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PyTorch/LanguageModeling/BERT/scripts/configs | configs | pretrain_config | #!/usr/bin/env bash
# Copyright (c) 2020-2021 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.
dgxa100-80g_8gpu_fp16 ()
{
train_batch_size="8192"
learning_rate="6e-3"
precision="fp16"
num_gpus=8
warmup_proportion="0.2843"
train_steps=7038
save_checkpoint_steps=200
resume_training="false"
create_logfile="true"
accumulate_gradients="true"
gradient_accumulation_steps=32
seed=42
job_name="bert_lamb_pretraining"
allreduce_post_accumulation="true"
allreduce_post_accumulation_fp16="true"
train_batch_size_phase2=4096
learning_rate_phase2="4e-3"
warmup_proportion_phase2="0.128"
train_steps_phase2=1563
gradient_accumulation_steps_phase2=128
DATASET=pretrain/phase1/unbinned/parquet # change this for other datasets
DATA_DIR_PHASE1="$BERT_PREP_WORKING_DIR/${DATASET}/"
BERT_CONFIG=bert_configs/large.json
CODEDIR="/workspace/bert"
init_checkpoint="None"
DATASET2=pretrain/phase2/bin_size_64/parquet # change this for other datasets
DATA_DIR_PHASE2="$BERT_PREP_WORKING_DIR/${DATASET2}/"
wikipedia_source=$BERT_PREP_WORKING_DIR/wikipedia/source/
num_dask_workers=128
num_shards_per_worker=128
num_workers=4
sample_ratio="0.9"
phase2_bin_size=64
masking=static
echo $train_batch_size $learning_rate $precision $num_gpus \
$warmup_proportion $train_steps $save_checkpoint_steps \
$resume_training $create_logfile $accumulate_gradients \
$gradient_accumulation_steps $seed $job_name $allreduce_post_accumulation \
$allreduce_post_accumulation_fp16 $train_batch_size_phase2 $learning_rate_phase2 \
$warmup_proportion_phase2 $train_steps_phase2 $gradient_accumulation_steps_phase2 \
$DATA_DIR_PHASE1 $DATA_DIR_PHASE2 $CODEDIR $init_checkpoint \
$wikipedia_source $num_dask_workers $num_shards_per_worker $num_workers \
$sample_ratio $phase2_bin_size $masking \
$BERT_CONFIG
}
dgxa100-80g_8gpu_tf32 ()
{
train_batch_size="8192"
learning_rate="6e-3"
precision="tf32"
num_gpus=8
warmup_proportion="0.2843"
train_steps=7038
save_checkpoint_steps=200
resume_training="false"
create_logfile="true"
accumulate_gradients="true"
gradient_accumulation_steps=64
seed=42
job_name="bert_lamb_pretraining"
allreduce_post_accumulation="true"
allreduce_post_accumulation_fp16="false"
train_batch_size_phase2=4096
learning_rate_phase2="4e-3"
warmup_proportion_phase2="0.128"
train_steps_phase2=1563
gradient_accumulation_steps_phase2=256
DATASET=pretrain/phase1/unbinned/parquet # change this for other datasets
DATA_DIR_PHASE1="$BERT_PREP_WORKING_DIR/${DATASET}/"
BERT_CONFIG=bert_configs/large.json
CODEDIR="/workspace/bert"
init_checkpoint="None"
DATASET2=pretrain/phase2/bin_size_64/parquet # change this for other datasets
DATA_DIR_PHASE2="$BERT_PREP_WORKING_DIR/${DATASET2}/"
wikipedia_source=$BERT_PREP_WORKING_DIR/wikipedia/source/
num_dask_workers=128
num_shards_per_worker=128
num_workers=4
sample_ratio="0.9"
phase2_bin_size=64
masking=static
echo $train_batch_size $learning_rate $precision $num_gpus \
$warmup_proportion $train_steps $save_checkpoint_steps \
$resume_training $create_logfile $accumulate_gradients \
$gradient_accumulation_steps $seed $job_name $allreduce_post_accumulation \
$allreduce_post_accumulation_fp16 $train_batch_size_phase2 $learning_rate_phase2 \
$warmup_proportion_phase2 $train_steps_phase2 $gradient_accumulation_steps_phase2 \
$DATA_DIR_PHASE1 $DATA_DIR_PHASE2 $CODEDIR $init_checkpoint \
$wikipedia_source $num_dask_workers $num_shards_per_worker $num_workers \
$sample_ratio $phase2_bin_size $masking \
$BERT_CONFIG
}
dgx1-32g_8gpu_fp16 ()
{
train_batch_size="8192"
learning_rate="6e-3"
precision="fp16"
num_gpus=8
warmup_proportion="0.2843"
train_steps=7038
save_checkpoint_steps=200
resume_training="false"
create_logfile="true"
accumulate_gradients="true"
gradient_accumulation_steps=64
seed=42
job_name="bert_lamb_pretraining"
allreduce_post_accumulation="true"
allreduce_post_accumulation_fp16="true"
train_batch_size_phase2=4096
learning_rate_phase2="4e-3"
warmup_proportion_phase2="0.128"
train_steps_phase2=1563
gradient_accumulation_steps_phase2=256
DATASET=pretrain/phase1/unbinned/parquet # change this for other datasets
DATA_DIR_PHASE1="$BERT_PREP_WORKING_DIR/${DATASET}/"
BERT_CONFIG=bert_configs/large.json
CODEDIR="/workspace/bert"
init_checkpoint="None"
DATASET2=pretrain/phase2/bin_size_64/parquet # change this for other datasets
DATA_DIR_PHASE2="$BERT_PREP_WORKING_DIR/${DATASET2}/"
wikipedia_source=$BERT_PREP_WORKING_DIR/wikipedia/source/
num_dask_workers=128
num_shards_per_worker=128
num_workers=4
sample_ratio="0.9"
phase2_bin_size=64
masking=static
echo $train_batch_size $learning_rate $precision $num_gpus \
$warmup_proportion $train_steps $save_checkpoint_steps \
$resume_training $create_logfile $accumulate_gradients \
$gradient_accumulation_steps $seed $job_name $allreduce_post_accumulation \
$allreduce_post_accumulation_fp16 $train_batch_size_phase2 $learning_rate_phase2 \
$warmup_proportion_phase2 $train_steps_phase2 $gradient_accumulation_steps_phase2 \
$DATA_DIR_PHASE1 $DATA_DIR_PHASE2 $CODEDIR $init_checkpoint \
$wikipedia_source $num_dask_workers $num_shards_per_worker $num_workers \
$sample_ratio $phase2_bin_size $masking \
$BERT_CONFIG
}
dgx1-32g_8gpu_fp32 ()
{
train_batch_size="8192"
learning_rate="6e-3"
precision="fp32"
num_gpus=8
warmup_proportion="0.2843"
train_steps=7038
save_checkpoint_steps=200
resume_training="false"
create_logfile="true"
accumulate_gradients="true"
gradient_accumulation_steps=128
seed=42
job_name="bert_lamb_pretraining"
allreduce_post_accumulation="true"
allreduce_post_accumulation_fp16="false"
train_batch_size_phase2=4096
learning_rate_phase2="4e-3"
warmup_proportion_phase2="0.128"
train_steps_phase2=1563
gradient_accumulation_steps_phase2=512
DATASET=pretrain/phase1/unbinned/parquet # change this for other datasets
DATA_DIR_PHASE1="$BERT_PREP_WORKING_DIR/${DATASET}/"
BERT_CONFIG=bert_configs/large.json
CODEDIR="/workspace/bert"
init_checkpoint="None"
DATASET2=pretrain/phase2/bin_size_64/parquet # change this for other datasets
DATA_DIR_PHASE2="$BERT_PREP_WORKING_DIR/${DATASET2}/"
wikipedia_source=$BERT_PREP_WORKING_DIR/wikipedia/source/
num_dask_workers=128
num_shards_per_worker=128
num_workers=4
sample_ratio="0.9"
phase2_bin_size=64
masking=static
echo $train_batch_size $learning_rate $precision $num_gpus \
$warmup_proportion $train_steps $save_checkpoint_steps \
$resume_training $create_logfile $accumulate_gradients \
$gradient_accumulation_steps $seed $job_name $allreduce_post_accumulation \
$allreduce_post_accumulation_fp16 $train_batch_size_phase2 $learning_rate_phase2 \
$warmup_proportion_phase2 $train_steps_phase2 $gradient_accumulation_steps_phase2 \
$DATA_DIR_PHASE1 $DATA_DIR_PHASE2 $CODEDIR $init_checkpoint \
$wikipedia_source $num_dask_workers $num_shards_per_worker $num_workers \
$sample_ratio $phase2_bin_size $masking \
$BERT_CONFIG
}
|
PyTorch/Forecasting/TFT/triton/runner | runner | __init__ | # Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
|
Tools/PyTorch/TimeSeriesPredictionPlatform/conf | conf | preproc_config | # 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.
defaults:
- dataset@_here_: ???
_target_: data.data_utils.Preprocessor
|
PyTorch/LanguageModeling/BERT/triton/large/runner | runner | pipeline_impl | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pathlib
if __name__ == "__main__" and __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from ...runner.pipeline import Pipeline
pipeline = Pipeline()
pipeline.model_export(
commands=(
r"""
if [[ "${EXPORT_FORMAT}" == "ts-trace" || "${EXPORT_FORMAT}" == "ts-script" ]]; then
export FORMAT_SUFFIX="pt"
else
export FORMAT_SUFFIX="${EXPORT_FORMAT}"
fi
if [[ "${EXPORT_FORMAT}" == "trt" ]]; then
export FLAG="--fixed-batch-dim"
else
export FLAG=""
fi
python3 triton/export_model.py \
--input-path triton/model.py \
--input-type pyt \
--output-path ${SHARED_DIR}/exported_model.${FORMAT_SUFFIX} \
--output-type ${EXPORT_FORMAT} \
--dataloader triton/dataloader.py \
--ignore-unknown-parameters \
--onnx-opset 13 \
${FLAG} \
\
--config-file bert_configs/large.json \
--checkpoint ${CHECKPOINT_DIR}/bert_large_qa.pt \
--precision ${EXPORT_PRECISION} \
\
--vocab-file ${DATASETS_DIR}/data/google_pretrained_weights/uncased_L-24_H-1024_A-16/vocab.txt \
--max-seq-length ${MAX_SEQ_LENGTH} \
--predict-file ${DATASETS_DIR}/data/squad/v1.1/dev-v1.1.json \
--batch-size ${MAX_BATCH_SIZE}
""",
)
)
pipeline.model_conversion(
commands=(
r"""
if [[ "${EXPORT_FORMAT}" == "ts-trace" || "${EXPORT_FORMAT}" == "ts-script" ]]; then
export FORMAT_SUFFIX="pt"
else
export FORMAT_SUFFIX="${EXPORT_FORMAT}"
fi
if [ "${EXPORT_FORMAT}" != "${FORMAT}" ]; then
model-navigator convert \
--model-name ${MODEL_NAME} \
--model-path ${SHARED_DIR}/exported_model.${FORMAT_SUFFIX} \
--output-path ${SHARED_DIR}/converted_model \
--target-formats ${FORMAT} \
--target-precisions ${PRECISION} \
--launch-mode local \
--override-workspace \
--verbose \
\
--onnx-opsets 13 \
--inputs input__0:${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH}:int32 \
--inputs input__1:${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH}:int32 \
--inputs input__2:${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH}:int32 \
--min-shapes input__0=${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH} \
input__1=${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH} \
input__2=${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH} \
--max-shapes input__0=${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH} \
input__1=${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH} \
input__2=${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH} \
--opt-shapes input__0=${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH} \
input__1=${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH} \
input__2=${MAX_BATCH_SIZE},${MAX_SEQ_LENGTH} \
--max-batch-size ${MAX_BATCH_SIZE} \
--tensorrt-max-workspace-size 8589934592 \
--atol 2 output__0=5.0 \
output__1=5.0 \
--rtol 1 output__0=5.0 \
output__1=5.0 \
| grep -v "broadcasting input1 to make tensors conform"
else
mv ${SHARED_DIR}/exported_model.${FORMAT_SUFFIX} ${SHARED_DIR}/converted_model
mv ${SHARED_DIR}/exported_model.${FORMAT_SUFFIX}.yaml ${SHARED_DIR}/converted_model.yaml 2>/dev/null || true
fi
""",
)
)
pipeline.model_deploy(
commands=(
r"""
if [[ "${FORMAT}" == "ts-trace" || "${FORMAT}" == "ts-script" ]]; then
export CONFIG_FORMAT="torchscript"
else
export CONFIG_FORMAT="${FORMAT}"
fi
if [[ "${FORMAT}" == "trt" ]]; then
export MBS="0"
else
export MBS="${MAX_BATCH_SIZE}"
fi
model-navigator triton-config-model \
--model-repository ${MODEL_REPOSITORY_PATH} \
--model-name ${MODEL_NAME} \
--model-version 1 \
--model-path ${SHARED_DIR}/converted_model \
--model-format ${CONFIG_FORMAT} \
--model-control-mode ${TRITON_LOAD_MODEL_METHOD} \
--verbose \
--load-model \
--load-model-timeout-s 100 \
\
--backend-accelerator ${ACCELERATOR} \
--tensorrt-precision ${ACCELERATOR_PRECISION} \
--max-batch-size ${MBS} \
--preferred-batch-sizes ${TRITON_PREFERRED_BATCH_SIZES} \
--max-queue-delay-us ${TRITON_MAX_QUEUE_DELAY} \
--engine-count-per-device gpu=${TRITON_GPU_ENGINE_COUNT}
""",
)
)
pipeline.triton_prepare_performance_profiling_data(
commands=(
r"""
mkdir -p ${SHARED_DIR}/input_data
""",
r"""
python triton/prepare_input_data.py \
--dataloader triton/dataloader.py \
--input-data-dir ${SHARED_DIR}/input_data \
\
--batch-size ${MAX_BATCH_SIZE} \
--max-seq-length ${MAX_SEQ_LENGTH} \
--predict-file ${DATASETS_DIR}/data/squad/v1.1/dev-v1.1.json \
--vocab-file ${DATASETS_DIR}/data/google_pretrained_weights/uncased_L-24_H-1024_A-16/vocab.txt
""",
)
)
pipeline.triton_performance_offline_tests(
commands=(
r"""
python triton/run_performance_on_triton.py \
--model-repository ${MODEL_REPOSITORY_PATH} \
--model-name ${MODEL_NAME} \
--input-data ${SHARED_DIR}/input_data/data.json \
--input-shapes input__0:${MAX_SEQ_LENGTH} \
--input-shapes input__1:${MAX_SEQ_LENGTH} \
--input-shapes input__2:${MAX_SEQ_LENGTH} \
--batch-sizes ${BATCH_SIZE} \
--number-of-triton-instances ${TRITON_INSTANCES} \
--number-of-model-instances ${TRITON_GPU_ENGINE_COUNT} \
--batching-mode static \
--evaluation-mode offline \
--performance-tool perf_analyzer \
--result-path ${SHARED_DIR}/triton_performance_offline.csv
""",
),
result_path="${SHARED_DIR}/triton_performance_offline.csv",
)
|
Tools/PyTorch/TimeSeriesPredictionPlatform | TimeSeriesPredictionPlatform | requirements | # 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.
pmdarima==1.8.0
wget==3.2
hydra-core==1.1.1
pyunpack==0.2.2
tensorboard
optuna
optuna-dashboard
hydra-optuna-sweeper==1.1.2
hydra-joblib-launcher==1.1.5
pandas==1.1.4
dgl-cu111
|
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.
*/
#include "taco2ProjectionLayerPlugin.h"
#include "taco2ProjectionKernel.h"
#include "taco2Utils.h"
#include <cassert>
#include <cstdlib>
#include <cstring>
#include <cuda_runtime.h> // cudaError_t
#include <iostream>
#include <sstream>
#include <stdexcept>
#include <string>
using namespace nvinfer1;
namespace nvinfer1
{
namespace plugin
{
using value_type = Taco2ProjectionLayerPlugin::value_type;
/******************************************************************************
* CONSTANTS ******************************************************************
*****************************************************************************/
namespace
{
constexpr const char* const PLUGIN_NAME = "Taco2Projection";
constexpr const char* const PLUGIN_VERSION = "0.1.0";
constexpr const int NUM_INPUTS = 2;
} // namespace
const float Taco2ProjectionLayerPlugin::ONE = 1.0f;
const float Taco2ProjectionLayerPlugin::ZERO = 0.0f;
/******************************************************************************
* HELPER FUNCTIONS ***********************************************************
*****************************************************************************/
namespace
{
std::vector<value_type> toVector(const Weights& weights)
{
if (weights.type != DataType::kFLOAT)
{
throw std::runtime_error(
"Invalid data type for Taco2Projection weights: " + std::to_string(static_cast<int>(weights.type)));
}
const value_type* const valuesBegin = static_cast<const value_type*>(weights.values);
const value_type* const valuesEnd = valuesBegin + weights.count;
return std::vector<value_type>(valuesBegin, valuesEnd);
}
const void* offset(const void* ptr, const size_t offset)
{
return reinterpret_cast<const void*>(static_cast<const uint8_t*>(ptr) + offset);
}
} // namespace
/******************************************************************************
* STATIC METHODS *************************************************************
*****************************************************************************/
const char* Taco2ProjectionLayerPlugin::getName()
{
return PLUGIN_NAME;
}
const char* Taco2ProjectionLayerPlugin::getVersion()
{
return PLUGIN_VERSION;
}
Taco2ProjectionLayerPlugin Taco2ProjectionLayerPlugin::deserialize(const void* const data, const size_t length)
{
if (length < 4 * sizeof(int32_t))
{
throw std::runtime_error("Invalid serialized size: " + std::to_string(length));
}
const int hiddenInputLength = static_cast<const int32_t*>(data)[0];
const int contextInputLength = static_cast<const int32_t*>(data)[1];
const int numChannelDimension = static_cast<const int32_t*>(data)[2];
const int numGateDimension = static_cast<const int32_t*>(data)[3];
const int inputLength = hiddenInputLength + contextInputLength;
const int numDimensions = numChannelDimension + numGateDimension;
const size_t reqSize = 4 * sizeof(int32_t) + sizeof(value_type) * ((inputLength * numDimensions) + numDimensions);
if (reqSize != length)
{
throw std::runtime_error(
"Invalid serialized size: " + std::to_string(length) + " / " + std::to_string(reqSize));
}
const Weights weightsChannel{
DataType::kFLOAT, offset(data, 4 * sizeof(int32_t)), numChannelDimension * inputLength};
const Weights weightsGate{DataType::kFLOAT,
offset(weightsChannel.values, sizeof(value_type) * weightsChannel.count), numGateDimension * inputLength};
const Weights biasChannel{
DataType::kFLOAT, offset(weightsGate.values, sizeof(value_type) * weightsGate.count), numChannelDimension};
const Weights biasGate{
DataType::kFLOAT, offset(biasChannel.values, sizeof(value_type) * biasChannel.count), numGateDimension};
Taco2ProjectionLayerPlugin layer(weightsChannel, weightsGate, biasChannel, biasGate, hiddenInputLength,
contextInputLength, numChannelDimension, numGateDimension);
return layer;
}
/******************************************************************************
* CONSTRUCTORS / DESTRUCTOR **************************************************
*****************************************************************************/
Taco2ProjectionLayerPlugin::Taco2ProjectionLayerPlugin(const nvinfer1::Weights& weightsChannel,
const nvinfer1::Weights& weightsGate, const nvinfer1::Weights& biasChannel, const nvinfer1::Weights& biasGate,
const int hiddenInputLength, const int contextInputLength, const int numChannelDimension,
const int numGateDimension)
: mHiddenInputLength(hiddenInputLength)
, mContextInputLength(contextInputLength)
, mNumChannelDimension(numChannelDimension)
, mNumGateDimension(numGateDimension)
, mWeightsChannel(toVector(weightsChannel))
, mWeightsGate(toVector(weightsGate))
, mBiasChannel(toVector(biasChannel))
, mBiasGate(toVector(biasGate))
, mKernel()
, mNamespace()
{
const size_t expectedWeightsChannel = getTotalInputLength() * mNumChannelDimension;
if (mWeightsChannel.size() != expectedWeightsChannel)
{
throw std::runtime_error("Taco2Projection expected " + std::to_string(expectedWeightsChannel)
+ " channel weights but given " + std::to_string(mWeightsChannel.size()));
}
const size_t expectedWeightsGate = getTotalInputLength() * mNumGateDimension;
if (mWeightsGate.size() != expectedWeightsGate)
{
throw std::runtime_error("Taco2Projection expected " + std::to_string(expectedWeightsGate)
+ " gate weights but given " + std::to_string(mWeightsGate.size()));
}
const size_t expectedBiasChannel = mNumChannelDimension;
if (mBiasChannel.size() != expectedBiasChannel)
{
throw std::runtime_error("Taco2Projection expected " + std::to_string(expectedBiasChannel)
+ " channel bias but given " + std::to_string(mBiasChannel.size()));
}
const size_t expectedBiasGate = mNumGateDimension;
if (mBiasGate.size() != expectedBiasGate)
{
throw std::runtime_error("Taco2Projection expected " + std::to_string(expectedBiasGate)
+ " gate bias but given " + std::to_string(mBiasGate.size()));
}
}
Taco2ProjectionLayerPlugin::Taco2ProjectionLayerPlugin(Taco2ProjectionLayerPlugin&& other)
: mHiddenInputLength(other.mHiddenInputLength)
, mContextInputLength(other.mContextInputLength)
, mNumChannelDimension(other.mNumChannelDimension)
, mNumGateDimension(other.mNumGateDimension)
, mWeightsChannel(std::move(other.mWeightsChannel))
, mWeightsGate(std::move(other.mWeightsGate))
, mBiasChannel(std::move(other.mBiasChannel))
, mBiasGate(std::move(other.mBiasGate))
, mKernel(std::move(other.mKernel))
, mNamespace(std::move(other.mNamespace))
{
other.mHiddenInputLength = 0;
other.mContextInputLength = 0;
other.mNumChannelDimension = 0;
other.mNumGateDimension = 0;
}
Taco2ProjectionLayerPlugin::~Taco2ProjectionLayerPlugin()
{
destroy();
}
/******************************************************************************
* PUBLIC METHODS *************************************************************
*****************************************************************************/
Taco2ProjectionLayerPlugin& Taco2ProjectionLayerPlugin::operator=(Taco2ProjectionLayerPlugin&& other)
{
// defere to constructor
*this = Taco2ProjectionLayerPlugin(std::move(other));
return *this;
}
DataType Taco2ProjectionLayerPlugin::getOutputDataType(
const int /* index */, const DataType* const /* inputTypes */, const int /* nbInputs */) const
{
return DataType::kFLOAT;
}
const char* Taco2ProjectionLayerPlugin::getPluginType() const
{
return getName();
}
const char* Taco2ProjectionLayerPlugin::getPluginVersion() const
{
return getVersion();
}
