:, :n_half, :]
audio_1 = (audio_1 - b) / torch.exp(s)
audio = torch.cat([audio_0, audio_1], 1)
audio = self.convinv[k].infer(audio)
if k % self.n_early_every == 0 and k > 0:
audio = torch.cat((z[:, :self.n_early_size, :], audio), 1)
z = z[:, self.n_early_size:self.n_group, :]
audio = audio.permute(0,2,1).contiguous().view(batch_size, (length_spect_group * self.n_group))
return audio
@staticmethod
def remove_weightnorm(model):
waveglow = model
for WN in waveglow.WN:
WN.start = torch.nn.utils.remove_weight_norm(WN.start)
WN.in_layers = remove(WN.in_layers)
WN.cond_layers = remove(WN.cond_layers)
WN.res_skip_layers = remove(WN.res_skip_layers)
return waveglow
def remove(conv_list):
new_conv_list = torch.nn.ModuleList()
for old_conv in conv_list:
old_conv = torch.nn.utils.remove_weight_norm(old_conv)
new_conv_list.append(old_conv)
return new_conv_list
|
PyTorch/SpeechSynthesis/HiFiGAN/common | common | stft | """
BSD 3-Clause License
Copyright (c) 2017, Prem Seetharaman
All rights reserved.
* Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this
list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import numpy as np
import torch
import torch.nn.functional as F
from librosa.util import pad_center, tiny
from scipy.signal import get_window
from torch.autograd import Variable
from common.audio_processing import window_sumsquare
class STFT(torch.nn.Module):
"""adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
def __init__(self, filter_length=800, hop_length=200, win_length=800,
window='hann'):
super(STFT, self).__init__()
self.filter_length = filter_length
self.hop_length = hop_length
self.win_length = win_length
self.window = window
self.forward_transform = None
scale = self.filter_length / self.hop_length
fourier_basis = np.fft.fft(np.eye(self.filter_length))
cutoff = int((self.filter_length / 2 + 1))
fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),
np.imag(fourier_basis[:cutoff, :])])
forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
inverse_basis = torch.FloatTensor(
np.linalg.pinv(scale * fourier_basis).T[:, None, :].copy())
if window is not None:
assert(filter_length >= win_length)
# get window and zero center pad it to filter_length
fft_window = get_window(window, win_length, fftbins=True)
fft_window = pad_center(fft_window, size=filter_length)
fft_window = torch.from_numpy(fft_window).float()
# window the bases
forward_basis *= fft_window
inverse_basis *= fft_window
self.register_buffer('forward_basis', forward_basis.float())
self.register_buffer('inverse_basis', inverse_basis.float())
def transform(self, input_data):
num_batches = input_data.size(0)
num_samples = input_data.size(1)
self.num_samples = num_samples
# similar to librosa, reflect-pad the input
input_data = input_data.view(num_batches, 1, num_samples)
input_data = F.pad(
input_data.unsqueeze(1),
(int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
mode='reflect')
input_data = input_data.squeeze(1)
forward_transform = F.conv1d(
input_data,
Variable(self.forward_basis, requires_grad=False),
stride=self.hop_length,
padding=0)
cutoff = int((self.filter_length / 2) + 1)
real_part = forward_transform[:, :cutoff, :]
imag_part = forward_transform[:, cutoff:, :]
magnitude = torch.sqrt(real_part**2 + imag_part**2)
phase = torch.autograd.Variable(
torch.atan2(imag_part.data, real_part.data))
return magnitude, phase
def inverse(self, magnitude, phase):
recombine_magnitude_phase = torch.cat(
[magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1)
with torch.no_grad():
inverse_transform = F.conv_transpose1d(
recombine_magnitude_phase, self.inverse_basis,
stride=self.hop_length, padding=0)
if self.window is not None:
window_sum = window_sumsquare(
self.window, magnitude.size(-1), hop_length=self.hop_length,
win_length=self.win_length, n_fft=self.filter_length,
dtype=np.float32)
# remove modulation effects
approx_nonzero_indices = torch.from_numpy(
np.where(window_sum > tiny(window_sum))[0])
window_sum = torch.autograd.Variable(
torch.from_numpy(window_sum), requires_grad=False)
window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum
inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
# scale by hop ratio
inverse_transform *= float(self.filter_length) / self.hop_length
inverse_transform = inverse_transform[:, :, int(self.filter_length/2):]
inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):]
return inverse_transform
def forward(self, input_data):
self.magnitude, self.phase = self.transform(input_data)
reconstruction = self.inverse(self.magnitude, self.phase)
return reconstruction
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/preprocessing/datasets | datasets | ogbn_mag | # 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 json
import os
import shutil
from typing import Optional
import cudf
import cupy as cp
import numpy as np
import pandas as pd
from ogb.nodeproppred import NodePropPredDataset
from syngen.configuration import SynGenDatasetFeatureSpec
from syngen.preprocessing.base_preprocessing import BasePreprocessing
from syngen.utils.io_utils import dump_dataframe
from syngen.utils.types import MetaData
class OGBN_MAG_Preprocessing(BasePreprocessing):
"""
The OGBN_MAG_Preprocessing class includes the transformation
operation for a subset of the Microsoft Academic Graph (MAG).
It's a heterogeneous network that contains four types of entities—papers
(736,389 nodes), authors (1,134,649 nodes), institutions (8,740 nodes),
and fields of study (59,965 nodes)—as well as four types of directed relations
connecting two types of entities—an author is “affiliated with” an institution,
an author “writes” a paper, a paper “cites” a paper, and a paper “has a topic
of” a field of study. For more information, please check
https://ogb.stanford.edu/docs/nodeprop/
"""
def __init__(
self,
source_path: str,
destination_path: Optional[str] = None,
download: bool = False,
**kwargs,
):
super().__init__(source_path, destination_path, download, **kwargs)
def download(self):
NodePropPredDataset(name="ogbn-mag", root=self.source_path)
def _check_files(self) -> bool:
return True
def transform(self, gpu=False, use_cache=False):
tabular_operator = cudf if gpu else pd
operator = cp if gpu else np
if use_cache and os.path.exists(self.destination_path):
return SynGenDatasetFeatureSpec.instantiate_from_preprocessed(self.destination_path)
shutil.rmtree(self.destination_path, ignore_errors=True)
os.makedirs(self.destination_path)
dataset = NodePropPredDataset(name="ogbn-mag", root=self.source_path)[0]
data = dataset[0]
labels = dataset[1]["paper"]
graph_metadata = {
MetaData.NODES: [],
MetaData.EDGES: [],
}
connections = {}
for e, edges in data["edge_index_dict"].items():
structural_data = pd.DataFrame(edges.T, columns=[MetaData.SRC, MetaData.DST])
connections[e[1]] = tabular_operator.DataFrame({
"src_id": edges[0, :],
"dst_id": edges[1, :],
})
edata = data["edge_reltype"][e]
edge_type = {
MetaData.NAME: e[1],
MetaData.COUNT: len(structural_data),
MetaData.SRC_NODE_TYPE: e[0],
MetaData.DST_NODE_TYPE: e[2],
MetaData.DIRECTED: False,
MetaData.FEATURES: [{
MetaData.NAME: 'feat',
MetaData.DTYPE: str(edata.dtype),
MetaData.FEATURE_TYPE: MetaData.CATEGORICAL,
}],
MetaData.FEATURES_PATH: f"{e[1]}_features.parquet",
MetaData.STRUCTURE_PATH: f"{e[1]}_list.parquet",
}
dump_dataframe(tabular_operator.DataFrame(edata, columns=['feat']),
os.path.join(self.destination_path, edge_type[MetaData.FEATURES_PATH]))
dump_dataframe(structural_data,
os.path.join(self.destination_path, edge_type[MetaData.STRUCTURE_PATH]))
graph_metadata[MetaData.EDGES].append(edge_type)
# paper node type
continuous_column_names = ["feat_" + str(i) for i in range(data["node_feat_dict"]["paper"].shape[1])]
paper_features_dataframe = tabular_operator.DataFrame(
data["node_feat_dict"]["paper"],
columns=continuous_column_names,
).astype("float32")
paper_features_dataframe["year"] = tabular_operator.DataFrame(data["node_year"]["paper"]).astype("int32")
paper_features_dataframe["venue"] = tabular_operator.DataFrame(labels).astype("int32")
paper_node_type = {
MetaData.NAME: "paper",
MetaData.COUNT: data["num_nodes_dict"]['paper'],
MetaData.FEATURES: [
{
MetaData.NAME: name,
MetaData.DTYPE: str(dtype),
MetaData.FEATURE_TYPE:
MetaData.CATEGORICAL if str(dtype).startswith('int') else MetaData.CONTINUOUS,
} for name, dtype in paper_features_dataframe.dtypes.items()
],
MetaData.FEATURES_PATH: "paper.parquet",
}
dump_dataframe(paper_features_dataframe,
os.path.join(self.destination_path, paper_node_type[MetaData.FEATURES_PATH]))
graph_metadata[MetaData.NODES].append(paper_node_type)
# author node type
paper_features_dataframe["paper_id"] = operator.arange(paper_features_dataframe.shape[0])
author_feat = connections["writes"].merge(
paper_features_dataframe,
left_on="dst_id",
right_on="paper_id",
how="left"
).groupby("src_id", sort=True).mean()
author_features_dataframe = author_feat[continuous_column_names]
author_node_type = {
MetaData.NAME: "author",
MetaData.COUNT: data["num_nodes_dict"]['author'],
MetaData.FEATURES: [
{
MetaData.NAME: name,
MetaData.DTYPE: str(dtype),
MetaData.FEATURE_TYPE: MetaData.CONTINUOUS,
} for name, dtype in author_features_dataframe.dtypes.items()
],
MetaData.FEATURES_PATH: "author.parquet",
}
dump_dataframe(author_features_dataframe,
os.path.join(self.destination_path, author_node_type[MetaData.FEATURES_PATH]))
graph_metadata[MetaData.NODES].append(author_node_type)
# institution node type
author_features_dataframe["author_id"] = operator.arange(author_features_dataframe.shape[0])
institution_feat = connections["affiliated_with"].merge(
author_features_dataframe,
left_on="src_id",
right_on="author_id"
).groupby("dst_id", sort=True).mean()
institution_dataframe = institution_feat[continuous_column_names]
institution_node_type = {
MetaData.NAME: "institution",
MetaData.COUNT: data["num_nodes_dict"]['institution'],
MetaData.FEATURES: [
{
MetaData.NAME: name,
MetaData.DTYPE: str(dtype),
MetaData.FEATURE_TYPE: MetaData.CONTINUOUS,
} for name, dtype in institution_dataframe.dtypes.items()
],
MetaData.FEATURES_PATH: "institution.parquet",
}
dump_dataframe(institution_dataframe,
os.path.join(self.destination_path, institution_node_type[MetaData.FEATURES_PATH]))
graph_metadata[MetaData.NODES].append(institution_node_type)
# field_of_study node type
field_of_study_feat = connections["has_topic"].merge(
paper_features_dataframe,
left_on="src_id",
right_on="paper_id"
).groupby("dst_id", sort=True).mean()
field_of_study_dataframe = field_of_study_feat[continuous_column_names]
field_of_study_node_type = {
MetaData.NAME: "field_of_study",
MetaData.COUNT: data["num_nodes_dict"]['field_of_study'],
MetaData.FEATURES: [
{
MetaData.NAME: name,
MetaData.DTYPE: str(dtype),
MetaData.FEATURE_TYPE: MetaData.CONTINUOUS,
} for name, dtype in field_of_study_dataframe.dtypes.items()
],
MetaData.FEATURES_PATH: "field_of_study.parquet",
}
dump_dataframe(field_of_study_dataframe,
os.path.join(self.destination_path, field_of_study_node_type[MetaData.FEATURES_PATH]))
graph_metadata[MetaData.NODES].append(field_of_study_node_type)
with open(os.path.join(self.destination_path, 'graph_metadata.json'), 'w') as f:
json.dump(graph_metadata, f, indent=4)
graph_metadata[MetaData.PATH] = self.destination_path
return SynGenDatasetFeatureSpec(graph_metadata)
|
CUDA-Optimized/FastSpeech/tacotron2 | tacotron2 | layers | # BSD 3-Clause License
# Copyright (c) 2018-2020, 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 copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""https://github.com/NVIDIA/tacotron2"""
import torch
from librosa.filters import mel as librosa_mel_fn
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(LinearNorm, self).__init__()
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
torch.nn.init.xavier_uniform_(
self.linear_layer.weight,
gain=torch.nn.init.calculate_gain(w_init_gain))
def forward(self, x):
return self.linear_layer(x)
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert(kernel_size % 2 == 1)
padding = int(dilation * (kernel_size - 1) / 2)
self.conv = torch.nn.Conv1d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation,
bias=bias)
torch.nn.init.xavier_uniform_(
self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
def forward(self, signal):
conv_signal = self.conv(signal)
return conv_signal
|
TensorFlow/Recommendation/WideAndDeep/utils | utils | schedulers | # 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 tensorflow as tf
__all__ = ['learning_rate_scheduler']
def learning_rate_scheduler(lr_init, warmup_steps, global_step):
warmup_lr = (lr_init * tf.cast(global_step, tf.float32) / tf.cast(warmup_steps, tf.float32))
return tf.cond(global_step < warmup_steps, lambda: warmup_lr, lambda: lr_init)
|
PyTorch/LanguageModeling/BERT/triton/deployment_toolkit/bermuda | bermuda | pyt | # 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 logging
import typing
from collections import Counter
from pathlib import Path
from typing import Dict, Optional, Union
import numpy as np
import torch # pytype: disable=import-error
import yaml
from model_navigator.model import ModelSignatureConfig
from model_navigator.tensor import TensorSpec
from model_navigator.utils.config import YamlConfigFile
from ..core import (
GET_MODEL_FN_NAME,
BaseLoader,
BaseRunner,
BaseRunnerSession,
BaseSaver,
Format,
Model,
Precision,
load_from_file,
)
from ..extensions import loaders, runners, savers
from .utils import get_dynamic_axes, get_shapes_with_dynamic_axes
LOGGER = logging.getLogger(__name__)
def get_sample_input(dataloader, device):
for batch in dataloader:
_, x, _ = batch
break
if isinstance(x, dict):
sample_input = list(x.values())
elif isinstance(x, list):
sample_input = x
else:
raise TypeError("The first element (x) of batch returned by dataloader must be a list or a dict")
for idx, s in enumerate(sample_input):
sample_input[idx] = torch.from_numpy(s).to(device)
return tuple(sample_input)
def get_model_device(torch_model):
if next(torch_model.parameters()).is_cuda:
return "cuda"
else:
return "cpu"
def infer_model_precision(model):
counter = Counter()
for param in model.parameters():
counter[param.dtype] += 1
if counter[torch.float16] > 0:
return Precision.FP16
else:
return Precision.FP32
def _get_tensor_dtypes(dataloader, precision):
def _get_dtypes(t):
def _get_dtype(v):
dtype = str(v.dtype)
if dtype == "float64":
dtype = "float32"
if precision == Precision.FP16 and dtype == "float32":
dtype = "float16"
return np.dtype(dtype)
return {k: _get_dtype(v) for k, v in t.items()}
batch = next(dataloader)
_, x, y = batch
input_dtypes = _get_dtypes(x)
output_dtypes = _get_dtypes(y)
return input_dtypes, output_dtypes
### TODO assumption: floating point input
### type has same precision as the model
def _get_model_signature(
inputs_names: typing.List[str],
outputs_names: typing.List[str],
precision,
dataloader_fn,
batch_size_dim: typing.Optional[int] = None,
):
dataloader = dataloader_fn()
input_dtypes, output_dtypes = _get_tensor_dtypes(dataloader, precision)
input_shapes, output_shapes = get_shapes_with_dynamic_axes(dataloader, batch_size_dim=batch_size_dim)
inputs = {
name: TensorSpec(name=name, dtype=input_dtypes[name], shape=tuple(input_shapes[name])) for name in inputs_names
}
outputs = {
name: TensorSpec(name=name, dtype=output_dtypes[name], shape=tuple(output_shapes[name]))
for name in outputs_names
}
return ModelSignatureConfig(inputs, outputs)
class PyTorchModelLoader(BaseLoader):
required_fn_name_for_signature_parsing: Optional[str] = GET_MODEL_FN_NAME
def __init__(self, **kwargs):
self._model_args = kwargs
def load(self, model_path: Union[str, Path], **kwargs) -> Model:
if isinstance(model_path, Path):
model_path = model_path.as_posix()
get_model = load_from_file(model_path, "model", GET_MODEL_FN_NAME)
model, io_names_dict = get_model(**self._model_args)
dataloader_fn = kwargs.get("dataloader_fn", None)
output_type = kwargs.get("output_type", None)
precision = infer_model_precision(model)
batch_axis = getattr(model, "bermuda_batch_axis", 0) # by default models supports batching; batch_axis=0
model_signature = _get_model_signature(
inputs_names=io_names_dict["inputs"],
outputs_names=io_names_dict["outputs"],
precision=precision,
dataloader_fn=dataloader_fn,
batch_size_dim=batch_axis,
)
model = Model(handle=model, precision=precision, inputs=model_signature.inputs, outputs=model_signature.outputs)
if output_type == Format.TS_TRACE.value:
return self._trace(model, dataloader_fn)
elif output_type == Format.TS_SCRIPT.value:
return self._script(model)
elif output_type == Format.ONNX.value:
return model
else:
raise ValueError(f"Not supported PyTorch format: {output_type}")
def _trace(self, model: Model, dataloader_fn) -> Model:
device = get_model_device(model.handle)
dummy_input = get_sample_input(dataloader_fn(), device)
traced_model = torch.jit.trace_module(model.handle, {"forward": dummy_input})
return Model(traced_model, precision=model.precision, inputs=model.inputs, outputs=model.outputs)
def _script(self, model: Model) -> Model:
scripted_model = torch.jit.script(model.handle)
return Model(scripted_model, precision=model.precision, inputs=model.inputs, outputs=model.outputs)
class TorchScriptLoader(BaseLoader):
def __init__(self, tensor_names_path: str = None, **kwargs):
self._model_args = kwargs
self._io_spec = None
if tensor_names_path is not None:
with Path(tensor_names_path).open("r") as fh:
tensor_infos = yaml.load(fh, Loader=yaml.SafeLoader)
self._io_spec = ModelSignatureConfig(tensor_infos["inputs"], tensor_infos["outputs"])
def load(self, model_path: Union[str, Path], **_) -> Model:
if not isinstance(model_path, Path):
model_path = Path(model_path)
model = torch.jit.load(model_path.as_posix())
precision = infer_model_precision(model)
io_spec = self._io_spec
if not io_spec:
yaml_path = model_path.parent / f"{model_path.name}.yaml"
if not yaml_path.is_file():
raise ValueError(
f"If `--tensor-names-path is not provided, "
f"TorchScript model loader expects file {yaml_path} with tensor information."
)
with yaml_path.open("r") as fh:
tensor_info = yaml.load(fh, Loader=yaml.SafeLoader)
io_spec = ModelSignatureConfig(tensor_info["inputs"], tensor_info["outputs"])
return Model(handle=model, precision=precision, inputs=io_spec.inputs, outputs=io_spec.outputs)
class PYT2ONNXSaver(BaseSaver):
def __init__(self, onnx_opset: int = None):
self._onnx_opset = onnx_opset
def save(self, model: Model, model_path: Union[str, Path], dataloader_fn) -> Model:
if isinstance(model_path, Path):
model_path = model_path.as_posix()
assert isinstance(model.handle, torch.jit.ScriptModule) or isinstance(
model.handle, torch.nn.Module
), "The model must be of type 'torch.jit.ScriptModule' or 'torch.nn.Module'. Converter aborted."
batch_axis = getattr(model.handle, "bermuda_batch_axis", 0) # by default models supports batching; batch_axis=0
dynamic_axes = get_dynamic_axes(dataloader_fn(), batch_size_dim=batch_axis)
device = get_model_device(model.handle)
dummy_input = get_sample_input(dataloader_fn(), device)
with torch.no_grad():
torch.onnx.export(
model.handle,
dummy_input,
model_path,
do_constant_folding=True,
input_names=list(model.inputs),
output_names=list(model.outputs),
dynamic_axes=dynamic_axes,
opset_version=self._onnx_opset,
enable_onnx_checker=True,
)
class TorchScriptSaver(BaseSaver):
def save(self, model: Model, model_path: Union[str, Path], dataloader_fn) -> None:
if not isinstance(model_path, Path):
model_path = Path(model_path)
if isinstance(model.handle, torch.jit.ScriptModule):
torch.jit.save(model.handle, model_path.as_posix())
else:
raise RuntimeError("The model must be of type 'torch.jit.ScriptModule'. Saving aborted.")
signature_config = ModelSignatureConfig(inputs=model.inputs, outputs=model.outputs)
annotation_path = model_path.parent / f"{model_path.name}.yaml"
with YamlConfigFile(annotation_path) as config_file:
config_file.save_config(signature_config)
class PyTorchRunner(BaseRunner):
def __init__(self):
pass
def init_inference(self, model: Model):
return PyTorchRunnerSession(model=model)
class PyTorchRunnerSession(BaseRunnerSession):
def __init__(self, model: Model):
super().__init__(model)
assert isinstance(model.handle, torch.jit.ScriptModule) or isinstance(
model.handle, torch.nn.Module
), "The model must be of type 'torch.jit.ScriptModule' or 'torch.nn.Module'. Runner aborted."
self._model = model
self._output_names = None
def __enter__(self):
self._output_names = list(self._model.outputs)
return self
def __exit__(self, exc_type, exc_value, traceback):
self._output_names = None
self._model = None
def __call__(self, x: Dict[str, object]):
with torch.no_grad():
feed_list = [torch.from_numpy(v).cuda() for k, v in x.items()]
y_pred = self._model.handle(*feed_list)
if isinstance(y_pred, torch.Tensor):
y_pred = (y_pred,)
y_pred = [t.cpu().numpy() for t in y_pred]
y_pred = dict(zip(self._output_names, y_pred))
return y_pred
loaders.register_extension(Format.PYT.value, PyTorchModelLoader)
loaders.register_extension(Format.TS_TRACE.value, TorchScriptLoader)
loaders.register_extension(Format.TS_SCRIPT.value, TorchScriptLoader)
savers.register_extension(Format.TS_SCRIPT.value, TorchScriptSaver)
savers.register_extension(Format.TS_TRACE.value, TorchScriptSaver)
savers.register_extension(f"{Format.PYT.value}--{Format.ONNX.value}", PYT2ONNXSaver)
runners.register_extension(Format.PYT.value, PyTorchRunner)
runners.register_extension(Format.TS_SCRIPT.value, PyTorchRunner)
runners.register_extension(Format.TS_TRACE.value, PyTorchRunner)
|
PyTorch/SpeechSynthesis/FastPitch/triton/scripts/docker | docker | interactive | #!/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 run -it --rm \
--gpus "device=all" \
--net=host \
--shm-size=1g \
--ulimit memlock=-1 \
--ulimit stack=67108864 \
--privileged \
-e WORKDIR=$(pwd) \
-v $(pwd):$(pwd) \
-v /var/run/docker.sock:/var/run/docker.sock \
fastpitch:latest bash
|
TensorFlow/Detection/SSD/models/research/object_detection/meta_architectures | meta_architectures | faster_rcnn_meta_arch | # 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.
# ==============================================================================
"""Faster R-CNN meta-architecture definition.
General tensorflow implementation of Faster R-CNN detection models.
See Faster R-CNN: Ren, Shaoqing, et al.
"Faster R-CNN: Towards real-time object detection with region proposal
networks." Advances in neural information processing systems. 2015.
We allow for three modes: number_of_stages={1, 2, 3}. In case of 1 stage,
all of the user facing methods (e.g., predict, postprocess, loss) can be used as
if the model consisted only of the RPN, returning class agnostic proposals
(these can be thought of as approximate detections with no associated class
information). In case of 2 stages, proposals are computed, then passed
through a second stage "box classifier" to yield (multi-class) detections.
Finally, in case of 3 stages which is only used during eval, proposals are
computed, then passed through a second stage "box classifier" that will compute
refined boxes and classes, and then features are pooled from the refined and
non-maximum suppressed boxes and are passed through the box classifier again. If
number of stages is 3 during training it will be reduced to two automatically.
Implementations of Faster R-CNN models must define a new
FasterRCNNFeatureExtractor and override three methods: `preprocess`,
`_extract_proposal_features` (the first stage of the model), and
`_extract_box_classifier_features` (the second stage of the model). Optionally,
the `restore_fn` method can be overridden. See tests for an example.
A few important notes:
+ Batching conventions: We support batched inference and training where
all images within a batch have the same resolution. Batch sizes are determined
dynamically via the shape of the input tensors (rather than being specified
directly as, e.g., a model constructor).
A complication is that due to non-max suppression, we are not guaranteed to get
the same number of proposals from the first stage RPN (region proposal network)
for each image (though in practice, we should often get the same number of
proposals). For this reason we pad to a max number of proposals per image
within a batch. This `self.max_num_proposals` property is set to the
`first_stage_max_proposals` parameter at inference time and the
`second_stage_batch_size` at training time since we subsample the batch to
be sent through the box classifier during training.
For the second stage of the pipeline, we arrange the proposals for all images
within the batch along a single batch dimension. For example, the input to
_extract_box_classifier_features is a tensor of shape
`[total_num_proposals, crop_height, crop_width, depth]` where
total_num_proposals is batch_size * self.max_num_proposals. (And note that per
the above comment, a subset of these entries correspond to zero paddings.)
+ Coordinate representations:
Following the API (see model.DetectionModel definition), our outputs after
postprocessing operations are always normalized boxes however, internally, we
sometimes convert to absolute --- e.g. for loss computation. In particular,
anchors and proposal_boxes are both represented as absolute coordinates.
Images are resized in the `preprocess` method.
The Faster R-CNN meta architecture has two post-processing methods
`_postprocess_rpn` which is applied after first stage and
`_postprocess_box_classifier` which is applied after second stage. There are
three different ways post-processing can happen depending on number_of_stages
configured in the meta architecture:
1. When number_of_stages is 1:
`_postprocess_rpn` is run as part of the `postprocess` method where
true_image_shapes is used to clip proposals, perform non-max suppression and
normalize them.
2. When number of stages is 2:
`_postprocess_rpn` is run as part of the `_predict_second_stage` method where
`resized_image_shapes` is used to clip proposals, perform non-max suppression
and normalize them. In this case `postprocess` method skips `_postprocess_rpn`
and only runs `_postprocess_box_classifier` using `true_image_shapes` to clip
detections, perform non-max suppression and normalize them.
3. When number of stages is 3:
`_postprocess_rpn` is run as part of the `_predict_second_stage` using
`resized_image_shapes` to clip proposals, perform non-max suppression and
normalize them. Subsequently, `_postprocess_box_classifier` is run as part of
`_predict_third_stage` using `true_image_shapes` to clip detections, peform
non-max suppression and normalize them. In this case, the `postprocess` method
skips both `_postprocess_rpn` and `_postprocess_box_classifier`.
"""
from abc import abstractmethod
from functools import partial
import tensorflow as tf
from object_detection.anchor_generators import grid_anchor_generator
from object_detection.builders import box_predictor_builder
from object_detection.core import box_list
from object_detection.core import box_list_ops
from object_detection.core import box_predictor
from object_detection.core import losses
from object_detection.core import model
from object_detection.core import standard_fields as fields
from object_detection.core import target_assigner
from object_detection.utils import ops
from object_detection.utils import shape_utils
slim = tf.contrib.slim
class FasterRCNNFeatureExtractor(object):
"""Faster R-CNN Feature Extractor definition."""
def __init__(self,
is_training,
first_stage_features_stride,
batch_norm_trainable=False,
reuse_weights=None,
weight_decay=0.0):
"""Constructor.
Args:
is_training: A boolean indicating whether the training version of the
computation graph should be constructed.
first_stage_features_stride: Output stride of extracted RPN feature map.
batch_norm_trainable: Whether to update batch norm parameters during
training or not. When training with a relative large batch size
(e.g. 8), it could be desirable to enable batch norm update.
reuse_weights: Whether to reuse variables. Default is None.
weight_decay: float weight decay for feature extractor (default: 0.0).
"""
self._is_training = is_training
self._first_stage_features_stride = first_stage_features_stride
self._train_batch_norm = (batch_norm_trainable and is_training)
self._reuse_weights = reuse_weights
self._weight_decay = weight_decay
@abstractmethod
def preprocess(self, resized_inputs):
"""Feature-extractor specific preprocessing (minus image resizing)."""
pass
def extract_proposal_features(self, preprocessed_inputs, scope):
"""Extracts first stage RPN features.
This function is responsible for extracting feature maps from preprocessed
images. These features are used by the region proposal network (RPN) to
predict proposals.
Args:
preprocessed_inputs: A [batch, height, width, channels] float tensor
representing a batch of images.
scope: A scope name.
Returns:
rpn_feature_map: A tensor with shape [batch, height, width, depth]
activations: A dictionary mapping activation tensor names to tensors.
"""
with tf.variable_scope(scope, values=[preprocessed_inputs]):
return self._extract_proposal_features(preprocessed_inputs, scope)
@abstractmethod
def _extract_proposal_features(self, preprocessed_inputs, scope):
"""Extracts first stage RPN features, to be overridden."""
pass
def extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name.
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(
scope, values=[proposal_feature_maps], reuse=tf.AUTO_REUSE):
return self._extract_box_classifier_features(proposal_feature_maps, scope)
@abstractmethod
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features, to be overridden."""
pass
def restore_from_classification_checkpoint_fn(
self,
first_stage_feature_extractor_scope,
second_stage_feature_extractor_scope):
"""Returns a map of variables to load from a foreign checkpoint.
Args:
first_stage_feature_extractor_scope: A scope name for the first stage
feature extractor.
second_stage_feature_extractor_scope: A scope name for the second stage
feature extractor.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
"""
variables_to_restore = {}
for variable in tf.global_variables():
for scope_name in [first_stage_feature_extractor_scope,
second_stage_feature_extractor_scope]:
if variable.op.name.startswith(scope_name):
var_name = variable.op.name.replace(scope_name + '/', '')
variables_to_restore[var_name] = variable
return variables_to_restore
class FasterRCNNMetaArch(model.DetectionModel):
"""Faster R-CNN Meta-architecture definition."""
def __init__(self,
is_training,
num_classes,
image_resizer_fn,
feature_extractor,
number_of_stages,
first_stage_anchor_generator,
first_stage_target_assigner,
first_stage_atrous_rate,
first_stage_box_predictor_arg_scope_fn,
first_stage_box_predictor_kernel_size,
first_stage_box_predictor_depth,
first_stage_minibatch_size,
first_stage_sampler,
first_stage_non_max_suppression_fn,
first_stage_max_proposals,
first_stage_localization_loss_weight,
first_stage_objectness_loss_weight,
crop_and_resize_fn,
initial_crop_size,
maxpool_kernel_size,
maxpool_stride,
second_stage_target_assigner,
second_stage_mask_rcnn_box_predictor,
second_stage_batch_size,
second_stage_sampler,
second_stage_non_max_suppression_fn,
second_stage_score_conversion_fn,
second_stage_localization_loss_weight,
second_stage_classification_loss_weight,
second_stage_classification_loss,
second_stage_mask_prediction_loss_weight=1.0,
hard_example_miner=None,
parallel_iterations=16,
add_summaries=True,
clip_anchors_to_image=False,
use_static_shapes=False,
resize_masks=True):
"""FasterRCNNMetaArch Constructor.
Args:
is_training: A boolean indicating whether the training version of the
computation graph should be constructed.
num_classes: Number of classes. Note that num_classes *does not*
include the background category, so if groundtruth labels take values
in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
assigned classification targets can range from {0,... K}).
image_resizer_fn: A callable for image resizing. This callable
takes a rank-3 image tensor of shape [height, width, channels]
(corresponding to a single image), an optional rank-3 instance mask
tensor of shape [num_masks, height, width] and returns a resized rank-3
image tensor, a resized mask tensor if one was provided in the input. In
addition this callable must also return a 1-D tensor of the form
[height, width, channels] containing the size of the true image, as the
image resizer can perform zero padding. See protos/image_resizer.proto.
feature_extractor: A FasterRCNNFeatureExtractor object.
number_of_stages: An integer values taking values in {1, 2, 3}. If
1, the function will construct only the Region Proposal Network (RPN)
part of the model. If 2, the function will perform box refinement and
other auxiliary predictions all in the second stage. If 3, it will
extract features from refined boxes and perform the auxiliary
predictions on the non-maximum suppressed refined boxes.
If is_training is true and the value of number_of_stages is 3, it is
reduced to 2 since all the model heads are trained in parallel in second
stage during training.
first_stage_anchor_generator: An anchor_generator.AnchorGenerator object
(note that currently we only support
grid_anchor_generator.GridAnchorGenerator objects)
first_stage_target_assigner: Target assigner to use for first stage of
Faster R-CNN (RPN).
first_stage_atrous_rate: A single integer indicating the atrous rate for
the single convolution op which is applied to the `rpn_features_to_crop`
tensor to obtain a tensor to be used for box prediction. Some feature
extractors optionally allow for producing feature maps computed at
denser resolutions. The atrous rate is used to compensate for the
denser feature maps by using an effectively larger receptive field.
(This should typically be set to 1).
first_stage_box_predictor_arg_scope_fn: A function to construct tf-slim
arg_scope for conv2d, separable_conv2d and fully_connected ops for the
RPN box predictor.
first_stage_box_predictor_kernel_size: Kernel size to use for the
convolution op just prior to RPN box predictions.
first_stage_box_predictor_depth: Output depth for the convolution op
just prior to RPN box predictions.
first_stage_minibatch_size: The "batch size" to use for computing the
objectness and location loss of the region proposal network. This
"batch size" refers to the number of anchors selected as contributing
to the loss function for any given image within the image batch and is
only called "batch_size" due to terminology from the Faster R-CNN paper.
first_stage_sampler: Sampler to use for first stage loss (RPN loss).
first_stage_non_max_suppression_fn: batch_multiclass_non_max_suppression
callable that takes `boxes`, `scores` and optional `clip_window`(with
all other inputs already set) and returns a dictionary containing
tensors with keys: `detection_boxes`, `detection_scores`,
`detection_classes`, `num_detections`. This is used to perform non max
suppression on the boxes predicted by the Region Proposal Network
(RPN).
See `post_processing.batch_multiclass_non_max_suppression` for the type
and shape of these tensors.
first_stage_max_proposals: Maximum number of boxes to retain after
performing Non-Max Suppression (NMS) on the boxes predicted by the
Region Proposal Network (RPN).
first_stage_localization_loss_weight: A float
first_stage_objectness_loss_weight: A float
crop_and_resize_fn: A differentiable resampler to use for cropping RPN
proposal features.
initial_crop_size: A single integer indicating the output size
(width and height are set to be the same) of the initial bilinear
interpolation based cropping during ROI pooling.
maxpool_kernel_size: A single integer indicating the kernel size of the
max pool op on the cropped feature map during ROI pooling.
maxpool_stride: A single integer indicating the stride of the max pool
op on the cropped feature map during ROI pooling.
second_stage_target_assigner: Target assigner to use for second stage of
Faster R-CNN. If the model is configured with multiple prediction heads,
this target assigner is used to generate targets for all heads (with the
correct `unmatched_class_label`).
second_stage_mask_rcnn_box_predictor: Mask R-CNN box predictor to use for
the second stage.
second_stage_batch_size: The batch size used for computing the
classification and refined location loss of the box classifier. This
"batch size" refers to the number of proposals selected as contributing
to the loss function for any given image within the image batch and is
only called "batch_size" due to terminology from the Faster R-CNN paper.
second_stage_sampler: Sampler to use for second stage loss (box
classifier loss).
second_stage_non_max_suppression_fn: batch_multiclass_non_max_suppression
callable that takes `boxes`, `scores`, optional `clip_window` and
optional (kwarg) `mask` inputs (with all other inputs already set)
and returns a dictionary containing tensors with keys:
`detection_boxes`, `detection_scores`, `detection_classes`,
`num_detections`, and (optionally) `detection_masks`. See
`post_processing.batch_multiclass_non_max_suppression` for the type and
shape of these tensors.
second_stage_score_conversion_fn: Callable elementwise nonlinearity
(that takes tensors as inputs and returns tensors). This is usually
used to convert logits to probabilities.
second_stage_localization_loss_weight: A float indicating the scale factor
for second stage localization loss.
second_stage_classification_loss_weight: A float indicating the scale
factor for second stage classification loss.
second_stage_classification_loss: Classification loss used by the second
stage classifier. Either losses.WeightedSigmoidClassificationLoss or
losses.WeightedSoftmaxClassificationLoss.
second_stage_mask_prediction_loss_weight: A float indicating the scale
factor for second stage mask prediction loss. This is applicable only if
second stage box predictor is configured to predict masks.
hard_example_miner: A losses.HardExampleMiner object (can be None).
parallel_iterations: (Optional) The number of iterations allowed to run
in parallel for calls to tf.map_fn.
add_summaries: boolean (default: True) controlling whether summary ops
should be added to tensorflow graph.
clip_anchors_to_image: Normally, anchors generated for a given image size
are pruned during training if they lie outside the image window. This
option clips the anchors to be within the image instead of pruning.
use_static_shapes: If True, uses implementation of ops with static shape
guarantees.
resize_masks: Indicates whether the masks presend in the groundtruth
should be resized in the model with `image_resizer_fn`
Raises:
ValueError: If `second_stage_batch_size` > `first_stage_max_proposals` at
training time.
ValueError: If first_stage_anchor_generator is not of type
grid_anchor_generator.GridAnchorGenerator.
"""
# TODO(rathodv): add_summaries is currently unused. Respect that directive
# in the future.
super(FasterRCNNMetaArch, self).__init__(num_classes=num_classes)
if not isinstance(first_stage_anchor_generator,
grid_anchor_generator.GridAnchorGenerator):
raise ValueError('first_stage_anchor_generator must be of type '
'grid_anchor_generator.GridAnchorGenerator.')
self._is_training = is_training
self._image_resizer_fn = image_resizer_fn
self._resize_masks = resize_masks
self._feature_extractor = feature_extractor
self._number_of_stages = number_of_stages
self._proposal_target_assigner = first_stage_target_assigner
self._detector_target_assigner = second_stage_target_assigner
# Both proposal and detector target assigners use the same box coder
self._box_coder = self._proposal_target_assigner.box_coder
# (First stage) Region proposal network parameters
self._first_stage_anchor_generator = first_stage_anchor_generator
self._first_stage_atrous_rate = first_stage_atrous_rate
self._first_stage_box_predictor_arg_scope_fn = (
first_stage_box_predictor_arg_scope_fn)
self._first_stage_box_predictor_kernel_size = (
first_stage_box_predictor_kernel_size)
self._first_stage_box_predictor_depth = first_stage_box_predictor_depth
self._first_stage_minibatch_size = first_stage_minibatch_size
self._first_stage_sampler = first_stage_sampler
self._first_stage_box_predictor = (
box_predictor_builder.build_convolutional_box_predictor(
is_training=self._is_training,
num_classes=1,
conv_hyperparams_fn=self._first_stage_box_predictor_arg_scope_fn,
use_dropout=False,
dropout_keep_prob=1.0,
box_code_size=self._box_coder.code_size,
kernel_size=1,
num_layers_before_predictor=0,
min_depth=0,
max_depth=0))
self._first_stage_nms_fn = first_stage_non_max_suppression_fn
self._first_stage_max_proposals = first_stage_max_proposals
self._use_static_shapes = use_static_shapes
self._first_stage_localization_loss = (
losses.WeightedSmoothL1LocalizationLoss())
self._first_stage_objectness_loss = (
losses.WeightedSoftmaxClassificationLoss())
self._first_stage_loc_loss_weight = first_stage_localization_loss_weight
self._first_stage_obj_loss_weight = first_stage_objectness_loss_weight
# Per-region cropping parameters
self._crop_and_resize_fn = crop_and_resize_fn
self._initial_crop_size = initial_crop_size
self._maxpool_kernel_size = maxpool_kernel_size
self._maxpool_stride = maxpool_stride
self._mask_rcnn_box_predictor = second_stage_mask_rcnn_box_predictor
self._second_stage_batch_size = second_stage_batch_size
self._second_stage_sampler = second_stage_sampler
self._second_stage_nms_fn = second_stage_non_max_suppression_fn
self._second_stage_score_conversion_fn = second_stage_score_conversion_fn
self._second_stage_localization_loss = (
losses.WeightedSmoothL1LocalizationLoss())
self._second_stage_classification_loss = second_stage_classification_loss
self._second_stage_mask_loss = (
losses.WeightedSigmoidClassificationLoss())
self._second_stage_loc_loss_weight = second_stage_localization_loss_weight
self._second_stage_cls_loss_weight = second_stage_classification_loss_weight
self._second_stage_mask_loss_weight = (
second_stage_mask_prediction_loss_weight)
self._hard_example_miner = hard_example_miner
self._parallel_iterations = parallel_iterations
self.clip_anchors_to_image = clip_anchors_to_image
if self._number_of_stages <= 0 or self._number_of_stages > 3:
raise ValueError('Number of stages should be a value in {1, 2, 3}.')
@property
def first_stage_feature_extractor_scope(self):
return 'FirstStageFeatureExtractor'
@property
def second_stage_feature_extractor_scope(self):
return 'SecondStageFeatureExtractor'
@property
def first_stage_box_predictor_scope(self):
return 'FirstStageBoxPredictor'
@property
def second_stage_box_predictor_scope(self):
return 'SecondStageBoxPredictor'
@property
def max_num_proposals(self):
"""Max number of proposals (to pad to) for each image in the input batch.
At training time, this is set to be the `second_stage_batch_size` if hard
example miner is not configured, else it is set to
`first_stage_max_proposals`. At inference time, this is always set to
`first_stage_max_proposals`.
Returns:
A positive integer.
"""
if self._is_training and not self._hard_example_miner:
return self._second_stage_batch_size
return self._first_stage_max_proposals
@property
def anchors(self):
if not self._anchors:
raise RuntimeError('anchors have not been constructed yet!')
if not isinstance(self._anchors, box_list.BoxList):
raise RuntimeError('anchors should be a BoxList object, but is not.')
return self._anchors
def preprocess(self, inputs):
"""Feature-extractor specific preprocessing.
See base class.
For Faster R-CNN, we perform image resizing in the base class --- each
class subclassing FasterRCNNMetaArch is responsible for any additional
preprocessing (e.g., scaling pixel values to be in [-1, 1]).
Args:
inputs: a [batch, height_in, width_in, channels] float tensor representing
a batch of images with values between 0 and 255.0.
Returns:
preprocessed_inputs: a [batch, height_out, width_out, channels] float
tensor representing a batch of images.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
Raises:
ValueError: if inputs tensor does not have type tf.float32
"""
if inputs.dtype is not tf.float32:
raise ValueError('`preprocess` expects a tf.float32 tensor')
with tf.name_scope('Preprocessor'):
outputs = shape_utils.static_or_dynamic_map_fn(
self._image_resizer_fn,
elems=inputs,
dtype=[tf.float32, tf.int32],
parallel_iterations=self._parallel_iterations)
resized_inputs = outputs[0]
true_image_shapes = outputs[1]
return (self._feature_extractor.preprocess(resized_inputs),
true_image_shapes)
def _compute_clip_window(self, image_shapes):
"""Computes clip window for non max suppression based on image shapes.
This function assumes that the clip window's left top corner is at (0, 0).
Args:
image_shapes: A 2-D int32 tensor of shape [batch_size, 3] containing
shapes of images in the batch. Each row represents [height, width,
channels] of an image.
Returns:
A 2-D float32 tensor of shape [batch_size, 4] containing the clip window
for each image in the form [ymin, xmin, ymax, xmax].
"""
clip_heights = image_shapes[:, 0]
clip_widths = image_shapes[:, 1]
clip_window = tf.to_float(tf.stack([tf.zeros_like(clip_heights),
tf.zeros_like(clip_heights),
clip_heights, clip_widths], axis=1))
return clip_window
def predict(self, preprocessed_inputs, true_image_shapes):
"""Predicts unpostprocessed tensors from input tensor.
This function takes an input batch of images and runs it through the
forward pass of the network to yield "raw" un-postprocessed predictions.
If `number_of_stages` is 1, this function only returns first stage
RPN predictions (un-postprocessed). Otherwise it returns both
first stage RPN predictions as well as second stage box classifier
predictions.
Other remarks:
+ Anchor pruning vs. clipping: following the recommendation of the Faster
R-CNN paper, we prune anchors that venture outside the image window at
training time and clip anchors to the image window at inference time.
+ Proposal padding: as described at the top of the file, proposals are
padded to self._max_num_proposals and flattened so that proposals from all
images within the input batch are arranged along the same batch dimension.
Args:
preprocessed_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
Returns:
prediction_dict: a dictionary holding "raw" prediction tensors:
1) rpn_box_predictor_features: A 4-D float32 tensor with shape
[batch_size, height, width, depth] to be used for predicting proposal
boxes and corresponding objectness scores.
2) rpn_features_to_crop: A 4-D float32 tensor with shape
[batch_size, height, width, depth] representing image features to crop
using the proposal boxes predicted by the RPN.
3) image_shape: a 1-D tensor of shape [4] representing the input
image shape.
4) rpn_box_encodings: 3-D float tensor of shape
[batch_size, num_anchors, self._box_coder.code_size] containing
predicted boxes.
5) rpn_objectness_predictions_with_background: 3-D float tensor of shape
[batch_size, num_anchors, 2] containing class
predictions (logits) for each of the anchors. Note that this
tensor *includes* background class predictions (at class index 0).
6) anchors: A 2-D tensor of shape [num_anchors, 4] representing anchors
for the first stage RPN (in absolute coordinates). Note that
`num_anchors` can differ depending on whether the model is created in
training or inference mode.
(and if number_of_stages > 1):
7) refined_box_encodings: a 3-D tensor with shape
[total_num_proposals, num_classes, self._box_coder.code_size]
representing predicted (final) refined box encodings, where
total_num_proposals=batch_size*self._max_num_proposals. If using
a shared box across classes the shape will instead be
[total_num_proposals, 1, self._box_coder.code_size].
8) class_predictions_with_background: a 3-D tensor with shape
[total_num_proposals, num_classes + 1] containing class
predictions (logits) for each of the anchors, where
total_num_proposals=batch_size*self._max_num_proposals.
Note that this tensor *includes* background class predictions
(at class index 0).
9) num_proposals: An int32 tensor of shape [batch_size] representing the
number of proposals generated by the RPN. `num_proposals` allows us
to keep track of which entries are to be treated as zero paddings and
which are not since we always pad the number of proposals to be
`self.max_num_proposals` for each image.
10) proposal_boxes: A float32 tensor of shape
[batch_size, self.max_num_proposals, 4] representing
decoded proposal bounding boxes in absolute coordinates.
11) mask_predictions: (optional) a 4-D tensor with shape
[total_num_padded_proposals, num_classes, mask_height, mask_width]
containing instance mask predictions.
Raises:
ValueError: If `predict` is called before `preprocess`.
"""
(rpn_box_predictor_features, rpn_features_to_crop, anchors_boxlist,
image_shape) = self._extract_rpn_feature_maps(preprocessed_inputs)
(rpn_box_encodings, rpn_objectness_predictions_with_background
) = self._predict_rpn_proposals(rpn_box_predictor_features)
# The Faster R-CNN paper recommends pruning anchors that venture outside
# the image window at training time and clipping at inference time.
clip_window = tf.to_float(tf.stack([0, 0, image_shape[1], image_shape[2]]))
if self._is_training:
if self.clip_anchors_to_image:
anchors_boxlist = box_list_ops.clip_to_window(
anchors_boxlist, clip_window, filter_nonoverlapping=False)
else:
(rpn_box_encodings, rpn_objectness_predictions_with_background,
anchors_boxlist) = self._remove_invalid_anchors_and_predictions(
rpn_box_encodings, rpn_objectness_predictions_with_background,
anchors_boxlist, clip_window)
else:
anchors_boxlist = box_list_ops.clip_to_window(
anchors_boxlist, clip_window,
filter_nonoverlapping=not self._use_static_shapes)
self._anchors = anchors_boxlist
prediction_dict = {
'rpn_box_predictor_features': rpn_box_predictor_features,
'rpn_features_to_crop': rpn_features_to_crop,
'image_shape': image_shape,
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'anchors': self._anchors.get()
}
if self._number_of_stages >= 2:
# If mixed-precision training on TPU is enabled, rpn_box_encodings and
# rpn_objectness_predictions_with_background are bfloat16 tensors.
# Considered prediction results, they need to be casted to float32
# tensors for correct postprocess_rpn computation in predict_second_stage.
prediction_dict.update(self._predict_second_stage(
tf.to_float(rpn_box_encodings),
tf.to_float(rpn_objectness_predictions_with_background),
rpn_features_to_crop,
self._anchors.get(), image_shape, true_image_shapes))
if self._number_of_stages == 3:
prediction_dict = self._predict_third_stage(
prediction_dict, true_image_shapes)
return prediction_dict
def _image_batch_shape_2d(self, image_batch_shape_1d):
"""Takes a 1-D image batch shape tensor and converts it to a 2-D tensor.
Example:
If 1-D image batch shape tensor is [2, 300, 300, 3]. The corresponding 2-D
image batch tensor would be [[300, 300, 3], [300, 300, 3]]
Args:
image_batch_shape_1d: 1-D tensor of the form [batch_size, height,
width, channels].
Returns:
image_batch_shape_2d: 2-D tensor of shape [batch_size, 3] were each row is
of the form [height, width, channels].
"""
return tf.tile(tf.expand_dims(image_batch_shape_1d[1:], 0),
[image_batch_shape_1d[0], 1])
def _predict_second_stage(self, rpn_box_encodings,
rpn_objectness_predictions_with_background,
rpn_features_to_crop,
anchors,
image_shape,
true_image_shapes):
"""Predicts the output tensors from second stage of Faster R-CNN.
Args:
rpn_box_encodings: 4-D float tensor of shape
[batch_size, num_valid_anchors, self._box_coder.code_size] containing
predicted boxes.
rpn_objectness_predictions_with_background: 2-D float tensor of shape
[batch_size, num_valid_anchors, 2] containing class
predictions (logits) for each of the anchors. Note that this
tensor *includes* background class predictions (at class index 0).
rpn_features_to_crop: A 4-D float32 or bfloat16 tensor with shape
[batch_size, height, width, depth] representing image features to crop
using the proposal boxes predicted by the RPN.
anchors: 2-D float tensor of shape
[num_anchors, self._box_coder.code_size].
image_shape: A 1D int32 tensors of size [4] containing the image shape.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
Returns:
prediction_dict: a dictionary holding "raw" prediction tensors:
1) refined_box_encodings: a 3-D tensor with shape
[total_num_proposals, num_classes, self._box_coder.code_size]
representing predicted (final) refined box encodings, where
total_num_proposals=batch_size*self._max_num_proposals. If using a
shared box across classes the shape will instead be
[total_num_proposals, 1, self._box_coder.code_size].
2) class_predictions_with_background: a 3-D tensor with shape
[total_num_proposals, num_classes + 1] containing class
predictions (logits) for each of the anchors, where
total_num_proposals=batch_size*self._max_num_proposals.
Note that this tensor *includes* background class predictions
(at class index 0).
3) num_proposals: An int32 tensor of shape [batch_size] representing the
number of proposals generated by the RPN. `num_proposals` allows us
to keep track of which entries are to be treated as zero paddings and
which are not since we always pad the number of proposals to be
`self.max_num_proposals` for each image.
4) proposal_boxes: A float32 tensor of shape
[batch_size, self.max_num_proposals, 4] representing
decoded proposal bounding boxes in absolute coordinates.
5) proposal_boxes_normalized: A float32 tensor of shape
[batch_size, self.max_num_proposals, 4] representing decoded proposal
bounding boxes in normalized coordinates. Can be used to override the
boxes proposed by the RPN, thus enabling one to extract features and
get box classification and prediction for externally selected areas
of the image.
6) box_classifier_features: a 4-D float32 or bfloat16 tensor
representing the features for each proposal.
"""
image_shape_2d = self._image_batch_shape_2d(image_shape)
proposal_boxes_normalized, _, num_proposals = self._postprocess_rpn(
rpn_box_encodings, rpn_objectness_predictions_with_background,
anchors, image_shape_2d, true_image_shapes)
# If mixed-precision training on TPU is enabled, the dtype of
# rpn_features_to_crop is bfloat16, otherwise it is float32. tf.cast is
# used to match the dtype of proposal_boxes_normalized to that of
# rpn_features_to_crop for further computation.
flattened_proposal_feature_maps = (
self._compute_second_stage_input_feature_maps(
rpn_features_to_crop,
tf.cast(proposal_boxes_normalized, rpn_features_to_crop.dtype)))
box_classifier_features = (
self._feature_extractor.extract_box_classifier_features(
flattened_proposal_feature_maps,
scope=self.second_stage_feature_extractor_scope))
if self._mask_rcnn_box_predictor.is_keras_model:
box_predictions = self._mask_rcnn_box_predictor(
[box_classifier_features],
prediction_stage=2)
else:
box_predictions = self._mask_rcnn_box_predictor.predict(
[box_classifier_features],
num_predictions_per_location=[1],
scope=self.second_stage_box_predictor_scope,
prediction_stage=2)
refined_box_encodings = tf.squeeze(
box_predictions[box_predictor.BOX_ENCODINGS],
axis=1, name='all_refined_box_encodings')
class_predictions_with_background = tf.squeeze(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1, name='all_class_predictions_with_background')
absolute_proposal_boxes = ops.normalized_to_image_coordinates(
proposal_boxes_normalized, image_shape, self._parallel_iterations)
prediction_dict = {
'refined_box_encodings': refined_box_encodings,
'class_predictions_with_background':
class_predictions_with_background,
'num_proposals': num_proposals,
'proposal_boxes': absolute_proposal_boxes,
'box_classifier_features': box_classifier_features,
'proposal_boxes_normalized': proposal_boxes_normalized,
}
return prediction_dict
def _predict_third_stage(self, prediction_dict, image_shapes):
"""Predicts non-box, non-class outputs using refined detections.
For training, masks as predicted directly on the box_classifier_features,
which are region-features from the initial anchor boxes.
For inference, this happens after calling the post-processing stage, such
that masks are only calculated for the top scored boxes.
Args:
prediction_dict: a dictionary holding "raw" prediction tensors:
1) refined_box_encodings: a 3-D tensor with shape
[total_num_proposals, num_classes, self._box_coder.code_size]
representing predicted (final) refined box encodings, where
total_num_proposals=batch_size*self._max_num_proposals. If using a
shared box across classes the shape will instead be
[total_num_proposals, 1, self._box_coder.code_size].
2) class_predictions_with_background: a 3-D tensor with shape
[total_num_proposals, num_classes + 1] containing class
predictions (logits) for each of the anchors, where
total_num_proposals=batch_size*self._max_num_proposals.
Note that this tensor *includes* background class predictions
(at class index 0).
3) num_proposals: An int32 tensor of shape [batch_size] representing the
number of proposals generated by the RPN. `num_proposals` allows us
to keep track of which entries are to be treated as zero paddings and
which are not since we always pad the number of proposals to be
`self.max_num_proposals` for each image.
4) proposal_boxes: A float32 tensor of shape
[batch_size, self.max_num_proposals, 4] representing
decoded proposal bounding boxes in absolute coordinates.
5) box_classifier_features: a 4-D float32 tensor representing the
features for each proposal.
image_shapes: A 2-D int32 tensors of shape [batch_size, 3] containing
shapes of images in the batch.
Returns:
prediction_dict: a dictionary that in addition to the input predictions
does hold the following predictions as well:
1) mask_predictions: a 4-D tensor with shape
[batch_size, max_detection, mask_height, mask_width] containing
instance mask predictions.
"""
if self._is_training:
curr_box_classifier_features = prediction_dict['box_classifier_features']
detection_classes = prediction_dict['class_predictions_with_background']
if self._mask_rcnn_box_predictor.is_keras_model:
mask_predictions = self._mask_rcnn_box_predictor(
[curr_box_classifier_features],
prediction_stage=3)
else:
mask_predictions = self._mask_rcnn_box_predictor.predict(
[curr_box_classifier_features],
num_predictions_per_location=[1],
scope=self.second_stage_box_predictor_scope,
prediction_stage=3)
prediction_dict['mask_predictions'] = tf.squeeze(mask_predictions[
box_predictor.MASK_PREDICTIONS], axis=1)
else:
detections_dict = self._postprocess_box_classifier(
prediction_dict['refined_box_encodings'],
prediction_dict['class_predictions_with_background'],
prediction_dict['proposal_boxes'],
prediction_dict['num_proposals'],
image_shapes)
prediction_dict.update(detections_dict)
detection_boxes = detections_dict[
fields.DetectionResultFields.detection_boxes]
detection_classes = detections_dict[
fields.DetectionResultFields.detection_classes]
rpn_features_to_crop = prediction_dict['rpn_features_to_crop']
batch_size = tf.shape(detection_boxes)[0]
max_detection = tf.shape(detection_boxes)[1]
flattened_detected_feature_maps = (
self._compute_second_stage_input_feature_maps(
rpn_features_to_crop, detection_boxes))
curr_box_classifier_features = (
self._feature_extractor.extract_box_classifier_features(
flattened_detected_feature_maps,
scope=self.second_stage_feature_extractor_scope))
if self._mask_rcnn_box_predictor.is_keras_model:
mask_predictions = self._mask_rcnn_box_predictor(
[curr_box_classifier_features],
prediction_stage=3)
else:
mask_predictions = self._mask_rcnn_box_predictor.predict(
[curr_box_classifier_features],
num_predictions_per_location=[1],
scope=self.second_stage_box_predictor_scope,
prediction_stage=3)
detection_masks = tf.squeeze(mask_predictions[
box_predictor.MASK_PREDICTIONS], axis=1)
_, num_classes, mask_height, mask_width = (
detection_masks.get_shape().as_list())
_, max_detection = detection_classes.get_shape().as_list()
prediction_dict['mask_predictions'] = tf.reshape(
detection_masks, [-1, num_classes, mask_height, mask_width])
if num_classes > 1:
detection_masks = self._gather_instance_masks(
detection_masks, detection_classes)
prediction_dict[fields.DetectionResultFields.detection_masks] = (
tf.reshape(tf.sigmoid(detection_masks),
[batch_size, max_detection, mask_height, mask_width]))
return prediction_dict
def _gather_instance_masks(self, instance_masks, classes):
"""Gathers the masks that correspond to classes.
Args:
instance_masks: A 4-D float32 tensor with shape
[K, num_classes, mask_height, mask_width].
classes: A 2-D int32 tensor with shape [batch_size, max_detection].
Returns:
masks: a 3-D float32 tensor with shape [K, mask_height, mask_width].
"""
_, num_classes, height, width = instance_masks.get_shape().as_list()
k = tf.shape(instance_masks)[0]
instance_masks = tf.reshape(instance_masks, [-1, height, width])
classes = tf.to_int32(tf.reshape(classes, [-1]))
gather_idx = tf.range(k) * num_classes + classes
return tf.gather(instance_masks, gather_idx)
def _extract_rpn_feature_maps(self, preprocessed_inputs):
"""Extracts RPN features.
This function extracts two feature maps: a feature map to be directly
fed to a box predictor (to predict location and objectness scores for
proposals) and a feature map from which to crop regions which will then
be sent to the second stage box classifier.
Args:
preprocessed_inputs: a [batch, height, width, channels] image tensor.
Returns:
rpn_box_predictor_features: A 4-D float32 tensor with shape
[batch, height, width, depth] to be used for predicting proposal boxes
and corresponding objectness scores.
rpn_features_to_crop: A 4-D float32 tensor with shape
[batch, height, width, depth] representing image features to crop using
the proposals boxes.
anchors: A BoxList representing anchors (for the RPN) in
absolute coordinates.
image_shape: A 1-D tensor representing the input image shape.
"""
image_shape = tf.shape(preprocessed_inputs)
rpn_features_to_crop, self.endpoints = (
self._feature_extractor.extract_proposal_features(
preprocessed_inputs,
scope=self.first_stage_feature_extractor_scope))
feature_map_shape = tf.shape(rpn_features_to_crop)
anchors = box_list_ops.concatenate(
self._first_stage_anchor_generator.generate([(feature_map_shape[1],
feature_map_shape[2])]))
with slim.arg_scope(self._first_stage_box_predictor_arg_scope_fn()):
kernel_size = self._first_stage_box_predictor_kernel_size
reuse = tf.get_variable_scope().reuse
rpn_box_predictor_features = slim.conv2d(
rpn_features_to_crop,
self._first_stage_box_predictor_depth,
kernel_size=[kernel_size, kernel_size],
rate=self._first_stage_atrous_rate,
activation_fn=tf.nn.relu6,
scope='Conv',
reuse=reuse)
return (rpn_box_predictor_features, rpn_features_to_crop,
anchors, image_shape)
def _predict_rpn_proposals(self, rpn_box_predictor_features):
"""Adds box predictors to RPN feature map to predict proposals.
Note resulting tensors will not have been postprocessed.
Args:
rpn_box_predictor_features: A 4-D float32 tensor with shape
[batch, height, width, depth] to be used for predicting proposal boxes
and corresponding objectness scores.
Returns:
box_encodings: 3-D float tensor of shape
[batch_size, num_anchors, self._box_coder.code_size] containing
predicted boxes.
objectness_predictions_with_background: 3-D float tensor of shape
[batch_size, num_anchors, 2] containing class
predictions (logits) for each of the anchors. Note that this
tensor *includes* background class predictions (at class index 0).
Raises:
RuntimeError: if the anchor generator generates anchors corresponding to
multiple feature maps. We currently assume that a single feature map
is generated for the RPN.
"""
num_anchors_per_location = (
self._first_stage_anchor_generator.num_anchors_per_location())
if len(num_anchors_per_location) != 1:
raise RuntimeError('anchor_generator is expected to generate anchors '
'corresponding to a single feature map.')
if self._first_stage_box_predictor.is_keras_model:
box_predictions = self._first_stage_box_predictor(
[rpn_box_predictor_features])
else:
box_predictions = self._first_stage_box_predictor.predict(
[rpn_box_predictor_features],
num_anchors_per_location,
scope=self.first_stage_box_predictor_scope)
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (tf.squeeze(box_encodings, axis=2),
objectness_predictions_with_background)
def _remove_invalid_anchors_and_predictions(
self,
box_encodings,
objectness_predictions_with_background,
anchors_boxlist,
clip_window):
"""Removes anchors that (partially) fall outside an image.
Also removes associated box encodings and objectness predictions.
Args:
box_encodings: 3-D float tensor of shape
[batch_size, num_anchors, self._box_coder.code_size] containing
predicted boxes.
objectness_predictions_with_background: 3-D float tensor of shape
[batch_size, num_anchors, 2] containing class
predictions (logits) for each of the anchors. Note that this
tensor *includes* background class predictions (at class index 0).
anchors_boxlist: A BoxList representing num_anchors anchors (for the RPN)
in absolute coordinates.
clip_window: a 1-D tensor representing the [ymin, xmin, ymax, xmax]
extent of the window to clip/prune to.
Returns:
box_encodings: 4-D float tensor of shape
[batch_size, num_valid_anchors, self._box_coder.code_size] containing
predicted boxes, where num_valid_anchors <= num_anchors
objectness_predictions_with_background: 2-D float tensor of shape
[batch_size, num_valid_anchors, 2] containing class
predictions (logits) for each of the anchors, where
num_valid_anchors <= num_anchors. Note that this
tensor *includes* background class predictions (at class index 0).
anchors: A BoxList representing num_valid_anchors anchors (for the RPN) in
absolute coordinates.
"""
pruned_anchors_boxlist, keep_indices = box_list_ops.prune_outside_window(
anchors_boxlist, clip_window)
def _batch_gather_kept_indices(predictions_tensor):
return shape_utils.static_or_dynamic_map_fn(
partial(tf.gather, indices=keep_indices),
elems=predictions_tensor,
dtype=tf.float32,
parallel_iterations=self._parallel_iterations,
back_prop=True)
return (_batch_gather_kept_indices(box_encodings),
_batch_gather_kept_indices(objectness_predictions_with_background),
pruned_anchors_boxlist)
def _flatten_first_two_dimensions(self, inputs):
"""Flattens `K-d` tensor along batch dimension to be a `(K-1)-d` tensor.
Converts `inputs` with shape [A, B, ..., depth] into a tensor of shape
[A * B, ..., depth].
Args:
inputs: A float tensor with shape [A, B, ..., depth]. Note that the first
two and last dimensions must be statically defined.
Returns:
A float tensor with shape [A * B, ..., depth] (where the first and last
dimension are statically defined.
"""
combined_shape = shape_utils.combined_static_and_dynamic_shape(inputs)
flattened_shape = tf.stack([combined_shape[0] * combined_shape[1]] +
combined_shape[2:])
return tf.reshape(inputs, flattened_shape)
def postprocess(self, prediction_dict, true_image_shapes):
"""Convert prediction tensors to final detections.
This function converts raw predictions tensors to final detection results.
See base class for output format conventions. Note also that by default,
scores are to be interpreted as logits, but if a score_converter is used,
then scores are remapped (and may thus have a different interpretation).
If number_of_stages=1, the returned results represent proposals from the
first stage RPN and are padded to have self.max_num_proposals for each
image; otherwise, the results can be interpreted as multiclass detections
from the full two-stage model and are padded to self._max_detections.
Args:
prediction_dict: a dictionary holding prediction tensors (see the
documentation for the predict method. If number_of_stages=1, we
expect prediction_dict to contain `rpn_box_encodings`,
`rpn_objectness_predictions_with_background`, `rpn_features_to_crop`,
and `anchors` fields. Otherwise we expect prediction_dict to
additionally contain `refined_box_encodings`,
`class_predictions_with_background`, `num_proposals`,
`proposal_boxes` and, optionally, `mask_predictions` fields.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
Returns:
detections: a dictionary containing the following fields
detection_boxes: [batch, max_detection, 4]
detection_scores: [batch, max_detections]
detection_classes: [batch, max_detections]
(this entry is only created if rpn_mode=False)
num_detections: [batch]
Raises:
ValueError: If `predict` is called before `preprocess`.
"""
with tf.name_scope('FirstStagePostprocessor'):
if self._number_of_stages == 1:
proposal_boxes, proposal_scores, num_proposals = self._postprocess_rpn(
prediction_dict['rpn_box_encodings'],
prediction_dict['rpn_objectness_predictions_with_background'],
prediction_dict['anchors'],
true_image_shapes,
true_image_shapes)
return {
fields.DetectionResultFields.detection_boxes: proposal_boxes,
fields.DetectionResultFields.detection_scores: proposal_scores,
fields.DetectionResultFields.num_detections:
tf.to_float(num_proposals),
}
# TODO(jrru): Remove mask_predictions from _post_process_box_classifier.
if (self._number_of_stages == 2 or
(self._number_of_stages == 3 and self._is_training)):
with tf.name_scope('SecondStagePostprocessor'):
mask_predictions = prediction_dict.get(box_predictor.MASK_PREDICTIONS)
detections_dict = self._postprocess_box_classifier(
prediction_dict['refined_box_encodings'],
prediction_dict['class_predictions_with_background'],
prediction_dict['proposal_boxes'],
prediction_dict['num_proposals'],
true_image_shapes,
mask_predictions=mask_predictions)
if 'rpn_features_to_crop' in prediction_dict and self._initial_crop_size:
self._add_detection_features_output_node(
detections_dict[fields.DetectionResultFields.detection_boxes],
prediction_dict['rpn_features_to_crop'])
return detections_dict
if self._number_of_stages == 3:
# Post processing is already performed in 3rd stage. We need to transfer
# postprocessed tensors from `prediction_dict` to `detections_dict`.
return prediction_dict
def _add_detection_features_output_node(self, detection_boxes,
rpn_features_to_crop):
"""Add the detection features to the output node.
The detection features are from cropping rpn_features with boxes.
Each bounding box has one feature vector of length depth, which comes from
mean_pooling of the cropped rpn_features.
Args:
detection_boxes: a 3-D float32 tensor of shape
[batch_size, max_detection, 4] which represents the bounding boxes.
rpn_features_to_crop: A 4-D float32 tensor with shape
[batch, height, width, depth] representing image features to crop using
the proposals boxes.
"""
with tf.name_scope('SecondStageDetectionFeaturesExtract'):
flattened_detected_feature_maps = (
self._compute_second_stage_input_feature_maps(
rpn_features_to_crop, detection_boxes))
detection_features_unpooled = (
self._feature_extractor.extract_box_classifier_features(
flattened_detected_feature_maps,
scope=self.second_stage_feature_extractor_scope))
batch_size = tf.shape(detection_boxes)[0]
max_detection = tf.shape(detection_boxes)[1]
detection_features_pool = tf.reduce_mean(
detection_features_unpooled, axis=[1, 2])
detection_features = tf.reshape(
detection_features_pool,
[batch_size, max_detection, tf.shape(detection_features_pool)[-1]])
detection_features = tf.identity(
detection_features, 'detection_features')
def _postprocess_rpn(self,
rpn_box_encodings_batch,
rpn_objectness_predictions_with_background_batch,
anchors,
image_shapes,
true_image_shapes):
"""Converts first stage prediction tensors from the RPN to proposals.
This function decodes the raw RPN predictions, runs non-max suppression
on the result.
Note that the behavior of this function is slightly modified during
training --- specifically, we stop the gradient from passing through the
proposal boxes and we only return a balanced sampled subset of proposals
with size `second_stage_batch_size`.
Args:
rpn_box_encodings_batch: A 3-D float32 tensor of shape
[batch_size, num_anchors, self._box_coder.code_size] containing
predicted proposal box encodings.
rpn_objectness_predictions_with_background_batch: A 3-D float tensor of
shape [batch_size, num_anchors, 2] containing objectness predictions
(logits) for each of the anchors with 0 corresponding to background
and 1 corresponding to object.
anchors: A 2-D tensor of shape [num_anchors, 4] representing anchors
for the first stage RPN. Note that `num_anchors` can differ depending
on whether the model is created in training or inference mode.
image_shapes: A 2-D tensor of shape [batch, 3] containing the shapes of
images in the batch.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
Returns:
proposal_boxes: A float tensor with shape
[batch_size, max_num_proposals, 4] representing the (potentially zero
padded) proposal boxes for all images in the batch. These boxes are
represented as normalized coordinates.
proposal_scores: A float tensor with shape
[batch_size, max_num_proposals] representing the (potentially zero
padded) proposal objectness scores for all images in the batch.
num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
representing the number of proposals predicted for each image in
the batch.
"""
rpn_box_encodings_batch = tf.expand_dims(rpn_box_encodings_batch, axis=2)
rpn_encodings_shape = shape_utils.combined_static_and_dynamic_shape(
rpn_box_encodings_batch)
tiled_anchor_boxes = tf.tile(
tf.expand_dims(anchors, 0), [rpn_encodings_shape[0], 1, 1])
proposal_boxes = self._batch_decode_boxes(rpn_box_encodings_batch,
tiled_anchor_boxes)
proposal_boxes = tf.squeeze(proposal_boxes, axis=2)
rpn_objectness_softmax_without_background = tf.nn.softmax(
rpn_objectness_predictions_with_background_batch)[:, :, 1]
clip_window = self._compute_clip_window(image_shapes)
(proposal_boxes, proposal_scores, _, _, _,
num_proposals) = self._first_stage_nms_fn(
tf.expand_dims(proposal_boxes, axis=2),
tf.expand_dims(rpn_objectness_softmax_without_background, axis=2),
clip_window=clip_window)
if self._is_training:
proposal_boxes = tf.stop_gradient(proposal_boxes)
if not self._hard_example_miner:
(groundtruth_boxlists, groundtruth_classes_with_background_list, _,
groundtruth_weights_list
) = self._format_groundtruth_data(true_image_shapes)
(proposal_boxes, proposal_scores,
num_proposals) = self._sample_box_classifier_batch(
proposal_boxes, proposal_scores, num_proposals,
groundtruth_boxlists, groundtruth_classes_with_background_list,
groundtruth_weights_list)
# normalize proposal boxes
def normalize_boxes(args):
proposal_boxes_per_image = args[0]
image_shape = args[1]
normalized_boxes_per_image = box_list_ops.to_normalized_coordinates(
box_list.BoxList(proposal_boxes_per_image), image_shape[0],
image_shape[1], check_range=False).get()
return normalized_boxes_per_image
normalized_proposal_boxes = shape_utils.static_or_dynamic_map_fn(
normalize_boxes, elems=[proposal_boxes, image_shapes], dtype=tf.float32)
return normalized_proposal_boxes, proposal_scores, num_proposals
def _sample_box_classifier_batch(
self,
proposal_boxes,
proposal_scores,
num_proposals,
groundtruth_boxlists,
groundtruth_classes_with_background_list,
groundtruth_weights_list):
"""Samples a minibatch for second stage.
Args:
proposal_boxes: A float tensor with shape
[batch_size, num_proposals, 4] representing the (potentially zero
padded) proposal boxes for all images in the batch. These boxes are
represented in absolute coordinates.
proposal_scores: A float tensor with shape
[batch_size, num_proposals] representing the (potentially zero
padded) proposal objectness scores for all images in the batch.
num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
representing the number of proposals predicted for each image in
the batch.
groundtruth_boxlists: A list of BoxLists containing (absolute) coordinates
of the groundtruth boxes.
groundtruth_classes_with_background_list: A list of 2-D one-hot
(or k-hot) tensors of shape [num_boxes, num_classes+1] containing the
class targets with the 0th index assumed to map to the background class.
groundtruth_weights_list: A list of 1-D tensors of shape [num_boxes]
indicating the weight associated with the groundtruth boxes.
Returns:
proposal_boxes: A float tensor with shape
[batch_size, second_stage_batch_size, 4] representing the (potentially
zero padded) proposal boxes for all images in the batch. These boxes
are represented in absolute coordinates.
proposal_scores: A float tensor with shape
[batch_size, second_stage_batch_size] representing the (potentially zero
padded) proposal objectness scores for all images in the batch.
num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
representing the number of proposals predicted for each image in
the batch.
"""
single_image_proposal_box_sample = []
single_image_proposal_score_sample = []
single_image_num_proposals_sample = []
for (single_image_proposal_boxes,
single_image_proposal_scores,
single_image_num_proposals,
single_image_groundtruth_boxlist,
single_image_groundtruth_classes_with_background,
single_image_groundtruth_weights) in zip(
tf.unstack(proposal_boxes),
tf.unstack(proposal_scores),
tf.unstack(num_proposals),
groundtruth_boxlists,
groundtruth_classes_with_background_list,
groundtruth_weights_list):
single_image_boxlist = box_list.BoxList(single_image_proposal_boxes)
single_image_boxlist.add_field(fields.BoxListFields.scores,
single_image_proposal_scores)
sampled_boxlist = self._sample_box_classifier_minibatch_single_image(
single_image_boxlist,
single_image_num_proposals,
single_image_groundtruth_boxlist,
single_image_groundtruth_classes_with_background,
single_image_groundtruth_weights)
sampled_padded_boxlist = box_list_ops.pad_or_clip_box_list(
sampled_boxlist,
num_boxes=self._second_stage_batch_size)
single_image_num_proposals_sample.append(tf.minimum(
sampled_boxlist.num_boxes(),
self._second_stage_batch_size))
bb = sampled_padded_boxlist.get()
single_image_proposal_box_sample.append(bb)
single_image_proposal_score_sample.append(
sampled_padded_boxlist.get_field(fields.BoxListFields.scores))
return (tf.stack(single_image_proposal_box_sample),
tf.stack(single_image_proposal_score_sample),
tf.stack(single_image_num_proposals_sample))
def _format_groundtruth_data(self, true_image_shapes):
"""Helper function for preparing groundtruth data for target assignment.
In order to be consistent with the model.DetectionModel interface,
groundtruth boxes are specified in normalized coordinates and classes are
specified as label indices with no assumed background category. To prepare
for target assignment, we:
1) convert boxes to absolute coordinates,
2) add a background class at class index 0
3) groundtruth instance masks, if available, are resized to match
image_shape.
Args:
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
Returns:
groundtruth_boxlists: A list of BoxLists containing (absolute) coordinates
of the groundtruth boxes.
groundtruth_classes_with_background_list: A list of 2-D one-hot
(or k-hot) tensors of shape [num_boxes, num_classes+1] containing the
class targets with the 0th index assumed to map to the background class.
groundtruth_masks_list: If present, a list of 3-D tf.float32 tensors of
shape [num_boxes, image_height, image_width] containing instance masks.
This is set to None if no masks exist in the provided groundtruth.
"""
groundtruth_boxlists = [
box_list_ops.to_absolute_coordinates(
box_list.BoxList(boxes), true_image_shapes[i, 0],
true_image_shapes[i, 1])
for i, boxes in enumerate(
self.groundtruth_lists(fields.BoxListFields.boxes))
]
groundtruth_classes_with_background_list = [
tf.to_float(
tf.pad(one_hot_encoding, [[0, 0], [1, 0]], mode='CONSTANT'))
for one_hot_encoding in self.groundtruth_lists(
fields.BoxListFields.classes)]
groundtruth_masks_list = self._groundtruth_lists.get(
fields.BoxListFields.masks)
# TODO(rathodv): Remove mask resizing once the legacy pipeline is deleted.
if groundtruth_masks_list is not None and self._resize_masks:
resized_masks_list = []
for mask in groundtruth_masks_list:
_, resized_mask, _ = self._image_resizer_fn(
# Reuse the given `image_resizer_fn` to resize groundtruth masks.
# `mask` tensor for an image is of the shape [num_masks,
# image_height, image_width]. Below we create a dummy image of the
# the shape [image_height, image_width, 1] to use with
# `image_resizer_fn`.
image=tf.zeros(tf.stack([tf.shape(mask)[1],
tf.shape(mask)[2], 1])),
masks=mask)
resized_masks_list.append(resized_mask)
groundtruth_masks_list = resized_masks_list
if self.groundtruth_has_field(fields.BoxListFields.weights):
groundtruth_weights_list = self.groundtruth_lists(
fields.BoxListFields.weights)
else:
# Set weights for all batch elements equally to 1.0
groundtruth_weights_list = []
for groundtruth_classes in groundtruth_classes_with_background_list:
num_gt = tf.shape(groundtruth_classes)[0]
groundtruth_weights = tf.ones(num_gt)
groundtruth_weights_list.append(groundtruth_weights)
return (groundtruth_boxlists, groundtruth_classes_with_background_list,
groundtruth_masks_list, groundtruth_weights_list)
def _sample_box_classifier_minibatch_single_image(
self, proposal_boxlist, num_valid_proposals, groundtruth_boxlist,
groundtruth_classes_with_background, groundtruth_weights):
"""Samples a mini-batch of proposals to be sent to the box classifier.
Helper function for self._postprocess_rpn.
Args:
proposal_boxlist: A BoxList containing K proposal boxes in absolute
coordinates.
num_valid_proposals: Number of valid proposals in the proposal boxlist.
groundtruth_boxlist: A Boxlist containing N groundtruth object boxes in
absolute coordinates.
groundtruth_classes_with_background: A tensor with shape
`[N, self.num_classes + 1]` representing groundtruth classes. The
classes are assumed to be k-hot encoded, and include background as the
zero-th class.
groundtruth_weights: Weights attached to the groundtruth_boxes.
Returns:
a BoxList contained sampled proposals.
"""
(cls_targets, cls_weights, _, _, _) = self._detector_target_assigner.assign(
proposal_boxlist,
groundtruth_boxlist,
groundtruth_classes_with_background,
unmatched_class_label=tf.constant(
[1] + self._num_classes * [0], dtype=tf.float32),
groundtruth_weights=groundtruth_weights)
# Selects all boxes as candidates if none of them is selected according
# to cls_weights. This could happen as boxes within certain IOU ranges
# are ignored. If triggered, the selected boxes will still be ignored
# during loss computation.
cls_weights = tf.reduce_mean(cls_weights, axis=-1)
positive_indicator = tf.greater(tf.argmax(cls_targets, axis=1), 0)
valid_indicator = tf.logical_and(
tf.range(proposal_boxlist.num_boxes()) < num_valid_proposals,
cls_weights > 0
)
selected_positions = self._second_stage_sampler.subsample(
valid_indicator,
self._second_stage_batch_size,
positive_indicator)
return box_list_ops.boolean_mask(
proposal_boxlist,
selected_positions,
use_static_shapes=self._use_static_shapes,
indicator_sum=(self._second_stage_batch_size
if self._use_static_shapes else None))
def _compute_second_stage_input_feature_maps(self, features_to_crop,
proposal_boxes_normalized):
"""Crops to a set of proposals from the feature map for a batch of images.
Helper function for self._postprocess_rpn. This function calls
`tf.image.crop_and_resize` to create the feature map to be passed to the
second stage box classifier for each proposal.
Args:
features_to_crop: A float32 tensor with shape
[batch_size, height, width, depth]
proposal_boxes_normalized: A float32 tensor with shape [batch_size,
num_proposals, box_code_size] containing proposal boxes in
normalized coordinates.
Returns:
A float32 tensor with shape [K, new_height, new_width, depth].
"""
cropped_regions = self._flatten_first_two_dimensions(
self._crop_and_resize_fn(
features_to_crop, proposal_boxes_normalized,
[self._initial_crop_size, self._initial_crop_size]))
return slim.max_pool2d(
cropped_regions,
[self._maxpool_kernel_size, self._maxpool_kernel_size],
stride=self._maxpool_stride)
def _postprocess_box_classifier(self,
refined_box_encodings,
class_predictions_with_background,
proposal_boxes,
num_proposals,
image_shapes,
mask_predictions=None):
"""Converts predictions from the second stage box classifier to detections.
Args:
refined_box_encodings: a 3-D float tensor with shape
[total_num_padded_proposals, num_classes, self._box_coder.code_size]
representing predicted (final) refined box encodings. If using a shared
box across classes the shape will instead be
[total_num_padded_proposals, 1, 4]
class_predictions_with_background: a 3-D tensor float with shape
[total_num_padded_proposals, num_classes + 1] containing class
predictions (logits) for each of the proposals. Note that this tensor
*includes* background class predictions (at class index 0).
proposal_boxes: a 3-D float tensor with shape
[batch_size, self.max_num_proposals, 4] representing decoded proposal
bounding boxes in absolute coordinates.
num_proposals: a 1-D int32 tensor of shape [batch] representing the number
of proposals predicted for each image in the batch.
image_shapes: a 2-D int32 tensor containing shapes of input image in the
batch.
mask_predictions: (optional) a 4-D float tensor with shape
[total_num_padded_proposals, num_classes, mask_height, mask_width]
containing instance mask prediction logits.
Returns:
A dictionary containing:
`detection_boxes`: [batch, max_detection, 4] in normalized co-ordinates.
`detection_scores`: [batch, max_detections]
`detection_classes`: [batch, max_detections]
`num_detections`: [batch]
`detection_masks`:
(optional) [batch, max_detections, mask_height, mask_width]. Note
that a pixel-wise sigmoid score converter is applied to the detection
masks.
"""
refined_box_encodings_batch = tf.reshape(
refined_box_encodings,
[-1,
self.max_num_proposals,
refined_box_encodings.shape[1],
self._box_coder.code_size])
class_predictions_with_background_batch = tf.reshape(
class_predictions_with_background,
[-1, self.max_num_proposals, self.num_classes + 1]
)
refined_decoded_boxes_batch = self._batch_decode_boxes(
refined_box_encodings_batch, proposal_boxes)
class_predictions_with_background_batch = (
self._second_stage_score_conversion_fn(
class_predictions_with_background_batch))
class_predictions_batch = tf.reshape(
tf.slice(class_predictions_with_background_batch,
[0, 0, 1], [-1, -1, -1]),
[-1, self.max_num_proposals, self.num_classes])
clip_window = self._compute_clip_window(image_shapes)
mask_predictions_batch = None
if mask_predictions is not None:
mask_height = mask_predictions.shape[2].value
mask_width = mask_predictions.shape[3].value
mask_predictions = tf.sigmoid(mask_predictions)
mask_predictions_batch = tf.reshape(
mask_predictions, [-1, self.max_num_proposals,
self.num_classes, mask_height, mask_width])
(nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks, _,
num_detections) = self._second_stage_nms_fn(
refined_decoded_boxes_batch,
class_predictions_batch,
clip_window=clip_window,
change_coordinate_frame=True,
num_valid_boxes=num_proposals,
masks=mask_predictions_batch)
detections = {
fields.DetectionResultFields.detection_boxes: nmsed_boxes,
fields.DetectionResultFields.detection_scores: nmsed_scores,
fields.DetectionResultFields.detection_classes: nmsed_classes,
fields.DetectionResultFields.num_detections: tf.to_float(num_detections)
}
if nmsed_masks is not None:
detections[fields.DetectionResultFields.detection_masks] = nmsed_masks
return detections
def _batch_decode_boxes(self, box_encodings, anchor_boxes):
"""Decodes box encodings with respect to the anchor boxes.
Args:
box_encodings: a 4-D tensor with shape
[batch_size, num_anchors, num_classes, self._box_coder.code_size]
representing box encodings.
anchor_boxes: [batch_size, num_anchors, self._box_coder.code_size]
representing decoded bounding boxes. If using a shared box across
classes the shape will instead be
[total_num_proposals, 1, self._box_coder.code_size].
Returns:
decoded_boxes: a
[batch_size, num_anchors, num_classes, self._box_coder.code_size]
float tensor representing bounding box predictions (for each image in
batch, proposal and class). If using a shared box across classes the
shape will instead be
[batch_size, num_anchors, 1, self._box_coder.code_size].
"""
combined_shape = shape_utils.combined_static_and_dynamic_shape(
box_encodings)
num_classes = combined_shape[2]
tiled_anchor_boxes = tf.tile(
tf.expand_dims(anchor_boxes, 2), [1, 1, num_classes, 1])
tiled_anchors_boxlist = box_list.BoxList(
tf.reshape(tiled_anchor_boxes, [-1, 4]))
decoded_boxes = self._box_coder.decode(
tf.reshape(box_encodings, [-1, self._box_coder.code_size]),
tiled_anchors_boxlist)
return tf.reshape(decoded_boxes.get(),
tf.stack([combined_shape[0], combined_shape[1],
num_classes, 4]))
def loss(self, prediction_dict, true_image_shapes, scope=None):
"""Compute scalar loss tensors given prediction tensors.
If number_of_stages=1, only RPN related losses are computed (i.e.,
`rpn_localization_loss` and `rpn_objectness_loss`). Otherwise all
losses are computed.
Args:
prediction_dict: a dictionary holding prediction tensors (see the
documentation for the predict method. If number_of_stages=1, we
expect prediction_dict to contain `rpn_box_encodings`,
`rpn_objectness_predictions_with_background`, `rpn_features_to_crop`,
`image_shape`, and `anchors` fields. Otherwise we expect
prediction_dict to additionally contain `refined_box_encodings`,
`class_predictions_with_background`, `num_proposals`, and
`proposal_boxes` fields.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
scope: Optional scope name.
Returns:
a dictionary mapping loss keys (`first_stage_localization_loss`,
`first_stage_objectness_loss`, 'second_stage_localization_loss',
'second_stage_classification_loss') to scalar tensors representing
corresponding loss values.
"""
with tf.name_scope(scope, 'Loss', prediction_dict.values()):
(groundtruth_boxlists, groundtruth_classes_with_background_list,
groundtruth_masks_list, groundtruth_weights_list
) = self._format_groundtruth_data(true_image_shapes)
loss_dict = self._loss_rpn(
prediction_dict['rpn_box_encodings'],
prediction_dict['rpn_objectness_predictions_with_background'],
prediction_dict['anchors'], groundtruth_boxlists,
groundtruth_classes_with_background_list, groundtruth_weights_list)
if self._number_of_stages > 1:
loss_dict.update(
self._loss_box_classifier(
prediction_dict['refined_box_encodings'],
prediction_dict['class_predictions_with_background'],
prediction_dict['proposal_boxes'],
prediction_dict['num_proposals'], groundtruth_boxlists,
groundtruth_classes_with_background_list,
groundtruth_weights_list, prediction_dict['image_shape'],
prediction_dict.get('mask_predictions'), groundtruth_masks_list,
prediction_dict.get(
fields.DetectionResultFields.detection_boxes),
prediction_dict.get(
fields.DetectionResultFields.num_detections)))
return loss_dict
def _loss_rpn(self, rpn_box_encodings,
rpn_objectness_predictions_with_background, anchors,
groundtruth_boxlists, groundtruth_classes_with_background_list,
groundtruth_weights_list):
"""Computes scalar RPN loss tensors.
Uses self._proposal_target_assigner to obtain regression and classification
targets for the first stage RPN, samples a "minibatch" of anchors to
participate in the loss computation, and returns the RPN losses.
Args:
rpn_box_encodings: A 4-D float tensor of shape
[batch_size, num_anchors, self._box_coder.code_size] containing
predicted proposal box encodings.
rpn_objectness_predictions_with_background: A 2-D float tensor of shape
[batch_size, num_anchors, 2] containing objectness predictions
(logits) for each of the anchors with 0 corresponding to background
and 1 corresponding to object.
anchors: A 2-D tensor of shape [num_anchors, 4] representing anchors
for the first stage RPN. Note that `num_anchors` can differ depending
on whether the model is created in training or inference mode.
groundtruth_boxlists: A list of BoxLists containing coordinates of the
groundtruth boxes.
groundtruth_classes_with_background_list: A list of 2-D one-hot
(or k-hot) tensors of shape [num_boxes, num_classes+1] containing the
class targets with the 0th index assumed to map to the background class.
groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape
[num_boxes] containing weights for groundtruth boxes.
Returns:
a dictionary mapping loss keys (`first_stage_localization_loss`,
`first_stage_objectness_loss`) to scalar tensors representing
corresponding loss values.
"""
with tf.name_scope('RPNLoss'):
(batch_cls_targets, batch_cls_weights, batch_reg_targets,
batch_reg_weights, _) = target_assigner.batch_assign_targets(
target_assigner=self._proposal_target_assigner,
anchors_batch=box_list.BoxList(anchors),
gt_box_batch=groundtruth_boxlists,
gt_class_targets_batch=(len(groundtruth_boxlists) * [None]),
gt_weights_batch=groundtruth_weights_list)
batch_cls_weights = tf.reduce_mean(batch_cls_weights, axis=2)
batch_cls_targets = tf.squeeze(batch_cls_targets, axis=2)
def _minibatch_subsample_fn(inputs):
cls_targets, cls_weights = inputs
return self._first_stage_sampler.subsample(
tf.cast(cls_weights, tf.bool),
self._first_stage_minibatch_size, tf.cast(cls_targets, tf.bool))
batch_sampled_indices = tf.to_float(shape_utils.static_or_dynamic_map_fn(
_minibatch_subsample_fn,
[batch_cls_targets, batch_cls_weights],
dtype=tf.bool,
parallel_iterations=self._parallel_iterations,
back_prop=True))
# Normalize by number of examples in sampled minibatch
normalizer = tf.reduce_sum(batch_sampled_indices, axis=1)
batch_one_hot_targets = tf.one_hot(
tf.to_int32(batch_cls_targets), depth=2)
sampled_reg_indices = tf.multiply(batch_sampled_indices,
batch_reg_weights)
losses_mask = None
if self.groundtruth_has_field(fields.InputDataFields.is_annotated):
losses_mask = tf.stack(self.groundtruth_lists(
fields.InputDataFields.is_annotated))
localization_losses = self._first_stage_localization_loss(
rpn_box_encodings, batch_reg_targets, weights=sampled_reg_indices,
losses_mask=losses_mask)
objectness_losses = self._first_stage_objectness_loss(
rpn_objectness_predictions_with_background,
batch_one_hot_targets,
weights=tf.expand_dims(batch_sampled_indices, axis=-1),
losses_mask=losses_mask)
localization_loss = tf.reduce_mean(
tf.reduce_sum(localization_losses, axis=1) / normalizer)
objectness_loss = tf.reduce_mean(
tf.reduce_sum(objectness_losses, axis=1) / normalizer)
localization_loss = tf.multiply(self._first_stage_loc_loss_weight,
localization_loss,
name='localization_loss')
objectness_loss = tf.multiply(self._first_stage_obj_loss_weight,
objectness_loss, name='objectness_loss')
loss_dict = {localization_loss.op.name: localization_loss,
objectness_loss.op.name: objectness_loss}
return loss_dict
def _loss_box_classifier(self,
refined_box_encodings,
class_predictions_with_background,
proposal_boxes,
num_proposals,
groundtruth_boxlists,
groundtruth_classes_with_background_list,
groundtruth_weights_list,
image_shape,
prediction_masks=None,
groundtruth_masks_list=None,
detection_boxes=None,
num_detections=None):
"""Computes scalar box classifier loss tensors.
Uses self._detector_target_assigner to obtain regression and classification
targets for the second stage box classifier, optionally performs
hard mining, and returns losses. All losses are computed independently
for each image and then averaged across the batch.
Please note that for boxes and masks with multiple labels, the box
regression and mask prediction losses are only computed for one label.
This function assumes that the proposal boxes in the "padded" regions are
actually zero (and thus should not be matched to).
Args:
refined_box_encodings: a 3-D tensor with shape
[total_num_proposals, num_classes, box_coder.code_size] representing
predicted (final) refined box encodings. If using a shared box across
classes this will instead have shape
[total_num_proposals, 1, box_coder.code_size].
class_predictions_with_background: a 2-D tensor with shape
[total_num_proposals, num_classes + 1] containing class
predictions (logits) for each of the anchors. Note that this tensor
*includes* background class predictions (at class index 0).
proposal_boxes: [batch_size, self.max_num_proposals, 4] representing
decoded proposal bounding boxes.
num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
representing the number of proposals predicted for each image in
the batch.
groundtruth_boxlists: a list of BoxLists containing coordinates of the
groundtruth boxes.
groundtruth_classes_with_background_list: a list of 2-D one-hot
(or k-hot) tensors of shape [num_boxes, num_classes + 1] containing the
class targets with the 0th index assumed to map to the background class.
groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape
[num_boxes] containing weights for groundtruth boxes.
image_shape: a 1-D tensor of shape [4] representing the image shape.
prediction_masks: an optional 4-D tensor with shape [total_num_proposals,
num_classes, mask_height, mask_width] containing the instance masks for
each box.
groundtruth_masks_list: an optional list of 3-D tensors of shape
[num_boxes, image_height, image_width] containing the instance masks for
each of the boxes.
detection_boxes: 3-D float tensor of shape [batch,
max_total_detections, 4] containing post-processed detection boxes in
normalized co-ordinates.
num_detections: 1-D int32 tensor of shape [batch] containing number of
valid detections in `detection_boxes`.
Returns:
a dictionary mapping loss keys ('second_stage_localization_loss',
'second_stage_classification_loss') to scalar tensors representing
corresponding loss values.
Raises:
ValueError: if `predict_instance_masks` in
second_stage_mask_rcnn_box_predictor is True and
`groundtruth_masks_list` is not provided.
"""
with tf.name_scope('BoxClassifierLoss'):
paddings_indicator = self._padded_batched_proposals_indicator(
num_proposals, proposal_boxes.shape[1])
proposal_boxlists = [
box_list.BoxList(proposal_boxes_single_image)
for proposal_boxes_single_image in tf.unstack(proposal_boxes)]
batch_size = len(proposal_boxlists)
num_proposals_or_one = tf.to_float(tf.expand_dims(
tf.maximum(num_proposals, tf.ones_like(num_proposals)), 1))
normalizer = tf.tile(num_proposals_or_one,
[1, self.max_num_proposals]) * batch_size
(batch_cls_targets_with_background, batch_cls_weights, batch_reg_targets,
batch_reg_weights, _) = target_assigner.batch_assign_targets(
target_assigner=self._detector_target_assigner,
anchors_batch=proposal_boxlists,
gt_box_batch=groundtruth_boxlists,
gt_class_targets_batch=groundtruth_classes_with_background_list,
unmatched_class_label=tf.constant(
[1] + self._num_classes * [0], dtype=tf.float32),
gt_weights_batch=groundtruth_weights_list)
class_predictions_with_background = tf.reshape(
class_predictions_with_background,
[batch_size, self.max_num_proposals, -1])
flat_cls_targets_with_background = tf.reshape(
batch_cls_targets_with_background,
[batch_size * self.max_num_proposals, -1])
one_hot_flat_cls_targets_with_background = tf.argmax(
flat_cls_targets_with_background, axis=1)
one_hot_flat_cls_targets_with_background = tf.one_hot(
one_hot_flat_cls_targets_with_background,
flat_cls_targets_with_background.get_shape()[1])
# If using a shared box across classes use directly
if refined_box_encodings.shape[1] == 1:
reshaped_refined_box_encodings = tf.reshape(
refined_box_encodings,
[batch_size, self.max_num_proposals, self._box_coder.code_size])
# For anchors with multiple labels, picks refined_location_encodings
# for just one class to avoid over-counting for regression loss and
# (optionally) mask loss.
else:
reshaped_refined_box_encodings = (
self._get_refined_encodings_for_postitive_class(
refined_box_encodings,
one_hot_flat_cls_targets_with_background, batch_size))
losses_mask = None
if self.groundtruth_has_field(fields.InputDataFields.is_annotated):
losses_mask = tf.stack(self.groundtruth_lists(
fields.InputDataFields.is_annotated))
second_stage_loc_losses = self._second_stage_localization_loss(
reshaped_refined_box_encodings,
batch_reg_targets,
weights=batch_reg_weights,
losses_mask=losses_mask) / normalizer
second_stage_cls_losses = ops.reduce_sum_trailing_dimensions(
self._second_stage_classification_loss(
class_predictions_with_background,
batch_cls_targets_with_background,
weights=batch_cls_weights,
losses_mask=losses_mask),
ndims=2) / normalizer
second_stage_loc_loss = tf.reduce_sum(
second_stage_loc_losses * tf.to_float(paddings_indicator))
second_stage_cls_loss = tf.reduce_sum(
second_stage_cls_losses * tf.to_float(paddings_indicator))
if self._hard_example_miner:
(second_stage_loc_loss, second_stage_cls_loss
) = self._unpad_proposals_and_apply_hard_mining(
proposal_boxlists, second_stage_loc_losses,
second_stage_cls_losses, num_proposals)
localization_loss = tf.multiply(self._second_stage_loc_loss_weight,
second_stage_loc_loss,
name='localization_loss')
classification_loss = tf.multiply(self._second_stage_cls_loss_weight,
second_stage_cls_loss,
name='classification_loss')
loss_dict = {localization_loss.op.name: localization_loss,
classification_loss.op.name: classification_loss}
second_stage_mask_loss = None
if prediction_masks is not None:
if groundtruth_masks_list is None:
raise ValueError('Groundtruth instance masks not provided. '
'Please configure input reader.')
if not self._is_training:
(proposal_boxes, proposal_boxlists, paddings_indicator,
one_hot_flat_cls_targets_with_background
) = self._get_mask_proposal_boxes_and_classes(
detection_boxes, num_detections, image_shape,
groundtruth_boxlists, groundtruth_classes_with_background_list,
groundtruth_weights_list)
unmatched_mask_label = tf.zeros(image_shape[1:3], dtype=tf.float32)
(batch_mask_targets, _, _, batch_mask_target_weights,
_) = target_assigner.batch_assign_targets(
target_assigner=self._detector_target_assigner,
anchors_batch=proposal_boxlists,
gt_box_batch=groundtruth_boxlists,
gt_class_targets_batch=groundtruth_masks_list,
unmatched_class_label=unmatched_mask_label,
gt_weights_batch=groundtruth_weights_list)
# Pad the prediction_masks with to add zeros for background class to be
# consistent with class predictions.
if prediction_masks.get_shape().as_list()[1] == 1:
# Class agnostic masks or masks for one-class prediction. Logic for
# both cases is the same since background predictions are ignored
# through the batch_mask_target_weights.
prediction_masks_masked_by_class_targets = prediction_masks
else:
prediction_masks_with_background = tf.pad(
prediction_masks, [[0, 0], [1, 0], [0, 0], [0, 0]])
prediction_masks_masked_by_class_targets = tf.boolean_mask(
prediction_masks_with_background,
tf.greater(one_hot_flat_cls_targets_with_background, 0))
mask_height = prediction_masks.shape[2].value
mask_width = prediction_masks.shape[3].value
reshaped_prediction_masks = tf.reshape(
prediction_masks_masked_by_class_targets,
[batch_size, -1, mask_height * mask_width])
batch_mask_targets_shape = tf.shape(batch_mask_targets)
flat_gt_masks = tf.reshape(batch_mask_targets,
[-1, batch_mask_targets_shape[2],
batch_mask_targets_shape[3]])
# Use normalized proposals to crop mask targets from image masks.
flat_normalized_proposals = box_list_ops.to_normalized_coordinates(
box_list.BoxList(tf.reshape(proposal_boxes, [-1, 4])),
image_shape[1], image_shape[2]).get()
flat_cropped_gt_mask = self._crop_and_resize_fn(
tf.expand_dims(flat_gt_masks, -1),
tf.expand_dims(flat_normalized_proposals, axis=1),
[mask_height, mask_width])
# Without stopping gradients into cropped groundtruth masks the
# performance with 100-padded groundtruth masks when batch size > 1 is
# about 4% worse.
# TODO(rathodv): Investigate this since we don't expect any variables
# upstream of flat_cropped_gt_mask.
flat_cropped_gt_mask = tf.stop_gradient(flat_cropped_gt_mask)
batch_cropped_gt_mask = tf.reshape(
flat_cropped_gt_mask,
[batch_size, -1, mask_height * mask_width])
mask_losses_weights = (
batch_mask_target_weights * tf.to_float(paddings_indicator))
mask_losses = self._second_stage_mask_loss(
reshaped_prediction_masks,
batch_cropped_gt_mask,
weights=tf.expand_dims(mask_losses_weights, axis=-1),
losses_mask=losses_mask)
total_mask_loss = tf.reduce_sum(mask_losses)
normalizer = tf.maximum(
tf.reduce_sum(mask_losses_weights * mask_height * mask_width), 1.0)
second_stage_mask_loss = total_mask_loss / normalizer
if second_stage_mask_loss is not None:
mask_loss = tf.multiply(self._second_stage_mask_loss_weight,
second_stage_mask_loss, name='mask_loss')
loss_dict[mask_loss.op.name] = mask_loss
return loss_dict
def _get_mask_proposal_boxes_and_classes(
self, detection_boxes, num_detections, image_shape, groundtruth_boxlists,
groundtruth_classes_with_background_list, groundtruth_weights_list):
"""Returns proposal boxes and class targets to compute evaluation mask loss.
During evaluation, detection boxes are used to extract features for mask
prediction. Therefore, to compute mask loss during evaluation detection
boxes must be used to compute correct class and mask targets. This function
returns boxes and classes in the correct format for computing mask targets
during evaluation.
Args:
detection_boxes: A 3-D float tensor of shape [batch, max_detection_boxes,
4] containing detection boxes in normalized co-ordinates.
num_detections: A 1-D float tensor of shape [batch] containing number of
valid boxes in `detection_boxes`.
image_shape: A 1-D tensor of shape [4] containing image tensor shape.
groundtruth_boxlists: A list of groundtruth boxlists.
groundtruth_classes_with_background_list: A list of groundtruth classes.
groundtruth_weights_list: A list of groundtruth weights.
Return:
mask_proposal_boxes: detection boxes to use for mask proposals in absolute
co-ordinates.
mask_proposal_boxlists: `mask_proposal_boxes` in a list of BoxLists in
absolute co-ordinates.
mask_proposal_paddings_indicator: a tensor indicating valid boxes.
mask_proposal_one_hot_flat_cls_targets_with_background: Class targets
computed using detection boxes.
"""
batch, max_num_detections, _ = detection_boxes.shape.as_list()
proposal_boxes = tf.reshape(box_list_ops.to_absolute_coordinates(
box_list.BoxList(tf.reshape(detection_boxes, [-1, 4])), image_shape[1],
image_shape[2]).get(), [batch, max_num_detections, 4])
proposal_boxlists = [
box_list.BoxList(detection_boxes_single_image)
for detection_boxes_single_image in tf.unstack(proposal_boxes)
]
paddings_indicator = self._padded_batched_proposals_indicator(
tf.to_int32(num_detections), detection_boxes.shape[1])
(batch_cls_targets_with_background, _, _, _,
_) = target_assigner.batch_assign_targets(
target_assigner=self._detector_target_assigner,
anchors_batch=proposal_boxlists,
gt_box_batch=groundtruth_boxlists,
gt_class_targets_batch=groundtruth_classes_with_background_list,
unmatched_class_label=tf.constant(
[1] + self._num_classes * [0], dtype=tf.float32),
gt_weights_batch=groundtruth_weights_list)
flat_cls_targets_with_background = tf.reshape(
batch_cls_targets_with_background, [-1, self._num_classes + 1])
one_hot_flat_cls_targets_with_background = tf.argmax(
flat_cls_targets_with_background, axis=1)
one_hot_flat_cls_targets_with_background = tf.one_hot(
one_hot_flat_cls_targets_with_background,
flat_cls_targets_with_background.get_shape()[1])
return (proposal_boxes, proposal_boxlists, paddings_indicator,
one_hot_flat_cls_targets_with_background)
def _get_refined_encodings_for_postitive_class(
self, refined_box_encodings, flat_cls_targets_with_background,
batch_size):
# We only predict refined location encodings for the non background
# classes, but we now pad it to make it compatible with the class
# predictions
refined_box_encodings_with_background = tf.pad(refined_box_encodings,
[[0, 0], [1, 0], [0, 0]])
refined_box_encodings_masked_by_class_targets = (
box_list_ops.boolean_mask(
box_list.BoxList(
tf.reshape(refined_box_encodings_with_background,
[-1, self._box_coder.code_size])),
tf.reshape(tf.greater(flat_cls_targets_with_background, 0), [-1]),
use_static_shapes=self._use_static_shapes,
indicator_sum=batch_size * self.max_num_proposals
if self._use_static_shapes else None).get())
return tf.reshape(
refined_box_encodings_masked_by_class_targets, [
batch_size, self.max_num_proposals,
self._box_coder.code_size
])
def _padded_batched_proposals_indicator(self,
num_proposals,
max_num_proposals):
"""Creates indicator matrix of non-pad elements of padded batch proposals.
Args:
num_proposals: Tensor of type tf.int32 with shape [batch_size].
max_num_proposals: Maximum number of proposals per image (integer).
Returns:
A Tensor of type tf.bool with shape [batch_size, max_num_proposals].
"""
batch_size = tf.size(num_proposals)
tiled_num_proposals = tf.tile(
tf.expand_dims(num_proposals, 1), [1, max_num_proposals])
tiled_proposal_index = tf.tile(
tf.expand_dims(tf.range(max_num_proposals), 0), [batch_size, 1])
return tf.greater(tiled_num_proposals, tiled_proposal_index)
def _unpad_proposals_and_apply_hard_mining(self,
proposal_boxlists,
second_stage_loc_losses,
second_stage_cls_losses,
num_proposals):
"""Unpads proposals and applies hard mining.
Args:
proposal_boxlists: A list of `batch_size` BoxLists each representing
`self.max_num_proposals` representing decoded proposal bounding boxes
for each image.
second_stage_loc_losses: A Tensor of type `float32`. A tensor of shape
`[batch_size, self.max_num_proposals]` representing per-anchor
second stage localization loss values.
second_stage_cls_losses: A Tensor of type `float32`. A tensor of shape
`[batch_size, self.max_num_proposals]` representing per-anchor
second stage classification loss values.
num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
representing the number of proposals predicted for each image in
the batch.
Returns:
second_stage_loc_loss: A scalar float32 tensor representing the second
stage localization loss.
second_stage_cls_loss: A scalar float32 tensor representing the second
stage classification loss.
"""
for (proposal_boxlist, single_image_loc_loss, single_image_cls_loss,
single_image_num_proposals) in zip(
proposal_boxlists,
tf.unstack(second_stage_loc_losses),
tf.unstack(second_stage_cls_losses),
tf.unstack(num_proposals)):
proposal_boxlist = box_list.BoxList(
tf.slice(proposal_boxlist.get(),
[0, 0], [single_image_num_proposals, -1]))
single_image_loc_loss = tf.slice(single_image_loc_loss,
[0], [single_image_num_proposals])
single_image_cls_loss = tf.slice(single_image_cls_loss,
[0], [single_image_num_proposals])
return self._hard_example_miner(
location_losses=tf.expand_dims(single_image_loc_loss, 0),
cls_losses=tf.expand_dims(single_image_cls_loss, 0),
decoded_boxlist_list=[proposal_boxlist])
def regularization_losses(self):
"""Returns a list of regularization losses for this model.
Returns a list of regularization losses for this model that the estimator
needs to use during training/optimization.
Returns:
A list of regularization loss tensors.
"""
return tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
def restore_map(self,
fine_tune_checkpoint_type='detection',
load_all_detection_checkpoint_vars=False):
"""Returns a map of variables to load from a foreign checkpoint.
See parent class for details.
Args:
fine_tune_checkpoint_type: whether to restore from a full detection
checkpoint (with compatible variable names) or to restore from a
classification checkpoint for initialization prior to training.
Valid values: `detection`, `classification`. Default 'detection'.
load_all_detection_checkpoint_vars: whether to load all variables (when
`fine_tune_checkpoint_type` is `detection`). If False, only variables
within the feature extractor scopes are included. Default False.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
Raises:
ValueError: if fine_tune_checkpoint_type is neither `classification`
nor `detection`.
"""
if fine_tune_checkpoint_type not in ['detection', 'classification']:
raise ValueError('Not supported fine_tune_checkpoint_type: {}'.format(
fine_tune_checkpoint_type))
if fine_tune_checkpoint_type == 'classification':
return self._feature_extractor.restore_from_classification_checkpoint_fn(
self.first_stage_feature_extractor_scope,
self.second_stage_feature_extractor_scope)
variables_to_restore = tf.global_variables()
variables_to_restore.append(slim.get_or_create_global_step())
# Only load feature extractor variables to be consistent with loading from
# a classification checkpoint.
include_patterns = None
if not load_all_detection_checkpoint_vars:
include_patterns = [
self.first_stage_feature_extractor_scope,
self.second_stage_feature_extractor_scope
]
feature_extractor_variables = tf.contrib.framework.filter_variables(
variables_to_restore, include_patterns=include_patterns)
return {var.op.name: var for var in feature_extractor_variables}
def updates(self):
"""Returns a list of update operators for this model.
Returns a list of update operators for this model that must be executed at
each training step. The estimator's train op needs to have a control
dependency on these updates.
Returns:
A list of update operators.
"""
return tf.get_collection(tf.GraphKeys.UPDATE_OPS)
|
TensorFlow/Classification/ConvNets/model/layers | layers | squeeze_excitation_layer | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# 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.
# ==============================================================================
import tensorflow as tf
from model import layers
from model import blocks
__all__ = ['squeeze_excitation_layer']
def squeeze_excitation_layer(
inputs,
ratio,
training=True,
data_format='NCHW',
kernel_initializer=tf.compat.v1.variance_scaling_initializer(),
bias_initializer=tf.zeros_initializer(),
name="squeeze_excitation_layer"
):
if data_format not in ['NHWC', 'NCHW']:
raise ValueError("Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" % data_format)
in_shape = inputs.get_shape()
num_channels = in_shape[1] if data_format == "NCHW" else in_shape[-1]
with tf.variable_scope(name):
net = inputs
# squeeze
squeeze = layers.reduce_mean(
net,
keepdims=False,
data_format=data_format,
name='squeeze_spatial_mean'
)
# fc + relu
excitation = layers.dense(
inputs=squeeze,
units=num_channels // ratio,
use_bias=True,
trainable=training,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer
)
excitation = layers.relu(excitation)
# fc + sigmoid
excitation = layers.dense(
inputs=excitation,
units=num_channels,
use_bias=True,
trainable=training,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer
)
excitation = layers.sigmoid(excitation)
out_shape = [-1, num_channels, 1, 1] if data_format == "NCHW" else [-1, 1, 1, num_channels]
excitation = tf.reshape(excitation, out_shape)
net = net * excitation
return net
|
PyTorch/Classification/ConvNets/efficientnet/training/FP32 | FP32 | DGX1V-16G_efficientnet-b0_FP32 | python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision FP32 --mode convergence --platform DGX1V-16G /imagenet --workspace ${1:-./} --raport-file raport.json
|
PyTorch/SpeechSynthesis/FastPitch/hifigan | hifigan | arg_parser | # 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 argparse
from ast import literal_eval
def parse_hifigan_args(parent, add_help=False):
"""
Parse model specific commandline arguments.
"""
parser = argparse.ArgumentParser(parents=[parent], add_help=add_help,
allow_abbrev=False)
hfg = parser.add_argument_group('HiFi-GAN generator parameters')
hfg.add_argument('--upsample_rates', default=[8, 8, 2, 2],
type=literal_eval_arg,
help='Upsample rates')
hfg.add_argument('--upsample_kernel_sizes', default=[16, 16, 4, 4],
type=literal_eval_arg,
help='Upsample kernel sizes')
hfg.add_argument('--upsample_initial_channel', default=512, type=int,
help='Upsample initial channel')
hfg.add_argument('--resblock', default='1', type=str,
help='Resblock module version')
hfg.add_argument('--resblock_kernel_sizes', default=[3, 7, 11],
type=literal_eval_arg,
help='Resblock kernel sizes')
hfg.add_argument('--resblock_dilation_sizes', type=literal_eval_arg,
default=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
help='Resblock dilation sizes'),
hfg = parser.add_argument_group('HiFi-GAN discriminator parameters')
hfg.add_argument('--mpd_periods', default=[2, 3, 5, 7, 11],
type=literal_eval_arg,
help='Periods of MultiPeriodDiscriminator')
hfg.add_argument('--concat_fwd', action='store_true',
help='Faster Discriminators (requires more GPU memory)')
hfg.add_argument('--hifigan-config', type=str, default=None, required=False,
help='Path to a HiFi-GAN config .json'
' (if provided, overrides model architecture flags)')
return parser
def literal_eval_arg(val):
try:
return literal_eval(val)
except SyntaxError as e: # Argparse does not handle SyntaxError
raise ValueError(str(e)) from e
|
TensorFlow2/Recommendation/DLRM_and_DCNv2/tensorflow-dot-based-interact/tensorflow_dot_based_interact/cc/kernels/launchers | launchers | dot_based_interact_fp16_launcher | // 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.
//
#ifndef FP16_LAUNCHER_CU
#define FP16_LAUNCHER_CU
#include "../cuda_kernels/dot_based_interact_fp16.cu"
inline void dotBasedInteractFP16Fwd(const void *input,
const void *bottom_mlp_output,
void *output,
uint batch_size,
uint num_rows,
uint num_cols,
cudaStream_t stream) {
// num_rows is num cat variables + 1
// num_cols is embedding dim
const uint kWarpSize = 32;
const uint kWarpSizeLog2 = Log2<kWarpSize>::value;
const uint kTileDim = 16;
const uint kTileDimLog2 = Log2<kTileDim>::value;
const uint warps_per_threadblock = 4;
const uint threadblock_size = warps_per_threadblock * 32;
const uint kRowTilesPerStep = 2;
const uint kColTilesPerStep = 1;
// num tiles - divide rounding up
uint num_row_tiles = (num_rows + kTileDim - 1) >> kTileDimLog2;
uint num_col_tiles = (num_cols + kTileDim - 1) >> kTileDimLog2;
// number of rows and columns after padding
uint num_rows_after_padding = kTileDim << 1; //TODO here we assume that kTileDim*2 >= num_rows.
uint num_cols_after_padding = num_col_tiles << kTileDimLog2; //
uint num_row_steps = num_row_tiles / kRowTilesPerStep;
uint num_col_steps = num_col_tiles / kColTilesPerStep;
const uint K_BLOCKS = 8; //todo what is this? is this 128//16, ie num_col_tiles?
const uint M_BLOCKS = 2; //todo what is this? is this 32//16, ie num_row_tiles?
const uint SKEW_HALF = ((K_BLOCKS % 2) == 0) ? 8 : 0;
const uint SMEM_STRIDE = (K_BLOCKS * 16 + SKEW_HALF); //num_cols padded for tensor cores, then padded again for alignment
// multiple of 2 to guarantee 256-bit alignment for start of the row, at least 16 to safeload a tile
const uint smem_rows_per_warp = M_BLOCKS << 4; //is this m_blocks * tile dim? how many rows we multiply in a single pass
const uint smem_elems_per_warp_mat = smem_rows_per_warp * SMEM_STRIDE;
const uint SKEW_HALF_ACC = ((M_BLOCKS % 2) == 0) ? 8 : 0;
const uint SMEM_STRIDE_ACC = (M_BLOCKS * 16 + SKEW_HALF_ACC);
const uint smem_elems_per_warp_acc = M_BLOCKS * 16 * SMEM_STRIDE_ACC * 2;
const uint smem_elems_per_warp =
(smem_elems_per_warp_mat > smem_elems_per_warp_acc) ? smem_elems_per_warp_mat : smem_elems_per_warp_acc;
uint raw_output_size = ((num_rows * (num_rows - 1)) >> 1) + num_cols;
uint output_size = ((raw_output_size-1)/8 + 1)*8; //round up to multiple of 8
uint padding_size = output_size-raw_output_size;
bool float4_predicate = !((num_cols & 7) || (output_size & 7));
if (float4_predicate) {
//aligned
dotBasedInteractFwdKernelFP16<warps_per_threadblock,
threadblock_size,
M_BLOCKS,
K_BLOCKS,
SMEM_STRIDE,
SMEM_STRIDE_ACC,
kWarpSize,
kWarpSizeLog2,
kTileDim,
kTileDimLog2,
true>
<<<(batch_size + warps_per_threadblock - 1) / warps_per_threadblock,
threadblock_size,
warps_per_threadblock * smem_elems_per_warp * sizeof(__half), stream>>>((const __half *)input,
(half *)output,
batch_size,
num_rows,
num_cols,
num_rows_after_padding,
num_cols_after_padding,
smem_elems_per_warp,
smem_rows_per_warp,
output_size,
num_row_steps,
num_col_steps,
padding_size);
} else {
//not aligned
dotBasedInteractFwdKernelFP16<warps_per_threadblock,
threadblock_size,
M_BLOCKS,
K_BLOCKS,
SMEM_STRIDE,
SMEM_STRIDE_ACC,
kWarpSize,
kWarpSizeLog2,
kTileDim,
kTileDimLog2,
false>
<<<(batch_size + warps_per_threadblock - 1) / warps_per_threadblock,
threadblock_size,
warps_per_threadblock * smem_elems_per_warp * sizeof(__half), stream>>>((const __half *)input,
(half *)output,
batch_size,
num_rows,
num_cols,
num_rows_after_padding,
num_cols_after_padding,
smem_elems_per_warp,
smem_rows_per_warp,
output_size,
num_row_steps,
num_col_steps,
padding_size);
}
}
inline void dotBasedInteractFP16Bwd(const void *input,
const void *upstream_grad,
void *grad,
void *bottom_mlp_grad,
uint batch_size,
uint num_rows,
uint num_cols,
cudaStream_t stream) {
const uint kWarpSize = 32;
const uint kWarpSizeLog2 = Log2<kWarpSize>::value;
const uint kTileDim = 16;
const uint kTileDimLog2 = Log2<kTileDim>::value;
const uint mem_skew_size = 8;
const uint kWarpsPerBlock = 4;
const uint kWarpsPerBlockLog2 = Log2<kWarpsPerBlock>::value;
const uint kNumThreads = kWarpsPerBlock * kWarpSize;
const uint kRowTilesPerStep = 2;
const uint kColTilesPerStep = 1;
uint row_tiles_per_step = num_rows > kTileDim ? kRowTilesPerStep : 1;
// num tiles
uint num_row_tiles = (num_rows + kTileDim - 1) >> kTileDimLog2;
uint num_col_tiles = (num_cols + kTileDim - 1) >> kTileDimLog2;
// number of rows and columns after padding
uint num_rows_after_padding = kTileDim << 1;
uint num_cols_after_padding = num_col_tiles << kTileDimLog2;
// 2D ugrad size and stride
uint interaction_ugrad_2D_stride = num_rows_after_padding + mem_skew_size;
uint interaction_ugrad_2D_size_elems = num_rows_after_padding * interaction_ugrad_2D_stride;
uint interaction_ugrad_2D_size_bytes = interaction_ugrad_2D_size_elems * sizeof(half);
// 1D ugrad size
uint interaction_ugrad_size = num_rows * (num_rows - 1) >> 1;
uint interaction_ugrad_size_with_padding = ((interaction_ugrad_size-1)/8 + 1)*8; //round up to multiple of 8
// in_out place size and stride
uint input_stride = num_cols_after_padding + mem_skew_size;
uint input_size_elems = num_rows_after_padding * input_stride;
uint input_size_bytes = input_size_elems * sizeof(half);
// sample size
uint sample_size = num_rows * num_cols;
// output size
uint output_size_elems = kTileDim * kTileDim * kRowTilesPerStep * kColTilesPerStep;
uint output_size_bytes = output_size_elems * sizeof(float);
// staging area size
uint staging_area_size_bytes =
output_size_bytes > interaction_ugrad_2D_size_bytes ? output_size_bytes : interaction_ugrad_2D_size_bytes;
// Shared memory size
uint shared_mem_per_warp_size_byte = input_size_bytes + staging_area_size_bytes;
uint shared_mem_size_bytes = kWarpsPerBlock * shared_mem_per_warp_size_byte;
uint num_blocks = (batch_size + kWarpsPerBlock - 1) >> kWarpsPerBlockLog2;
uint num_row_steps = num_row_tiles / row_tiles_per_step;
uint num_col_steps = num_col_tiles / kColTilesPerStep;
bool float4_predicate = !((interaction_ugrad_size_with_padding & 7) || (num_cols & 7));
if (float4_predicate) {
//aligned
dotBasedInteractBwdKernelFP16<kWarpsPerBlock,
kNumThreads,
kRowTilesPerStep,
kColTilesPerStep,
kWarpSize,
kWarpSizeLog2,
kTileDim,
kTileDimLog2,
true>
<<<num_blocks, kNumThreads, shared_mem_size_bytes, stream>>>((const half *)input,
(const half *)upstream_grad,
(half *)grad,
(half *)bottom_mlp_grad,
batch_size,
num_rows,
num_cols,
num_rows_after_padding,
num_cols_after_padding,
sample_size,
interaction_ugrad_size,
interaction_ugrad_size_with_padding,
interaction_ugrad_2D_size_elems,
interaction_ugrad_2D_stride,
input_size_elems,
input_stride,
num_row_steps,
num_col_steps,
row_tiles_per_step,
shared_mem_per_warp_size_byte);
} else {
//unaligned
dotBasedInteractBwdKernelFP16<kWarpsPerBlock,
kNumThreads,
kRowTilesPerStep,
kColTilesPerStep,
kWarpSize,
kWarpSizeLog2,
kTileDim,
kTileDimLog2,
false>
<<<num_blocks, kNumThreads, shared_mem_size_bytes, stream>>>((const half *)input,
(const half *)upstream_grad,
(half *)grad,
(half *)bottom_mlp_grad,
batch_size,
num_rows,
num_cols,
num_rows_after_padding,
num_cols_after_padding,
sample_size,
interaction_ugrad_size,
interaction_ugrad_size_with_padding,
interaction_ugrad_2D_size_elems,
interaction_ugrad_2D_stride,
input_size_elems,
input_stride,
num_row_steps,
num_col_steps,
row_tiles_per_step,
shared_mem_per_warp_size_byte);
}
}
#endif /* FP16_LAUNCHER_CU */
|
PaddlePaddle/Classification/RN50v1.5 | RN50v1.5 | program | # 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 logging
import time
from profile import Profiler
import dllogger
import models
import numpy as np
from lr_scheduler import build_lr_scheduler
from optimizer import build_optimizer
from utils.misc import AverageMeter
from utils.mode import Mode, RunScope
from utils.utility import get_num_trainers
import paddle
import paddle.nn.functional as F
from paddle.distributed import fleet
from paddle.distributed.fleet import DistributedStrategy
from paddle.distributed.fleet.meta_optimizers.common import CollectiveHelper
from paddle.incubate import asp as sparsity
def create_feeds(image_shape):
"""
Create feeds mapping for the inputs of Pragrm execution.
Args:
image_shape(list[int]): Model input shape, such as [4, 224, 224].
Returns:
feeds(dict): A dict to map variables'name to their values.
key (string): Name of variable to feed.
Value (tuple): paddle.static.data.
"""
feeds = {}
feeds['data'] = paddle.static.data(
name="data", shape=[None] + image_shape, dtype="float32"
)
feeds['label'] = paddle.static.data(
name="label", shape=[None, 1], dtype="int64"
)
return feeds
def create_fetchs(out, feeds, class_num, label_smoothing=0, mode=Mode.TRAIN):
"""
Create fetchs to obtain specific outputs from Pragrm execution (included loss and measures).
Args:
out(variable): The model output variable.
feeds(dict): A dict of mapping variables'name to their values
(The input of Program execution).
class_num(int): The number of classes.
label_smoothing(float, optional): Epsilon of label smoothing. Default: 0.
mode(utils.Mode, optional): Train or eval mode. Default: Mode.TRAIN
Returns:
fetchs(dict): A dict of outputs from Program execution (included loss and measures).
key (string): Name of variable to fetch.
Value (tuple): (variable, AverageMeter).
"""
fetchs = {}
target = paddle.reshape(feeds['label'], [-1, 1])
if mode == Mode.TRAIN:
if label_smoothing == 0:
loss = F.cross_entropy(out, target)
else:
label_one_hot = F.one_hot(target, class_num)
soft_target = F.label_smooth(label_one_hot, epsilon=label_smoothing)
soft_target = paddle.reshape(soft_target, shape=[-1, class_num])
log_softmax = -F.log_softmax(out, axis=-1)
loss = paddle.sum(log_softmax * soft_target, axis=-1)
else:
loss = F.cross_entropy(out, target)
label = paddle.argmax(out, axis=-1, dtype='int32')
fetchs['label'] = (label, None)
loss = loss.mean()
fetchs['loss'] = (loss, AverageMeter('loss', '7.4f', need_avg=True))
acc_top1 = paddle.metric.accuracy(input=out, label=target, k=1)
acc_top5 = paddle.metric.accuracy(input=out, label=target, k=5)
metric_dict = {}
metric_dict["top1"] = acc_top1
metric_dict["top5"] = acc_top5
for key in metric_dict:
if mode != Mode.TRAIN and paddle.distributed.get_world_size() > 1:
paddle.distributed.all_reduce(
metric_dict[key], op=paddle.distributed.ReduceOp.SUM
)
metric_dict[key] = (
metric_dict[key] / paddle.distributed.get_world_size()
)
fetchs[key] = (
metric_dict[key],
AverageMeter(key, '7.4f', need_avg=True),
)
return fetchs
def create_strategy(args, is_train=True):
"""
Create paddle.static.BuildStrategy and paddle.static.ExecutionStrategy with arguments.
Args:
args(Namespace): Arguments obtained from ArgumentParser.
is_train(bool, optional): Indicate the prupose of strategy is for training
of not. Default is True.
Returns:
build_strategy(paddle.static.BuildStrategy): A instance of BuildStrategy.
exec_strategy(paddle.static.ExecutionStrategy): A instance of ExecutionStrategy.
"""
build_strategy = paddle.static.BuildStrategy()
exec_strategy = paddle.static.ExecutionStrategy()
exec_strategy.num_threads = 1
exec_strategy.num_iteration_per_drop_scope = (
10000 if args.amp and args.use_pure_fp16 else 10
)
paddle.set_flags(
{
'FLAGS_cudnn_exhaustive_search': True,
'FLAGS_conv_workspace_size_limit': 4096,
}
)
if not is_train:
build_strategy.fix_op_run_order = True
if args.amp:
build_strategy.fuse_bn_act_ops = True
build_strategy.fuse_elewise_add_act_ops = True
build_strategy.fuse_bn_add_act_ops = True
build_strategy.enable_addto = True
if args.fuse_resunit and is_train:
build_strategy.fuse_resunit = True
return build_strategy, exec_strategy
def dist_optimizer(args, optimizer):
"""
Create a distributed optimizer based on a given optimizer.
Args:
args(Namespace): Arguments obtained from ArgumentParser.
optimizer(paddle.optimizer): A normal optimizer.
Returns:
optimizer(fleet.distributed_optimizer): A distributed optimizer.
"""
build_strategy, exec_strategy = create_strategy(args)
dist_strategy = DistributedStrategy()
dist_strategy.execution_strategy = exec_strategy
dist_strategy.build_strategy = build_strategy
dist_strategy.fuse_all_reduce_ops = True
all_reduce_size = 16
dist_strategy.fuse_grad_size_in_MB = all_reduce_size
dist_strategy.nccl_comm_num = 1
dist_strategy.sync_nccl_allreduce = True
if args.amp:
dist_strategy.cudnn_batchnorm_spatial_persistent = True
dist_strategy.amp = True
dist_strategy.amp_configs = {
"init_loss_scaling": args.scale_loss,
"use_dynamic_loss_scaling": args.use_dynamic_loss_scaling,
"use_pure_fp16": args.use_pure_fp16,
}
dist_strategy.asp = args.asp
optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy)
return optimizer
def build(args, main_prog, startup_prog, step_each_epoch, is_train=True):
"""
Build a executable paddle.static.Program via following four steps:
1. Create feeds.
2. Create a model.
3. Create fetchs.
4. Create an optimizer if is_train==True.
Args:
args(Namespace): Arguments obtained from ArgumentParser.
main_prog(paddle.static.Program):The main program.
startup_prog(paddle.static.Program):The startup program.
step_each_epoch(int): The number of steps in each epoch.
is_train(bool, optional): Whether the main programe created is for training. Default: True.
Returns:
fetchs(dict): A dict of outputs from Program execution (included loss and measures).
lr_scheduler(paddle.optimizer.lr.LRScheduler): A learning rate scheduler.
feeds(dict): A dict to map variables'name to their values.
optimizer(Optimizer): An optimizer with distributed/AMP/ASP strategy.
"""
with paddle.static.program_guard(main_prog, startup_prog):
with paddle.utils.unique_name.guard():
mode = Mode.TRAIN if is_train else Mode.EVAL
feeds = create_feeds(args.image_shape)
model_name = args.model_arch_name
class_num = args.num_of_class
input_image_channel = args.image_channel
data_format = args.data_layout
use_pure_fp16 = args.use_pure_fp16
bn_weight_decay = args.bn_weight_decay
model = models.__dict__[model_name](
class_num=class_num,
input_image_channel=input_image_channel,
data_format=data_format,
use_pure_fp16=use_pure_fp16,
bn_weight_decay=bn_weight_decay,
)
out = model(feeds["data"])
fetchs = create_fetchs(
out, feeds, class_num, args.label_smoothing, mode=mode
)
if args.asp:
sparsity.set_excluded_layers(main_program=main_prog, param_names=[model.fc.weight.name])
lr_scheduler = None
optimizer = None
if is_train:
lr_scheduler = build_lr_scheduler(args, step_each_epoch)
optimizer = build_optimizer(args, lr_scheduler)
optimizer = dist_optimizer(args, optimizer)
optimizer.minimize(fetchs['loss'][0], startup_prog)
# This is a workaround to "Communicator of ring id 0 has not been initialized.".
# Since Paddle's design, the initialization would be done inside train program,
# eval_only need to manually call initialization.
if (
args.run_scope == RunScope.EVAL_ONLY
and paddle.distributed.get_world_size() > 1
):
collective_helper = CollectiveHelper(
role_maker=fleet.PaddleCloudRoleMaker(is_collective=True)
)
collective_helper.update_startup_program(startup_prog)
return fetchs, lr_scheduler, feeds, optimizer
def compile_prog(args, program, loss_name=None, is_train=True):
"""
Compile the given program, which would fuse computing ops or optimize memory footprint
based building strategy in config.