int Taco2ProjectionLayerPlugin::getNbOutputs() const
{
return 1;
}
DimsExprs Taco2ProjectionLayerPlugin::getOutputDimensions(
const int outputIndex, const DimsExprs* inputs, const int nbInputs, IExprBuilder& exprBuilder)
{
if (outputIndex >= getNbOutputs())
{
throw std::runtime_error("Only has one output.");
}
if (nbInputs != NUM_INPUTS)
{
throw std::runtime_error(
"Can only handle " + std::to_string(NUM_INPUTS) + " input tensors: " + std::to_string(nbInputs));
}
return DimsExprs{3, {inputs[0].d[0], exprBuilder.constant(1), exprBuilder.constant(getTotalDimensions())}};
}
bool Taco2ProjectionLayerPlugin::supportsFormatCombination(
const int pos, const PluginTensorDesc* const inOut, const int /* nbInputs */, const int /* nbOutputs */)
{
return inOut[pos].format == TensorFormat::kLINEAR && inOut[pos].type == DataType::kFLOAT;
}
void Taco2ProjectionLayerPlugin::configurePlugin(const DynamicPluginTensorDesc* const in, const int nbInputs,
const DynamicPluginTensorDesc* const out, const int nbOutputs)
{
if (nbInputs != NUM_INPUTS)
{
throw std::runtime_error(
"Can only handle " + std::to_string(NUM_INPUTS) + " input tensors: " + std::to_string(nbInputs));
}
for (int i = 0; i < nbInputs; ++i)
{
if (in[i].desc.type != DataType::kFLOAT)
{
throw std::runtime_error("Only FLOAT supported as input " + std::to_string(i) + " : "
+ std::to_string(static_cast<int>(in[i].desc.type)));
}
}
// assert dimensions
{
bool foundDim = false;
const Dims dims = in[0].desc.dims;
for (int d = 1; d < dims.nbDims; ++d)
{
if (dims.d[d] != 1)
{
if (foundDim || dims.d[d] != mHiddenInputLength)
{
throw std::runtime_error(
"First projection input must be 1 x hiddenInputLength"
" : "
+ taco2::Taco2Utils::dimsToString(dims));
}
foundDim = true;
}
}
if (!foundDim)
{
throw std::runtime_error(
"First projection input must be 1 x hiddenInputLength"
" : "
+ taco2::Taco2Utils::dimsToString(dims));
}
}
{
bool foundDim = false;
const Dims dims = in[1].desc.dims;
for (int d = 1; d < dims.nbDims; ++d)
{
if (dims.d[d] != 1)
{
if (foundDim || dims.d[d] != mContextInputLength)
{
throw std::runtime_error(
"Second projection input must be 1 x contextInputLength"
" : "
+ taco2::Taco2Utils::dimsToString(dims));
}
foundDim = true;
}
}
if (!foundDim)
{
throw std::runtime_error(
"Second projection input must be 1 x contextInputLength"
" : "
+ taco2::Taco2Utils::dimsToString(dims));
}
}
for (int i = 0; i < nbOutputs; ++i)
{
if (out[i].desc.type != DataType::kFLOAT)
{
throw std::runtime_error("Only FLOAT supported as output: " + std::to_string(i) + " : "
+ std::to_string(static_cast<int>(out[i].desc.type)));
}
}
}
int Taco2ProjectionLayerPlugin::initialize()
{
try
{
// concat projection and gate FC layers
std::vector<float> hostWeightCat;
hostWeightCat.insert(hostWeightCat.end(), mWeightsChannel.begin(), mWeightsChannel.end());
hostWeightCat.insert(hostWeightCat.end(), mWeightsGate.begin(), mWeightsGate.end());
std::vector<float> hostBiasCat;
hostBiasCat.insert(hostBiasCat.end(), mBiasChannel.begin(), mBiasChannel.end());
hostBiasCat.insert(hostBiasCat.end(), mBiasGate.begin(), mBiasGate.end());
mKernel.reset(new Taco2ProjectionKernel(hostWeightCat, hostBiasCat, mHiddenInputLength, mContextInputLength,
mNumChannelDimension + mNumGateDimension));
}
catch (const std::exception& e)
{
std::cerr << "Taco2ProjectionLayerPlugin initialization failed: " << e.what() << std::endl;
return 1;
}
return 0;
}
void Taco2ProjectionLayerPlugin::terminate()
{
mKernel.reset();
}
size_t Taco2ProjectionLayerPlugin::getWorkspaceSize(
const PluginTensorDesc* const /* in */, const int /* nbInputs */, const PluginTensorDesc* const /* out */, const int /* nbOutputs */) const
{
return 0;
}
int Taco2ProjectionLayerPlugin::enqueue(const PluginTensorDesc* const inputDesc, const PluginTensorDesc* /* outputDesc */,
const void* const* const inputs, void* const* const outputs, void* const /* workspace */, cudaStream_t stream)
{
const int batchSize = inputDesc[0].dims.d[0];
if (batchSize != 1)
{
// we only support batch size of 1 right now
std::cerr << "Taco2ProjectionLayerPlugin plugin does not support batch size other "
"than 1: got "
<< batchSize << std::endl;
std::cerr << "Recompile without plugins to use a larger batch size." << std::endl;
return 1;
}
else if (!mKernel)
{
std::cerr << "Taco2ProjectionLayerPlugin is not initialized properly." << std::endl;
return 1;
}
// name inputs and outputs
const value_type* const hiddenDevice = static_cast<const value_type*>(inputs[0]);
const value_type* const contextDevice = static_cast<const value_type*>(inputs[1]);
value_type* const outputDevice = static_cast<value_type*>(outputs[0]);
mKernel->execute(hiddenDevice, contextDevice, outputDevice, stream);
return 0;
}
size_t Taco2ProjectionLayerPlugin::getSerializationSize() const
{
return 4 * sizeof(int32_t)
+ sizeof(value_type) * (getTotalInputLength() * getTotalDimensions() + getTotalDimensions());
}
void Taco2ProjectionLayerPlugin::serialize(void* const buffer) const
{
static_cast<int32_t*>(buffer)[0] = mHiddenInputLength;
static_cast<int32_t*>(buffer)[1] = mContextInputLength;
static_cast<int32_t*>(buffer)[2] = mNumChannelDimension;
static_cast<int32_t*>(buffer)[3] = mNumGateDimension;
float* const weightsChannel = reinterpret_cast<float*>(static_cast<int32_t*>(buffer) + 4);
float* const weightsGate = weightsChannel + (getTotalInputLength() * mNumChannelDimension);
float* const biasChannel = weightsGate + (getTotalInputLength() * mNumGateDimension);
float* const biasGate = biasChannel + mNumChannelDimension;
memcpy(weightsChannel, mWeightsChannel.data(), sizeof(value_type) * mWeightsChannel.size());
memcpy(weightsGate, mWeightsGate.data(), sizeof(value_type) * mWeightsGate.size());
memcpy(biasChannel, mBiasChannel.data(), sizeof(value_type) * mBiasChannel.size());
memcpy(biasGate, mBiasGate.data(), sizeof(value_type) * mBiasGate.size());
}
void Taco2ProjectionLayerPlugin::destroy()
{
terminate();
}
IPluginV2DynamicExt* Taco2ProjectionLayerPlugin::clone() const
{
// call constructor which copy's data
Taco2ProjectionLayerPlugin clone(
Weights{DataType::kFLOAT, mWeightsChannel.data(), static_cast<int64_t>(mWeightsChannel.size())},
Weights{DataType::kFLOAT, mWeightsGate.data(), static_cast<int64_t>(mWeightsGate.size())},
Weights{DataType::kFLOAT, mBiasChannel.data(), static_cast<int64_t>(mBiasChannel.size())},
Weights{DataType::kFLOAT, mBiasGate.data(), static_cast<int64_t>(mBiasGate.size())}, mHiddenInputLength,
mContextInputLength, mNumChannelDimension, mNumGateDimension);
if (mKernel)
{
// initialize the clone too
clone.initialize();
}
// move it to the heap last to avoid exceptions causing memory leaks
return new Taco2ProjectionLayerPlugin(std::move(clone));
}
void Taco2ProjectionLayerPlugin::setPluginNamespace(const char* pluginNamespace)
{
mNamespace = pluginNamespace;
}
const char* Taco2ProjectionLayerPlugin::getPluginNamespace() const
{
return mNamespace.c_str();
}
/******************************************************************************
* PRIVATE METHODS ************************************************************
*****************************************************************************/
int Taco2ProjectionLayerPlugin::getTotalDimensions() const
{
return mNumChannelDimension + mNumGateDimension;
}
int Taco2ProjectionLayerPlugin::getTotalInputLength() const
{
return mHiddenInputLength + mContextInputLength;
}
} // namespace plugin
} // namespace nvinfer1
|
PyTorch/SpeechRecognition/Jasper/triton/model_repo_configs/fp32/jasper-ts-trace | jasper-ts-trace | config | name: "jasper-ts-trace"
platform: "pytorch_libtorch"
default_model_filename: "model.pt"
max_batch_size: 8#MAX_BATCH
input [
{
name: "input__0"
data_type: TYPE_FP32
dims: [64, -1]
}
]
output [
{
name: "output__0"
data_type: TYPE_FP32
dims: [-1, 29]
}
]
instance_group {
count: 1#NUM_ENGINES
gpus: 0
kind: KIND_GPU
}
#db#dynamic_batching {
#db# preferred_batch_size: 8#MAX_BATCH
#db# max_queue_delay_microseconds: #MAX_QUEUE
#db#}
|
TensorFlow/Detection/SSD/models/research/object_detection/samples/configs | configs | faster_rcnn_resnet101_kitti | # Faster R-CNN with Resnet-101 (v1)
# Trained on KITTI dataset (cars and pedestrian), initialized from COCO
# detection checkpoint.
# 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: 2
image_resizer {
keep_aspect_ratio_resizer {
# Raw KITTI images have a resolution of 1242x375, if we wish to resize
# them to have a height of 600 then their width should be
# 1242/(375/600)=1987.2
min_dimension: 600
max_dimension: 1987
}
}
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: 0.0001
schedule {
step: 500000
learning_rate: .00001
}
schedule {
step: 700000
learning_rate: .000001
}
}
}
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
num_steps: 800000
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/kitti_label_map.pbtxt"
tf_record_input_reader: {
input_path: "PATH_TO_BE_CONFIGURED/kitti_train.tfrecord"
}
}
eval_config: {
use_moving_averages: false
num_examples: 500
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/kitti_label_map.pbtxt"
tf_record_input_reader: {
input_path: "PATH_TO_BE_CONFIGURED/kitti_val.tfrecord"
}
}
|
PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/modeling | modeling | registry | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from maskrcnn_benchmark.utils.registry import Registry
BACKBONES = Registry()
ROI_BOX_FEATURE_EXTRACTORS = Registry()
RPN_HEADS = Registry()
|
TensorFlow/Segmentation/UNet_3D_Medical/scripts | scripts | unet3d_train_benchmark_TF-AMP | # 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 TF-AMP train benchmark.
# Usage:
# bash examples/unet3d_train_benchmark.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 --exec_mode train --max_steps 80 --benchmark --fold 0 --batch_size $4 --amp --xla --augment |
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/layers | layers | lstm | /*
* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "lstm.h"
#include "NvInfer.h"
using namespace nvinfer1;
namespace tts
{
/******************************************************************************
* PUBLIC STATIC METHODS ******************************************************
*****************************************************************************/
ILayer* LSTM::addPaddedBidirectional(INetworkDefinition* const network, ITensor* const input,
ITensor* const inputLength, const int numDimensions, const LayerData& lstmData)
{
// build LSTM
const int hiddenSize = numDimensions / 2;
IRNNv2Layer* lstm = network->addRNNv2(*input, 1, hiddenSize, input->getDimensions().d[1], RNNOperation::kLSTM);
lstm->setDirection(RNNDirection::kBIDIRECTION);
lstm->setSequenceLengths(*inputLength);
{
const int64_t inputBlockSize = numDimensions * hiddenSize;
// pytorch weights are stored in "weight_ih_l0" = {W_ii|W_if|W_ig|W_io}
const float* inputWeights = (const float*) lstmData.get("weight_ih_l0").values;
Weights wii{DataType::kFLOAT, (void*) (inputWeights), inputBlockSize};
Weights wif{DataType::kFLOAT, (void*) (inputWeights + inputBlockSize), inputBlockSize};
Weights wig{DataType::kFLOAT, (void*) (inputWeights + 2 * inputBlockSize), inputBlockSize};
Weights wio{DataType::kFLOAT, (void*) (inputWeights + 3 * inputBlockSize), inputBlockSize};
lstm->setWeightsForGate(0, RNNGateType::kINPUT, true, wii);
lstm->setWeightsForGate(0, RNNGateType::kCELL, true, wig);
lstm->setWeightsForGate(0, RNNGateType::kFORGET, true, wif);
lstm->setWeightsForGate(0, RNNGateType::kOUTPUT, true, wio);
const float* inputBias = (const float*) lstmData.get("bias_ih_l0").values;
Weights bii{DataType::kFLOAT, (void*) (inputBias), hiddenSize};
Weights bif{DataType::kFLOAT, (void*) (inputBias + hiddenSize), hiddenSize};
Weights big{DataType::kFLOAT, (void*) (inputBias + 2 * hiddenSize), hiddenSize};
Weights bio{DataType::kFLOAT, (void*) (inputBias + 3 * hiddenSize), hiddenSize};
lstm->setBiasForGate(0, RNNGateType::kINPUT, true, bii);
lstm->setBiasForGate(0, RNNGateType::kCELL, true, big);
lstm->setBiasForGate(0, RNNGateType::kFORGET, true, bif);
lstm->setBiasForGate(0, RNNGateType::kOUTPUT, true, bio);
const int64_t hiddenBlockSize = hiddenSize * hiddenSize;
// pytorch weights are stored in "weight_hh_l0" = {W_hi|W_hf|W_hg|W_ho}
const float* hiddenWeights = (const float*) lstmData.get("weight_hh_l0").values;
Weights whi{DataType::kFLOAT, (void*) (hiddenWeights), hiddenBlockSize};
Weights whf{DataType::kFLOAT, (void*) (hiddenWeights + hiddenBlockSize), hiddenBlockSize};
Weights whg{DataType::kFLOAT, (void*) (hiddenWeights + 2 * hiddenBlockSize), hiddenBlockSize};
Weights who{DataType::kFLOAT, (void*) (hiddenWeights + 3 * hiddenBlockSize), hiddenBlockSize};
lstm->setWeightsForGate(0, RNNGateType::kINPUT, false, whi);
lstm->setWeightsForGate(0, RNNGateType::kCELL, false, whg);
lstm->setWeightsForGate(0, RNNGateType::kFORGET, false, whf);
lstm->setWeightsForGate(0, RNNGateType::kOUTPUT, false, who);
const float* hiddenBias = (const float*) lstmData.get("bias_hh_l0").values;
Weights bhi{DataType::kFLOAT, (void*) (hiddenBias), hiddenSize};
Weights bhf{DataType::kFLOAT, (void*) (hiddenBias + hiddenSize), hiddenSize};
Weights bhg{DataType::kFLOAT, (void*) (hiddenBias + 2 * hiddenSize), hiddenSize};
Weights bho{DataType::kFLOAT, (void*) (hiddenBias + 3 * hiddenSize), hiddenSize};
lstm->setBiasForGate(0, RNNGateType::kINPUT, false, bhi);
lstm->setBiasForGate(0, RNNGateType::kCELL, false, bhg);
lstm->setBiasForGate(0, RNNGateType::kFORGET, false, bhf);
lstm->setBiasForGate(0, RNNGateType::kOUTPUT, false, bho);
}
{
const int64_t inputBlockSize = numDimensions * hiddenSize;
// pytorch weights are stored in "weight_ih_l0" = {W_ii|W_if|W_ig|W_io}
const float* inputWeights = (const float*) lstmData.get("weight_ih_l0_reverse").values;
Weights wii{DataType::kFLOAT, (void*) (inputWeights), inputBlockSize};
Weights wif{DataType::kFLOAT, (void*) (inputWeights + inputBlockSize), inputBlockSize};
Weights wig{DataType::kFLOAT, (void*) (inputWeights + 2 * inputBlockSize), inputBlockSize};
Weights wio{DataType::kFLOAT, (void*) (inputWeights + 3 * inputBlockSize), inputBlockSize};
lstm->setWeightsForGate(1, RNNGateType::kINPUT, true, wii);
lstm->setWeightsForGate(1, RNNGateType::kCELL, true, wig);
lstm->setWeightsForGate(1, RNNGateType::kFORGET, true, wif);
lstm->setWeightsForGate(1, RNNGateType::kOUTPUT, true, wio);
const float* inputBias = (const float*) lstmData.get("bias_ih_l0_reverse").values;
Weights bii{DataType::kFLOAT, (void*) (inputBias), hiddenSize};
Weights bif{DataType::kFLOAT, (void*) (inputBias + hiddenSize), hiddenSize};
Weights big{DataType::kFLOAT, (void*) (inputBias + 2 * hiddenSize), hiddenSize};
Weights bio{DataType::kFLOAT, (void*) (inputBias + 3 * hiddenSize), hiddenSize};
lstm->setBiasForGate(1, RNNGateType::kINPUT, true, bii);
lstm->setBiasForGate(1, RNNGateType::kCELL, true, big);
lstm->setBiasForGate(1, RNNGateType::kFORGET, true, bif);
lstm->setBiasForGate(1, RNNGateType::kOUTPUT, true, bio);
const int64_t hiddenBlockSize = hiddenSize * hiddenSize;
// pytorch weights are stored in "weight_hh_l0" = {W_hi|W_hf|W_hg|W_ho}
const float* hiddenWeights = (const float*) lstmData.get("weight_hh_l0_reverse").values;
Weights whi{DataType::kFLOAT, (void*) (hiddenWeights), hiddenBlockSize};
Weights whf{DataType::kFLOAT, (void*) (hiddenWeights + hiddenBlockSize), hiddenBlockSize};
Weights whg{DataType::kFLOAT, (void*) (hiddenWeights + 2 * hiddenBlockSize), hiddenBlockSize};
Weights who{DataType::kFLOAT, (void*) (hiddenWeights + 3 * hiddenBlockSize), hiddenBlockSize};
lstm->setWeightsForGate(1, RNNGateType::kINPUT, false, whi);
lstm->setWeightsForGate(1, RNNGateType::kCELL, false, whg);
lstm->setWeightsForGate(1, RNNGateType::kFORGET, false, whf);
lstm->setWeightsForGate(1, RNNGateType::kOUTPUT, false, who);
const float* hiddenBias = (const float*) lstmData.get("bias_hh_l0_reverse").values;
Weights bhi{DataType::kFLOAT, (void*) (hiddenBias), hiddenSize};
Weights bhf{DataType::kFLOAT, (void*) (hiddenBias + hiddenSize), hiddenSize};
Weights bhg{DataType::kFLOAT, (void*) (hiddenBias + 2 * hiddenSize), hiddenSize};
Weights bho{DataType::kFLOAT, (void*) (hiddenBias + 3 * hiddenSize), hiddenSize};
lstm->setBiasForGate(1, RNNGateType::kINPUT, false, bhi);
lstm->setBiasForGate(1, RNNGateType::kCELL, false, bhg);
lstm->setBiasForGate(1, RNNGateType::kFORGET, false, bhf);
lstm->setBiasForGate(1, RNNGateType::kOUTPUT, false, bho);
}
return lstm;
}
ILayer* LSTM::addUnidirectionalCell(INetworkDefinition* const network, ITensor* const input,
ITensor* const hiddenStatesIn, ITensor* const cellStatesIn, const int numDimensions, const LayerData& lstmData)
{
// build LSTM
const int hiddenSize = numDimensions;
const int inputLength = input->getDimensions().d[2];
IRNNv2Layer* lstm = network->addRNNv2(*input, 1, hiddenSize, input->getDimensions().d[1], RNNOperation::kLSTM);
lstm->setDirection(RNNDirection::kUNIDIRECTION);
const int64_t inputBlockSize = inputLength * hiddenSize;
// pytorch weights are stored in "weight_ih" = {W_ii|W_if|W_ig|W_io}
const float* inputWeights = (const float*) lstmData.get("weight_ih").values;
Weights wii{DataType::kFLOAT, (void*) (inputWeights), inputBlockSize};
Weights wif{DataType::kFLOAT, (void*) (inputWeights + inputBlockSize), inputBlockSize};
Weights wig{DataType::kFLOAT, (void*) (inputWeights + 2 * inputBlockSize), inputBlockSize};
Weights wio{DataType::kFLOAT, (void*) (inputWeights + 3 * inputBlockSize), inputBlockSize};
lstm->setWeightsForGate(0, RNNGateType::kINPUT, true, wii);
lstm->setWeightsForGate(0, RNNGateType::kCELL, true, wig);
lstm->setWeightsForGate(0, RNNGateType::kFORGET, true, wif);
lstm->setWeightsForGate(0, RNNGateType::kOUTPUT, true, wio);
const float* inputBias = (const float*) lstmData.get("bias_ih").values;
Weights bii{DataType::kFLOAT, (void*) (inputBias), hiddenSize};
Weights bif{DataType::kFLOAT, (void*) (inputBias + hiddenSize), hiddenSize};
Weights big{DataType::kFLOAT, (void*) (inputBias + 2 * hiddenSize), hiddenSize};
Weights bio{DataType::kFLOAT, (void*) (inputBias + 3 * hiddenSize), hiddenSize};
lstm->setBiasForGate(0, RNNGateType::kINPUT, true, bii);
lstm->setBiasForGate(0, RNNGateType::kCELL, true, big);
lstm->setBiasForGate(0, RNNGateType::kFORGET, true, bif);
lstm->setBiasForGate(0, RNNGateType::kOUTPUT, true, bio);
const int64_t hiddenBlockSize = hiddenSize * hiddenSize;
// pytorch weights are stored in "weight_hh" = {W_hi|W_hf|W_hg|W_ho}
const float* hiddenWeights = (const float*) lstmData.get("weight_hh").values;
Weights whi{DataType::kFLOAT, (void*) (hiddenWeights), hiddenBlockSize};
Weights whf{DataType::kFLOAT, (void*) (hiddenWeights + hiddenBlockSize), hiddenBlockSize};
Weights whg{DataType::kFLOAT, (void*) (hiddenWeights + 2 * hiddenBlockSize), hiddenBlockSize};
Weights who{DataType::kFLOAT, (void*) (hiddenWeights + 3 * hiddenBlockSize), hiddenBlockSize};
lstm->setWeightsForGate(0, RNNGateType::kINPUT, false, whi);
lstm->setWeightsForGate(0, RNNGateType::kCELL, false, whg);
lstm->setWeightsForGate(0, RNNGateType::kFORGET, false, whf);
lstm->setWeightsForGate(0, RNNGateType::kOUTPUT, false, who);
const float* hiddenBias = (const float*) lstmData.get("bias_hh").values;
Weights bhi{DataType::kFLOAT, (void*) (hiddenBias), hiddenSize};
Weights bhf{DataType::kFLOAT, (void*) (hiddenBias + hiddenSize), hiddenSize};
Weights bhg{DataType::kFLOAT, (void*) (hiddenBias + 2 * hiddenSize), hiddenSize};
Weights bho{DataType::kFLOAT, (void*) (hiddenBias + 3 * hiddenSize), hiddenSize};
lstm->setBiasForGate(0, RNNGateType::kINPUT, false, bhi);
lstm->setBiasForGate(0, RNNGateType::kCELL, false, bhg);
lstm->setBiasForGate(0, RNNGateType::kFORGET, false, bhf);
lstm->setBiasForGate(0, RNNGateType::kOUTPUT, false, bho);
lstm->setHiddenState(*hiddenStatesIn);
lstm->setCellState(*cellStatesIn);
return lstm;
}
} // namespace tts
|
TensorFlow/Classification/ConvNets/utils/hooks | hooks | prefill_hook | #! /usr/bin/python
# -*- coding: utf-8 -*-
# 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 time
import numpy as np
import tensorflow as tf
__all__ = ['PrefillStagingAreasHook']
class PrefillStagingAreasHook(tf.estimator.SessionRunHook):
def after_create_session(self, session, coord):