Args:
args(Namespace): Arguments obtained from ArgumentParser.
program(paddle.static.Program): The main program to be compiled.
loss_name(str, optional): The name of loss variable. Default: None.
is_train(bool, optional): Indicate the prupose of strategy is for
training of not. Default is True.
Returns:
compiled_program(paddle.static.CompiledProgram): A compiled program.
"""
build_strategy, exec_strategy = create_strategy(args, is_train)
compiled_program = paddle.static.CompiledProgram(
program, build_strategy=build_strategy
)
return compiled_program
def run(
args,
dataloader,
exe,
program,
fetchs,
epoch,
mode=Mode.TRAIN,
lr_scheduler=None,
):
"""
Execute program.
Args:
args(Namespace): Arguments obtained from ArgumentParser.
dataloader(nvidia.dali.plugin.paddle.DALIGenericIterator):
Iteratable output of NVIDIA DALI pipeline,
please refer to dali_dataloader in dali.py for details.
exe(paddle.static.Executor): A executor to run program.
program(paddle.static.Program): The program to be executed.
fetchs(dict): A dict of outputs from Program execution (included loss and measures).
epoch(int): Current epoch id to run.
mode(utils.Mode, optional): Train or eval mode. Default: Mode.TRAIN.
lr_scheduler(paddle.optimizer.lr.LRScheduler, optional): A learning rate scheduler.
Default: None.
Returns:
metrics (dict): A dictionary to collect values of metrics.
"""
num_trainers = get_num_trainers()
fetch_list = [f[0] for f in fetchs.values()]
metric_dict = {"lr": AverageMeter('lr', 'f', postfix=",", need_avg=False)}
for k in fetchs:
if fetchs[k][1] is not None:
metric_dict[k] = fetchs[k][1]
metric_dict["batch_time"] = AverageMeter('batch_time', '.5f', postfix=" s,")
metric_dict["data_time"] = AverageMeter('data_time', '.5f', postfix=" s,")
metric_dict["compute_time"] = AverageMeter(
'compute_time', '.5f', postfix=" s,"
)
for m in metric_dict.values():
m.reset()
profiler = Profiler()
tic = time.perf_counter()
idx = 0
batch_size = None
latency = []
total_benchmark_steps = args.benchmark_steps + args.benchmark_warmup_steps
dataloader.reset()
while True:
# profiler.profile_setup return True only when
# profile is enable and idx == stop steps
if profiler.profile_setup(idx):
break
idx += 1
try:
batch = next(dataloader)
except StopIteration:
# Reset dataloader when run benchmark to fill required steps.
if args.benchmark and (idx < total_benchmark_steps):
dataloader.reset()
# Reset tic timestamp to ignore exception handling time.
tic = time.perf_counter()
continue
break
except RuntimeError:
logging.warning(
"Except RuntimeError when reading data from dataloader, try to read once again..."
)
continue
reader_toc = time.perf_counter()
metric_dict['data_time'].update(reader_toc - tic)
batch_size = batch[0]["data"].shape()[0]
feed_dict = batch[0]
with profiler.profile_tag(
idx, "Training" if mode == Mode.TRAIN else "Evaluation"
):
results = exe.run(
program=program, feed=feed_dict, fetch_list=fetch_list
)
for name, m in zip(fetchs.keys(), results):
if name in metric_dict:
metric_dict[name].update(np.mean(m), batch_size)
metric_dict["compute_time"].update(time.perf_counter() - reader_toc)
metric_dict["batch_time"].update(time.perf_counter() - tic)
if mode == Mode.TRAIN:
metric_dict['lr'].update(lr_scheduler.get_lr())
if lr_scheduler is not None:
with profiler.profile_tag(idx, "LR Step"):
lr_scheduler.step()
tic = time.perf_counter()
if idx % args.print_interval == 0:
log_msg = {}
log_msg['loss'] = metric_dict['loss'].val.item()
log_msg['top1'] = metric_dict['top1'].val.item()
log_msg['top5'] = metric_dict['top5'].val.item()
log_msg['data_time'] = metric_dict['data_time'].val
log_msg['compute_time'] = metric_dict['compute_time'].val
log_msg['batch_time'] = metric_dict['batch_time'].val
log_msg['ips'] = (
batch_size * num_trainers / metric_dict['batch_time'].val
)
if mode == Mode.TRAIN:
log_msg['lr'] = metric_dict['lr'].val
log_info((epoch, idx), log_msg, mode)
if args.benchmark:
latency.append(metric_dict['batch_time'].val)
# Ignore the warmup iters
if idx == args.benchmark_warmup_steps:
metric_dict["compute_time"].reset()
metric_dict["data_time"].reset()
metric_dict["batch_time"].reset()
latency.clear()
logging.info("Begin benchmark at step %d", idx + 1)
if idx == total_benchmark_steps:
benchmark_data = {}
benchmark_data['ips'] = (
batch_size * num_trainers / metric_dict['batch_time'].avg
)
if mode == mode.EVAL:
latency = np.array(latency) * 1000
quantile = np.quantile(latency, [0.9, 0.95, 0.99])
benchmark_data['latency_avg'] = np.mean(latency)
benchmark_data['latency_p90'] = quantile[0]
benchmark_data['latency_p95'] = quantile[1]
benchmark_data['latency_p99'] = quantile[2]
logging.info("End benchmark at epoch step %d", idx)
return benchmark_data
epoch_data = {}
epoch_data['loss'] = metric_dict['loss'].avg.item()
epoch_data['epoch_time'] = metric_dict['batch_time'].total
epoch_data['ips'] = (
batch_size
* num_trainers
* metric_dict["batch_time"].count
/ metric_dict["batch_time"].sum
)
if mode == Mode.EVAL:
epoch_data['top1'] = metric_dict['top1'].avg.item()
epoch_data['top5'] = metric_dict['top5'].avg.item()
log_info((epoch,), epoch_data, mode)
return epoch_data
def log_info(step, metrics, mode):
"""
Log metrics with step and mode information.
Args:
step(tuple): Step, coulbe (epoch-id, iter-id). Use tuple() for summary.
metrics(dict): A dictionary collected values of metrics.
mode(utils.Mode): Train or eval mode.
"""
prefix = 'train' if mode == Mode.TRAIN else 'val'
dllogger_iter_data = {}
for key in metrics:
dllogger_iter_data[f"{prefix}.{key}"] = metrics[key]
dllogger.log(step=step, data=dllogger_iter_data)
|
PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils | utils | README | # Utility functions
This folder contain utility functions that are not used in the
core library, but are useful for building models or training
code using the config system.
|
PyTorch/Segmentation/MaskRCNN/pytorch/configs/gn_baselines | gn_baselines | scratch_e2e_mask_rcnn_R_50_FPN_3x_gn | INPUT:
MIN_SIZE_TRAIN: 800
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "" # no pretrained model
BACKBONE:
CONV_BODY: "R-50-FPN"
OUT_CHANNELS: 256
FREEZE_CONV_BODY_AT: 0 # finetune all layers
RESNETS: # use GN for backbone
TRANS_FUNC: "BottleneckWithGN"
STEM_FUNC: "StemWithGN"
FPN:
USE_GN: True # use GN for FPN
RPN:
USE_FPN: True
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
PRE_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TEST: 1000
ROI_HEADS:
USE_FPN: True
BATCH_SIZE_PER_IMAGE: 512
POSITIVE_FRACTION: 0.25
ROI_BOX_HEAD:
USE_GN: True # use GN for bbox head
POOLER_RESOLUTION: 7
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POOLER_SAMPLING_RATIO: 2
FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
PREDICTOR: "FPNPredictor"
ROI_MASK_HEAD:
USE_GN: True # use GN for mask head
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
CONV_LAYERS: (256, 256, 256, 256)
FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
PREDICTOR: "MaskRCNNC4Predictor"
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
RESOLUTION: 28
SHARE_BOX_FEATURE_EXTRACTOR: False
MASK_ON: True
DATASETS:
TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
TEST: ("coco_2014_minival",)
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
# Assume 8 gpus
BASE_LR: 0.02
WEIGHT_DECAY: 0.0001
STEPS: (210000, 250000)
MAX_ITER: 270000
IMS_PER_BATCH: 16
TEST:
IMS_PER_BATCH: 8
|
TensorFlow/Detection/SSD/models/research/object_detection/configs | configs | mask_rcnn_resnet50_atrous_coco_8GPU | # Mask R-CNN with Resnet-50 (v1), Atrous version
# Configured for MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.
#
# 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.
model {
faster_rcnn {
num_classes: 90
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 800
max_dimension: 1365
}
}
number_of_stages: 3
feature_extractor {
type: 'faster_rcnn_resnet50'
first_stage_features_stride: 8
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 8
width_stride: 8
}
}
first_stage_atrous_rate: 2
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
predict_instance_masks: true
mask_height: 33
mask_width: 33
mask_prediction_conv_depth: 0
mask_prediction_num_conv_layers: 4
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
second_stage_mask_prediction_loss_weight: 4.0
}
}
train_config: {
batch_size: 1
optimizer {
momentum_optimizer: {
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 0.0024
schedule {
step: 900000
learning_rate: .00024
}
schedule {
step: 1200000
learning_rate: .000024
}
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "/checkpoints/resnet_v1_50/model.ckpt"
fine_tune_checkpoint_type: "classification"
# Note: The below line limits the training process to 200K steps, which we
# empirically found to be sufficient enough to train the pets dataset. This
# effectively bypasses the learning rate schedule (the learning rate will
# never decay). Remove the below line to train indefinitely.
num_steps: 200000
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "/data/coco_train.record*"
}
label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
num_examples: 8000
}
eval_input_reader: {
tf_record_input_reader {
input_path: "/data/coco_val.record*"
}
label_map_path: "object_detection/data/mscoco_label_map.pbtxt"
shuffle: false
num_readers: 1
}
|
PyTorch/Translation/GNMT/seq2seq/train | train | table | # Copyright (c) 2018-2020, NVIDIA CORPORATION. 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.
from pytablewriter import MarkdownTableWriter
class TrainingTable:
def __init__(self, acc_unit='BLEU', time_unit='min', perf_unit='tok/s'):
self.data = []
self.acc_unit = acc_unit
self.time_unit = time_unit
self.perf_unit = perf_unit
self.time_unit_convert = {'s': 1, 'min': 1/60, 'h': 1/3600}
def add(self, gpus, batch_size, accuracy, perf, time_to_train):
time_to_train *= self.time_unit_convert[self.time_unit]
if not accuracy:
accuracy = 0.0
accuracy = round(accuracy, 2)
self.data.append([gpus, batch_size, accuracy, perf, time_to_train])
def write(self, title, math):
writer = MarkdownTableWriter()
writer.table_name = f'{title}'
header = [f'**GPUs**',
f'**Batch Size / GPU**',
f'**Accuracy - {math.upper()} ({self.acc_unit})**',
f'**Throughput - {math.upper()} ({self.perf_unit})**',
f'**Time to Train - {math.upper()} ({self.time_unit})**',
]
writer.headers = header
writer.value_matrix = self.data
writer.write_table()
|
PyTorch/SpeechSynthesis/Tacotron2/platform | platform | DGX1_tacotron2_AMP_8NGPU_train | mkdir -p output
python -m multiproc train.py -m Tacotron2 -o output/ --amp -lr 1e-3 --epochs 1501 -bs 104 --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 0.3
|
TensorFlow2/LanguageModeling/BERT/official/utils/misc | misc | callstack_sampler | """A simple Python callstack sampler."""
import contextlib
import datetime
import signal
import traceback
class CallstackSampler(object):
"""A simple signal-based Python callstack sampler.
"""
def __init__(self, interval=None):
self.stacks = []
self.interval = 0.001 if interval is None else interval
def _sample(self, signum, frame):
"""Samples the current stack."""
del signum
stack = traceback.extract_stack(frame)
formatted_stack = []
formatted_stack.append(datetime.datetime.utcnow())
for filename, lineno, function_name, text in stack:
formatted_frame = '{}:{}({})({})'.format(filename, lineno, function_name,
text)
formatted_stack.append(formatted_frame)
self.stacks.append(formatted_stack)
signal.setitimer(signal.ITIMER_VIRTUAL, self.interval, 0)
@contextlib.contextmanager
def profile(self):
signal.signal(signal.SIGVTALRM, self._sample)
signal.setitimer(signal.ITIMER_VIRTUAL, self.interval, 0)
try:
yield
finally:
signal.setitimer(signal.ITIMER_VIRTUAL, 0)
def save(self, fname):
with open(fname, 'w') as f:
for s in self.stacks:
for l in s:
f.write('%s\n' % l)
f.write('\n')
@contextlib.contextmanager
def callstack_sampling(filename, interval=None):
"""Periodically samples the Python callstack.
Args:
filename: the filename
interval: the sampling interval, in seconds. Defaults to 0.001.
Yields:
nothing
"""
sampler = CallstackSampler(interval=interval)
with sampler.profile():
yield
sampler.save(filename)
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | ops | # 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.
# ==============================================================================
"""A module for helper tensorflow ops."""
import collections
import math
import numpy as np
import six
import tensorflow as tf
from object_detection.core import standard_fields as fields
from object_detection.utils import shape_utils
from object_detection.utils import static_shape
def expanded_shape(orig_shape, start_dim, num_dims):
"""Inserts multiple ones into a shape vector.
Inserts an all-1 vector of length num_dims at position start_dim into a shape.
Can be combined with tf.reshape to generalize tf.expand_dims.
Args:
orig_shape: the shape into which the all-1 vector is added (int32 vector)
start_dim: insertion position (int scalar)
num_dims: length of the inserted all-1 vector (int scalar)
Returns:
An int32 vector of length tf.size(orig_shape) + num_dims.
"""
with tf.name_scope('ExpandedShape'):
start_dim = tf.expand_dims(start_dim, 0) # scalar to rank-1
before = tf.slice(orig_shape, [0], start_dim)
add_shape = tf.ones(tf.reshape(num_dims, [1]), dtype=tf.int32)
after = tf.slice(orig_shape, start_dim, [-1])
new_shape = tf.concat([before, add_shape, after], 0)
return new_shape
def normalized_to_image_coordinates(normalized_boxes, image_shape,
parallel_iterations=32):
"""Converts a batch of boxes from normal to image coordinates.
Args:
normalized_boxes: a float32 tensor of shape [None, num_boxes, 4] in
normalized coordinates.
image_shape: a float32 tensor of shape [4] containing the image shape.
parallel_iterations: parallelism for the map_fn op.
Returns:
absolute_boxes: a float32 tensor of shape [None, num_boxes, 4] containing
the boxes in image coordinates.
"""
x_scale = tf.cast(image_shape[2], tf.float32)
y_scale = tf.cast(image_shape[1], tf.float32)
def _to_absolute_coordinates(normalized_boxes):
y_min, x_min, y_max, x_max = tf.split(
value=normalized_boxes, num_or_size_splits=4, axis=1)
y_min = y_scale * y_min
y_max = y_scale * y_max
x_min = x_scale * x_min
x_max = x_scale * x_max
scaled_boxes = tf.concat([y_min, x_min, y_max, x_max], 1)
return scaled_boxes
absolute_boxes = shape_utils.static_or_dynamic_map_fn(
_to_absolute_coordinates,
elems=(normalized_boxes),
dtype=tf.float32,
parallel_iterations=parallel_iterations,
back_prop=True)
return absolute_boxes
def meshgrid(x, y):
"""Tiles the contents of x and y into a pair of grids.
Multidimensional analog of numpy.meshgrid, giving the same behavior if x and y
are vectors. Generally, this will give:
xgrid(i1, ..., i_m, j_1, ..., j_n) = x(j_1, ..., j_n)
ygrid(i1, ..., i_m, j_1, ..., j_n) = y(i_1, ..., i_m)
Keep in mind that the order of the arguments and outputs is reverse relative
to the order of the indices they go into, done for compatibility with numpy.
The output tensors have the same shapes. Specifically:
xgrid.get_shape() = y.get_shape().concatenate(x.get_shape())
ygrid.get_shape() = y.get_shape().concatenate(x.get_shape())
Args:
x: A tensor of arbitrary shape and rank. xgrid will contain these values
varying in its last dimensions.
y: A tensor of arbitrary shape and rank. ygrid will contain these values
varying in its first dimensions.
Returns:
A tuple of tensors (xgrid, ygrid).
"""
with tf.name_scope('Meshgrid'):
x = tf.convert_to_tensor(x)
y = tf.convert_to_tensor(y)
x_exp_shape = expanded_shape(tf.shape(x), 0, tf.rank(y))
y_exp_shape = expanded_shape(tf.shape(y), tf.rank(y), tf.rank(x))
xgrid = tf.tile(tf.reshape(x, x_exp_shape), y_exp_shape)
ygrid = tf.tile(tf.reshape(y, y_exp_shape), x_exp_shape)
new_shape = y.get_shape().concatenate(x.get_shape())
xgrid.set_shape(new_shape)
ygrid.set_shape(new_shape)
return xgrid, ygrid
def fixed_padding(inputs, kernel_size, rate=1):
"""Pads the input along the spatial dimensions independently of input size.
Args:
inputs: A tensor of size [batch, height_in, width_in, channels].
kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
Should be a positive integer.
rate: An integer, rate for atrous convolution.
Returns:
output: A tensor of size [batch, height_out, width_out, channels] with the
input, either intact (if kernel_size == 1) or padded (if kernel_size > 1).
"""
kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
pad_total = kernel_size_effective - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
[pad_beg, pad_end], [0, 0]])
return padded_inputs
def pad_to_multiple(tensor, multiple):
"""Returns the tensor zero padded to the specified multiple.
Appends 0s to the end of the first and second dimension (height and width) of
the tensor until both dimensions are a multiple of the input argument
'multiple'. E.g. given an input tensor of shape [1, 3, 5, 1] and an input
multiple of 4, PadToMultiple will append 0s so that the resulting tensor will
be of shape [1, 4, 8, 1].
Args:
tensor: rank 4 float32 tensor, where
tensor -> [batch_size, height, width, channels].
multiple: the multiple to pad to.
Returns:
padded_tensor: the tensor zero padded to the specified multiple.
"""
if multiple == 1:
return tensor
tensor_shape = tensor.get_shape()
batch_size = static_shape.get_batch_size(tensor_shape)
tensor_height = static_shape.get_height(tensor_shape)
tensor_width = static_shape.get_width(tensor_shape)
tensor_depth = static_shape.get_depth(tensor_shape)
if batch_size is None:
batch_size = tf.shape(tensor)[0]
if tensor_height is None:
tensor_height = tf.shape(tensor)[1]
padded_tensor_height = tf.to_int32(
tf.ceil(tf.to_float(tensor_height) / tf.to_float(multiple))) * multiple
else:
padded_tensor_height = int(
math.ceil(float(tensor_height) / multiple) * multiple)
if tensor_width is None:
tensor_width = tf.shape(tensor)[2]
padded_tensor_width = tf.to_int32(
tf.ceil(tf.to_float(tensor_width) / tf.to_float(multiple))) * multiple
else:
padded_tensor_width = int(
math.ceil(float(tensor_width) / multiple) * multiple)
if tensor_depth is None:
tensor_depth = tf.shape(tensor)[3]
# Use tf.concat instead of tf.pad to preserve static shape
if padded_tensor_height != tensor_height:
height_pad = tf.zeros([
batch_size, padded_tensor_height - tensor_height, tensor_width,
tensor_depth
])
tensor = tf.concat([tensor, height_pad], 1)
if padded_tensor_width != tensor_width:
width_pad = tf.zeros([
batch_size, padded_tensor_height, padded_tensor_width - tensor_width,
tensor_depth
])
tensor = tf.concat([tensor, width_pad], 2)
return tensor
def padded_one_hot_encoding(indices, depth, left_pad):
"""Returns a zero padded one-hot tensor.
This function converts a sparse representation of indices (e.g., [4]) to a
zero padded one-hot representation (e.g., [0, 0, 0, 0, 1] with depth = 4 and
left_pad = 1). If `indices` is empty, the result will simply be a tensor of
shape (0, depth + left_pad). If depth = 0, then this function just returns
`None`.
Args:
indices: an integer tensor of shape [num_indices].
depth: depth for the one-hot tensor (integer).
left_pad: number of zeros to left pad the one-hot tensor with (integer).
Returns:
padded_onehot: a tensor with shape (num_indices, depth + left_pad). Returns
`None` if the depth is zero.
Raises:
ValueError: if `indices` does not have rank 1 or if `left_pad` or `depth are
either negative or non-integers.
TODO(rathodv): add runtime checks for depth and indices.
"""
if depth < 0 or not isinstance(depth, six.integer_types):
raise ValueError('`depth` must be a non-negative integer.')
if left_pad < 0 or not isinstance(left_pad, six.integer_types):
raise ValueError('`left_pad` must be a non-negative integer.')
if depth == 0:
return None
rank = len(indices.get_shape().as_list())
if rank != 1:
raise ValueError('`indices` must have rank 1, but has rank=%s' % rank)
def one_hot_and_pad():
one_hot = tf.cast(tf.one_hot(tf.cast(indices, tf.int64), depth,
on_value=1, off_value=0), tf.float32)
return tf.pad(one_hot, [[0, 0], [left_pad, 0]], mode='CONSTANT')
result = tf.cond(tf.greater(tf.size(indices), 0), one_hot_and_pad,
lambda: tf.zeros((depth + left_pad, 0)))
return tf.reshape(result, [-1, depth + left_pad])
def dense_to_sparse_boxes(dense_locations, dense_num_boxes, num_classes):
"""Converts bounding boxes from dense to sparse form.
Args:
dense_locations: a [max_num_boxes, 4] tensor in which only the first k rows
are valid bounding box location coordinates, where k is the sum of
elements in dense_num_boxes.
dense_num_boxes: a [max_num_classes] tensor indicating the counts of
various bounding box classes e.g. [1, 0, 0, 2] means that the first
bounding box is of class 0 and the second and third bounding boxes are
of class 3. The sum of elements in this tensor is the number of valid
bounding boxes.
num_classes: number of classes
Returns:
box_locations: a [num_boxes, 4] tensor containing only valid bounding
boxes (i.e. the first num_boxes rows of dense_locations)
box_classes: a [num_boxes] tensor containing the classes of each bounding
box (e.g. dense_num_boxes = [1, 0, 0, 2] => box_classes = [0, 3, 3]
"""
num_valid_boxes = tf.reduce_sum(dense_num_boxes)
box_locations = tf.slice(dense_locations,
tf.constant([0, 0]), tf.stack([num_valid_boxes, 4]))
tiled_classes = [tf.tile([i], tf.expand_dims(dense_num_boxes[i], 0))
for i in range(num_classes)]
box_classes = tf.concat(tiled_classes, 0)
box_locations.set_shape([None, 4])
return box_locations, box_classes
def indices_to_dense_vector(indices,
size,
indices_value=1.,
default_value=0,
dtype=tf.float32):
"""Creates dense vector with indices set to specific value and rest to zeros.
This function exists because it is unclear if it is safe to use
tf.sparse_to_dense(indices, [size], 1, validate_indices=False)
with indices which are not ordered.
This function accepts a dynamic size (e.g. tf.shape(tensor)[0])
Args:
indices: 1d Tensor with integer indices which are to be set to
indices_values.
size: scalar with size (integer) of output Tensor.
indices_value: values of elements specified by indices in the output vector
default_value: values of other elements in the output vector.
dtype: data type.
Returns:
dense 1D Tensor of shape [size] with indices set to indices_values and the
rest set to default_value.
"""
size = tf.to_int32(size)
zeros = tf.ones([size], dtype=dtype) * default_value
values = tf.ones_like(indices, dtype=dtype) * indices_value
return tf.dynamic_stitch([tf.range(size), tf.to_int32(indices)],
[zeros, values])
def reduce_sum_trailing_dimensions(tensor, ndims):
"""Computes sum across all dimensions following first `ndims` dimensions."""
return tf.reduce_sum(tensor, axis=tuple(range(ndims, tensor.shape.ndims)))
def retain_groundtruth(tensor_dict, valid_indices):
"""Retains groundtruth by valid indices.
Args:
tensor_dict: a dictionary of following groundtruth tensors -
fields.InputDataFields.groundtruth_boxes
fields.InputDataFields.groundtruth_classes
fields.InputDataFields.groundtruth_keypoints
fields.InputDataFields.groundtruth_instance_masks
fields.InputDataFields.groundtruth_is_crowd
fields.InputDataFields.groundtruth_area
fields.InputDataFields.groundtruth_label_types
fields.InputDataFields.groundtruth_difficult
valid_indices: a tensor with valid indices for the box-level groundtruth.
Returns:
a dictionary of tensors containing only the groundtruth for valid_indices.
Raises:
ValueError: If the shape of valid_indices is invalid.
ValueError: field fields.InputDataFields.groundtruth_boxes is
not present in tensor_dict.
"""
input_shape = valid_indices.get_shape().as_list()
if not (len(input_shape) == 1 or
(len(input_shape) == 2 and input_shape[1] == 1)):
raise ValueError('The shape of valid_indices is invalid.')
valid_indices = tf.reshape(valid_indices, [-1])
valid_dict = {}
if fields.InputDataFields.groundtruth_boxes in tensor_dict:
# Prevents reshape failure when num_boxes is 0.
num_boxes = tf.maximum(tf.shape(
tensor_dict[fields.InputDataFields.groundtruth_boxes])[0], 1)
for key in tensor_dict:
if key in [fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
fields.InputDataFields.groundtruth_keypoints,
fields.InputDataFields.groundtruth_instance_masks]:
valid_dict[key] = tf.gather(tensor_dict[key], valid_indices)
# Input decoder returns empty tensor when these fields are not provided.
# Needs to reshape into [num_boxes, -1] for tf.gather() to work.
elif key in [fields.InputDataFields.groundtruth_is_crowd,
fields.InputDataFields.groundtruth_area,
fields.InputDataFields.groundtruth_difficult,
fields.InputDataFields.groundtruth_label_types]:
valid_dict[key] = tf.reshape(
tf.gather(tf.reshape(tensor_dict[key], [num_boxes, -1]),
valid_indices), [-1])
# Fields that are not associated with boxes.
else:
valid_dict[key] = tensor_dict[key]
else:
raise ValueError('%s not present in input tensor dict.' % (
fields.InputDataFields.groundtruth_boxes))
return valid_dict
def retain_groundtruth_with_positive_classes(tensor_dict):
"""Retains only groundtruth with positive class ids.
Args:
tensor_dict: a dictionary of following groundtruth tensors -
fields.InputDataFields.groundtruth_boxes
fields.InputDataFields.groundtruth_classes
fields.InputDataFields.groundtruth_keypoints
fields.InputDataFields.groundtruth_instance_masks
fields.InputDataFields.groundtruth_is_crowd
fields.InputDataFields.groundtruth_area
fields.InputDataFields.groundtruth_label_types
fields.InputDataFields.groundtruth_difficult
Returns:
a dictionary of tensors containing only the groundtruth with positive
classes.
Raises:
ValueError: If groundtruth_classes tensor is not in tensor_dict.
"""
if fields.InputDataFields.groundtruth_classes not in tensor_dict:
raise ValueError('`groundtruth classes` not in tensor_dict.')
keep_indices = tf.where(tf.greater(
tensor_dict[fields.InputDataFields.groundtruth_classes], 0))
return retain_groundtruth(tensor_dict, keep_indices)
def replace_nan_groundtruth_label_scores_with_ones(label_scores):
"""Replaces nan label scores with 1.0.
Args:
label_scores: a tensor containing object annoation label scores.
Returns:
a tensor where NaN label scores have been replaced by ones.
"""
return tf.where(
tf.is_nan(label_scores), tf.ones(tf.shape(label_scores)), label_scores)
def filter_groundtruth_with_crowd_boxes(tensor_dict):
"""Filters out groundtruth with boxes corresponding to crowd.
Args:
tensor_dict: a dictionary of following groundtruth tensors -
fields.InputDataFields.groundtruth_boxes
fields.InputDataFields.groundtruth_classes
fields.InputDataFields.groundtruth_keypoints
fields.InputDataFields.groundtruth_instance_masks
fields.InputDataFields.groundtruth_is_crowd
fields.InputDataFields.groundtruth_area
fields.InputDataFields.groundtruth_label_types
Returns:
a dictionary of tensors containing only the groundtruth that have bounding
boxes.
"""
if fields.InputDataFields.groundtruth_is_crowd in tensor_dict:
is_crowd = tensor_dict[fields.InputDataFields.groundtruth_is_crowd]
is_not_crowd = tf.logical_not(is_crowd)
is_not_crowd_indices = tf.where(is_not_crowd)
tensor_dict = retain_groundtruth(tensor_dict, is_not_crowd_indices)
return tensor_dict
def filter_groundtruth_with_nan_box_coordinates(tensor_dict):
"""Filters out groundtruth with no bounding boxes.
Args:
tensor_dict: a dictionary of following groundtruth tensors -
fields.InputDataFields.groundtruth_boxes
fields.InputDataFields.groundtruth_classes
fields.InputDataFields.groundtruth_keypoints
fields.InputDataFields.groundtruth_instance_masks
fields.InputDataFields.groundtruth_is_crowd
fields.InputDataFields.groundtruth_area
fields.InputDataFields.groundtruth_label_types
Returns:
a dictionary of tensors containing only the groundtruth that have bounding
boxes.
"""
groundtruth_boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
nan_indicator_vector = tf.greater(tf.reduce_sum(tf.to_int32(
tf.is_nan(groundtruth_boxes)), reduction_indices=[1]), 0)
valid_indicator_vector = tf.logical_not(nan_indicator_vector)
valid_indices = tf.where(valid_indicator_vector)
return retain_groundtruth(tensor_dict, valid_indices)
def normalize_to_target(inputs,
target_norm_value,
dim,
epsilon=1e-7,
trainable=True,
scope='NormalizeToTarget',
summarize=True):
"""L2 normalizes the inputs across the specified dimension to a target norm.
This op implements the L2 Normalization layer introduced in
Liu, Wei, et al. "SSD: Single Shot MultiBox Detector."
and Liu, Wei, Andrew Rabinovich, and Alexander C. Berg.
"Parsenet: Looking wider to see better." and is useful for bringing
activations from multiple layers in a convnet to a standard scale.
Note that the rank of `inputs` must be known and the dimension to which
normalization is to be applied should be statically defined.
TODO(jonathanhuang): Add option to scale by L2 norm of the entire input.
Args:
inputs: A `Tensor` of arbitrary size.
target_norm_value: A float value that specifies an initial target norm or
a list of floats (whose length must be equal to the depth along the
dimension to be normalized) specifying a per-dimension multiplier
after normalization.
dim: The dimension along which the input is normalized.
epsilon: A small value to add to the inputs to avoid dividing by zero.
trainable: Whether the norm is trainable or not
scope: Optional scope for variable_scope.
summarize: Whether or not to add a tensorflow summary for the op.
Returns:
The input tensor normalized to the specified target norm.
Raises:
ValueError: If dim is smaller than the number of dimensions in 'inputs'.
ValueError: If target_norm_value is not a float or a list of floats with
length equal to the depth along the dimension to be normalized.
"""
with tf.variable_scope(scope, 'NormalizeToTarget', [inputs]):
if not inputs.get_shape():
raise ValueError('The input rank must be known.')
input_shape = inputs.get_shape().as_list()
input_rank = len(input_shape)
if dim < 0 or dim >= input_rank:
raise ValueError(
'dim must be non-negative but smaller than the input rank.')
if not input_shape[dim]:
raise ValueError('input shape should be statically defined along '
'the specified dimension.')
depth = input_shape[dim]
if not (isinstance(target_norm_value, float) or
(isinstance(target_norm_value, list) and
len(target_norm_value) == depth) and
all([isinstance(val, float) for val in target_norm_value])):
raise ValueError('target_norm_value must be a float or a list of floats '
'with length equal to the depth along the dimension to '
'be normalized.')
if isinstance(target_norm_value, float):
initial_norm = depth * [target_norm_value]
else:
initial_norm = target_norm_value
target_norm = tf.contrib.framework.model_variable(
name='weights', dtype=tf.float32,
initializer=tf.constant(initial_norm, dtype=tf.float32),
trainable=trainable)
if summarize:
mean = tf.reduce_mean(target_norm)
mean = tf.Print(mean, ['NormalizeToTarget:', mean])
tf.summary.scalar(tf.get_variable_scope().name, mean)
lengths = epsilon + tf.sqrt(tf.reduce_sum(tf.square(inputs), dim, True))
mult_shape = input_rank*[1]
mult_shape[dim] = depth
return tf.reshape(target_norm, mult_shape) * tf.truediv(inputs, lengths)
def batch_position_sensitive_crop_regions(images,
boxes,
crop_size,
num_spatial_bins,
global_pool,
parallel_iterations=64):
"""Position sensitive crop with batches of images and boxes.
This op is exactly like `position_sensitive_crop_regions` below but operates
on batches of images and boxes. See `position_sensitive_crop_regions` function
below for the operation applied per batch element.
Args:
images: A `Tensor`. Must be one of the following types: `uint8`, `int8`,
`int16`, `int32`, `int64`, `half`, `float32`, `float64`.
A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
Both `image_height` and `image_width` need to be positive.
boxes: A `Tensor` of type `float32`.
A 3-D tensor of shape `[batch, num_boxes, 4]`. Each box is specified in
normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value
of `y` is mapped to the image coordinate at `y * (image_height - 1)`, so
as the `[0, 1]` interval of normalized image height is mapped to
`[0, image_height - 1] in image height coordinates. We do allow y1 > y2,
in which case the sampled crop is an up-down flipped version of the
original image. The width dimension is treated similarly.
crop_size: See `position_sensitive_crop_regions` below.
num_spatial_bins: See `position_sensitive_crop_regions` below.
global_pool: See `position_sensitive_crop_regions` below.
parallel_iterations: Number of batch items to process in parallel.
Returns:
"""
def _position_sensitive_crop_fn(inputs):
images, boxes = inputs
return position_sensitive_crop_regions(
images,
boxes,
crop_size=crop_size,
num_spatial_bins=num_spatial_bins,
global_pool=global_pool)
return shape_utils.static_or_dynamic_map_fn(
_position_sensitive_crop_fn,
elems=[images, boxes],
dtype=tf.float32,
parallel_iterations=parallel_iterations)
def position_sensitive_crop_regions(image,
boxes,
crop_size,
num_spatial_bins,
global_pool):
"""Position-sensitive crop and pool rectangular regions from a feature grid.
The output crops are split into `spatial_bins_y` vertical bins
and `spatial_bins_x` horizontal bins. For each intersection of a vertical
and a horizontal bin the output values are gathered by performing
`tf.image.crop_and_resize` (bilinear resampling) on a a separate subset of
channels of the image. This reduces `depth` by a factor of
`(spatial_bins_y * spatial_bins_x)`.
When global_pool is True, this function implements a differentiable version
of position-sensitive RoI pooling used in
[R-FCN detection system](https://arxiv.org/abs/1605.06409).
When global_pool is False, this function implements a differentiable version
of position-sensitive assembling operation used in
[instance FCN](https://arxiv.org/abs/1603.08678).
Args:
image: A `Tensor`. Must be one of the following types: `uint8`, `int8`,
`int16`, `int32`, `int64`, `half`, `float32`, `float64`.
A 3-D tensor of shape `[image_height, image_width, depth]`.
Both `image_height` and `image_width` need to be positive.
boxes: A `Tensor` of type `float32`.
A 2-D tensor of shape `[num_boxes, 4]`. Each box is specified in
normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value
of `y` is mapped to the image coordinate at `y * (image_height - 1)`, so
as the `[0, 1]` interval of normalized image height is mapped to
`[0, image_height - 1] in image height coordinates. We do allow y1 > y2,
in which case the sampled crop is an up-down flipped version of the
original image. The width dimension is treated similarly.
crop_size: A list of two integers `[crop_height, crop_width]`. All
cropped image patches are resized to this size. The aspect ratio of the
image content is not preserved. Both `crop_height` and `crop_width` need
to be positive.
num_spatial_bins: A list of two integers `[spatial_bins_y, spatial_bins_x]`.
Represents the number of position-sensitive bins in y and x directions.
Both values should be >= 1. `crop_height` should be divisible by
`spatial_bins_y`, and similarly for width.
The number of image channels should be divisible by
(spatial_bins_y * spatial_bins_x).
Suggested value from R-FCN paper: [3, 3].
global_pool: A boolean variable.
If True, we perform average global pooling on the features assembled from
the position-sensitive score maps.
If False, we keep the position-pooled features without global pooling
over the spatial coordinates.
Note that using global_pool=True is equivalent to but more efficient than
running the function with global_pool=False and then performing global
average pooling.
Returns:
position_sensitive_features: A 4-D tensor of shape
`[num_boxes, K, K, crop_channels]`,
where `crop_channels = depth / (spatial_bins_y * spatial_bins_x)`,
where K = 1 when global_pool is True (Average-pooled cropped regions),
and K = crop_size when global_pool is False.
Raises:
ValueError: Raised in four situations:
`num_spatial_bins` is not >= 1;
`num_spatial_bins` does not divide `crop_size`;
`(spatial_bins_y*spatial_bins_x)` does not divide `depth`;
`bin_crop_size` is not square when global_pool=False due to the
constraint in function space_to_depth.
"""
total_bins = 1
bin_crop_size = []
for (num_bins, crop_dim) in zip(num_spatial_bins, crop_size):
if num_bins < 1:
raise ValueError('num_spatial_bins should be >= 1')
if crop_dim % num_bins != 0:
raise ValueError('crop_size should be divisible by num_spatial_bins')
total_bins *= num_bins
bin_crop_size.append(crop_dim // num_bins)
if not global_pool and bin_crop_size[0] != bin_crop_size[1]:
raise ValueError('Only support square bin crop size for now.')
ymin, xmin, ymax, xmax = tf.unstack(boxes, axis=1)
spatial_bins_y, spatial_bins_x = num_spatial_bins
# Split each box into spatial_bins_y * spatial_bins_x bins.
position_sensitive_boxes = []
for bin_y in range(spatial_bins_y):
step_y = (ymax - ymin) / spatial_bins_y
for bin_x in range(spatial_bins_x):
step_x = (xmax - xmin) / spatial_bins_x
box_coordinates = [ymin + bin_y * step_y,
xmin + bin_x * step_x,
ymin + (bin_y + 1) * step_y,
xmin + (bin_x + 1) * step_x,
]
position_sensitive_boxes.append(tf.stack(box_coordinates, axis=1))
image_splits = tf.split(value=image, num_or_size_splits=total_bins, axis=2)
image_crops = []
for (split, box) in zip(image_splits, position_sensitive_boxes):
if split.shape.is_fully_defined() and box.shape.is_fully_defined():
crop = tf.squeeze(
matmul_crop_and_resize(
tf.expand_dims(split, axis=0), tf.expand_dims(box, axis=0),
bin_crop_size),
axis=0)
else:
crop = tf.image.crop_and_resize(
tf.expand_dims(split, 0), box,
tf.zeros(tf.shape(boxes)[0], dtype=tf.int32), bin_crop_size)
image_crops.append(crop)
if global_pool:
# Average over all bins.
position_sensitive_features = tf.add_n(image_crops) / len(image_crops)
# Then average over spatial positions within the bins.
position_sensitive_features = tf.reduce_mean(
position_sensitive_features, [1, 2], keep_dims=True)
else:
# Reorder height/width to depth channel.
block_size = bin_crop_size[0]
if block_size >= 2:
image_crops = [tf.space_to_depth(
crop, block_size=block_size) for crop in image_crops]
# Pack image_crops so that first dimension is for position-senstive boxes.
position_sensitive_features = tf.stack(image_crops, axis=0)
# Unroll the position-sensitive boxes to spatial positions.
position_sensitive_features = tf.squeeze(
tf.batch_to_space_nd(position_sensitive_features,
block_shape=[1] + num_spatial_bins,
crops=tf.zeros((3, 2), dtype=tf.int32)),
squeeze_dims=[0])
# Reorder back the depth channel.
if block_size >= 2:
position_sensitive_features = tf.depth_to_space(
position_sensitive_features, block_size=block_size)
return position_sensitive_features
def reframe_box_masks_to_image_masks(box_masks, boxes, image_height,
image_width):
"""Transforms the box masks back to full image masks.
Embeds masks in bounding boxes of larger masks whose shapes correspond to
image shape.
Args:
box_masks: A tf.float32 tensor of size [num_masks, mask_height, mask_width].
boxes: A tf.float32 tensor of size [num_masks, 4] containing the box
corners. Row i contains [ymin, xmin, ymax, xmax] of the box
corresponding to mask i. Note that the box corners are in
normalized coordinates.
image_height: Image height. The output mask will have the same height as
the image height.
image_width: Image width. The output mask will have the same width as the
image width.
Returns:
A tf.float32 tensor of size [num_masks, image_height, image_width].
"""
# TODO(rathodv): Make this a public function.
def reframe_box_masks_to_image_masks_default():
"""The default function when there are more than 0 box masks."""
def transform_boxes_relative_to_boxes(boxes, reference_boxes):
boxes = tf.reshape(boxes, [-1, 2, 2])
min_corner = tf.expand_dims(reference_boxes[:, 0:2], 1)
max_corner = tf.expand_dims(reference_boxes[:, 2:4], 1)
transformed_boxes = (boxes - min_corner) / (max_corner - min_corner)
return tf.reshape(transformed_boxes, [-1, 4])
box_masks_expanded = tf.expand_dims(box_masks, axis=3)
num_boxes = tf.shape(box_masks_expanded)[0]
unit_boxes = tf.concat(
[tf.zeros([num_boxes, 2]), tf.ones([num_boxes, 2])], axis=1)
reverse_boxes = transform_boxes_relative_to_boxes(unit_boxes, boxes)
return tf.image.crop_and_resize(
image=box_masks_expanded,
boxes=reverse_boxes,
box_ind=tf.range(num_boxes),
crop_size=[image_height, image_width],
extrapolation_value=0.0)
image_masks = tf.cond(
tf.shape(box_masks)[0] > 0,
reframe_box_masks_to_image_masks_default,
lambda: tf.zeros([0, image_height, image_width, 1], dtype=tf.float32))
return tf.squeeze(image_masks, axis=3)
def merge_boxes_with_multiple_labels(boxes,
classes,
confidences,
num_classes,
quantization_bins=10000):
"""Merges boxes with same coordinates and returns K-hot encoded classes.
Args:
boxes: A tf.float32 tensor with shape [N, 4] holding N boxes. Only
normalized coordinates are allowed.
classes: A tf.int32 tensor with shape [N] holding class indices.
The class index starts at 0.
confidences: A tf.float32 tensor with shape [N] holding class confidences.
num_classes: total number of classes to use for K-hot encoding.
quantization_bins: the number of bins used to quantize the box coordinate.
Returns:
merged_boxes: A tf.float32 tensor with shape [N', 4] holding boxes,
where N' <= N.
class_encodings: A tf.int32 tensor with shape [N', num_classes] holding
K-hot encodings for the merged boxes.
confidence_encodings: A tf.float32 tensor with shape [N', num_classes]
holding encodings of confidences for the merged boxes.
merged_box_indices: A tf.int32 tensor with shape [N'] holding original
indices of the boxes.
"""
boxes_shape = tf.shape(boxes)
classes_shape = tf.shape(classes)
confidences_shape = tf.shape(confidences)
box_class_shape_assert = shape_utils.assert_shape_equal_along_first_dimension(
boxes_shape, classes_shape)
box_confidence_shape_assert = (
shape_utils.assert_shape_equal_along_first_dimension(
boxes_shape, confidences_shape))
box_dimension_assert = tf.assert_equal(boxes_shape[1], 4)
box_normalized_assert = shape_utils.assert_box_normalized(boxes)
with tf.control_dependencies(
[box_class_shape_assert, box_confidence_shape_assert,
box_dimension_assert, box_normalized_assert]):
quantized_boxes = tf.to_int64(boxes * (quantization_bins - 1))
ymin, xmin, ymax, xmax = tf.unstack(quantized_boxes, axis=1)
hashcodes = (
ymin +
xmin * quantization_bins +
ymax * quantization_bins * quantization_bins +
xmax * quantization_bins * quantization_bins * quantization_bins)
unique_hashcodes, unique_indices = tf.unique(hashcodes)
num_boxes = tf.shape(boxes)[0]
num_unique_boxes = tf.shape(unique_hashcodes)[0]
merged_box_indices = tf.unsorted_segment_min(
tf.range(num_boxes), unique_indices, num_unique_boxes)
merged_boxes = tf.gather(boxes, merged_box_indices)
def map_box_encodings(i):
"""Produces box K-hot and score encodings for each class index."""
box_mask = tf.equal(
unique_indices, i * tf.ones(num_boxes, dtype=tf.int32))
box_mask = tf.reshape(box_mask, [-1])
box_indices = tf.boolean_mask(classes, box_mask)
box_confidences = tf.boolean_mask(confidences, box_mask)
box_class_encodings = tf.sparse_to_dense(
box_indices, [num_classes], 1, validate_indices=False)
box_confidence_encodings = tf.sparse_to_dense(
box_indices, [num_classes], box_confidences, validate_indices=False)
return box_class_encodings, box_confidence_encodings
class_encodings, confidence_encodings = tf.map_fn(
map_box_encodings,
tf.range(num_unique_boxes),
back_prop=False,
dtype=(tf.int32, tf.float32))
merged_boxes = tf.reshape(merged_boxes, [-1, 4])
class_encodings = tf.reshape(class_encodings, [-1, num_classes])
confidence_encodings = tf.reshape(confidence_encodings, [-1, num_classes])
merged_box_indices = tf.reshape(merged_box_indices, [-1])
return (merged_boxes, class_encodings, confidence_encodings,
merged_box_indices)
def nearest_neighbor_upsampling(input_tensor, scale=None, height_scale=None,
width_scale=None):
"""Nearest neighbor upsampling implementation.
Nearest neighbor upsampling function that maps input tensor with shape
[batch_size, height, width, channels] to [batch_size, height * scale
, width * scale, channels]. This implementation only uses reshape and
broadcasting to make it TPU compatible.
Args:
input_tensor: A float32 tensor of size [batch, height_in, width_in,
channels].
scale: An integer multiple to scale resolution of input data in both height
and width dimensions.
height_scale: An integer multiple to scale the height of input image. This
option when provided overrides `scale` option.
width_scale: An integer multiple to scale the width of input image. This
option when provided overrides `scale` option.
Returns:
data_up: A float32 tensor of size
[batch, height_in*scale, width_in*scale, channels].
Raises:
ValueError: If both scale and height_scale or if both scale and width_scale
are None.
"""
if not scale and (height_scale is None or width_scale is None):
raise ValueError('Provide either `scale` or `height_scale` and'
' `width_scale`.')
with tf.name_scope('nearest_neighbor_upsampling'):
h_scale = scale if height_scale is None else height_scale
w_scale = scale if width_scale is None else width_scale
(batch_size, height, width,
channels) = shape_utils.combined_static_and_dynamic_shape(input_tensor)
output_tensor = tf.reshape(
input_tensor, [batch_size, height, 1, width, 1, channels]) * tf.ones(
[1, 1, h_scale, 1, w_scale, 1], dtype=input_tensor.dtype)
return tf.reshape(output_tensor,
[batch_size, height * h_scale, width * w_scale, channels])
def matmul_gather_on_zeroth_axis(params, indices, scope=None):
"""Matrix multiplication based implementation of tf.gather on zeroth axis.
TODO(rathodv, jonathanhuang): enable sparse matmul option.
Args:
params: A float32 Tensor. The tensor from which to gather values.
Must be at least rank 1.
indices: A Tensor. Must be one of the following types: int32, int64.
Must be in range [0, params.shape[0])
scope: A name for the operation (optional).
Returns:
A Tensor. Has the same type as params. Values from params gathered
from indices given by indices, with shape indices.shape + params.shape[1:].
"""
with tf.name_scope(scope, 'MatMulGather'):
params_shape = shape_utils.combined_static_and_dynamic_shape(params)
indices_shape = shape_utils.combined_static_and_dynamic_shape(indices)
params2d = tf.reshape(params, [params_shape[0], -1])
indicator_matrix = tf.one_hot(indices, params_shape[0])
gathered_result_flattened = tf.matmul(indicator_matrix, params2d)
return tf.reshape(gathered_result_flattened,
tf.stack(indices_shape + params_shape[1:]))
def matmul_crop_and_resize(image, boxes, crop_size, scope=None):
"""Matrix multiplication based implementation of the crop and resize op.
Extracts crops from the input image tensor and bilinearly resizes them
(possibly with aspect ratio change) to a common output size specified by
crop_size. This is more general than the crop_to_bounding_box op which
extracts a fixed size slice from the input image and does not allow
resizing or aspect ratio change.
Returns a tensor with crops from the input image at positions defined at
the bounding box locations in boxes. The cropped boxes are all resized
(with bilinear interpolation) to a fixed size = `[crop_height, crop_width]`.
The result is a 5-D tensor `[batch, num_boxes, crop_height, crop_width,
depth]`.
Running time complexity:
O((# channels) * (# boxes) * (crop_size)^2 * M), where M is the number
of pixels of the longer edge of the image.
Note that this operation is meant to replicate the behavior of the standard
tf.image.crop_and_resize operation but there are a few differences.
Specifically:
1) The extrapolation value (the values that are interpolated from outside
the bounds of the image window) is always zero
2) Only XLA supported operations are used (e.g., matrix multiplication).
3) There is no `box_indices` argument --- to run this op on multiple images,
one must currently call this op independently on each image.
4) All shapes and the `crop_size` parameter are assumed to be statically
defined. Moreover, the number of boxes must be strictly nonzero.
Args:
image: A `Tensor`. Must be one of the following types: `uint8`, `int8`,
`int16`, `int32`, `int64`, `half`, 'bfloat16', `float32`, `float64`.
A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
Both `image_height` and `image_width` need to be positive.
boxes: A `Tensor` of type `float32` or 'bfloat16'.
A 3-D tensor of shape `[batch, num_boxes, 4]`. The boxes are specified in
normalized coordinates and are of the form `[y1, x1, y2, x2]`. A
normalized coordinate value of `y` is mapped to the image coordinate at
`y * (image_height - 1)`, so as the `[0, 1]` interval of normalized image
height is mapped to `[0, image_height - 1] in image height coordinates.
We do allow y1 > y2, in which case the sampled crop is an up-down flipped
version of the original image. The width dimension is treated similarly.
Normalized coordinates outside the `[0, 1]` range are allowed, in which
case we use `extrapolation_value` to extrapolate the input image values.
crop_size: A list of two integers `[crop_height, crop_width]`. All
cropped image patches are resized to this size. The aspect ratio of the
image content is not preserved. Both `crop_height` and `crop_width` need
to be positive.
scope: A name for the operation (optional).
Returns:
A 5-D tensor of shape `[batch, num_boxes, crop_height, crop_width, depth]`
Raises:
ValueError: if image tensor does not have shape
`[batch, image_height, image_width, depth]` and all dimensions statically
defined.
ValueError: if boxes tensor does not have shape `[batch, num_boxes, 4]`
where num_boxes > 0.
ValueError: if crop_size is not a list of two positive integers
"""
img_shape = image.shape.as_list()
boxes_shape = boxes.shape.as_list()
_, img_height, img_width, _ = img_shape
if not isinstance(crop_size, list) or len(crop_size) != 2:
raise ValueError('`crop_size` must be a list of length 2')
dimensions = img_shape + crop_size + boxes_shape
if not all([isinstance(dim, int) for dim in dimensions]):
raise ValueError('all input shapes must be statically defined')
if len(boxes_shape) != 3 or boxes_shape[2] != 4:
raise ValueError('`boxes` should have shape `[batch, num_boxes, 4]`')
if len(img_shape) != 4:
raise ValueError('image should have shape '
'`[batch, image_height, image_width, depth]`')
num_crops = boxes_shape[0]
if not num_crops > 0:
raise ValueError('number of boxes must be > 0')
if not (crop_size[0] > 0 and crop_size[1] > 0):
raise ValueError('`crop_size` must be a list of two positive integers.')
def _lin_space_weights(num, img_size):
if num > 1:
start_weights = tf.linspace(img_size - 1.0, 0.0, num)
stop_weights = img_size - 1 - start_weights
else:
start_weights = tf.constant(num * [.5 * (img_size - 1)], dtype=tf.float32)
stop_weights = tf.constant(num * [.5 * (img_size - 1)], dtype=tf.float32)
return (start_weights, stop_weights)
with tf.name_scope(scope, 'MatMulCropAndResize'):
y1_weights, y2_weights = _lin_space_weights(crop_size[0], img_height)
x1_weights, x2_weights = _lin_space_weights(crop_size[1], img_width)
y1_weights = tf.cast(y1_weights, boxes.dtype)
y2_weights = tf.cast(y2_weights, boxes.dtype)
x1_weights = tf.cast(x1_weights, boxes.dtype)
x2_weights = tf.cast(x2_weights, boxes.dtype)
[y1, x1, y2, x2] = tf.unstack(boxes, axis=2)
# Pixel centers of input image and grid points along height and width
image_idx_h = tf.constant(
np.reshape(np.arange(img_height), (1, 1, 1, img_height)),
dtype=boxes.dtype)
image_idx_w = tf.constant(
np.reshape(np.arange(img_width), (1, 1, 1, img_width)),
dtype=boxes.dtype)
grid_pos_h = tf.expand_dims(
tf.einsum('ab,c->abc', y1, y1_weights) + tf.einsum(
'ab,c->abc', y2, y2_weights),
axis=3)
grid_pos_w = tf.expand_dims(
tf.einsum('ab,c->abc', x1, x1_weights) + tf.einsum(
'ab,c->abc', x2, x2_weights),
axis=3)
# Create kernel matrices of pairwise kernel evaluations between pixel
# centers of image and grid points.
kernel_h = tf.nn.relu(1 - tf.abs(image_idx_h - grid_pos_h))
kernel_w = tf.nn.relu(1 - tf.abs(image_idx_w - grid_pos_w))
# Compute matrix multiplication between the spatial dimensions of the image
# and height-wise kernel using einsum.
intermediate_image = tf.einsum('abci,aiop->abcop', kernel_h, image)
# Compute matrix multiplication between the spatial dimensions of the
# intermediate_image and width-wise kernel using einsum.
return tf.einsum('abno,abcop->abcnp', kernel_w, intermediate_image)
def native_crop_and_resize(image, boxes, crop_size, scope=None):
"""Same as `matmul_crop_and_resize` but uses tf.image.crop_and_resize."""
def get_box_inds(proposals):
proposals_shape = proposals.get_shape().as_list()
if any(dim is None for dim in proposals_shape):
proposals_shape = tf.shape(proposals)
ones_mat = tf.ones(proposals_shape[:2], dtype=tf.int32)
multiplier = tf.expand_dims(
tf.range(start=0, limit=proposals_shape[0]), 1)
return tf.reshape(ones_mat * multiplier, [-1])
with tf.name_scope(scope, 'CropAndResize'):
cropped_regions = tf.image.crop_and_resize(
image, tf.reshape(boxes, [-1] + boxes.shape.as_list()[2:]),
get_box_inds(boxes), crop_size)
final_shape = tf.concat([tf.shape(boxes)[:2],
tf.shape(cropped_regions)[1:]], axis=0)
return tf.reshape(cropped_regions, final_shape)
EqualizationLossConfig = collections.namedtuple('EqualizationLossConfig',
['weight', 'exclude_prefixes'])
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/configuration | configuration | configuration | # 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 copy
import json
import os
import warnings
from typing import Dict, Optional, Union
from syngen.configuration.utils import optional_comparison, one_field_from_list_of_dicts
from syngen.utils.io_utils import load_dataframe, load_graph
from syngen.utils.types import MetaData, DataSourceInputType
class SynGenDatasetFeatureSpec(dict):
""" SynGenDatasetFeatureSpec is an util class to simply the work with SynGen Dataset Format
Args:
graph_metadata (Dict): dict in SynGen Format
"""
def __init__(self, graph_metadata: Dict):
super().__init__(graph_metadata)
@staticmethod
def instantiate_from_preprocessed(path: str):
""" Creates a SynGenDatasetFeatureSpec and checks all specified files
Args:
path: path to the directory with a dataset in SynGen Format
"""
if os.path.isfile(path):
file_path = path
dir_path = os.path.dirname(file_path)
elif os.path.isdir(path):
file_path = os.path.join(path, 'graph_metadata.json')
dir_path = path
else:
raise ValueError(f"expected path to existing file or directory. got {path}")
with open(file_path, 'r') as f:
graph_metadata = json.load(f)
graph_metadata[MetaData.PATH] = dir_path
config = SynGenDatasetFeatureSpec(graph_metadata)
config.validate()
return config
def get_tabular_data(self, part, name, cache=False, absolute_path=None, return_cat_feats=False):
part_info = self.get_info(part, name)
if MetaData.FEATURES_DATA in part_info:
return part_info[MetaData.FEATURES_DATA]
part_features_info = part_info[MetaData.FEATURES]
part_features_path = part_info[MetaData.FEATURES_PATH]
if part_features_path is None:
raise ValueError()
if MetaData.PATH not in self:
if absolute_path is None:
raise ValueError("Please specify the absolute path for the feature spec: "
"by passing absolute_path argument or specifying MetaData.PATH in the Feature Spec")
else:
self[MetaData.PATH] = absolute_path
features_df = load_dataframe(os.path.join(self[MetaData.PATH], part_features_path),
feature_info=part_features_info)
if cache:
part_info[MetaData.FEATURES_DATA] = features_df
if return_cat_feats:
cat_features = [
feature_info[MetaData.NAME]
for feature_info in part_info[MetaData.FEATURES]
if feature_info[MetaData.FEATURE_TYPE] == MetaData.CATEGORICAL
]
return features_df, cat_features
return features_df
def get_structural_data(self, edge_name, cache=False, absolute_path=None, ):
edge_info = self.get_edge_info(edge_name)
if MetaData.STRUCTURE_DATA in edge_info:
return edge_info[MetaData.STRUCTURE_DATA]
structure_path = edge_info[MetaData.STRUCTURE_PATH]
if structure_path is None:
raise ValueError()
if MetaData.PATH not in self:
if absolute_path is None:
raise ValueError("Please specify the absolute path for the feature spec: "
"by passing absolute_path argument or specifying MetaData.PATH in the Feature Spec")
else:
self[MetaData.PATH] = absolute_path
graph = load_graph(os.path.join(self[MetaData.PATH], structure_path))
if cache:
edge_info[MetaData.STRUCTURE_DATA] = graph
return graph
def get_edge_info(self, name: Union[str, list], src_node_type: Optional[str] = None,
dst_node_type: Optional[str] = None):
if isinstance(name, list):
src_node_type, name, dst_node_type = name
for edge_type in self[MetaData.EDGES]:
if edge_type[MetaData.NAME] == name \
and optional_comparison(src_node_type, edge_type[MetaData.SRC_NODE_TYPE]) \
and optional_comparison(dst_node_type, edge_type[MetaData.DST_NODE_TYPE]):
return edge_type
def get_node_info(self, name: str):
for node_type in self[MetaData.NODES]:
if node_type[MetaData.NAME] == name:
return node_type
def get_info(self, part, name):
if part == MetaData.NODES:
return self.get_node_info(name)
elif part == MetaData.EDGES:
return self.get_edge_info(name)
else:
raise ValueError(f"unsupported FeatureSpec part expected [{MetaData.NODES}, {MetaData.EDGES}], got {part}")
def validate(self):
for part in [MetaData.NODES, MetaData.EDGES]:
for part_info in self[part]:
if part_info[MetaData.FEATURES_PATH]:
tab_path = os.path.join(self[MetaData.PATH], part_info[MetaData.FEATURES_PATH])
assert os.path.exists(tab_path), f"{part}-{part_info[MetaData.NAME]}: {tab_path} does not exist"
assert len(part_info[MetaData.FEATURES]) > 0, \
f"{part}-{part_info[MetaData.NAME]}: tabular features are not specified"
feature_files = one_field_from_list_of_dicts(
part_info[MetaData.FEATURES], MetaData.FEATURE_FILE, res_aggregator=set)
if len(feature_files) > 1:
assert os.path.isdir(tab_path), \
"different feature files are specified MetaData. FEATURES_PATH should be a directory"
for ff in feature_files:
ff_path = os.path.join(tab_path, ff)
assert os.path.exists(ff_path), \
f"{part}-{part_info[MetaData.NAME]}: {ff_path} does not exist"
if part == MetaData.EDGES:
struct_path = os.path.join(self[MetaData.PATH], part_info[MetaData.STRUCTURE_PATH])
assert os.path.exists(struct_path), \
f"{part}-{part_info[MetaData.NAME]}: {struct_path} does not exist"
def copy(self):
res = {}
keys_to_ignore = {MetaData.STRUCTURE_DATA, MetaData.FEATURES_DATA}
for part in (MetaData.EDGES, MetaData.NODES):
res[part] = [
{
k: copy.deepcopy(v)
for k, v in part_info.items() if k not in keys_to_ignore
}
for part_info in self[part]
]
return SynGenDatasetFeatureSpec(res)
class SynGenConfiguration(SynGenDatasetFeatureSpec):
""" SynGen Configuration
"""
def __init__(self, configuration: Dict):
super().__init__(configuration)
self._fill_missing_values()
self.validate()
def validate(self):
if MetaData.ALIGNERS in self:
for aligner_info in self[MetaData.ALIGNERS]:
for edge_name in aligner_info[MetaData.EDGES]:
if not self.get_edge_info(edge_name)[MetaData.FEATURES_PATH].endswith(".parquet"):
raise ValueError("Alignment supports only .parquet files right now")
for node_name in aligner_info[MetaData.NODES]:
if not self.get_node_info(node_name)[MetaData.FEATURES_PATH].endswith(".parquet"):
raise ValueError("Alignment supports only .parquet files right now")
def _process_tabular_generators(self, graph_part_info, part):
if MetaData.TABULAR_GENERATORS not in graph_part_info:
return
if graph_part_info[MetaData.FEATURES] == -1:
assert len(graph_part_info[MetaData.TABULAR_GENERATORS]) == 1
tab_gen_cfg = graph_part_info[MetaData.TABULAR_GENERATORS][0]
assert tab_gen_cfg[MetaData.DATA_SOURCE][MetaData.TYPE] == DataSourceInputType.CONFIGURATION
cfg = SynGenConfiguration.instantiate_from_preprocessed(tab_gen_cfg[MetaData.DATA_SOURCE][MetaData.PATH])
data_source_part_info = cfg.get_info(part, tab_gen_cfg[MetaData.DATA_SOURCE][MetaData.NAME])
graph_part_info[MetaData.FEATURES] = data_source_part_info[MetaData.FEATURES]
for tab_gen_cfg in graph_part_info[MetaData.TABULAR_GENERATORS]:
if tab_gen_cfg[MetaData.FEATURES_LIST] == -1:
assert len(graph_part_info[MetaData.TABULAR_GENERATORS]) == 1, \
"you may use mimic value (-1) only if you specify a single tabular generator"
tab_gen_cfg[MetaData.FEATURES_LIST] = [f[MetaData.NAME] for f in graph_part_info[MetaData.FEATURES]]
if tab_gen_cfg[MetaData.DATA_SOURCE][MetaData.TYPE] == DataSourceInputType.RANDOM:
edge_features = [f[MetaData.NAME] for f in graph_part_info[MetaData.FEATURES]]
for feature_name in tab_gen_cfg[MetaData.FEATURES_LIST]:
if feature_name not in edge_features:
graph_part_info[MetaData.FEATURES].append(
{
MetaData.NAME: feature_name,
MetaData.DTYPE: 'float32',
MetaData.FEATURE_TYPE: MetaData.CONTINUOUS,
# Now random generator supports only continuous features
}
)
def _fill_missing_values(self):
for part in [MetaData.NODES, MetaData.EDGES]:
for part_info in self[part]:
if MetaData.FEATURES not in part_info:
part_info[MetaData.FEATURES] = []
warnings.warn(
f"{part}-{part_info[MetaData.NAME]}: no {MetaData.FEATURES} specified, default is []")
if MetaData.FEATURES_PATH not in part_info:
part_info[MetaData.FEATURES_PATH] = None
warnings.warn(
f"{part}-{part_info[MetaData.NAME]}: no {MetaData.FEATURES_PATH} specified, default is None")
if MetaData.COUNT not in part_info:
part_info[MetaData.COUNT] = -1
warnings.warn(
f"{part}-{part_info[MetaData.NAME]}: no {MetaData.COUNT} specified, "
f"try to mimic based on generators data")
self._process_tabular_generators(part_info, part)
if part == MetaData.EDGES:
if MetaData.DIRECTED not in part_info:
part_info[MetaData.DIRECTED] = False
if part_info[MetaData.COUNT] == -1:
data_source_info = part_info[MetaData.STRUCTURE_GENERATOR][MetaData.DATA_SOURCE]
if data_source_info[MetaData.TYPE] == DataSourceInputType.CONFIGURATION:
cfg = SynGenConfiguration.instantiate_from_preprocessed(data_source_info[MetaData.PATH])
data_source_part_info = cfg.get_info(part, data_source_info[MetaData.NAME])
elif data_source_info[MetaData.TYPE] == DataSourceInputType.RANDOM:
raise ValueError('Can\'t fill the ')
else:
raise ValueError("unsupported structure generator datasource type")
if part_info[MetaData.COUNT] == -1:
part_info[MetaData.COUNT] = data_source_part_info[MetaData.COUNT]
def copy(self):
res = super().copy()
if MetaData.ALIGNERS in self:
res[MetaData.ALIGNERS] = copy.deepcopy(self[MetaData.ALIGNERS])
return SynGenConfiguration(res)
|
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Sand
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1778
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146
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Ethics
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Protocol
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139
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Woody
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154
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153
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156
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168
composing
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Calvert
Casper
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littered
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Experiment
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Mace
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Annabelle
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din
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Fielding
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Pound
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Dyke
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Arroyo
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Hawke
Noctuidae
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|
TensorFlow/Detection/SSD/models/research/object_detection/samples/configs | configs | faster_rcnn_inception_v2_coco | # Faster R-CNN with Inception v2, configuration for MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.
model {
faster_rcnn {
num_classes: 90
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
feature_extractor {
type: 'faster_rcnn_inception_v2'
first_stage_features_stride: 16
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: {
batch_size: 1
optimizer {
momentum_optimizer: {
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 0.0002
schedule {
step: 900000
learning_rate: .00002
}
schedule {
step: 1200000
learning_rate: .000002
}
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
from_detection_checkpoint: true
# Note: The below line limits the training process to 200K steps, which we
# empirically found to be sufficient enough to train the COCO dataset. This
# effectively bypasses the learning rate schedule (the learning rate will
# never decay). Remove the below line to train indefinitely.
num_steps: 200000
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
}
label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
}
eval_config: {
num_examples: 8000
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 10
}
eval_input_reader: {
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
}
label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
shuffle: false
num_readers: 1
}
|
TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools | dataset_tools | oid_hierarchical_labels_expansion | # 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"""An executable to expand hierarchically image-level labels and boxes.