# TODO: This assumes TF collections are ordered; is this safe?
enqueue_ops = tf.get_collection('STAGING_AREA_PUTS')
for i in range(len(enqueue_ops)):
session.run(enqueue_ops[:i + 1])
|
PyTorch/SpeechRecognition/Jasper/triton/model_repo_configs/fp16/jasper-tensorrt | jasper-tensorrt | config | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
name: "jasper-tensorrt"
platform: "tensorrt_plan"
default_model_filename: "model.plan"
max_batch_size: 8#MAX_BATCH
input [
{
name: "input__0"
data_type: TYPE_FP16
dims: [64, -1]
}
]
output [
{
name: "output__0"
data_type: TYPE_FP16
dims: [-1, 29 ]
}
]
instance_group {
count: 1#NUM_ENGINES
gpus: 0
kind: KIND_GPU
}
#db#dynamic_batching {
#db# preferred_batch_size: 8#MAX_BATCH
#db# max_queue_delay_microseconds: #MAX_QUEUE
#db#}
|
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/layers | layers | attentionLayerCreator | /*
* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "attentionLayerCreator.h"
#include "dims5.h"
#include "layerData.h"
#include "NvInfer.h"
using namespace nvinfer1;
namespace tts
{
/******************************************************************************
* PUBLIC METHODS *************************************************************
*****************************************************************************/
ILayer* AttentionLayerCreator::addLocation(INetworkDefinition& network, ITensor* const input, const int attentionDim,
const int numFilters, const int kernelSize, const LayerData& convData, const LayerData& linearData,
const std::string& name)
{
// conv layer
const int padding = (kernelSize - 1) / 2;
#if NV_TENSORRT_MAJOR < 7
IConvolutionLayer* const convLayer = network.addConvolution(
*input, numFilters, DimsHW{kernelSize, 1}, convData.get("weight"), {DataType::kFLOAT, nullptr, 0});
convLayer->setPadding({padding, 0});
#else
IConvolutionLayer* const convLayer = network.addConvolutionNd(
*input, numFilters, Dims2(kernelSize, 1), convData.get("weight"), {DataType::kFLOAT, nullptr, 0});
convLayer->setPaddingNd(Dims2(padding, 0));
#endif
convLayer->setName((name + ".conv_layer").c_str());
// need to tranpose
IShuffleLayer* const transLayer = network.addShuffle(*convLayer->getOutput(0));
transLayer->setFirstTranspose({0, 2, 1, 3});
transLayer->setReshapeDimensions(Dims5{1, convLayer->getOutput(0)->getDimensions().d[2],
convLayer->getOutput(0)->getDimensions().d[1], 1, convLayer->getOutput(0)->getDimensions().d[3]});
transLayer->setName((name + ".transpose").c_str());
// fully connected layer
ILayer* const linearLayer = network.addFullyConnected(
*transLayer->getOutput(0), attentionDim, linearData.get("weight"), Weights{DataType::kFLOAT, 0, 0});
linearLayer->setName((name + ".linear_layer").c_str());
return linearLayer;
}
ILayer* AttentionLayerCreator::addEnergy(INetworkDefinition& network, ITensor* const input1, ITensor* const input2,
ITensor* const input3, const LayerData& linearData, const std::string& name)
{
// summation
ILayer* const add1Layer = network.addElementWise(*input1, *input2, ElementWiseOperation::kSUM);
add1Layer->setName((name + ".0.elementwise_sum").c_str());
ILayer* const add2Layer = network.addElementWise(*add1Layer->getOutput(0), *input3, ElementWiseOperation::kSUM);
add2Layer->setName((name + ".1.elementwise_sum").c_str());
// activation
ILayer* const actLayer = network.addActivation(*add2Layer->getOutput(0), ActivationType::kTANH);
actLayer->setName((name + ".tanh").c_str());
// fully connected layer
ILayer* const linearLayer = network.addFullyConnected(
*actLayer->getOutput(0), 1, linearData.get("weight"), Weights{DataType::kFLOAT, 0, 0});
linearLayer->setName((name + ".linear_layer").c_str());
return linearLayer;
}
ILayer* AttentionLayerCreator::addPaddedSoftMax(INetworkDefinition& network, ITensor* const input,
ITensor* const inputMask, ITensor* const inputSegments, const std::string& name)
{
// make our inputs 2 dimensional
IShuffleLayer* const maskShuffleLayer = network.addShuffle(*inputMask);
maskShuffleLayer->setReshapeDimensions(Dims2{1, -1});
maskShuffleLayer->setName((name + ".mask_reshape").c_str());
IShuffleLayer* const inputShuffleLayer = network.addShuffle(*input);
inputShuffleLayer->setReshapeDimensions(Dims2{1, -1});
inputShuffleLayer->setName((name + ".input_reshape").c_str());
// perform softmax over non-padding elements
ILayer* const softMaxLayer = network.addRaggedSoftMax(*inputShuffleLayer->getOutput(0), *inputSegments);
softMaxLayer->setName((name + ".ragged_softmax").c_str());
// zero padding
ILayer* const maskLayer = network.addElementWise(
*softMaxLayer->getOutput(0), *maskShuffleLayer->getOutput(0), ElementWiseOperation::kPROD);
maskLayer->setName((name + ".mask").c_str());
// return three dimensional output
IShuffleLayer* const outShuffle = network.addShuffle(*maskLayer->getOutput(0));
outShuffle->setReshapeDimensions(Dims3{-1, 1, 1});
outShuffle->setName((name + ".transpose").c_str());
return outShuffle;
}
} // namespace tts
|
PyTorch/Recommendation/DLRM/preproc | preproc | spark_data_utils | # 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 json
import os
import sys
from argparse import ArgumentParser
from collections import OrderedDict
from contextlib import contextmanager
from operator import itemgetter
from time import time
from pyspark import broadcast
from pyspark.sql import Row, SparkSession, Window
from pyspark.sql.functions import *
from pyspark.sql.types import *
LABEL_COL = 0
INT_COLS = list(range(1, 14))
CAT_COLS = list(range(14, 40))
def get_column_counts_with_frequency_limit(df, frequency_limit = None):
cols = ['_c%d' % i for i in CAT_COLS]
df = (df
.select(posexplode(array(*cols)))
.withColumnRenamed('pos', 'column_id')
.withColumnRenamed('col', 'data')
.filter('data is not null')
.groupBy('column_id', 'data')
.count())
if frequency_limit:
frequency_limit = frequency_limit.split(",")
exclude = []
default_limit = None
for fl in frequency_limit:
frequency_pair = fl.split(":")
if len(frequency_pair) == 1:
default_limit = int(frequency_pair[0])
elif len(frequency_pair) == 2:
df = df.filter((col('column_id') != int(frequency_pair[0]) - CAT_COLS[0]) | (col('count') >= int(frequency_pair[1])))
exclude.append(int(frequency_pair[0]))
if default_limit:
remain = [x - CAT_COLS[0] for x in CAT_COLS if x not in exclude]
df = df.filter((~col('column_id').isin(remain)) | (col('count') >= default_limit))
# for comparing isin and separate filter
# for i in remain:
# df = df.filter((col('column_id') != i - CAT_COLS[0]) | (col('count') >= default_limit))
return df
def assign_id_with_window(df):
windowed = Window.partitionBy('column_id').orderBy(desc('count'))
return (df
.withColumn('id', row_number().over(windowed))
.withColumnRenamed('count', 'model_count'))
def assign_low_mem_partial_ids(df):
# To avoid some scaling issues with a simple window operation, we use a more complex method
# to compute the same thing, but in a more distributed spark specific way
df = df.orderBy(asc('column_id'), desc('count'))
# The monotonically_increasing_id is the partition id in the top 31 bits and the rest
# is an increasing count of the rows within that partition. So we split it into two parts,
# the partion id part_id and the count mono_id
df = df.withColumn('part_id', spark_partition_id())
return df.withColumn('mono_id', monotonically_increasing_id() - shiftLeft(col('part_id'), 33))
def assign_low_mem_final_ids(df):
# Now we can find the minimum and maximum mono_ids within a given column/partition pair
sub_model = df.groupBy('column_id', 'part_id').agg(max('mono_id').alias('top'), min('mono_id').alias('bottom'))
sub_model = sub_model.withColumn('diff', col('top') - col('bottom') + 1)
sub_model = sub_model.drop('top')
# This window function is over aggregated column/partition pair table. It will do a running sum of the rows
# within that column
windowed = Window.partitionBy('column_id').orderBy('part_id').rowsBetween(Window.unboundedPreceding, -1)
sub_model = sub_model.withColumn('running_sum', sum('diff').over(windowed)).na.fill(0, ["running_sum"])
joined = df.withColumnRenamed('column_id', 'i_column_id')
joined = joined.withColumnRenamed('part_id', 'i_part_id')
joined = joined.withColumnRenamed('count', 'model_count')
# Then we can join the original input with the pair it is a part of
joined = joined.join(sub_model, (col('i_column_id') == col('column_id')) & (col('part_id') == col('i_part_id')))
# So with all that we can subtract bottom from mono_id makeing it start at 0 for each partition
# and then add in the running_sum so the id is contiguous and unique for the entire column. + 1 to make it match the 1 based indexing
# for row_number
ret = joined.select(col('column_id'),
col('data'),
(col('mono_id') - col('bottom') + col('running_sum') + 1).cast(IntegerType()).alias('id'),
col('model_count'))
return ret
def get_column_models(combined_model):
for i in CAT_COLS:
model = (combined_model
.filter('column_id == %d' % (i - CAT_COLS[0]))
.drop('column_id'))
yield i, model
def col_of_rand_long():
return (rand() * (1 << 52)).cast(LongType())
def skewed_join(df, model, col_name, cutoff):
# Most versions of spark don't have a good way
# to deal with a skewed join out of the box.
# Some do and if you want to replace this with
# one of those that would be great.
# Because we have statistics about the skewedness
# that we can used we divide the model up into two parts
# one part is the highly skewed part and we do a
# broadcast join for that part, but keep the result in
# a separate column
b_model = broadcast(model.filter(col('model_count') >= cutoff)
.withColumnRenamed('data', col_name)
.drop('model_count'))
df = (df
.join(b_model, col_name, how='left')
.withColumnRenamed('id', 'id_tmp'))
# We also need to spread the skewed data that matched
# evenly. We will use a source of randomness for this
# but use a -1 for anything that still needs to be matched
if 'ordinal' in df.columns:
rand_column = col('ordinal')
else:
rand_column = col_of_rand_long()
df = df.withColumn('join_rand',
# null values are not in the model, they are filtered out
# but can be a source of skewedness so include them in
# the even distribution
when(col('id_tmp').isNotNull() | col(col_name).isNull(), rand_column)
.otherwise(lit(-1)))
# Null out the string data that already matched to save memory
df = df.withColumn(col_name,
when(col('id_tmp').isNotNull(), None)
.otherwise(col(col_name)))
# Now do the second join, which will be a non broadcast join.
# Sadly spark is too smart for its own good and will optimize out
# joining on a column it knows will always be a constant value.
# So we have to make a convoluted version of assigning a -1 to the
# randomness column for the model itself to work around that.
nb_model = (model
.withColumn('join_rand', when(col('model_count') < cutoff, lit(-1)).otherwise(lit(-2)))
.filter(col('model_count') < cutoff)
.withColumnRenamed('data', col_name)
.drop('model_count'))
df = (df
.join(nb_model, ['join_rand', col_name], how='left')
.drop(col_name, 'join_rand')
# Pick either join result as an answer
.withColumn(col_name, coalesce(col('id'), col('id_tmp')))
.drop('id', 'id_tmp'))
return df
def apply_models(df, models, broadcast_model = False, skew_broadcast_pct = 1.0):
# sort the models so broadcast joins come first. This is
# so we reduce the amount of shuffle data sooner than later
# If we parsed the string hex values to ints early on this would
# not make a difference.
models = sorted(models, key=itemgetter(3), reverse=True)
for i, model, original_rows, would_broadcast in models:
col_name = '_c%d' % i
if not (would_broadcast or broadcast_model):
# The data is highly skewed so we need to offset that
cutoff = int(original_rows * skew_broadcast_pct/100.0)
df = skewed_join(df, model, col_name, cutoff)
else:
# broadcast joins can handle skewed data so no need to
# do anything special
model = (model.drop('model_count')
.withColumnRenamed('data', col_name))
model = broadcast(model) if broadcast_model else model
df = (df
.join(model, col_name, how='left')
.drop(col_name)
.withColumnRenamed('id', col_name))
return df.fillna(0, ['_c%d' % i for i in CAT_COLS])
def transform_log(df, transform_log = False):
cols = ['_c%d' % i for i in INT_COLS]
if transform_log:
for col_name in cols:
df = df.withColumn(col_name, log(df[col_name] + 3))
return df.fillna(0, cols)
def would_broadcast(spark, str_path):
sc = spark.sparkContext
config = sc._jsc.hadoopConfiguration()
path = sc._jvm.org.apache.hadoop.fs.Path(str_path)
fs = sc._jvm.org.apache.hadoop.fs.FileSystem.get(config)
stat = fs.listFiles(path, True)
sum = 0
while stat.hasNext():
sum = sum + stat.next().getLen()
sql_conf = sc._jvm.org.apache.spark.sql.internal.SQLConf()
cutoff = sql_conf.autoBroadcastJoinThreshold() * sql_conf.fileCompressionFactor()
return sum <= cutoff
def delete_data_source(spark, path):
sc = spark.sparkContext
config = sc._jsc.hadoopConfiguration()
path = sc._jvm.org.apache.hadoop.fs.Path(path)
sc._jvm.org.apache.hadoop.fs.FileSystem.get(config).delete(path, True)
def load_raw(spark, folder, day_range):
label_fields = [StructField('_c%d' % LABEL_COL, IntegerType())]
int_fields = [StructField('_c%d' % i, IntegerType()) for i in INT_COLS]
str_fields = [StructField('_c%d' % i, StringType()) for i in CAT_COLS]
schema = StructType(label_fields + int_fields + str_fields)
paths = [os.path.join(folder, 'day_%d' % i) for i in day_range]
return (spark
.read
.schema(schema)
.option('sep', '\t')
.csv(paths))
def rand_ordinal(df):
# create a random long from the double precision float.
# The fraction part of a double is 52 bits, so we try to capture as much
# of that as possible
return df.withColumn('ordinal', col_of_rand_long())
def day_from_ordinal(df, num_days):
return df.withColumn('day', (col('ordinal') % num_days).cast(IntegerType()))
def day_from_input_file(df):
return df.withColumn('day', substring_index(input_file_name(), '_', -1).cast(IntegerType()))
def psudo_sort_by_day_plus(spark, df, num_days):
# Sort is very expensive because it needs to calculate the partitions
# which in our case may involve rereading all of the data. In some cases
# we can avoid this by repartitioning the data and sorting within a single partition
shuffle_parts = int(spark.conf.get('spark.sql.shuffle.partitions'))
extra_parts = int(shuffle_parts/num_days)
if extra_parts <= 0:
df = df.repartition('day')
else:
#We want to spread out the computation to about the same amount as shuffle_parts
divided = (col('ordinal') / num_days).cast(LongType())
extra_ident = divided % extra_parts
df = df.repartition(col('day'), extra_ident)
return df.sortWithinPartitions('day', 'ordinal')
def load_combined_model(spark, model_folder):
path = os.path.join(model_folder, 'combined.parquet')
return spark.read.parquet(path)
def save_combined_model(df, model_folder, mode=None):
path = os.path.join(model_folder, 'combined.parquet')
df.write.parquet(path, mode=mode)
def delete_combined_model(spark, model_folder):
path = os.path.join(model_folder, 'combined.parquet')
delete_data_source(spark, path)
def load_low_mem_partial_ids(spark, model_folder):
path = os.path.join(model_folder, 'partial_ids.parquet')
return spark.read.parquet(path)
def save_low_mem_partial_ids(df, model_folder, mode=None):
path = os.path.join(model_folder, 'partial_ids.parquet')
df.write.parquet(path, mode=mode)
def delete_low_mem_partial_ids(spark, model_folder):
path = os.path.join(model_folder, 'partial_ids.parquet')
delete_data_source(spark, path)
def load_column_models(spark, model_folder, count_required):
for i in CAT_COLS:
path = os.path.join(model_folder, '%d.parquet' % i)
df = spark.read.parquet(path)
if count_required:
values = df.agg(sum('model_count').alias('sum'), count('*').alias('size')).collect()
else:
values = df.agg(sum('model_count').alias('sum')).collect()
yield i, df, values[0], would_broadcast(spark, path)
def save_column_models(column_models, model_folder, mode=None):
for i, model in column_models:
path = os.path.join(model_folder, '%d.parquet' % i)
model.write.parquet(path, mode=mode)
def save_model_size(model_size, path, write_mode):
if os.path.exists(path) and write_mode == 'errorifexists':
print('Error: model size file %s exists' % path)
sys.exit(1)
os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
with open(path, 'w') as fp:
json.dump(model_size, fp, indent=4)
_benchmark = {}
@contextmanager
def _timed(step):
start = time()
yield
end = time()
_benchmark[step] = end - start
def _parse_args():
parser = ArgumentParser()
parser.add_argument(
'--mode',
required=True,
choices=['generate_models', 'transform'])
parser.add_argument('--days', required=True)
parser.add_argument('--input_folder', required=True)
parser.add_argument('--output_folder')
parser.add_argument('--model_size_file')
parser.add_argument('--model_folder', required=True)
parser.add_argument(
'--write_mode',
choices=['overwrite', 'errorifexists'],
default='errorifexists')
parser.add_argument('--frequency_limit')
parser.add_argument('--no_numeric_log_col', action='store_true')
#Support for running in a lower memory environment
parser.add_argument('--low_mem', action='store_true')
parser.add_argument(
'--output_ordering',
choices=['total_random', 'day_random', 'any', 'input'],
default='total_random')
parser.add_argument(
'--output_partitioning',
choices=['day', 'none'],
default='none')
parser.add_argument('--dict_build_shuffle_parallel_per_day', type=int, default=2)
parser.add_argument('--apply_shuffle_parallel_per_day', type=int, default=25)
parser.add_argument('--skew_broadcast_pct', type=float, default=1.0)
parser.add_argument('--debug_mode', action='store_true')
args = parser.parse_args()
start, end = args.days.split('-')
args.day_range = list(range(int(start), int(end) + 1))
args.days = len(args.day_range)
return args
def _main():
args = _parse_args()
spark = SparkSession.builder.getOrCreate()
df = load_raw(spark, args.input_folder, args.day_range)
if args.mode == 'generate_models':
spark.conf.set('spark.sql.shuffle.partitions', args.days * args.dict_build_shuffle_parallel_per_day)
with _timed('generate models'):
col_counts = get_column_counts_with_frequency_limit(df, args.frequency_limit)
if args.low_mem:
# in low memory mode we have to save an intermediate result
# because if we try to do it in one query spark ends up assigning the
# partial ids in two different locations that are not guaranteed to line up
# this prevents that from happening by assigning the partial ids
# and then writeing them out.
save_low_mem_partial_ids(
assign_low_mem_partial_ids(col_counts),
args.model_folder,
args.write_mode)
save_combined_model(
assign_low_mem_final_ids(load_low_mem_partial_ids(spark, args.model_folder)),
args.model_folder,
args.write_mode)
if not args.debug_mode:
delete_low_mem_partial_ids(spark, args.model_folder)
else:
save_combined_model(
assign_id_with_window(col_counts),
args.model_folder,
args.write_mode)
save_column_models(
get_column_models(load_combined_model(spark, args.model_folder)),
args.model_folder,
args.write_mode)
if not args.debug_mode:
delete_combined_model(spark, args.model_folder)
if args.mode == 'transform':
with _timed('transform'):
if args.output_ordering == 'total_random':
df = rand_ordinal(df)
if args.output_partitioning == 'day':
df = day_from_ordinal(df, args.days)
elif args.output_ordering == 'day_random':
df = rand_ordinal(df)
df = day_from_input_file(df)
elif args.output_ordering == 'input':
df = df.withColumn('ordinal', monotonically_increasing_id())
if args.output_partitioning == 'day':
df = day_from_input_file(df)
else: # any ordering
if args.output_partitioning == 'day':
df = day_from_input_file(df)
models = list(load_column_models(spark, args.model_folder, bool(args.model_size_file)))
if args.model_size_file:
save_model_size(
OrderedDict(('_c%d' % i, agg.size) for i, _, agg, _ in models),
args.model_size_file,
args.write_mode)
models = [(i, df, agg.sum, flag) for i, df, agg, flag in models]
df = apply_models(
df,
models,
not args.low_mem,
args.skew_broadcast_pct)
df = transform_log(df, not args.no_numeric_log_col)
if args.output_partitioning == 'day':
partitionBy = 'day'
else:
partitionBy = None
if args.output_ordering == 'total_random':
if args.output_partitioning == 'day':
df = psudo_sort_by_day_plus(spark, df, args.days)
else: # none
# Don't do a full sort it is expensive. Order is random so
# just make it random
df = df.repartition('ordinal').sortWithinPartitions('ordinal')
df = df.drop('ordinal')
elif args.output_ordering == 'day_random':
df = psudo_sort_by_day_plus(spark, df, args.days)
df = df.drop('ordinal')
if args.output_partitioning != 'day':
df = df.drop('day')
elif args.output_ordering == 'input':
if args.low_mem:
# This is the slowest option. We totally messed up the order so we have to put
# it back in the correct order
df = df.orderBy('ordinal')
else:
# Applying the dictionary happened within a single task so we are already really
# close to the correct order, just need to sort within the partition
df = df.sortWithinPartitions('ordinal')
df = df.drop('ordinal')
if args.output_partitioning != 'day':
df = df.drop('day')
# else: any ordering so do nothing the ordering does not matter
df.write.parquet(
args.output_folder,
mode=args.write_mode,
partitionBy=partitionBy)
print('=' * 100)
print(_benchmark)
if __name__ == '__main__':
_main()
|
TensorFlow2/Recommendation/WideAndDeep/triton/deployment_toolkit/triton_inference_runner | triton_inference_runner | http | # 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 json
import logging
from pathlib import Path
from typing import Optional
# pytype: disable=import-error
try:
from tritonclient import utils as client_utils # noqa: F401
except ImportError:
import tritonclientutils as client_utils # noqa: F401
try:
import tritonclient.http as http_client
except (ImportError, RuntimeError):
import tritonhttpclient as http_client
# pytype: enable=import-error
# method from PEP-366 to support relative import in executed modules
if __package__ is None:
__package__ = Path(__file__).parent.name
from .base import BaseRunner
LOGGER = logging.getLogger("triton_inference_runner.http")
class HTTPInferenceRunner(BaseRunner):
def _parse_content(self, response):
return json.dumps(response, indent=4)
class SyncInferenceRunner(HTTPInferenceRunner):
def __iter__(self):
LOGGER.debug(f"Connecting to {self._server_url}")
client = http_client.InferenceServerClient(
url=self._server_url,
verbose=self._verbose,
connection_timeout=self._response_wait_t,
network_timeout=self._response_wait_t,
)
error = self._verify_triton_state(client)
if error:
raise RuntimeError(f"Could not communicate to Triton Server: {error}")
LOGGER.debug(
f"Triton server {self._server_url} and model {self._model_name}:{self._model_version} " f"are up and ready!"
)
model_config = client.get_model_config(self._model_name, self._model_version)
model_metadata = client.get_model_metadata(self._model_name, self._model_version)
LOGGER.info(f"Model config {self._parse_content(model_config)}")
LOGGER.info(f"Model metadata {self._parse_content(model_metadata)}")
inputs = {tm["name"]: tm for tm in model_metadata["inputs"]}
outputs = {tm["name"]: tm for tm in model_metadata["outputs"]}
output_names = list(outputs)
outputs_req = [http_client.InferRequestedOutput(name) for name in outputs]
for ids, x, y_real in self._dataloader:
infer_inputs = []
for name in inputs:
data = x[name]
datatype = inputs[name]["datatype"]
infer_input = http_client.InferInput(name, data.shape, datatype)
target_np_dtype = client_utils.triton_to_np_dtype(datatype)
data = data.astype(target_np_dtype)
infer_input.set_data_from_numpy(data)
infer_inputs.append(infer_input)
results = client.infer(
model_name=self._model_name,
model_version=self._model_version,
inputs=infer_inputs,
outputs=outputs_req,
timeout=self._response_wait_t_ms,
)
y_pred = {name: results.as_numpy(name) for name in output_names}
yield ids, x, y_pred, y_real
class AsyncInferenceRunner(HTTPInferenceRunner):
DEFAULT_MAX_UNRESP_REQS = 128
def __init__(
self,
server_url: str,
model_name: str,
model_version: str,
*,
dataloader,
verbose=False,
response_wait_time: Optional[float] = None,
max_unresponded_requests: Optional[int] = None,
):
super().__init__(
server_url,
model_name,
model_version,
dataloader=dataloader,
verbose=verbose,
response_wait_time=response_wait_time,
)
self._max_unresp_reqs = (
self.DEFAULT_MAX_UNRESP_REQS if max_unresponded_requests is None else max_unresponded_requests
)
def __iter__(self):
client = http_client.InferenceServerClient(
url=self._server_url,
verbose=self._verbose,
concurrency=self._max_unresp_reqs,
connection_timeout=self._response_wait_t,
network_timeout=self._response_wait_t,
)
self._errors = self._verify_triton_state(client)
if self._errors:
return
LOGGER.debug(
f"Triton server {self._server_url} and model {self._model_name}:{self._model_version} " f"are up and ready!"
)
model_config = client.get_model_config(self._model_name, self._model_version)
model_metadata = client.get_model_metadata(self._model_name, self._model_version)
LOGGER.info(f"Model config {self._parse_content(model_config)}")
LOGGER.info(f"Model metadata {self._parse_content(model_metadata)}")
inputs = {tm["name"]: tm for tm in model_metadata["inputs"]}
outputs = {tm["name"]: tm for tm in model_metadata["outputs"]}
output_names = list(outputs)
async_requests = []
for ids, x, y_real in self._dataloader:
infer_inputs = []
for name in inputs:
data = x[name]
datatype = inputs[name]["datatype"]
infer_input = http_client.InferInput(name, data.shape, datatype)
target_np_dtype = client_utils.triton_to_np_dtype(datatype)
data = data.astype(target_np_dtype)
infer_input.set_data_from_numpy(data)
infer_inputs.append(infer_input)
outputs_req = [http_client.InferRequestedOutput(name) for name in outputs]
request_id = str(ids[0])
async_request = client.async_infer(
model_name=self._model_name,
model_version=self._model_version,
inputs=infer_inputs,
outputs=outputs_req,
request_id=request_id,
timeout=self._response_wait_t_ms,
)
async_requests.append((ids, x, y_real, async_request))
if len(async_requests) > self._max_unresp_reqs:
yield from self._yield_response(async_requests, output_names)
async_requests = []
yield from self._yield_response(async_requests, output_names)
LOGGER.debug("Finished request thread")
def _yield_response(self, async_requests, output_names):
for ids, x, y_real, async_response in async_requests:
result = async_response.get_result()
y_pred = {name: result.as_numpy(name) for name in output_names}
yield ids, x, y_pred, y_real
|
PyTorch/SpeechRecognition/Jasper/common/text | text | symbols | # Copyright (c) 2017 Keith Ito
""" from https://github.com/keithito/tacotron """
'''
Defines the set of symbols used in text input to the model.
The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
from . import cmudict
_pad = '_'
_punctuation = '!\'(),.:;? '
_special = '-'
_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
_arpabet = ['@' + s for s in cmudict.valid_symbols]
# Export all symbols:
symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet
|
PyTorch/Recommendation/NCF | NCF | transcode | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
import os
import torch
import pandas as pd
from feature_spec import FeatureSpec
from neumf_constants import USER_CHANNEL_NAME, ITEM_CHANNEL_NAME, LABEL_CHANNEL_NAME
def parse_args():
parser = ArgumentParser()
parser.add_argument('--path', type=str, default='',
help='Path to input data directory')
parser.add_argument('--feature_spec_in', type=str, default='feature_spec.yaml',
help='Name of the input feature specification file, or path relative to data directory.')
parser.add_argument('--output', type=str, default='/data',
help='Path to output data directory')
parser.add_argument('--feature_spec_out', type=str, default='feature_spec.yaml',
help='Name of the output feature specification file, or path relative to data directory.')
return parser.parse_args()
def main():
args = parse_args()
args_output = args.output
args_path = args.path
args_feature_spec_in = args.feature_spec_in
args_feature_spec_out = args.feature_spec_out
feature_spec_path = os.path.join(args_path, args_feature_spec_in)
feature_spec = FeatureSpec.from_yaml(feature_spec_path)
# Only three features are transcoded - this is NCF specific
user_feature_name = feature_spec.channel_spec[USER_CHANNEL_NAME][0]
item_feature_name = feature_spec.channel_spec[ITEM_CHANNEL_NAME][0]
label_feature_name = feature_spec.channel_spec[LABEL_CHANNEL_NAME][0]
categorical_features = [user_feature_name, item_feature_name]
found_cardinalities = {f: 0 for f in categorical_features}
new_source_spec = {}
for mapping_name, mapping in feature_spec.source_spec.items():
# Load all chunks and link into one df
chunk_dfs = []
for chunk in mapping:
assert chunk['type'] == 'csv', "Only csv files supported in this transcoder"
file_dfs = []
for file in chunk['files']:
path_to_load = os.path.join(feature_spec.base_directory, file)
file_dfs.append(pd.read_csv(path_to_load, header=None))
chunk_df = pd.concat(file_dfs, ignore_index=True)
chunk_df.columns = chunk['features']
chunk_df.reset_index(drop=True, inplace=True)
chunk_dfs.append(chunk_df)
mapping_df = pd.concat(chunk_dfs, axis=1) # This takes care of making sure feature names are unique
for feature in categorical_features:
mapping_cardinality = mapping_df[feature].max() + 1
previous_cardinality = found_cardinalities[feature]
found_cardinalities[feature] = max(previous_cardinality, mapping_cardinality)
# We group together users and items, while separating labels. This is because of the target dtypes: ids are int,
# while labels are float to compute loss.
ints_tensor = torch.from_numpy(mapping_df[[user_feature_name, item_feature_name]].values).long()
ints_file = f"{mapping_name}_data_0.pt"
ints_chunk = {"type": "torch_tensor",
"features": [user_feature_name, item_feature_name],
"files": [ints_file]}
torch.save(ints_tensor, os.path.join(args_output, ints_file))
floats_tensor = torch.from_numpy(mapping_df[[label_feature_name]].values).float()
floats_file = f"{mapping_name}_data_1.pt"
floats_chunk = {"type": "torch_tensor",
"features": [label_feature_name],
"files": [floats_file]}
torch.save(floats_tensor, os.path.join(args_output, floats_file))
new_source_spec[mapping_name] = [ints_chunk, floats_chunk]
for feature in categorical_features:
found_cardinality = found_cardinalities[feature]
declared_cardinality = feature_spec.feature_spec[feature].get('cardinality', 'auto')
if declared_cardinality != "auto":
declared = int(declared_cardinality)
assert declared >= found_cardinality, "Specified cardinality conflicts data"
found_cardinalities[feature] = declared
new_inner_feature_spec = {
user_feature_name: {
"dtype": "torch.int64",
"cardinality": int(found_cardinalities[user_feature_name])
},
item_feature_name: {
"dtype": "torch.int64",
"cardinality": int(found_cardinalities[item_feature_name])
},
label_feature_name: {
"dtype": "torch.float32"
}
}
new_feature_spec = FeatureSpec(feature_spec=new_inner_feature_spec,
source_spec=new_source_spec,
channel_spec=feature_spec.channel_spec,
metadata=feature_spec.metadata,
base_directory="")
feature_spec_save_path = os.path.join(args_output, args_feature_spec_out)
new_feature_spec.to_yaml(output_path=feature_spec_save_path)
if __name__ == '__main__':
main()
|
PyTorch/LanguageModeling/BERT/triton | triton | metrics | # 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 json
import logging
import os
import subprocess
import sys
from argparse import Namespace
from typing import Any, Dict, List, Optional
import numpy as np
from run_squad import RawResult, convert_examples_to_features, get_answers, read_squad_examples
from tokenization import BertTokenizer
#
from triton.deployment_toolkit.core import BaseMetricsCalculator
class MetricsCalculator(BaseMetricsCalculator):
def __init__(
self,
eval_script: str = "data/squad/v1.1/evaluate-v1.1.py",
predict_file: str = "",
output_dir: str = "./",
n_best_size: int = 20,
max_answer_length: int = 30,
version_2_with_negative: bool = False,
max_seq_length: int = 384,
doc_stride: int = 128,
max_query_length: int = 64,
vocab_file: str = "",
do_lower_case: bool = True,
max_len: int = 512,
):
tokenizer = BertTokenizer(vocab_file, do_lower_case=do_lower_case, max_len=max_len) # for bert large
self.eval_examples = read_squad_examples(
input_file=predict_file, is_training=False, version_2_with_negative=version_2_with_negative
)
self.eval_features = convert_examples_to_features(
examples=self.eval_examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=False,
)
self.output_dir = output_dir
self.eval_script = eval_script
self.predict_file = predict_file
args = Namespace(
version_2_with_negative=version_2_with_negative,
n_best_size=n_best_size,
max_answer_length=max_answer_length,
verbose_logging=False,
do_lower_case=do_lower_case,
)
self.args = args
self.all_results: List[RawResult] = []
def _calc(self) -> Dict[str, float]:
dataset_size = len(self.eval_features)
self.all_results = self.all_results[:dataset_size]
output_prediction_file = os.path.join(self.output_dir, "predictions.json")
answers, _ = get_answers(self.eval_examples, self.eval_features, self.all_results, self.args)
with open(output_prediction_file, "w") as f:
f.write(json.dumps(answers, indent=4) + "\n")
eval_out = subprocess.check_output(
[sys.executable, self.eval_script, self.predict_file, output_prediction_file]
)
scores = str(eval_out).strip()
# exact_match = float(scores.split(":")[1].split(",")[0])
f1 = float(scores.split(":")[2].split("}")[0])
return {"f1": f1}
def update(
self,
ids: List[Any],
y_pred: Dict[str, np.ndarray],
x: Optional[Dict[str, np.ndarray]],
y_real: Optional[Dict[str, np.ndarray]],
):
start_logits = y_pred["output__0"]
end_logits = y_pred["output__1"]
for unique_id, start_logit, end_logit in zip(ids, start_logits, end_logits):
start_logit = start_logit.tolist()
end_logit = end_logit.tolist()
raw_result = RawResult(unique_id=unique_id, start_logits=start_logit, end_logits=end_logit)
self.all_results.append(raw_result)
@property
def metrics(self) -> Dict[str, float]:
return self._calc()
|
Tools/PyTorch/TimeSeriesPredictionPlatform/triton | triton | export_model | # 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 logging
import os
from pathlib import Path
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
os.environ["TF_ENABLE_DEPRECATION_WARNINGS"] = "1"
# method from PEP-366 to support relative import in executed modules
if __name__ == "__main__" and __package__ is None:
__package__ = Path(__file__).parent.name
from .deployment_toolkit.args import ArgParserGenerator # noqa: E402 module level import not at top of file
from .deployment_toolkit.core import ( # noqa: E402 module level import not at top of file
DATALOADER_FN_NAME,
BaseLoader,
BaseSaver,
Format,
load_from_file,
)
from .deployment_toolkit.extensions import loaders, savers # noqa: E402 module level import not at top of file
LOGGER = logging.getLogger("export_model")
INPUT_MODEL_TYPES = [Format.TF_ESTIMATOR, Format.TF_KERAS, Format.PYT]
OUTPUT_MODEL_TYPES = [Format.TF_SAVEDMODEL, Format.TS_TRACE, Format.TS_SCRIPT, Format.ONNX]
def _get_args():
parser = argparse.ArgumentParser(
description="Script for exporting models from supported frameworks.", allow_abbrev=False
)
parser.add_argument("--input-path", help="Path to input python module", required=True)
parser.add_argument(
"--input-type", help="Input model type", choices=[f.value for f in INPUT_MODEL_TYPES], required=True
)
parser.add_argument("--output-path", help="Path to output model file", required=True)
parser.add_argument(
"--output-type", help="Output model type", choices=[f.value for f in OUTPUT_MODEL_TYPES], required=True
)
parser.add_argument("--dataloader", help="Path to python module containing data loader")
parser.add_argument("-v", "--verbose", help="Verbose logs", action="store_true", default=False)
parser.add_argument(
"--ignore-unknown-parameters",
help="Ignore unknown parameters (argument often used in CI where set of arguments is constant)",
action="store_true",
default=False,
)
args, unparsed_args = parser.parse_known_args()
Loader: BaseLoader = loaders.get(args.input_type)
ArgParserGenerator(Loader, module_path=args.input_path).update_argparser(parser)
if args.input_type == Format.PYT.value and args.output_type == Format.ONNX.value:
saver_type = f"{Format.PYT.value}--{Format.ONNX.value}"
else:
saver_type = args.output_type
Saver: BaseSaver = savers.get(saver_type)
ArgParserGenerator(Saver).update_argparser(parser)
if args.dataloader is not None:
get_dataloader_fn = load_from_file(args.dataloader, label="dataloader", target=DATALOADER_FN_NAME)
ArgParserGenerator(get_dataloader_fn).update_argparser(parser)
if args.ignore_unknown_parameters:
args, unknown_args = parser.parse_known_args()
LOGGER.warning(f"Got additional args {unknown_args}")
else:
args = parser.parse_args()
return args
def main():
args = _get_args()
log_level = logging.INFO if not args.verbose else logging.DEBUG
log_format = "%(asctime)s %(levelname)s %(name)s %(message)s"
logging.basicConfig(level=log_level, format=log_format)
LOGGER.info("args:")
for key, value in vars(args).items():
LOGGER.info(f" {key} = {value}")
dataloader_fn = None
if args.dataloader is not None:
get_dataloader_fn = load_from_file(args.dataloader, label="dataloader", target=DATALOADER_FN_NAME)
dataloader_fn = ArgParserGenerator(get_dataloader_fn).from_args(args)
Loader: BaseLoader = loaders.get(args.input_type)
loader = ArgParserGenerator(Loader, module_path=args.input_path).from_args(args)
model = loader.load(args.input_path, dataloader_fn=dataloader_fn, output_type=args.output_type)
LOGGER.info("inputs: %s", model.inputs)
LOGGER.info("outputs: %s", model.outputs)
if args.input_type == Format.PYT.value and args.output_type == Format.ONNX.value:
saver_type = f"{Format.PYT.value}--{Format.ONNX.value}"
else:
saver_type = args.output_type
Saver: BaseSaver = savers.get(saver_type)
saver = ArgParserGenerator(Saver).from_args(args)
saver.save(model, args.output_path, dataloader_fn)
if __name__ == "__main__":
main()
|
Tools/PyTorch/TimeSeriesPredictionPlatform | TimeSeriesPredictionPlatform | launch_training | # 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 os
import hydra
from omegaconf import OmegaConf
import torch
import conf.conf_utils
from distributed_utils import is_main_process, init_distributed, init_parallel
from training.utils import set_seed, get_optimization_objectives
from loggers.log_helper import log_parameters
warnings.filterwarnings("ignore")
@hydra.main(config_path="conf", config_name="train_config")
def main(config):
trainer_type = config.trainer._target_
set_seed(config.get("seed", None))
model = hydra.utils.instantiate(config.model)
train, valid, test = hydra.utils.call(config.dataset)
evaluator = hydra.utils.instantiate(config.evaluator, test_data=test)
if 'CTLTrainer' in trainer_type:
init_parallel()
init_distributed()
model = model.to(device=config.model.config.device)
trainer = hydra.utils.instantiate(
config.trainer,
optimizer={'params': model.parameters()},
model=model,
train_dataset=train,
valid_dataset=valid,
)
log_parameters(trainer.logger, config)
trainer.train()
if is_main_process():
checkpoint = torch.load("best_checkpoint.zip", map_location=evaluator.device)
model.load_state_dict(checkpoint["model_state_dict"])
preds, labels, ids, weights = evaluator.predict(model)
eval_metrics = evaluator.evaluate(preds, labels, ids, weights)
trainer.logger.log(step=[], data=eval_metrics, verbosity=0)
trainer.logger.flush()
del train, valid, test, model, trainer
torch.cuda.empty_cache()
objectives = get_optimization_objectives(config, eval_metrics)
return objectives
elif 'XGBTrainer' in trainer_type or "StatTrainer" in trainer_type:
del config.trainer.criterion
trainer = hydra.utils.instantiate(
config.trainer,
model=model,
train_dataset=train,
valid_dataset=valid,
)
trainer.train()
preds, labels, ids, weights = evaluator.predict(model)
eval_metrics = evaluator.evaluate(preds, labels, ids, weights)
trainer.logger.log(step=[], data=eval_metrics, verbosity=0)
objectives = get_optimization_objectives(config, eval_metrics)
return objectives
else:
raise AttributeError(f"Not supported Trainer provided {trainer_type}")
if __name__ == "__main__":
main()
|
PyTorch/SpeechSynthesis/FastPitch/common/text/zh | zh | pinyin_dict | NIN N IN
FA F A
BAI B AI
YIN Y IN
DE D E
SHEN SH EN
TAN T AN
PAO P AO
WENG W ENG
LAN L AN
CHUAN CH U AN
SEI S EI
DANG D ANG
XUE X VE
YUAN Y V AN
HU H U
CUAN C U AN
BO B O
SHAI SH AI
CHUI CH UI
SHOU SH OU
QIU Q IU
SONG S ONG
KAI K AI
LING L ING
SUO S U O
ZHUAI ZH U AI
ZHEN ZH EN
GENG G ENG
YAN Y AN
CU C U
ZHUA ZH U A
MA M A
SOU S OU
GOU G OU
PU P U
GUA G U A
RONG R ONG
JIAN J I AN
FOU F OU
FO F O
ZHUAN ZH U AN
DIU D IU
TIAN T I AN
QUN Q VN
NE N E
LIN L IN
QIE Q IE
LANG L ANG
CAO C AO
PANG P ANG
GAN G AN
KUI K UI
ROU R OU
NING N ING
NOU N OU
CUI C UI
NA N A
MING M ING
JUAN J V AN
NIAN N I AN
JIONG J I ONG
LE L E
GEN G EN
CHUO CH U O
SANG S ANG
MANG M ANG
GANG G ANG
SHENG SH ENG
KENG K ENG
ANG ^ ANG
ZHONG ZH ONG
PEI P EI
LO L O
BEN B EN
SAN S AN
WAI W AI
BA B A
ZEI Z EI
BANG B ANG
MENG M ENG
HA H A
SHAO SH AO
RENG R ENG
XUAN X V AN
GUAI G U AI
QUAN Q V AN
DIE D IE
CEN C EN
QIONG Q I ONG
QIAO Q I AO
NAN N AN
CANG C ANG
NANG N ANG
LA L A
KU K U
KAO K AO
XI X I
MO M O
CHAN CH AN
DUO D U O
DIAO D I AO
HUN H UN
LOU L OU
HANG H ANG
CENG C ENG
ZHI ZH I
RUAN R U AN
QIANG Q I ANG
MIU M IU
WO W O
GEI G EI
EI ^ EI
CHAI CH AI
ZHUI ZH UI
CHU CH U
YONG Y ONG
SHUO SH U O
DING D ING
CHE CH E
YO Y O
PENG P ENG
RANG R ANG
BU B U
NIU N IU
KE K E
MI M I
GUAN G U AN
RE R E
NI N I
TI T I
DIA D I A
NUO N U O
WANG W ANG
QIAN Q I AN
LUO L U O
YA Y A
CI C I
GUN G UN
GAO G AO
DOU D OU
DAI D AI
BAO B AO
BIN B IN
NAI N AI
SE S E
PA P A
ZAO Z AO
AO ^ AO
NIE N IE
BENG B ENG
ZHU ZH U
JU J V
XIU X IU
XIAN X I AN
RUI R UI
SAI S AI
SHUANG SH U ANG
SHUAI SH U AI
HEN H EN
OU ^ OU
HUA H U A
LONG L ONG
ZI Z I
SHE SH E
JUN J VN
YE Y E
TUI T UI
GUANG G U ANG
MAN M AN
LAI L AI
ZHUN ZH UN
CHUANG CH U ANG
ZUI Z UI
SU S U
TE T E
TAO T AO
CONG C ONG
TONG T ONG
HENG H ENG
ZUO Z U O
LU L U
BAN B AN
PIAO P I AO
XIANG X I ANG
LIANG L I ANG
ZU Z U
NIANG N I ANG
LIU L IU
BIE B IE
CHA CH A
YANG Y ANG
LVE L VE
LENG L ENG
KOU K OU
AN ^ AN
CHUN CH UN
ZAI Z AI
DONG D ONG
SHI SH I
CHAO CH AO
ZHAI ZH AI
RI R I
HUAI H U AI
TOU T OU
SENG S ENG
GUO G U O
NENG N ENG
ZUN Z UN
XIONG X I ONG
ZEN Z EN
TANG T ANG
BIAN B I AN
QU Q V
QI Q I
ZHAN ZH AN
JIAO J I AO
CHENG CH ENG
CHONG CH ONG
KEI K EI
MEI M EI
LV L V
SHUA SH U A
CA C A
DENG D ENG
TING T ING
YAO Y AO
TIAO T I AO
ME M E
CE C E
ZUAN Z U AN
SEN S EN
O ^ O
ZENG Z ENG
RAO R AO
WEI W EI
KUAN K U AN
PING P ING
MAI M AI
HUAN H U AN
DEN D EN
BING B ING
QING Q ING
PIN P IN
GAI G AI
LI L I
ZHENG ZH ENG
ZAN Z AN
BEI B EI
SHU SH U
MU M U
KUO K U O
JIE J IE
CHUAI CH U AI
FAN F AN
PI P I
SHUI SH UI
YING Y ING
QIN Q IN
SHA SH A
KANG K ANG
CHEN CH EN
JIANG J I ANG
RAN R AN
LUAN L U AN
HEI H EI
XING X ING
WAN W AN
TA T A
XU X V
TENG T ENG
ZA Z A
KEN K EN
DAN D AN
TU T U
KUANG K U ANG
JING J ING
REN R EN
CHOU CH OU
KUA K U A
HE H E
DAO D AO
NEI N EI
KUAI K U AI
HAO H AO
MIAO M I AO
YI Y I
ZHAO ZH AO
TUO T U O
ZHEI ZH EI
FU F U
FEN F EN
JIA J I A
WA W A
CUO C U O
WU W U
MEN M EN
XUN X VN
MOU M OU
SHAN SH AN
PAI P AI
GONG G ONG
NONG N ONG
COU C OU
KONG K ONG
HUO H U O
HUANG H U ANG
JIU J IU
HONG H ONG
MIE M IE
HUI H UI
WEN W EN
ZHUO ZH U O
MIAN M I AN
BI B I
ZE Z E
YUN Y VN
GA G A
SUAN S U AN
SUN S UN
MAO M AO
XIA X I A
KA K A
NAO N AO
TIE T IE
GE G E
GUI G UI
LAO L AO
ZOU Z OU
SAO S AO
PO P O
JIN J IN
DUAN D U AN
DU D U
RUN R UN
YUE Y VE
DUN D UN
A ^ A
PIE P IE
SHANG SH ANG
XIN X IN
CAN C AN
PAN P AN
LIE L IE
QIA Q I A
GU G U
ZHE ZH E
ZONG Z ONG
DIAN D I AN
LIA L I A
FENG F ENG
JUE J VE
LIAO L I AO
SA S A
TAI T AI
LEI L EI
SHUN SH UN
HAI H AI
NEN N EN
MIN M IN
PIAN P I AN
CHI CH I
CHANG CH ANG
NIAO N I AO
JI J I
TEI T EI
FANG F ANG
POU P OU
QUE Q VE
ZHOU ZH OU
NV N V
ER ^ ER
YU Y V
XIE X IE
FAI F AI
EN ^ EN
NVE N VE
KAN K AN
LUN L UN
ZHUANG ZH U ANG
HAN H AN
NG N EN
DI D I
SHEI SH EI
RUO R U O
KUN K UN
DUI D UI
TUAN T U AN
ZANG Z ANG
CUN C UN
YOU Y OU
SUI S UI
DEI D EI
RU R U
NU N U
ZHANG ZH ANG
BIAO B I AO
NUAN N U AN
SHUAN SH U AN
XIAO X I AO
TUN T UN
E ^ E
SI S I
HOU H OU
FEI F EI
ZHA ZH A
CAI C AI
KIU K IU
DA D A
PEN P EN
LIAN L I AN
AI ^ AI
|
CUDA-Optimized/FastSpeech/tacotron2 | tacotron2 | utils | # 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 numpy as np
from scipy.io.wavfile import read
import torch
def get_mask_from_lengths(lengths):
max_len = torch.max(lengths).item()
ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len))
mask = (ids < lengths.unsqueeze(1)).bool()
return mask
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
def load_filepaths_and_text(filename, split="|"):
with open(filename, encoding='utf-8') as f:
filepaths_and_text = [line.strip().split(split) for line in f]
return filepaths_and_text
def to_gpu(x):
x = x.contiguous()
if torch.cuda.is_available():
x = x.cuda(non_blocking=True)
return torch.autograd.Variable(x)
|
PyTorch/LanguageModeling/BERT/distillation/BERT_6L_768D | BERT_6L_768D | config | {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"max_position_embeddings": 512,
"num_attention_heads": 12,
"num_hidden_layers": 6,
"type_vocab_size": 2,
"vocab_size": 30528
}
|
PyTorch/LanguageModeling/BART/bart/tokenization | tokenization | tokenization_xlnet | # coding=utf-8