Example usage:
python models/research/object_detection/dataset_tools/\
oid_hierarchical_labels_expansion.py \
--json_hierarchy_file=<path to JSON hierarchy> \
--input_annotations=<input csv file> \
--output_annotations=<output csv file> \
--annotation_type=<1 (for boxes) or 2 (for image-level labels)>
"""
from __future__ import print_function
import argparse
import json
def _update_dict(initial_dict, update):
"""Updates dictionary with update content.
Args:
initial_dict: initial dictionary.
update: updated dictionary.
"""
for key, value_list in update.iteritems():
if key in initial_dict:
initial_dict[key].extend(value_list)
else:
initial_dict[key] = value_list
def _build_plain_hierarchy(hierarchy, skip_root=False):
"""Expands tree hierarchy representation to parent-child dictionary.
Args:
hierarchy: labels hierarchy as JSON file.
skip_root: if true skips root from the processing (done for the case when all
classes under hierarchy are collected under virtual node).
Returns:
keyed_parent - dictionary of parent - all its children nodes.
keyed_child - dictionary of children - all its parent nodes
children - all children of the current node.
"""
all_children = []
all_keyed_parent = {}
all_keyed_child = {}
if 'Subcategory' in hierarchy:
for node in hierarchy['Subcategory']:
keyed_parent, keyed_child, children = _build_plain_hierarchy(node)
# Update is not done through dict.update() since some children have multi-
# ple parents in the hiearchy.
_update_dict(all_keyed_parent, keyed_parent)
_update_dict(all_keyed_child, keyed_child)
all_children.extend(children)
if not skip_root:
all_keyed_parent[hierarchy['LabelName']] = all_children
all_children = [hierarchy['LabelName']] + all_children
for child, _ in all_keyed_child.iteritems():
all_keyed_child[child].append(hierarchy['LabelName'])
all_keyed_child[hierarchy['LabelName']] = []
return all_keyed_parent, all_keyed_child, all_children
class OIDHierarchicalLabelsExpansion(object):
""" Main class to perform labels hierachical expansion."""
def __init__(self, hierarchy):
"""Constructor.
Args:
hierarchy: labels hierarchy as JSON object.
"""
self._hierarchy_keyed_parent, self._hierarchy_keyed_child, _ = (
_build_plain_hierarchy(hierarchy, skip_root=True))
def expand_boxes_from_csv(self, csv_row):
"""Expands a row containing bounding boxes from CSV file.
Args:
csv_row: a single row of Open Images released groundtruth file.
Returns:
a list of strings (including the initial row) corresponding to the ground
truth expanded to multiple annotation for evaluation with Open Images
Challenge 2018 metric.
"""
# Row header is expected to be exactly:
# ImageID,Source,LabelName,Confidence,XMin,XMax,YMin,YMax,IsOccluded,
# IsTruncated,IsGroupOf,IsDepiction,IsInside
cvs_row_splitted = csv_row.split(',')
assert len(cvs_row_splitted) == 13
result = [csv_row]
assert cvs_row_splitted[2] in self._hierarchy_keyed_child
parent_nodes = self._hierarchy_keyed_child[cvs_row_splitted[2]]
for parent_node in parent_nodes:
cvs_row_splitted[2] = parent_node
result.append(','.join(cvs_row_splitted))
return result
def expand_labels_from_csv(self, csv_row):
"""Expands a row containing bounding boxes from CSV file.
Args:
csv_row: a single row of Open Images released groundtruth file.
Returns:
a list of strings (including the initial row) corresponding to the ground
truth expanded to multiple annotation for evaluation with Open Images
Challenge 2018 metric.
"""
# Row header is expected to be exactly:
# ImageID,Source,LabelName,Confidence
cvs_row_splited = csv_row.split(',')
assert len(cvs_row_splited) == 4
result = [csv_row]
if int(cvs_row_splited[3]) == 1:
assert cvs_row_splited[2] in self._hierarchy_keyed_child
parent_nodes = self._hierarchy_keyed_child[cvs_row_splited[2]]
for parent_node in parent_nodes:
cvs_row_splited[2] = parent_node
result.append(','.join(cvs_row_splited))
else:
assert cvs_row_splited[2] in self._hierarchy_keyed_parent
child_nodes = self._hierarchy_keyed_parent[cvs_row_splited[2]]
for child_node in child_nodes:
cvs_row_splited[2] = child_node
result.append(','.join(cvs_row_splited))
return result
def main(parsed_args):
with open(parsed_args.json_hierarchy_file) as f:
hierarchy = json.load(f)
expansion_generator = OIDHierarchicalLabelsExpansion(hierarchy)
labels_file = False
if parsed_args.annotation_type == 2:
labels_file = True
elif parsed_args.annotation_type != 1:
print('--annotation_type expected value is 1 or 2.')
return -1
with open(parsed_args.input_annotations, 'r') as source:
with open(parsed_args.output_annotations, 'w') as target:
header = None
for line in source:
if not header:
header = line
target.writelines(header)
continue
if labels_file:
expanded_lines = expansion_generator.expand_labels_from_csv(line)
else:
expanded_lines = expansion_generator.expand_boxes_from_csv(line)
target.writelines(expanded_lines)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Hierarchically expand annotations (excluding root node).')
parser.add_argument(
'--json_hierarchy_file',
required=True,
help='Path to the file containing label hierarchy in JSON format.')
parser.add_argument(
'--input_annotations',
required=True,
help="""Path to Open Images annotations file (either bounding boxes or
image-level labels).""")
parser.add_argument(
'--output_annotations',
required=True,
help="""Path to the output file.""")
parser.add_argument(
'--annotation_type',
type=int,
required=True,
help="""Type of the input annotations: 1 - boxes, 2 - image-level
labels"""
)
args = parser.parse_args()
main(args)
|
TensorFlow2/LanguageModeling/BERT/official/nlp/modeling/layers | layers | transformer_scaffold_test | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Keras-based transformer block layer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import numpy as np
import tensorflow as tf
from tensorflow.python.keras import keras_parameterized # pylint: disable=g-direct-tensorflow-import
from official.nlp.modeling.layers import attention
from official.nlp.modeling.layers import transformer_scaffold
# Test class that wraps a standard attention layer. If this layer is called
# at any point, the list passed to the config object will be filled with a
# boolean 'True'. We register this class as a Keras serializable so we can
# test serialization below.
# @tf.keras.utils.register_keras_serializable(package='TestOnly')
class ValidatedAttentionLayer(attention.Attention):
def __init__(self, call_list, **kwargs):
super(ValidatedAttentionLayer, self).__init__(**kwargs)
self.list = call_list
def call(self, inputs):
self.list.append(True)
return super(ValidatedAttentionLayer, self).call(inputs)
def get_config(self):
config = super(ValidatedAttentionLayer, self).get_config()
config['call_list'] = []
return config
# This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It
# guarantees forward compatibility of this code for the V2 switchover.
@keras_parameterized.run_all_keras_modes
class TransformerLayerTest(keras_parameterized.TestCase):
def test_layer_creation(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
output_tensor = test_layer(data_tensor)
# The default output of a transformer layer should be the same as the input.
self.assertEqual(data_tensor.shape.as_list(), output_tensor.shape.as_list())
# If call_list[0] exists and is True, the passed layer class was
# instantiated from the given config properly.
self.assertNotEmpty(call_list)
self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
def test_layer_creation_with_mask(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
# Create a 2-dimensional input (the first dimension is implicit).
mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))
output_tensor = test_layer([data_tensor, mask_tensor])
# The default output of a transformer layer should be the same as the input.
self.assertEqual(data_tensor.shape.as_list(), output_tensor.shape.as_list())
# If call_list[0] exists and is True, the passed layer class was
# instantiated from the given config properly.
self.assertNotEmpty(call_list)
self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
def test_layer_creation_with_incorrect_mask_fails(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
# Create a 2-dimensional input (the first dimension is implicit).
mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length - 3))
with self.assertRaisesRegex(ValueError, 'When passing a mask tensor.*'):
_ = test_layer([data_tensor, mask_tensor])
def test_layer_invocation(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
output_tensor = test_layer(data_tensor)
# Create a model from the test layer.
model = tf.keras.Model(data_tensor, output_tensor)
# Invoke the model on test data. We can't validate the output data itself
# (the NN is too complex) but this will rule out structural runtime errors.
batch_size = 6
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, width))
_ = model.predict(input_data)
# If call_list[0] exists and is True, the passed layer class was
# instantiated from the given config properly.
self.assertNotEmpty(call_list)
self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
def test_layer_invocation_with_mask(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
# Create a 2-dimensional input (the first dimension is implicit).
mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))
output_tensor = test_layer([data_tensor, mask_tensor])
# Create a model from the test layer.
model = tf.keras.Model([data_tensor, mask_tensor], output_tensor)
# Invoke the model on test data. We can't validate the output data itself
# (the NN is too complex) but this will rule out structural runtime errors.
batch_size = 6
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, width))
# The attention mask should be of shape (batch, from_seq_len, to_seq_len),
# which here is (batch, sequence_length, sequence_length)
mask_data = np.random.randint(
2, size=(batch_size, sequence_length, sequence_length))
_ = model.predict([input_data, mask_data])
# If call_list[0] exists and is True, the passed layer class was
# instantiated from the given config properly.
self.assertNotEmpty(call_list)
self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
def test_layer_invocation_with_float16_dtype(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu',
dtype='float16')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(
shape=(sequence_length, width), dtype=tf.float16)
# Create a 2-dimensional input (the first dimension is implicit).
mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))
output_tensor = test_layer([data_tensor, mask_tensor])
# Create a model from the test layer.
model = tf.keras.Model([data_tensor, mask_tensor], output_tensor)
# Invoke the model on test data. We can't validate the output data itself
# (the NN is too complex) but this will rule out structural runtime errors.
batch_size = 6
input_data = (10 * np.random.random_sample(
(batch_size, sequence_length, width))).astype(np.float16)
# The attention mask should be of shape (batch, from_seq_len, to_seq_len),
# which here is (batch, sequence_length, sequence_length)
mask_data = np.random.randint(
2, size=(batch_size, sequence_length, sequence_length))
_ = model.predict([input_data, mask_data])
# If call_list[0] exists and is True, the passed layer class was
# instantiated from the given config properly.
self.assertNotEmpty(call_list)
self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
def test_transform_with_initializer(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu',
kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02))
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
output = test_layer(data_tensor)
# The default output of a transformer layer should be the same as the input.
self.assertEqual(data_tensor.shape.as_list(), output.shape.as_list())
# If call_list[0] exists and is True, the passed layer class was
# instantiated from the given config properly.
self.assertNotEmpty(call_list)
self.assertTrue(call_list[0])
def test_layer_restoration_from_config(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
'name': 'test_layer',
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
# Create a 2-dimensional input (the first dimension is implicit).
mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))
output_tensor = test_layer([data_tensor, mask_tensor])
# Create a model from the test layer.
model = tf.keras.Model([data_tensor, mask_tensor], output_tensor)
# Invoke the model on test data. We can't validate the output data itself
# (the NN is too complex) but this will rule out structural runtime errors.
batch_size = 6
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, width))
# The attention mask should be of shape (batch, from_seq_len, to_seq_len),
# which here is (batch, sequence_length, sequence_length)
mask_data = np.random.randint(
2, size=(batch_size, sequence_length, sequence_length))
pre_serialization_output = model.predict([input_data, mask_data])
# Serialize the model config. Pass the serialized data through json to
# ensure that we can serialize this layer to disk.
serialized_data = json.dumps(model.get_config())
post_string_serialized_data = json.loads(serialized_data)
# Create a new model from the old config, and copy the weights. These models
# should have identical outputs.
new_model = tf.keras.Model.from_config(post_string_serialized_data)
new_model.set_weights(model.get_weights())
output = new_model.predict([input_data, mask_data])
self.assertAllClose(pre_serialization_output, output)
# If the layer was configured correctly, it should have a list attribute
# (since it should have the custom class and config passed to it).
new_model.summary()
new_call_list = new_model.get_layer(
name='transformer_scaffold')._attention_layer.list
self.assertNotEmpty(new_call_list)
self.assertTrue(new_call_list[0],
"The passed layer class wasn't instantiated.")
if __name__ == '__main__':
tf.test.main()
|
TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools | dataset_tools | create_pascal_tf_record | # 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"""Convert raw PASCAL dataset to TFRecord for object_detection.
Example usage:
python object_detection/dataset_tools/create_pascal_tf_record.py \
--data_dir=/home/user/VOCdevkit \
--year=VOC2012 \
--output_path=/home/user/pascal.record
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import hashlib
import io
import logging
import os
from lxml import etree
import PIL.Image
import tensorflow as tf
from object_detection.utils import dataset_util
from object_detection.utils import label_map_util
flags = tf.app.flags
flags.DEFINE_string('data_dir', '', 'Root directory to raw PASCAL VOC dataset.')
flags.DEFINE_string('set', 'train', 'Convert training set, validation set or '
'merged set.')
flags.DEFINE_string('annotations_dir', 'Annotations',
'(Relative) path to annotations directory.')
flags.DEFINE_string('year', 'VOC2007', 'Desired challenge year.')
flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
flags.DEFINE_string('label_map_path', 'data/pascal_label_map.pbtxt',
'Path to label map proto')
flags.DEFINE_boolean('ignore_difficult_instances', False, 'Whether to ignore '
'difficult instances')
FLAGS = flags.FLAGS
SETS = ['train', 'val', 'trainval', 'test']
YEARS = ['VOC2007', 'VOC2012', 'merged']
def dict_to_tf_example(data,
dataset_directory,
label_map_dict,
ignore_difficult_instances=False,
image_subdirectory='JPEGImages'):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
dataset_directory: Path to root directory holding PASCAL dataset
label_map_dict: A map from string label names to integers ids.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
image_subdirectory: String specifying subdirectory within the
PASCAL dataset directory holding the actual image data.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
img_path = os.path.join(data['folder'], image_subdirectory, data['filename'])
full_path = os.path.join(dataset_directory, img_path)
with tf.gfile.GFile(full_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width = int(data['size']['width'])
height = int(data['size']['height'])
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
if 'object' in data:
for obj in data['object']:
difficult = bool(int(obj['difficult']))
if ignore_difficult_instances and difficult:
continue
difficult_obj.append(int(difficult))
xmin.append(float(obj['bndbox']['xmin']) / width)
ymin.append(float(obj['bndbox']['ymin']) / height)
xmax.append(float(obj['bndbox']['xmax']) / width)
ymax.append(float(obj['bndbox']['ymax']) / height)
classes_text.append(obj['name'].encode('utf8'))
classes.append(label_map_dict[obj['name']])
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(
data['filename'].encode('utf8')),
'image/source_id': dataset_util.bytes_feature(
data['filename'].encode('utf8')),
'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses),
}))
return example
def main(_):
if FLAGS.set not in SETS:
raise ValueError('set must be in : {}'.format(SETS))
if FLAGS.year not in YEARS:
raise ValueError('year must be in : {}'.format(YEARS))
data_dir = FLAGS.data_dir
years = ['VOC2007', 'VOC2012']
if FLAGS.year != 'merged':
years = [FLAGS.year]
writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path)
for year in years:
logging.info('Reading from PASCAL %s dataset.', year)
examples_path = os.path.join(data_dir, year, 'ImageSets', 'Main',
'aeroplane_' + FLAGS.set + '.txt')
annotations_dir = os.path.join(data_dir, year, FLAGS.annotations_dir)
examples_list = dataset_util.read_examples_list(examples_path)
for idx, example in enumerate(examples_list):
if idx % 100 == 0:
logging.info('On image %d of %d', idx, len(examples_list))
path = os.path.join(annotations_dir, example + '.xml')
with tf.gfile.GFile(path, 'r') as fid:
xml_str = fid.read()
xml = etree.fromstring(xml_str)
data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation']
tf_example = dict_to_tf_example(data, FLAGS.data_dir, label_map_dict,
FLAGS.ignore_difficult_instances)
writer.write(tf_example.SerializeToString())
writer.close()
if __name__ == '__main__':
tf.app.run()
|
PyTorch/SpeechRecognition/QuartzNet/common | common | optimizers | # 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 torch
from torch.optim import Optimizer
import math
def lr_policy(step, epoch, initial_lr, optimizer, steps_per_epoch, warmup_epochs,
hold_epochs, num_epochs=None, policy='linear', min_lr=1e-5,
exp_gamma=None):
"""
learning rate decay
Args:
initial_lr: base learning rate
step: current iteration number
N: total number of iterations over which learning rate is decayed
lr_steps: list of steps to apply exp_gamma
"""
warmup_steps = warmup_epochs * steps_per_epoch
hold_steps = hold_epochs * steps_per_epoch
if policy == 'legacy':
assert num_epochs is not None
tot_steps = num_epochs * steps_per_epoch
if step < warmup_steps:
a = (step + 1) / (warmup_steps + 1)
elif step < warmup_steps + hold_steps:
a = 1.0
else:
a = (((tot_steps - step)
/ (tot_steps - warmup_steps - hold_steps)) ** 2)
elif policy == 'exponential':
assert exp_gamma is not None
if step < warmup_steps:
a = (step + 1) / (warmup_steps + 1)
elif step < warmup_steps + hold_steps:
a = 1.0
else:
a = exp_gamma ** (epoch - warmup_epochs - hold_epochs)
else:
raise ValueError
new_lr = max(a * initial_lr, min_lr)
for param_group in optimizer.param_groups:
param_group['lr'] = new_lr
class AdamW(Optimizer):
"""Implements AdamW algorithm.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
Adam: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, amsgrad=False):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay, amsgrad=amsgrad)
super(AdamW, self).__init__(params, defaults)
def __setstate__(self, state):
super(AdamW, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
amsgrad = group['amsgrad']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
p.data.add_(torch.mul(p.data, group['weight_decay']).addcdiv_(1, exp_avg, denom), alpha=-step_size)
return loss
class Novograd(Optimizer):
"""
Implements Novograd algorithm.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.95, 0))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
grad_averaging: gradient averaging
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False)
"""
def __init__(self, params, lr=1e-3, betas=(0.95, 0), eps=1e-8,
weight_decay=0, grad_averaging=False, amsgrad=False):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay,
grad_averaging=grad_averaging,
amsgrad=amsgrad)
super(Novograd, self).__init__(params, defaults)
def __setstate__(self, state):
super(Novograd, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('Sparse gradients are not supported.')
amsgrad = group['amsgrad']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
norm = torch.sum(torch.pow(grad, 2))
if exp_avg_sq == 0:
exp_avg_sq.copy_(norm)
else:
exp_avg_sq.mul_(beta2).add_(norm, alpha=1 - beta2)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
grad.div_(denom)
if group['weight_decay'] != 0:
grad.add_(p.data, alpha=group['weight_decay'])
if group['grad_averaging']:
grad.mul_(1 - beta1)
exp_avg.mul_(beta1).add_(grad)
p.data.add_(exp_avg, alpha=-group['lr'])
return loss
|
PyTorch/LanguageModeling/BERT/data | data | WikiDownloader | # Copyright (c) 2019-2021 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 bz2
import os
import urllib.request
import subprocess
import sys
import subprocess
class WikiDownloader:
def __init__(self, language, save_path):
self.save_path = save_path + '/wikicorpus_' + language
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
self.language = language
# Use a mirror from https://dumps.wikimedia.org/mirrors.html if the below links do not work
self.download_urls = {
'en' : 'https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2',
'zh' : 'https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2'
}
self.output_files = {
'en' : 'wikicorpus_en.xml.bz2',
'zh' : 'wikicorpus_zh.xml.bz2'
}
def download(self):
if self.language in self.download_urls:
url = self.download_urls[self.language]
filename = self.output_files[self.language]
print('Downloading:', url)
if os.path.isfile(self.save_path + '/' + filename):
print('** Download file already exists, skipping download')
else:
cmd = ['wget', url, '--output-document={}'.format(self.save_path + '/' + filename)]
print('Running:', cmd)
status = subprocess.run(cmd)
if status.returncode != 0:
raise RuntimeError('Wiki download not successful')
# Always unzipping since this is relatively fast and will overwrite
print('Unzipping:', self.output_files[self.language])
subprocess.run('bzip2 -dk ' + self.save_path + '/' + filename, shell=True, check=True)
else:
assert False, 'WikiDownloader not implemented for this language yet.'
|
TensorFlow/Segmentation/UNet_Industrial/scripts/benchmarking | benchmarking | UNet_evalbench | #!/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 FP32/TF32 on 1 GPUs using 16 batch size
# Usage ./UNet_evalbench.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 \
--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
|
PyTorch/Detection/SSD/examples | examples | SSD300_A100_FP16_1GPU | # This script launches SSD300 training in FP16 on 1 GPUs using 256 batch size
# Usage bash SSD300_FP16_1GPU.sh <path to this repository> <path to dataset> <additional flags>
python $1/main.py --backbone resnet50 --warmup 300 --bs 256 --data $2 ${@:3}
|
TensorFlow/Detection/SSD/models/research/object_detection/builders | builders | box_coder_builder | # 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.
# ==============================================================================
"""A function to build an object detection box coder from configuration."""
from object_detection.box_coders import faster_rcnn_box_coder
from object_detection.box_coders import keypoint_box_coder
from object_detection.box_coders import mean_stddev_box_coder
from object_detection.box_coders import square_box_coder
from object_detection.protos import box_coder_pb2
def build(box_coder_config):
"""Builds a box coder object based on the box coder config.
Args:
box_coder_config: A box_coder.proto object containing the config for the
desired box coder.
Returns:
BoxCoder based on the config.
Raises:
ValueError: On empty box coder proto.
"""
if not isinstance(box_coder_config, box_coder_pb2.BoxCoder):
raise ValueError('box_coder_config not of type box_coder_pb2.BoxCoder.')
if box_coder_config.WhichOneof('box_coder_oneof') == 'faster_rcnn_box_coder':
return faster_rcnn_box_coder.FasterRcnnBoxCoder(scale_factors=[
box_coder_config.faster_rcnn_box_coder.y_scale,
box_coder_config.faster_rcnn_box_coder.x_scale,
box_coder_config.faster_rcnn_box_coder.height_scale,
box_coder_config.faster_rcnn_box_coder.width_scale
])
if box_coder_config.WhichOneof('box_coder_oneof') == 'keypoint_box_coder':
return keypoint_box_coder.KeypointBoxCoder(
box_coder_config.keypoint_box_coder.num_keypoints,
scale_factors=[
box_coder_config.keypoint_box_coder.y_scale,
box_coder_config.keypoint_box_coder.x_scale,
box_coder_config.keypoint_box_coder.height_scale,
box_coder_config.keypoint_box_coder.width_scale
])
if (box_coder_config.WhichOneof('box_coder_oneof') ==
'mean_stddev_box_coder'):
return mean_stddev_box_coder.MeanStddevBoxCoder(
stddev=box_coder_config.mean_stddev_box_coder.stddev)
if box_coder_config.WhichOneof('box_coder_oneof') == 'square_box_coder':
return square_box_coder.SquareBoxCoder(scale_factors=[
box_coder_config.square_box_coder.y_scale,
box_coder_config.square_box_coder.x_scale,
box_coder_config.square_box_coder.length_scale
])
raise ValueError('Empty box coder.')
|
PyTorch/SpeechSynthesis/Tacotron2/notebooks/conversationalai/client/speech_ai_demo/utils/tacotron2 | tacotron2 | cleaners | """ from https://github.com/keithito/tacotron """
'''
Cleaners are transformations that run over the input text at both training and eval time.
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You'll typically want to use:
1. "english_cleaners" for English text
2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
the Unidecode library (https://pypi.python.org/pypi/Unidecode)
3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
the symbols in symbols.py to match your data).
'''
import re
from .numbers import normalize_numbers
from .unidecoder import unidecoder
# Regular expression matching whitespace:
_whitespace_re = re.compile(r'\s+')
# List of (regular expression, replacement) pairs for abbreviations:
_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
('mrs', 'misess'),
('mr', 'mister'),
('dr', 'doctor'),
('st', 'saint'),
('co', 'company'),
('jr', 'junior'),
('maj', 'major'),
('gen', 'general'),
('drs', 'doctors'),
('rev', 'reverend'),
('lt', 'lieutenant'),
('hon', 'honorable'),
('sgt', 'sergeant'),
('capt', 'captain'),
('esq', 'esquire'),
('ltd', 'limited'),
('col', 'colonel'),
('ft', 'fort'),
]]
def expand_abbreviations(text):
for regex, replacement in _abbreviations:
text = re.sub(regex, replacement, text)
return text
def expand_numbers(text):
return normalize_numbers(text)
def lowercase(text):
return text.lower()
def collapse_whitespace(text):
return re.sub(_whitespace_re, ' ', text)
def convert_to_ascii(text):
return unidecoder(text)
def basic_cleaners(text):
'''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
text = lowercase(text)
text = collapse_whitespace(text)
return text
def transliteration_cleaners(text):
'''Pipeline for non-English text that transliterates to ASCII.'''
text = convert_to_ascii(text)
text = lowercase(text)
text = collapse_whitespace(text)
return text
def english_cleaners(text):
'''Pipeline for English text, including number and abbreviation expansion.'''
text = convert_to_ascii(text)
text = lowercase(text)
text = expand_numbers(text)
text = expand_abbreviations(text)
text = collapse_whitespace(text)
return text
|
PyTorch/Segmentation/MaskRCNN/pytorch | pytorch | CODE_OF_CONDUCT | # Code of Conduct
Facebook has adopted a Code of Conduct that we expect project participants to adhere to.
Please read the [full text](https://code.fb.com/codeofconduct/)
so that you can understand what actions will and will not be tolerated.
|
PyTorch/Detection/Efficientdet | Efficientdet | .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/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
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
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# 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
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# PyCharm
.idea
# PyTorch weights
*.tar
*.pth
*.gz
|
PyTorch/SpeechRecognition/wav2vec2 | wav2vec2 | requirements | editdistance==0.6.0
librosa==0.10.1
omegaconf==2.0.6 # optional for handling certain Fairseq ckpts
pyarrow==6.0.1
soundfile==0.12.1
sox==1.4.1
tqdm==4.53.0
git+https://github.com/NVIDIA/dllogger@v1.0.0#egg=dllogger
|
TensorFlow/Detection/SSD/models/research/object_detection/anchor_generators | anchor_generators | multiscale_grid_anchor_generator | # 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.
# ==============================================================================
"""Generates grid anchors on the fly corresponding to multiple CNN layers.
Generates grid anchors on the fly corresponding to multiple CNN layers as
described in:
"Focal Loss for Dense Object Detection" (https://arxiv.org/abs/1708.02002)
T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar
"""
from object_detection.anchor_generators import grid_anchor_generator
from object_detection.core import anchor_generator
from object_detection.core import box_list_ops
class MultiscaleGridAnchorGenerator(anchor_generator.AnchorGenerator):
"""Generate a grid of anchors for multiple CNN layers of different scale."""
def __init__(self, min_level, max_level, anchor_scale, aspect_ratios,
scales_per_octave, normalize_coordinates=True):
"""Constructs a MultiscaleGridAnchorGenerator.
To construct anchors, at multiple scale resolutions, one must provide a
the minimum level and maximum levels on a scale pyramid. To define the size
of anchor, the anchor scale is provided to decide the size relatively to the
stride of the corresponding feature map. The generator allows one pixel
location on feature map maps to multiple anchors, that have different aspect
ratios and intermediate scales.
Args:
min_level: minimum level in feature pyramid.
max_level: maximum level in feature pyramid.
anchor_scale: anchor scale and feature stride define the size of the base
anchor on an image. For example, given a feature pyramid with strides
[2^3, ..., 2^7] and anchor scale 4. The base anchor size is
4 * [2^3, ..., 2^7].
aspect_ratios: list or tuple of (float) aspect ratios to place on each
grid point.
scales_per_octave: integer number of intermediate scales per scale octave.
normalize_coordinates: whether to produce anchors in normalized
coordinates. (defaults to True).
"""
self._anchor_grid_info = []
self._aspect_ratios = aspect_ratios
self._scales_per_octave = scales_per_octave
self._normalize_coordinates = normalize_coordinates
scales = [2**(float(scale) / scales_per_octave)
for scale in range(scales_per_octave)]
aspects = list(aspect_ratios)
for level in range(min_level, max_level + 1):
anchor_stride = [2**level, 2**level]
base_anchor_size = [2**level * anchor_scale, 2**level * anchor_scale]
self._anchor_grid_info.append({
'level': level,
'info': [scales, aspects, base_anchor_size, anchor_stride]
})
def name_scope(self):
return 'MultiscaleGridAnchorGenerator'
def num_anchors_per_location(self):
"""Returns the number of anchors per spatial location.
Returns:
a list of integers, one for each expected feature map to be passed to
the Generate function.
"""
return len(self._anchor_grid_info) * [
len(self._aspect_ratios) * self._scales_per_octave]
def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
"""Generates a collection of bounding boxes to be used as anchors.
Currently we require the input image shape to be statically defined. That
is, im_height and im_width should be integers rather than tensors.
Args:
feature_map_shape_list: list of pairs of convnet layer resolutions in the
format [(height_0, width_0), (height_1, width_1), ...]. For example,
setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
correspond to an 8x8 layer followed by a 7x7 layer.
im_height: the height of the image to generate the grid for. If both
im_height and im_width are 1, anchors can only be generated in
absolute coordinates.
im_width: the width of the image to generate the grid for. If both
im_height and im_width are 1, anchors can only be generated in
absolute coordinates.
Returns:
boxes_list: a list of BoxLists each holding anchor boxes corresponding to
the input feature map shapes.
Raises:
ValueError: if im_height and im_width are not integers.
ValueError: if im_height and im_width are 1, but normalized coordinates
were requested.
"""
anchor_grid_list = []
for feat_shape, grid_info in zip(feature_map_shape_list,
self._anchor_grid_info):
# TODO(rathodv) check the feature_map_shape_list is consistent with
# self._anchor_grid_info
level = grid_info['level']
stride = 2**level
scales, aspect_ratios, base_anchor_size, anchor_stride = grid_info['info']
feat_h = feat_shape[0]
feat_w = feat_shape[1]
anchor_offset = [0, 0]
if isinstance(im_height, int) and isinstance(im_width, int):
if im_height % 2.0**level == 0 or im_height == 1:
anchor_offset[0] = stride / 2.0
if im_width % 2.0**level == 0 or im_width == 1:
anchor_offset[1] = stride / 2.0
ag = grid_anchor_generator.GridAnchorGenerator(
scales,
aspect_ratios,
base_anchor_size=base_anchor_size,
anchor_stride=anchor_stride,
anchor_offset=anchor_offset)
(anchor_grid,) = ag.generate(feature_map_shape_list=[(feat_h, feat_w)])
if self._normalize_coordinates:
if im_height == 1 or im_width == 1:
raise ValueError(
'Normalized coordinates were requested upon construction of the '
'MultiscaleGridAnchorGenerator, but a subsequent call to '
'generate did not supply dimension information.')
anchor_grid = box_list_ops.to_normalized_coordinates(
anchor_grid, im_height, im_width, check_range=False)
anchor_grid_list.append(anchor_grid)
return anchor_grid_list
|
TensorFlow/Detection/SSD/models/research/object_detection/models | models | faster_rcnn_nas_feature_extractor | # 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.
# ==============================================================================
"""NASNet Faster R-CNN implementation.
Learning Transferable Architectures for Scalable Image Recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le
https://arxiv.org/abs/1707.07012
"""
import tensorflow as tf
from object_detection.meta_architectures import faster_rcnn_meta_arch
from nets.nasnet import nasnet
from nets.nasnet import nasnet_utils
arg_scope = tf.contrib.framework.arg_scope
slim = tf.contrib.slim
def nasnet_large_arg_scope_for_detection(is_batch_norm_training=False):
"""Defines the default arg scope for the NASNet-A Large for object detection.
This provides a small edit to switch batch norm training on and off.
Args:
is_batch_norm_training: Boolean indicating whether to train with batch norm.
Returns:
An `arg_scope` to use for the NASNet Large Model.
"""
imagenet_scope = nasnet.nasnet_large_arg_scope()
with arg_scope(imagenet_scope):
with arg_scope([slim.batch_norm], is_training=is_batch_norm_training) as sc:
return sc
# Note: This is largely a copy of _build_nasnet_base inside nasnet.py but
# with special edits to remove instantiation of the stem and the special
# ability to receive as input a pair of hidden states.
def _build_nasnet_base(hidden_previous,
hidden,
normal_cell,
reduction_cell,
hparams,
true_cell_num,
start_cell_num):
"""Constructs a NASNet image model."""
# Find where to place the reduction cells or stride normal cells
reduction_indices = nasnet_utils.calc_reduction_layers(
hparams.num_cells, hparams.num_reduction_layers)
# Note: The None is prepended to match the behavior of _imagenet_stem()
cell_outputs = [None, hidden_previous, hidden]
net = hidden
# NOTE: In the nasnet.py code, filter_scaling starts at 1.0. We instead
# start at 2.0 because 1 reduction cell has been created which would
# update the filter_scaling to 2.0.
filter_scaling = 2.0
# Run the cells
for cell_num in range(start_cell_num, hparams.num_cells):
stride = 1
if hparams.skip_reduction_layer_input:
prev_layer = cell_outputs[-2]
if cell_num in reduction_indices:
filter_scaling *= hparams.filter_scaling_rate
net = reduction_cell(
net,
scope='reduction_cell_{}'.format(reduction_indices.index(cell_num)),
filter_scaling=filter_scaling,
stride=2,
prev_layer=cell_outputs[-2],
cell_num=true_cell_num)
true_cell_num += 1
cell_outputs.append(net)
if not hparams.skip_reduction_layer_input:
prev_layer = cell_outputs[-2]
net = normal_cell(
net,
scope='cell_{}'.format(cell_num),
filter_scaling=filter_scaling,
stride=stride,
prev_layer=prev_layer,
cell_num=true_cell_num)
true_cell_num += 1
cell_outputs.append(net)
# Final nonlinearity.
# Note that we have dropped the final pooling, dropout and softmax layers
# from the default nasnet version.
with tf.variable_scope('final_layer'):
net = tf.nn.relu(net)
return net
# TODO(shlens): Only fixed_shape_resizer is currently supported for NASNet
# featurization. The reason for this is that nasnet.py only supports
# inputs with fully known shapes. We need to update nasnet.py to handle
# shapes not known at compile time.
class FasterRCNNNASFeatureExtractor(
faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
"""Faster R-CNN with NASNet-A feature extractor implementation."""
def __init__(self,
is_training,
first_stage_features_stride,
batch_norm_trainable=False,
reuse_weights=None,
weight_decay=0.0):
"""Constructor.
Args:
is_training: See base class.
first_stage_features_stride: See base class.
batch_norm_trainable: See base class.
reuse_weights: See base class.
weight_decay: See base class.
Raises:
ValueError: If `first_stage_features_stride` is not 16.
"""
if first_stage_features_stride != 16:
raise ValueError('`first_stage_features_stride` must be 16.')
super(FasterRCNNNASFeatureExtractor, self).__init__(
is_training, first_stage_features_stride, batch_norm_trainable,
reuse_weights, weight_decay)
def preprocess(self, resized_inputs):
"""Faster R-CNN with NAS preprocessing.
Maps pixel values to the range [-1, 1].
Args:
resized_inputs: A [batch, height_in, width_in, channels] float32 tensor
representing a batch of images with values between 0 and 255.0.
Returns:
preprocessed_inputs: A [batch, height_out, width_out, channels] float32
tensor representing a batch of images.
"""
return (2.0 / 255.0) * resized_inputs - 1.0
def _extract_proposal_features(self, preprocessed_inputs, scope):
"""Extracts first stage RPN features.
Extracts features using the first half of the NASNet network.
We construct the network in `align_feature_maps=True` mode, which means
that all VALID paddings in the network are changed to SAME padding so that
the feature maps are aligned.
Args:
preprocessed_inputs: A [batch, height, width, channels] float32 tensor
representing a batch of images.
scope: A scope name.
Returns:
rpn_feature_map: A tensor with shape [batch, height, width, depth]
end_points: A dictionary mapping feature extractor tensor names to tensors
Raises:
ValueError: If the created network is missing the required activation.
"""
del scope
if len(preprocessed_inputs.get_shape().as_list()) != 4:
raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
'tensor of shape %s' % preprocessed_inputs.get_shape())
with slim.arg_scope(nasnet_large_arg_scope_for_detection(
is_batch_norm_training=self._train_batch_norm)):
with arg_scope([slim.conv2d,
slim.batch_norm,
slim.separable_conv2d],
reuse=self._reuse_weights):
_, end_points = nasnet.build_nasnet_large(
preprocessed_inputs, num_classes=None,
is_training=self._is_training,
final_endpoint='Cell_11')
# Note that both 'Cell_10' and 'Cell_11' have equal depth = 2016.
rpn_feature_map = tf.concat([end_points['Cell_10'],
end_points['Cell_11']], 3)
# nasnet.py does not maintain the batch size in the first dimension.
# This work around permits us retaining the batch for below.
batch = preprocessed_inputs.get_shape().as_list()[0]
shape_without_batch = rpn_feature_map.get_shape().as_list()[1:]
rpn_feature_map_shape = [batch] + shape_without_batch
rpn_feature_map.set_shape(rpn_feature_map_shape)
return rpn_feature_map, end_points
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
This function reconstructs the "second half" of the NASNet-A
network after the part defined in `_extract_proposal_features`.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name.
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
del scope
# Note that we always feed into 2 layers of equal depth
# where the first N channels corresponds to previous hidden layer
# and the second N channels correspond to the final hidden layer.
hidden_previous, hidden = tf.split(proposal_feature_maps, 2, axis=3)
# Note that what follows is largely a copy of build_nasnet_large() within
# nasnet.py. We are copying to minimize code pollution in slim.
# TODO(shlens,skornblith): Determine the appropriate drop path schedule.
# For now the schedule is the default (1.0->0.7 over 250,000 train steps).
hparams = nasnet.large_imagenet_config()
if not self._is_training:
hparams.set_hparam('drop_path_keep_prob', 1.0)
# Calculate the total number of cells in the network
# -- Add 2 for the reduction cells.
total_num_cells = hparams.num_cells + 2
# -- And add 2 for the stem cells for ImageNet training.
total_num_cells += 2
normal_cell = nasnet_utils.NasNetANormalCell(
hparams.num_conv_filters, hparams.drop_path_keep_prob,
total_num_cells, hparams.total_training_steps)
reduction_cell = nasnet_utils.NasNetAReductionCell(
hparams.num_conv_filters, hparams.drop_path_keep_prob,
total_num_cells, hparams.total_training_steps)
with arg_scope([slim.dropout, nasnet_utils.drop_path],
is_training=self._is_training):
with arg_scope([slim.batch_norm], is_training=self._train_batch_norm):
with arg_scope([slim.avg_pool2d,
slim.max_pool2d,
slim.conv2d,
slim.batch_norm,
slim.separable_conv2d,
nasnet_utils.factorized_reduction,
nasnet_utils.global_avg_pool,
nasnet_utils.get_channel_index,
nasnet_utils.get_channel_dim],
data_format=hparams.data_format):
# This corresponds to the cell number just past 'Cell_11' used by
# by _extract_proposal_features().
start_cell_num = 12
# Note that this number equals:
# start_cell_num + 2 stem cells + 1 reduction cell
true_cell_num = 15
with slim.arg_scope(nasnet.nasnet_large_arg_scope()):
net = _build_nasnet_base(hidden_previous,
hidden,
normal_cell=normal_cell,
reduction_cell=reduction_cell,
hparams=hparams,
true_cell_num=true_cell_num,
start_cell_num=start_cell_num)
proposal_classifier_features = net
return proposal_classifier_features
def restore_from_classification_checkpoint_fn(
self,
first_stage_feature_extractor_scope,
second_stage_feature_extractor_scope):
"""Returns a map of variables to load from a foreign checkpoint.
Note that this overrides the default implementation in
faster_rcnn_meta_arch.FasterRCNNFeatureExtractor which does not work for
NASNet-A checkpoints.
Args:
first_stage_feature_extractor_scope: A scope name for the first stage
feature extractor.
second_stage_feature_extractor_scope: A scope name for the second stage
feature extractor.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
"""
# Note that the NAS checkpoint only contains the moving average version of
# the Variables so we need to generate an appropriate dictionary mapping.
variables_to_restore = {}
for variable in tf.global_variables():
if variable.op.name.startswith(
first_stage_feature_extractor_scope):
var_name = variable.op.name.replace(
first_stage_feature_extractor_scope + '/', '')
var_name += '/ExponentialMovingAverage'
variables_to_restore[var_name] = variable
if variable.op.name.startswith(
second_stage_feature_extractor_scope):
var_name = variable.op.name.replace(
second_stage_feature_extractor_scope + '/', '')
var_name += '/ExponentialMovingAverage'
variables_to_restore[var_name] = variable
return variables_to_restore
|
DGLPyTorch/DrugDiscovery/SE3Transformer/se3_transformer/model | model | fiber | # 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 collections import namedtuple
from itertools import product
from typing import Dict
import torch
from torch import Tensor
from se3_transformer.runtime.utils import degree_to_dim
FiberEl = namedtuple('FiberEl', ['degree', 'channels'])
class Fiber(dict):
"""
Describes the structure of some set of features.
Features are split into types (0, 1, 2, 3, ...). A feature of type k has a dimension of 2k+1.
Type-0 features: invariant scalars
Type-1 features: equivariant 3D vectors
Type-2 features: equivariant symmetric traceless matrices
...
As inputs to a SE3 layer, there can be many features of the same types, and many features of different types.
The 'multiplicity' or 'number of channels' is the number of features of a given type.
This class puts together all the degrees and their multiplicities in order to describe
the inputs, outputs or hidden features of SE3 layers.
"""
def __init__(self, structure):
if isinstance(structure, dict):
structure = [FiberEl(int(d), int(m)) for d, m in sorted(structure.items(), key=lambda x: x[1])]
elif not isinstance(structure[0], FiberEl):
structure = list(map(lambda t: FiberEl(*t), sorted(structure, key=lambda x: x[1])))
self.structure = structure
super().__init__({d: m for d, m in self.structure})
@property
def degrees(self):
return sorted([t.degree for t in self.structure])
@property
def channels(self):
return [self[d] for d in self.degrees]
@property
def num_features(self):
""" Size of the resulting tensor if all features were concatenated together """
return sum(t.channels * degree_to_dim(t.degree) for t in self.structure)
@staticmethod
def create(num_degrees: int, num_channels: int):
""" Create a Fiber with degrees 0..num_degrees-1, all with the same multiplicity """
return Fiber([(degree, num_channels) for degree in range(num_degrees)])
@staticmethod
def from_features(feats: Dict[str, Tensor]):
""" Infer the Fiber structure from a feature dict """
structure = {}
for k, v in feats.items():
degree = int(k)
assert len(v.shape) == 3, 'Feature shape should be (N, C, 2D+1)'
assert v.shape[-1] == degree_to_dim(degree)
structure[degree] = v.shape[-2]
return Fiber(structure)
def __getitem__(self, degree: int):
""" fiber[degree] returns the multiplicity for this degree """
return dict(self.structure).get(degree, 0)
def __iter__(self):
""" Iterate over namedtuples (degree, channels) """
return iter(self.structure)
def __mul__(self, other):
"""
If other in an int, multiplies all the multiplicities by other.
If other is a fiber, returns the cartesian product.
"""
if isinstance(other, Fiber):
return product(self.structure, other.structure)
elif isinstance(other, int):
return Fiber({t.degree: t.channels * other for t in self.structure})
def __add__(self, other):
"""
If other in an int, add other to all the multiplicities.
If other is a fiber, add the multiplicities of the fibers together.
"""
if isinstance(other, Fiber):
return Fiber({t.degree: t.channels + other[t.degree] for t in self.structure})
elif isinstance(other, int):
return Fiber({t.degree: t.channels + other for t in self.structure})
def __repr__(self):
return str(self.structure)
@staticmethod
def combine_max(f1, f2):
""" Combine two fiber by taking the maximum multiplicity for each degree in both fibers """
new_dict = dict(f1.structure)
for k, m in f2.structure:
new_dict[k] = max(new_dict.get(k, 0), m)
return Fiber(list(new_dict.items()))
@staticmethod
def combine_selectively(f1, f2):
""" Combine two fiber by taking the sum of multiplicities for each degree in the first fiber """
# only use orders which occur in fiber f1
new_dict = dict(f1.structure)
for k in f1.degrees:
if k in f2.degrees:
new_dict[k] += f2[k]
return Fiber(list(new_dict.items()))
def to_attention_heads(self, tensors: Dict[str, Tensor], num_heads: int):
# dict(N, num_channels, 2d+1) -> (N, num_heads, -1)
fibers = [tensors[str(degree)].reshape(*tensors[str(degree)].shape[:-2], num_heads, -1) for degree in
self.degrees]
fibers = torch.cat(fibers, -1)
return fibers
|
Tools/DGLPyTorch/SyntheticGraphGeneration/demos/performance | performance | struct_generator | #!/usr/bin/env python
# coding: utf-8
# Copyright 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.
# ==============================================================================
# # Graph structure generation demo
# ## Overview
#
# In this notebbok we compare the performance (throughput) of graph structure generators presented in the SynGen tool.
#
# Available generators:
#
# 1. [Exact RMAT generator (GPU)](#1)
# 1. [Exact RMAT generator (CPU)](#2)
# 1. [Approximate RMAT generator (GPU)](#3)
# 1. [Approximate RMAT generator (CPU)](#4)
# In[1]:
# Generator
from syngen.generator.graph import RMATGenerator
# Others
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.pyplot import set_loglevel
set_loglevel('warning')
import time
from itertools import product
# In[2]:
def get_xy(data):
x = [edges for edges, _, _ in data]
y = [eps for _, eps, _ in data]
return x, y
# ## Exact generator
# <a id="1"></a>
# ### GPU
# In[3]:
static_graph_generator=RMATGenerator()
static_graph_generator.gpu=True
# In[4]:
static_graph_generator._fit_results = 0.4, 0.25, 0.2, 0.15
# In[5]:
get_ipython().run_cell_magic('time', '', '\nstart = time.perf_counter()\ndata_proper = static_graph_generator.generate(num_nodes=6_797_556, \n num_edges=168_114, \n is_directed=False, \n has_self_loop=False)\nelapsed = time.perf_counter() - start\n\nprint(elapsed)\n')
# In[6]:
n_range = [15, 20, 30, 40]
n_range = [2 ** x for x in n_range]
edges_range = [1e6, 1e7, 1e8]
edges_range = [int(x // 2) for x in edges_range]
gpu_res = {
int(np.log2(n)): [] for n in n_range
}
# Random run
data_proper = static_graph_generator.generate(num_nodes=168_114,
num_edges=6_797_556,
is_directed=False,
has_self_loop=False)
for n, edges in product(n_range, edges_range):
max_edges = (n * (n - 1)) // 2
density = edges / max_edges
if density > 0.75:
res = "FAIL"
else:
res = "GOOD"
f_string = f"{n:<13} | {edges:<13} | {density:>8.3f} | {res}"
print(f_string)
if res == "FAIL":
continue
start = time.perf_counter()
data_proper = static_graph_generator.generate(num_nodes=n,
num_edges=edges,
is_directed=False,
has_self_loop=False)
elapsed = time.perf_counter() - start
gen_edges = data_proper.shape[0]
edges_per_second = data_proper.shape[0] / elapsed
calculated = (gen_edges, edges_per_second, elapsed)
f_string = f"{n:<13} | {edges:<13} | {edges_per_second}"
print(f_string)
l = gpu_res[int(np.log2(n))]
l.append(calculated)
# In[7]:
plt.figure(figsize=(15, 6))
for n, data in gpu_res.items():
x, y = get_xy(data)
plt.plot(x, y, '--x', label=f'{n}')
plt.xlabel('Total edges to generate')
plt.ylabel('Edges per seconds')
plt.legend()
plt.legend()
plt.yscale('log')
plt.xscale('log')
# <a id="2"></a>
# ### CPU
# In[8]:
static_graph_generator=RMATGenerator()
static_graph_generator.gpu=False
# In[9]:
static_graph_generator._fit_results = 0.4, 0.25, 0.2, 0.15
# In[10]:
n_range = [15, 20, 30, 40]
n_range = [2 ** x for x in n_range]
edges_range = [1e6, 1e7, 1e8]
edges_range = [int(x // 2) for x in edges_range]
cpu_res = {
int(np.log2(n)): [] for n in n_range
}
# Random run
data_proper = static_graph_generator.generate(num_nodes=168_114,
num_edges=6_797_556,
is_directed=False,
has_self_loop=False)
for n, edges in product(n_range, edges_range):
max_edges = (n * (n - 1)) // 2
density = edges / max_edges
if density > 0.75:
res = "FAIL"
else:
res = "GOOD"
f_string = f"{n:<13} | {edges:<13} | {density:>8.3f} | {res}"
print(f_string)
if res == "FAIL":
continue
start = time.perf_counter()
data_proper = static_graph_generator.generate(num_nodes=n,
num_edges=edges,
is_directed=False,
has_self_loop=False)
elapsed = time.perf_counter() - start
gen_edges = data_proper.shape[0]
edges_per_second = data_proper.shape[0] / elapsed
calculated = (gen_edges, edges_per_second, elapsed)
f_string = f"{n:<13} | {edges:<13} | {edges_per_second}"
print(f_string)
l = cpu_res[int(np.log2(n))]
l.append(calculated)
# In[11]:
plt.figure(figsize=(15, 6))
for n, data in cpu_res.items():
x, y = get_xy(data)
plt.plot(x, y, '--x', label=f'{n}')
plt.xlabel('Total edges to generate')
plt.ylabel('Edges per seconds')
plt.legend()
plt.legend()
plt.yscale('log')
plt.xscale('log')
# In[12]:
plt.figure(figsize=(15, 6), dpi=800)
for n, data in gpu_res.items():
x, y = get_xy(data)
plt.plot(x, y, '--x', label=f'2^{n} nodes')
for n, data in cpu_res.items():
x, y = get_xy(data)
plt.plot(x, y, '--o', label=f'2^{n} nodes')
plt.annotate('dense \n graph', (cpu_res[15][2][0], cpu_res[15][2][1]))
plt.annotate('gpu generators', (gpu_res[15][1][0], gpu_res[15][1][1]+2e7))
plt.annotate('cpu generators', (cpu_res[15][1][0], cpu_res[15][1][1]+7e4))
plt.title('Exact generator performance')
plt.xlabel('Total edges to generate')
plt.ylabel('Edges per seconds')
plt.legend()
plt.yscale('log')
plt.xscale('log')
# plt.savefig("myImage.png", format="png", dpi=800)
plt.show()
# ## Approximate generator
# <a id="3"></a>
# ### GPU
# In[13]:
import cupy as cp
import numpy as np
from pylibraft.random import rmat
def generate_gpu_rmat_approx(a, b, c, d,
r_scale, c_scale,
n_edges,
noise=0.5):
gen_graph = None
theta_len = max(r_scale, c_scale) * 4
base_theta = [a, b, c, d]
if noise > 0:
full_theta = []
for i in range(theta_len):
noise_uniform = noise * np.random.uniform(-1, 1, size=len(base_theta))
noise_to_add = np.multiply(base_theta, noise_uniform)
theta_n = base_theta + noise_to_add
theta_n = theta_n / np.sum(theta_n)
full_theta.append(theta_n)
else:
full_theta = base_theta * theta_len
theta_cpu = np.array(full_theta, dtype=np.float32)
theta = cp.asarray(theta_cpu)
tmp = cp.empty((n_edges, 2), dtype=cp.int32)
rmat(tmp, theta, r_scale, c_scale)
return tmp
# In[14]:
a, b, c, d = 0.4, 0.25, 0.2, 0.15
# In[15]:
n_range = [15, 20, 30, 40]
edges_range = [1e6, 1e7, 1e8]
edges_range = [int(x) for x in edges_range]
gpu_res_approx = {
n: [] for n in n_range
}
# Random run
data_proper = generate_gpu_rmat_approx(a, b, c, d,
18, 18,
6_797_556)
for n, edges in product(n_range, edges_range):
max_edges = (2**n * (2**n - 1)) // 2
density = edges / max_edges
if density > 0.75:
res = "FAIL"
else:
res = "GOOD"
f_string = f"{n:<13} | {edges:<13} | {density:>8.3f} | {res}"
print(f_string)
if res == "FAIL":
continue
start = time.perf_counter()
data_proper = generate_gpu_rmat_approx(a, b, c, d, n, n, edges)
elapsed = time.perf_counter() - start
gen_edges = data_proper.shape[0]
edges_per_second = data_proper.shape[0] / elapsed
calculated = (gen_edges, edges_per_second, elapsed)
f_string = f"{n:<13} | {edges:<13} | {edges_per_second}"
print(f_string)
l = gpu_res_approx[n]
l.append(calculated)
# In[16]:
plt.figure(figsize=(15, 6))
for n, data in gpu_res_approx.items():
x, y = get_xy(data)
plt.plot(x, y, '--x', label=f'{n}')
plt.xlabel('Total edges to generate')
plt.ylabel('Edges per seconds')
plt.legend()
plt.legend()
plt.yscale('log')
plt.xscale('log')
# <a id="4"></a>
# ### CPU
# In[17]:
from syngen.generator.graph.utils import effective_nonsquare_rmat_approximate
# In[18]:
a, b, c, d = 0.4, 0.25, 0.2, 0.15
theta = np.array([[a, b], [c, d]])
theta /= a + b + c + d
# In[19]:
part, _, _ = effective_nonsquare_rmat_approximate(
theta,
6_797_556,
(18, 18),
noise_scaling=0.5,
batch_size=1_000_000,
dtype=np.int64,
custom_samplers=None,
generate_back_edges=False
)
# In[20]:
part.shape
# In[21]:
n_range = [15, 20, 30, 40]
#n_range = [2 ** x for x in n_range]
edges_range = [1e6, 1e7, 1e8]
edges_range = [int(x) for x in edges_range]
cpu_res_approx = {
n: [] for n in n_range
}
# Random run
part, _, _ = effective_nonsquare_rmat_approximate(
theta,
6_797_556,
(18, 18),
noise_scaling=0.5,
batch_size=1_000_000,
dtype=np.int64,
custom_samplers=None,
generate_back_edges=False
)
for n, edges in product(n_range, edges_range):
max_edges = (2**n * (2**n - 1)) // 2
density = edges / max_edges
if density > 0.75:
res = "FAIL"
else:
res = "GOOD"
f_string = f"{n:<13} | {edges:<13} | {density:>8.3f} | {res}"
print(f_string)
if res == "FAIL":
continue
start = time.perf_counter()
data_proper, _, _ = effective_nonsquare_rmat_approximate(
theta,
edges,
(n, n),
noise_scaling=0.5,
batch_size=1_000_000,
dtype=np.int64,
custom_samplers=None,
generate_back_edges=False
)
elapsed = time.perf_counter() - start
gen_edges = data_proper.shape[0]
edges_per_second = data_proper.shape[0] / elapsed
calculated = (gen_edges, edges_per_second, elapsed)
f_string = f"{n:<13} | {edges:<13} | {edges_per_second}"
print(f_string)
l = cpu_res_approx[n]
l.append(calculated)
# In[22]:
plt.figure(figsize=(15, 6))
for n, data in cpu_res_approx.items():
x, y = get_xy(data)
plt.plot(x, y, '--x', label=f'{n}')
plt.xlabel('Total edges to generate')
plt.ylabel('Edges per seconds')
plt.legend()
plt.legend()
plt.yscale('log')
plt.xscale('log')
# In[24]:
plt.figure(figsize=(15, 6), dpi=800)
for n, data in gpu_res_approx.items():
x, y = get_xy(data)
plt.plot(x, y, '--x', label=f'2^{n} nodes')
for n, data in cpu_res_approx.items():
x, y = get_xy(data)
plt.plot(x, y, '--o', label=f'2^{n} nodes')
plt.annotate('gpu generators', (gpu_res_approx[15][1][0], gpu_res_approx[15][1][1]+5e9))
plt.annotate('cpu generators', (cpu_res_approx[15][1][0], cpu_res_approx[15][1][1]+5e5))
plt.title('Approximate generator performance')
plt.xlabel('Total edges to generate')
plt.ylabel('Edges per seconds')
plt.legend()
plt.yscale('log')
plt.xscale('log')
plt.savefig("/workspace/img/edge_perf.png")
plt.show()
# In[ ]:
|
CUDA-Optimized/FastSpeech/fastspeech/trt/plugins/add_pos_enc | add_pos_enc | test_add_pos_enc_plugin | # 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.
from __future__ import print_function
import numpy as np
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
import ctypes
import os
import sys
import time
logger = trt.Logger(trt.Logger.INFO)
PLUGIN_PATH = '/home/dahn/git/fastspeech/fastspeech/trt/plugins/add_pos_enc/AddPosEncPlugin.so'
ctypes.cdll.LoadLibrary(PLUGIN_PATH)
def get_plugin_creator(plugin_name):
trt.init_libnvinfer_plugins(logger, '')
plugin_creator_list = trt.get_plugin_registry().plugin_creator_list
plugin_creator = None
for c in plugin_creator_list:
if c.name == plugin_name:
plugin_creator = c
return plugin_creator
def build_engine(shape):
plugin_creator = get_plugin_creator('AddPosEncPlugin')
if plugin_creator == None:
print('Plugin not found. Exiting')
exit()
builder = trt.Builder(logger)
builder.max_batch_size = 1024
builder.max_workspace_size = 1 << 20
builder.fp16_mode = use_fp16
network = builder.create_network()
tensor = network.add_input('data', trt.DataType.FLOAT, shape)
tensor = network.add_plugin_v2(
[tensor],
plugin_creator.create_plugin('AddPosEncPlugin', trt.PluginFieldCollection())
).get_output(0)
network.mark_output(tensor)
return builder.build_cuda_engine(network)
def run_trt(data):
engine = build_engine(data.shape[1:])
context = engine.create_execution_context()
d_data = cuda.mem_alloc(data.nbytes)
output = np.zeros_like(data, dtype=np.float32)
d_output = cuda.mem_alloc(output.nbytes)
cuda.memcpy_htod(d_data, data)
bindings = [int(d_data), int(d_output)]
start = time.time()
context.execute(data.shape[0], bindings)
end = time.time()
time_elapsed = end - start
print("time elapsed: {:06f}".format(time_elapsed))
cuda.memcpy_dtoh(output, d_output)
return output
use_fp16 = len(sys.argv) > 1 and sys.argv[1].isdigit() and int(sys.argv[1]) == 1
print('Use FP16:', use_fp16)
output = run_trt(np.zeros((16, 128, 384), np.float32))
print(output)
print(output.shape) |
TensorFlow2/Recommendation/WideAndDeep/triton/runner | runner | stages | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pathlib
from typing import 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/SpeechSynthesis/FastPitch/common/text | text | numerical | """ adapted from https://github.com/keithito/tacotron """
import inflect
import re
_magnitudes = ['trillion', 'billion', 'million', 'thousand', 'hundred', 'm', 'b', 't']
_magnitudes_key = {'m': 'million', 'b': 'billion', 't': 'trillion'}
_measurements = '(f|c|k|d|m)'
_measurements_key = {'f': 'fahrenheit',
'c': 'celsius',
'k': 'thousand',
'm': 'meters'}
_currency_key = {'$': 'dollar', '£': 'pound', '€': 'euro', '₩': 'won'}
_inflect = inflect.engine()
_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
_currency_re = re.compile(r'([\$€£₩])([0-9\.\,]*[0-9]+)(?:[ ]?({})(?=[^a-zA-Z]|$))?'.format("|".join(_magnitudes)), re.IGNORECASE)
_measurement_re = re.compile(r'([0-9\.\,]*[0-9]+(\s)?{}\b)'.format(_measurements), re.IGNORECASE)
_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
# _range_re = re.compile(r'(?<=[0-9])+(-)(?=[0-9])+.*?')
_roman_re = re.compile(r'\b(?=[MDCLXVI]+\b)M{0,4}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{2,3})\b') # avoid I
_multiply_re = re.compile(r'(\b[0-9]+)(x)([0-9]+)')
_number_re = re.compile(r"[0-9]+'s|[0-9]+s|[0-9]+")
def _remove_commas(m):
return m.group(1).replace(',', '')
def _expand_decimal_point(m):
return m.group(1).replace('.', ' point ')
def _expand_currency(m):
currency = _currency_key[m.group(1)]
quantity = m.group(2)
magnitude = m.group(3)
# remove commas from quantity to be able to convert to numerical
quantity = quantity.replace(',', '')
# check for million, billion, etc...
if magnitude is not None and magnitude.lower() in _magnitudes:
if len(magnitude) == 1:
magnitude = _magnitudes_key[magnitude.lower()]
return "{} {} {}".format(_expand_hundreds(quantity), magnitude, currency+'s')
parts = quantity.split('.')
if len(parts) > 2:
return quantity + " " + currency + "s" # Unexpected format
dollars = int(parts[0]) if parts[0] else 0
cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
if dollars and cents:
dollar_unit = currency if dollars == 1 else currency+'s'
cent_unit = 'cent' if cents == 1 else 'cents'
return "{} {}, {} {}".format(
_expand_hundreds(dollars), dollar_unit,
_inflect.number_to_words(cents), cent_unit)
elif dollars:
dollar_unit = currency if dollars == 1 else currency+'s'
return "{} {}".format(_expand_hundreds(dollars), dollar_unit)
elif cents:
cent_unit = 'cent' if cents == 1 else 'cents'
return "{} {}".format(_inflect.number_to_words(cents), cent_unit)
else:
return 'zero' + ' ' + currency + 's'
def _expand_hundreds(text):
number = float(text)
if 1000 < number < 10000 and (number % 100 == 0) and (number % 1000 != 0):
return _inflect.number_to_words(int(number / 100)) + " hundred"
else:
return _inflect.number_to_words(text)
def _expand_ordinal(m):
return _inflect.number_to_words(m.group(0))
def _expand_measurement(m):
_, number, measurement = re.split('(\d+(?:\.\d+)?)', m.group(0))
number = _inflect.number_to_words(number)
measurement = "".join(measurement.split())
measurement = _measurements_key[measurement.lower()]
return "{} {}".format(number, measurement)
def _expand_range(m):
return ' to '
def _expand_multiply(m):
left = m.group(1)
right = m.group(3)
return "{} by {}".format(left, right)
def _expand_roman(m):
# from https://stackoverflow.com/questions/19308177/converting-roman-numerals-to-integers-in-python
roman_numerals = {'I':1, 'V':5, 'X':10, 'L':50, 'C':100, 'D':500, 'M':1000}
result = 0
num = m.group(0)
for i, c in enumerate(num):
if (i+1) == len(num) or roman_numerals[c] >= roman_numerals[num[i+1]]:
result += roman_numerals[c]
else:
result -= roman_numerals[c]
return str(result)
def _expand_number(m):
_, number, suffix = re.split(r"(\d+(?:'?\d+)?)", m.group(0))
number = int(number)
if number > 1000 < 10000 and (number % 100 == 0) and (number % 1000 != 0):
text = _inflect.number_to_words(number // 100) + " hundred"
elif number > 1000 and number < 3000:
if number == 2000:
text = 'two thousand'
elif number > 2000 and number < 2010:
text = 'two thousand ' + _inflect.number_to_words(number % 100)
elif number % 100 == 0:
text = _inflect.number_to_words(number // 100) + ' hundred'
else:
number = _inflect.number_to_words(number, andword='', zero='oh', group=2).replace(', ', ' ')
number = re.sub(r'-', ' ', number)
text = number
else:
number = _inflect.number_to_words(number, andword='and')
number = re.sub(r'-', ' ', number)
number = re.sub(r',', '', number)
text = number
if suffix in ("'s", "s"):
if text[-1] == 'y':
text = text[:-1] + 'ies'
else:
text = text + suffix
return text
def normalize_numbers(text):
text = re.sub(_comma_number_re, _remove_commas, text)
text = re.sub(_currency_re, _expand_currency, text)
text = re.sub(_decimal_number_re, _expand_decimal_point, text)
text = re.sub(_ordinal_re, _expand_ordinal, text)
# text = re.sub(_range_re, _expand_range, text)
# text = re.sub(_measurement_re, _expand_measurement, text)
text = re.sub(_roman_re, _expand_roman, text)
text = re.sub(_multiply_re, _expand_multiply, text)
text = re.sub(_number_re, _expand_number, text)
return text
|
PyTorch/Segmentation/nnUNet/triton | triton | preprocess | # 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 time
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
from data_preprocessing.preprocessor import Preprocessor
from utils.utils import get_task_code
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument("--data", type=str, default="/data", help="Path to data directory")
parser.add_argument("--results", type=str, default="/data", help="Path for saving results directory")
parser.add_argument(
"--exec_mode",
type=str,
default="training",
choices=["training", "val", "test"],
help="Mode for data preprocessing",
)
parser.add_argument("--dilation", action="store_true", help="Perform morphological label dilation")
parser.add_argument("--task", type=str, help="Number of task to be run. MSD uses numbers 01-10")
parser.add_argument("--dim", type=int, default=3, choices=[2, 3], help="Data dimension to prepare")
parser.add_argument("--n_jobs", type=int, default=-1, help="Number of parallel jobs for data preprocessing")
if __name__ == "__main__":
args = parser.parse_args()
start = time.time()
Preprocessor(args).run()
task_code = get_task_code(args)
path = os.path.join(args.data, task_code)
if args.exec_mode == "test":
path = os.path.join(path, "test")
end = time.time()
print(f"Preprocessing time: {(end - start):.2f}")
|
TensorFlow/Detection/SSD/models/research/object_detection/samples/configs | configs | ssd_mobilenet_v2_quantized_300x300_coco | # Quantized trained SSD with Mobilenet v2 on MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.
model {
ssd {
num_classes: 90
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
}
}
similarity_calculator {
iou_similarity {
}
}
anchor_generator {
ssd_anchor_generator {
num_layers: 6
min_scale: 0.2
max_scale: 0.95
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.3333
}
}
image_resizer {
fixed_shape_resizer {
height: 300
width: 300
}
}
box_predictor {
convolutional_box_predictor {
min_depth: 0
max_depth: 0
num_layers_before_predictor: 0
use_dropout: false
dropout_keep_probability: 0.8
kernel_size: 1
box_code_size: 4
apply_sigmoid_to_scores: false
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.9997,
epsilon: 0.001,
}
}
}
}
feature_extractor {
type: 'ssd_mobilenet_v2'
min_depth: 16
depth_multiplier: 1.0
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.9997,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid {
}
}
localization_loss {
weighted_smooth_l1 {
}
}
hard_example_miner {
num_hard_examples: 3000
iou_threshold: 0.99
loss_type: CLASSIFICATION
max_negatives_per_positive: 3
min_negatives_per_image: 3
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
batch_size: 24
optimizer {
rms_prop_optimizer: {
learning_rate: {
exponential_decay_learning_rate {
initial_learning_rate: 0.004
decay_steps: 800720
decay_factor: 0.95
}
}
momentum_optimizer_value: 0.9
decay: 0.9
epsilon: 1.0
}
}
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
fine_tune_checkpoint_type: "detection"
# Note: The below line limits the training process to 200K steps, which we
# empirically found to be sufficient enough to train the pets dataset. This
# effectively bypasses the learning rate schedule (the learning rate will
# never decay). Remove the below line to train indefinitely.
num_steps: 200000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
}
label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
}
eval_config: {
num_examples: 8000
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 10
}
eval_input_reader: {
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
}
label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
shuffle: false
num_readers: 1
}
graph_rewriter {
quantization {
delay: 48000
weight_bits: 8
activation_bits: 8
}
} |
TensorFlow2/Segmentation/Contrib/UNet3P/models | models | model | """
Returns Unet3+ model
"""
import tensorflow as tf
from omegaconf import DictConfig
from .backbones import vgg16_backbone, vgg19_backbone, unet3plus_backbone
from .unet3plus import unet3plus
from .unet3plus_deep_supervision import unet3plus_deepsup
from .unet3plus_deep_supervision_cgm import unet3plus_deepsup_cgm
def prepare_model(cfg: DictConfig, training=False):
"""
Creates and return model object based on given model type.