# Copyright (c) 2022 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
#
# 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.
""" Tokenization classes for XLNet model."""
import os
import unicodedata
from shutil import copyfile
from typing import List, Optional
from bart.tokenization.tokenization_utils import PreTrainedTokenizer
from utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"xlnet-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-spiece.model",
"xlnet-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-spiece.model",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
SPIECE_UNDERLINE = "▁"
# Segments (not really needed)
SEG_ID_A = 0
SEG_ID_B = 1
SEG_ID_CLS = 2
SEG_ID_SEP = 3
SEG_ID_PAD = 4
class XLNetTokenizer(PreTrainedTokenizer):
"""
Constructs an XLNet tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`__
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users
should refer to the superclass for more information regarding methods.
Args:
vocab_file (:obj:`string`):
`SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a .spm extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether to lowercase the input when tokenizing.
remove_space (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether to strip the text when tokenizing (removing excess spaces before and after the string).
keep_accents (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to keep accents when tokenizing.
bos_token (:obj:`string`, `optional`, defaults to "<s>"):
The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the beginning
of sequence. The token used is the :obj:`cls_token`.
eos_token (:obj:`string`, `optional`, defaults to "</s>"):
The end of sequence token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the end
of sequence. The token used is the :obj:`sep_token`.
unk_token (:obj:`string`, `optional`, defaults to "<unk>"):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (:obj:`string`, `optional`, defaults to "<sep>"):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences
for sequence classification or for a text and a question for question answering.
It is also used as the last token of a sequence built with special tokens.
pad_token (:obj:`string`, `optional`, defaults to "<pad>"):
The token used for padding, for example when batching sequences of different lengths.
cls_token (:obj:`string`, `optional`, defaults to "<cls>"):
The classifier token which is used when doing sequence classification (classification of the whole
sequence instead of per-token classification). It is the first token of the sequence when built with
special tokens.
mask_token (:obj:`string`, `optional`, defaults to "<mask>"):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
additional_special_tokens (:obj:`List[str]`, `optional`, defaults to :obj:`["<eop>", "<eod>"]`):
Additional special tokens used by the tokenizer.
Attributes:
sp_model (:obj:`SentencePieceProcessor`):
The `SentencePiece` processor that is used for every conversion (string, tokens and IDs).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
padding_side = "left"
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=True,
keep_accents=False,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
sep_token="<sep>",
pad_token="<pad>",
cls_token="<cls>",
mask_token="<mask>",
additional_special_tokens=["<eop>", "<eod>"],
**kwargs
):
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
additional_special_tokens=additional_special_tokens,
**kwargs,
)
self._pad_token_type_id = 3
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"You need to install SentencePiece to use XLNetTokenizer: https://github.com/google/sentencepiece"
"pip install sentencepiece"
)
raise
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(vocab_file)
@property
def vocab_size(self):
return len(self.sp_model)
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"You need to install SentencePiece to use XLNetTokenizer: https://github.com/google/sentencepiece"
"pip install sentencepiece"
)
raise
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file)
def preprocess_text(self, inputs):
if self.remove_space:
outputs = " ".join(inputs.strip().split())
else:
outputs = inputs
outputs = outputs.replace("``", '"').replace("''", '"')
if not self.keep_accents:
outputs = unicodedata.normalize("NFKD", outputs)
outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
if self.do_lower_case:
outputs = outputs.lower()
return outputs
def _tokenize(self, text, sample=False):
""" Tokenize a string. """
text = self.preprocess_text(text)
if not sample:
pieces = self.sp_model.EncodeAsPieces(text)
else:
pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1)
new_pieces = []
for piece in pieces:
if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, ""))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
cur_pieces = cur_pieces[1:]
else:
cur_pieces[0] = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(cur_pieces)
else:
new_pieces.append(piece)
return new_pieces
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.sp_model.IdToPiece(index)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
return out_string
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
An XLNet sequence has the following format:
- single sequence: ``X <sep> <cls>``
- pair of sequences: ``A <sep> B <sep> <cls>``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return token_ids_0 + sep + cls
return token_ids_0 + sep + token_ids_1 + sep + cls
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` methods.
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True if the token list is already formatted with special tokens for the model
Returns:
:obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formated with special tokens for the model."
)
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is not None:
return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1]
return ([0] * len(token_ids_0)) + [1, 1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
An XLNet sequence pair mask has the following format:
0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2
| first sequence | second sequence | CLS segment ID
if token_ids_1 is None, only returns the first portion of the mask (0's).
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
sequence(s).
"""
sep = [self.sep_token_id]
cls_segment_id = [2]
if token_ids_1 is None:
return len(token_ids_0 + sep) * [0] + cls_segment_id
return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id
def save_vocabulary(self, save_directory):
"""
Save the sentencepiece vocabulary (copy original file) and special tokens file to a directory.
Args:
save_directory (:obj:`str`):
The directory in which to save the vocabulary.
Returns:
:obj:`Tuple(str)`: Paths to the files saved.
"""
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
out_vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,) |
TensorFlow/Classification/ConvNets/dataprep | dataprep | imagenet_2012_validation_synset_labels | n01751748
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|
TensorFlow/Detection/SSD/models/research/slim/nets | nets | inception_v4 | # 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.
# ==============================================================================
"""Contains the definition of the Inception V4 architecture.
As described in http://arxiv.org/abs/1602.07261.
Inception-v4, Inception-ResNet and the Impact of Residual Connections
on Learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import inception_utils
slim = tf.contrib.slim
def block_inception_a(inputs, scope=None, reuse=None):
"""Builds Inception-A block for Inception v4 network."""
# By default use stride=1 and SAME padding
with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
stride=1, padding='SAME'):
with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1')
return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
def block_reduction_a(inputs, scope=None, reuse=None):
"""Builds Reduction-A block for Inception v4 network."""
# By default use stride=1 and SAME padding
with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
stride=1, padding='SAME'):
with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2, padding='VALID',
scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2,
padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
def block_inception_b(inputs, scope=None, reuse=None):
"""Builds Inception-B block for Inception v4 network."""
# By default use stride=1 and SAME padding
with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
stride=1, padding='SAME'):
with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
def block_reduction_b(inputs, scope=None, reuse=None):
"""Builds Reduction-B block for Inception v4 network."""
# By default use stride=1 and SAME padding
with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
stride=1, padding='SAME'):
with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2,
padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1')
branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2,
padding='VALID', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
def block_inception_c(inputs, scope=None, reuse=None):
"""Builds Inception-C block for Inception v4 network."""
# By default use stride=1 and SAME padding
with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
stride=1, padding='SAME'):
with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat(axis=3, values=[
slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')])
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1')
branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3')
branch_2 = tf.concat(axis=3, values=[
slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'),
slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')])
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1')
return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
"""Creates the Inception V4 network up to the given final endpoint.
Args:
inputs: a 4-D tensor of size [batch_size, height, width, 3].
final_endpoint: specifies the endpoint to construct the network up to.
It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e',
'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c',
'Mixed_7d']
scope: Optional variable_scope.
Returns:
logits: the logits outputs of the model.
end_points: the set of end_points from the inception model.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
"""
end_points = {}
def add_and_check_final(name, net):
end_points[name] = net
return name == final_endpoint
with tf.variable_scope(scope, 'InceptionV4', [inputs]):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
# 299 x 299 x 3
net = slim.conv2d(inputs, 32, [3, 3], stride=2,
padding='VALID', scope='Conv2d_1a_3x3')
if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
# 149 x 149 x 32
net = slim.conv2d(net, 32, [3, 3], padding='VALID',
scope='Conv2d_2a_3x3')
if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
# 147 x 147 x 32
net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3')
if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
# 147 x 147 x 64
with tf.variable_scope('Mixed_3a'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
scope='MaxPool_0a_3x3')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID',
scope='Conv2d_0a_3x3')
net = tf.concat(axis=3, values=[branch_0, branch_1])
if add_and_check_final('Mixed_3a', net): return net, end_points
# 73 x 73 x 160
with tf.variable_scope('Mixed_4a'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID',
scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1')
branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID',
scope='Conv2d_1a_3x3')
net = tf.concat(axis=3, values=[branch_0, branch_1])
if add_and_check_final('Mixed_4a', net): return net, end_points
# 71 x 71 x 192
with tf.variable_scope('Mixed_5a'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [3, 3], stride=2, padding='VALID',
scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
scope='MaxPool_1a_3x3')
net = tf.concat(axis=3, values=[branch_0, branch_1])
if add_and_check_final('Mixed_5a', net): return net, end_points
# 35 x 35 x 384
# 4 x Inception-A blocks
for idx in range(4):
block_scope = 'Mixed_5' + chr(ord('b') + idx)
net = block_inception_a(net, block_scope)
if add_and_check_final(block_scope, net): return net, end_points
# 35 x 35 x 384
# Reduction-A block
net = block_reduction_a(net, 'Mixed_6a')
if add_and_check_final('Mixed_6a', net): return net, end_points
# 17 x 17 x 1024
# 7 x Inception-B blocks
for idx in range(7):
block_scope = 'Mixed_6' + chr(ord('b') + idx)
net = block_inception_b(net, block_scope)
if add_and_check_final(block_scope, net): return net, end_points
# 17 x 17 x 1024
# Reduction-B block
net = block_reduction_b(net, 'Mixed_7a')
if add_and_check_final('Mixed_7a', net): return net, end_points
# 8 x 8 x 1536
# 3 x Inception-C blocks
for idx in range(3):
block_scope = 'Mixed_7' + chr(ord('b') + idx)
net = block_inception_c(net, block_scope)
if add_and_check_final(block_scope, net): return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint)
def inception_v4(inputs, num_classes=1001, is_training=True,
dropout_keep_prob=0.8,
reuse=None,
scope='InceptionV4',
create_aux_logits=True):
"""Creates the Inception V4 model.
Args:
inputs: a 4-D tensor of size [batch_size, height, width, 3].
num_classes: number of predicted classes. If 0 or None, the logits layer
is omitted and the input features to the logits layer (before dropout)
are returned instead.
is_training: whether is training or not.
dropout_keep_prob: float, the fraction to keep before final layer.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
create_aux_logits: Whether to include the auxiliary logits.
Returns:
net: a Tensor with the logits (pre-softmax activations) if num_classes
is a non-zero integer, or the non-dropped input to the logits layer
if num_classes is 0 or None.
end_points: the set of end_points from the inception model.
"""
end_points = {}
with tf.variable_scope(scope, 'InceptionV4', [inputs], reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
net, end_points = inception_v4_base(inputs, scope=scope)
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
# Auxiliary Head logits
if create_aux_logits and num_classes:
with tf.variable_scope('AuxLogits'):
# 17 x 17 x 1024
aux_logits = end_points['Mixed_6h']
aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3,
padding='VALID',
scope='AvgPool_1a_5x5')
aux_logits = slim.conv2d(aux_logits, 128, [1, 1],
scope='Conv2d_1b_1x1')
aux_logits = slim.conv2d(aux_logits, 768,
aux_logits.get_shape()[1:3],
padding='VALID', scope='Conv2d_2a')
aux_logits = slim.flatten(aux_logits)
aux_logits = slim.fully_connected(aux_logits, num_classes,
activation_fn=None,
scope='Aux_logits')
end_points['AuxLogits'] = aux_logits
# Final pooling and prediction
# TODO(sguada,arnoegw): Consider adding a parameter global_pool which
# can be set to False to disable pooling here (as in resnet_*()).
with tf.variable_scope('Logits'):
# 8 x 8 x 1536
kernel_size = net.get_shape()[1:3]
if kernel_size.is_fully_defined():
net = slim.avg_pool2d(net, kernel_size, padding='VALID',
scope='AvgPool_1a')
else:
net = tf.reduce_mean(net, [1, 2], keep_dims=True,
name='global_pool')
end_points['global_pool'] = net
if not num_classes:
return net, end_points
# 1 x 1 x 1536
net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b')
net = slim.flatten(net, scope='PreLogitsFlatten')
end_points['PreLogitsFlatten'] = net
# 1536
logits = slim.fully_connected(net, num_classes, activation_fn=None,
scope='Logits')
end_points['Logits'] = logits
end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions')
return logits, end_points
inception_v4.default_image_size = 299
inception_v4_arg_scope = inception_utils.inception_arg_scope
|
TensorFlow2/Detection/Efficientdet | Efficientdet | inspector | # Copyright 2020 Google Research. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tool to inspect a model."""
import os
from absl import app
from absl import flags
from absl import logging
import numpy as np
from PIL import Image
import tensorflow as tf
from dllogger import StdOutBackend, JSONStreamBackend, Verbosity
import dllogger as DLLogger
from model import inference
from utils import hparams_config
from utils import model_utils
from utils import setup
flags.DEFINE_string('model_name', 'efficientdet-d0', 'Model.')
flags.DEFINE_string('mode', 'benchmark',
'Run mode: {dry, export, benchmark}')
flags.DEFINE_string('trace_filename', None, 'Trace file name.')
flags.DEFINE_integer('bm_runs', 100, 'Number of benchmark runs.')
flags.DEFINE_string('tensorrt', None, 'TensorRT mode: {None, FP32, FP16, INT8}')
flags.DEFINE_integer('batch_size', 1, 'Batch size for inference.')
flags.DEFINE_string('ckpt_path', '_', 'checkpoint dir used for eval.')
flags.DEFINE_string('export_ckpt', None, 'Output model ckpt path.')
flags.DEFINE_string(
'hparams', '', 'Comma separated k=v pairs of hyperparameters or a module'
' containing attributes to use as hyperparameters.')
flags.DEFINE_bool('amp', True, 'Enable mixed precision training')
flags.DEFINE_bool('use_xla', True, 'Use XLA')
flags.DEFINE_string('input_image', None, 'Input image path for inference.')
flags.DEFINE_string('output_image_dir', None, 'Output dir for inference.')
flags.DEFINE_string('dllogger_path', '/tmp/time_log.txt', 'Filepath for dllogger logs')
# For video.
flags.DEFINE_string('input_video', None, 'Input video path for inference.')
flags.DEFINE_string('output_video', None,
'Output video path. If None, play it online instead.')
# For visualization.
flags.DEFINE_integer('max_boxes_to_draw', 100, 'Max number of boxes to draw.')
flags.DEFINE_float('min_score_thresh', 0.4, 'Score threshold to show box.')
flags.DEFINE_string('nms_method', 'hard', 'nms method, hard or gaussian.')
# For saved model.
flags.DEFINE_string('saved_model_dir', None,
'Folder path for saved model.')
flags.DEFINE_string('tflite_path', None, 'Path for exporting tflite file.')
flags.DEFINE_bool('debug', False, 'Debug mode.')
FLAGS = flags.FLAGS
def main(_):
model_config = hparams_config.get_detection_config(FLAGS.model_name)
model_config.override(FLAGS.hparams) # Add custom overrides
model_config.is_training_bn = False
model_config.image_size = model_utils.parse_image_size(model_config.image_size)
# A hack to make flag consistent with nms configs.
if FLAGS.min_score_thresh:
model_config.nms_configs.score_thresh = FLAGS.min_score_thresh
if FLAGS.nms_method:
model_config.nms_configs.method = FLAGS.nms_method
if FLAGS.max_boxes_to_draw:
model_config.nms_configs.max_output_size = FLAGS.max_boxes_to_draw
model_config.mixed_precision = FLAGS.amp
setup.set_flags(FLAGS, model_config, training=False)
model_params = model_config.as_dict()
ckpt_path_or_file = FLAGS.ckpt_path
if tf.io.gfile.isdir(ckpt_path_or_file):
ckpt_path_or_file = tf.train.latest_checkpoint(ckpt_path_or_file)
driver = inference.ServingDriver(FLAGS.model_name, ckpt_path_or_file,
FLAGS.batch_size or None,
FLAGS.min_score_thresh,
FLAGS.max_boxes_to_draw, model_params)
# dllogger setup
backends = []
backends+=[
JSONStreamBackend(verbosity=Verbosity.VERBOSE, filename=FLAGS.dllogger_path),
StdOutBackend(verbosity=Verbosity.DEFAULT)]
DLLogger.init(backends=backends)
DLLogger.metadata('inference_fps', {'unit': 'images/s'})
DLLogger.metadata('inference_latency_ms', {'unit': 'ms'})
DLLogger.metadata('latency_avg', {'unit': 's'})
DLLogger.metadata('latency_90', {'unit': 's'})
DLLogger.metadata('latency_95', {'unit': 's'})
DLLogger.metadata('latency_99', {'unit': 's'})
if FLAGS.mode == 'export':
if tf.io.gfile.exists(FLAGS.saved_model_dir):
tf.io.gfile.rmtree(FLAGS.saved_model_dir)
driver.export(FLAGS.saved_model_dir, FLAGS.tflite_path, FLAGS.tensorrt)
elif FLAGS.mode == 'benchmark':
if FLAGS.saved_model_dir:
driver.load(FLAGS.saved_model_dir)
batch_size = FLAGS.batch_size or 1
if FLAGS.input_image:
image_file = tf.io.read_file(FLAGS.input_image)
image_arrays = tf.image.decode_image(image_file)
image_arrays.set_shape((None, None, 3))
image_arrays = tf.expand_dims(image_arrays, 0)
if batch_size > 1:
image_arrays = tf.tile(image_arrays, [batch_size, 1, 1, 1])
else:
# use synthetic data if no image is provided.
image_arrays = tf.ones((batch_size, *model_config.image_size, 3),
dtype=tf.uint8)
driver.benchmark(image_arrays, FLAGS.bm_runs, FLAGS.trace_filename)
elif FLAGS.mode == 'dry':
# transfer to tf2 format ckpt
driver.build()
if FLAGS.export_ckpt:
driver.model.save_weights(FLAGS.export_ckpt)
elif FLAGS.mode == 'video':
import cv2 # pylint: disable=g-import-not-at-top
if tf.saved_model.contains_saved_model(FLAGS.saved_model_dir):
driver.load(FLAGS.saved_model_dir)
cap = cv2.VideoCapture(FLAGS.input_video)
if not cap.isOpened():
print('Error opening input video: {}'.format(FLAGS.input_video))
out_ptr = None
if FLAGS.output_video:
frame_width, frame_height = int(cap.get(3)), int(cap.get(4))
out_ptr = cv2.VideoWriter(FLAGS.output_video,
cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), 25,
(frame_width, frame_height))
while cap.isOpened():
# Capture frame-by-frame
ret, frame = cap.read()
if not ret:
break
raw_frames = np.array([frame])
detections_bs = driver.serve(raw_frames)
boxes, scores, classes, _ = tf.nest.map_structure(np.array, detections_bs)
new_frame = driver.visualize(
raw_frames[0],
boxes[0],
scores[0],
classes[0],
min_score_thresh=model_config.nms_configs.score_thresh,
max_boxes_to_draw=model_config.nms_configs.max_output_size)
if out_ptr:
# write frame into output file.
out_ptr.write(new_frame)
else:
# show the frame online, mainly used for real-time speed test.
cv2.imshow('Frame', new_frame)
# Press Q on keyboard to exit
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if __name__ == '__main__':
logging.set_verbosity(logging.ERROR)
app.run(main)
|
TensorFlow2/LanguageModeling/BERT/official/utils/flags | flags | _benchmark | # 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.
# ==============================================================================
"""Flags for benchmarking models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
from official.utils.flags._conventions import help_wrap
def define_benchmark(benchmark_log_dir=True, bigquery_uploader=True):
"""Register benchmarking flags.
Args:
benchmark_log_dir: Create a flag to specify location for benchmark logging.
bigquery_uploader: Create flags for uploading results to BigQuery.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
flags.DEFINE_enum(
name="benchmark_logger_type", default="BaseBenchmarkLogger",
enum_values=["BaseBenchmarkLogger", "BenchmarkFileLogger",
"BenchmarkBigQueryLogger"],
help=help_wrap("The type of benchmark logger to use. Defaults to using "
"BaseBenchmarkLogger which logs to STDOUT. Different "
"loggers will require other flags to be able to work."))
flags.DEFINE_string(
name="benchmark_test_id", short_name="bti", default=None,
help=help_wrap("The unique test ID of the benchmark run. It could be the "
"combination of key parameters. It is hardware "
"independent and could be used compare the performance "
"between different test runs. This flag is designed for "
"human consumption, and does not have any impact within "
"the system."))
flags.DEFINE_integer(
name='log_steps', default=100,
help='For every log_steps, we log the timing information such as '
'examples per second. Besides, for every log_steps, we store the '
'timestamp of a batch end.')
if benchmark_log_dir:
flags.DEFINE_string(
name="benchmark_log_dir", short_name="bld", default=None,
help=help_wrap("The location of the benchmark logging.")
)
if bigquery_uploader:
flags.DEFINE_string(
name="gcp_project", short_name="gp", default=None,
help=help_wrap(
"The GCP project name where the benchmark will be uploaded."))
flags.DEFINE_string(
name="bigquery_data_set", short_name="bds", default="test_benchmark",
help=help_wrap(
"The Bigquery dataset name where the benchmark will be uploaded."))
flags.DEFINE_string(
name="bigquery_run_table", short_name="brt", default="benchmark_run",
help=help_wrap("The Bigquery table name where the benchmark run "
"information will be uploaded."))
flags.DEFINE_string(
name="bigquery_run_status_table", short_name="brst",
default="benchmark_run_status",
help=help_wrap("The Bigquery table name where the benchmark run "
"status information will be uploaded."))
flags.DEFINE_string(
name="bigquery_metric_table", short_name="bmt",
default="benchmark_metric",
help=help_wrap("The Bigquery table name where the benchmark metric "
"information will be uploaded."))
@flags.multi_flags_validator(
["benchmark_logger_type", "benchmark_log_dir"],
message="--benchmark_logger_type=BenchmarkFileLogger will require "
"--benchmark_log_dir being set")
def _check_benchmark_log_dir(flags_dict):
benchmark_logger_type = flags_dict["benchmark_logger_type"]
if benchmark_logger_type == "BenchmarkFileLogger":
return flags_dict["benchmark_log_dir"]
return True
return key_flags
|
PyTorch/Recommendation/DLRM/preproc | preproc | preproc_NVTabular | # 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.
"""Preprocess Criteo 1TB Click Logs dataset with frequency thresholding and filling missing values.
This script accepts input in either tsv or parquet format.
"""
import argparse
from collections import OrderedDict
import json
import os
import subprocess
from time import time
from typing import List, Optional
import numpy as np
import nvtabular as nvt
import rmm
import cudf
from dask.base import tokenize
from dask.dataframe.io.parquet.utils import _analyze_paths
from dask.delayed import Delayed
from dask.distributed import Client
from dask.highlevelgraph import HighLevelGraph
from dask.utils import natural_sort_key
from dask_cuda import LocalCUDACluster
from fsspec.core import get_fs_token_paths
from nvtabular import Workflow
from nvtabular.io import Dataset, Shuffle
from nvtabular.utils import device_mem_size
from nvtabular.ops import Normalize, Categorify, LogOp, FillMissing, Clip, get_embedding_sizes, \
LambdaOp
from cudf.io.parquet import ParquetWriter
CRITEO_CONTINUOUS_COLUMNS = [f'_c{x}' for x in range(1, 14)]
CRITEO_CATEGORICAL_COLUMNS = [f'_c{x}' for x in range(14, 40)]
CRITEO_CLICK_COLUMNS = ['_c0']
COLUMNS = CRITEO_CONTINUOUS_COLUMNS + CRITEO_CATEGORICAL_COLUMNS + CRITEO_CLICK_COLUMNS
CRITEO_TRAIN_DAYS = list(range(0, 23))
ALL_DS_MEM_FRAC = 0.04
TRAIN_DS_MEM_FRAC = 0.045
TEST_DS_MEM_FRAC = 0.3
VALID_DS_MEM_FRAC = 0.3
def _pool(frac=0.8):
initial_pool_size = frac * device_mem_size()
if initial_pool_size % 256 != 0:
new_initial_pool_size = initial_pool_size // 256 * 256
print(
f"Initial pool size for rmm has to be a multiply of 256. Got {initial_pool_size}, reducing to {new_initial_pool_size}")
initial_pool_size = new_initial_pool_size
rmm.reinitialize(
pool_allocator=True,
initial_pool_size=initial_pool_size,
)
def _convert_file(path, name, out_dir, gpu_mem_frac, fs, cols, dtypes):
fn = f"{name}.parquet"
out_path = fs.sep.join([out_dir, f"{name}.parquet"])
writer = ParquetWriter(out_path, compression=None)
for gdf in nvt.Dataset(
path,
engine="csv",
names=cols,
part_memory_fraction=gpu_mem_frac,
sep='\t',
dtypes=dtypes,
).to_iter():
writer.write_table(gdf)
del gdf
md = writer.close(metadata_file_path=fn)
return md
def _write_metadata(md_list, fs, path):
if md_list:
metadata_path = fs.sep.join([path, "_metadata"])
_meta = (
cudf.io.merge_parquet_filemetadata(md_list)
if len(md_list) > 1
else md_list[0]
)
with fs.open(metadata_path, "wb") as f:
_meta.tofile(f)
return True
def convert_criteo_to_parquet(
input_path: str,
output_path: str,
client,
gpu_mem_frac: float = 0.05,
):
print("Converting tsv to parquet files")
if not output_path:
raise RuntimeError("Intermediate directory must be defined, if the dataset is tsv.")
os.makedirs(output_path, exist_ok=True)
# split last day into two parts
number_of_lines = int(
subprocess.check_output((f'wc -l {os.path.join(input_path, "day_23")}').split()).split()[0])
valid_set_size = number_of_lines // 2
test_set_size = number_of_lines - valid_set_size
with open(os.path.join(input_path, "day_23.part1"), "w") as f:
subprocess.run(['head', '-n', str(test_set_size), str(os.path.join(input_path, "day_23"))], stdout=f)
with open(os.path.join(input_path, "day_23.part2"), "w") as f:
subprocess.run(['tail', '-n', str(valid_set_size), str(os.path.join(input_path, "day_23"))], stdout=f)
fs = get_fs_token_paths(input_path, mode="rb")[0]
file_list = [
x for x in fs.glob(fs.sep.join([input_path, "day_*"]))
if not x.endswith("parquet")
]
file_list = sorted(file_list, key=natural_sort_key)
name_list = _analyze_paths(file_list, fs)[1]
cols = CRITEO_CLICK_COLUMNS + CRITEO_CONTINUOUS_COLUMNS + CRITEO_CATEGORICAL_COLUMNS
dtypes = {}
dtypes[CRITEO_CLICK_COLUMNS[0]] = np.int64
for x in CRITEO_CONTINUOUS_COLUMNS:
dtypes[x] = np.int64
for x in CRITEO_CATEGORICAL_COLUMNS:
dtypes[x] = "hex"
dsk = {}
token = tokenize(file_list, name_list, output_path, gpu_mem_frac, fs, cols, dtypes)
convert_file_name = "convert_file-" + token
for i, (path, name) in enumerate(zip(file_list, name_list)):
key = (convert_file_name, i)
dsk[key] = (_convert_file, path, name, output_path, gpu_mem_frac, fs, cols, dtypes)
write_meta_name = "write-metadata-" + token
dsk[write_meta_name] = (
_write_metadata,
[(convert_file_name, i) for i in range(len(file_list))],
fs,
output_path,
)
graph = HighLevelGraph.from_collections(write_meta_name, dsk, dependencies=[])
conversion_delayed = Delayed(write_meta_name, graph)
if client:
conversion_delayed.compute()
else:
conversion_delayed.compute(scheduler="synchronous")
print("Converted")
def save_model_size_config(workflow: Workflow, output_path: str):
embeddings = {}
for k, v in get_embedding_sizes(workflow).items():
embeddings[k] = v[0] - 1 # we have to subtract one, as the model expects to get a maximal id for each category
ordered_dict = OrderedDict()
for k, v in sorted(list(embeddings.items()), key=lambda x: x[0]):
ordered_dict[k] = v
with open(os.path.join(output_path, "model_size.json"), 'w') as file:
file.write(json.dumps(ordered_dict))
def preprocess_criteo_parquet(
input_path: str,
output_path: str,
client,
frequency_threshold: int,
):
train_days = [str(x) for x in CRITEO_TRAIN_DAYS]
train_files = [
os.path.join(input_path, x)
for x in os.listdir(input_path)
if x.startswith("day") and x.split(".")[0].split("_")[-1] in train_days
]
valid_file = os.path.join(input_path, "day_23.part2.parquet")
test_file = os.path.join(input_path, "day_23.part1.parquet")
all_set = train_files + [valid_file] + [test_file]
print(all_set, train_files, valid_file, test_file)
print("Creating Workflow Object")
workflow = Workflow(
cat_names=CRITEO_CATEGORICAL_COLUMNS,
cont_names=CRITEO_CONTINUOUS_COLUMNS,
label_name=CRITEO_CLICK_COLUMNS
)
# We want to assign 0 to all missing values, and calculate log(x+3) for present values
# so if we set missing values to -2, then the result of log(1+2+(-2)) would be 0
workflow.add_cont_feature([
FillMissing(fill_val=-2.0),
LambdaOp(op_name='Add3ButMinusOneCauseLogAddsOne', f=lambda col, _: col.add(2.0)),
LogOp(), # Log(1+x)
])
workflow.add_cat_preprocess(
Categorify(freq_threshold=frequency_threshold, out_path=output_path)
)
workflow.finalize()
print("Creating Dataset Iterator")
all_ds = Dataset(all_set, engine="parquet", part_mem_fraction=ALL_DS_MEM_FRAC)
trains_ds = Dataset(train_files, engine="parquet", part_mem_fraction=TRAIN_DS_MEM_FRAC)
valid_ds = Dataset(valid_file, engine="parquet", part_mem_fraction=TEST_DS_MEM_FRAC)
test_ds = Dataset(test_file, engine="parquet", part_mem_fraction=VALID_DS_MEM_FRAC)
print("Running apply")
out_train = os.path.join(output_path, "train")
out_valid = os.path.join(output_path, "validation")
out_test = os.path.join(output_path, "test")
start = time()
workflow.update_stats(all_ds)
print(f"Gathering statistics time: {time() - start}")
start = time()
workflow.apply(
trains_ds,
record_stats=False,
output_path=out_train
)
print(f"train preprocess time: {time() - start}")
start = time()
workflow.apply(
valid_ds,
record_stats=False,
output_path=out_valid
)
print(f"valid preprocess time: {time() - start}")
start = time()
workflow.apply(
test_ds,
record_stats=False,
output_path=out_test
)
print(f"test preprocess time: {time() - start}")
save_model_size_config(workflow, output_path)
def parse_args():
parser = argparse.ArgumentParser(description="Process some integers.")
parser.add_argument(
"input_dir",
help="directory with either csv or parquet dataset files inside"
)
parser.add_argument(
"output_dir",
help="directory to save preprocessed dataset files"
)
parser.add_argument(
"--intermediate_dir",
required=False,
default=None,
help="directory for converted to parquet dataset files inside"
)
parser.add_argument(
"--devices",
required=True,
help="available gpus, separated with commas; e.g 0,1,2,3"
)
parser.add_argument(
"--freq_threshold",
required=False,
default=15,
help="frequency threshold for categorical can be int or dict {column_name: threshold}"
)
parser.add_argument(
"--pool",
required=False,
default=False,
help="bool value to use a RMM pooled allocator"
)
args = parser.parse_args()
args.devices = args.devices.split(",")
return args
def is_input_parquet(input_dir: str):
for f in os.listdir(input_dir):
if 'parquet' in f:
return True
return False
def start_local_CUDA_cluster(devices, pool):
if len(devices) > 1:
cluster = LocalCUDACluster(
n_workers=len(devices),
CUDA_VISIBLE_DEVICES=",".join(str(x) for x in devices),
)
client = Client(cluster)
if pool:
client.run(_pool)
elif pool:
_pool()
return client
def main():
args = parse_args()
client = start_local_CUDA_cluster(args.devices, args.pool)
if not is_input_parquet(args.input_dir):
convert_criteo_to_parquet(
input_path=args.input_dir,
output_path=args.intermediate_dir,
client=client,
)
args.input_dir = args.intermediate_dir
print("Preprocessing data")
preprocess_criteo_parquet(
input_path=args.input_dir,
output_path=args.output_dir,
client=client,
frequency_threshold=int(args.freq_threshold),
)
print("Done")
if __name__ == '__main__':
main()
|
TensorFlow/Detection/SSD/models/research/object_detection/metrics | metrics | oid_od_challenge_evaluation | # 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.
# ==============================================================================
r"""Runs evaluation using OpenImages groundtruth and predictions.
Example usage:
python models/research/object_detection/metrics/oid_od_challenge_evaluation.py \
--input_annotations_boxes=/path/to/input/annotations-human-bbox.csv \
--input_annotations_labels=/path/to/input/annotations-label.csv \
--input_class_labelmap=/path/to/input/class_labelmap.pbtxt \
--input_predictions=/path/to/input/predictions.csv \
--output_metrics=/path/to/output/metric.csv \
CSVs with bounding box annotations and image label (including the image URLs)
can be downloaded from the Open Images Challenge website:
https://storage.googleapis.com/openimages/web/challenge.html
The format of the input csv and the metrics itself are described on the
challenge website.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import pandas as pd
from google.protobuf import text_format
from object_detection.metrics import io_utils
from object_detection.metrics import oid_od_challenge_evaluation_utils as utils
from object_detection.protos import string_int_label_map_pb2
from object_detection.utils import object_detection_evaluation
def _load_labelmap(labelmap_path):
"""Loads labelmap from the labelmap path.
Args:
labelmap_path: Path to the labelmap.
Returns:
A dictionary mapping class name to class numerical id
A list with dictionaries, one dictionary per category.
"""
label_map = string_int_label_map_pb2.StringIntLabelMap()
with open(labelmap_path, 'r') as fid:
label_map_string = fid.read()
text_format.Merge(label_map_string, label_map)
labelmap_dict = {}
categories = []
for item in label_map.item:
labelmap_dict[item.name] = item.id
categories.append({'id': item.id, 'name': item.name})
return labelmap_dict, categories
def main(parsed_args):
all_box_annotations = pd.read_csv(parsed_args.input_annotations_boxes)
all_label_annotations = pd.read_csv(parsed_args.input_annotations_labels)
all_label_annotations.rename(
columns={'Confidence': 'ConfidenceImageLabel'}, inplace=True)
all_annotations = pd.concat([all_box_annotations, all_label_annotations])
class_label_map, categories = _load_labelmap(parsed_args.input_class_labelmap)
challenge_evaluator = (
object_detection_evaluation.OpenImagesDetectionChallengeEvaluator(
categories))
for _, groundtruth in enumerate(all_annotations.groupby('ImageID')):
image_id, image_groundtruth = groundtruth
groundtruth_dictionary = utils.build_groundtruth_boxes_dictionary(
image_groundtruth, class_label_map)
challenge_evaluator.add_single_ground_truth_image_info(
image_id, groundtruth_dictionary)
all_predictions = pd.read_csv(parsed_args.input_predictions)
for _, prediction_data in enumerate(all_predictions.groupby('ImageID')):
image_id, image_predictions = prediction_data
prediction_dictionary = utils.build_predictions_dictionary(
image_predictions, class_label_map)
challenge_evaluator.add_single_detected_image_info(image_id,
prediction_dictionary)
metrics = challenge_evaluator.evaluate()
with open(parsed_args.output_metrics, 'w') as fid:
io_utils.write_csv(fid, metrics)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Evaluate Open Images Object Detection Challenge predictions.'