"""
input_shape = [cfg.INPUT.HEIGHT, cfg.INPUT.WIDTH, cfg.INPUT.CHANNELS]
input_layer = tf.keras.layers.Input(
shape=input_shape,
name="input_layer"
) # 320*320*3
filters = [64, 128, 256, 512, 1024]
# create backbone
if cfg.MODEL.BACKBONE.TYPE == "unet3plus":
backbone_layers = unet3plus_backbone(
input_layer,
filters
)
elif cfg.MODEL.BACKBONE.TYPE == "vgg16":
backbone_layers = vgg16_backbone(input_layer, )
elif cfg.MODEL.BACKBONE.TYPE == "vgg19":
backbone_layers = vgg19_backbone(input_layer, )
else:
raise ValueError(
"Wrong backbone type passed."
"\nPlease check config file for possible options."
)
print(f"Using {cfg.MODEL.BACKBONE.TYPE} as a backbone.")
if cfg.MODEL.TYPE == "unet3plus":
# training parameter does not matter in this case
outputs, model_name = unet3plus(
backbone_layers,
cfg.OUTPUT.CLASSES,
filters
)
elif cfg.MODEL.TYPE == "unet3plus_deepsup":
outputs, model_name = unet3plus_deepsup(
backbone_layers,
cfg.OUTPUT.CLASSES,
filters,
training
)
elif cfg.MODEL.TYPE == "unet3plus_deepsup_cgm":
if cfg.OUTPUT.CLASSES != 1:
raise ValueError(
"UNet3+ with Deep Supervision and Classification Guided Module"
"\nOnly works when model output classes are equal to 1"
)
outputs, model_name = unet3plus_deepsup_cgm(
backbone_layers,
cfg.OUTPUT.CLASSES,
filters,
training
)
else:
raise ValueError(
"Wrong model type passed."
"\nPlease check config file for possible options."
)
return tf.keras.Model(
inputs=input_layer,
outputs=outputs,
name=model_name
)
if __name__ == "__main__":
"""## Test model Compilation,"""
from omegaconf import OmegaConf
cfg = {
"WORK_DIR": "H:\\Projects\\UNet3P",
"INPUT": {"HEIGHT": 320, "WIDTH": 320, "CHANNELS": 3},
"OUTPUT": {"CLASSES": 1},
# available variants are unet3plus, unet3plus_deepsup, unet3plus_deepsup_cgm
"MODEL": {"TYPE": "unet3plus",
# available variants are unet3plus, vgg16, vgg19
"BACKBONE": {"TYPE": "vgg19", }
}
}
unet_3P = prepare_model(OmegaConf.create(cfg), True)
unet_3P.summary()
# tf.keras.utils.plot_model(unet_3P, show_layer_names=True, show_shapes=True)
# unet_3P.save("unet_3P.hdf5")
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | np_box_list_ops_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for object_detection.utils.np_box_list_ops."""
import numpy as np
import tensorflow as tf
from object_detection.utils import np_box_list
from object_detection.utils import np_box_list_ops
class AreaRelatedTest(tf.test.TestCase):
def setUp(self):
boxes1 = np.array([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
dtype=float)
boxes2 = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
[0.0, 0.0, 20.0, 20.0]],
dtype=float)
self.boxlist1 = np_box_list.BoxList(boxes1)
self.boxlist2 = np_box_list.BoxList(boxes2)
def test_area(self):
areas = np_box_list_ops.area(self.boxlist1)
expected_areas = np.array([6.0, 5.0], dtype=float)
self.assertAllClose(expected_areas, areas)
def test_intersection(self):
intersection = np_box_list_ops.intersection(self.boxlist1, self.boxlist2)
expected_intersection = np.array([[2.0, 0.0, 6.0], [1.0, 0.0, 5.0]],
dtype=float)
self.assertAllClose(intersection, expected_intersection)
def test_iou(self):
iou = np_box_list_ops.iou(self.boxlist1, self.boxlist2)
expected_iou = np.array([[2.0 / 16.0, 0.0, 6.0 / 400.0],
[1.0 / 16.0, 0.0, 5.0 / 400.0]],
dtype=float)
self.assertAllClose(iou, expected_iou)
def test_ioa(self):
boxlist1 = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
np.float32))
boxlist2 = np_box_list.BoxList(
np.array(
[[0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype=np.float32))
ioa21 = np_box_list_ops.ioa(boxlist2, boxlist1)
expected_ioa21 = np.array([[0.5, 0.0],
[1.0, 1.0]],
dtype=np.float32)
self.assertAllClose(ioa21, expected_ioa21)
def test_scale(self):
boxlist = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
np.float32))
boxlist_scaled = np_box_list_ops.scale(boxlist, 2.0, 3.0)
expected_boxlist_scaled = np_box_list.BoxList(
np.array(
[[0.5, 0.75, 1.5, 2.25], [0.0, 0.0, 1.0, 2.25]], dtype=np.float32))
self.assertAllClose(expected_boxlist_scaled.get(), boxlist_scaled.get())
def test_clip_to_window(self):
boxlist = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
[-0.2, -0.3, 0.7, 1.5]],
dtype=np.float32))
boxlist_clipped = np_box_list_ops.clip_to_window(boxlist,
[0.0, 0.0, 1.0, 1.0])
expected_boxlist_clipped = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
[0.0, 0.0, 0.7, 1.0]],
dtype=np.float32))
self.assertAllClose(expected_boxlist_clipped.get(), boxlist_clipped.get())
def test_prune_outside_window(self):
boxlist = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
[-0.2, -0.3, 0.7, 1.5]],
dtype=np.float32))
boxlist_pruned, _ = np_box_list_ops.prune_outside_window(
boxlist, [0.0, 0.0, 1.0, 1.0])
expected_boxlist_pruned = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
np.float32))
self.assertAllClose(expected_boxlist_pruned.get(), boxlist_pruned.get())
def test_concatenate(self):
boxlist1 = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
np.float32))
boxlist2 = np_box_list.BoxList(
np.array(
[[0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype=np.float32))
boxlists = [boxlist1, boxlist2]
boxlist_concatenated = np_box_list_ops.concatenate(boxlists)
boxlist_concatenated_expected = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
[0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]],
dtype=np.float32))
self.assertAllClose(boxlist_concatenated_expected.get(),
boxlist_concatenated.get())
def test_change_coordinate_frame(self):
boxlist = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
np.float32))
boxlist_coord = np_box_list_ops.change_coordinate_frame(
boxlist, np.array([0, 0, 0.5, 0.5], dtype=np.float32))
expected_boxlist_coord = np_box_list.BoxList(
np.array([[0.5, 0.5, 1.5, 1.5], [0, 0, 1.0, 1.5]], dtype=np.float32))
self.assertAllClose(boxlist_coord.get(), expected_boxlist_coord.get())
def test_filter_scores_greater_than(self):
boxlist = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
np.float32))
boxlist.add_field('scores', np.array([0.8, 0.2], np.float32))
boxlist_greater = np_box_list_ops.filter_scores_greater_than(boxlist, 0.5)
expected_boxlist_greater = np_box_list.BoxList(
np.array([[0.25, 0.25, 0.75, 0.75]], dtype=np.float32))
self.assertAllClose(boxlist_greater.get(), expected_boxlist_greater.get())
class GatherOpsTest(tf.test.TestCase):
def setUp(self):
boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
[0.0, 0.0, 20.0, 20.0]],
dtype=float)
self.boxlist = np_box_list.BoxList(boxes)
self.boxlist.add_field('scores', np.array([0.5, 0.7, 0.9], dtype=float))
self.boxlist.add_field('labels',
np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0],
[0, 0, 0, 0, 1]],
dtype=int))
def test_gather_with_out_of_range_indices(self):
indices = np.array([3, 1], dtype=int)
boxlist = self.boxlist
with self.assertRaises(ValueError):
np_box_list_ops.gather(boxlist, indices)
def test_gather_with_invalid_multidimensional_indices(self):
indices = np.array([[0, 1], [1, 2]], dtype=int)
boxlist = self.boxlist
with self.assertRaises(ValueError):
np_box_list_ops.gather(boxlist, indices)
def test_gather_without_fields_specified(self):
indices = np.array([2, 0, 1], dtype=int)
boxlist = self.boxlist
subboxlist = np_box_list_ops.gather(boxlist, indices)
expected_scores = np.array([0.9, 0.5, 0.7], dtype=float)
self.assertAllClose(expected_scores, subboxlist.get_field('scores'))
expected_boxes = np.array([[0.0, 0.0, 20.0, 20.0], [3.0, 4.0, 6.0, 8.0],
[14.0, 14.0, 15.0, 15.0]],
dtype=float)
self.assertAllClose(expected_boxes, subboxlist.get())
expected_labels = np.array([[0, 0, 0, 0, 1], [0, 0, 0, 1, 0],
[0, 1, 0, 0, 0]],
dtype=int)
self.assertAllClose(expected_labels, subboxlist.get_field('labels'))
def test_gather_with_invalid_field_specified(self):
indices = np.array([2, 0, 1], dtype=int)
boxlist = self.boxlist
with self.assertRaises(ValueError):
np_box_list_ops.gather(boxlist, indices, 'labels')
with self.assertRaises(ValueError):
np_box_list_ops.gather(boxlist, indices, ['objectness'])
def test_gather_with_fields_specified(self):
indices = np.array([2, 0, 1], dtype=int)
boxlist = self.boxlist
subboxlist = np_box_list_ops.gather(boxlist, indices, ['labels'])
self.assertFalse(subboxlist.has_field('scores'))
expected_boxes = np.array([[0.0, 0.0, 20.0, 20.0], [3.0, 4.0, 6.0, 8.0],
[14.0, 14.0, 15.0, 15.0]],
dtype=float)
self.assertAllClose(expected_boxes, subboxlist.get())
expected_labels = np.array([[0, 0, 0, 0, 1], [0, 0, 0, 1, 0],
[0, 1, 0, 0, 0]],
dtype=int)
self.assertAllClose(expected_labels, subboxlist.get_field('labels'))
class SortByFieldTest(tf.test.TestCase):
def setUp(self):
boxes = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
[0.0, 0.0, 20.0, 20.0]],
dtype=float)
self.boxlist = np_box_list.BoxList(boxes)
self.boxlist.add_field('scores', np.array([0.5, 0.9, 0.4], dtype=float))
self.boxlist.add_field('labels',
np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0],
[0, 0, 0, 0, 1]],
dtype=int))
def test_with_invalid_field(self):
with self.assertRaises(ValueError):
np_box_list_ops.sort_by_field(self.boxlist, 'objectness')
with self.assertRaises(ValueError):
np_box_list_ops.sort_by_field(self.boxlist, 'labels')
def test_with_invalid_sorting_order(self):
with self.assertRaises(ValueError):
np_box_list_ops.sort_by_field(self.boxlist, 'scores', 'Descending')
def test_with_descending_sorting(self):
sorted_boxlist = np_box_list_ops.sort_by_field(self.boxlist, 'scores')
expected_boxes = np.array([[14.0, 14.0, 15.0, 15.0], [3.0, 4.0, 6.0, 8.0],
[0.0, 0.0, 20.0, 20.0]],
dtype=float)
self.assertAllClose(expected_boxes, sorted_boxlist.get())
expected_scores = np.array([0.9, 0.5, 0.4], dtype=float)
self.assertAllClose(expected_scores, sorted_boxlist.get_field('scores'))
def test_with_ascending_sorting(self):
sorted_boxlist = np_box_list_ops.sort_by_field(
self.boxlist, 'scores', np_box_list_ops.SortOrder.ASCEND)
expected_boxes = np.array([[0.0, 0.0, 20.0, 20.0],
[3.0, 4.0, 6.0, 8.0],
[14.0, 14.0, 15.0, 15.0],],
dtype=float)
self.assertAllClose(expected_boxes, sorted_boxlist.get())
expected_scores = np.array([0.4, 0.5, 0.9], dtype=float)
self.assertAllClose(expected_scores, sorted_boxlist.get_field('scores'))
class NonMaximumSuppressionTest(tf.test.TestCase):
def setUp(self):
self._boxes = np.array([[0, 0, 1, 1],
[0, 0.1, 1, 1.1],
[0, -0.1, 1, 0.9],
[0, 10, 1, 11],
[0, 10.1, 1, 11.1],
[0, 100, 1, 101]],
dtype=float)
self._boxlist = np_box_list.BoxList(self._boxes)
def test_with_no_scores_field(self):
boxlist = np_box_list.BoxList(self._boxes)
max_output_size = 3
iou_threshold = 0.5
with self.assertRaises(ValueError):
np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
def test_nms_disabled_max_output_size_equals_three(self):
boxlist = np_box_list.BoxList(self._boxes)
boxlist.add_field('scores',
np.array([.9, .75, .6, .95, .2, .3], dtype=float))
max_output_size = 3
iou_threshold = 1. # No NMS
expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 0.1, 1, 1.1]],
dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_select_from_three_clusters(self):
boxlist = np_box_list.BoxList(self._boxes)
boxlist.add_field('scores',
np.array([.9, .75, .6, .95, .2, .3], dtype=float))
max_output_size = 3
iou_threshold = 0.5
expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 100, 1, 101]],
dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_select_at_most_two_from_three_clusters(self):
boxlist = np_box_list.BoxList(self._boxes)
boxlist.add_field('scores',
np.array([.9, .75, .6, .95, .5, .3], dtype=float))
max_output_size = 2
iou_threshold = 0.5
expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1]], dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_select_at_most_thirty_from_three_clusters(self):
boxlist = np_box_list.BoxList(self._boxes)
boxlist.add_field('scores',
np.array([.9, .75, .6, .95, .5, .3], dtype=float))
max_output_size = 30
iou_threshold = 0.5
expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1], [0, 100, 1, 101]],
dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_select_from_ten_indentical_boxes(self):
boxes = np.array(10 * [[0, 0, 1, 1]], dtype=float)
boxlist = np_box_list.BoxList(boxes)
boxlist.add_field('scores', np.array(10 * [0.8]))
iou_threshold = .5
max_output_size = 3
expected_boxes = np.array([[0, 0, 1, 1]], dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_different_iou_threshold(self):
boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80], [200, 200, 210, 300],
[200, 200, 210, 250]],
dtype=float)
boxlist = np_box_list.BoxList(boxes)
boxlist.add_field('scores', np.array([0.9, 0.8, 0.7, 0.6]))
max_output_size = 4
iou_threshold = .4
expected_boxes = np.array([[0, 0, 20, 100],
[200, 200, 210, 300],],
dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
iou_threshold = .5
expected_boxes = np.array([[0, 0, 20, 100], [200, 200, 210, 300],
[200, 200, 210, 250]],
dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
iou_threshold = .8
expected_boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80],
[200, 200, 210, 300], [200, 200, 210, 250]],
dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_multiclass_nms(self):
boxlist = np_box_list.BoxList(
np.array(
[[0.2, 0.4, 0.8, 0.8], [0.4, 0.2, 0.8, 0.8], [0.6, 0.0, 1.0, 1.0]],
dtype=np.float32))
scores = np.array([[-0.2, 0.1, 0.5, -0.4, 0.3],
[0.7, -0.7, 0.6, 0.2, -0.9],
[0.4, 0.34, -0.9, 0.2, 0.31]],
dtype=np.float32)
boxlist.add_field('scores', scores)
boxlist_clean = np_box_list_ops.multi_class_non_max_suppression(
boxlist, score_thresh=0.25, iou_thresh=0.1, max_output_size=3)
scores_clean = boxlist_clean.get_field('scores')
classes_clean = boxlist_clean.get_field('classes')
boxes = boxlist_clean.get()
expected_scores = np.array([0.7, 0.6, 0.34, 0.31])
expected_classes = np.array([0, 2, 1, 4])
expected_boxes = np.array([[0.4, 0.2, 0.8, 0.8],
[0.4, 0.2, 0.8, 0.8],
[0.6, 0.0, 1.0, 1.0],
[0.6, 0.0, 1.0, 1.0]],
dtype=np.float32)
self.assertAllClose(scores_clean, expected_scores)
self.assertAllClose(classes_clean, expected_classes)
self.assertAllClose(boxes, expected_boxes)
if __name__ == '__main__':
tf.test.main()
|
TensorFlow2/Recommendation/DLRM_and_DCNv2/preproc | preproc | prepare_dataset | #! /bin/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.
# Examples:
# to run on a DGX2 with a frequency limit of 3 (will need 8xV100-32GB to fit the model in GPU memory)
# ./prepare_dataset.sh DGX2 3
#
# to run on a DGX2 with a frequency limit of 15 (should fit on a single V100-32GB):
# ./prepare_dataset.sh DGX2 15
#
# to run on CPU with a frequency limit of 15:
# ./prepare_dataset.sh CPU 15
set -e
set -x
ls -ltrash
download_dir=${download_dir:-'/data/criteo_orig'}
./verify_criteo_downloaded.sh ${download_dir}
spark_output_path=${spark_output_path:-'/data/spark/output'}
if [ -f ${spark_output_path}/train/_SUCCESS ] \
&& [ -f ${spark_output_path}/validation/_SUCCESS ] \
&& [ -f ${spark_output_path}/test/_SUCCESS ]; then
echo "Spark preprocessing already carried out"
else
echo "Performing spark preprocessing"
./run_spark.sh $1 ${download_dir} ${spark_output_path} $2
fi
conversion_intermediate_dir=${conversion_intermediate_dir:-'/data/intermediate_binary'}
final_output_dir=${final_output_dir:-'/data/preprocessed'}
if [ -d ${final_output_dir}/train ] \
&& [ -d ${final_output_dir}/validation ] \
&& [ -d ${final_output_dir}/test ] \
&& [ -f ${final_output_dir}/feature_spec.yaml ]; then
echo "Final conversion already done"
else
echo "Performing final conversion to a custom data format"
python parquet_to_binary.py --parallel_jobs 40 --src_dir ${spark_output_path} \
--intermediate_dir ${conversion_intermediate_dir} \
--dst_dir ${final_output_dir}
cp "${spark_output_path}/model_size.json" "${final_output_dir}/model_size.json"
python split_dataset.py --dataset "${final_output_dir}" --output "${final_output_dir}/split"
rm ${final_output_dir}/train_data.bin
rm ${final_output_dir}/validation_data.bin
rm ${final_output_dir}/test_data.bin
rm ${final_output_dir}/model_size.json
mv ${final_output_dir}/split/* ${final_output_dir}
rm -rf ${final_output_dir}/split
fi
echo "Done preprocessing the Criteo Kaggle Dataset"
|
TensorFlow/LanguageModeling/BERT/scripts | scripts | run_pretraining_adam | #! /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 = " $NVIDIA_BUILD_ID
train_batch_size=${1:-16}
eval_batch_size=${2:-8}
learning_rate=${3:-"1e-4"}
precision=${4:-"fp16"}
use_xla=${5:-"true"}
num_gpus=${6:-8}
warmup_steps=${7:-"10000"}
train_steps=${8:-1144000}
save_checkpoints_steps=${9:-5000}
bert_model=${10:-"large"}
num_accumulation_steps=${11:-1}
seq_len=${12:-512}
max_pred_per_seq=${13:-80}
DATA_DIR=data/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
if [ "$bert_model" = "large" ] ; then
export BERT_DIR=data/download/nvidia_pretrained/bert_tf_pretraining_large_lamb
else
export BERT_DIR=data/download/nvidia_pretrained/bert_tf_squad11_base_128
fi
PREC=""
if [ "$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
export GBS=$(expr $train_batch_size \* $num_gpus \* $num_accumulation_steps)
printf -v TAG "tf_bert_pretraining_adam_%s_%s_gbs%d" "$bert_model" "$precision" $GBS
DATESTAMP=`date +'%y%m%d%H%M%S'`
#Edit to save logs & checkpoints in a different directory
RESULTS_DIR=${RESULTS_DIR:-/results/${TAG}_${DATESTAMP}}
LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log
mkdir -m 777 -p $RESULTS_DIR
printf "Saving checkpoints to %s\n" "$RESULTS_DIR"
printf "Logs written to %s\n" "$LOGFILE"
INPUT_FILES="$DATA_DIR/training"
EVAL_FILES="$DATA_DIR/test"
horovod_str=""
mpi=""
if [ $num_gpus -gt 1 ] ; then
mpi="mpiexec --allow-run-as-root -np $num_gpus --bind-to socket"
horovod_str="--horovod"
fi
CMD="$mpi python3 /workspace/bert/run_pretraining.py"
CMD+=" --input_files_dir=$INPUT_FILES"
CMD+=" --eval_files_dir=$EVAL_FILES"
CMD+=" --output_dir=$RESULTS_DIR"
CMD+=" --bert_config_file=$BERT_CONFIG"
CMD+=" --do_train=True"
CMD+=" --do_eval=True"
CMD+=" --train_batch_size=$train_batch_size"
CMD+=" --eval_batch_size=$eval_batch_size"
CMD+=" --max_seq_length=$seq_len"
CMD+=" --max_predictions_per_seq=$max_pred_per_seq"
CMD+=" --num_train_steps=$train_steps"
CMD+=" --num_warmup_steps=$warmup_steps"
CMD+=" --num_accumulation_steps=$num_accumulation_steps"
CMD+=" --save_checkpoints_steps=$save_checkpoints_steps"
CMD+=" --learning_rate=$learning_rate"
CMD+=" --optimizer_type=adam"
CMD+=" $horovod_str $PREC"
CMD+=" --allreduce_post_accumulation=True"
#Check if all necessary files are available before training
for DIR_or_file in $DATA_DIR $BERT_CONFIG $RESULTS_DIR; do
if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then
echo "Error! $DIR_or_file directory missing. Please mount correctly"
exit -1
fi
done
set -x
if [ -z "$LOGFILE" ] ; then
$CMD
else
(
$CMD
) |& tee $LOGFILE
fi
set +x
|
TensorFlow2/Segmentation/Contrib/UNet3P/data_preparation | data_preparation | verify_data | """
Verify for each image corresponding mask exist or not.
Check against both train and val data
"""
import os
import sys
from omegaconf import DictConfig
from tqdm import tqdm
sys.path.append(os.path.abspath("./"))
from utils.general_utils import join_paths
from utils.images_utils import image_to_mask_name
def check_image_and_mask(cfg, mode):
"""
Check and print names of those images whose mask are not found.
"""
images_path = join_paths(
cfg.WORK_DIR,
cfg.DATASET[mode].IMAGES_PATH
)
mask_path = join_paths(
cfg.WORK_DIR,
cfg.DATASET[mode].MASK_PATH
)
all_images = os.listdir(images_path)
both_found = True
for image in tqdm(all_images):
mask_name = image_to_mask_name(image)
if not (
os.path.exists(
join_paths(images_path, image)
) and
os.path.exists(
join_paths(mask_path, mask_name)
)
):
print(f"{mask_name} did not found against {image}")
both_found = False
return both_found
def verify_data(cfg: DictConfig):
"""
For both train and val data, check for each image its
corresponding mask exist or not. If not then stop the program.
"""
assert check_image_and_mask(cfg, "TRAIN"), \
"Train images and mask should be same in length"
assert check_image_and_mask(cfg, "VAL"), \
"Validation images and mask should be same in length"
|
TensorFlow2/Classification/ConvNets/config/efficientnet_v1 | efficientnet_v1 | b4_cfg | import tensorflow as tf
from config.defaults import Config
# NOTE: this confile file can further be overridden by user-defined params provided at the command line
config = dict(
path_to_impl='model.efficientnet_model_v1',
#data-related model params
num_classes=1000, # must be the same as data.num_classes
input_channels= 3,
rescale_input= 1, # binary,
mean_rgb=(0.485 * 255, 0.456 * 255, 0.406 * 255), # used when rescale_input=True
std_rgb=(0.229 * 255, 0.224 * 255, 0.225 * 255), # used when rescale_input=True
dtype= tf.float32, #used for input image normalization/casting, # tf.float32, tf.bfloat16, tf.float16, tf.float32, tf.bfloat16,
# GUIDE
# width depth resolution dropout
# efficientnet_v1-b0 1.0 1.0 224 0.2
# 'efficientnet_v1-b1 1.0 1.1 240 0.2
# 'efficientnet_v1-b2 1.1 1.2 260 0.3
# 'efficientnet_v1-b3 1.2 1.4 300 0.3
# 'efficientnet_v1-b4 1.4 1.8 380 0.4
# 'efficientnet_v1-b5 1.6 2.2 456 0.4
# 'efficientnet_v1-b6 1.8 2.6 528 0.5
# 'efficientnet_v1-b7 2.0 3.1 600 0.5
# 'efficientnet_v1-b8 2.2 3.6 672 0.5
# 'efficientnet_v1-l2 4.3 5.3 800 0.5
width_coefficient= 1.4,
depth_coefficient= 1.8,
dropout_rate= 0.4,
# image resolution must be set in tr/eval/predict configs below
drop_connect_rate= 0.2,
stem_base_filters= 32,
top_base_filters= 1280,
activation= 'swish',
depth_divisor= 8,
min_depth= None,
use_se= 1, # binary
batch_norm= 'syncbn',
bn_momentum= 0.99,
bn_epsilon= 1e-3,
weight_init= 'fan_out',
blocks= (
# (input_filters, output_filters, kernel_size, num_repeat,expand_ratio, strides, se_ratio)
# pylint: disable=bad-whitespace
dict(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),
dict(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),
dict(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),
dict(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),
dict(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),
dict(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),
dict(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),
# pylint: enable=bad-whitespace
),
)
# train_config = dict(lr_decay='cosine',
#
# max_epochs=500,
# img_size=380,
# batch_size=256,
# save_checkpoint_freq=5,
# lr_init=0.005,
# weight_decay=5e-6,
# epsilon=0.001,
# resume_checkpoint=1,
# enable_tensorboard=0
# )
#
# eval_config = dict(img_size=380,
# batch_size=256)
#
# data_config = dict(
# data_dir='/data/',
# augmenter_name='autoaugment',
# mixup_alpha=0.0,
#
#
# )
# runtime_config = dict(mode='train_and_eval',
# model_dir='./output/',
# use_amp=1,
# use_xla=1,
# log_steps=100
# )
#
# config = dict(model=model_config,
# train=train_config,
# eval=eval_config,
# data=data_config,
# runtime=runtime_config,
# )
|
PyTorch/Classification/GPUNet/triton/085ms/runner | runner | config_NVIDIA-DGX-1-(1x-V100-32GB) | batching: dynamic
checkpoints:
- name: 0.85ms
url: https://api.ngc.nvidia.com/v2/models/nvidia/dle/gpunet_1_pyt_ckpt/versions/21.12.0_amp/zip
configurations:
- checkpoint: 0.85ms
parameters:
backend_accelerator: trt
checkpoint: 0.85ms
device_kind: gpu
export_format: onnx
export_precision: fp16
format: onnx
max_batch_size: 64
number_of_model_instances: 2
precision: fp16
tensorrt_capture_cuda_graph: 0
torch_jit: none
container_version: '21.12'
datasets:
- name: imagenet
datasets_dir: datasets
ensemble_model_name: null
framework: PyTorch
measurement_steps_offline: 8
measurement_steps_online: 32
model_name: GPUnet
performance_tool: model_analyzer
triton_container_image: nvcr.io/nvidia/tritonserver:21.12-py3
triton_custom_operations: null
triton_dockerfile: null
triton_load_model_method: explicit
|
TensorFlow/Detection/SSD/models/research/object_detection/builders | builders | input_reader_builder | # 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.
# ==============================================================================
"""Input reader builder.
Creates data sources for DetectionModels from an InputReader config. See
input_reader.proto for options.
Note: If users wishes to also use their own InputReaders with the Object
Detection configuration framework, they should define their own builder function
that wraps the build function.
"""
import tensorflow as tf
from object_detection.data_decoders import tf_example_decoder
from object_detection.protos import input_reader_pb2
parallel_reader = tf.contrib.slim.parallel_reader
def build(input_reader_config):
"""Builds a tensor dictionary based on the InputReader config.
Args:
input_reader_config: A input_reader_pb2.InputReader object.
Returns:
A tensor dict based on the input_reader_config.
Raises:
ValueError: On invalid input reader proto.
ValueError: If no input paths are specified.
"""
if not isinstance(input_reader_config, input_reader_pb2.InputReader):
raise ValueError('input_reader_config not of type '
'input_reader_pb2.InputReader.')
if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
config = input_reader_config.tf_record_input_reader
if not config.input_path:
raise ValueError('At least one input path must be specified in '
'`input_reader_config`.')
_, string_tensor = parallel_reader.parallel_read(
config.input_path[:], # Convert `RepeatedScalarContainer` to list.
reader_class=tf.TFRecordReader,
num_epochs=(input_reader_config.num_epochs
if input_reader_config.num_epochs else None),
num_readers=input_reader_config.num_readers,
shuffle=input_reader_config.shuffle,
dtypes=[tf.string, tf.string],
capacity=input_reader_config.queue_capacity,
min_after_dequeue=input_reader_config.min_after_dequeue)
label_map_proto_file = None
if input_reader_config.HasField('label_map_path'):
label_map_proto_file = input_reader_config.label_map_path
decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=input_reader_config.load_instance_masks,
instance_mask_type=input_reader_config.mask_type,
label_map_proto_file=label_map_proto_file)
return decoder.decode(string_tensor)
raise ValueError('Unsupported input_reader_config.')
|
PyTorch/SpeechRecognition/QuartzNet/common/dali | dali | iterator | # 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 numpy as np
import torch
from nvidia.dali.plugin.base_iterator import LastBatchPolicy
from nvidia.dali.plugin.pytorch import DALIGenericIterator
from common.helpers import print_once
from common.text import _clean_text, punctuation_map
def normalize_string(s, symbols, punct_map):
"""
Normalizes string.
Example:
'call me at 8:00 pm!' -> 'call me at eight zero pm'
"""
labels = set(symbols)
try:
text = _clean_text(s, ["english_cleaners"], punct_map).strip()
return ''.join([tok for tok in text if all(t in labels for t in tok)])
except Exception as e:
print_once(f"WARNING: Normalizing failed: {s} {e}")
class DaliIterator(object):
"""Returns batches of data.
Batches are in the form:
(preprocessed_signal, preprocessed_signal_length, transcript,
transcript_length)
This iterator is not meant to be the entry point to a Dali pipeline.
Use DataLoader instead.
"""
def __init__(self, dali_pipelines, transcripts, symbols, batch_size,
reader_name, train_iterator: bool):
self.transcripts = transcripts
self.symbols = symbols
self.batch_size = batch_size
# in train pipeline shard_size is set to divisable by batch_size,
# so PARTIAL policy is safe
self.dali_it = DALIGenericIterator(
dali_pipelines,
["audio", "label", "audio_shape"],
reader_name=reader_name,
dynamic_shape=True,
auto_reset=True,
last_batch_policy=LastBatchPolicy.DROP)
@staticmethod
def _str2list(s: str):
"""
Returns list of floats, that represents given string.
'0.' denotes separator
'1.' denotes 'a'
'27.' denotes "'"
Assumes, that the string is lower case.
"""
list = []
for c in s:
if c == "'":
list.append(27.)
else:
list.append(max(0., ord(c) - 96.))
return list
@staticmethod
def _pad_lists(lists: list, pad_val=0):
"""
Pads lists, so that all have the same size.
Returns list with actual sizes of corresponding input lists
"""
max_length = 0
sizes = []
for li in lists:
sizes.append(len(li))
max_length = max_length if len(li) < max_length else len(li)
for li in lists:
li += [pad_val] * (max_length - len(li))
return sizes
def _gen_transcripts(self, labels, normalize_transcripts: bool = True):
"""
Generate transcripts in format expected by NN
"""
if normalize_transcripts:
lists = [
self._str2list(normalize_string(self.transcripts[lab.item()],
self.symbols, punctuation_map(self.symbols)))
for lab in labels]
else:
lists = [self._str2list(self.transcripts[lab.item()])
for lab in labels]
sizes = self._pad_lists(lists)
return (torch.tensor(lists).cuda(),
torch.tensor(sizes, dtype=torch.int32).cuda())
def __next__(self):
data = self.dali_it.__next__()
transcripts, transcripts_lengths = self._gen_transcripts(
data[0]["label"])
return (data[0]["audio"], data[0]["audio_shape"][:, 1], transcripts,
transcripts_lengths)
def next(self):
return self.__next__()
def __iter__(self):
return self
# TODO: refactor
class SyntheticDataIterator(object):
def __init__(self, batch_size, nfeatures, feat_min=-5., feat_max=0.,
txt_min=0., txt_max=23., feat_lens_max=1760, txt_lens_max=231,
regenerate=False):
"""
Args:
batch_size
nfeatures: number of features for melfbanks
feat_min: minimum value in `feat` tensor, used for randomization
feat_max: maximum value in `feat` tensor, used for randomization
txt_min: minimum value in `txt` tensor, used for randomization
txt_max: maximum value in `txt` tensor, used for randomization
regenerate: If True, regenerate random tensors for every iterator
step. If False, generate them only at start.
"""
self.batch_size = batch_size
self.nfeatures = nfeatures
self.feat_min = feat_min
self.feat_max = feat_max
self.feat_lens_max = feat_lens_max
self.txt_min = txt_min
self.txt_max = txt_max
self.txt_lens_max = txt_lens_max
self.regenerate = regenerate
if not self.regenerate:
(self.feat, self.feat_lens, self.txt, self.txt_lens
) = self._generate_sample()
def _generate_sample(self):
feat = ((self.feat_max - self.feat_min)
* np.random.random_sample(
(self.batch_size, self.nfeatures, self.feat_lens_max))
+ self.feat_min)
feat_lens = np.random.randint(0, int(self.feat_lens_max) - 1,
size=self.batch_size)
txt = (self.txt_max - self.txt_min) * np.random.random_sample(
(self.batch_size, self.txt_lens_max)) + self.txt_min
txt_lens = np.random.randint(0, int(self.txt_lens_max) - 1,
size=self.batch_size)
return (torch.Tensor(feat).cuda(),
torch.Tensor(feat_lens).cuda(),
torch.Tensor(txt).cuda(),
torch.Tensor(txt_lens).cuda())
def __next__(self):
if self.regenerate:
return self._generate_sample()
return self.feat, self.feat_lens, self.txt, self.txt_lens
def next(self):
return self.__next__()
def __iter__(self):
return self
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/util | util | dims1 | /*
* 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_DIMS1
#define TT2I_DIMS1
#include "NvInfer.h"
namespace tts
{
class Dims1 : public nvinfer1::Dims
{
public:
/**
* @brief Create a new 1-dimension Dims struct.
*
* @param n The size of the single dimension.
*/
Dims1(const int n)
: Dims()
{
nbDims = 1;
d[0] = n;
}
};
} // namespace tts
#endif
|
TensorFlow/LanguageModeling/BERT | BERT | fused_layer_norm | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
# 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 collections
import copy
import json
import math
import re
import six
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.contrib.layers.python.layers import utils
from tensorflow.contrib.framework.python.ops import variables
from tensorflow.python.ops import init_ops
import numpy
from tensorflow.python.ops import array_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import nn
def fused_layer_norm(inputs,
center=True,
scale=True,
activation_fn=None,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
begin_norm_axis=1,
begin_params_axis=-1,
scope=None,
use_fused_batch_norm=False):
with tf.variable_scope(
scope, 'LayerNorm', [inputs], reuse=reuse) as sc:
inputs = ops.convert_to_tensor(inputs)
inputs_shape = inputs.shape
inputs_rank = inputs_shape.ndims
if inputs_rank is None:
raise ValueError('Inputs %s has undefined rank.' % inputs.name)
dtype = inputs.dtype.base_dtype
if begin_norm_axis < 0:
begin_norm_axis = inputs_rank + begin_norm_axis
if begin_params_axis >= inputs_rank or begin_norm_axis >= inputs_rank:
raise ValueError('begin_params_axis (%d) and begin_norm_axis (%d) '
'must be < rank(inputs) (%d)' %
(begin_params_axis, begin_norm_axis, inputs_rank))
params_shape = inputs_shape[begin_params_axis:]
if not params_shape.is_fully_defined():
raise ValueError(
'Inputs %s: shape(inputs)[%s:] is not fully defined: %s' %
(inputs.name, begin_params_axis, inputs_shape))
# Allocate parameters for the beta and gamma of the normalization.
beta, gamma = None, None
if center:
beta_collections = utils.get_variable_collections(variables_collections,
'beta')
beta = variables.model_variable(
'beta',
shape=params_shape,
dtype=dtype,
initializer=init_ops.zeros_initializer(),
collections=beta_collections,
trainable=trainable)
if scale:
gamma_collections = utils.get_variable_collections(
variables_collections, 'gamma')
gamma = variables.model_variable(
'gamma',
shape=params_shape,
dtype=dtype,
initializer=init_ops.ones_initializer(),
collections=gamma_collections,
trainable=trainable)
if use_fused_batch_norm:
# get static TensorShape if fully defined,
# otherwise retrieve shape tensor
norm_shape = inputs.shape[begin_norm_axis:]
if norm_shape.is_fully_defined():
bn_shape = [1, -1, 1, numpy.prod(norm_shape.as_list())]
else:
norm_shape = tf.shape(inputs)[begin_norm_axis:]
bn_shape = [1, -1, 1, tf.reduce_prod(norm_shape)]
if inputs.get_shape().is_fully_defined():
outputs_shape = inputs.get_shape()
else:
outputs_shape = tf.shape(inputs)
inputs = array_ops.reshape(inputs, bn_shape)
if inputs.get_shape().is_fully_defined():
# static inputs TensorShape fully defined after reshape.
ones = array_ops.ones(inputs.get_shape()[1], dtype=dtypes.float32)
zeros = array_ops.zeros(inputs.get_shape()[1], dtype=dtypes.float32)
else:
# static inputs TensorShape NOT fully defined after reshape.
# must use dynamic shape, which means these input tensors
# have to be created at runtime, which causes a slowdown.
scale_shape = tf.shape(inputs)[1]
ones = array_ops.ones(scale_shape, dtype=dtypes.float32)
zeros = array_ops.zeros(scale_shape, dtype=dtypes.float32)
outputs, mean, variance = nn.fused_batch_norm(
inputs,
ones, zeros,
epsilon=1e-4,
data_format="NCHW")
outputs = array_ops.reshape(outputs, outputs_shape)
if center and scale:
outputs = outputs * gamma + beta
elif center:
outputs = outputs + beta
elif scale:
outputs = outputs * gamma
else:
# Calculate the moments on the last axis (layer activations).
norm_axes = list(range(begin_norm_axis, inputs_rank))
mean, variance = nn.moments(inputs, norm_axes, keep_dims=True)
# Compute layer normalization using the batch_normalization function.
variance_epsilon = 1e-4
outputs = nn.batch_normalization(
inputs,
mean,
variance,
offset=beta,
scale=gamma,
variance_epsilon=variance_epsilon)
outputs.set_shape(inputs_shape)
if activation_fn is not None:
outputs = activation_fn(outputs)
return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
|
TensorFlow/Segmentation/UNet_Industrial | UNet_Industrial | export_saved_model | # !/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.
#
# ==============================================================================
"""
Usage:
python export_saved_model.py \
--activation_fn='relu' \
--batch_size=16 \
--data_format='NCHW' \
--input_dtype="fp32" \
--export_dir="exported_models" \
--model_checkpoint_path="path/to/checkpoint/model.ckpt-2500" \
--unet_variant='tinyUNet' \
--xla \
--amp
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import argparse
import pprint
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import tensorflow as tf
from dllogger.logger import LOGGER
from model.unet import UNet_v1
from model.blocks.activation_blck import authorized_activation_fn
from utils.cmdline_helper import _add_bool_argument
def get_export_flags():
parser = argparse.ArgumentParser(description="JoC-UNet_v1-TF-ExportFlags")
parser.add_argument('--export_dir', default=None, required=True, type=str, help='The export directory.')
parser.add_argument('--model_checkpoint_path', default=None, required=True, help='Checkpoint path.')
parser.add_argument(
'--data_format',
choices=['NHWC', 'NCHW'],
type=str,
default="NCHW",
required=False,
help="""Which Tensor format is used for computation inside the mode"""
)
parser.add_argument(
'--input_dtype',
choices=['fp32', 'fp16'],
type=str,
default="fp32",
required=False,
help="""Tensorflow dtype of the input tensor"""
)
parser.add_argument(
'--unet_variant',
default="tinyUNet",
choices=UNet_v1.authorized_models_variants,
type=str,
required=False,
help="""Which model size is used. This parameter control directly the size and the number of parameters"""
)
parser.add_argument(
'--activation_fn',
choices=authorized_activation_fn,
type=str,
default="relu",
required=False,
help="""Which activation function is used after the convolution layers"""
)
_add_bool_argument(
parser=parser,
name="amp",
default=False,
required=False,
help="Enable Automatic Mixed Precision Computation to maximise performance."
)
_add_bool_argument(
parser=parser,
name="xla",
default=False,
required=False,
help="Enable Tensorflow XLA to maximise performance."
)
parser.add_argument('--batch_size', default=16, type=int, help='Evaluation batch size.')
FLAGS, unknown_args = parser.parse_known_args()
if len(unknown_args) > 0:
for bad_arg in unknown_args:
print("ERROR: Unknown command line arg: %s" % bad_arg)
raise ValueError("Invalid command line arg(s)")
return FLAGS
def export_model(RUNNING_CONFIG):
if RUNNING_CONFIG.amp:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
model = UNet_v1(
model_name="UNet_v1",
input_format="NHWC",
compute_format=RUNNING_CONFIG.data_format,
n_output_channels=1,
unet_variant=RUNNING_CONFIG.unet_variant,
weight_init_method="he_normal",
activation_fn=RUNNING_CONFIG.activation_fn
)
config_proto = tf.ConfigProto()
config_proto.allow_soft_placement = True
config_proto.log_device_placement = False
config_proto.gpu_options.allow_growth = True
if RUNNING_CONFIG.xla: # Only working on single GPU
LOGGER.log("XLA is activated - Experimental Feature")
config_proto.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
config_proto.gpu_options.force_gpu_compatible = True # Force pinned memory
run_config = tf.estimator.RunConfig(
model_dir=None,
tf_random_seed=None,
save_summary_steps=1e9, # disabled
save_checkpoints_steps=None,
save_checkpoints_secs=None,
session_config=config_proto,
keep_checkpoint_max=None,
keep_checkpoint_every_n_hours=1e9, # disabled
log_step_count_steps=1e9,
train_distribute=None,
device_fn=None,
protocol=None,
eval_distribute=None,
experimental_distribute=None
)
estimator = tf.estimator.Estimator(
model_fn=model,
model_dir=RUNNING_CONFIG.model_checkpoint_path,
config=run_config,
params={'debug_verbosity': 0}
)
LOGGER.log('[*] Exporting the model ...')
input_type = tf.float32 if RUNNING_CONFIG.input_dtype else tf.float16
def get_serving_input_receiver_fn():
input_shape = [RUNNING_CONFIG.batch_size, 512, 512, 1]
def serving_input_receiver_fn():
features = tf.placeholder(dtype=input_type, shape=input_shape, name='input_tensor')
return tf.estimator.export.TensorServingInputReceiver(features=features, receiver_tensors=features)
return serving_input_receiver_fn
export_path = estimator.export_saved_model(
export_dir_base=RUNNING_CONFIG.export_dir,
serving_input_receiver_fn=get_serving_input_receiver_fn(),
checkpoint_path=RUNNING_CONFIG.model_checkpoint_path
)
LOGGER.log('[*] Done! path: `%s`' % export_path.decode())
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.ERROR)
tf.disable_eager_execution()
flags = get_export_flags()
for endpattern in [".index", ".meta"]:
file_to_check = flags.model_checkpoint_path + endpattern
if not os.path.isfile(file_to_check):
raise FileNotFoundError("The checkpoint file `%s` does not exist" % file_to_check)
print(" ========================= Export Flags =========================\n")
pprint.pprint(dict(flags._get_kwargs()))
print("\n %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
export_model(flags)
|
PyTorch/SpeechRecognition/wav2vec2/common | common | fairseq_fake_modules | # 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.
'''Fake fairseq.* modules allowing to torch.load fairseq checkpoints.'''
import sys
class Dummy:
pass
class FakeModule:
def __init__(self, classes=["AverageMeter", "TimeMeter", "StopwatchMeter"]):
[setattr(self, cls, Dummy) for cls in classes]
sys.modules["fairseq"] = Dummy()
sys.modules["fairseq.data"] = Dummy()
sys.modules["fairseq.data.dictionary"] = FakeModule(["Dictionary"])
sys.modules["fairseq.logging.meters"] = FakeModule()
sys.modules["fairseq.meters"] = FakeModule()
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/generator/graph | graph | utils | # 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 logging
import math
import multiprocessing
from datetime import datetime
from functools import partial
from typing import Tuple, Union, Optional
import cupy as cp
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
from pylibraft.random import rmat
from scipy import stats
from syngen.utils import NDArray, infer_operator
from syngen.utils.utils import infer_operator
from syngen.utils.io_utils import dump_generated_graph
from syngen.utils.memory_manager import MemoryManager
from syngen.utils.types import NDArray
logger = logging.getLogger(__name__)
def move_ndarray_to_host(ndarray: NDArray):
if isinstance(ndarray, np.ndarray):
return ndarray
elif isinstance(ndarray, cp.ndarray):
return cp.asnumpy(ndarray)
else:
raise ValueError('supports only numpy and cupy ndarrays')
def rearrange_graph(
edge_list: NDArray,
src_nodes: NDArray,
dst_nodes: NDArray,
assume_unique: bool = False,
) -> Tuple[NDArray, NDArray]:
"""
Transforms a bipartite graph from edge list format to lower_left and upper_right adjacency matrices.
Returned matrices are in coordinate list format.
"""
operator = infer_operator(edge_list)
if not isinstance(src_nodes, (np.ndarray, cp.ndarray)):
raise ValueError('src_nodes: expected type NDArray, but %s was passed', type(src_nodes))
if not isinstance(dst_nodes, (np.ndarray, cp.ndarray)):
raise ValueError('dst_nodes: expected type NDArray, but %s was passed', type(dst_nodes))
if not assume_unique:
src_nodes = operator.unique(src_nodes)
dst_nodes = operator.unique(dst_nodes)
if operator.intersect1d(src_nodes, dst_nodes, assume_unique=True).size != 0:
raise ValueError('node sets cannot intersect')
edge_list = edge_list.flatten()
node_set = operator.hstack([src_nodes, dst_nodes])
pos_to_new_id = operator.argsort(node_set)
sorted_node_set = node_set[pos_to_new_id]
pos_in_sorted_nodeset = operator.searchsorted(sorted_node_set, edge_list)
# need to validate since errors could be ignored
# https://docs.cupy.dev/en/stable/user_guide/difference.html#out-of-bounds-indices
message = 'all ids in a graph should be in one of the node sets'
if operator.any(pos_in_sorted_nodeset == len(sorted_node_set)):
raise ValueError(message)
if operator.any(sorted_node_set[pos_in_sorted_nodeset] != edge_list):
raise ValueError(message)
edge_list_mapped = pos_to_new_id[pos_in_sorted_nodeset].reshape(-1, 2)
upper_right = edge_list_mapped[edge_list_mapped[:, 0] < len(src_nodes)]
upper_right[:, 1] -= len(src_nodes)
lower_left = edge_list_mapped[edge_list_mapped[:, 0] >= len(src_nodes)]
lower_left[:, 0] -= len(src_nodes)
return lower_left, upper_right
def reindex_graph(
edge_list: NDArray,
return_counts: bool = False,
) -> Union[NDArray, Tuple[NDArray, int, int]]:
"""
Reindexes a graph by assigning node ids starting from 0.
Returns the processed graph and, optionally, number of nodes and number of edges.
"""
operator = infer_operator(edge_list)
nodes, inverse_flat = operator.unique(edge_list, return_inverse=True)
edge_list_reindexed = inverse_flat.reshape(edge_list.shape)
if return_counts:
return edge_list_reindexed, len(nodes), len(edge_list)
else:
return edge_list_reindexed
def get_reversed_part(part, gpu=False, operator=None):
operator = operator or (cp if gpu else np)
new_part = operator.empty_like(part)
new_part[:, 0] = part[:, 1]
new_part[:, 1] = part[:, 0]
return new_part
# Postprocessing
def recreate_graph(lower: NDArray, upper: NDArray, offset: int, gpu=False):
assert (
lower is not None and upper is not None
), "Upper and lower cannot be None"
operator = cp if gpu else np
lower[:, 0] = lower[:, 0] + offset
upper[:, 1] = upper[:, 1] + offset
new_graph = operator.concatenate((lower, upper), axis=0)
return new_graph
def recreate_bipartite_nondirected(graph, row_shape):
upper = [(row, col + row_shape) for row, col in graph]
lower = [(col, row) for row, col in upper]
new_graph = upper + lower
return new_graph
def to_adj_matrix(graph, shape):
matrix = np.zeros(shape=shape, dtype=np.bool)
arr_indicies = np.array(graph)
matrix[arr_indicies[:, 0], arr_indicies[:, 1]] = 1
return matrix
def plot_graph_adj(graph, shape):
graph_adj = to_adj_matrix(graph, shape=shape)
return plt.imshow(graph_adj, cmap="binary", interpolation="nearest")
def graph_to_snap_file(A, filename):
np.savetxt(filename, A, fmt="%i", delimiter="\t")
def effective_nonsquare_rmat_approximate(
theta,
E,
A_shape,
noise_scaling=1.0,
batch_size=1000,
dtype=np.int64,
custom_samplers=None,
generate_back_edges=False,
verbose=False,
):
""" This function generates list of edges using modified RMat approach
Args:
theta (np.array): seeding matrix, needs to be shape 2x2
E (int): number of edges to be generated
A_shape (tuple): shape of resulting adjacency matrix. numbers has to be powers of 2
A_shape should be equal to (ceil(log2(X)),ceil(log2(Y))) X,Y are
dimensions of original adjacency
noise_scaling (float 0..1): noise scaling factor for good degree distribution
batch_size (int): edges are generated in batches of batch_size size
dtype (numpy dtype np.int32/np.int64): dtype of nodes id's
custom_samplers (List[scipy.stats.rv_discrete]): samplers for each step of genration
process
generate_back_edges (bool): if True then generated edges will also have "back" edges. Not
that setting to True for partite graphs makes no sense.
Returns:
A (np.array 2 x E): matrix containing in every row a signle edge. Edge is always directed
0'th column is FROM edge 1st is TO edge
mtx_shape (tuple) - shape of adjecency matrix (A contains list of edges, this is Adjecency
metrix shape)
custom_samplers (List[scipy.stats.rv_discrete]) - list of samplers needed to generate edges
from the same disctribution for multiple runs of the function
Description:
The generation will consist of theta^[n] (x) theta_p^[m] (x) theta_q^[l]
^[n] is kronecker power
(x) is matrix kronecker product
theta_p (2x1) and theta_q(1x2) are marginals of theta
This way we can generate rectangular shape of adjecency matrix e.g. for bipatrite
graphs
"""
def get_row_col_addres(thetas_n):
thetas_r = [t.shape[0] for t in thetas_n]
thetas_c = [t.shape[1] for t in thetas_n]
row_n = np.prod(thetas_r) # theta_r**quadrant_sequence.shape[1]
col_n = np.prod(thetas_c) # theta_c**quadrant_sequence.shape[1]
row_adders = np.array(
[
int(row_n / thetas_r[i] ** (i + 1)) % row_n
for i in range(len(thetas_n))
]
) # there has to be % as we can have thetas_r[i]==1
col_adders = np.array(
[
int(col_n / thetas_c[i] ** (i + 1)) % col_n
for i in range(len(thetas_n))
]
)
return row_adders, col_adders, thetas_r, thetas_c, row_n, col_n
def parse_quadrants(
quadrant_sequence,
thetas_n,
row_adders,
col_addres,
thetas_r,
thetas_c,
row_n,
col_n,
dtype=np.int64,
):
N = len(thetas_n)
new_edges = np.zeros(
shape=(quadrant_sequence.shape[0], 2)
) # 2 because 0 col=rows_addresses, 1st col = columns
row_addr = np.array(quadrant_sequence // thetas_c, dtype=dtype)
col_addr = np.array(quadrant_sequence % thetas_c, dtype=dtype)
row_adders = np.array(
[int(row_n / thetas_r[i] ** (i + 1)) % row_n for i in range(N)]
) # there has to be % as we can have thetas_r[i]==1
col_adders = np.array(
[int(col_n / thetas_c[i] ** (i + 1)) % col_n for i in range(N)]
)
new_edges[:, 0] = np.sum(np.multiply(row_addr, row_adders), axis=1)
new_edges[:, 1] = np.sum(np.multiply(col_addr, col_adders), axis=1)
return new_edges
if batch_size > E: # if bs>E
batch_size = int(E // 2 * 2)
if generate_back_edges:
assert (
batch_size % 2 == 0 and batch_size >= 2
), "batch size has to be odd and >1"
assert (
np.abs((np.sum(theta) - 1.0)) < 1e-6
), "Theta probabilities has to sum to 1.0"
assert (theta.shape[0] == 2) and (
theta.shape[1] == 2
), "Only 2x2 seeding matrixes are acceptable"
assert len(A_shape) == 2, "A_shape needs to be of len 2"
# get appropriate number of n,m,l always m=0 or l=0 (or both for rectangular adjecency)
r = A_shape[0]
c = A_shape[1]
n = min(r, c) # theta^[n] (x) theta_p^[m] (x) theta_q^[l]
m = max(0, r - c)
# flake8: noqa
l = max(0, c - r)
# calc values of marginal theta matrixes
theta_p = theta.sum(axis=1).reshape((2, -1)) # 2x1
theta_q = theta.sum(axis=0).reshape((1, -1)) # 1x2
# get all thetas
thetas_n = [theta] * n + [theta_p] * m + [theta_q] * l
# prepare samplers for each of n+m+l steps
if custom_samplers is None:
custom_samplers = []
for i in range(n + m + l):
theta_n = thetas_n[
i
] # each of n+m+l steps have their own theta_n which can be theta/theta_p or theta_q +
# noise
noise = noise_scaling * np.random.uniform(
-1, 1, size=theta_n.shape
)
noise_to_add = np.multiply(theta_n, noise)
theta_n = theta_n + noise_to_add
theta_n = theta_n / np.sum(theta_n)
cstm_n = "step_" + str(i)
theta_r = theta_n.shape[0]
theta_c = theta_n.shape[1]
xk = tuple(range(theta_r * theta_c))
pk = theta_n.reshape(-1)
cstm_s = stats.rv_discrete(name=cstm_n, values=(xk, pk))
custom_samplers.append(cstm_s)
# Prepare all batch sizes needed for generation
if batch_size == 0:
batch_count = 0 # XXX: why does this happen anyways?
else:
batch_count = E // batch_size
last_batch_size = E - batch_count * batch_size
if last_batch_size % 2 > 0 and generate_back_edges:
last_batch_size -= 1
A = np.zeros((E, 2), dtype=np.int64)
num_sequences = batch_size
last_num_sequences = last_batch_size
if (
generate_back_edges
): # in case of generating back edges we need to sample just E/2
last_num_sequences = last_batch_size // 2
num_sequences = batch_size // 2
new_back_edges = np.zeros(shape=(num_sequences, 2))
quadrant_sequence = np.zeros(shape=(num_sequences, n + m + l), dtype=dtype)
(
row_adders,
col_addres,
thetas_r,
thetas_c,
row_n,
col_n,
) = get_row_col_addres(thetas_n)
# generate sequences of quadrants from previously prepared samplers
batch_itr = range(batch_count)
if verbose:
batch_itr = tqdm(batch_itr)
for e in batch_itr:
for i in range(
n + m + l
): # each steps in generation has its own sampler
smpl = custom_samplers[i].rvs(size=num_sequences)
quadrant_sequence[:, i] = smpl
# produce new edges
new_edges = parse_quadrants(
quadrant_sequence,
thetas_n,
row_adders,
col_addres,
thetas_r,
thetas_c,
row_n,
col_n,
dtype=dtype,
)
if generate_back_edges:
new_back_edges[:, [0, 1]] = new_edges[:, [1, 0]] # swap columns
A[
e * batch_size: (e + 1) * batch_size: 2, :
] = new_edges # we need interleave so that back edges are "right after" normal edges
A[
e * batch_size + 1: (e + 1) * batch_size: 2, :
] = new_back_edges
else:
A[e * batch_size: (e + 1) * batch_size, :] = new_edges
# generate last batch
if last_batch_size > 0:
for i in range(n + m + l):
smpl = custom_samplers[i].rvs(size=last_num_sequences)
quadrant_sequence[:last_num_sequences, i] = smpl
new_edges = parse_quadrants(
quadrant_sequence[:last_num_sequences, :],
thetas_n,
row_adders,
col_addres,
thetas_r,
thetas_c,
row_n,
col_n,
dtype=dtype,
)
if generate_back_edges:
new_back_edges[:last_num_sequences, [0, 1]] = new_edges[
:last_num_sequences, [1, 0]
]
# we need interleave so that back edges are "right after" normal edges
A[
batch_count * batch_size: batch_count * batch_size
+ last_batch_size: 2,
:,
] = new_edges
# np.concatenate((new_edges,new_back_edges[:last_num_sequences,:]),axis=0)
A[
batch_count * batch_size
+ 1: batch_count * batch_size
+ last_batch_size: 2,
:,
] = new_back_edges[:last_num_sequences, :]
else:
A[
batch_count * batch_size: batch_count * batch_size
+ last_batch_size,
:,
] = new_edges
mtx_shape = (
np.prod([t.shape[0] for t in thetas_n]),
np.prod([t.shape[1] for t in thetas_n]),
) # shape of resulting adjacency matrix
return A, mtx_shape, custom_samplers
def effective_nonsquare_rmat_exact(
theta,
E,
A_shape,
noise_scaling=1.0,
batch_size=1000,
dtype=np.int64,
custom_samplers=None,
remove_selfloops=False,
generate_back_edges=False,
return_node_ids=0,
verbose=False,
):
""" This function generates list of edges using modified RMat approach based on effective_nonsuqare_rmat_approximate
Args:
theta (np.array): seeding matrix, needs to be shape 2x2
E (int): number of edges to be generated
A_shape (tuple): shape of resulting adjacency matrix. numbers has to be powers of 2
A_shape should be equal to (ceil(log2(X)),ceil(log2(Y))) X,Y are
dimensions of original adjacency
noise_scaling (float 0..1): noise scaling factor for good degree distribution
batch_size (int): edges are generated in batches of batch_size size
dtype (numpy dtype np.int32/np.int64): dtype of nodes id's
remove_selfloops (bool): If true edges n->n will not be generated. Note that for partite
graphs this makes no sense
generate_back_edges (bool): if True then generated edges will also have "back" edges. Not
that setting to True for partite graphs makes no sense.
Returns:
A (np.array 2 x E) - matrix containing in every row a signle edge. Edge is always directed
0'th column is FROM edge 1st is TO edge
mtx_shape (tuple) - shape of adjecency matrix (A contains list of edges, this is Adjecency
metrix shape)
custom_samplers (List[scipy.stats.rv_discrete]) - list of samplers needed to generate edges
from the same disctribution for multiple runs of the function
Description:
see effective_nonsuqare_rmat_approximate
"""
heuristics = 1.5
if verbose:
print("Getting egdes")
A, mtx_shape, cs = effective_nonsquare_rmat_approximate(
theta,
int(heuristics * E),
A_shape,
noise_scaling=noise_scaling,
batch_size=batch_size,
dtype=dtype,
custom_samplers=custom_samplers,
generate_back_edges=generate_back_edges,
verbose=verbose,
)
if generate_back_edges:
A = A[
np.sort(np.unique(A, return_index=True, axis=0)[1])
] # permutation is not needed here
else:
if verbose:
print("Getting unique egdes")
A = np.unique(A, axis=0)
if verbose:
print("Permuting edges")
perm = np.random.permutation(
A.shape[0]
) # we need to permute it as othervise unique returns edges in order
A = A[perm]
if remove_selfloops:
if verbose:
print("Removing selfloops")
A = np.delete(A, np.where(A[:, 0] == A[:, 1]), axis=0)
E_already_generated = A.shape[0]
if E_already_generated >= E:
if return_node_ids == 2:
return A[:E, :], np.unique(A[:E, :][:, 0]), np.unique(A[:E, :][:, 1]), mtx_shape, cs
if return_node_ids == 1:
return A[:E, :], np.unique(A[:E, :]), mtx_shape, cs
return A[:E, :], mtx_shape, cs
else:
while E_already_generated < E:
if verbose:
print("Generating some additional edges")
E_to_generate = int(heuristics * (E - E_already_generated))
A_next, mtx_shape, cs = effective_nonsquare_rmat_approximate(
theta,
E_to_generate,
A_shape,
noise_scaling=noise_scaling,
batch_size=batch_size,
dtype=dtype,
custom_samplers=cs,
verbose=verbose,
)
if remove_selfloops:
A_next = np.delete(
A_next, np.where(A_next[:, 0] == A_next[:, 1]), axis=0
)
A = np.concatenate((A, A_next), axis=0)
if generate_back_edges:
A = A[np.sort(np.unique(A, return_index=True, axis=0)[1])]
else:
A = np.unique(A, axis=0)
perm = np.random.permutation(A.shape[0])
A = A[perm]
E_already_generated = A.shape[0]
if return_node_ids == 2:
return A[:E, :], np.unique(A[:E, :][:, 0]), np.unique(A[:E, :][:, 1]), mtx_shape, cs
if return_node_ids == 1:
return A[:E, :], np.unique(A[:E, :]), mtx_shape, cs
return A[:E, :], mtx_shape, cs
def cupy_unique_axis0(array):
# https://stackoverflow.com/questions/58662085/is-there-a-cupy-version-supporting-axis-option-in-cupy-unique-function-any
sortarr = array[cp.lexsort(array.T[::-1])]
mask = cp.empty(array.shape[0], dtype=cp.bool_)
mask[0] = True
mask[1:] = cp.any(sortarr[1:] != sortarr[:-1], axis=1)
return sortarr[mask]
def unique_axis0(ar: NDArray) -> NDArray:
"""
Uniform way of calling operator.unique(ar, axis=0).
axis != None is not supported in cupy yet.
This function provides a workaround for one of the cases.
"""
operator = infer_operator(ar)
if operator == cp:
return cupy_unique_axis0(ar)
else:
return np.unique(ar, axis=0)
def generate_gpu_rmat(
a,
b,
c,
d,
r_scale,
c_scale,
n_edges,
noise=0.5,
is_directed=False,
has_self_loop=False,
return_node_ids=0,
):
if not is_directed and r_scale != c_scale:
raise ValueError('undirected generation works only for square adj matrix')
if not is_directed:
n_edges = n_edges // 2
gen_graph = None
HEURISTIC = 1.2
edges_to_generate = int(HEURISTIC * n_edges)
theta_len = max(r_scale, c_scale)
base_theta = [a, b, c, d]
if noise > 0:
full_theta = []
for i in range(theta_len):
noise_uniform = noise * np.random.uniform(
-1, 1, size=len(base_theta)
)
noise_to_add = np.multiply(base_theta, noise_uniform)
theta_n = base_theta + noise_to_add
theta_n = theta_n / np.sum(theta_n)
full_theta.append(theta_n)
else:
full_theta = base_theta * theta_len
theta_cpu = np.array(full_theta, dtype=np.float32)
theta = cp.asarray(theta_cpu)
while gen_graph is None or gen_graph.shape[0] < n_edges:
tmp = cp.empty((edges_to_generate, 2), dtype=cp.int32)
seed = cp.random.randint(0, high=1_000_000, size=None, dtype=int)
rmat(tmp, theta, r_scale, c_scale, seed=seed)
# Remove self loops
if not has_self_loop:
tmp = tmp[tmp[:, 0] != tmp[:, 1]]
# Keep only one sided edges
if not is_directed:
tmp = tmp[tmp[:, 0] <= tmp[:, 1]]
if gen_graph is None:
# Remove duplicates
gen_graph = cupy_unique_axis0(tmp)
else:
gen_graph = cp.concatenate((gen_graph, tmp), axis=0)
# Remove duplicates
gen_graph = cupy_unique_axis0(gen_graph)
gen_graph = gen_graph[:n_edges]
if not is_directed:
gen_graph_backward = cp.empty((n_edges, 2), dtype=cp.int32)
gen_graph_backward[:, 0] = gen_graph[:, 1]
gen_graph_backward[:, 1] = gen_graph[:, 0]
gen_graph = cp.concatenate((gen_graph, gen_graph_backward), axis=0)
gen_graph = cupy_unique_axis0(
gen_graph
)
if not has_self_loop:
gen_graph = gen_graph[gen_graph[:, 0] != gen_graph[:, 1]]
if return_node_ids == 2:
return cp.asnumpy(gen_graph), cp.asnumpy(cp.unique(gen_graph[:, 0])), cp.asnumpy(cp.unique(gen_graph[:, 1]))
if return_node_ids == 1:
return cp.asnumpy(gen_graph), cp.asnumpy(cp.unique(gen_graph))
return cp.asnumpy(gen_graph)
def generate_theta(base_theta, noise, theta_len, is_directed):
if noise > 0:
full_theta = []
for i in range(theta_len):
noise_uniform = noise * np.random.uniform(
-1, 1, size=len(base_theta)
)
noise_to_add = np.multiply(base_theta, noise_uniform)
theta_n = base_theta + noise_to_add
if not is_directed:
theta_n[2] = theta_n[1]
theta_n = theta_n / np.sum(theta_n)
full_theta.append(theta_n)
else:
full_theta = [base_theta] * theta_len
return full_theta
def prepare_chunks(full_theta, r_scale, c_scale, gpu_bytes_to_use, edges_to_generate):
if r_scale > 32 or c_scale > 32:
bytes_per_edge = 8
max_id = 9223372036854775807 # int64 max
else:
bytes_per_edge = 4
max_id = 2147483647 # int32 max
bytes_to_generate = edges_to_generate * 2 * bytes_per_edge
skip_theta = 0
# approximation
while (bytes_to_generate >> 2 * skip_theta) > gpu_bytes_to_use \
or (bytes_to_generate >> 2 * skip_theta) > max_id:
skip_theta += 1
if skip_theta == 0:
return [], np.array([edges_to_generate]), full_theta, 0, r_scale, c_scale
# chunk size is limited by the smaller side of the rectangular graph
while abs(r_scale - c_scale) > skip_theta:
skip_theta += 1
def repeat(a, scale):
if scale == 1:
return a
return np.repeat(np.repeat(a, scale, axis=0), scale, axis=1)
def tile(a, scale):
if scale == 1:
return a
return np.tile(a, (scale, scale))
def prepare_prefixes(skip_theta):
if skip_theta > 0:
prefix_theta = full_theta[:skip_theta]
gen_theta_len = max(r_scale, c_scale) - skip_theta
prefix_edges = np.ones((1 << skip_theta, 1 << skip_theta), dtype=np.float64)
prefixes = np.zeros((2, 1 << skip_theta, 1 << skip_theta), dtype=np.int32)
for theta_idx, theta in enumerate(prefix_theta):
pref_src = np.array([[0, 0], [1, 1]]) << theta_idx
pref_dst = np.array([[0, 1], [0, 1]]) << theta_idx
theta = np.array(theta, dtype=np.float64).reshape(2, 2)
repeat_scale = 1 << (skip_theta - theta_idx - 1)
tile_scale = 1 << theta_idx
prefix_edges = prefix_edges * tile(repeat(theta, repeat_scale), tile_scale)
prefixes[0] = prefixes[0] + tile(repeat(pref_src, repeat_scale), tile_scale)
prefixes[1] = prefixes[1] + tile(repeat(pref_dst, repeat_scale), tile_scale)
if r_scale != c_scale: # probabilities in the rectangular matrix should sum up to 1.0
r_len = 2 ** (r_scale - gen_theta_len)
c_len = 2 ** (c_scale - gen_theta_len)
prefix_edges[:r_len, :c_len] = prefix_edges[:r_len, :c_len] / prefix_edges[:r_len, :c_len].sum()
prefixes[int(r_scale > c_scale), :r_len, :c_len] = \
prefixes[int(r_scale > c_scale), :r_len, :c_len] >> abs(r_scale - c_scale)
prefix_edges = np.ceil(prefix_edges * edges_to_generate).astype(np.int32).reshape(-1)
prefixes = prefixes.reshape(2, -1)
else:
prefixes = []
prefix_edges = np.array([edges_to_generate])
return prefixes, prefix_edges
prefixes, prefix_edges = prepare_prefixes(skip_theta)
while prefix_edges.max() * 2 * bytes_per_edge > gpu_bytes_to_use:
skip_theta += 1
prefixes, prefix_edges = prepare_prefixes(skip_theta)
generation_theta = full_theta[skip_theta:]
return prefixes, prefix_edges, generation_theta, skip_theta, len(generation_theta), len(generation_theta)
def _generate_gpu_chunk_rmat(
chunk_info,
prefixes,
prefix_edges,
has_self_loop,
is_directed,
generation_theta,
r_log2_nodes,
c_log2_nodes,
r_pref_len,
c_pref_len,
row_len,
gpus,
dtype='int32',
return_node_ids=0,
memmap_kwargs: Optional = None,
chunk_save_path_format: Optional[str] = None):
chunk_id, chunk_end = chunk_info
chunk_size = prefix_edges[chunk_id]
if gpus > 1:
gpu_id = int(multiprocessing.current_process()._identity[0]) % gpus
else:
gpu_id = 0
theta_cpu = np.array(generation_theta, dtype=np.float32)
edge_list = None
is_diagonal_chunk = ((chunk_id // row_len) == (chunk_id % row_len))
use_memmap = memmap_kwargs is not None
if use_memmap:
memmap_outfile = np.load(file=memmap_kwargs['filename'], mmap_mode='r+')
with cp.cuda.Device(gpu_id):
theta = cp.asarray(theta_cpu)
while edge_list is None or edge_list.shape[0] < prefix_edges[chunk_id]:
tmp = cp.empty((prefix_edges[chunk_id], 2), dtype=dtype)
seed = cp.random.randint(0, high=1_000_000, size=None, dtype=int)
rmat(tmp, theta, r_log2_nodes, c_log2_nodes, seed=seed)
if not has_self_loop and is_diagonal_chunk:
tmp = tmp[tmp[:, 0] != tmp[:, 1]]
# undirected diagonal_case
if not is_directed and is_diagonal_chunk:
tmp = tmp[tmp[:, 0] <= tmp[:, 1]]
tmp = cupy_unique_axis0(tmp)
if edge_list is None:
edge_list = tmp
else:
edge_list = cp.concatenate((edge_list, tmp), axis=0)
del tmp
edge_list = cupy_unique_axis0(edge_list)
if len(prefix_edges) > 1:
edge_list[:, 0] = (edge_list[:, 0] << r_pref_len) + prefixes[0][chunk_id]
edge_list[:, 1] = (edge_list[:, 1] << c_pref_len) + prefixes[1][chunk_id]
edge_list = edge_list[:prefix_edges[chunk_id]]
if return_node_ids == 2:
src_nodes_ids = cp.asnumpy(cp.unique(edge_list[:, 0]))
dst_nodes_ids = cp.asnumpy(cp.unique(edge_list[:, 1]))
if return_node_ids == 1:
nodes_ids = cp.asnumpy(cp.unique(edge_list))
result = cp.asnumpy(edge_list)
if use_memmap:
memmap_outfile[chunk_end-chunk_size:chunk_end] = result
del edge_list
if chunk_save_path_format is not None:
dump_generated_graph(chunk_save_path_format.format(chunk_id=chunk_id), result)
result = len(result)
if use_memmap:
result = None
if return_node_ids == 2:
return result, src_nodes_ids, dst_nodes_ids
if return_node_ids == 1:
return result, nodes_ids
return result
def generate_gpu_chunked_rmat(
a,
b,
c,
d,
r_scale,
c_scale,
n_edges,
noise=0.5,
is_directed=False,
has_self_loop=False,
gpus=None,
return_node_ids=0,
save_path: Optional[str] = None,
verbose: bool = False,
):
if not is_directed and r_scale != c_scale:
raise ValueError('undirected generation works only for square adj matrix')
base_theta = [a, b, c, d]
theta_len = max(r_scale, c_scale)
full_theta = generate_theta(base_theta, noise, theta_len, is_directed)
if gpus is None:
gpus = MemoryManager().get_available_gpus()
gpu_bytes_to_use = MemoryManager().get_min_available_across_gpus_memory(gpus=gpus)
gpu_bytes_to_use = math.floor(gpu_bytes_to_use * 0.10)
prefixes, prefix_edges, generation_theta, prefix_len, r_log2_nodes, c_log2_nodes = \
prepare_chunks(full_theta, r_scale, c_scale, gpu_bytes_to_use, n_edges)
chunk_ids = list(range(len(prefix_edges)))
row_len = 1 << prefix_len
r_pref_len = r_scale - len(generation_theta)
c_pref_len = c_scale - len(generation_theta)
if not is_directed: # generate a triangular adj matrix
chunk_ids = [i for i in chunk_ids if (i // row_len) <= (i % row_len)]
# reduce the diagonal chunks
for i in range(prefix_len * 2):
prefix_edges[i * row_len + i] = prefix_edges[i * row_len + i] // 2
if r_scale != c_scale:
chunk_ids = [i for i in chunk_ids if (i // row_len) < 2 ** r_pref_len and (i % row_len) < 2 ** c_pref_len]
is_single_chunk = len(chunk_ids) == 1
memmap_kwargs = None
chunk_save_path_format = None
use_memmap = False
if save_path and os.path.isdir(save_path):
chunk_save_path_format = os.path.join(save_path, 'chunk_{chunk_id}.npy')
elif save_path and save_path.endswith('.npy'):
use_memmap = True
memmap_shape = (sum(prefix_edges[chunk_ids]), 2)
memmap_dtype = np.uint64 if theta_len > 32 else np.uint32
memmap_kwargs = dict(
filename=save_path,
)
memmap_outfile = np.lib.format.open_memmap(save_path, dtype=memmap_dtype, shape=memmap_shape, mode='w+')
dtype = cp.int64 if theta_len > 32 else cp.int32
_generate_gpu_chunk_rmat_p = partial(
_generate_gpu_chunk_rmat,
prefixes=prefixes,
prefix_edges=prefix_edges,
has_self_loop=has_self_loop,
is_directed=is_directed,
generation_theta=generation_theta,
r_log2_nodes=r_log2_nodes,
c_log2_nodes=c_log2_nodes,
r_pref_len=r_pref_len,
c_pref_len=c_pref_len,
row_len=row_len,
dtype=dtype,
return_node_ids=return_node_ids,
chunk_save_path_format=chunk_save_path_format,
memmap_kwargs=memmap_kwargs,
gpus=1 if is_single_chunk else gpus,
)
if is_single_chunk:
chunk_res = _generate_gpu_chunk_rmat_p((chunk_ids[0], prefix_edges[chunk_ids[0]]))
if return_node_ids == 2:
result, src_node_ids, dst_node_ids = chunk_res
elif return_node_ids == 1:
result, node_ids = chunk_res
else:
result = chunk_res
if use_memmap:
result = memmap_outfile
else:
multiprocessing.set_start_method('spawn', force=True)
sub_res_lists = []
if return_node_ids == 2:
src_node_ids_presence = np.full(2**r_scale, False)
dst_node_ids_presence = np.full(2**c_scale, False)
elif return_node_ids == 1:
node_ids_presence = np.full(2**theta_len, False)
with multiprocessing.Pool(processes=gpus) as pool:
chunk_res = pool.imap_unordered(_generate_gpu_chunk_rmat_p,
zip(chunk_ids, np.cumsum(prefix_edges[chunk_ids])),
chunksize=(len(chunk_ids)+gpus-1) // gpus )
if verbose:
chunk_res = tqdm(chunk_res, total=len(chunk_ids))
if return_node_ids == 2:
for res, src_n_ids, dst_n_ids in chunk_res:
sub_res_lists.append(res)
src_node_ids_presence[src_n_ids] = True
dst_node_ids_presence[dst_n_ids] = True
elif return_node_ids == 1:
for res, n_ids in chunk_res:
sub_res_lists.append(res)
node_ids_presence[n_ids] = True
else:
sub_res_lists = list(chunk_res)
if use_memmap:
result = memmap_outfile
elif chunk_save_path_format is None:
result = np.concatenate(sub_res_lists)
else:
result = int(np.sum(sub_res_lists))
if return_node_ids == 2:
src_node_ids, = np.where(src_node_ids_presence)
dst_node_ids, = np.where(dst_node_ids_presence)
elif return_node_ids == 1:
node_ids, = np.where(node_ids_presence)
if return_node_ids == 2:
return result, src_node_ids, dst_node_ids
if return_node_ids == 1:
return result, node_ids
return result
def get_degree_distribution(vertices, gpu=False, operator=None):
operator = operator or (cp if gpu else np)
_, degrees = operator.unique(vertices, return_counts=True)
degree_values, degree_counts = operator.unique(degrees, return_counts=True)
return degree_values, degree_counts
class BaseLogger:
""" Base logger class
Args:
logdir (str): path to the logging directory
"""
def __init__(self, logdir: str = "tmp"):
self.logdir = logdir
os.makedirs(self.logdir, exist_ok=True)
currentDateAndTime = datetime.now()
self.logname = (
f'{currentDateAndTime.strftime("%Y_%m_%d_%H_%M_%S")}.txt'
)
self.logpath = os.path.join(self.logdir, self.logname)
self.setup_logger()
self.log("Initialized logger")
def setup_logger(self):
""" This function setups logger """
logging.basicConfig(
filename=self.logpath,
filemode="a",
format="%(asctime)s| %(message)s",
datefmt="%Y/%m/%d %H:%M:%S",
level=logging.DEBUG,
)
def log(self, msg: str):
""" This function logs messages in debug mode
Args:
msg (str): message to be printed
"""
logging.debug(msg)
def _reshuffle(X: NDArray, mask: NDArray, max_node_id: int) -> None:
"""
Shuffles dst nodes of edges specified by idx.
Preserves degree distribution and keeps edge list sorted.
"""
operator = infer_operator(X)
if not operator.any(mask):
return
target = X[mask, 1]
operator.random.shuffle(target)
X[mask, 1] = target
src_node_mask = operator.zeros(max_node_id + 1, dtype=operator.bool_)
src_node_mask[X[mask, 0]] = True
to_sort_mask = operator.zeros(X.shape[0], dtype=operator.bool_)
to_sort_mask = src_node_mask[X[:, 0]]
to_sort = X[to_sort_mask]
to_sort = to_sort[operator.lexsort(to_sort.T[::-1])]
X[to_sort_mask] = to_sort
def _find_correct_edges(
X: NDArray,
self_loops: bool = False,
assume_sorted: bool = False,
) -> Tuple[NDArray, NDArray]:
""" Finds duplicates and self loops in an edge list. """
operator = infer_operator(X)
if not assume_sorted:
X = X[operator.lexsort(X.T[::-1])]
mask = operator.empty(X.shape[0], dtype=operator.bool_)
mask[0] = True
mask[1:] = operator.any(X[1:] != X[:-1], axis=1)
if not self_loops:
mask &= X[:, 0] != X[:, 1]
return X, mask
def postprocess_edge_list(X: NDArray, n_reshuffle: int = 0, self_loops: bool = False) -> NDArray:
"""
Removes multi-edges and (optionally) self-loops.
If n_reshuffle > 0 is specified, edges are shuffled between nodes
so that the degree distribution is preserved and less edges will be removed.
Assumes node set is reindexed from min_id > 0 to max_id ~ N.
"""
max_node_id = X.max().item()
X, mask = _find_correct_edges(X, self_loops=self_loops)
for _ in range(n_reshuffle):
_reshuffle(X, ~mask, max_node_id)
X, mask = _find_correct_edges(X, self_loops=self_loops, assume_sorted=True)
return X[mask]
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/trtis_client | trtis_client | CMakeLists | cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(tacotron2_inference)
if (DEFINED DEVEL AND NOT DEVEL EQUAL 0)
if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "GNU")
# g++ warnings
set(CPP_DEVEL_FLAGS "${CPP_DEVEL_FLAGS} -Wall")
set(CPP_DEVEL_FLAGS "${CPP_DEVEL_FLAGS} -Werror")
set(CPP_DEVEL_FLAGS "${CPP_DEVEL_FLAGS} -Wpedantic")
set(CPP_DEVEL_FLAGS "${CPP_DEVEL_FLAGS} -Weffc++")
set(CPP_DEVEL_FLAGS "${CPP_DEVEL_FLAGS} -Wextra")
set(CPP_DEVEL_FLAGS "${CPP_DEVEL_FLAGS} -DDEVEL=1")
# nvcc warnings
set(CUDA_DEVEL_FLAGS "${CUDA_DEVEL_FLAGS} -Xcompiler -Wall")
set(CUDA_DEVEL_FLAGS "${CUDA_DEVEL_FLAGS} -Xcompiler -Werror")
set(CUDA_DEVEL_FLAGS "${CUDA_DEVEL_FLAGS} -Xcompiler -Weffc++")
set(CUDA_DEVEL_FLAGS "${CUDA_DEVEL_FLAGS} -Xcompiler -Wextra")
set(CUDA_DEVEL_FLAGS "${CUDA_DEVEL_FLAGS} -Xcompiler -DDEVEL=1")
endif()
endif()
set(CMAKE_CXX_FLAGS_DEBUG "-g -O0")
set(CMAKE_CXX_STANDARD 14)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC")
separate_arguments(CPP_DEVEL_FLAGS)
separate_arguments(CUDA_DEVEL_FLAGS)
add_subdirectory("src")
|
PyTorch/Recommendation/DLRM/preproc | preproc | parquet_to_binary | # Copyright (c) 2021 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import pandas as pd
import os
from joblib import Parallel, delayed
import glob
import argparse
import tqdm
import subprocess
def process_file(f, dst):
label = '_c0'
dense_columns = [f'_c{i}' for i in range(1, 14)]
categorical_columns = [f'_c{i}' for i in range(14, 40)]
all_columns_sorted = [f'_c{i}' for i in range(0, 40)]
data = pd.read_parquet(f)
data = data[all_columns_sorted]
data[label] = data[label].astype(np.int32)
data[dense_columns] = data[dense_columns].astype(np.float32)
data[categorical_columns] = data[categorical_columns].astype(np.int32)
data = data.to_records(index=False)
data = data.tobytes()
dst_file = dst + '/' + f.split('/')[-1] + '.bin'
with open(dst_file, 'wb') as dst_fd:
dst_fd.write(data)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--src_dir', type=str)
parser.add_argument('--intermediate_dir', type=str)
parser.add_argument('--dst_dir', type=str)
parser.add_argument('--parallel_jobs', default=40, type=int)
args = parser.parse_args()
print('Processing train files...')
train_src_files = glob.glob(args.src_dir + '/train/*.parquet')
train_intermediate_dir = os.path.join(args.intermediate_dir, 'train')
os.makedirs(train_intermediate_dir, exist_ok=True)
Parallel(n_jobs=args.parallel_jobs)(delayed(process_file)(f, train_intermediate_dir) for f in tqdm.tqdm(train_src_files))
print('Train files conversion done')
print('Processing test files...')
test_src_files = glob.glob(args.src_dir + '/test/*.parquet')
test_intermediate_dir = os.path.join(args.intermediate_dir, 'test')
os.makedirs(test_intermediate_dir, exist_ok=True)
Parallel(n_jobs=args.parallel_jobs)(delayed(process_file)(f, test_intermediate_dir) for f in tqdm.tqdm(test_src_files))
print('Test files conversion done')
print('Processing validation files...')
valid_src_files = glob.glob(args.src_dir + '/validation/*.parquet')
valid_intermediate_dir = os.path.join(args.intermediate_dir, 'validation')
os.makedirs(valid_intermediate_dir, exist_ok=True)
Parallel(n_jobs=args.parallel_jobs)(delayed(process_file)(f, valid_intermediate_dir) for f in tqdm.tqdm(valid_src_files))
print('Validation files conversion done')
os.makedirs(args.dst_dir, exist_ok=True)
print('Concatenating train files')
os.system(f'cat {train_intermediate_dir}/*.bin > {args.dst_dir}/train_data.bin')
print('Concatenating test files')
os.system(f'cat {test_intermediate_dir}/*.bin > {args.dst_dir}/test_data.bin')
print('Concatenating validation files')
os.system(f'cat {valid_intermediate_dir}/*.bin > {args.dst_dir}/validation_data.bin')
print('Done')
if __name__ == '__main__':
main()
|
PyTorch/Segmentation/MaskRCNN/pytorch/configs/pascal_voc | pascal_voc | e2e_faster_rcnn_R_50_C4_1x_1_gpu_voc | MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
RPN:
PRE_NMS_TOP_N_TEST: 6000
POST_NMS_TOP_N_TEST: 300
ANCHOR_SIZES: (128, 256, 512)
ROI_BOX_HEAD:
NUM_CLASSES: 21
DATASETS:
TRAIN: ("voc_2007_train", "voc_2007_val")
TEST: ("voc_2007_test",)
SOLVER:
BASE_LR: 0.001
WEIGHT_DECAY: 0.0001
STEPS: (50000, )
MAX_ITER: 70000
IMS_PER_BATCH: 1
TEST:
IMS_PER_BATCH: 1
|
TensorFlow2/Recommendation/WideAndDeep/triton/deployment_toolkit/triton_performance_runner/perf_analyzer | perf_analyzer | perf_config | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any
from .exceptions import PerfAnalyzerException
class PerfAnalyzerConfig:
"""
A config class to set arguments to the perf_analyzer.
An argument set to None will use the perf_analyzer's default.
"""
perf_analyzer_args = [
"async",
"sync",
"measurement-interval",
"measurement-mode",
"measurement-request-count",
"concurrency-range",
"request-rate-range",
"request-distribution",
"request-intervals",
"binary-search",
"num-of-sequence",
"latency-threshold",
"max-threads",
"stability-percentage",
"max-trials",
"percentile",
"input-data",
"shared-memory",
"output-shared-memory-size",
"sequence-length",
"string-length",
"string-data",
]
perf_analyzer_multiple_args = [
"shape",
]
input_to_options = [
"model-name",
"model-version",
"batch-size",
"url",
"protocol",
"latency-report-file",
"streaming",
]
input_to_verbose = ["verbose", "extra-verbose"]
def __init__(self):
"""
Construct a PerfAnalyzerConfig
"""
self._args = {k: None for k in self.perf_analyzer_args}
self._multiple_args = {k: [] for k in self.perf_analyzer_multiple_args}
self._options = {
"-m": None,
"-x": None,
"-b": None,
"-u": None,
"-i": None,
"-f": None,
"-H": None,
"-c": None,
"-t": None,
}
self._verbose = {"-v": None, "-v -v": None}
self._input_to_options = {
"model-name": "-m",
"model-version": "-x",
"batch-size": "-b",
"url": "-u",
"protocol": "-i",
"latency-report-file": "-f",
"streaming": "-H",
"concurrency": "-c",
"threads": "-t",
}
self._input_to_verbose = {"verbose": "-v", "extra-verbose": "-v -v"}
@classmethod
def allowed_keys(cls):
"""
Returns
-------
list of str
The keys that are allowed to be
passed into perf_analyzer
"""
return (
list(cls.perf_analyzer_args)
+ list(cls.perf_analyzer_multiple_args)
+ list(cls.input_to_options)
+ list(cls.input_to_verbose)
)
def update_config(self, params=None):
"""
Allows setting values from a
params dict
Parameters
----------
params: dict
keys are allowed args to perf_analyzer
"""
if params:
for key in params:
self[key] = params[key]
def to_cli_string(self):
"""
Utility function to convert a config into a
string of arguments to the perf_analyzer with CLI.
Returns
-------
str
cli command string consisting of all arguments
to the perf_analyzer set in the config, without
the executable name.
"""
# single dashed options, then verbose flags, then main args
args = [f"{k} {v}" for k, v in self._options.items() if v]
args += [k for k, v in self._verbose.items() if v]
args += [f"--{k}={v}" for k, v in self._args.items() if v]
for k, v in self._multiple_args.items():
for item in v:
args.append(f"--{k}={item}")
return " ".join(args)
def __getitem__(self, key: str):
"""
Gets an arguments value in config
Parameters
----------
key : str
The name of the argument to the perf_analyzer
Returns
-------
The value that the argument is set to in this config
Raises
------
TritonModelAnalyzerException
If argument not found in the config
"""
if key in self._args:
return self._args[key]
elif key in self._multiple_args:
return self._multiple_args[key]
elif key in self._input_to_options:
return self._options[self._input_to_options[key]]
elif key in self._input_to_verbose:
return self._verbose[self._input_to_verbose[key]]
else:
raise PerfAnalyzerException(f"'{key}' Key not found in config")
def __setitem__(self, key: str, value: Any):
"""
Sets an arguments value in config
after checking if defined/supported.
Parameters
----------
key : str
The name of the argument to the perf_analyzer
value : (any)
The value to which the argument is being set
Raises
------
TritonModelAnalyzerException
If key is unsupported or undefined in the
config class
"""
if key in self._args:
self._args[key] = value
elif key in self._multiple_args:
self._multiple_args[key].append(value)
elif key in self._input_to_options:
self._options[self._input_to_options[key]] = value
elif key in self._input_to_verbose:
self._verbose[self._input_to_verbose[key]] = value
else:
raise PerfAnalyzerException(
f"The argument '{key}' to the perf_analyzer " "is not supported by the model analyzer."
)
|
TensorFlow2/Recommendation/DLRM_and_DCNv2/deployment/tf | tf | __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 constants import dense_model_name, hps_model_name
from deployment.tf.deploy_dense import deploy_dense
from deployment.tf.deploy_ensemble import deploy_ensemble
from deployment.tf.deploy_sparse import deploy_sparse
from deployment.tf.deploy_monolithic import deploy_monolithic
|
TensorFlow2/Recommendation/WideAndDeep/data/outbrain | outbrain | dataloader | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cupy
import horovod.tensorflow as hvd
import tensorflow as tf
from nvtabular.loader.tensorflow import KerasSequenceLoader
from data.outbrain.defaults import LABEL_CHANNEL, MAP_FEATURE_CHANNEL, NUMERICAL_CHANNEL, ONEHOT_CHANNEL, \
MULTIHOT_CHANNEL
cupy.random.seed(None)
def seed_fn():
min_int, max_int = tf.int32.limits
max_rand = max_int // hvd.size()
# Generate a seed fragment on each worker
seed_fragment = cupy.random.randint(0, max_rand).get()
# Aggregate seed fragments from all Horovod workers
seed_tensor = tf.constant(seed_fragment)
reduced_seed = hvd.allreduce(seed_tensor, name="shuffle_seed", op=hvd.mpi_ops.Sum)
return reduced_seed % max_rand
def get_dataset(feature_spec, mapping, batch_size, buffer_size=0.1, parts_per_chunk=1,
map_channel_enabled=False, shuffle=True):
data_paths = feature_spec.get_paths_by_mapping(mapping)
label_names = feature_spec.get_names_by_channel(LABEL_CHANNEL)
cat_names = feature_spec.get_names_by_channel(ONEHOT_CHANNEL) + feature_spec.get_names_by_channel(MULTIHOT_CHANNEL)
cont_names = feature_spec.get_names_by_channel(NUMERICAL_CHANNEL)
if map_channel_enabled:
cat_names += feature_spec.get_names_by_channel(MAP_FEATURE_CHANNEL)
tf_dataset = KerasSequenceLoader(
data_paths,
batch_size=batch_size,
label_names=label_names,
cat_names=cat_names,
cont_names=cont_names,
engine="parquet",
shuffle=shuffle,
buffer_size=buffer_size,
parts_per_chunk=parts_per_chunk,
global_size=hvd.size(),
global_rank=hvd.rank(),
seed_fn=seed_fn,
)
return tf_dataset
def make_padding_function(multihot_hotness_dict):
@tf.function(experimental_relax_shapes=True)
def pad_batch(batch):
batch = batch.copy()
for feature, hotness in multihot_hotness_dict.items():
multihot_tuple = batch[feature]
values = multihot_tuple[0][:, 0]
row_lengths = multihot_tuple[1][:, 0]
padded = tf.RaggedTensor.from_row_lengths(
values, row_lengths, validate=False
).to_tensor(default_value=-1, shape=[None, hotness])
batch[feature] = padded
return batch
return pad_batch
|
TensorFlow2/Recommendation/WideAndDeep/triton/deployment_toolkit/triton_performance_runner | triton_performance_runner | runner | # 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.
# method from PEP-366 to support relative import in executed modules
import logging
import pathlib
from typing import List, Optional
if __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from ..core import EvaluationMode, MeasurementMode, OfflineMode, PerformanceTool
from .model_analyzer import ModelAnalyzerRunner
from .perf_analyzer import PerfAnalyzerRunner, PerfAnalyzerWarmupRunner
LOGGER = logging.getLogger("triton_performance_runner")
class TritonPerformanceRunner:
def __init__(
self,
server_url: str,
model_name: str,
input_data: str,
input_shapes: List[str],
batch_sizes: List[int],
concurrency: List[int],
measurement_mode: MeasurementMode,
measurement_interval: int,
measurement_request_count: int,
evaluation_mode: EvaluationMode,
offline_mode: OfflineMode,
output_shared_memory_size: int,
performance_tool: PerformanceTool,
model_repository: str,
result_path: pathlib.Path,
warmup: bool,
timeout: Optional[int],
verbose: bool,
):
self._warmup_runner = None
if warmup:
LOGGER.info("Running warmup before the main test")
self._warmup_runner = PerfAnalyzerWarmupRunner(
server_url=server_url,
model_name=model_name,
input_data=input_data,
input_shapes=input_shapes,
batch_sizes=batch_sizes,
concurrency=concurrency,
measurement_mode=measurement_mode,
measurement_interval=measurement_interval,
measurement_request_count=measurement_request_count,
evaluation_mode=evaluation_mode,
offline_mode=offline_mode,
output_shared_memory_size=output_shared_memory_size,
timeout=timeout,
)
if performance_tool == PerformanceTool.MODEL_ANALYZER:
LOGGER.info("Using Model Analyzer for performance evaluation")
self._runner = ModelAnalyzerRunner(
server_url=server_url,
model_name=model_name,
input_data=input_data,
input_shapes=input_shapes,
batch_sizes=batch_sizes,
concurrency=concurrency,
measurement_mode=measurement_mode,
measurement_interval=measurement_interval,
measurement_request_count=measurement_request_count,
evaluation_mode=evaluation_mode,
offline_mode=offline_mode,
output_shared_memory_size=output_shared_memory_size,
model_repository=model_repository,
result_path=result_path,
timeout=timeout,
verbose=verbose,
)
elif performance_tool == PerformanceTool.PERF_ANALYZER:
LOGGER.info("Using Perf Analyzer for performance evaluation")
self._runner = PerfAnalyzerRunner(
server_url=server_url,
model_name=model_name,
input_data=input_data,
input_shapes=input_shapes,
batch_sizes=batch_sizes,
measurement_mode=measurement_mode,
measurement_interval=measurement_interval,
measurement_request_count=measurement_request_count,
concurrency=concurrency,
evaluation_mode=evaluation_mode,
offline_mode=offline_mode,
output_shared_memory_size=output_shared_memory_size,
result_path=result_path,
timeout=timeout,
verbose=verbose,
)
else:
raise ValueError(f"Unsupported performance tool {performance_tool}")
def run(self):
if self._warmup_runner:
self._warmup_runner.run()
self._runner.run()
|
TensorFlow/Detection/SSD/models/research/slim/nets | nets | cyclegan_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 tensorflow.contrib.slim.nets.cyclegan."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import cyclegan
# TODO(joelshor): Add a test to check generator endpoints.
class CycleganTest(tf.test.TestCase):
def test_generator_inference(self):
"""Check one inference step."""
img_batch = tf.zeros([2, 32, 32, 3])
model_output, _ = cyclegan.cyclegan_generator_resnet(img_batch)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(model_output)
def _test_generator_graph_helper(self, shape):
"""Check that generator can take small and non-square inputs."""
output_imgs, _ = cyclegan.cyclegan_generator_resnet(tf.ones(shape))
self.assertAllEqual(shape, output_imgs.shape.as_list())
def test_generator_graph_small(self):
self._test_generator_graph_helper([4, 32, 32, 3])
def test_generator_graph_medium(self):
self._test_generator_graph_helper([3, 128, 128, 3])
def test_generator_graph_nonsquare(self):
self._test_generator_graph_helper([2, 80, 400, 3])
def test_generator_unknown_batch_dim(self):
"""Check that generator can take unknown batch dimension inputs."""
img = tf.placeholder(tf.float32, shape=[None, 32, None, 3])
output_imgs, _ = cyclegan.cyclegan_generator_resnet(img)
self.assertAllEqual([None, 32, None, 3], output_imgs.shape.as_list())
def _input_and_output_same_shape_helper(self, kernel_size):
img_batch = tf.placeholder(tf.float32, shape=[None, 32, 32, 3])
output_img_batch, _ = cyclegan.cyclegan_generator_resnet(
img_batch, kernel_size=kernel_size)
self.assertAllEqual(img_batch.shape.as_list(),
output_img_batch.shape.as_list())
def input_and_output_same_shape_kernel3(self):
self._input_and_output_same_shape_helper(3)
def input_and_output_same_shape_kernel4(self):
self._input_and_output_same_shape_helper(4)
def input_and_output_same_shape_kernel5(self):
self._input_and_output_same_shape_helper(5)
def input_and_output_same_shape_kernel6(self):
self._input_and_output_same_shape_helper(6)
def _error_if_height_not_multiple_of_four_helper(self, height):
self.assertRaisesRegexp(
ValueError,
'The input height must be a multiple of 4.',
cyclegan.cyclegan_generator_resnet,
tf.placeholder(tf.float32, shape=[None, height, 32, 3]))
def test_error_if_height_not_multiple_of_four_height29(self):
self._error_if_height_not_multiple_of_four_helper(29)
def test_error_if_height_not_multiple_of_four_height30(self):
self._error_if_height_not_multiple_of_four_helper(30)
def test_error_if_height_not_multiple_of_four_height31(self):
self._error_if_height_not_multiple_of_four_helper(31)
def _error_if_width_not_multiple_of_four_helper(self, width):
self.assertRaisesRegexp(
ValueError,
'The input width must be a multiple of 4.',
cyclegan.cyclegan_generator_resnet,
tf.placeholder(tf.float32, shape=[None, 32, width, 3]))
def test_error_if_width_not_multiple_of_four_width29(self):
self._error_if_width_not_multiple_of_four_helper(29)
def test_error_if_width_not_multiple_of_four_width30(self):
self._error_if_width_not_multiple_of_four_helper(30)
def test_error_if_width_not_multiple_of_four_width31(self):
self._error_if_width_not_multiple_of_four_helper(31)
if __name__ == '__main__':
tf.test.main()
|
PaddlePaddle/LanguageModeling/BERT/vocab | vocab | bert-large-uncased-vocab | [PAD]
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40
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reported
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saying
allowed
master
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smith
winning
try
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moving
campaign
los
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breath
nearly
mid
1987
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girls
date
italian
african
standing
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artist
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shows
deal
mine
industry
1986
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republic
provide
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1985
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success
meet
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1984
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source
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guy
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husband
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entered
weeks
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1980
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films
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sense
operation
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1983
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twenty
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hour
edition
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places
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movie
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report
chicago
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1982
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month
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contract
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lines
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writer
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championships
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giving
access
attended
test
couple
stand
catholic
martin
caught
executive
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eye
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chair
quite
shoulder
1979
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decision
plays
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whether
structure
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paper
mission
1981
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200
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managed
nature
lives
plant
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computer
figure
relationship
issue
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loss
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gun
ago
highest
1972
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male
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distance
commercial
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1976
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1978
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caused
italy
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hotel
comes
appearance
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double
issues
musical
companies
castle
income
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assembly
bass
initially
parliament
artists
experience
1974
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walk
foot
engineering
talking
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dropped
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miss
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boys
break
1975
stars
edge
remember
policy
carried
train
stadium
bar
sex
angeles
evidence
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becoming
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soviet
1977
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step
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1970
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1968
1973
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1945
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launched
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retired
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prize
contains
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1971
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watch
legal
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45
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1969
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mm
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winter
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problems
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1967
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journal
35
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65
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1964
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1965
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32
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1960
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1944
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1963
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1962
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nations
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wild
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1942
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80
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1961
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walking
vote
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lieutenant
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proposed
perfect
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affairs
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seconds
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paid
1943
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kitchen
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academic
nice
teacher
races
1956
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corporation
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issued
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1958
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housing
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1959
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von
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picked
build
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shortly
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exchange
elections
1980s
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percent
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fish
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1941
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1940
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1948
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1957
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tonight
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1939
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1946
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1950
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pair
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33
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1970s
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70
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1955
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1952
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1947
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44
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36
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1954
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steps
1949
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34
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1960s
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1938
37
relations
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plants
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1936
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kids
begins
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1918
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laws
400
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classes
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thanks
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reaching
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1937
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1935
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1920
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1930
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1933
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66
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73
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verses
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interference
objectives
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sectors
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initiatives
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integrity
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robe
tap
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gazed
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distracted
rulers
111
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cart
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depths
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makers
ethiopia
longtime
detected
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rely
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anticipation
iso
morton
successive
agnes
screenwriter
straightened
philippe
playwright
haunted
licence
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intentions
sutton
112
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correctly
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algebra
ira
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240
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designers
raids
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mikhail
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wei
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kosovo
macedonia
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occupy
mastering
stephanie
janeiro
para
unaware
nurses
noon
135
cam
hopefully
ranger
combine
sociology
polar
rica
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neill
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holocaust
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doubled
lust
1828
109
decent
cooling
unveiled
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1829
nsw
homer
chapman
meyer
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dive
mae
reagan
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brooke
sided
prosecution
investigating
comprised
petroleum
genres
reluctant
differently
trilogy
johns
vegetables
corpse
highlighted
lounge
pension
unsuccessfully
elegant
aided
ivory
beatles
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immigrant
babe
click
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pepper
combining
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homeless
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prizes
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modes
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overnight
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generals
guidelines
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addresses
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cord
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cyclone
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bacon
petty
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hampton
diagnosis
1824
throws
complexity
rita
disputed
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trafficking
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formats
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modeling
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clergy
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cowboys
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providers
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void
storey
preceded
masses
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laughs
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wards
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pad
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measurements
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toxic
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precision
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expressway
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1821
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scanned
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outlets
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lucien
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sweep
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missionaries
websites
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sentences
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val
trademark
spells
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starter
slim
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toe
incredibly
shoots
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nobility
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gardner
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organisms
emissions
kazakhstan
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boring
chips
themed
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llc
constantinople
chasing
systematic
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borrowed
erin
carey
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highlands
struggles
1810
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wong
exceptions
develops
enlarged
kindergarten
castro
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leigh
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juvenile
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consul
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intensive
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clayton
stuffed
exceptional
ix
apostolic
230
transactions
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exempt
swinging
cove
religions
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shields
dairy
bypass
190
pursuing
bug
joyce
bombay
chassis
southampton
chat
interact
redesignated
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publicity
constituted
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purely
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weakened
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defines
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ming
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fu
narrowly
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standings
virtue
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gloves
lid
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conservatory
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sympathy
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strode
advocated
optional
chronic
discharge
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compatible
laurel
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shi
fails
wage
dodge
128
informal
sorts
levi
buddha
villagers
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chronicles
heavier
summoned
gateway
3000
eleventh
jewelry
translations
accordingly
seas
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fiber
pyramid
cubic
dragging
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caring
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android
contacted
lunar
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kai
lisbon
patted
1826
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subtropical
disputes
ta
holidays
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mare
cane
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benny
companions
dong
raj
observe
roar
charming
plaque
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fossils
enacted
manning
bubble
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tanzania
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funk
swamp
deputies
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scenario
par
scratch
metals
anthem
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engaging
specially
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sidewalk
cope
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benson
princes
peterson
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bedford
sharks
eli
wreck
alberto
gasp
archaeology
lgbt
teaches
securities
madness
compromise
waving
coordination
davidson
visions
leased
possibilities
eighty
jun
fernandez
enthusiasm
assassin
sponsorship
reviewer
kingdoms
estonian
laboratories
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applies
verb
celebrations
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rowing
lightweight
sadness
submit
mvp
balanced
dude
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explicitly
metric
magnificent
mound
brett
mohammad
mistakes
irregular
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sanders
betrayed
shipped
surge
##enburg
reporters
termed
georg
pity
verbal
bulls
abbreviated
enabling
appealed
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sicily
sting
heel
sweetheart
bart
spacecraft
brutal
monarchy
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aberdeen
cameo
diane
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survivor
clyde
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complaint
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clarinet
delicious
chilean
karnataka
coordinates
1818
panties
##rst
pretending
ar
dramatically
kiev
bella
tends
distances
113
catalog
launching
instances
telecommunications
portable
lindsay
vatican
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angles
aliens
marker
stint
screens
bolton
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judy
wool
benedict
plasma
europa
spark
imaging
filmmaker
swiftly
##een
contributor
##nor
opted
stamps
apologize
financing
butter
gideon
sophisticated
alignment
avery
chemicals
yearly
speculation
prominence
professionally
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immortal
institutional
inception
wrists
identifying
tribunal
derives
gains
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papal
preference
linguistic
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operative
brewery
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unemployment
boyd
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prophet
1813
bi
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confirm
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beaches
midway
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gambling
bonnie
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regulated
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hermann
tricks
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specialty
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overtime
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mon
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feathers
angelo
thirds
nuts
surveys
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gill
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dissolution
dedication
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elvis
reds
chaired
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functioning
niece
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advocacy
robbie
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yugoslav
passport
constable
congressman
hastings
vera
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consecrated
sparks
ecclesiastical
confined
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floyd
nora
1822
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1827
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ned
saga
spiral
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appreciated
yi
collaborative
treating
similarities
feminine
finishes
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import
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champagne
mice
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celebrities
helsinki
attributes
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cousins
phases
ache
lucia
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submission
vicar
spear
shine
tasmania
biting
detention
constitute
tighter
seasonal
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terrestrial
matthews
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effectiveness
parody
philharmonic
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1816
strangers
encoded
consortium
guaranteed
regards
shifts
tortured
collision
supervisor
inform
broader
insight
theaters
armour
emeritus
blink
incorporates
mapping
##50
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generous
thief
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carr
loses
1793
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ucla
romeo
generic
metallic
realization
damages
mk
commissioners
zach
default
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helicopters
lengthy
stems
spa
partnered
spectators
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indication
penalties
teresa
1801
sen
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dalton
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photographic
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dell
deaf
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crawled
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pipes
employers
maharashtra
comparable
va
textile
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alphabet
binary
irrigation
chartered
choked
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offs
waking
supplement
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quantities
demolition
regain
locate
urdu
folks
alt
114
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scary
andreas
whites
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classrooms
mw
aesthetic
publishes
valleys
guides
cubs
johannes
bryant
conventions
affecting
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drain
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isolation
prosecutor
ambitious
apology
captive
downs
atmospheric
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aisle
beef
foul
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kidding
composite
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illusion
natives
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rockets
riverside
wartime
painters
adolf
melted
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uncertainty
simulation
hawks
progressed
meantime
builder
spray
breach
unhappy
regina
russians
##urg
determining
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tram
1806
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aging
##12
1823
garion
rented
mister
diaz
terminated
clip
1817
depend
nervously
disco
owe
defenders
shiva
notorious
disbelief
shiny
worcester
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trailing
undertook
islander
belarus
limitations
watershed
fuller
overlooking
utilized
raphael
1819
synthetic
breakdown
klein
##nate
moaned
memoir
lamb
practicing
##erly
cellular
arrows
exotic
##graphy
witches
117
charted
rey
hut
hierarchy
subdivision
freshwater
giuseppe
aloud
reyes
qatar
marty
sideways
utterly
sexually
jude
prayers
mccarthy
softball
blend
damien
##gging
##metric
wholly
erupted
lebanese
negro
revenues
tasted
comparative
teamed
transaction
labeled
maori
sovereignty
parkway
trauma
gran
malay
121
advancement
descendant
2020
buzz
salvation
inventory
symbolic
##making
antarctica
mps
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mohammed
myanmar
holt
submarines
tones
##lman
locker
patriarch
bangkok
emerson
remarks
predators
kin
afghan
confession
norwich
rental
emerge
advantages
##zel
rca
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shortened
storms
aidan
##matic
autonomy
compliance
##quet
dudley
atp
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1803
motto
documentation
summary
professors
spectacular
christina
archdiocese
flashing
innocence
remake
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psychic
reef
scare
employ
rs
sticks
meg
gus
leans
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accompany
bergen
tomas
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doom
wages
pools
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breasts
scholarly
alison
outline
brittany
breakthrough
willis
realistic
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competitor
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pike
picnic
icon
designing
commercials
washing
villain
skiing
micro
costumes
auburn
halted
executives
##hat
logistics
cycles
vowel
applicable
barrett
exclaimed
eurovision
eternity
ramon
##umi
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modifications
sweeping
disgust
##uck
torch
aviv
ensuring
rude
dusty
sonic
donovan
outskirts
cu
pathway
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disciplines
acids
cadet
paired
##40
sketches
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marriages
##⁺
folding
peers
slovak
implies
admired
##beck
1880s
leopold
instinct
attained
weston
megan
horace
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dorsal
ingredients
evolutionary
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complications
deity
lethal
brushing
levy
deserted
institutes
posthumously
delivering
telescope
coronation
motivated
rapids
luc
flicked
pays
volcano
tanner
weighed
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crowds
frankie
gifted
addressing
granddaughter
winding
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constantine
gomez
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landscapes
rudolf
anthropology
slate
werewolf
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astronomy
circa
rouge
dreaming
sack
knelt
drowned
naomi
prolific
tracked
freezing
herb
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randall
twisting
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retaining
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compare
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daemon
nigel
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recommendation
czechoslovakia
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ng
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inheritance
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cries
examining
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ko
retrieve
tomatoes
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polished
defining
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lantern
personalities
begging
tract
swore
1809
175
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brotherhood
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exeter
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steele
dumb
pearson
210
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elisabeth
trends
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premium
bugs
fraction
calmly
viking
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tug
inserted
unusually
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confronted
distress
crashing
brent
turks
resign
##olo
cambodia
gabe
sauce
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evelyn
116
extant
clusters
quarry
teenagers
luna
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affiliation
drill
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panthers
scenic
libya
anita
strengthen
inscriptions
##cated
lace
sued
judith
riots
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mint
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preparations
midst
dub
challenger
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mock
cf
displaced
wicket
breaths
enables
schmidt
analyst
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ag
highlight
automotive
axe
josef
newark
sufficiently
resembles
50th
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flushed
mum
traits
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commodore
incomplete
warming
titular
ceremonial
ethical
118
celebrating
eighteenth
cao
lima
medalist
mobility
strips
snakes
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miniature
zagreb
barton
escapes
umbrella
automated
doubted
differs
cooled
georgetown
dresden
cooked
fade
wyatt
rna
jacobs
carlton
abundant
stereo
boost
madras
inning
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spur
ip
malayalam
begged
osaka
groan
escaping
charging
dose
vista
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bud
papa
communists
advocates
edged
tri
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resemble
peaking
necklace
fried
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glances
stuttgart
curator
recruit
grocery
sympathetic
##tting
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127
lotus
randolph
ancestor
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succeeding
jupiter
1798
macedonian
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hiking
1808
handing
fischer
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garbage
node
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prone
singular
papua
inclined
attractions
italia
pouring
motioned
grandma
garnered
jacksonville
corp
ego
ringing
aluminum
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ordering
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drawer
traders
synagogue
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resistant
wandering
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teased
var
hardcore
soaked
jubilee
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valencia
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kuwait
1811
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tavern
gamma
122
johan
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airways
amino
gil
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feb
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jax
motorway
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stalin
1805
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passive
tensions
straw
123
frequencies
1804
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participant
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shire
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inmates
nielsen
councillors
loaned
uncommon
omar
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daniels
formations
jokes
1794
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sigma
licensing
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wheelchair
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1807
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trustee
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nm
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gram
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119
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disappearance
farewell
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wimbledon
124
rue
1792
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imagery
bloom
280
brake
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po
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lacrosse
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5000
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precipitation
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1802
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declare
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260
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incorporating
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gore
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rican
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discovering
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lifts
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physicist
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##page
##ographic
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juliet
reformation
sparhawk
320
complement
suppressed
jewel
##½
floated
##kas
continuity
sadly
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inability
melting
scanning
paula
flour
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vague
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solving
curb
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financially
gable
bees
expired
miserable
cassidy
dominion
1789
cupped
145
robbery
facto
amos
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resume
tallest
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ing
pounded
usd
declaring
gasoline
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darkened
270
650
sophomore
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blows
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algorithms
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cherokee
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sexuality
platoon
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meditation
poetic
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dreamed
ensuing
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mattered
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1799
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cameroon
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wax
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librarian
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shipyard
pharmaceutical
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pt
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acquiring
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nikolai
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kannada
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reilly
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waitress
abdomen
devastated
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pseudonym
pharmacy
fulfill
paraguay
1796
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clifford
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mafia
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sensors
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upstream
##eg
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spanning
165
trombone
basque
seeded
interred
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batsman
portrayal
mara
pushes
spears
og
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reside
nathaniel
brennan
1776
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caucus
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markings
yemen
nobles
ku
lazy
viewer
catalan
encompasses
sawyer
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sparked
substances
patents
braves
arranger
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sergio
persuade
dover
tolerance
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townships
occupying
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projection
puppet
flanders
introduces
liability
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gymnastics
antwerp
taipei
hobart
candles
jeep
wes
observers
126
chaplain
bundle
glorious
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sol
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stares
sh
bangalore
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expressing
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crafts
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rite
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entertaining
comply
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wrestlers
basel
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marian
rivera
helpful
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caution
downward
networking
##atory
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darted
genocide
emergence
replies
specializing
spokesman
convenient
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elijah
investigator
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promotes
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simone
announcer
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lydia
weaver
132
residency
modification
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stretches
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nat
lowe
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pam
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installations
1797
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werner
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155
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330
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1775
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1791
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xii
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dexter
##pf
lionel
129
debates
lemon
tiffany
volunteered
dom
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sic
tremendous
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colts
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pac
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133
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diabetes
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131
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pencil
wines
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144
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newest
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1200
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1600
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1870s
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143
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205
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146
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370
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148
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147
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151
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139
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haley
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154
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153
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156
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167
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202
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168
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guerrero
racist
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cyber
derivatives
culminated
allie
annals
panzer
sainte
wikipedia
pops
zu
austro
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algerian
politely
nicholson
mornings
educate
tastes
thrill
dartmouth
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db
##jee
regan
differing
concentrating
choreography
divinity
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pledged
alexandre
routing
gregor
madeline
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apocalypse
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gunfire
culminating
elves
fined
liang
lam
programmed
tar
guessing
transparency
gabrielle
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cancellation
flexibility
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accession
shea
stronghold
nets
specializes
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abused
hasan
sgt
ling
exceeding
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admiration
supermarket
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photographers
specialised
tilt
resonance
hmm
perfume
380
sami
threatens
garland
botany
guarding
boiled
greet
puppy
russo
supplier
wilmington
vibrant
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paralympic
grumbled
paige
faa
licking
margins
hurricanes
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fest
grenade
ripping
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counseling
weigh
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needles
wiltshire
edison
costly
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fulton
tramway
redesigned
staffordshire
cache
gasping
watkins
sleepy
candidacy
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monkeys
timeline
throbbing
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berth
uzbekistan
vanderbilt
bothering
overturned
ballots
gem
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sunglasses
subscribers
hooker
compelling
ang
exceptionally
saloon
stab
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carla
terrifying
rom
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coil
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satisfying
vendors
31st
mackay
deities
overlooked
ambient
bahamas
felipe
olympia
whirled
botanist
advertised
tugging
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disciples
morales
unionist
rites
foley
morse
motives
creepy
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bargain
highness
frightening
turnpike
tory
reorganization
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depict
biographer
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unopposed
manifesto
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institut
emile
accidental
kapoor
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kilkenny
cortex
lively
##13
romanesque
jain
shan
cannons
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petrol
echoing
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disappears
cautious
proposes
sanctions
trenton
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flotilla
aus
contempt
tor
canary
cote
theirs
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conceptual
deleted
fascinating
paso
blazing
elf
honourable
hutchinson
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surveyor
tee
amidst
wooded
reissue
intro
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cobb
shelters
newsletter
hanson
brace
encoding
confiscated
dem
caravan
marino
scroll
melodic
cows
imam
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northward
searches
biodiversity
cora
310
roaring
##bers
connell
theologian
halo
compose
pathetic
unmarried
dynamo
##oot
az
calculation
toulouse
deserves
humour
nr
forgiveness
tam
undergone
martyr
pamela
myths
whore
counselor
hicks
290
heavens
battleship
electromagnetic
##bbs
stellar
establishments
presley
hopped
##chin
temptation
90s
wills
nas
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nhs
##nya
seminars
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adaptations
gong
asher
lex
indicator
sikh
tobago
cites
goin
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satirical
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characterised
correspond
bubbles
lure
participates
##vid
eruption
skate
therapeutic
1785
canals
wholesale
defaulted
sac
460
petit
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virgil
leak
ravens
256
portraying
##yx
ghetto
creators
dams
portray
vicente
##rington
fae
namesake
bounty
##arium
joachim
##ota
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aforementioned
axle
snout
depended
dismantled
reuben
480
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gallagher
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##pd
earnest
##ieu
##iary
inflicted
objections
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asa
gritted
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jericho
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flick
underside
ceramics
undead
substituted
195
eastward
undoubtedly
wheeled
chimney
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guinness
cb
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siding
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traitor
baptiste
disguised
inauguration
149
tipperary
choreographer
perched
warmed
stationary
eco
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bacterial
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flores
phosphate
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attacker
invaders
alvin
intersects
a1
indirectly
immigrated
businessmen
cornelius
valves
narrated
pill
sober
ul
nationale
monastic
applicants
scenery
##jack
161
motifs
constitutes
cpu
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jurisdictions
sd
tuning
irritation
woven
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fertility
gao
##erie
antagonist
impatient
glacial
hides
boarded
denominations
interception
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cookie
nicola
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algebraic
marquess
bahn
parole
buyers
bait
turbines
paperwork
bestowed
natasha
renee
oceans
purchases
157
vaccine
215
##tock
fixtures
playhouse
integrate
jai
oswald
intellectuals
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booked
nests
mortimer
##isi
obsession
sept
##gler
##sum
440
scrutiny
simultaneous
squinted
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collects
oven
shankar
penned
remarkably
##я
slips
luggage
spectral
1786
collaborations
louie
consolidation
##ailed
##ivating
420
hoover
blackpool
harness
ignition
vest
tails
belmont
mongol
skinner
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visually
mage
derry
##tism
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stevie
transitional
##rdy
redskins
drying
prep
prospective
##21
annoyance
oversee
##loaded
fills
##books
##iki
announces
fda
scowled
respects
prasad
mystic
tucson
##vale
revue
springer
bankrupt
1772
aristotle
salvatore
habsburg
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dal
natal
nut
pod
chewing
darts
moroccan
walkover
rosario
lenin
punjabi
##ße
grossed
scattering
wired
invasive
hui
polynomial
corridors
wakes
gina
portrays
##cratic
arid
retreating
erich
irwin
sniper
##dha
linen
lindsey
maneuver
butch
shutting
socio
bounce
commemorative
postseason
jeremiah
pines
275
mystical
beads
bp
abbas
furnace
bidding
consulted
assaulted
empirical
rubble
enclosure
sob
weakly
cancel
polly
yielded
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curly
prediction
battered
70s
vhs
jacqueline
render
sails
barked
detailing
grayson
riga
sloane
raging
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herbs
bravo
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alloy
giggle
imminent
suffers
assumptions
waltz
##itate
accomplishments
##ited
bathing
remixed
deception
prefix
##emia
deepest
##tier
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balkan
frogs
##rong
slab
##pate
philosophers
peterborough
grains
imports
dickinson
rwanda
##atics
1774
dirk
lan
tablets
##rove
clone
##rice
caretaker
hostilities
mclean
##gre
regimental
treasures
norms
impose
tsar
tango
diplomacy
variously
complain
192
recognise
arrests
1779
celestial
pulitzer
##dus
bing
libretto
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adele
splash
##rite
expectation
lds
confronts
##izer
spontaneous
harmful
wedge
entrepreneurs
buyer
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bilingual
translate
rugged
conner
circulated
uae
eaton
##gra
##zzle
lingered
lockheed
vishnu
reelection
alonso
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joints
yankee
headline
cooperate
heinz
laureate
invading
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echoes
scandinavian
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hugging
vitamin
salute
micah
hind
trader
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radioactive
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militants
poisoned
ratified
remark
campeonato
deprived
wander
prop
##dong
outlook
##tani
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chiang
darcy
##oping
mandolin
spice
statesman
babylon
182
walled
forgetting
afro
##cap
158
giorgio
buffer
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planetary
##gis
overlap
terminals
kinda
centenary
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arising
manipulate
elm
ke
1770
ak
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chrysler
mapped
moose
pomeranian
quad
macarthur
assemblies
shoreline
recalls
stratford
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noticeable
##evic
imp
##rita
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accustomed
supplying
tents
disgusted
vogue
sipped
filters
khz
reno
selecting
luftwaffe
mcmahon
tyne
masterpiece
carriages
collided
dunes
exercised
flare
remembers
muzzle
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heck
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burgess
lunged
middleton
boycott
bilateral
##sity
hazardous
lumpur
multiplayer
spotlight
jackets
goldman
liege
porcelain
rag
waterford
benz
attracts
hopeful
battling
ottomans
kensington
baked
hymns
cheyenne
lattice
levine
borrow
polymer
clashes
michaels
monitored
commitments
denounced
##25
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cavity
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hobby
akin
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futures
intricate
cornish
patty
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illegally
dolphin
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barlow
yellowish
maddie
apologized
luton
plagued
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nana
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sway
fanny
łodz
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psi
suspicions
hanged
##eding
initiate
charlton
##por
nak
competent
235
analytical
annex
wardrobe
reservations
##rma
sect
162
fairfax
hedge
piled
buckingham
uneven
bauer
simplicity
snyder
interpret
accountability
donors
moderately
byrd
continents
##cite
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disciple
hr
jamaican
ping
nominees
##uss
mongolian
diver
attackers
eagerly
ideological
pillows
miracles
apartheid
revolver
sulfur
clinics
moran
163
##enko
ile
katy
rhetoric
##icated
chronology
recycling
##hrer
elongated
mughal
pascal
profiles
vibration
databases
domination
##fare
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matthias
digest
rehearsal
polling
weiss
initiation
reeves
clinging
flourished
impress
ngo
##hoff
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buckley
symposium
rhythms
weed
emphasize
transforming
##taking
##gence
##yman
accountant
analyze
flicker
foil
priesthood
voluntarily
decreases
##80
##hya
slater
sv
charting
mcgill
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moreno
##iu
besieged
zur
robes
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admitting
api
deported
turmoil
peyton
earthquakes
##ares
nationalists
beau
clair
brethren
interrupt
welch
curated
galerie
requesting
164
##ested
impending
steward
viper
##vina
complaining
beautifully
brandy
foam
nl
1660
##cake
alessandro
punches
laced
explanations
##lim
attribute
clit
reggie
discomfort
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smoothed
whales
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adler
countered
duffy
disciplinary
widening
recipe
reliance
conducts
goats
gradient
preaching
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matilda
quasi
striped
meridian
cannabis
cordoba
certificates
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graffiti
hangs
pilgrims
repeats
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revive
urine
etat
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fueled
belts
fuzzy
susceptible
##hang
mauritius
salle
sincere
beers
hooks
##cki
arbitration
entrusted
advise
sniffed
seminar
junk
donnell
processors
principality
strapped
celia
mendoza
everton
fortunes
prejudice
starving
reassigned
steamer
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tuck
evenly
foreman
##ffen
dans
375
envisioned
slit
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baseman
liberia
rosemary
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electrified
periodically
potassium
stride
contexts
sperm
slade
mariners
influx
bianca
subcommittee
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spilling
icao
estuary
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delivers
iphone
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isa
mira
bohemian
dessert
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welcoming
proudly
slowing
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musee
ascension
russ
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waits
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africans
exploit
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gov
eccentric
crab
peck
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entrances
formidable
marketplace
groom
bolted
metabolism
patton
robbins
courier
payload
endure
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andes
refrigerator
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ornate
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ruthless
illegitimate
masonry
strasbourg
bikes
adobe
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apples
quintet
willingly
niche
bakery
corpses
energetic
##cliffe
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##ards
177
centimeters
centro
fuscous
cretaceous
rancho
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andrei
telecom
tottenham
oasis
ordination
vulnerability
presiding
corey
cp
penguins
sims
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malawi
piss
##48
correction
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##ryn
countdown
detectives
psychiatrist
psychedelic
dinosaurs
blouse
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choi
vowed
##oz
randomly
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49ers
scrub
blanche
bruins
dusseldorf
##using
unwanted
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212
dominique
elevations
headlights
om
laguna
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1750
famously
ignorance
shrewsbury
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ajax
breuning
che
confederacy
greco
overhaul
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paz
skirts
disagreement
cruelty
jagged
phoebe
shifter
hovered
viruses
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mandy
##lined
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landlord
squirrel
dashed
##ι
ornamental
gag
wally
grange
literal
spurs
undisclosed
proceeding
yin
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billie
orphan
spanned
humidity
indy
weighted
presentations
explosions
lucian
##tary
vaughn
hindus
##anga
##hell
psycho
171
daytona
protects
efficiently
rematch
sly
tandem
##oya
rebranded
impaired
hee
metropolis
peach
godfrey
diaspora
ethnicity
prosperous
gleaming
dar
grossing
playback
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stripe
pistols
##tain
births
labelled
##cating
172
rudy
alba
##onne
aquarium
hostility
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shudder
sumatra
hardest
lakers
consonant
creeping
demos
homicide
capsule
zeke
liberties
expulsion
pueblo
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trait
transporting
##ddin
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depart
gregg
mold
ledge
hangar
oldham
playboy
termination
analysts
gmbh
romero
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insist
cradle
filthy
brightness
slash
shootout
deposed
bordering
##truct
isis
microwave
tumbled
sheltered
cathy
werewolves
messy
andersen
convex
clapped
clinched
satire
wasting
edo
vc
rufus
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mont
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poznan
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restructuring
transverse
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azerbaijani
slovene
gestures
roommate
choking
shear
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vanguard
oblivious
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disagreed
baptism
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coliseum
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salvage
societe
cory
locke
relocation
relying
versailles
ahl
swelling
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cheerful
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gin
sarajevo
obstacle
diverted
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messed
thoroughbred
fluttered
utrecht
chewed
acquaintance
assassins
dispatch
mirza
##wart
nike
salzburg
swell
yen
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idle
ligue
samson
##nds
##igh
playful
spawned
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tease
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burgundy
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stirring
skeptical
interceptions
marathi
##dies
bedrooms
aroused
pinch
##lik
preferences
tattoos
buster
digitally
projecting
rust
##ital
kitten
priorities
addison
pseudo
##guard
dusk
icons
sermon
##psis
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bt
##lift
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ju
truce
rink
##dah
##wy
defects
psychiatry
offences
calculate
glucose
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##unda
francaise
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richest
warwickshire
carly
1763
purity
redemption
lending
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muse
bruises
cerebral
aero
carving
##name
preface
terminology
invade
monty
##int
anarchist
blurred
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rossi
treats
guts
shu
foothills
ballads
undertaking
premise
cecilia
affiliates
blasted
conditional
wilder
minors
drone
rudolph
buffy
swallowing
horton
attested
##hop
rutherford
howell
primetime
livery
penal
##bis
minimize
hydro
wrecked
wrought
palazzo
##gling
cans
vernacular
friedman
nobleman
shale
walnut
danielle
##ection
##tley
sears
##kumar
chords
lend
flipping
streamed
por
dracula
gallons
sacrifices
gamble
orphanage
##iman
mckenzie
##gible
boxers
daly
##balls
##ان
208
##ific
##rative
##iq
exploited
slated
##uity
circling
hillary
pinched
goldberg
provost
campaigning
lim
piles
ironically
jong
mohan
successors
usaf
##tem
##ught
autobiographical
haute
preserves
##ending
acquitted
comparisons
203
hydroelectric
gangs
cypriot
torpedoes
rushes
chrome
derive
bumps
instability
fiat
pets
##mbe
silas
dye
reckless
settler
##itation
info
heats
##writing
176
canonical
maltese
fins
mushroom
stacy
aspen
avid
##kur
##loading
vickers
gaston
hillside