)
parser.add_argument(
'--input_annotations_boxes',
required=True,
help='File with groundtruth boxes annotations.')
parser.add_argument(
'--input_annotations_labels',
required=True,
help='File with groundtruth labels annotations')
parser.add_argument(
'--input_predictions',
required=True,
help="""File with detection predictions; NOTE: no postprocessing is
applied in the evaluation script.""")
parser.add_argument(
'--input_class_labelmap',
required=True,
help='Open Images Challenge labelmap.')
parser.add_argument(
'--output_metrics', required=True, help='Output file with csv metrics')
args = parser.parse_args()
main(args)
|
PyTorch/SpeechRecognition/Jasper/platform | platform | DGX1-32GB_Jasper_AMP_8GPU | #!/bin/bash
NUM_GPUS=8 AMP=true BATCH_SIZE=64 GRAD_ACCUMULATION_STEPS=1 bash scripts/train.sh "$@"
|
PyTorch/SpeechSynthesis/HiFiGAN/hifigan | hifigan | __init__ | from .entrypoints import nvidia_hifigan
|
PyTorch/SpeechSynthesis/FastPitch/common/text | text | text_processing | """ adapted from https://github.com/keithito/tacotron """
import re
import numpy as np
from . import cleaners
from .symbols import get_symbols
from . import cmudict
from .numerical import _currency_re, _expand_currency
#########
# REGEX #
#########
# Regular expression matching text enclosed in curly braces for encoding
_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
# Regular expression matching words and not words
_words_re = re.compile(r"([a-zA-ZÀ-ž]+['][a-zA-ZÀ-ž]{1,2}|[a-zA-ZÀ-ž]+)|([{][^}]+[}]|[^a-zA-ZÀ-ž{}]+)")
# Regular expression separating words enclosed in curly braces for cleaning
_arpa_re = re.compile(r'{[^}]+}|\S+')
class TextProcessing(object):
def __init__(self, symbol_set, cleaner_names, p_arpabet=0.0,
handle_arpabet='word', handle_arpabet_ambiguous='ignore',
expand_currency=True):
self.symbols = get_symbols(symbol_set)
self.cleaner_names = cleaner_names
# Mappings from symbol to numeric ID and vice versa:
self.symbol_to_id = {s: i for i, s in enumerate(self.symbols)}
self.id_to_symbol = {i: s for i, s in enumerate(self.symbols)}
self.expand_currency = expand_currency
# cmudict
self.p_arpabet = p_arpabet
self.handle_arpabet = handle_arpabet
self.handle_arpabet_ambiguous = handle_arpabet_ambiguous
def text_to_sequence(self, text):
sequence = []
# Check for curly braces and treat their contents as ARPAbet:
while len(text):
m = _curly_re.match(text)
if not m:
sequence += self.symbols_to_sequence(text)
break
sequence += self.symbols_to_sequence(m.group(1))
sequence += self.arpabet_to_sequence(m.group(2))
text = m.group(3)
return sequence
def sequence_to_text(self, sequence):
result = ''
for symbol_id in sequence:
if symbol_id in self.id_to_symbol:
s = self.id_to_symbol[symbol_id]
# Enclose ARPAbet back in curly braces:
if len(s) > 1 and s[0] == '@':
s = '{%s}' % s[1:]
result += s
return result.replace('}{', ' ')
def clean_text(self, text):
for name in self.cleaner_names:
cleaner = getattr(cleaners, name)
if not cleaner:
raise Exception('Unknown cleaner: %s' % name)
text = cleaner(text)
return text
def symbols_to_sequence(self, symbols):
return [self.symbol_to_id[s] for s in symbols if s in self.symbol_to_id]
def arpabet_to_sequence(self, text):
return self.symbols_to_sequence(['@' + s for s in text.split()])
def get_arpabet(self, word):
arpabet_suffix = ''
if word.lower() in cmudict.heteronyms:
return word
if len(word) > 2 and word.endswith("'s"):
arpabet = cmudict.lookup(word)
if arpabet is None:
arpabet = self.get_arpabet(word[:-2])
arpabet_suffix = ' Z'
elif len(word) > 1 and word.endswith("s"):
arpabet = cmudict.lookup(word)
if arpabet is None:
arpabet = self.get_arpabet(word[:-1])
arpabet_suffix = ' Z'
else:
arpabet = cmudict.lookup(word)
if arpabet is None:
return word
elif arpabet[0] == '{':
arpabet = [arpabet[1:-1]]
# XXX arpabet might not be a list here
if type(arpabet) is not list:
return word
if len(arpabet) > 1:
if self.handle_arpabet_ambiguous == 'first':
arpabet = arpabet[0]
elif self.handle_arpabet_ambiguous == 'random':
arpabet = np.random.choice(arpabet)
elif self.handle_arpabet_ambiguous == 'ignore':
return word
else:
arpabet = arpabet[0]
arpabet = "{" + arpabet + arpabet_suffix + "}"
return arpabet
def encode_text(self, text, return_all=False):
if self.expand_currency:
text = re.sub(_currency_re, _expand_currency, text)
text_clean = [self.clean_text(split) if split[0] != '{' else split
for split in _arpa_re.findall(text)]
text_clean = ' '.join(text_clean)
text_clean = cleaners.collapse_whitespace(text_clean)
text = text_clean
text_arpabet = ''
if self.p_arpabet > 0:
if self.handle_arpabet == 'sentence':
if np.random.uniform() < self.p_arpabet:
words = _words_re.findall(text)
text_arpabet = [
self.get_arpabet(word[0])
if (word[0] != '') else word[1]
for word in words]
text_arpabet = ''.join(text_arpabet)
text = text_arpabet
elif self.handle_arpabet == 'word':
words = _words_re.findall(text)
text_arpabet = [
word[1] if word[0] == '' else (
self.get_arpabet(word[0])
if np.random.uniform() < self.p_arpabet
else word[0])
for word in words]
text_arpabet = ''.join(text_arpabet)
text = text_arpabet
elif self.handle_arpabet != '':
raise Exception("{} handle_arpabet is not supported".format(
self.handle_arpabet))
text_encoded = self.text_to_sequence(text)
if return_all:
return text_encoded, text_clean, text_arpabet
return text_encoded
def get_text_processing(symbol_set, text_cleaners, p_arpabet):
if symbol_set in ['english_basic', 'english_basic_lowercase', 'english_expanded']:
return TextProcessing(symbol_set, text_cleaners, p_arpabet=p_arpabet)
elif symbol_set == 'english_mandarin_basic':
from common.text.zh.mandarin_text_processing import MandarinTextProcessing
return MandarinTextProcessing(symbol_set, text_cleaners, p_arpabet=p_arpabet)
else:
raise ValueError(f"No TextProcessing for symbol set {symbol_set} unknown.")
|
TensorFlow/Segmentation/UNet_3D_Medical | UNet_3D_Medical | .gitignore | .idea/
*.tar
.ipynb_checkpoints
/_python_build
*.pyc
__pycache__
*.swp
/datasets
/results
results
./data
|
PyTorch/Classification/ConvNets/image_classification | image_classification | mixup | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
import numpy as np
def mixup(alpha, data, target):
with torch.no_grad():
bs = data.size(0)
c = np.random.beta(alpha, alpha)
perm = torch.randperm(bs).cuda()
md = c * data + (1 - c) * data[perm, :]
mt = c * target + (1 - c) * target[perm, :]
return md, mt
class MixUpWrapper(object):
def __init__(self, alpha, dataloader):
self.alpha = alpha
self.dataloader = dataloader
def mixup_loader(self, loader):
for input, target in loader:
i, t = mixup(self.alpha, input, target)
yield i, t
def __iter__(self):
return self.mixup_loader(self.dataloader)
def __len__(self):
return len(self.dataloader)
class NLLMultiLabelSmooth(nn.Module):
def __init__(self, smoothing=0.0):
super(NLLMultiLabelSmooth, self).__init__()
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
def forward(self, x, target):
if self.training:
x = x.float()
target = target.float()
logprobs = torch.nn.functional.log_softmax(x, dim=-1)
nll_loss = -logprobs * target
nll_loss = nll_loss.sum(-1)
smooth_loss = -logprobs.mean(dim=-1)
loss = self.confidence * nll_loss + self.smoothing * smooth_loss
return loss.mean()
else:
return torch.nn.functional.cross_entropy(x, target)
|
TensorFlow2/Classification/ConvNets/efficientnet_v1/B0/evaluation | evaluation | evaluation_FP32_V100-32G | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
python3 main.py --cfg config/efficientnet_v1/b0_cfg.py \
--mode eval \
--use_xla \
--model_dir ./output \
--data_dir /data \
--eval_batch_size 256
|
TensorFlow2/Segmentation/nnUNet/models | models | unet | import tensorflow as tf
from models import layers
class UNet(tf.keras.Model):
def __init__(
self,
input_shape,
n_class,
kernels,
strides,
normalization_layer,
negative_slope,
dimension,
deep_supervision,
):
super().__init__()
self.dim = dimension
self.n_class = n_class
self.negative_slope = negative_slope
self.norm = normalization_layer
self.deep_supervision = deep_supervision
filters = [min(2 ** (5 + i), 320 if dimension == 3 else 512) for i in range(len(strides))]
self.filters = filters
self.kernels = kernels
self.strides = strides
down_block = layers.ConvBlock
self.input_block = self.get_conv_block(
conv_block=down_block,
filters=filters[0],
kernel_size=kernels[0],
stride=strides[0],
input_shape=input_shape,
)
self.downsamples = self.get_block_list(
conv_block=down_block, filters=filters[1:], kernels=kernels[1:-1], strides=strides[1:-1]
)
self.bottleneck = self.get_conv_block(
conv_block=down_block, filters=filters[-1], kernel_size=kernels[-1], stride=strides[-1]
)
self.upsamples = self.get_block_list(
conv_block=layers.UpsampleBlock,
filters=filters[:-1][::-1],
kernels=kernels[1:][::-1],
strides=strides[1:][::-1],
)
self.output_block = self.get_output_block()
if self.deep_supervision:
self.deep_supervision_heads = [self.get_output_block(), self.get_output_block()]
self.n_layers = len(self.upsamples) - 1
def call(self, x, training=True):
skip_connections = []
out = self.input_block(x)
skip_connections.append(out)
for down_block in self.downsamples:
out = down_block(out)
skip_connections.append(out)
out = self.bottleneck(out)
decoder_outputs = []
for up_block in self.upsamples:
out = up_block(out, skip_connections.pop())
decoder_outputs.append(out)
out = self.output_block(out)
if training and self.deep_supervision:
out = [
out,
self.deep_supervision_heads[0](decoder_outputs[-2]),
self.deep_supervision_heads[1](decoder_outputs[-3]),
]
return out
def get_output_block(self):
return layers.OutputBlock(filters=self.n_class, dim=self.dim, negative_slope=self.negative_slope)
def get_conv_block(self, conv_block, filters, kernel_size, stride, **kwargs):
return conv_block(
dim=self.dim,
stride=stride,
norm=self.norm,
kernel_size=kernel_size,
filters=filters,
negative_slope=self.negative_slope,
**kwargs,
)
def get_block_list(self, conv_block, filters, kernels, strides):
layers = []
for filter, kernel, stride in zip(filters, kernels, strides):
conv_layer = self.get_conv_block(conv_block, filter, kernel, stride)
layers.append(conv_layer)
return layers
|
TensorFlow/Detection/SSD/models/research/object_detection | object_detection | model_main | # 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.
# ==============================================================================
#
# 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.
"""Binary to run train and evaluation on object detection model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
import tensorflow as tf
import horovod.tensorflow as hvd
import dllogger
import time
import os
from object_detection import model_hparams
from object_detection import model_lib
from object_detection.utils.exp_utils import AverageMeter, setup_dllogger
flags.DEFINE_string(
'model_dir', None, 'Path to output model directory '
'where event and checkpoint files will be written.')
flags.DEFINE_string('pipeline_config_path', None, 'Path to pipeline config '
'file.')
flags.DEFINE_string("raport_file", default="summary.json",
help="Path to dlloger json")
flags.DEFINE_integer('num_train_steps', None, 'Number of train steps.')
flags.DEFINE_boolean('eval_training_data', False,
'If training data should be evaluated for this job. Note '
'that one call only use this in eval-only mode, and '
'`checkpoint_dir` must be supplied.')
flags.DEFINE_integer('sample_1_of_n_eval_examples', 1, 'Will sample one of '
'every n eval input examples, where n is provided.')
flags.DEFINE_integer('sample_1_of_n_eval_on_train_examples', 5, 'Will sample '
'one of every n train input examples for evaluation, '
'where n is provided. This is only used if '
'`eval_training_data` is True.')
flags.DEFINE_integer('eval_count', 1, 'How many times the evaluation should be run')
flags.DEFINE_string(
'hparams_overrides', None, 'Hyperparameter overrides, '
'represented as a string containing comma-separated '
'hparam_name=value pairs.')
flags.DEFINE_string(
'checkpoint_dir', None, 'Path to directory holding a checkpoint. If '
'`checkpoint_dir` is provided, this binary operates in eval-only mode, '
'writing resulting metrics to `model_dir`.')
flags.DEFINE_boolean(
'allow_xla', False, 'Enable XLA compilation')
flags.DEFINE_boolean(
'amp', False, 'Whether to enable AMP ops. When false, uses TF32 on A100 and FP32 on V100 GPUS.')
flags.DEFINE_boolean(
'run_once', False, 'If running in eval-only mode, whether to run just '
'one round of eval vs running continuously (default).'
)
FLAGS = flags.FLAGS
class DLLoggerHook(tf.estimator.SessionRunHook):
def __init__(self, global_batch_size, rank=-1):
self.global_batch_size = global_batch_size
self.rank = rank
setup_dllogger(enabled=True, filename=FLAGS.raport_file, rank=rank)
def after_create_session(self, session, coord):
self.meters = {}
warmup = 100
self.meters['train_throughput'] = AverageMeter(warmup=warmup)
def before_run(self, run_context):
self.t0 = time.time()
return tf.estimator.SessionRunArgs(fetches=['global_step:0', 'learning_rate:0'])
def after_run(self, run_context, run_values):
throughput = self.global_batch_size/(time.time() - self.t0)
global_step, lr = run_values.results
self.meters['train_throughput'].update(throughput)
def end(self, session):
summary = {
'train_throughput': self.meters['train_throughput'].avg,
}
dllogger.log(step=tuple(), data=summary)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.amp:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION"] = "1"
else:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION"] = "0"
hvd.init()
flags.mark_flag_as_required('model_dir')
flags.mark_flag_as_required('pipeline_config_path')
session_config = tf.ConfigProto()
session_config.gpu_options.per_process_gpu_memory_fraction=0.9
session_config.gpu_options.visible_device_list = str(hvd.local_rank())
if FLAGS.allow_xla:
session_config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
model_dir = FLAGS.model_dir if hvd.rank() == 0 else None
config = tf.estimator.RunConfig(model_dir=model_dir, session_config=session_config)
train_and_eval_dict = model_lib.create_estimator_and_inputs(
run_config=config,
eval_count=FLAGS.eval_count,
hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
pipeline_config_path=FLAGS.pipeline_config_path,
train_steps=FLAGS.num_train_steps,
sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
sample_1_of_n_eval_on_train_examples=(
FLAGS.sample_1_of_n_eval_on_train_examples))
estimator = train_and_eval_dict['estimator']
train_input_fn = train_and_eval_dict['train_input_fn']
eval_input_fns = train_and_eval_dict['eval_input_fns']
eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
predict_input_fn = train_and_eval_dict['predict_input_fn']
train_steps = train_and_eval_dict['train_steps']
if FLAGS.checkpoint_dir:
if FLAGS.eval_training_data:
name = 'training_data'
input_fn = eval_on_train_input_fn
else:
name = 'validation_data'
# The first eval input will be evaluated.
input_fn = eval_input_fns[0]
if FLAGS.run_once:
estimator.evaluate(input_fn,
steps=None,
checkpoint_path=tf.train.latest_checkpoint(
FLAGS.checkpoint_dir))
else:
model_lib.continuous_eval(estimator, FLAGS.checkpoint_dir, input_fn,
train_steps, name)
else:
train_spec, eval_specs = model_lib.create_train_and_eval_specs(
train_input_fn,
eval_input_fns,
eval_on_train_input_fn,
predict_input_fn,
train_steps,
eval_on_train_data=False)
train_hooks = [hvd.BroadcastGlobalVariablesHook(0), DLLoggerHook(hvd.size()*train_and_eval_dict['train_batch_size'], hvd.rank())]
eval_hooks = []
for x in range(FLAGS.eval_count):
estimator.train(train_input_fn,
hooks=train_hooks,
steps=train_steps // FLAGS.eval_count)
if hvd.rank() == 0:
eval_input_fn = eval_input_fns[0]
results = estimator.evaluate(eval_input_fn,
steps=None,
hooks=eval_hooks)
if __name__ == '__main__':
tf.app.run()
|
TensorFlow2/Classification/ConvNets/efficientnet_v1/B4/inference | inference | inference_FP32 | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
python3 main.py --cfg config/efficientnet_v1/b4_cfg.py \
--mode predict \
--use_xla \
--predict_ckpt /model \
--predict_img_dir /infer_data \
--predict_batch_size 50 \
--predict_img_size 380
|
PyTorch/SpeechSynthesis/FastPitch/fastpitch | fastpitch | data_function | # *****************************************************************************
# 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.
#
# *****************************************************************************
import functools
import json
import re
from pathlib import Path
import librosa
import numpy as np
import torch
import torch.nn.functional as F
from scipy import ndimage
from scipy.stats import betabinom
import common.layers as layers
from common.text.text_processing import get_text_processing
from common.utils import load_wav_to_torch, load_filepaths_and_text, to_gpu
class BetaBinomialInterpolator:
"""Interpolates alignment prior matrices to save computation.
Calculating beta-binomial priors is costly. Instead cache popular sizes
and use img interpolation to get priors faster.
"""
def __init__(self, round_mel_len_to=100, round_text_len_to=20):
self.round_mel_len_to = round_mel_len_to
self.round_text_len_to = round_text_len_to
self.bank = functools.lru_cache(beta_binomial_prior_distribution)
def round(self, val, to):
return max(1, int(np.round((val + 1) / to))) * to
def __call__(self, w, h):
bw = self.round(w, to=self.round_mel_len_to)
bh = self.round(h, to=self.round_text_len_to)
ret = ndimage.zoom(self.bank(bw, bh).T, zoom=(w / bw, h / bh), order=1)
assert ret.shape[0] == w, ret.shape
assert ret.shape[1] == h, ret.shape
return ret
def beta_binomial_prior_distribution(phoneme_count, mel_count, scaling=1.0):
P = phoneme_count
M = mel_count
x = np.arange(0, P)
mel_text_probs = []
for i in range(1, M+1):
a, b = scaling * i, scaling * (M + 1 - i)
rv = betabinom(P, a, b)
mel_i_prob = rv.pmf(x)
mel_text_probs.append(mel_i_prob)
return torch.tensor(np.array(mel_text_probs))
def estimate_pitch(wav, mel_len, method='pyin', normalize_mean=None,
normalize_std=None, n_formants=1):
if type(normalize_mean) is float or type(normalize_mean) is list:
normalize_mean = torch.tensor(normalize_mean)
if type(normalize_std) is float or type(normalize_std) is list:
normalize_std = torch.tensor(normalize_std)
if method == 'pyin':
snd, sr = librosa.load(wav)
pitch_mel, voiced_flag, voiced_probs = librosa.pyin(
snd, fmin=librosa.note_to_hz('C2'),
fmax=librosa.note_to_hz('C7'), frame_length=1024)
assert np.abs(mel_len - pitch_mel.shape[0]) <= 1.0
pitch_mel = np.where(np.isnan(pitch_mel), 0.0, pitch_mel)
pitch_mel = torch.from_numpy(pitch_mel).unsqueeze(0)
pitch_mel = F.pad(pitch_mel, (0, mel_len - pitch_mel.size(1)))
if n_formants > 1:
raise NotImplementedError
else:
raise ValueError
pitch_mel = pitch_mel.float()
if normalize_mean is not None:
assert normalize_std is not None
pitch_mel = normalize_pitch(pitch_mel, normalize_mean, normalize_std)
return pitch_mel
def normalize_pitch(pitch, mean, std):
zeros = (pitch == 0.0)
pitch -= mean[:, None]
pitch /= std[:, None]
pitch[zeros] = 0.0
return pitch
class TTSDataset(torch.utils.data.Dataset):
"""
1) loads audio,text pairs
2) normalizes text and converts them to sequences of one-hot vectors
3) computes mel-spectrograms from audio files.
"""
def __init__(self,
dataset_path,
audiopaths_and_text,
text_cleaners,
n_mel_channels,
symbol_set='english_basic',
p_arpabet=1.0,
n_speakers=1,
load_mel_from_disk=True,
load_pitch_from_disk=True,
pitch_mean=214.72203, # LJSpeech defaults
pitch_std=65.72038,
max_wav_value=None,
sampling_rate=None,
filter_length=None,
hop_length=None,
win_length=None,
mel_fmin=None,
mel_fmax=None,
prepend_space_to_text=False,
append_space_to_text=False,
pitch_online_dir=None,
betabinomial_online_dir=None,
use_betabinomial_interpolator=True,
pitch_online_method='pyin',
**ignored):
# Expect a list of filenames
if type(audiopaths_and_text) is str:
audiopaths_and_text = [audiopaths_and_text]
self.dataset_path = dataset_path
self.audiopaths_and_text = load_filepaths_and_text(
dataset_path, audiopaths_and_text,
has_speakers=(n_speakers > 1))
self.load_mel_from_disk = load_mel_from_disk
if not load_mel_from_disk:
self.max_wav_value = max_wav_value
self.sampling_rate = sampling_rate
self.stft = layers.TacotronSTFT(
filter_length, hop_length, win_length,
n_mel_channels, sampling_rate, mel_fmin, mel_fmax)
self.load_pitch_from_disk = load_pitch_from_disk
self.prepend_space_to_text = prepend_space_to_text
self.append_space_to_text = append_space_to_text
assert p_arpabet == 0.0 or p_arpabet == 1.0, (
'Only 0.0 and 1.0 p_arpabet is currently supported. '
'Variable probability breaks caching of betabinomial matrices.')
self.tp = get_text_processing(symbol_set, text_cleaners, p_arpabet)
self.n_speakers = n_speakers
self.pitch_tmp_dir = pitch_online_dir
self.f0_method = pitch_online_method
self.betabinomial_tmp_dir = betabinomial_online_dir
self.use_betabinomial_interpolator = use_betabinomial_interpolator
if use_betabinomial_interpolator:
self.betabinomial_interpolator = BetaBinomialInterpolator()
expected_columns = (2 + int(load_pitch_from_disk) + (n_speakers > 1))
assert not (load_pitch_from_disk and self.pitch_tmp_dir is not None)
if len(self.audiopaths_and_text[0]) < expected_columns:
raise ValueError(f'Expected {expected_columns} columns in audiopaths file. '
'The format is <mel_or_wav>|[<pitch>|]<text>[|<speaker_id>]')
if len(self.audiopaths_and_text[0]) > expected_columns:
print('WARNING: Audiopaths file has more columns than expected')
to_tensor = lambda x: torch.Tensor([x]) if type(x) is float else x
self.pitch_mean = to_tensor(pitch_mean)
self.pitch_std = to_tensor(pitch_std)
def __getitem__(self, index):
# Separate filename and text
if self.n_speakers > 1:
audiopath, *extra, text, speaker = self.audiopaths_and_text[index]
speaker = int(speaker)
else:
audiopath, *extra, text = self.audiopaths_and_text[index]
speaker = None
mel = self.get_mel(audiopath)
text = self.get_text(text)
pitch = self.get_pitch(index, mel.size(-1))
energy = torch.norm(mel.float(), dim=0, p=2)
attn_prior = self.get_prior(index, mel.shape[1], text.shape[0])
assert pitch.size(-1) == mel.size(-1)
# No higher formants?
if len(pitch.size()) == 1:
pitch = pitch[None, :]
return (text, mel, len(text), pitch, energy, speaker, attn_prior,
audiopath)
def __len__(self):
return len(self.audiopaths_and_text)
def get_mel(self, filename):
if not self.load_mel_from_disk:
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.stft.sampling_rate:
raise ValueError("{} SR doesn't match target {} SR".format(
sampling_rate, self.stft.sampling_rate))
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
audio_norm = torch.autograd.Variable(audio_norm,
requires_grad=False)
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = torch.squeeze(melspec, 0)
else:
melspec = torch.load(filename)
# assert melspec.size(0) == self.stft.n_mel_channels, (
# 'Mel dimension mismatch: given {}, expected {}'.format(
# melspec.size(0), self.stft.n_mel_channels))
return melspec
def get_text(self, text):
text = self.tp.encode_text(text)
space = [self.tp.encode_text("A A")[1]]
if self.prepend_space_to_text:
text = space + text
if self.append_space_to_text:
text = text + space
return torch.LongTensor(text)
def get_prior(self, index, mel_len, text_len):
if self.use_betabinomial_interpolator:
return torch.from_numpy(self.betabinomial_interpolator(mel_len,
text_len))
if self.betabinomial_tmp_dir is not None:
audiopath, *_ = self.audiopaths_and_text[index]
fname = Path(audiopath).relative_to(self.dataset_path)
fname = fname.with_suffix('.pt')
cached_fpath = Path(self.betabinomial_tmp_dir, fname)
if cached_fpath.is_file():
return torch.load(cached_fpath)
attn_prior = beta_binomial_prior_distribution(text_len, mel_len)
if self.betabinomial_tmp_dir is not None:
cached_fpath.parent.mkdir(parents=True, exist_ok=True)
torch.save(attn_prior, cached_fpath)
return attn_prior
def get_pitch(self, index, mel_len=None):
audiopath, *fields = self.audiopaths_and_text[index]
if self.n_speakers > 1:
spk = int(fields[-1])
else:
spk = 0
if self.load_pitch_from_disk:
pitchpath = fields[0]
pitch = torch.load(pitchpath)
if self.pitch_mean is not None:
assert self.pitch_std is not None
pitch = normalize_pitch(pitch, self.pitch_mean, self.pitch_std)
return pitch
if self.pitch_tmp_dir is not None:
fname = Path(audiopath).relative_to(self.dataset_path)
fname_method = fname.with_suffix('.pt')
cached_fpath = Path(self.pitch_tmp_dir, fname_method)
if cached_fpath.is_file():
return torch.load(cached_fpath)
# No luck so far - calculate
wav = audiopath
if not wav.endswith('.wav'):
wav = re.sub('/mels/', '/wavs/', wav)
wav = re.sub('.pt$', '.wav', wav)
pitch_mel = estimate_pitch(wav, mel_len, self.f0_method,
self.pitch_mean, self.pitch_std)
if self.pitch_tmp_dir is not None and not cached_fpath.is_file():
cached_fpath.parent.mkdir(parents=True, exist_ok=True)
torch.save(pitch_mel, cached_fpath)
return pitch_mel
def ensure_disjoint(*tts_datasets):
paths = [set(list(zip(*d.audiopaths_and_text))[0]) for d in tts_datasets]
assert sum(len(p) for p in paths) == len(set().union(*paths)), (
"Your datasets (train, val) are not disjoint. "
"Review filelists and restart training."