statutes
wilde
gail
kung
sabine
comfortably
motorcycles
##rgo
169
pneumonia
fetch
##sonic
axel
faintly
parallels
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mclaren
spouse
compton
interdisciplinary
miner
##eni
181
clamped
##chal
##llah
separates
versa
##mler
scarborough
labrador
##lity
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rutgers
hurdles
como
166
burt
divers
##100
wichita
cade
coincided
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bruised
mla
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vineyard
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notch
mentioning
jase
hearted
kits
doe
##acle
pomerania
##ady
ronan
seizure
pavel
problematic
##zaki
domenico
##ulin
catering
penelope
dependence
parental
emilio
ministerial
atkinson
##bolic
clarkson
chargers
colby
grill
peeked
arises
summon
##aged
fools
##grapher
faculties
qaeda
##vial
garner
refurbished
##hwa
geelong
disasters
nudged
bs
shareholder
lori
algae
reinstated
rot
##ades
##nous
invites
stainless
183
inclusive
##itude
diocesan
til
##icz
denomination
##xa
benton
floral
registers
##ider
##erman
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absurd
brunei
guangzhou
hitter
retaliation
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blanc
nh
consistency
contamination
##eres
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dire
palermo
broadcasters
diaries
inspire
vols
brewer
tightening
ky
mixtape
hormone
##tok
stokes
##color
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pg
##ometer
##lington
sanitation
##tility
intercontinental
apps
##adt
¹⁄₂
cylinders
economies
favourable
unison
croix
gertrude
odyssey
vanity
dangling
##logists
upgrades
dice
middleweight
practitioner
##ight
206
henrik
parlor
orion
angered
lac
python
blurted
##rri
sensual
intends
swings
angled
##phs
husky
attain
peerage
precinct
textiles
cheltenham
shuffled
dai
confess
tasting
bhutan
##riation
tyrone
segregation
abrupt
ruiz
##rish
smirked
blackwell
confidential
browning
amounted
##put
vase
scarce
fabulous
raided
staple
guyana
unemployed
glider
shay
##tow
carmine
troll
intervene
squash
superstar
##uce
cylindrical
len
roadway
researched
handy
##rium
##jana
meta
lao
declares
##rring
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##elin
##kova
willem
shrubs
napoleonic
realms
skater
qi
volkswagen
##ł
tad
hara
archaeologist
awkwardly
eerie
##kind
wiley
##heimer
##24
titus
organizers
cfl
crusaders
lama
usb
vent
enraged
thankful
occupants
maximilian
##gaard
possessing
textbooks
##oran
collaborator
quaker
##ulo
avalanche
mono
silky
straits
isaiah
mustang
surged
resolutions
potomac
descend
cl
kilograms
plato
strains
saturdays
##olin
bernstein
##ype
holstein
ponytail
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belize
conversely
heroine
perpetual
##ylus
charcoal
piedmont
glee
negotiating
backdrop
prologue
##jah
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pasadena
climbs
ramos
sunni
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anand
deficiency
hertfordshire
stout
##avi
aperture
orioles
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doncaster
intrigued
bombed
coating
otis
##mat
cocktail
##jit
##eto
amir
arousal
sar
##proof
##act
##ories
dixie
pots
##bow
whereabouts
159
##fted
drains
bullying
cottages
scripture
coherent
fore
poe
appetite
##uration
sampled
##ators
##dp
derrick
rotor
jays
peacock
installment
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advisors
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rodeo
scotch
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##db
##fen
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ensued
rodrigo
dictatorship
martyrs
twenties
##н
towed
incidence
marta
rainforest
sai
scaled
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oceanic
qualifiers
symphonic
mcbride
dislike
generalized
aubrey
colonization
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##lion
##ssing
disliked
lublin
salesman
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spherical
whatsoever
sweating
avalon
contention
punt
severity
alderman
atari
##dina
##grant
##rop
scarf
seville
vertices
annexation
fairfield
fascination
inspiring
launches
palatinate
regretted
##rca
feral
##iom
elk
nap
olsen
reddy
yong
##leader
##iae
garment
transports
feng
gracie
outrage
viceroy
insides
##esis
breakup
grady
organizer
softer
grimaced
222
murals
galicia
arranging
vectors
##rsten
bas
##sb
##cens
sloan
##eka
bitten
ara
fender
nausea
bumped
kris
banquet
comrades
detector
persisted
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adjustment
endowed
cinemas
##shot
sellers
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peek
epa
kindly
neglect
simpsons
talon
mausoleum
runaway
hangul
lookout
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rewards
coughed
acquainted
chloride
##ald
quicker
accordion
neolithic
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artemis
coefficient
lenny
pandora
tx
##xed
ecstasy
litter
segunda
chairperson
gemma
hiss
rumor
vow
nasal
antioch
compensate
patiently
transformers
##eded
judo
morrow
penis
posthumous
philips
bandits
husbands
denote
flaming
##any
##phones
langley
yorker
1760
walters
##uo
##kle
gubernatorial
fatty
samsung
leroy
outlaw
##nine
unpublished
poole
jakob
##ᵢ
##ₙ
crete
distorted
superiority
##dhi
intercept
crust
mig
claus
crashes
positioning
188
stallion
301
frontal
armistice
##estinal
elton
aj
encompassing
camel
commemorated
malaria
woodward
calf
cigar
penetrate
##oso
willard
##rno
##uche
illustrate
amusing
convergence
noteworthy
##lma
##rva
journeys
realise
manfred
##sable
410
##vocation
hearings
fiance
##posed
educators
provoked
adjusting
##cturing
modular
stockton
paterson
vlad
rejects
electors
selena
maureen
##tres
uber
##rce
swirled
##num
proportions
nanny
pawn
naturalist
parma
apostles
awoke
ethel
wen
##bey
monsoon
overview
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mccain
rendition
risky
adorned
##ih
equestrian
germain
nj
conspicuous
confirming
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shivering
##imeter
milestone
rumours
flinched
bounds
smacked
token
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lectured
automobiles
##shore
impacted
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nouns
nero
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ismail
prostitute
trams
##lace
bridget
sud
stimulus
impressions
reins
revolves
##oud
##gned
giro
honeymoon
##swell
criterion
##sms
##uil
libyan
prefers
##osition
211
preview
sucks
accusation
bursts
metaphor
diffusion
tolerate
faye
betting
cinematographer
liturgical
specials
bitterly
humboldt
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flux
rattled
##itzer
archaeologists
odor
authorised
marshes
discretion
##ов
alarmed
archaic
inverse
##leton
explorers
##pine
drummond
tsunami
woodlands
##minate
##tland
booklet
insanity
owning
insert
crafted
calculus
##tore
receivers
##bt
stung
##eca
##nched
prevailing
travellers
eyeing
lila
graphs
##borne
178
julien
##won
morale
adaptive
therapist
erica
cw
libertarian
bowman
pitches
vita
##ional
crook
##ads
##entation
caledonia
mutiny
##sible
1840s
automation
##ß
flock
##pia
ironic
pathology
##imus
remarried
##22
joker
withstand
energies
##att
shropshire
hostages
madeleine
tentatively
conflicting
mateo
recipes
euros
ol
mercenaries
nico
##ndon
albuquerque
augmented
mythical
bel
freud
##child
cough
##lica
365
freddy
lillian
genetically
nuremberg
calder
209
bonn
outdoors
paste
suns
urgency
vin
restraint
tyson
##cera
##selle
barrage
bethlehem
kahn
##par
mounts
nippon
barony
happier
ryu
makeshift
sheldon
blushed
castillo
barking
listener
taped
bethel
fluent
headlines
pornography
rum
disclosure
sighing
mace
doubling
gunther
manly
##plex
rt
interventions
physiological
forwards
emerges
##tooth
##gny
compliment
rib
recession
visibly
barge
faults
connector
exquisite
prefect
##rlin
patio
##cured
elevators
brandt
italics
pena
173
wasp
satin
ea
botswana
graceful
respectable
##jima
##rter
##oic
franciscan
generates
##dl
alfredo
disgusting
##olate
##iously
sherwood
warns
cod
promo
cheryl
sino
##ة
##escu
twitch
##zhi
brownish
thom
ortiz
##dron
densely
##beat
carmel
reinforce
##bana
187
anastasia
downhill
vertex
contaminated
remembrance
harmonic
homework
##sol
fiancee
gears
olds
angelica
loft
ramsay
quiz
colliery
sevens
##cape
autism
##hil
walkway
##boats
ruben
abnormal
ounce
khmer
##bbe
zachary
bedside
morphology
punching
##olar
sparrow
convinces
##35
hewitt
queer
remastered
rods
mabel
solemn
notified
lyricist
symmetric
##xide
174
encore
passports
wildcats
##uni
baja
##pac
mildly
##ease
bleed
commodity
mounds
glossy
orchestras
##omo
damian
prelude
ambitions
##vet
awhile
remotely
##aud
asserts
imply
##iques
distinctly
modelling
remedy
##dded
windshield
dani
xiao
##endra
audible
powerplant
1300
invalid
elemental
acquisitions
##hala
immaculate
libby
plata
smuggling
ventilation
denoted
minh
##morphism
430
differed
dion
kelley
lore
mocking
sabbath
spikes
hygiene
drown
runoff
stylized
tally
liberated
aux
interpreter
righteous
aba
siren
reaper
pearce
millie
##cier
##yra
gaius
##iso
captures
##ttering
dorm
claudio
##sic
benches
knighted
blackness
##ored
discount
fumble
oxidation
routed
##ς
novak
perpendicular
spoiled
fracture
splits
##urt
pads
topology
##cats
axes
fortunate
offenders
protestants
esteem
221
broadband
convened
frankly
hound
prototypes
isil
facilitated
keel
##sher
sahara
awaited
bubba
orb
prosecutors
186
hem
520
##xing
relaxing
remnant
romney
sorted
slalom
stefano
ulrich
##active
exemption
folder
pauses
foliage
hitchcock
epithet
204
criticisms
##aca
ballistic
brody
hinduism
chaotic
youths
equals
##pala
pts
thicker
analogous
capitalist
improvised
overseeing
sinatra
ascended
beverage
##tl
straightforward
##kon
curran
##west
bois
325
induce
surveying
emperors
sax
unpopular
##kk
cartoonist
fused
##mble
unto
##yuki
localities
##cko
##ln
darlington
slain
academie
lobbying
sediment
puzzles
##grass
defiance
dickens
manifest
tongues
alumnus
arbor
coincide
184
appalachian
mustafa
examiner
cabaret
traumatic
yves
bracelet
draining
heroin
magnum
baths
odessa
consonants
mitsubishi
##gua
kellan
vaudeville
##fr
joked
null
straps
probation
##ław
ceded
interfaces
##pas
##zawa
blinding
viet
224
rothschild
museo
640
huddersfield
##vr
tactic
##storm
brackets
dazed
incorrectly
##vu
reg
glazed
fearful
manifold
benefited
irony
##sun
stumbling
##rte
willingness
balkans
mei
wraps
##aba
injected
##lea
gu
syed
harmless
##hammer
bray
takeoff
poppy
timor
cardboard
astronaut
purdue
weeping
southbound
cursing
stalls
diagonal
##neer
lamar
bryce
comte
weekdays
harrington
##uba
negatively
##see
lays
grouping
##cken
##henko
affirmed
halle
modernist
##lai
hodges
smelling
aristocratic
baptized
dismiss
justification
oilers
##now
coupling
qin
snack
healer
##qing
gardener
layla
battled
formulated
stephenson
gravitational
##gill
##jun
1768
granny
coordinating
suites
##cd
##ioned
monarchs
##cote
##hips
sep
blended
apr
barrister
deposition
fia
mina
policemen
paranoid
##pressed
churchyard
covert
crumpled
creep
abandoning
tr
transmit
conceal
barr
understands
readiness
spire
##cology
##enia
##erry
610
startling
unlock
vida
bowled
slots
##nat
##islav
spaced
trusting
admire
rig
##ink
slack
##70
mv
207
casualty
##wei
classmates
##odes
##rar
##rked
amherst
furnished
evolve
foundry
menace
mead
##lein
flu
wesleyan
##kled
monterey
webber
##vos
wil
##mith
##на
bartholomew
justices
restrained
##cke
amenities
191
mediated
sewage
trenches
ml
mainz
##thus
1800s
##cula
##inski
caine
bonding
213
converts
spheres
superseded
marianne
crypt
sweaty
ensign
historia
##br
spruce
##post
##ask
forks
thoughtfully
yukon
pamphlet
ames
##uter
karma
##yya
bryn
negotiation
sighs
incapable
##mbre
##ntial
actresses
taft
##mill
luce
prevailed
##amine
1773
motionless
envoy
testify
investing
sculpted
instructors
provence
kali
cullen
horseback
##while
goodwin
##jos
gaa
norte
##ldon
modify
wavelength
abd
214
skinned
sprinter
forecast
scheduling
marries
squared
tentative
##chman
boer
##isch
bolts
swap
fisherman
assyrian
impatiently
guthrie
martins
murdoch
194
tanya
nicely
dolly
lacy
med
##45
syn
decks
fashionable
millionaire
##ust
surfing
##ml
##ision
heaved
tammy
consulate
attendees
routinely
197
fuse
saxophonist
backseat
malaya
##lord
scowl
tau
##ishly
193
sighted
steaming
##rks
303
911
##holes
##hong
ching
##wife
bless
conserved
jurassic
stacey
unix
zion
chunk
rigorous
blaine
198
peabody
slayer
dismay
brewers
nz
##jer
det
##glia
glover
postwar
int
penetration
sylvester
imitation
vertically
airlift
heiress
knoxville
viva
##uin
390
macon
##rim
##fighter
##gonal
janice
##orescence
##wari
marius
belongings
leicestershire
196
blanco
inverted
preseason
sanity
sobbing
##due
##elt
##dled
collingwood
regeneration
flickering
shortest
##mount
##osi
feminism
##lat
sherlock
cabinets
fumbled
northbound
precedent
snaps
##mme
researching
##akes
guillaume
insights
manipulated
vapor
neighbour
sap
gangster
frey
f1
stalking
scarcely
callie
barnett
tendencies
audi
doomed
assessing
slung
panchayat
ambiguous
bartlett
##etto
distributing
violating
wolverhampton
##hetic
swami
histoire
##urus
liable
pounder
groin
hussain
larsen
popping
surprises
##atter
vie
curt
##station
mute
relocate
musicals
authorization
richter
##sef
immortality
tna
bombings
##press
deteriorated
yiddish
##acious
robbed
colchester
cs
pmid
ao
verified
balancing
apostle
swayed
recognizable
oxfordshire
retention
nottinghamshire
contender
judd
invitational
shrimp
uhf
##icient
cleaner
longitudinal
tanker
##mur
acronym
broker
koppen
sundance
suppliers
##gil
4000
clipped
fuels
petite
##anne
landslide
helene
diversion
populous
landowners
auspices
melville
quantitative
##xes
ferries
nicky
##llus
doo
haunting
roche
carver
downed
unavailable
##pathy
approximation
hiroshima
##hue
garfield
valle
comparatively
keyboardist
traveler
##eit
congestion
calculating
subsidiaries
##bate
serb
modernization
fairies
deepened
ville
averages
##lore
inflammatory
tonga
##itch
co₂
squads
##hea
gigantic
serum
enjoyment
retailer
verona
35th
cis
##phobic
magna
technicians
##vati
arithmetic
##sport
levin
##dation
amtrak
chow
sienna
##eyer
backstage
entrepreneurship
##otic
learnt
tao
##udy
worcestershire
formulation
baggage
hesitant
bali
sabotage
##kari
barren
enhancing
murmur
pl
freshly
putnam
syntax
aces
medicines
resentment
bandwidth
##sier
grins
chili
guido
##sei
framing
implying
gareth
lissa
genevieve
pertaining
admissions
geo
thorpe
proliferation
sato
bela
analyzing
parting
##gor
awakened
##isman
huddled
secrecy
##kling
hush
gentry
540
dungeons
##ego
coasts
##utz
sacrificed
##chule
landowner
mutually
prevalence
programmer
adolescent
disrupted
seaside
gee
trusts
vamp
georgie
##nesian
##iol
schedules
sindh
##market
etched
hm
sparse
bey
beaux
scratching
gliding
unidentified
216
collaborating
gems
jesuits
oro
accumulation
shaping
mbe
anal
##xin
231
enthusiasts
newscast
##egan
janata
dewey
parkinson
179
ankara
biennial
towering
dd
inconsistent
950
##chet
thriving
terminate
cabins
furiously
eats
advocating
donkey
marley
muster
phyllis
leiden
##user
grassland
glittering
iucn
loneliness
217
memorandum
armenians
##ddle
popularized
rhodesia
60s
lame
##illon
sans
bikini
header
orbits
##xx
##finger
##ulator
sharif
spines
biotechnology
strolled
naughty
yates
##wire
fremantle
milo
##mour
abducted
removes
##atin
humming
wonderland
##chrome
##ester
hume
pivotal
##rates
armand
grams
believers
elector
rte
apron
bis
scraped
##yria
endorsement
initials
##llation
eps
dotted
hints
buzzing
emigration
nearer
##tom
indicators
##ulu
coarse
neutron
protectorate
##uze
directional
exploits
pains
loire
1830s
proponents
guggenheim
rabbits
ritchie
305
hectare
inputs
hutton
##raz
verify
##ako
boilers
longitude
##lev
skeletal
yer
emilia
citrus
compromised
##gau
pokemon
prescription
paragraph
eduard
cadillac
attire
categorized
kenyan
weddings
charley
##bourg
entertain
monmouth
##lles
nutrients
davey
mesh
incentive
practised
ecosystems
kemp
subdued
overheard
##rya
bodily
maxim
##nius
apprenticeship
ursula
##fight
lodged
rug
silesian
unconstitutional
patel
inspected
coyote
unbeaten
##hak
34th
disruption
convict
parcel
##cl
##nham
collier
implicated
mallory
##iac
##lab
susannah
winkler
##rber
shia
phelps
sediments
graphical
robotic
##sner
adulthood
mart
smoked
##isto
kathryn
clarified
##aran
divides
convictions
oppression
pausing
burying
##mt
federico
mathias
eileen
##tana
kite
hunched
##acies
189
##atz
disadvantage
liza
kinetic
greedy
paradox
yokohama
dowager
trunks
ventured
##gement
gupta
vilnius
olaf
##thest
crimean
hopper
##ej
progressively
arturo
mouthed
arrondissement
##fusion
rubin
simulcast
oceania
##orum
##stra
##rred
busiest
intensely
navigator
cary
##vine
##hini
##bies
fife
rowe
rowland
posing
insurgents
shafts
lawsuits
activate
conor
inward
culturally
garlic
265
##eering
eclectic
##hui
##kee
##nl
furrowed
vargas
meteorological
rendezvous
##aus
culinary
commencement
##dition
quota
##notes
mommy
salaries
overlapping
mule
##iology
##mology
sums
wentworth
##isk
##zione
mainline
subgroup
##illy
hack
plaintiff
verdi
bulb
differentiation
engagements
multinational
supplemented
bertrand
caller
regis
##naire
##sler
##arts
##imated
blossom
propagation
kilometer
viaduct
vineyards
##uate
beckett
optimization
golfer
songwriters
seminal
semitic
thud
volatile
evolving
ridley
##wley
trivial
distributions
scandinavia
jiang
##ject
wrestled
insistence
##dio
emphasizes
napkin
##ods
adjunct
rhyme
##ricted
##eti
hopeless
surrounds
tremble
32nd
smoky
##ntly
oils
medicinal
padded
steer
wilkes
219
255
concessions
hue
uniquely
blinded
landon
yahoo
##lane
hendrix
commemorating
dex
specify
chicks
##ggio
intercity
1400
morley
##torm
highlighting
##oting
pang
oblique
stalled
##liner
flirting
newborn
1769
bishopric
shaved
232
currie
##ush
dharma
spartan
##ooped
favorites
smug
novella
sirens
abusive
creations
espana
##lage
paradigm
semiconductor
sheen
##rdo
##yen
##zak
nrl
renew
##pose
##tur
adjutant
marches
norma
##enity
ineffective
weimar
grunt
##gat
lordship
plotting
expenditure
infringement
lbs
refrain
av
mimi
mistakenly
postmaster
1771
##bara
ras
motorsports
tito
199
subjective
##zza
bully
stew
##kaya
prescott
1a
##raphic
##zam
bids
styling
paranormal
reeve
sneaking
exploding
katz
akbar
migrant
syllables
indefinitely
##ogical
destroys
replaces
applause
##phine
pest
##fide
218
articulated
bertie
##thing
##cars
##ptic
courtroom
crowley
aesthetics
cummings
tehsil
hormones
titanic
dangerously
##ibe
stadion
jaenelle
auguste
ciudad
##chu
mysore
partisans
##sio
lucan
philipp
##aly
debating
henley
interiors
##rano
##tious
homecoming
beyonce
usher
henrietta
prepares
weeds
##oman
ely
plucked
##pire
##dable
luxurious
##aq
artifact
password
pasture
juno
maddy
minsk
##dder
##ologies
##rone
assessments
martian
royalist
1765
examines
##mani
##rge
nino
223
parry
scooped
relativity
##eli
##uting
##cao
congregational
noisy
traverse
##agawa
strikeouts
nickelodeon
obituary
transylvania
binds
depictions
polk
trolley
##yed
##lard
breeders
##under
dryly
hokkaido
1762
strengths
stacks
bonaparte
connectivity
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245
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226
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470
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264
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254
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228
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241
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246
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242
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251
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1757
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startup
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compilations
vibrations
embankment
jurist
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bard
juventus
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kern
palaces
helium
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marissa
soto
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jae
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faso
bazaar
warmly
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229
pairing
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wien
freaked
ulysses
rebirth
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mummy
guzman
jimenez
stilled
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trajectory
tha
woken
archival
professions
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hilly
shadowy
shrink
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norwood
glued
migrate
stereotypes
devoid
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625
evacuate
horrors
infancy
gotham
knowles
optic
downloaded
sachs
kingsley
parramatta
darryl
mor
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shady
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confesses
kan
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revoked
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intruder
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banged
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bourgeois
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footing
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penthouse
sane
720
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stakeholders
neumann
bb
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comb
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catchment
pinning
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typing
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forefront
freiburg
sweetie
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widowed
goodwill
worshipped
aspirations
midday
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fishery
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bournemouth
turk
243
hearth
ethanol
guadalajara
murmurs
sl
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afforded
scripted
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wah
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coroner
translucent
252
memorials
puck
progresses
clumsy
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315
candace
recounted
##27
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filtering
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heron
leveled
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citations
exhibiting
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injunction
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antibodies
##44
organise
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cardiovascular
cushion
inverness
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dia
cocoa
sibling
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expanse
feasible
tunisian
algiers
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rus
bloomberg
dso
westphalia
bro
tacoma
281
downloads
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konrad
duran
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continuum
jett
compares
legislator
secession
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translating
reacher
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orchards
trapping
linguist
versatile
drumming
postage
calhoun
superiors
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barefoot
leary
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ignacio
alfa
kaplan
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bratislava
mori
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disturb
haas
313
cartridges
gilmore
radiated
salford
tunic
hades
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archeological
delilah
magistrates
auditioned
brewster
charters
empowerment
blogs
cappella
dynasties
iroquois
whipping
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raceway
truths
myra
weaken
judah
mcgregor
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mic
refueling
37th
burnley
bosses
markus
premio
query
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dunbar
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darkest
lyndon
sealing
commendation
reappeared
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addicted
ezio
slaughtered
satisfactory
shuffle
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fortification
warrington
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resurrected
fargo
mane
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foreword
ox
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abrams
hua
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sakura
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sentimental
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midfield
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sturdy
scrolls
macleod
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mitochondrial
cicero
excelled
thinner
convoys
perceive
##oslav
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systematically
grind
burkina
287
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ops
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guantanamo
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forcefully
wavy
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pointless
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layton
portico
superficial
clerical
outlaws
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burials
muir
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creditors
hauling
rattle
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calais
monde
archers
reclaimed
dwell
wexford
hellenic
falsely
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dough
furnishings
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neurological
novice
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contemplated
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saratoga
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documenting
pulsing
taluk
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busted
marital
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disagreements
wasps
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hodge
mcdonnell
mimic
fran
pendant
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musa
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congratulations
argent
darrell
concussion
losers
regrets
thessaloniki
reversal
donaldson
hardwood
thence
achilles
ritter
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demonic
jurgen
prophets
goethe
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classmate
buff
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irrational
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perished
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barre
horizontally
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phylogenetic
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intercourse
seduce
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ferris
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amar
nik
unarmed
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evaluating
kyrgyzstan
sweetness
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mccormick
meiji
notoriety
stimulate
disrupt
figuring
instructional
mcgrath
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groundbreaking
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flinch
khorasan
agrarian
bengals
mixer
radiating
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ingram
pitchers
nad
tariff
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tata
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appellate
lehigh
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brawl
duct
texans
##ciation
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skipper
speculative
vomit
doctrines
stresses
253
davy
graders
whitehead
jozef
timely
cumulative
haryana
paints
appropriately
boon
cactus
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dow
legions
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perceptions
1730
picturesque
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periphery
rune
wr
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celtics
sentencing
whoa
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confirms
variance
425
moines
mathews
spade
rave
m1
fronted
fx
blending
alleging
reared
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237
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grassroots
eroded
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directs
ordeal
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accelerate
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rooftop
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buys
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specialising
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choreographed
repetition
warehouses
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tuscany
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exclude
nix
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ito
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jana
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longed
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chattanooga
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careless
precedence
frescoes
##uet
chilled
consult
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snatch
peat
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caucasian
humane
relaxation
spins
temperance
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occupations
lambda
hybrids
moons
mp3
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247
rolf
societal
yerevan
ness
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befriended
mechanized
nominate
trough
boasted
cues
seater
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bends
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emptiness
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tian
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anxiously
lark
propellers
chichester
jock
ev
2a
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credible
recounts
tori
loyalist
abduction
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ventral
tempting
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steered
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dipping
laborers
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looming
titanium
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badges
emir
tensor
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rash
denies
hawthorne
lombard
showers
wehrmacht
dietary
trojan
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welles
executing
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lifeboat
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elsa
infirmary
nearing
roberta
boyer
mutter
trillion
joanne
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sinks
vortex
uruguayan
clasp
sirius
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accelerator
prohibit
sunken
byu
chronological
diplomats
ochreous
510
symmetrical
1644
maia
##tology
salts
reigns
atrocities
##ия
hess
bared
issn
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saturated
##cycle
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sable
voyager
dyer
yusuf
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fountains
wolff
##39
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rollins
atheist
ominous
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herr
chariot
martina
strung
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horrific
sahib
gazes
saetan
erased
ptolemy
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flushing
lauderdale
analytic
##ices
530
navarro
beak
gorilla
herrera
broom
guadalupe
raiding
sykes
311
bsc
deliveries
1720
invasions
carmichael
tajikistan
thematic
ecumenical
sentiments
onstage
##rians
##brand
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catastrophic
flanks
molten
##arns
waller
aimee
terminating
##icing
alternately
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nehru
printers
outraged
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empires
template
banners
repetitive
za
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vegetarian
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guiana
opt
cavendish
lucknow
synthesized
##hani
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finalized
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fictitious
mayoral
unreliable
##enham
embracing
peppers
rbis
##chio
##neo
inhibition
slashed
togo
orderly
embroidered
safari
salty
236
barron
benito
totaled
##dak
pubs
simulated
caden
devin
tolkien
momma
welding
sesame
##ept
gottingen
hardness
630
shaman
temeraire
620
adequately
pediatric
##kit
ck
assertion
radicals
composure
cadence
seafood
beaufort
lazarus
mani
warily
cunning
kurdistan
249
cantata
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ares
##41
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nape
townland
geared
insulted
flutter
boating
violate
draper
dumping
malmo
##hh
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firearm
alta
bono
obscured
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exceeds
panorama
unbelievable
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preschool
##essed
disconnected
installing
rescuing
secretaries
accessibility
##castle
##drive
##ifice
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bouts
slug
waterway
mindanao
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halves
##ل
calming
liter
maternity
adorable
bragg
electrification
mcc
##dote
roxy
schizophrenia
##body
munoz
kaye
whaling
239
mil
tingling
tolerant
##ago
unconventional
volcanoes
##finder
deportivo
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robson
kaufman
neuroscience
wai
deportation
masovian
scraping
converse
##bh
hacking
bulge
##oun
administratively
yao
580
amp
mammoth
booster
claremont
hooper
nomenclature
pursuits
mclaughlin
melinda
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catfish
barclay
substrates
taxa
zee
originals
kimberly
packets
padma
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borrowing
ostensibly
solvent
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lukas
shreveport
veracruz
##ь
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cheney
tt
anatolia
hobbs
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cyclic
radiant
alistair
greenish
siena
dat
independents
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conform
pieter
hyper
applicant
bradshaw
spores
telangana
vinci
inexpensive
nuclei
322
jang
nme
soho
spd
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cradled
receptionist
pow
##43
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fascism
##ifer
experimenting
##ading
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##region
345
jocelyn
maris
stair
nocturnal
toro
constabulary
elgin
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msc
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doherty
doping
sarcastically
batter
maneuvers
##cano
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intrinsic
##nst
##stor
1753
showtime
cafes
gasps
lviv
ushered
##thed
fours
restart
astonishment
transmitting
flyer
shrugs
##sau
intriguing
cones
dictated
mushrooms
medial
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escorting
gaped
##26
godfather
##door
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djs
recaptured
timetable
vila
1710
3a
aerodrome
mortals
scientology
##orne
angelina
mag
convection
unpaid
insertion
intermittent
lego
##nated
endeavor
kota
pereira
##lz
304
bwv
glamorgan
insults
agatha
fey
##cend
fleetwood
mahogany
protruding
steamship
zeta
##arty
mcguire
suspense
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advising
urges
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hurriedly
meteor
gilded
inline
arroyo
stalker
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excitedly
revered
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earle
introductory
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mutants
puff
pulses
reinforcement
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curses
lizards
stalk
correlated
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fallout
macquarie
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bearded
denton
heaving
802
##ocation
winery
assign
dortmund
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everest
invariant
charismatic
susie
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bled
lesley
telegram
sumner
bk
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##к
wilcox
needy
colbert
duval
##iferous
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allotted
attends
imperative
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replacements
hawker
##inda
insurgency
##zee
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casts
##yla
680
ives
transitioned
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authoritative
baylor
flex
cringed
plaintiffs
woodrow
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drastic
ape
aroma
unfolded
commotion
nt
preoccupied
theta
routines
lasers
privatization
wand
domino
ek
clenching
nsa
strategically
showered
bile
handkerchief
pere
storing
christophe
insulting
316
nakamura
romani
asiatic
magdalena
palma
cruises
stripping
405
konstantin
soaring
##berman
colloquially
forerunner
havilland
incarcerated
parasites
sincerity
##utus
disks
plank
saigon
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corbin
homo
ornaments
powerhouse
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chong
fastened
feasibility
idf
morphological
usable
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aqueduct
jaguars
keepers
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aleksandr
faust
assigns
ewing
bacterium
hurled
tricky
hungarians
integers
wallis
321
yamaha
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hushed
oblivion
aviator
evangelist
friars
##eller
monograph
ode
##nary
airplanes
labourers
charms
##nee
1661
hagen
tnt
rudder
fiesta
transcript
dorothea
ska
inhibitor
maccabi
retorted
raining
encompassed
clauses
menacing
1642
lineman
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vamps
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gloom
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dealings
easing
seekers
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unmanned
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basics
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adjustments
1688
brutality
horne
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sui
##55
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aggregator
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rhino
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counters
zoom
##01
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mn
montenegrin
packard
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##♭
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reclaim
scholastic
thugs
pulsed
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syriac
quan
saddam
banda
kobe
blaming
buddies
dissent
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corbett
jaya
delle
erratic
lexie
##hesis
435
amiga
hermes
##pressing
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chapels
gospels
jamal
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compute
revolving
warp
##sso
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armory
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antrim
loki
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braid
handwriting
subdistrict
funky
pantheon
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concurrency
estimation
improper
juliana
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newcomers
johnstone
staten
communicated
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sausage
stormy
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superfamily
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acidic
collateral
tabloid
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bladder
austen
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mcgraw
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hannibal
mein
aquino
lucifer
wo
badger
boar
cher
christensen
greenberg
interruption
##kken
jem
244
mocked
bottoms
cambridgeshire
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sprawling
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eastwood
ghent
synth
##buck
advisers
##bah
nominally
hapoel
qu
daggers
estranged
fabricated
towels
vinnie
wcw
misunderstanding
anglia
nothin
unmistakable
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chilly
marquette
truss
##edge
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reece
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272
308
41st
bash
raion
waterfalls
##ump
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labyrinth
queue
theorist
##istle
bharatiya
flexed
soundtracks
rooney
leftist
patrolling
wharton
plainly
alleviate
eastman
schuster
topographic
engages
immensely
unbearable
fairchild
1620
dona
lurking
parisian
oliveira
ia
indictment
hahn
bangladeshi
##aster
vivo
##uming
##ential
antonia
expects
indoors
kildare
harlan
##logue
##ogenic
##sities
forgiven
##wat
childish
tavi
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plausible
grimm
successively
scooted
##bola
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spartans
emery
flatly
azure
epilogue
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flourish
##iny
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##oshi
bestseller
distressed
receipt
spitting
hermit
topological
##cot
drilled
subunit
francs
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eel
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octopus
footprint
petitions
ufo
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interfering
leaking
palo
##metry
thistle
valiant
##pic
narayan
mcpherson
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gonzales
##ym
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dustin
novgorod
solos
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doin
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soluble
ashland
cuffs
carole
pendleton
whistling
vassal
##river
deviation
revisited
constituents
rallied
rotate
loomed
##eil
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amateurs
augsburg
auschwitz
crowns
skeletons
##cona
bonnet
257
dummy
globalization
simeon
sleeper
mandal
differentiated
##crow
##mare
milne
bundled
exasperated
talmud
owes
segregated
##feng
##uary
dentist
piracy
props
##rang
devlin
##torium
malicious
paws
##laid
dependency
##ergy
##fers
##enna
258
pistons
rourke
jed
grammatical
tres
maha
wig
512
ghostly
jayne
##achal
##creen
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##lins
##rence
designate
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arrogance
cambodian
clones
showdown
throttle
twain
##ception
lobes
metz
nagoya
335
braking
##furt
385
roaming
##minster
amin
crippled
##37
##llary
indifferent
hoffmann
idols
intimidating
1751
261
influenza
memo
onions
1748
bandage
consciously
##landa
##rage
clandestine
observes
swiped
tangle
##ener
##jected
##trum
##bill
##lta
hugs
congresses
josiah
spirited
##dek
humanist
managerial
filmmaking
inmate
rhymes
debuting
grimsby
ur
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duplicate
vigor
##tf
republished
bolshevik
refurbishment
antibiotics
martini
methane
newscasts
royale
horizons
levant
iain
visas
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paler
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manifestation
snuck
alf
chop
futile
pedestal
rehab
##kat
bmg
kerman
res
fairbanks
jarrett
abstraction
saharan
##zek
1746
procedural
clearer
kincaid
sash
luciano
##ffey
crunch
helmut
##vara
revolutionaries
##tute
creamy
leach
##mmon
1747
permitting
nes
plight
wendell
##lese
contra
ts
clancy
ipa
mach
staples
autopsy
disturbances
nueva
karin
pontiac
##uding
proxy
venerable
haunt
leto
bergman
expands
##helm
wal
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canning
celine
cords
obesity
##enary
intrusion
planner
##phate
reasoned
sequencing
307
harrow
##chon
##dora
marred
mcintyre
repay
tarzan
darting
248
harrisburg
margarita
repulsed
##hur
##lding
belinda
hamburger
novo
compliant
runways
bingham
registrar
skyscraper
ic
cuthbert
improvisation
livelihood
##corp
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admiring
##dened
sporadic
believer
casablanca
popcorn
##29
asha
shovel
##bek
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coiled
tangible
##dez
casper
elsie
resin
tenderness
rectory
##ivision
avail
sonar
##mori
boutique
##dier
guerre
bathed
upbringing
vaulted
sandals
blessings
##naut
##utnant
1680
306
foxes
pia
corrosion
hesitantly
confederates
crystalline
footprints
shapiro
tirana
valentin
drones
45th
microscope
shipments
texted
inquisition
wry
guernsey
unauthorized
resigning
760
ripple
schubert
stu
reassure
felony
##ardo
brittle
koreans
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dun
implicit
tyres
##aldi
##lth
magnolia
##ehan
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aggressively
fei
gr
familiarity
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indicative
##trust
fundamentally
jimmie
overrun
395
anchors
moans
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cosmopolitan
geometridae
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caf
415
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engulfed
gleam
purge
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jurisprudence
guerra
revisions
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1749
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cloudy
conde
hermitage
278
simulations
torches
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matteo
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accomplishment
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1752
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404
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288
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hornets
multiplication
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forte
illustrates
erika
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570
dew
nationalities
bran
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thirsty
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reborn
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286
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yuki
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myspace
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enthusiastically
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ns
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1630
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ci
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yourselves
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peg
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etudes
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roast
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309
illicit
suriname
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overture
1685
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analytics
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raju
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271
disneyland
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sociologist
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2500
faulkner
louvre
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276
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afterlife
mannheim
peptide
referees
comedians
meaningless
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renal
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firth
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portage
reset
narrows
268
commandos
expansive
speechless
tubular
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eyelashes
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chet
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orton
266
bane
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impairment
offenses
undermine
moi
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590
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259
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980
qc
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rockwell
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crusader
glue
revolutions
scrambling
1714
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263
contemplating
coven
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preach
triumphant
tufts
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rotational
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328
falkland
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1741
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passions
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distraught
draught
1727
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sy
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adapting
kidd
shortstop
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spiked
mcleod
reprint
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pretoria
windmill
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singled
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273
reunite
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747
bankers
outlying
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apologies
cosmetics
patsy
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323
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dq
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ua
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psychologists
stryker
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screenings
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urgently
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sanitary
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spicy
drugged
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westchester
##caster
267
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nonstop
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aromatic
centrally
cerro
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modulation
sedimentary
283
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outrageous
goldstein
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spaceship
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als
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1643
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keynote
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lilith
tinted
277
wrestle
mobilization
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sequential
siam
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274
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presenters
ringo
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haydn
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molina
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001
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wb
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hysterical
1743
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warped
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525
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1603
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1725
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supplements
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announcements
anthologies
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2021
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gorman
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writ
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alla
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1641
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1742
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adherents
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vitro
ferns
yanking
269
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confines
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tully
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49th
docked
roam
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craftsmen
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scramble
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sponge
helix
zaragoza
279
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43rd
backlash
fontaine
seizures
posse
cowan
nonfiction
telenovela
wwii
hammered
undone
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encircled
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musique
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racers
tingle
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introductions
radically
292
##hiff
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1610
1739
munchen
plead
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scissors
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marne
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blasts
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740
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rios
simulator
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thrusts
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1744
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ley
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412
ammonia
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incompatible
violins
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grooves
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rampant
fabrication
kyushu
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vanish
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999
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1662
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superliga
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upgrading
299
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testosterone
collapses
greer
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mingled
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6000
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disclose
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1701
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1735
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1645
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whereupon
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plastics
accommodations
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transcribed
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357
seventies
staggering
alam
horticultural
hs
regression
timbers
blasting
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manipulating
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catalytic
1550
troopers
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condemnation
fitzpatrick
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inexperienced
1670
castes
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outing
314
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flicking
quarrel
ste
learners
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whistled
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282
classify
tariffs
temperament
355
folly
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ceasefire
apparel
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44th
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thierry
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1724
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cognition
radha
319
liechtenstein
meade
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trumpets
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lear
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reused
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nirvana
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headlining
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jug
tko
1649
naga
intersections
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nawab
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gulp
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brentford
frazier
pleasures
dunne
potsdam
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dentistry
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prem
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relinquished
sutra
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flaps
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poly
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homme
aback
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linger
womb
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doorstep
orthodoxy
threaded
westfield
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dioceses
fridays
subsided
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loyalists
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letterman
lunatic
prelate
tenderly
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thug
winslow
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furlongs
gogh
jeopardy
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pegasus
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humiliated
standalone
tagged
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freshmen
klan
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attaining
initiating
transatlantic
logged
viz
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1723
combatants
intervening
stephane
chieftain
despised
grazed
317
cdc
galveston
godzilla
macro
simulate
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parades
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960
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overdose
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epstein
sonora
treacherous
aquatics
manchu
responsive
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supervisory
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karlsruhe
mab
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ignores
phonetic
reuters
spaghetti
820
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danzig
rumbling
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designations
lured
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supermarkets
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grupo
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comprehension
genealogy
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redding
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1722
bowing
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lest
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valkyrie
sikhs
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swans
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clerks
leasing
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dt
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pods
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xp
attendants
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stale
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plump
asteroids
rediscovered
buds
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hive
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1737
classifications
debuts
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olympus
scala
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snort
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lodges
riches
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socrates
regulates
mueller
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1702
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solids
himalayas
nutrient
pup
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nec
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immortals
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cleansing
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servicing
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2010s
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axial
liquids
mora
sho
yoo
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bundles
oldies
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sparkle
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1728
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highs
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immature
880
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ignatius
mansions
monterrey
sweets
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addict
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heaviest
lodging
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ymca
snuggled
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284
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chromosomes
risking
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cynical
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yeomanry
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grading
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magdalene
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upton
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longevity
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graz
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70th
fairness
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lei
newsweek
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293
finer
guerrillas
athenian
deng
disused
stepmother
accuse
gingerly
seduction
521
confronting
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nostalgia
sabres
virginity
wrenched
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syndication
wielding
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gallant
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amplified
geraldine
scrape
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fresco
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1718
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osborn
selector
partnering
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318
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gables
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softness
immersion
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1713
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pcs
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lina
purported
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teaming
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proficient
rouen
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selects
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fluffy
1621
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mukherjee
polgara
thrash
nicholls
secluded
smoothing
thru
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loaf
whitaker
inquiries
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289
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myles
peking
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pastry
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soc
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workout
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joyah
triggers
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grandmaster
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clapping
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ina
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unsigned
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watered
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chemotherapy
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refreshing
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planners
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elects
childbirth
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291
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fontana
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tyrol
1675
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296
nylon
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obstruction
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polymers
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atm
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piloted
settles
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mayfield
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persians
1733
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pep
324
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298
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occurrences
adversary
ahmedabad
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1672
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charlemagne
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racks
unicode
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mbc
pic
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fellowships
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kawasaki
reacts
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lass
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arching
passageway
1708
researches
tia
internationals
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distinguishes
javanese
divert
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plotted
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affirmative
signifies
validation
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felicity
georgina
zulu
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overcoming
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1734
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ratification
windy
earls
parapet
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hunan
pristine
astrid
punta
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malaga
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rouse
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portals
reclamation
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parentheses
quoting
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showcases
benefactor
heartland
nonlinear
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bladed
cheerfully
scans
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1666
girlfriends
pedersen
hiram
sous
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1683
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primaries
smiley
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unearthed
uniformly
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1635
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recoil
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406
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jamestown
mcmillan
tulane
seychelles
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antics
coli
fated
stucco
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1654
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accolades
arrays
caledonian
carnage
optimism
puebla
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seo
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aeronautics
chimed
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quieter
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spontaneously
townsville
buena
southport
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1638
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stiffly
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297
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realising
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bytes
straightening
356
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soloists
411
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417
coping
fission
hardin
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1717
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331
gaunt
neighbourhoods
1540
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behold
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1732
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ow
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compulsion
recapture
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1667
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ported
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875
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1665
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1721
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holiness
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dawned
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910
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dispose
paxton
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1704
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hounds
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rutland
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disqualification
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footballers
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48th
rein
scribe
stabilize
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exemplary
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pantry
traversed
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disrepair
identifiable
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interviewing
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greaves
wealthiest
343
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jogged
£5
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respecting
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defiant
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strife
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354
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1629
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ob
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bromwich
egan
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utilization
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contradictory
provoke
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338
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utmost
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1679
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sonya
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wiring
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skepticism
np
townspeople
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somethin
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336
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legislatures
flirt
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trolls
umar
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crank
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46th
constantin
molded
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seriousness
00pm
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compartments
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statehood
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730
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1738
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nbl
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441
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nightfall
robbers
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replicate
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rainer
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boosted
reunification
kathmandu
loco
robyn
402
acknowledges
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newell
redeveloped
restraints
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barbarians
chopper
1609
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investigates
wrestlemania
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690
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stupidity
volley
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malvern
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imperialism
1910s
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stat
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840
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moravian
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1622
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294
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anatomical
excerpt
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housemates
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451
1719
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£3
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326
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1736
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1086
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1605
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1632
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heterosexual
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jogging
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475
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wastewater
rained
favourites
bedrock
fisted
hallways
likeness
upscale
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1580
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tn
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1659
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tractors
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himalayan
prodigy
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demonstrators
handcuffs
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sublime
1726
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shrill
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341
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shelly
whitehall
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peacekeeping
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1703
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ousted
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translators
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hackney
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bedfordshire
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1656
racetrack
variability
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1655
austrians
deteriorating
madman
theorists
aix
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weathered
1731
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eruptions
1729
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1711
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1712
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mouthful
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##保
##信
##健
##元
##光
##八
##公
##内
##出
##分
##前
##劉
##力
##加
##勝
##北
##区
##十
##千
##南
##博
##原
##口
##古
##史
##司
##合
##吉
##同
##名
##和
##囗
##四
##国
##國
##土
##地
##坂
##城
##堂
##場
##士
##夏
##外
##大
##天
##太
##夫
##奈
##女
##子
##学
##宀
##宇
##安
##宗
##定
##宣
##宮
##家
##宿
##寺
##將
##小
##尚
##山
##岡
##島
##崎
##川
##州
##巿
##帝
##平
##年
##幸
##广
##弘
##張
##彳
##後
##御
##德
##心
##忄
##志
##忠
##愛
##成
##我
##戦
##戸
##手
##扌
##政
##文
##新
##方
##日
##明
##星
##春
##昭
##智
##曲
##書
##月
##有
##朝
##木
##本
##李
##村
##東
##松
##林
##森
##楊
##樹
##橋
##歌
##止
##正
##武
##比
##氏
##民
##水
##氵
##氷
##永
##江
##沢
##河
##治
##法
##海
##清
##漢
##瀬
##火
##版
##犬
##王
##生
##田
##男
##疒
##発
##白
##的
##皇
##目
##相
##省
##真
##石
##示
##社
##神
##福
##禾
##秀
##秋
##空
##立
##章
##竹
##糹
##美
##義
##耳
##良
##艹
##花
##英
##華
##葉
##藤
##行
##街
##西
##見
##訁
##語
##谷
##貝
##貴
##車
##軍
##辶
##道
##郎
##郡
##部
##都
##里
##野
##金
##鈴
##镇
##長
##門
##間
##阝
##阿
##陳
##陽
##雄
##青
##面
##風
##食
##香
##馬
##高
##龍
##龸
##fi
##fl
##！
##（
##）
##，
##－
##．
##／
##：
##？
##～
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/cli | cli | __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.
import argparse
from syngen.cli.commands.synthesize import SynthesizeCommand
from syngen.cli.commands.preprocess import PreprocessingCommand
from syngen.cli.commands.mimic_dataset import MimicDatasetCommand
from syngen.cli.commands.pretrain import PretrainCommand
def get_parser():
parser = argparse.ArgumentParser(
description="Synthetic Graph Generation Tool",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
command = parser.add_subparsers(title="command")
command.required = True
SynthesizeCommand().init_parser(command)
PreprocessingCommand().init_parser(command)
MimicDatasetCommand().init_parser(command)
PretrainCommand().init_parser(command)
return parser
|
CUDA-Optimized/FastSpeech/fastspeech/trt/plugins/repeat | repeat | test_repeat_plugin | # 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.
from __future__ import print_function
import numpy as np
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
import ctypes
import os
import time
import sys
logger = trt.Logger(trt.Logger.INFO)
PLUGIN_PATH = '/home/dahn/git/fastspeech/fastspeech/trt/plugins/repeat/RepeatPlugin.so'
ctypes.cdll.LoadLibrary(PLUGIN_PATH)
def get_plugin_creator(plugin_name):
trt.init_libnvinfer_plugins(logger, '')
plugin_creator_list = trt.get_plugin_registry().plugin_creator_list
plugin_creator = None
for c in plugin_creator_list:
if c.name == plugin_name:
plugin_creator = c
return plugin_creator
def build_engine(shape, shape2):
plugin_creator = get_plugin_creator('RepeatPlugin')
if plugin_creator == None:
print('Plugin not found. Exiting')
exit()
builder = trt.Builder(logger)
builder.max_batch_size = 1024
builder.max_workspace_size = 1 << 20
builder.fp16_mode = use_fp16
network = builder.create_network()
tensor = network.add_input('input1', trt.DataType.FLOAT, shape)
tensor2 = network.add_input('input2', trt.DataType.FLOAT, shape2)
tensor = network.add_plugin_v2(
[tensor, tensor2],
plugin_creator.create_plugin('RepeatPlugin', trt.PluginFieldCollection([
trt.PluginField('maxOutputLength', np.array([MAX_OUTPUT_LENGTH], dtype=np.int32), trt.PluginFieldType.INT32)
]))
).get_output(0)
network.mark_output(tensor)
return builder.build_cuda_engine(network)
def run_trt(input1, input2):
batch_size = input1.shape[0]
engine = build_engine(input1.shape[1:], input2.shape[1:])
context = engine.create_execution_context()
d_input1 = cuda.mem_alloc(input1.nbytes)
d_input2 = cuda.mem_alloc(input2.nbytes)
output = np.zeros(shape=(batch_size, MAX_OUTPUT_LENGTH, input1.shape[2]), dtype=np.float32)
d_output = cuda.mem_alloc(output.nbytes)
cuda.memcpy_htod(d_input1, input1)
cuda.memcpy_htod(d_input2, input2)
bindings = [int(d_input1), int(d_input2), int(d_output)]
start = time.time()
context.execute(batch_size, bindings)
end = time.time()
time_elapsed = end - start
print("time elapsed: {:06f}".format(time_elapsed))
cuda.memcpy_dtoh(output, d_output)
return output
use_fp16 = len(sys.argv) > 1 and sys.argv[1].isdigit() and int(sys.argv[1]) == 1
print('Use FP16:', use_fp16)
##
# accuray test
##
MAX_OUTPUT_LENGTH=8
inputs = np.array([
[[1, 2], [4, 5], [7, 8]],
[[3, 4], [5, 6], [8, 9]]
], np.float32)
masks = np.ones((2,3,1), np.float32)
repeats = np.array([
[[0, 2, 10]],
[[1, 2, 1]]
], np.float32)
output = run_trt(inputs, repeats)
print(output)
print(output.shape)
print(type(output))
output_mask = run_trt(masks, repeats)
print(output_mask)
print(output_mask.shape)
print(type(output_mask))
##
# latency test
##
# MAX_OUTPUT_LENGTH=1024
# inputs = np.full((16, 256, 384), 2, np.float32)
# masks = np.ones((16, 256, 384), np.float32)
# repeats = np.full((16, 256), 4, np.float32)
# output = run_trt(inputs, repeats)
# output_mask = run_trt(masks, repeats) |
PyTorch/Classification/ConvNets/se-resnext101-32x4d/training/AMP | AMP | DGX1V_se-resnext101-32x4d_AMP_90E | python ./multiproc.py --nproc_per_node 8 ./launch.py --model se-resnext101-32x4d --precision AMP --mode convergence --platform DGX1V /imagenet --epochs 90 --mixup 0.0 --workspace ${1:-./} --raport-file raport.json
|
PyTorch/LanguageModeling/BERT/triton/deployment_toolkit/bermuda | bermuda | tensorrt | # 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 logging
import sys
from pathlib import Path
from typing import Dict, NamedTuple, Optional, Union
import numpy as np
# pytype: disable=import-error
try:
import pycuda.autoinit
import pycuda.driver as cuda
except Exception as e:
logging.getLogger(__name__).warning(f"Problems with importing pycuda package; {e}")
# pytype: enable=import-error
import tensorrt as trt # pytype: disable=import-error
from ..core import BaseLoader, BaseRunner, BaseRunnerSession, Format, Model, TensorSpec
from ..extensions import loaders, runners
LOGGER = logging.getLogger(__name__)
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
# documentation:
# https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/index.html
# https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#python_samples_section
_NP_DTYPE2TRT_DTYPE = {
np.dtype("float32"): trt.DataType.FLOAT,
np.dtype("float16"): trt.DataType.HALF,
np.dtype("int8"): trt.DataType.INT8,
np.dtype("int32"): trt.DataType.INT32,
np.dtype("bool"): trt.DataType.BOOL,
}
class TensorRTLoader(BaseLoader):
def load(self, model_path: Union[str, Path], **_) -> Model:
model_path = Path(model_path)
LOGGER.debug(f"Loading TensorRT engine from {model_path}")
engine = self._load_engine(model_path)
if engine is None:
LOGGER.debug("Unable to load engine without plugins. Loading plugins.")
trt.init_libnvinfer_plugins(logger=TRT_LOGGER, namespace="")
LOGGER.debug(f"Loading TensorRT engine with plugins from {model_path}")
engine = self._load_engine(model_path)
if engine is None:
raise RuntimeError(f"Could not load ICudaEngine from {model_path}")
inputs = {}
outputs = {}
for binding_idx in range(engine.num_bindings):
name = engine.get_binding_name(binding_idx)
is_input = engine.binding_is_input(binding_idx)
dtype = np.dtype(trt.nptype(engine.get_binding_dtype(binding_idx))).name
shape = engine.get_binding_shape(binding_idx)
if is_input:
inputs[name] = TensorSpec(name, dtype, shape)
else:
outputs[name] = TensorSpec(name, dtype, shape)
return Model(engine, None, inputs, outputs)
def _load_engine(self, model_path: Path):
with model_path.open("rb") as fh, trt.Runtime(TRT_LOGGER) as runtime:
engine = runtime.deserialize_cuda_engine(fh.read())
return engine
class TRTBuffers(NamedTuple):
x_host: Optional[Dict[str, object]]
x_dev: Dict[str, object]
y_pred_host: Dict[str, object]
y_pred_dev: Dict[str, object]
class TensorRTRunner(BaseRunner):
def __init__(self):
pass
def init_inference(self, model: Model):
return TensorRTRunnerSession(model=model)
class TensorRTRunnerSession(BaseRunnerSession):
def __init__(self, model: Model):
super().__init__(model)
assert isinstance(model.handle, trt.ICudaEngine)
self._model = model
self._has_dynamic_shapes = None
self._context = None
self._engine: trt.ICudaEngine = self._model.handle
self._cuda_context = pycuda.autoinit.context
self._input_names = None
self._output_names = None
self._buffers = None
def __enter__(self):
self._context = self._engine.create_execution_context()
self._context.__enter__()
self._input_names = [
self._engine[idx] for idx in range(self._engine.num_bindings) if self._engine.binding_is_input(idx)
]
self._output_names = [
self._engine[idx] for idx in range(self._engine.num_bindings) if not self._engine.binding_is_input(idx)
]
# all_binding_shapes_specified is True for models without dynamic shapes
# so initially this variable is False for models with dynamic shapes
self._has_dynamic_shapes = not self._context.all_binding_shapes_specified
return self
def __exit__(self, exc_type, exc_value, traceback):
self._context.__exit__(exc_type, exc_value, traceback)
self._input_names = None
self._output_names = None
# TODO: are cuda buffers dealloc automatically?
self._buffers = None
def __call__(self, x):
buffers = self._prepare_buffers_if_needed(x)
bindings = self._update_bindings(buffers)
for name in self._input_names:
cuda.memcpy_htod(buffers.x_dev[name], buffers.x_host[name])
self._cuda_context.push()
self._context.execute_v2(bindings=bindings)
self._cuda_context.pop()
for name in self._output_names:
cuda.memcpy_dtoh(buffers.y_pred_host[name], buffers.y_pred_dev[name])
return buffers.y_pred_host
def _update_bindings(self, buffers: TRTBuffers):
bindings = [None] * self._engine.num_bindings
for name in buffers.y_pred_dev:
binding_idx: int = self._engine[name]
bindings[binding_idx] = buffers.y_pred_dev[name]
for name in buffers.x_dev:
binding_idx: int = self._engine[name]
bindings[binding_idx] = buffers.x_dev[name]
return bindings
def _set_dynamic_input_shapes(self, x_host):
def _is_shape_dynamic(input_shape):
return any([dim is None or dim == -1 for dim in input_shape])
for name in self._input_names:
bindings_idx = self._engine[name]
data_shape = x_host[name].shape # pytype: disable=attribute-error
if self._engine.is_shape_binding(bindings_idx):
input_shape = self._context.get_shape(bindings_idx)
if _is_shape_dynamic(input_shape):
self._context.set_shape_input(bindings_idx, data_shape)
else:
input_shape = self._engine.get_binding_shape(bindings_idx)
if _is_shape_dynamic(input_shape):
self._context.set_binding_shape(bindings_idx, data_shape)
assert self._context.all_binding_shapes_specified and self._context.all_shape_inputs_specified
def _prepare_buffers_if_needed(self, x_host: Dict[str, object]):
# pytype: disable=attribute-error
new_batch_size = list(x_host.values())[0].shape[0]
current_batch_size = list(self._buffers.y_pred_host.values())[0].shape[0] if self._buffers else 0
# pytype: enable=attribute-error
if self._has_dynamic_shapes or new_batch_size != current_batch_size:
# TODO: are CUDA buffers dealloc automatically?
self._set_dynamic_input_shapes(x_host)
y_pred_host = {}
for name in self._output_names:
shape = self._context.get_binding_shape(self._engine[name])
binding_idx: int = self._engine[name]
dtype_from_trt_binding = np.dtype(trt.nptype(self._engine.get_binding_dtype(binding_idx)))
dtype_from_model_spec = np.dtype(self._model.outputs[name].dtype)
assert dtype_from_model_spec == dtype_from_trt_binding
y_pred_host[name] = np.zeros(shape, dtype=dtype_from_model_spec)
y_pred_dev = {name: cuda.mem_alloc(data.nbytes) for name, data in y_pred_host.items()}
# cast host input into binding dtype
def _cast_input(name, data):
binding_idx: int = self._engine[name]
np_dtype = trt.nptype(self._engine.get_binding_dtype(binding_idx))
return data.astype(np_dtype)
x_host = {name: _cast_input(name, host_input) for name, host_input in x_host.items()}
x_dev = {
name: cuda.mem_alloc(host_input.nbytes)
for name, host_input in x_host.items()
if name in self._input_names # pytype: disable=attribute-error
}
self._buffers = TRTBuffers(None, x_dev, y_pred_host, y_pred_dev)
return self._buffers._replace(x_host=x_host)
if "pycuda.driver" in sys.modules:
loaders.register_extension(Format.TRT.value, TensorRTLoader)
runners.register_extension(Format.TRT.value, TensorRTRunner)
else:
LOGGER.warning("Do not register TensorRT extension due problems with importing pycuda.driver package.")
|
PyTorch/Forecasting/TFT | TFT | tft_torchhub | # 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 os
import sys
import urllib.request
from zipfile import ZipFile
import torch
from torch.utils.data import DataLoader
NGC_CHECKPOINT_URLS = {}
NGC_CHECKPOINT_URLS["electricity"] = "https://api.ngc.nvidia.com/v2/models/nvidia/dle/tft_base_pyt_ckpt_ds-electricity/versions/22.11.0_amp/zip"
NGC_CHECKPOINT_URLS["traffic"] = "https://api.ngc.nvidia.com/v2/models/nvidia/dle/tft_base_pyt_ckpt_ds-traffic/versions/22.11.0_amp/zip"
def _download_checkpoint(checkpoint, force_reload):
model_dir = os.path.join(torch.hub._get_torch_home(), 'checkpoints')
if not os.path.exists(model_dir):
os.makedirs(model_dir)
ckpt_file = os.path.join(model_dir, os.path.basename(checkpoint))
if not os.path.exists(ckpt_file) or force_reload:
sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
urllib.request.urlretrieve(checkpoint, ckpt_file)
with ZipFile(ckpt_file, "r") as zf:
zf.extractall(path=model_dir)
return os.path.join(model_dir, "checkpoint.pt")
def nvidia_tft(pretrained=True, **kwargs):
from .modeling import TemporalFusionTransformer
"""Constructs a TFT model.
For detailed information on model input and output, training recipies, inference and performance
visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
Args (type[, default value]):
pretrained (bool, True): If True, returns a pretrained model.
dataset (str, 'electricity'): loads selected model type electricity or traffic. Defaults to electricity
"""
ds_type = kwargs.get("dataset", "electricity")
ckpt = _download_checkpoint(NGC_CHECKPOINT_URLS[ds_type], True)
state_dict = torch.load(ckpt)
config = state_dict['config']
model = TemporalFusionTransformer(config)
if pretrained:
model.load_state_dict(state_dict['model'])
model.eval()
return model
def nvidia_tft_data_utils(**kwargs):
from .data_utils import TFTDataset
from .configuration import ElectricityConfig
class Processing:
@staticmethod
def download_data(path):
if not os.path.exists(os.path.join(path, "raw")):
os.makedirs(os.path.join(path, "raw"), exist_ok=True)
dataset_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00321/LD2011_2014.txt.zip"
ckpt_file = os.path.join(path, "raw/electricity.zip")
if not os.path.exists(ckpt_file):
sys.stderr.write('Downloading checkpoint from {}\n'.format(dataset_url))
urllib.request.urlretrieve(dataset_url, ckpt_file)
with ZipFile(ckpt_file, "r") as zf:
zf.extractall(path=os.path.join(path, "raw/electricity/"))
@staticmethod
def preprocess(path):
config = ElectricityConfig()
if not os.path.exists(os.path.join(path, "processed")):
os.makedirs(os.path.join(path, "processed"), exist_ok=True)
from data_utils import standarize_electricity as standarize
from data_utils import preprocess
standarize(os.path.join(path, "raw/electricity"))
preprocess(os.path.join(path, "raw/electricity/standarized.csv"), os.path.join(path, "processed/electricity_bin/"), config)
@staticmethod
def get_batch(path):
config = ElectricityConfig()
test_split = TFTDataset(os.path.join(path, "processed/electricity_bin/", "test.csv"), config)
data_loader = DataLoader(test_split, batch_size=16, num_workers=0)
for i, batch in enumerate(data_loader):
if i == 40:
break
return batch
return Processing()
|
PyTorch/SpeechRecognition/wav2vec2/scripts/docker | docker | build | #!/usr/bin/env bash
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
docker build . --rm -t wav2vec2
|
TensorFlow2/Classification/ConvNets/efficientnet_v1 | efficientnet_v1 | README | # EfficientNet v1 For TensorFlow 2.6
This repository provides scripts and recipes to train EfficientNet v1-B0 and v1-B4 to achieve state-of-the-art accuracy.
The content of the repository is maintained by NVIDIA and is tested against each NGC monthly released container to ensure consistent accuracy and performance over time.
## 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)
* [Training process](#training-process)
* [Multi-node](#multi-node)
* [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 for EfficientNet v1-B0](#training-accuracy-results-for-efficientnet-v1-b0)
* [Training accuracy: NVIDIA DGX A100 (8x A100 80GB)](#training-accuracy-nvidia-dgx-a100-8x-a100-80gb)
* [Training accuracy: NVIDIA DGX-1 (8x V100 32GB)](#training-accuracy-nvidia-dgx-1-8x-v100-32gb)
* [Training accuracy results for EfficientNet v1-B4](#training-accuracy-results-for-efficientnet-v1-b4)
* [Training accuracy: NVIDIA DGX A100 (8x A100 80GB)](#training-accuracy-nvidia-dgx-a100-8x-a100-80gb)
* [Training accuracy: NVIDIA DGX-1 (8x V100 32GB)](#training-accuracy-nvidia-dgx-1-8x-v100-32gb)
* [Training performance results for EfficientNet v1-B0](#training-performance-results-for-efficientnet-v1-b0)
* [Training performance: NVIDIA DGX A100 (8x A100 80GB)](#training-performance-nvidia-dgx-a100-8x-a100-80gb)
* [Training performance: NVIDIA DGX-1 (8x V100 32GB)](#training-performance-nvidia-dgx-1-8x-v100-32gb)
* [Training performance results for EfficientNet v1-B4](#training-performance-results-for-efficientnet-v1-b4)
* [Training performance: NVIDIA DGX A100 (8x A100 80GB)](#training-performance-nvidia-dgx-a100-8x-a100-80gb)
* [Training performance: NVIDIA DGX-1 (8x V100 32GB)](#training-performance-nvidia-dgx-1-8x-v100-32gb)
* [Inference performance results for EfficientNet v1-B0](#inference-performance-results-for-efficientnet-v1-b0)
* [Inference performance: NVIDIA DGX A100 (1x A100 80GB)](#inference-performance-nvidia-dgx-a100-1x-a100-80gb)
* [Inference performance: NVIDIA DGX-1 (1x V100 32GB)](#inference-performance-nvidia-dgx-1-1x-v100-32gb)
* [Inference performance results for EfficientNet v1-B4](#inference-performance-results-for-efficientnet-v1-b4)
* [Inference performance: NVIDIA DGX A100 (1x A100 80GB)](#inference-performance-nvidia-dgx-a100-1x-a100-80gb)
* [Inference performance: NVIDIA DGX-1 (1x V100 32GB)](#inference-performance-nvidia-dgx-1-1x-v100-32gb)
- [Release notes](#release-notes)
* [Changelog](#changelog)
* [Known issues](#known-issues)
## Model overview
### Model architecture
EfficientNet v1 is developed based on AutoML and Compound Scaling. In particular,
a mobile-size baseline network called EfficientNet v1-B0 is developed from AutoML MNAS Mobile
framework, the building block is mobile inverted bottleneck MBConv with squeeze-and-excitation optimization.
Then, through a compound scaling method, this baseline is scaled up to obtain EfficientNet v1-B1
to B7.
### Default configuration
Here is the Baseline EfficientNet v1-B0 structure.
The following features are supported by this implementation:
- General:
- XLA support
- Mixed precision support
- Multi-GPU support using Horovod
- Multi-node support using Horovod
- Cosine LR Decay
- Inference:
- Support for inference on a single image is included
- Support for inference on a batch of images is included
### Feature support matrix
| Feature | EfficientNet
|-----------------------|-------------------------- |
|Horovod Multi-GPU training (NCCL) | Yes |
|Multi-GPU training | Yes |
|Multi-node training | Yes |
|Automatic mixed precision (AMP) | Yes |
|XLA | Yes |
|Gradient Accumulation| Yes |
|Stage-wise Training| Yes |
#### Features
**Multi-GPU training with Horovod**
Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, refer to example sources in this repository or refer to the [TensorFlow tutorial](https://github.com/horovod/horovod/#usage).
**Multi-node training with Horovod**
Our model also uses Horovod to implement efficient multi-node training.
**Automatic Mixed Precision (AMP)**
Computation graphs can be modified by TensorFlow on runtime to support mixed precision training. A detailed explanation of mixed precision can be found in Appendix.
**Gradient Accumulation**
Gradient Accumulation is supported through a custom train_step function. This feature is enabled only when grad_accum_steps is greater than 1.
### 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 NVIDIA Volta, and following with both the NVIDIA Turing and NVIDIA 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 NVIDIA Volta, NVIDIA 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, refer to 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, refer to 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, refer to [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 `--use_amp` to the command-line used to run the model. This will enable the following code:
```
if params.use_amp:
policy = tf.keras.mixed_precision.experimental.Policy('mixed_float16', loss_scale='dynamic')
tf.keras.mixed_precision.experimental.set_policy(policy)
```
#### 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 NVIDIA 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 a 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 EfficientNet 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 21.09-py3] NGC container or later
- Supported GPUs:
- [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
- [NVIDIA Turing architecture](https://www.nvidia.com/en-us/geforce/turing/)
- [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)
For more information about how to get started with NGC containers, refer to the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:
- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
- [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#accessing_registry)
- [Running TensorFlow](https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/running.html#running)
As an alternative to the use of the Tensorflow2 NGC container, to set up the required environment or create your own container, refer to the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).
For multi-node, the sample provided in this repository requires [Enroot](https://github.com/NVIDIA/enroot) and [Pyxis](https://github.com/NVIDIA/pyxis) set up on a [SLURM](https://slurm.schedmd.com) cluster.
## 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 EfficientNet model on the ImageNet dataset. For the specifics concerning training and inference, refer to the [Advanced](#advanced) section.
1. Clone the repository.
```
git clone https://github.com/NVIDIA/DeepLearningExamples.git
cd DeepLearningExamples/TensorFlow2/Classification/ConvNets/efficientnet
```
2. Download and prepare the dataset.
`Runner.py` supports ImageNet with [TensorFlow Datasets (TFDS)](https://www.tensorflow.org/datasets/overview). Refer to the [TFDS ImageNet readme](https://github.com/tensorflow/datasets/blob/master/docs/catalog/imagenet2012.md) for manual download instructions.
3. Build EfficientNet on top of the NGC container.
`bash ./scripts/docker/build.sh YOUR_DESIRED_CONTAINER_NAME`
4. Start an interactive session in the NGC container to run training/inference. **Ensure that `launch.sh` has the correct path to ImageNet on your machine and that this path is mounted onto the `/data` directory because this is where training and evaluation scripts search for data.**
`bash ./scripts/docker/launch.sh YOUR_DESIRED_CONTAINER_NAME`
5. Start training.
To run training for a standard configuration, **under the container default entry point `/workspace`**, run one of the scripts in the `./efficinetnet_v1/{B0,B4}/training/{AMP,TF32,FP32}/convergence_8x{A100-80G, V100-32G}.sh`. For example:
`bash ./efficinetnet_v1/B0/training/AMP/convergence_8xA100-80G.sh`
6. Start validation/evaluation.
To run validation/evaluation for a standard configuration, **under the container default entry point `/workspace`**, run one of the scripts in the `./efficinetnet_v1/{B0,B4}/evaluation/evaluation_{AMP,FP32,TF32}_8x{A100-80G, V100-32G}.sh`. The evaluation script is configured to use the checkpoint specified in `checkpoint` for evaluation. The specified checkpoint will be read from the location passed by `--model_dir'.For example:
`bash ./efficinetnet_v1/B0/evaluation/evaluation_AMP_A100-80G.sh`
7. Start inference/predictions.
To run inference for a standard configuration, **under the container default entry point `/workspace`**, run one of the scripts in the `./efficinetnet_v1/{B0,B4}/inference/inference_{AMP,FP32,TF32}.sh`.
Ensure your JPEG images used to run inference on are mounted in the `/infer_data` directory with this folder structure :
```
infer_data
| ├── images
| | ├── image1.JPEG
| | ├── image2.JPEG
```
For example:
`bash ./efficinetnet_v1/B0/inference/inference_AMP.sh`
Now that you have your model trained and evaluated, you can choose to compare your training results with our [Training accuracy results](#training-accuracy-results). You can also choose to benchmark your performance to [Training performance benchmark](#training-performance-results) or [Inference performance benchmark](#inference-performance-results). Following the steps in these sections will ensure that you achieve the same accuracy and performance results as stated in the [Results](#results) section.
## Advanced
The following sections provide greater details of the dataset, running training and inference, and the training results.
### Scripts and sample code
The repository is structured as follows:
- `scripts/` - shell scripts to build and launch EfficientNet container on top of NGC container
- `efficientnet_{v1,v2}` scripts to launch training, evaluation and inference
- `model/` - building blocks and EfficientNet model definitions
- `runtime/` - detailed procedure for each running mode
- `utils/` - support util functions for learning rates, optimizers, etc.
- `dataloader/` provides data pipeline utils
- `config/` contains model definitions
### Parameters
The hyper parameters can be grouped into model-specific hyperparameters (e.g., #layers ) and general hyperparameters (e.g., #training epochs).
The model-specific hyperparameters are to be defined in a python module, which must be passed in the command line via --cfg ( `python main.py --cfg config/efficientnet_v1/b0_cfg.py`). To override model-specific hyperparameters, you can use a comma-separated list of k=v pairs (e.g., `python main.py --cfg config/efficientnet_v1/b0_cfg.py --mparams=bn_momentum=0.9,dropout=0.5`).
The general hyperparameters and their default values can be found in `utils/cmdline_helper.py`. The user can override these hyperparameters in the command line (e.g., `python main.py --cfg config/efficientnet_v1/b0_cfg.py --data_dir xx --train_batch_size 128`). Here is a list of important hyperparameters:
- `--mode` (`train_and_eval`,`train`,`eval`,`prediction`) - the default is `train_and_eval`.
- `--use_amp` Set to True to enable AMP
- `--use_xla` Set to True to enable XLA
- `--model_dir` The folder where model checkpoints are saved (the default is `/workspace/output`)
- `--data_dir` The folder where data resides (the default is `/data/`)
- `--log_steps` The interval of steps between logging of batch level stats.
- `--augmenter_name` Type of data augmentation
- `--raug_num_layers` Number of layers used in the random data augmentation scheme
- `--raug_magnitude` Strength of transformations applied in the random data augmentation scheme
- `--cutmix_alpha` Cutmix parameter used in the last stage of training.
- `--mixup_alpha` Mixup parameter used in the last stage of training.
- `--defer_img_mixing` Move image mixing ops from the data loader to the model/GPU (faster training)
- `--eval_img_size` Size of images used for evaluation
- `--eval_batch_size` The evaluation batch size per GPU
- `--n_stages` Number of stages used for stage-wise training
- `--train_batch_size` The training batch size per GPU
- `--train_img_size` Size of images used in the last stage of training
- `--base_img_size` Size of images used in the first stage of training
- `--max_epochs` The number of training epochs
- `--warmup_epochs` The number of epochs of warmup
- `--moving_average_decay` The decay weight used for EMA
- `--lr_init` The learning rate for a batch size of 128, effective learning rate will be automatically scaled according to the global training batch size: `lr=lr_init * global_BS/128 where global_BS=train_batch_size*n_GPUs`
- `--lr_decay` Learning rate decay policy
- `--weight_decay` Weight decay coefficient
- `--save_checkpoint_freq` Number of epochs to save checkpoints
**NOTE**: Avoid changing the default values of the general hyperparameters provided in `utils/cmdline_helper.py`. The reason is that some other models supported by this repository may rely on such default values. If you want to change the values, override them via the command line.
### Command-line options
To display the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:
`python main.py --help`
### Getting the data
Refer to the [TFDS ImageNet readme](https://github.com/tensorflow/datasets/blob/master/docs/catalog/imagenet2012.md) for manual download instructions.
To train on the ImageNet dataset, pass `$path_to_ImageNet_tfrecords` to `$data_dir` in the command-line.
Name the TFRecords in the following scheme:
- Training images - `/data/train-*`
- Validation images - `/data/validation-*`
### Training process
The training process can start from scratch or resume from a checkpoint.
By default, bash script `scripts/{B0,B4}/training/{AMP,FP32,TF32}/convergence_8x{A100-80G,V100-32G}.sh` will start the training process with the following settings.
- Use 8 GPUs by Horovod
- Has XLA enabled
- Saves checkpoints after every 10 epochs to `/workspace/output/` folder
- AMP or FP32 or TF32 based on the folder `scripts/{B0,B4}/training/{AMP, FP32, TF32}`
The training starts from scratch if `--model_dir` has no checkpoints in it. To resume from a checkpoint, place the checkpoint into `--model_dir` and make sure the `checkpoint` file points to it.
#### Multi-node
Multi-node runs can be launched on a Pyxis/enroot Slurm cluster (refer to [Requirements](#requirements)) with the `run_{B0,B4}_multinode.sub` script with the following command for a 4-node NVIDIA DGX A100 example:
```
PARTITION=<partition_name> sbatch N 4 --ntasks-per-node=8 run_B0_multinode.sub
PARTITION=<partition_name> sbatch N 4 --ntasks-per-node=8 run_B4_multinode.sub
```
Checkpoints will be saved after `--save_checkpoint_freq` epochs at `checkpointdir`. The latest checkpoint will be automatically picked up to resume training in case it needs to be resumed. Cluster partition name has to be provided `<partition_name>`.
Note that the `run_{B0,B4}_multinode.sub` script is a starting point that has to be adapted depending on the environment. In particular, pay attention to the variables such as `--container-image`, which handles the container image to train, and `--datadir`, which handles the location of the ImageNet data.
Refer to the scripts to find the full list of variables to adjust for your system.
## Inference process
Validation can be done either during training (when `--mode train_and_eval` is used) or in a post-training setting (`--mode eval`) on a checkpointed model. The evaluation script expects data in the tfrecord format.
`bash ./scripts/{B0,B4}/evaluation/evaluation_{AMP,FP32,TF32}_{A100-80G,V100-32G}.sh`
Metrics gathered through this process are as follows:
```
- eval_loss
- eval_accuracy_top_1
- eval_accuracy_top_5
- avg_exp_per_second_eval
- avg_exp_per_second_eval_per_GPU
- avg_time_per_exp_eval : Average Latency
- latency_90pct : 90% Latency
- latency_95pct : 95% Latency
- latency_99pct : 99% Latency
```
The scripts used for inference expect the inference data in the following directory structure:
```
infer_data
| ├── images
| | ├── image1.JPEG
| | ├── image2.JPEG
```
Run:
`bash ./scripts/{B0,B4}/inference/inference_{AMP,FP32,TF32}.sh`
## 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 for EfficientNet v1-B0 was run on NVIDIA DGX A100 80GB and NVIDIA DGX-1 V100 32GB.
To benchmark training performance with other parameters, run:
`bash ./scripts/B0/training/{AMP, FP32, TF32}/train_benchmark_8x{A100-80G, V100-32G}.sh`
Training benchmark for EfficientNet v1-B4 was run on NVIDIA DGX A100 80GB and NVIDIA DGX-1 V100 32GB.
`bash ./scripts/B4/training/{AMP, FP32, TF32}/train_benchmark_8x{A100-80G, V100-32G}.sh`
#### Inference performance benchmark
Inference benchmark for EfficientNet v1-B0 was run on NVIDIA DGX A100 80GB and NVIDIA DGX-1 V100 32GB.
Inference benchmark for EfficientNet v1-B4 was run on NVIDIA DGX A100 80GB and NVIDIA DGX-1 V100 32GB.
### Results
The following sections provide details on how we achieved our performance and accuracy in training and inference.
#### Training accuracy results for EfficientNet v1-B0
##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)
Our results were obtained by running the training scripts in the tensorflow:21.09-tf2-py3 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs. We evaluated the models using both the original and EMA weights and selected the higher accuracy to report.
| GPUs | Accuracy - TF32 | Accuracy - mixed precision | Time to train - TF32 | Time to train - mixed precision | Time to train speedup (TF32 to mixed precision) |
|----------|------------------|-----------------------------|-------------------------|----------------------------------|--------------------------------------------------------|
| 8 | 77.60% | 77.59% | 19.5hrs | 8.5hrs | 2.29 |
| 16 | 77.51% | 77.48% | 10hrs | 4.5hrs | 2.22 |
##### Training accuracy: NVIDIA DGX-1 (8x V100 32GB)
Our results were obtained by running the training scripts in the tensorflow:21.09-tf2-py3 NGC container on NVIDIA DGX V100 (8x V100 32GB) GPUs. We evaluated the models using both the original and EMA weights and selected the higher accuracy to report.
| GPUs | Accuracy - FP32 | Accuracy - mixed precision | Time to train - FP32 | Time to train - mixed precision | Time to train speedup (FP32 to mixed precision) |
|----------|------------------|-----------------------------|-------------------------|----------------------------------|--------------------------------------------------------|
| 8 | 77.67% | 77.69% | 49.0hrs | 38.0hrs | 1.29 |
| 32 | 77.55% | 77.53% | 11.5hrs | 10hrs | 1.15 |
#### Training accuracy results for EfficientNet v1-B4
##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)
Our results were obtained by running the training scripts in the tensorflow:21.09-tf2-py3 NGC container on multi-node NVIDIA DGX A100 (8x A100 80GB) GPUs. We evaluated the models using both the original and EMA weights and selected the higher accuracy to report.
| GPUs | Accuracy - TF32 | Accuracy - mixed precision | Time to train - TF32 | Time to train - mixed precision | Time to train speedup (TF32 to mixed precision) |
|----------|------------------|-----------------------------|-------------------------|----------------------------------|--------------------------------------------------------|
| 32 | 82.98% | 83.13% | 38hrs | 14hrs | 2.00 |
| 64 | 83.14% | 83.05% | 19hrs | 7hrs | 2.00 |
##### Training accuracy: NVIDIA DGX V100 (8x V100 32GB)
Our results were obtained by running the training scripts in the tensorflow:21.09-tf2-py3 NGC container on NVIDIA DGX V100 (8x A100 32GB) GPUs. We evaluated the models using both the original and EMA weights and selected the higher accuracy to report.
| GPUs | Accuracy - FP32 | Accuracy - mixed precision | Time to train - TF32 | Time to train - mixed precision | Time to train speedup (FP32 to mixed precision) |
|----------|------------------|-----------------------------|-------------------------|----------------------------------|--------------------------------------------------------|
| 32 | 82.64% | 82.88% | 97.0hrs | 41.0hrs | 2.37 |
| 64 | 82.74% | 83.16% | 50.0hrs | 20.5hrs | 2.43 |
#### Training performance results for EfficientNet v1-B0
##### Training performance: NVIDIA DGX A100 (8x A100 80GB)
| GPUs | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 - mixed precision) | Weak scaling - TF32 | Weak scaling - mixed precision |
|-----|------------------------|---------------------------------|-----------------------------------------------|------------------------|----------------------------------|
| 1 | 1209 | 3454 | 2.85 | 1 | 1 |
| 8 | 9119 | 20647 | 2.26 | 7.54 | 5.98 |
| 16 | 17815 | 40644 | 2.28 | 14.74 | 11.77 |
##### Training performance: NVIDIA DGX-1 (8x V100 32GB)
| GPUs | Throughput - FP32 | Throughput - mixed precision | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision |
|-----|------------------------|---------------------------------|-----------------------------------------------|------------------------|----------------------------------|
| 1 | 752 | 868 | 1.15 | 1 | 1 |
| 8 | 4504 | 4880 | 1.08 | 5.99 | 5.62 |
| 32 | 15309 | 18424 | 1.20 | 20.36 | 21.23 |
#### Training performance results for EfficientNet v1-B4
##### Training performance: NVIDIA DGX A100 (8x A100 80GB)
| GPUs | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 - mixed precision) | Weak scaling - TF32 | Weak scaling - mixed precision |
|-----|------------------------|---------------------------------|-----------------------------------------------|------------------------|----------------------------------|
| 1 | 165 | 470 | 2.85 | 1 | 1 |
| 8 | 1308 | 3550 | 2.71 | 7.93 | 7.55 |
| 32 | 4782 | 12908 | 2.70 | 28.98 | 27.46 |
| 64 | 9473 | 25455 | 2.69 | 57.41 | 54.16 |
##### Training performance: NVIDIA DGX-1 (8x V100 32GB)
| GPUs | Throughput - FP32 | Throughput - mixed precision | Throughput speedup (FP32 - mixed precision) | Weak scaling - TF32 | Weak scaling - mixed precision |
|-----|------------------------|---------------------------------|-----------------------------------------------|------------------------|----------------------------------|
| 1 | 79 | 211 | 2.67 | 1 | 1 |
| 8 | 570 | 1258 | 2.21 | 7.22 | 5.96 |
| 32 | 1855 | 4325 | 2.33 | 23.48 | 20.50 |
| 64 | 3568 | 8643 | 2.42 | 45.16 | 40.96 |
#### Inference performance results for EfficientNet v1-B0
##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)
Our results were obtained by running the inferencing benchmarking script in the tensorflow:21.09-tf2-py3 NGC container on the NVIDIA DGX A100 (1x A100 80GB) GPU.
FP16 Inference Latency
| Batch size | Resolution | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) |Latency 95% (ms) |Latency 99% (ms) |
|-------------|------------------|--------|--------|--------|--------|--------|
| 1 | 224x224 | 110.97 | 9.09 | 9.02 | 9.04 | 9.09 | #| 95.71 | 10.45 | 10.34 | 10.38 | 10.42 |
| 8 | 224x224 | 874.91 | 9.12 | 9.04 | 9.08 | 9.12 | #| 616.02 | 12.99 | 12.81 | 12.88 | 12.98 |
| 32 | 224x224 | 2188.84| 14.62 | 14.35 | 14.43 | 14.52 |
| 1024 | 224x224 | 9729.85| 105.24 | 101.50 | 103.20 | 105.24 |
TF32 Inference Latency
| Batch size | Resolution | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) | Latency 95% (ms) | Latency 99% (ms) |
|------------|-----------------|---------|--------|--------|--------|--------|
| 1 | 224x224 | 127.95 | 7.88 | 7.83 | 7.84 | 7.87 | # 119.26 | 8.38 | 8.30 | 8.34 | 8.37 |
| 8 | 224x224 | 892.27 | 8.97 | 8.88 | 8.91 | 8.94 | # | 803.61 | 9.96 | 9.82 | 9.87 | 9.93
| 32 | 224x224 | 2185.02 | 14.65 | 14.33 | 14.43 | 14.54 |
| 512 | 224x224 | 5253.19 | 97.46 | 96.57 | 97.03 | 97.46 |
##### Inference performance: NVIDIA DGX-1 (1x V100 32GB)
FP16 Inference Latency
| Batch size | Resolution | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) |Latency 95% (ms) |Latency 99% (ms) |
|-------------|------------------|--------|--------|--------|--------|--------|
| 1 | 224x224 | 97.53 | 10.25 | 10.11 | 10.13 | 10.21 |
| 8 | 224x224 | 752.72 | 10.63 | 10.49 | 10.54 | 10.59 |
| 32 | 224x224 | 1768.05| 18.10 | 17.88 | 17.96 | 18.04 |
| 512 | 224x224 | 5399.88| 94.82 | 92.85 | 93.89 | 94.82 |
FP32 Inference Latency
| Batch size | Resolution | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) | Latency 95% (ms) | Latency 99% (ms) |
|------------|-----------------|---------|--------|--------|--------|--------|
| 1 | 224x224 | 97.01 | 10.31 | 10.17 | 10.22 | 10.28 |
| 8 | 224x224 | 649.79 | 12.31 | 12.16 | 12.22 | 12.28 |
| 32 | 224x224 | 1861.65 | 17.19 | 16.98 | 17.03 | 17.10 |
| 256 | 224x224 | 2829.34 | 90.48 | 89.80 | 90.13 | 90.43 |
#### Inference performance results for EfficientNet v1-B4
##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)
Our results were obtained by running the inferencing benchmarking script in the tensorflow:21.09-tf2-py3 NGC container on the NVIDIA DGX A100 (1x A100 80GB) GPU.
FP16 Inference Latency
| Batch size | Resolution | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) |Latency 95% (ms) |Latency 99% (ms) |
|------------|------------------|--------|--------|--------|--------|--------|
| 1 | 380x380 | 61.36 | 16.30 | 16.20 | 16.24 | 16.28 | #61.36 16.30 16.20 16.24 16.28| 45.40 | 22.03 | 21.82 | 21.90 | 21.99 |
| 8 | 380x380 | 338.60 | 23.63 | 23.34 | 23.46 | 23.58 |
| 32 | 380x380 | 971.68 | 32.93 | 32.46 | 32.61 | 32.76 |
| 128 | 380x380 | 1497.21| 85.28 | 83.01 | 83.68 | 84.70 |
TF32 Inference Latency
| Batch size | Resolution | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) | Latency 95% (ms) | Latency 99% (ms) |
|------------|-----------------|---------|--------|--------|--------|--------|
| 1 | 380x380 | 60.54 | 16.52 | 16.34 | 16.41 | 16.49 |
| 8 | 380x380 | 366.82 | 21.81 | 21.48 | 21.61 | 21.75 |
| 32 | 380x380 | 642.78 | 49.78 | 49.41 | 49.53 | 49.65 |
| 64 | 380x380 | 714.55 | 89.54 | 89.00 | 89.17 | 89.34 |
##### Inference performance: NVIDIA DGX-1 (1x V100 32GB)
FP16 Inference Latency
| Batch size | Resolution | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) |Latency 95% (ms) |Latency 99% (ms) |
|------------|------------------|--------|--------|--------|--------|--------|
| 1 | 380x380 | 55.71 | 17.95 | 17.68 | 17.93 | 17.86 | #61.36 16.30 16.20 16.24 16.28| 45.40 | 22.03 | 21.82 | 21.90 | 21.99 |
| 8 | 380x380 | 256.72 | 31.16 | 30.92 | 31.02 | 31.12 |
| 16 | 380x380 | 350.14 | 45.75 | 45.44 | 45.57 | 45.68 |
| 64 | 380x380 | 805.21 | 79.46 | 78.74 | 78.86 | 79.01 |
TF32 Inference Latency
| Batch size | Resolution | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) | Latency 95% (ms) | Latency 99% (ms) |
|------------|-----------------|---------|--------|--------|--------|--------|
| 1 | 380x380 | 49.03 | 20.40 | 20.03 | 20.18 | 20.34 |
| 8 | 380x380 | 258.21 | 30.98 | 30.83 | 30.89 | 30.95 |
| 16 | 380x380 | 310.84 | 51.47 | 51.26 | 51.34 | 51.42 |
| 32 | 380x380 | 372.23 | 85.97 | 85.70 | 85.79 | 85.89 |
## Release notes
### Changelog
February 2022
- Second release
- Code Refactoring
- Add support of graduate accumulation
- Add support of exponential moving average evaluation
- Update all accuracy and performance tables on V100 and A100 results
March 2021
- Initial release
### Known issues
- EfficientNet v1-B0 does not improve training speed by using AMP as compared to FP32, because of the CPU-bound Auto-augmentation.
|
TensorFlow/LanguageModeling/BERT/data | data | BooksDownloader | # 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 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'
bookscorpus_download_process = subprocess.run(bookscorpus_download_command, shell=True, check=True) |
TensorFlow/Detection/SSD/models/research/object_detection/samples/configs | configs | faster_rcnn_resnet101_coco | # Faster R-CNN with Resnet-101 (v1), configuration for MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.
model {
faster_rcnn {
num_classes: 90
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
feature_extractor {
type: 'faster_rcnn_resnet101'
first_stage_features_stride: 16
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: {
batch_size: 1
optimizer {
momentum_optimizer: {
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 0.0003
schedule {
step: 900000
learning_rate: .00003
}
schedule {
step: 1200000
learning_rate: .000003
}
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
from_detection_checkpoint: true
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100"
}
label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
}
eval_config: {
num_examples: 8000
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 10
}
eval_input_reader: {
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010"
}
label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
shuffle: false
num_readers: 1
}
|
PyTorch/Classification/GPUNet/triton/deployment_toolkit/library | library | utils | # 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 collections import Counter
from typing import Callable, Dict, List, Optional
import networkx as nx
from ..core import ShapeSpec
def infer_precision(
nx_graph: nx.Graph,
input_names: List[str],
output_names: List[str],
get_node_dtype_fn: Callable,
):
node_dtypes = [nx_graph.nodes[node_name].get("dtype", None) for node_name in nx_graph.nodes]
node_dtypes = [dt for dt in node_dtypes if dt is None or dt.kind not in ["i", "b"]]
dtypes_counter = Counter(node_dtypes)
return dtypes_counter.most_common()[0][0]
def get_shapes_with_dynamic_axes(dataloader, batch_size_dim: Optional[int] = None):
def _set_dynamic_shapes(t, shapes):
for k, v in t.items():
shape = list(v.shape)
for dim, s in enumerate(shape):
if shapes[k][dim] != -1 and shapes[k][dim] != s:
shapes[k][dim] = -1
def _mark_batch_axis(shape, batch_axis: int):
shape = list(shape)
shape[batch_axis] = -1
return tuple(shape)
## get all shapes from input and output tensors
input_shapes = {}
output_shapes = {}
for batch in dataloader:
_, x, y = batch
for k, v in x.items():
input_shapes[k] = list(v.shape)
for k, v in y.items():
output_shapes[k] = list(v.shape)
break
# based on max <max_num_iters> iterations, check which
# dimensions differ to determine dynamic_axes
max_num_iters = 100
for idx, batch in enumerate(dataloader):
if idx >= max_num_iters:
break
_, x, y = batch
_set_dynamic_shapes(x, input_shapes)
_set_dynamic_shapes(y, output_shapes)
if batch_size_dim is not None:
input_shapes = {name: _mark_batch_axis(shape, batch_size_dim) for name, shape in input_shapes.items()}
output_shapes = {name: _mark_batch_axis(shape, batch_size_dim) for name, shape in output_shapes.items()}
return input_shapes, output_shapes
def get_dynamic_axes(dataloader, batch_size_dim: Optional[int] = None):
input_shapes, output_shapes = get_shapes_with_dynamic_axes(dataloader, batch_size_dim=batch_size_dim)
all_shapes = {**input_shapes, **output_shapes}
dynamic_axes = {}
for k, shape in all_shapes.items():
for idx, s in enumerate(shape):
if s == -1:
dynamic_axes[k] = {idx: k + "_" + str(idx)}
for k in all_shapes:
if k in dynamic_axes:
dynamic_axes[k].update({batch_size_dim: "batch_size_" + str(batch_size_dim)})
else:
dynamic_axes[k] = {batch_size_dim: "batch_size_" + str(batch_size_dim)}
return dynamic_axes
def get_input_shapes(dataloader, max_batch_size=1) -> Dict[str, ShapeSpec]:
def init_counters_and_shapes(x, counters, min_shapes, max_shapes):
for k, v in x.items():
counters[k] = Counter()
min_shapes[k] = [float("inf")] * v.ndim
max_shapes[k] = [float("-inf")] * v.ndim
counters = {}
min_shapes: Dict[str, tuple] = {}
max_shapes: Dict[str, tuple] = {}
for idx, batch in enumerate(dataloader):
ids, x, y = batch
if idx == 0:
init_counters_and_shapes(x, counters, min_shapes, max_shapes)
for k, v in x.items():
shape = v.shape
counters[k][shape] += 1
min_shapes[k] = tuple(min(a, b) for a, b in zip(min_shapes[k], shape))
max_shapes[k] = tuple(max(a, b) for a, b in zip(max_shapes[k], shape))
opt_shapes: Dict[str, tuple] = {}
for k, v in counters.items():
opt_shapes[k] = v.most_common(1)[0][0]
shapes = {}
for k in opt_shapes.keys(): # same keys in min_shapes and max_shapes
shapes[k] = ShapeSpec(
min=(1,) + min_shapes[k][1:],
max=(max_batch_size,) + max_shapes[k][1:],
opt=(max_batch_size,) + opt_shapes[k][1:],
)
return shapes
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/generator/graph | graph | base_graph_generator | # 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 abc
import json
import pickle
from abc import ABC
from typing import List, Optional, Set, Tuple
import numpy as np
from syngen.generator.graph.utils import BaseLogger
from syngen.generator.graph.seeder import BaseSeeder
class BaseGenerator(abc.ABC):
""" BaseGenerator class """
JSON_ASSERTION = "Expected file to be json"
@classmethod
def get_generators(cls, include_parents=True):
""" Recursively find subclasses
Args:
include_parents (bool): whether to include parents to other classes. (default: `True`)
Returns:
generators: dictionary with all the subclasses
"""
generators = dict()
for child in cls.__subclasses__():
children = child.get_generators(include_parents)
generators.update(children)
if include_parents or not children:
if abc.ABC not in child.__bases__ and BaseGenerator not in child.__bases__:
generators[child.__name__] = child
return generators
def save(self, path):
raise NotImplementedError()
@classmethod
def load(cls, path):
raise NotImplementedError()
class BaseGraphGenerator(BaseGenerator, ABC):
""" Base class for all graph generators
Args:
*args: optional positional args
**kwargs: optional key-word args
"""
def __init__(
self,
seed: Optional[int] = None,
logdir: str = "./logs",
gpu: bool = True,
verbose: bool = False,
*args,
**kwargs,
):
self._fit_results = None
self.seeder = BaseSeeder(seed)
self.seeder.reseed()
self.logger = BaseLogger(logdir)
self.logger.log(f"Using seed: {self.seeder.seed}")
self.gpu = gpu
self.verbose = verbose
def fit(
self, graph: List[Tuple[int, int]], is_directed: bool, *args, **kwargs
):
""" Fits generator on the graph
Args:
graph (List[Tuple[int, int]]): graph to be fitted on
is_directed (bool): flag indicating whether the graph is directed
*args: optional positional args
**kwargs: optional key-word args
"""
raise NotImplementedError()
def generate(
self,
num_nodes: int,
num_edges: int,
is_directed: bool,
*args,
return_node_ids: bool = False,
**kwargs,
) -> np.ndarray:
""" Generates graph with approximately `num_nodes` and exactly `num_edges` from generator
Args:
num_nodes (int): approximate number of nodes to be generated
num_edges (int): exact number of edges to be generated
is_directed (bool): flag indicating whether the generated graph has to be directed
return_node_ids (bool): flag indicating whether the generator has to return nodes_ids as the second output
*args: optional positional args
**kwargs: optional key-word args
"""
raise NotImplementedError()
def set_fit_results(self, fit_results):
self._fit_results = fit_results
def get_fit_results(self):
return self._fit_results
def save_fit_results(self, save_path: str = "./fit_results.json"):
""" Store fitted results into json file
Args:
save_path (str): path to the json file with the fitted result
"""
assert (
self._fit_results
), "There are no fit results to be saved, \
call fit method first or load the results from the file"
assert save_path.endswith(".json"), self.JSON_ASSERTION
with open(save_path, "w") as fjson:
json.dump(self._fit_results, fjson)
def load_fit_results(self, load_path: str = "./fit_results.json"):
"""load fitted results from json file
Args:
load_path (str): path to the json file with the fitted result
"""
assert load_path.endswith(".json"), self.JSON_ASSERTION
with open(load_path, "r") as fjson:
self._fit_results = json.load(fjson)
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
@staticmethod
def add_args(parser):
return parser
class BaseBipartiteGraphGenerator(BaseGenerator, ABC):
""" Base class for all bipartite graph generators
Args:
*args: optional positional args
**kwargs: optional key-word args
"""
def __init__(
self,
seed: Optional[int] = None,
logdir: str = "./logs",
gpu: bool = True,
verbose: bool = False,
*args,
**kwargs,
):
self._fit_src_dst_results = None
self._fit_dst_src_results = None
self.seeder = BaseSeeder(seed)
self.seeder.reseed()
self.logger = BaseLogger(logdir)
self.logger.log(f"Using seed: {self.seeder.seed}")
self.gpu = gpu
self.verbose = verbose
def fit(
self,
graph: List[Tuple[int, int]],
src_set: Set[int],
dst_set: Set[int],
is_directed: bool,
transform_graph: bool,
*args,
**kwargs,
):
""" Fits generator on the graph
Args:
graph (List[Tuple[int, int]]): graph to be fitted on
src_set (Set[int]): set of source nodes
dst_set (Set[int]): set of destination nodes
is_directed (bool): flag indicating whether the graph is directed
*args: optional positional args
**kwargs: optional key-word args
"""
raise NotImplementedError()
def generate(
self,
num_nodes_src_set: int,
num_nodes_dst_set: int,
num_edges_src_dst: int,
num_edges_dst_src: int,
is_directed: bool,
return_node_ids: bool = False,
transform_graph: bool = True,
*args,
**kwargs,
):
""" Generates graph with approximately `num_nodes_src_set`/`num_nodes_dst_set` nodes
and exactly `num_edges_src_dst`/`num_edges_dst_src` edges from generator
Args:
num_nodes_src_set (int): approximate number of source nodes to be generated
num_nodes_dst_set (int): approximate number of destination nodes to be generated
num_edges_src_dst (int): exact number of source->destination edges to be generated
num_edges_dst_src (int): exact number of destination->source to be generated
is_directed (bool) flag indicating whether the generated graph has to be directed
return_node_ids (bool): flag indicating whether the generator has to return nodes_ids as the second output
*args: optional positional args
**kwargs: optional key-word args
"""
raise NotImplementedError()
def set_fit_results(self, fit_results):
self._fit_src_dst_results, self._fit_dst_src_results = fit_results
def get_fit_results(self):
return self._fit_src_dst_results, self._fit_dst_src_results
def save_fit_results(self, save_path: str = "./fit_results.json"):
""" Stores fitted results into json file
Args:
save_path (str): path to the json file with the fitted result
"""
assert (
self._fit_src_dst_results or self._fit_dst_src_results
), "There are no fit results to be saved, \
call fit method first or load the results from the file"
assert save_path.endswith(".json"), self.JSON_ASSERTION
wrapped_results = {
"fit_src_dst_results": self._fit_src_dst_results,
"fit_dst_src_results": self._fit_dst_src_results,
}
with open(save_path, "w") as fjson:
json.dump(wrapped_results, fjson)
def load_fit_results(self, load_path: str = "./fit_results.json"):
""" Loads fitted results from json file
Args:
load_path (str): path to the json file with the fitted result
"""
assert load_path.endswith(".json"), self.JSON_ASSERTION
with open(load_path, "r") as fjson:
wrapped_results = json.load(fjson)
assert (
"fit_src_dst_results" in wrapped_results
and "fit_dst_src_results" in wrapped_results
), "Required keys fit_src_dst_results and fit_dst_src_results keys in the json not found"
self._fit_src_dst_results = wrapped_results["fit_src_dst_results"]
self._fit_dst_src_results = wrapped_results["fit_dst_src_results"]
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
@staticmethod
def add_args(parser):
return parser
|
CUDA-Optimized/FastSpeech/tacotron2 | tacotron2 | distributed | # BSD 3-Clause License
# Copyright (c) 2018-2020, 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 copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""https://github.com/NVIDIA/tacotron2"""
import torch
import torch.distributed as dist
from torch.nn.modules import Module
from torch.autograd import Variable
def _flatten_dense_tensors(tensors):
"""Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
same dense type.
Since inputs are dense, the resulting tensor will be a concatenated 1D
buffer. Element-wise operation on this buffer will be equivalent to
operating individually.
Arguments:
tensors (Iterable[Tensor]): dense tensors to flatten.
Returns:
A contiguous 1D buffer containing input tensors.
"""
if len(tensors) == 1:
return tensors[0].contiguous().view(-1)
flat = torch.cat([t.contiguous().view(-1) for t in tensors], dim=0)
return flat
def _unflatten_dense_tensors(flat, tensors):
"""View a flat buffer using the sizes of tensors. Assume that tensors are of
same dense type, and that flat is given by _flatten_dense_tensors.
Arguments:
flat (Tensor): flattened dense tensors to unflatten.
tensors (Iterable[Tensor]): dense tensors whose sizes will be used to
unflatten flat.
Returns:
Unflattened dense tensors with sizes same as tensors and values from
flat.
"""
outputs = []
offset = 0
for tensor in tensors:
numel = tensor.numel()
outputs.append(flat.narrow(0, offset, numel).view_as(tensor))
offset += numel
return tuple(outputs)
'''
This version of DistributedDataParallel is designed to be used in conjunction with the multiproc.py
launcher included with this example. It assumes that your run is using multiprocess with 1
GPU/process, that the model is on the correct device, and that torch.set_device has been
used to set the device.
Parameters are broadcasted to the other processes on initialization of DistributedDataParallel,
and will be allreduced at the finish of the backward pass.
'''
class DistributedDataParallel(Module):
def __init__(self, module):
super(DistributedDataParallel, self).__init__()
#fallback for PyTorch 0.3
if not hasattr(dist, '_backend'):
self.warn_on_half = True
else:
self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
self.module = module
for p in self.module.state_dict().values():
if not torch.is_tensor(p):
continue
dist.broadcast(p, 0)
def allreduce_params():
if(self.needs_reduction):
self.needs_reduction = False
buckets = {}
for param in self.module.parameters():
if param.requires_grad and param.grad is not None:
tp = type(param.data)
if tp not in buckets:
buckets[tp] = []
buckets[tp].append(param)
if self.warn_on_half:
if torch.cuda.HalfTensor in buckets:
print("WARNING: gloo dist backend for half parameters may be extremely slow." +
" It is recommended to use the NCCL backend in this case. This currently requires" +
"PyTorch built from top of tree master.")
self.warn_on_half = False
for tp in buckets:
bucket = buckets[tp]
grads = [param.grad.data for param in bucket]
coalesced = _flatten_dense_tensors(grads)
dist.all_reduce(coalesced)
coalesced /= dist.get_world_size()
for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
buf.copy_(synced)
for param in list(self.module.parameters()):
def allreduce_hook(*unused):
param._execution_engine.queue_callback(allreduce_params)
if param.requires_grad:
param.register_hook(allreduce_hook)
def forward(self, *inputs, **kwargs):
self.needs_reduction = True
return self.module(*inputs, **kwargs)
'''
def _sync_buffers(self):
buffers = list(self.module._all_buffers())
if len(buffers) > 0:
# cross-node buffer sync
flat_buffers = _flatten_dense_tensors(buffers)
dist.broadcast(flat_buffers, 0)
for buf, synced in zip(buffers, _unflatten_dense_tensors(flat_buffers, buffers)):
buf.copy_(synced)
def train(self, mode=True):
# Clear NCCL communicator and CUDA event cache of the default group ID,
# These cache will be recreated at the later call. This is currently a
# work-around for a potential NCCL deadlock.
if dist._backend == dist.dist_backend.NCCL:
dist._clear_group_cache()
super(DistributedDataParallel, self).train(mode)
self.module.train(mode)
'''
'''
Modifies existing model to do gradient allreduce, but doesn't change class
so you don't need "module"
'''
def apply_gradient_allreduce(module):
if not hasattr(dist, '_backend'):
module.warn_on_half = True
else:
module.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
for p in module.state_dict().values():
if not torch.is_tensor(p):
continue
dist.broadcast(p, 0)
def allreduce_params():
if(module.needs_reduction):
module.needs_reduction = False
buckets = {}
for param in module.parameters():
if param.requires_grad and param.grad is not None:
tp = param.data.dtype
if tp not in buckets:
buckets[tp] = []
buckets[tp].append(param)
if module.warn_on_half:
if torch.cuda.HalfTensor in buckets:
print("WARNING: gloo dist backend for half parameters may be extremely slow." +
" It is recommended to use the NCCL backend in this case. This currently requires" +
"PyTorch built from top of tree master.")
module.warn_on_half = False
for tp in buckets:
bucket = buckets[tp]
grads = [param.grad.data for param in bucket]
coalesced = _flatten_dense_tensors(grads)
dist.all_reduce(coalesced)
coalesced /= dist.get_world_size()
for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
buf.copy_(synced)
for param in list(module.parameters()):
def allreduce_hook(*unused):
Variable._execution_engine.queue_callback(allreduce_params)
if param.requires_grad:
param.register_hook(allreduce_hook)
def set_needs_reduction(self, input, output):
self.needs_reduction = True
module.register_forward_hook(set_needs_reduction)
return module
|
TensorFlow/Detection/SSD/models/research/slim/preprocessing | preprocessing | inception_preprocessing | # 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 utilities to preprocess images for the Inception networks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
def apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
for case in range(num_cases)])[0]
def distort_color(image, color_ordering=0, fast_mode=True, scope=None):
"""Distort the color of a Tensor image.
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a distinct ordering
of color ops for each preprocessing thread.
Args:
image: 3-D Tensor containing single image in [0, 1].
color_ordering: Python int, a type of distortion (valid values: 0-3).
fast_mode: Avoids slower ops (random_hue and random_contrast)
scope: Optional scope for name_scope.
Returns:
3-D Tensor color-distorted image on range [0, 1]
Raises:
ValueError: if color_ordering not in [0, 3]
"""
with tf.name_scope(scope, 'distort_color', [image]):
if fast_mode:
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
else:
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_brightness(image, max_delta=32. / 255.)
else:
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
elif color_ordering == 1:
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
elif color_ordering == 2:
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
elif color_ordering == 3:
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_brightness(image, max_delta=32. / 255.)
else:
raise ValueError('color_ordering must be in [0, 3]')
# The random_* ops do not necessarily clamp.
return tf.clip_by_value(image, 0.0, 1.0)
def distorted_bounding_box_crop(image,
bbox,
min_object_covered=0.1,
aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0),
max_attempts=100,
scope=None):
"""Generates cropped_image using a one of the bboxes randomly distorted.
See `tf.image.sample_distorted_bounding_box` for more documentation.
Args:
image: 3-D Tensor of image (it will be converted to floats in [0, 1]).
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates are arranged
as [ymin, xmin, ymax, xmax]. If num_boxes is 0 then it would use the whole
image.
min_object_covered: An optional `float`. Defaults to `0.1`. The cropped
area of the image must contain at least this fraction of any bounding box
supplied.
aspect_ratio_range: An optional list of `floats`. The cropped area of the
image must have an aspect ratio = width / height within this range.
area_range: An optional list of `floats`. The cropped area of the image
must contain a fraction of the supplied image within in this range.
max_attempts: An optional `int`. Number of attempts at generating a cropped
region of the image of the specified constraints. After `max_attempts`
failures, return the entire image.
scope: Optional scope for name_scope.
Returns:
A tuple, a 3-D Tensor cropped_image and the distorted bbox
"""
with tf.name_scope(scope, 'distorted_bounding_box_crop', [image, bbox]):
# Each bounding box has shape [1, num_boxes, box coords] and
# the coordinates are ordered [ymin, xmin, ymax, xmax].
# A large fraction of image datasets contain a human-annotated bounding
# box delineating the region of the image containing the object of interest.
# We choose to create a new bounding box for the object which is a randomly
# distorted version of the human-annotated bounding box that obeys an
# allowed range of aspect ratios, sizes and overlap with the human-annotated
# bounding box. If no box is supplied, then we assume the bounding box is
# the entire image.
sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
tf.shape(image),
bounding_boxes=bbox,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
max_attempts=max_attempts,
use_image_if_no_bounding_boxes=True)
bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box
# Crop the image to the specified bounding box.
cropped_image = tf.slice(image, bbox_begin, bbox_size)
return cropped_image, distort_bbox
def preprocess_for_train(image, height, width, bbox,
fast_mode=True,
scope=None,
add_image_summaries=True):
"""Distort one image for training a network.
Distorting images provides a useful technique for augmenting the data
set during training in order to make the network invariant to aspects
of the image that do not effect the label.
Additionally it would create image_summaries to display the different
transformations applied to the image.
Args:
image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
[0, 1], otherwise it would converted to tf.float32 assuming that the range
is [0, MAX], where MAX is largest positive representable number for
int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
height: integer
width: integer
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates are arranged
as [ymin, xmin, ymax, xmax].
fast_mode: Optional boolean, if True avoids slower transformations (i.e.
bi-cubic resizing, random_hue or random_contrast).
scope: Optional scope for name_scope.
add_image_summaries: Enable image summaries.
Returns:
3-D float Tensor of distorted image used for training with range [-1, 1].
"""
with tf.name_scope(scope, 'distort_image', [image, height, width, bbox]):
if bbox is None:
bbox = tf.constant([0.0, 0.0, 1.0, 1.0],
dtype=tf.float32,
shape=[1, 1, 4])
if image.dtype != tf.float32:
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
# Each bounding box has shape [1, num_boxes, box coords] and
# the coordinates are ordered [ymin, xmin, ymax, xmax].
image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
bbox)
if add_image_summaries:
tf.summary.image('image_with_bounding_boxes', image_with_box)
distorted_image, distorted_bbox = distorted_bounding_box_crop(image, bbox)
# Restore the shape since the dynamic slice based upon the bbox_size loses
# the third dimension.
distorted_image.set_shape([None, None, 3])
image_with_distorted_box = tf.image.draw_bounding_boxes(
tf.expand_dims(image, 0), distorted_bbox)
if add_image_summaries:
tf.summary.image('images_with_distorted_bounding_box',
image_with_distorted_box)
# This resizing operation may distort the images because the aspect
# ratio is not respected. We select a resize method in a round robin
# fashion based on the thread number.
# Note that ResizeMethod contains 4 enumerated resizing methods.
# We select only 1 case for fast_mode bilinear.
num_resize_cases = 1 if fast_mode else 4
distorted_image = apply_with_random_selector(
distorted_image,
lambda x, method: tf.image.resize_images(x, [height, width], method),
num_cases=num_resize_cases)
if add_image_summaries:
tf.summary.image('cropped_resized_image',
tf.expand_dims(distorted_image, 0))
# Randomly flip the image horizontally.
distorted_image = tf.image.random_flip_left_right(distorted_image)
# Randomly distort the colors. There are 1 or 4 ways to do it.
num_distort_cases = 1 if fast_mode else 4
distorted_image = apply_with_random_selector(
distorted_image,
lambda x, ordering: distort_color(x, ordering, fast_mode),
num_cases=num_distort_cases)
if add_image_summaries:
tf.summary.image('final_distorted_image',
tf.expand_dims(distorted_image, 0))
distorted_image = tf.subtract(distorted_image, 0.5)
distorted_image = tf.multiply(distorted_image, 2.0)
return distorted_image
def preprocess_for_eval(image, height, width,
central_fraction=0.875, scope=None):
"""Prepare one image for evaluation.
If height and width are specified it would output an image with that size by
applying resize_bilinear.
If central_fraction is specified it would crop the central fraction of the
input image.
Args:
image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
[0, 1], otherwise it would converted to tf.float32 assuming that the range
is [0, MAX], where MAX is largest positive representable number for
int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
height: integer
width: integer
central_fraction: Optional Float, fraction of the image to crop.
scope: Optional scope for name_scope.
Returns:
3-D float Tensor of prepared image.
"""
with tf.name_scope(scope, 'eval_image', [image, height, width]):
if image.dtype != tf.float32:
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
# Crop the central region of the image with an area containing 87.5% of
# the original image.
if central_fraction:
image = tf.image.central_crop(image, central_fraction=central_fraction)
if height and width:
# Resize the image to the specified height and width.
image = tf.expand_dims(image, 0)
image = tf.image.resize_bilinear(image, [height, width],
align_corners=False)
image = tf.squeeze(image, [0])
image = tf.subtract(image, 0.5)
image = tf.multiply(image, 2.0)
return image
def preprocess_image(image, height, width,
is_training=False,
bbox=None,
fast_mode=True,
add_image_summaries=True):
"""Pre-process one image for training or evaluation.
Args:
image: 3-D Tensor [height, width, channels] with the image. If dtype is
tf.float32 then the range should be [0, 1], otherwise it would converted
to tf.float32 assuming that the range is [0, MAX], where MAX is largest
positive representable number for int(8/16/32) data type (see
`tf.image.convert_image_dtype` for details).
height: integer, image expected height.
width: integer, image expected width.
is_training: Boolean. If true it would transform an image for train,
otherwise it would transform it for evaluation.
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates are arranged as
[ymin, xmin, ymax, xmax].
fast_mode: Optional boolean, if True avoids slower transformations.
add_image_summaries: Enable image summaries.
Returns:
3-D float Tensor containing an appropriately scaled image
Raises:
ValueError: if user does not provide bounding box
"""
if is_training:
return preprocess_for_train(image, height, width, bbox, fast_mode,
add_image_summaries=add_image_summaries)
else:
return preprocess_for_eval(image, height, width)
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | test_utils | # 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 functions which are convenient for unit testing."""
import numpy as np
import tensorflow as tf
from object_detection.core import anchor_generator
from object_detection.core import box_coder
from object_detection.core import box_list
from object_detection.core import box_predictor
from object_detection.core import matcher
from object_detection.utils import shape_utils
# Default size (both width and height) used for testing mask predictions.
DEFAULT_MASK_SIZE = 5
class MockBoxCoder(box_coder.BoxCoder):
"""Simple `difference` BoxCoder."""
@property
def code_size(self):
return 4
def _encode(self, boxes, anchors):
return boxes.get() - anchors.get()
def _decode(self, rel_codes, anchors):
return box_list.BoxList(rel_codes + anchors.get())
class MockMaskHead(object):
"""Simple maskhead that returns all zeros as mask predictions."""
def __init__(self, num_classes):
self._num_classes = num_classes
def predict(self, features):
batch_size = tf.shape(features)[0]
return tf.zeros((batch_size, 1, self._num_classes, DEFAULT_MASK_SIZE,
DEFAULT_MASK_SIZE),
dtype=tf.float32)
class MockBoxPredictor(box_predictor.BoxPredictor):
"""Simple box predictor that ignores inputs and outputs all zeros."""
def __init__(self, is_training, num_classes, add_background_class=True):
super(MockBoxPredictor, self).__init__(is_training, num_classes)
self._add_background_class = add_background_class
def _predict(self, image_features, num_predictions_per_location):
image_feature = image_features[0]
combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
image_feature)
batch_size = combined_feature_shape[0]
num_anchors = (combined_feature_shape[1] * combined_feature_shape[2])
code_size = 4
zero = tf.reduce_sum(0 * image_feature)
num_class_slots = self.num_classes
if self._add_background_class:
num_class_slots = num_class_slots + 1
box_encodings = zero + tf.zeros(
(batch_size, num_anchors, 1, code_size), dtype=tf.float32)
class_predictions_with_background = zero + tf.zeros(
(batch_size, num_anchors, num_class_slots), dtype=tf.float32)
predictions_dict = {
box_predictor.BOX_ENCODINGS:
box_encodings,
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
class_predictions_with_background
}
return predictions_dict
class MockKerasBoxPredictor(box_predictor.KerasBoxPredictor):
"""Simple box predictor that ignores inputs and outputs all zeros."""
def __init__(self, is_training, num_classes, add_background_class=True):
super(MockKerasBoxPredictor, self).__init__(
is_training, num_classes, False, False)
self._add_background_class = add_background_class
def _predict(self, image_features, **kwargs):
image_feature = image_features[0]
combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
image_feature)
batch_size = combined_feature_shape[0]
num_anchors = (combined_feature_shape[1] * combined_feature_shape[2])
code_size = 4
zero = tf.reduce_sum(0 * image_feature)
num_class_slots = self.num_classes
if self._add_background_class:
num_class_slots = num_class_slots + 1
box_encodings = zero + tf.zeros(
(batch_size, num_anchors, 1, code_size), dtype=tf.float32)
class_predictions_with_background = zero + tf.zeros(
(batch_size, num_anchors, num_class_slots), dtype=tf.float32)
predictions_dict = {
box_predictor.BOX_ENCODINGS:
box_encodings,
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
class_predictions_with_background
}
return predictions_dict
class MockAnchorGenerator(anchor_generator.AnchorGenerator):
"""Mock anchor generator."""
def name_scope(self):
return 'MockAnchorGenerator'
def num_anchors_per_location(self):
return [1]
def _generate(self, feature_map_shape_list):
num_anchors = sum([shape[0] * shape[1] for shape in feature_map_shape_list])
return box_list.BoxList(tf.zeros((num_anchors, 4), dtype=tf.float32))
class MockMatcher(matcher.Matcher):
"""Simple matcher that matches first anchor to first groundtruth box."""
def _match(self, similarity_matrix, valid_rows):
return tf.constant([0, -1, -1, -1], dtype=tf.int32)
def create_diagonal_gradient_image(height, width, depth):
"""Creates pyramid image. Useful for testing.
For example, pyramid_image(5, 6, 1) looks like:
# [[[ 5. 4. 3. 2. 1. 0.]
# [ 6. 5. 4. 3. 2. 1.]
# [ 7. 6. 5. 4. 3. 2.]
# [ 8. 7. 6. 5. 4. 3.]
# [ 9. 8. 7. 6. 5. 4.]]]
Args:
height: height of image
width: width of image
depth: depth of image
Returns:
pyramid image
"""
row = np.arange(height)
col = np.arange(width)[::-1]
image_layer = np.expand_dims(row, 1) + col
image_layer = np.expand_dims(image_layer, 2)
image = image_layer
for i in range(1, depth):
image = np.concatenate((image, image_layer * pow(10, i)), 2)
return image.astype(np.float32)
def create_random_boxes(num_boxes, max_height, max_width):
"""Creates random bounding boxes of specific maximum height and width.
Args:
num_boxes: number of boxes.
max_height: maximum height of boxes.
max_width: maximum width of boxes.
Returns:
boxes: numpy array of shape [num_boxes, 4]. Each row is in form
[y_min, x_min, y_max, x_max].
"""
y_1 = np.random.uniform(size=(1, num_boxes)) * max_height
y_2 = np.random.uniform(size=(1, num_boxes)) * max_height
x_1 = np.random.uniform(size=(1, num_boxes)) * max_width
x_2 = np.random.uniform(size=(1, num_boxes)) * max_width
boxes = np.zeros(shape=(num_boxes, 4))
boxes[:, 0] = np.minimum(y_1, y_2)
boxes[:, 1] = np.minimum(x_1, x_2)
boxes[:, 2] = np.maximum(y_1, y_2)
boxes[:, 3] = np.maximum(x_1, x_2)
return boxes.astype(np.float32)
def first_rows_close_as_set(a, b, k=None, rtol=1e-6, atol=1e-6):
"""Checks if first K entries of two lists are close, up to permutation.
Inputs to this assert are lists of items which can be compared via
numpy.allclose(...) and can be sorted.
Args:
a: list of items which can be compared via numpy.allclose(...) and are
sortable.
b: list of items which can be compared via numpy.allclose(...) and are
sortable.
k: a non-negative integer. If not provided, k is set to be len(a).
rtol: relative tolerance.
atol: absolute tolerance.
Returns:
boolean, True if input lists a and b have the same length and
the first k entries of the inputs satisfy numpy.allclose() after
sorting entries.
"""
if not isinstance(a, list) or not isinstance(b, list) or len(a) != len(b):
return False
if not k:
k = len(a)
k = min(k, len(a))
a_sorted = sorted(a[:k])
b_sorted = sorted(b[:k])
return all([
np.allclose(entry_a, entry_b, rtol, atol)
for (entry_a, entry_b) in zip(a_sorted, b_sorted)
])
|
TensorFlow/LanguageModeling/BERT/biobert/scripts | scripts | run_pretraining_pubmed_base_phase_2 | #! /bin/bash
echo "Container nvidia build = " $NVIDIA_BUILD_ID
init_checkpoint=${1}
train_batch_size=${2:-16}
learning_rate=${3:-"2.9e-4"}
cased=${4:-false}
precision=${5:-"fp16"}
use_xla=${6:-true}
num_gpu=${7:-16}
warmup_steps=${8:-"434"}
train_steps=${9:-4340}
num_accumulation_steps=${10:-128}
save_checkpoint_steps=${11:-5000}
eval_batch_size=${12:-26}
use_fp16=""
if [ "$precision" = "fp16" ] ; then
echo "fp16 activated!"
use_fp16="--amp"
else
echo "fp32/tf32 activated!"
use_fp16="--noamp"
fi
if [ "$use_xla" = "true" ] ; then
use_xla_tag="--use_xla"
echo "XLA activated"
else
use_xla_tag="--nouse_xla"
fi
if [ "$cased" = "true" ] ; then
DO_LOWER_CASE=0
CASING_DIR_PREFIX="cased"
else
DO_LOWER_CASE=1
CASING_DIR_PREFIX="uncased"
fi
BERT_CONFIG=/workspace/bert/data/download/google_pretrained_weights/${CASING_DIR_PREFIX}_L-12_H-768_A-12/bert_config.json
RESULTS_DIR=/results
CHECKPOINTS_DIR=${RESULTS_DIR}/biobert_phase_2
mkdir -p ${CHECKPOINTS_DIR}
INPUT_FILES_DIR="/workspace/bert/data/tfrecord/lower_case_${DO_LOWER_CASE}_seq_len_512_max_pred_80_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/pubmed_baseline/training"
EVAL_FILES_DIR="/workspace/bert/data/tfrecord/lower_case_${DO_LOWER_CASE}_seq_len_512_max_pred_80_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/pubmed_baseline/test"
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
export GBS=$(expr $train_batch_size \* $num_gpus \* num_accumulation_steps)
printf -v TAG "tf_bert_bio_1n_phase2_cased_%s_%s_gbs%d" "$cased" "$precision" $GBS
DATESTAMP=`date +'%y%m%d%H%M%S'`
LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log
printf "Logs written to %s\n" "$LOGFILE"
$mpi python3 /workspace/bert/run_pretraining.py \
--input_files_dir=$INPUT_FILES_DIR \
--eval_files_dir=$EVAL_FILES_DIR \
--output_dir=$CHECKPOINTS_DIR \
--bert_config_file=$BERT_CONFIG \
--do_train=True \
--do_eval=True \
--train_batch_size=$train_batch_size \
--eval_batch_size=$eval_batch_size \
--max_seq_length=512 \
--max_predictions_per_seq=80 \
--num_train_steps=$train_steps \
--num_warmup_steps=$warmup_steps \
--save_checkpoints_steps=$save_checkpoint_steps \
--num_accumulation_steps=$num_accumulation_steps \
--learning_rate=$learning_rate \
--report_loss \
$use_hvd $use_xla_tag $use_fp16 \
--init_checkpoint=$INIT_CHECKPOINT |& tee $LOGFILE |
PyTorch/Recommendation/DLRM/preproc/gpu | gpu | get_gpu_resources | #! /bin/bash
ADDRS=`nvidia-smi --query-gpu=index --format=csv,noheader | sed -e ':a' -e 'N' -e'$!ba' -e 's/\n/","/g'`
echo {\"name\": \"gpu\", \"addresses\":[\"$ADDRS\"]}
|
TensorFlow2/Recommendation/WideAndDeep/tests/feature_specs | feature_specs | less_numerical | channel_spec:
label:
- clicked
map: []
multihot_categorical:
- topic_id_list
- entity_id_list
- category_id_list
numerical:
- document_id_promo_ctr
- publisher_id_promo_ctr
- source_id_promo_ctr
- document_id_promo_count
- publish_time_days_since_published
- ad_id_ctr
- advertiser_id_ctr
- campaign_id_ctr
- ad_id_count
- publish_time_promo_days_since_published
onehot_categorical:
- ad_id
- document_id
- platform
- document_id_promo
- campaign_id
- advertiser_id
- source_id
- geo_location
- geo_location_country
- geo_location_state
- publisher_id
- source_id_promo
- publisher_id_promo
feature_spec:
ad_id:
cardinality: 250000
ad_id_count: {}
ad_id_ctr: {}
advertiser_id:
cardinality: 2500
advertiser_id_ctr: {}
campaign_id:
cardinality: 5000
campaign_id_ctr: {}
category_id_list:
cardinality: 100
max_hotness: 3
clicked: {}
document_id:
cardinality: 300000
document_id_promo:
cardinality: 100000
document_id_promo_count: {}
document_id_promo_ctr: {}
entity_id_list:
cardinality: 10000
max_hotness: 3
geo_location:
cardinality: 2500
geo_location_country:
cardinality: 300
geo_location_state:
cardinality: 2000
platform:
cardinality: 4
publish_time_days_since_published: {}
publish_time_promo_days_since_published: {}
publisher_id:
cardinality: 1000
publisher_id_promo:
cardinality: 1000
publisher_id_promo_ctr: {}
source_id:
cardinality: 4000
source_id_promo:
cardinality: 4000
source_id_promo_ctr: {}
topic_id_list:
cardinality: 350
max_hotness: 3
metadata: {}
source_spec:
test:
- features:
- clicked
- ad_id
- document_id
- platform
- document_id_promo
- campaign_id
- advertiser_id
- source_id
- geo_location
- geo_location_country
- geo_location_state
- publisher_id
- source_id_promo
- publisher_id_promo
- topic_id_list
- entity_id_list
- category_id_list
- document_id_promo_ctr
- publisher_id_promo_ctr
- source_id_promo_ctr
- document_id_promo_count
- publish_time_days_since_published
- ad_id_ctr
- advertiser_id_ctr
- campaign_id_ctr
- ad_id_count
- publish_time_promo_days_since_published
files:
- valid.csv
type: csv
train:
- features:
- clicked
- ad_id
- document_id
- platform
- document_id_promo
- campaign_id
- advertiser_id
- source_id
- geo_location
- geo_location_country
- geo_location_state
- publisher_id
- source_id_promo
- publisher_id_promo
- topic_id_list
- entity_id_list
- category_id_list
- document_id_promo_ctr
- publisher_id_promo_ctr
- source_id_promo_ctr
- document_id_promo_count
- publish_time_days_since_published
- ad_id_ctr
- advertiser_id_ctr
- campaign_id_ctr
- ad_id_count
- publish_time_promo_days_since_published
files:
- train.csv
type: csv
|
Tools/PyTorch/TimeSeriesPredictionPlatform | TimeSeriesPredictionPlatform | launch_preproc | # 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 warnings
import hydra
warnings.filterwarnings("ignore")
@hydra.main(config_path="conf/", config_name="preproc_config")
def main(cfg):
print(cfg)
preprocessor = hydra.utils.instantiate(cfg, _recursive_=False)
train, valid, test = preprocessor.preprocess()
preprocessor.fit_scalers(train)
train = preprocessor.apply_scalers(train)
valid = preprocessor.apply_scalers(valid)
test = preprocessor.apply_scalers(test)
train = preprocessor.impute(train)
valid = preprocessor.impute(valid)
test = preprocessor.impute(test)
preprocessor.save_state()
preprocessor.save_datasets(train, valid, test)
if __name__ == "__main__":
main()
|
PyTorch/LanguageModeling/BERT/triton/large/runner | runner | start_NVIDIA-DGX-1-(1x-V100-32GB) | # 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.large.runner.__main__" \
--config-path "triton/large/runner/config_NVIDIA-DGX-1-(1x-V100-32GB).yaml" \
--device 0 |