)
class TTSCollate:
"""Zero-pads model inputs and targets based on number of frames per step"""
def __call__(self, batch):
"""Collate training batch from normalized text and mel-spec"""
# Right zero-pad all one-hot text sequences to max input length
input_lengths, ids_sorted_decreasing = torch.sort(
torch.LongTensor([len(x[0]) for x in batch]),
dim=0, descending=True)
max_input_len = input_lengths[0]
text_padded = torch.LongTensor(len(batch), max_input_len)
text_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
text = batch[ids_sorted_decreasing[i]][0]
text_padded[i, :text.size(0)] = text
# Right zero-pad mel-spec
num_mels = batch[0][1].size(0)
max_target_len = max([x[1].size(1) for x in batch])
# Include mel padded and gate padded
mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
mel_padded.zero_()
output_lengths = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
mel = batch[ids_sorted_decreasing[i]][1]
mel_padded[i, :, :mel.size(1)] = mel
output_lengths[i] = mel.size(1)
n_formants = batch[0][3].shape[0]
pitch_padded = torch.zeros(mel_padded.size(0), n_formants,
mel_padded.size(2), dtype=batch[0][3].dtype)
energy_padded = torch.zeros_like(pitch_padded[:, 0, :])
for i in range(len(ids_sorted_decreasing)):
pitch = batch[ids_sorted_decreasing[i]][3]
energy = batch[ids_sorted_decreasing[i]][4]
pitch_padded[i, :, :pitch.shape[1]] = pitch
energy_padded[i, :energy.shape[0]] = energy
if batch[0][5] is not None:
speaker = torch.zeros_like(input_lengths)
for i in range(len(ids_sorted_decreasing)):
speaker[i] = batch[ids_sorted_decreasing[i]][5]
else:
speaker = None
attn_prior_padded = torch.zeros(len(batch), max_target_len,
max_input_len)
attn_prior_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
prior = batch[ids_sorted_decreasing[i]][6]
attn_prior_padded[i, :prior.size(0), :prior.size(1)] = prior
# Count number of items - characters in text
len_x = [x[2] for x in batch]
len_x = torch.Tensor(len_x)
audiopaths = [batch[i][7] for i in ids_sorted_decreasing]
return (text_padded, input_lengths, mel_padded, output_lengths, len_x,
pitch_padded, energy_padded, speaker, attn_prior_padded,
audiopaths)
def batch_to_gpu(batch):
(text_padded, input_lengths, mel_padded, output_lengths, len_x,
pitch_padded, energy_padded, speaker, attn_prior, audiopaths) = batch
text_padded = to_gpu(text_padded).long()
input_lengths = to_gpu(input_lengths).long()
mel_padded = to_gpu(mel_padded).float()
output_lengths = to_gpu(output_lengths).long()
pitch_padded = to_gpu(pitch_padded).float()
energy_padded = to_gpu(energy_padded).float()
attn_prior = to_gpu(attn_prior).float()
if speaker is not None:
speaker = to_gpu(speaker).long()
# Alignments act as both inputs and targets - pass shallow copies
x = [text_padded, input_lengths, mel_padded, output_lengths,
pitch_padded, energy_padded, speaker, attn_prior, audiopaths]
y = [mel_padded, input_lengths, output_lengths]
len_x = torch.sum(output_lengths)
return (x, y, len_x)
|
PyTorch/SpeechSynthesis/FastPitch/triton | triton | config_model_on_triton | #!/usr/bin/env python3
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""
To configure model on Triton, you can use `config_model_on_triton.py` script.
This will prepare layout of Model Repository, including Model Configuration.
```shell script
python ./triton/config_model_on_triton.py \
--model-repository /model_repository \
--model-path /models/exported/model.onnx \
--model-format onnx \
--model-name ResNet50 \
--model-version 1 \
--max-batch-size 32 \
--precision fp16 \
--backend-accelerator trt \
--load-model explicit \
--timeout 120 \
--verbose
```
If Triton server to which we prepare model repository is running with **explicit model control mode**,
use `--load-model` argument to send request load_model request to Triton Inference Server.
If server is listening on non-default address or port use `--server-url` argument to point server control endpoint.
If it is required to use HTTP protocol to communicate with Triton server use `--http` argument.
To improve inference throughput you can use
[dynamic batching](https://github.com/triton-inference-server/server/blob/master/docs/model_configuration.md#dynamic-batcher)
for your model by providing `--preferred-batch-sizes` and `--max-queue-delay-us` parameters.
For models which doesn't support batching, set `--max-batch-sizes` to 0.
By default Triton will [automatically obtain inputs and outputs definitions](https://github.com/triton-inference-server/server/blob/master/docs/model_configuration.md#auto-generated-model-configuration).
but for TorchScript ang TF GraphDef models script uses file with I/O specs. This file is automatically generated
when the model is converted to ScriptModule (either traced or scripted).
If there is a need to pass different than default path to I/O spec file use `--io-spec` CLI argument.
I/O spec file is yaml file with below structure:
```yaml
- inputs:
- name: input
dtype: float32 # np.dtype name
shape: [None, 224, 224, 3]
- outputs:
- name: probabilities
dtype: float32
shape: [None, 1001]
- name: classes
dtype: int32
shape: [None, 1]
```
"""
import argparse
import logging
import time
from model_navigator.triton.config import BackendAccelerator as Accelerator
from model_navigator.triton.config import TensorRTOptPrecision as Precision
from model_navigator.model import Format
from model_navigator.log import set_logger, log_dict
from model_navigator.triton import ModelConfig, TritonClient, TritonModelStore
LOGGER = logging.getLogger("config_model")
def _available_enum_values(my_enum):
return [item.value for item in my_enum]
def main():
parser = argparse.ArgumentParser(
description="Create Triton model repository and model configuration", allow_abbrev=False
)
parser.add_argument("--model-repository", required=True, help="Path to Triton model repository.")
parser.add_argument("--model-path", required=True, help="Path to model to configure")
# TODO: automation
parser.add_argument(
"--model-format",
required=True,
choices=_available_enum_values(Format),
help="Format of model to deploy",
)
parser.add_argument("--model-name", required=True, help="Model name")
parser.add_argument("--model-version", default="1", help="Version of model (default 1)")
parser.add_argument(
"--max-batch-size",
type=int,
default=32,
help="Maximum batch size allowed for inference. "
"A max_batch_size value of 0 indicates that batching is not allowed for the model",
)
# TODO: automation
parser.add_argument(
"--precision",
type=str,
default=Precision.FP16.value,
choices=_available_enum_values(Precision),
help="Model precision (parameter used only by Tensorflow backend with TensorRT optimization)",
)
# Triton Inference Server endpoint
parser.add_argument(
"--server-url",
type=str,
default="grpc://localhost:8001",
help="Inference server URL in format protocol://host[:port] (default grpc://localhost:8001)",
)
parser.add_argument(
"--load-model",
choices=["none", "poll", "explicit"],
help="Loading model while Triton Server is in given model control mode",
)
parser.add_argument(
"--timeout", default=120, help="Timeout in seconds to wait till model load (default=120)", type=int
)
# optimization related
parser.add_argument(
"--backend-accelerator",
type=str,
choices=_available_enum_values(Accelerator),
default=Accelerator.TRT.value,
help="Select Backend Accelerator used to serve model",
)
parser.add_argument("--number-of-model-instances", type=int, default=1, help="Number of model instances per GPU")
parser.add_argument(
"--preferred-batch-sizes",
type=int,
nargs="*",
help="Batch sizes that the dynamic batcher should attempt to create. "
"In case --max-queue-delay-us is set and this parameter is not, default value will be --max-batch-size",
)
parser.add_argument(
"--max-queue-delay-us",
type=int,
default=0,
help="Max delay time which dynamic batcher shall wait to form a batch (default 0)",
)
parser.add_argument(
"--capture-cuda-graph",
type=int,
default=0,
help="Use cuda capture graph (used only by TensorRT platform)",
)
parser.add_argument("-v", "--verbose", help="Provide verbose logs", action='store_true')
args = parser.parse_args()
set_logger(verbose=args.verbose)
log_dict("args", vars(args))
config = ModelConfig.create(
model_path=args.model_path,
# model definition
model_name=args.model_name,
model_version=args.model_version,
model_format=args.model_format,
precision=args.precision,
max_batch_size=args.max_batch_size,
# optimization
accelerator=args.backend_accelerator,
gpu_engine_count=args.number_of_model_instances,
preferred_batch_sizes=args.preferred_batch_sizes or [],
max_queue_delay_us=args.max_queue_delay_us,
capture_cuda_graph=args.capture_cuda_graph,
)
model_store = TritonModelStore(args.model_repository)
model_store.deploy_model(model_config=config, model_path=args.model_path)
if args.load_model != "none":
client = TritonClient(server_url=args.server_url, verbose=args.verbose)
client.wait_for_server_ready(timeout=args.timeout)
if args.load_model == "explicit":
client.load_model(model_name=args.model_name)
if args.load_model == "poll":
time.sleep(15)
client.wait_for_model(model_name=args.model_name, model_version=args.model_version, timeout_s=args.timeout)
if __name__ == "__main__":
main()
|
PyTorch/LanguageModeling/BART/utils | utils | logging | # coding=utf-8
# Copyright (c) 2022 NVIDIA CORPORATION. All rights reserved.
# Copyright 2020 Optuna, Hugging Face
#
# 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.
""" Logging utilities. """
import logging
import os
import sys
import threading
from logging import CRITICAL # NOQA
from logging import DEBUG # NOQA
from logging import ERROR # NOQA
from logging import FATAL # NOQA
from logging import INFO # NOQA
from logging import NOTSET # NOQA
from logging import WARN # NOQA
from logging import WARNING # NOQA
from typing import Optional
_lock = threading.Lock()
_default_handler: Optional[logging.Handler] = None
log_levels = {
"debug": logging.DEBUG,
"info": logging.INFO,
"warning": logging.WARNING,
"error": logging.ERROR,
"critical": logging.CRITICAL,
}
_default_log_level = logging.WARNING
def _get_default_logging_level():
"""
If TRANSFORMERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
not - fall back to ``_default_log_level``
"""
env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None)
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, "
f"has to be one of: { ', '.join(log_levels.keys()) }"
)
return _default_log_level
def _get_library_name() -> str:
return __name__.split(".")[0]
def _get_library_root_logger() -> logging.Logger:
return logging.getLogger(_get_library_name())
def _configure_library_root_logger() -> None:
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
_default_handler = logging.StreamHandler() # Set sys.stderr as stream.
_default_handler.flush = sys.stderr.flush
# Apply our default configuration to the library root logger.
library_root_logger = _get_library_root_logger()
library_root_logger.addHandler(_default_handler)
library_root_logger.setLevel(_get_default_logging_level())
library_root_logger.propagate = False
def _reset_library_root_logger() -> None:
global _default_handler
with _lock:
if not _default_handler:
return
library_root_logger = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler)
library_root_logger.setLevel(logging.NOTSET)
_default_handler = None
def get_logger(name: Optional[str] = None) -> logging.Logger:
"""
Return a logger with the specified name.
This function is not supposed to be directly accessed unless you are writing a custom transformers module.
"""
if name is None:
name = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(name)
def get_verbosity() -> int:
"""
Return the current level for the 🤗 Transformers's root logger as an int.
Returns:
:obj:`int`: The logging level.
.. note::
🤗 Transformers has following logging levels:
- 50: ``transformers.logging.CRITICAL`` or ``transformers.logging.FATAL``
- 40: ``transformers.logging.ERROR``
- 30: ``transformers.logging.WARNING`` or ``transformers.logging.WARN``
- 20: ``transformers.logging.INFO``
- 10: ``transformers.logging.DEBUG``
"""
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def set_verbosity(verbosity: int) -> None:
"""
Set the vebosity level for the 🤗 Transformers's root logger.
Args:
verbosity (:obj:`int`):
Logging level, e.g., one of:
- ``transformers.logging.CRITICAL`` or ``transformers.logging.FATAL``
- ``transformers.logging.ERROR``
- ``transformers.logging.WARNING`` or ``transformers.logging.WARN``
- ``transformers.logging.INFO``
- ``transformers.logging.DEBUG``
"""
_configure_library_root_logger()
_get_library_root_logger().setLevel(verbosity)
def set_verbosity_info():
"""Set the verbosity to the :obj:`INFO` level."""
return set_verbosity(INFO)
def set_verbosity_warning():
"""Set the verbosity to the :obj:`WARNING` level."""
return set_verbosity(WARNING)
def set_verbosity_debug():
"""Set the verbosity to the :obj:`DEBUG` level."""
return set_verbosity(DEBUG)
def set_verbosity_error():
"""Set the verbosity to the :obj:`ERROR` level."""
return set_verbosity(ERROR)
def disable_default_handler() -> None:
"""Disable the default handler of the HuggingFace Transformers's root logger."""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler)
def enable_default_handler() -> None:
"""Enable the default handler of the HuggingFace Transformers's root logger."""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler)
def disable_propagation() -> None:
"""
Disable propagation of the library log outputs. Note that log propagation is disabled by default.
"""
_configure_library_root_logger()
_get_library_root_logger().propagate = False
def enable_propagation() -> None:
"""
Enable propagation of the library log outputs. Please disable the HuggingFace Transformers's default handler to
prevent double logging if the root logger has been configured.
"""
_configure_library_root_logger()
_get_library_root_logger().propagate = True
def enable_explicit_format() -> None:
"""
Enable explicit formatting for every HuggingFace Transformers's logger. The explicit formatter is as follows:
::
[LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE
All handlers currently bound to the root logger are affected by this method.
"""
handlers = _get_library_root_logger().handlers
for handler in handlers:
formatter = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s")
handler.setFormatter(formatter)
def reset_format() -> None:
"""
Resets the formatting for HuggingFace Transformers's loggers.
All handlers currently bound to the root logger are affected by this method.
"""
handlers = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(None) |
TensorFlow2/Recommendation/WideAndDeep/triton/deployment_toolkit/library | library | tensorrt | # 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 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/Segmentation/MaskRCNN/pytorch/configs | configs | e2e_faster_rcnn_R_50_FPN_1x | MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
BACKBONE:
CONV_BODY: "R-50-FPN"
OUT_CHANNELS: 256
RPN:
USE_FPN: True
ANCHOR_STRIDE: (4, 8, 16, 32, 64)
PRE_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TEST: 1000
FPN_POST_NMS_TOP_N_TEST: 1000
ROI_HEADS:
USE_FPN: True
ROI_BOX_HEAD:
POOLER_RESOLUTION: 7
POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
POOLER_SAMPLING_RATIO: 2
FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
PREDICTOR: "FPNPredictor"
DATASETS:
TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
TEST: ("coco_2014_minival",)
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
BASE_LR: 0.02
WEIGHT_DECAY: 0.0001
STEPS: (60000, 80000)
MAX_ITER: 90000
|
PyTorch/Recommendation/DLRM/tests/feature_specs | feature_specs | 10_num | channel_spec:
categorical:
- cat_0.bin
- cat_1.bin
- cat_2.bin
- cat_3.bin
- cat_4.bin
- cat_5.bin
- cat_6.bin
- cat_7.bin
- cat_8.bin
- cat_9.bin
- cat_10.bin
- cat_11.bin
- cat_12.bin
- cat_13.bin
- cat_14.bin
- cat_15.bin
- cat_16.bin
- cat_17.bin
- cat_18.bin
- cat_19.bin
- cat_20.bin
- cat_21.bin
- cat_22.bin
- cat_23.bin
- cat_24.bin
- cat_25.bin
label:
- label
numerical: &id001
- num_0
- num_1
- num_2
- num_3
- num_4
- num_5
- num_6
- num_7
- num_8
- num_9
feature_spec:
cat_0.bin:
cardinality: 100000
dtype: int32
cat_1.bin:
cardinality: 100001
dtype: int32
cat_10.bin:
cardinality: 100010
dtype: int32
cat_11.bin:
cardinality: 100011
dtype: int32
cat_12.bin:
cardinality: 100012
dtype: int32
cat_13.bin:
cardinality: 100013
dtype: int32
cat_14.bin:
cardinality: 100014
dtype: int32
cat_15.bin:
cardinality: 100015
dtype: int32
cat_16.bin:
cardinality: 100016
dtype: int32
cat_17.bin:
cardinality: 100017
dtype: int32
cat_18.bin:
cardinality: 100018
dtype: int32
cat_19.bin:
cardinality: 100019
dtype: int32
cat_2.bin:
cardinality: 100002
dtype: int32
cat_20.bin:
cardinality: 100020
dtype: int32
cat_21.bin:
cardinality: 100021
dtype: int32
cat_22.bin:
cardinality: 100022
dtype: int32
cat_23.bin:
cardinality: 100023
dtype: int32
cat_24.bin:
cardinality: 100024
dtype: int32
cat_25.bin:
cardinality: 100025
dtype: int32
cat_3.bin:
cardinality: 100003
dtype: int32
cat_4.bin:
cardinality: 100004
dtype: int32
cat_5.bin:
cardinality: 100005
dtype: int32
cat_6.bin:
cardinality: 100006
dtype: int32
cat_7.bin:
cardinality: 100007
dtype: int32
cat_8.bin:
cardinality: 100008
dtype: int32
cat_9.bin:
cardinality: 100009
dtype: int32
label:
dtype: bool
num_0:
dtype: float16
num_1:
dtype: float16
num_2:
dtype: float16
num_3:
dtype: float16
num_4:
dtype: float16
num_5:
dtype: float16
num_6:
dtype: float16
num_7:
dtype: float16
num_8:
dtype: float16
num_9:
dtype: float16
metadata: {}
source_spec:
test:
- features: *id001
files:
- test/numerical.bin
type: split_binary
- features:
- label
files:
- test/label.bin
type: split_binary
- features:
- cat_0.bin
files:
- test/cat_0.bin
type: split_binary
- features:
- cat_1.bin
files:
- test/cat_1.bin
type: split_binary
- features:
- cat_2.bin
files:
- test/cat_2.bin
type: split_binary
- features:
- cat_3.bin
files:
- test/cat_3.bin
type: split_binary
- features:
- cat_4.bin
files:
- test/cat_4.bin
type: split_binary
- features:
- cat_5.bin
files:
- test/cat_5.bin
type: split_binary
- features:
- cat_6.bin
files:
- test/cat_6.bin
type: split_binary
- features:
- cat_7.bin
files:
- test/cat_7.bin
type: split_binary
- features:
- cat_8.bin
files:
- test/cat_8.bin
type: split_binary
- features:
- cat_9.bin
files:
- test/cat_9.bin
type: split_binary
- features:
- cat_10.bin
files:
- test/cat_10.bin
type: split_binary
- features:
- cat_11.bin
files:
- test/cat_11.bin
type: split_binary
- features:
- cat_12.bin
files:
- test/cat_12.bin
type: split_binary
- features:
- cat_13.bin
files:
- test/cat_13.bin
type: split_binary
- features:
- cat_14.bin
files:
- test/cat_14.bin
type: split_binary
- features:
- cat_15.bin
files:
- test/cat_15.bin
type: split_binary
- features:
- cat_16.bin
files:
- test/cat_16.bin
type: split_binary
- features:
- cat_17.bin
files:
- test/cat_17.bin
type: split_binary
- features:
- cat_18.bin
files:
- test/cat_18.bin
type: split_binary
- features:
- cat_19.bin
files:
- test/cat_19.bin
type: split_binary
- features:
- cat_20.bin
files:
- test/cat_20.bin
type: split_binary
- features:
- cat_21.bin
files:
- test/cat_21.bin
type: split_binary
- features:
- cat_22.bin
files:
- test/cat_22.bin
type: split_binary
- features:
- cat_23.bin
files:
- test/cat_23.bin
type: split_binary
- features:
- cat_24.bin
files:
- test/cat_24.bin
type: split_binary
- features:
- cat_25.bin
files:
- test/cat_25.bin
type: split_binary
train:
- features: *id001
files:
- train/numerical.bin
type: split_binary
- features:
- label
files:
- train/label.bin
type: split_binary
- features:
- cat_0.bin
files:
- train/cat_0.bin
type: split_binary
- features:
- cat_1.bin
files:
- train/cat_1.bin
type: split_binary
- features:
- cat_2.bin
files:
- train/cat_2.bin
type: split_binary
- features:
- cat_3.bin
files:
- train/cat_3.bin
type: split_binary
- features:
- cat_4.bin
files:
- train/cat_4.bin
type: split_binary
- features:
- cat_5.bin
files:
- train/cat_5.bin
type: split_binary
- features:
- cat_6.bin
files:
- train/cat_6.bin
type: split_binary
- features:
- cat_7.bin
files:
- train/cat_7.bin
type: split_binary
- features:
- cat_8.bin
files:
- train/cat_8.bin
type: split_binary
- features:
- cat_9.bin
files:
- train/cat_9.bin
type: split_binary
- features:
- cat_10.bin
files:
- train/cat_10.bin
type: split_binary
- features:
- cat_11.bin
files:
- train/cat_11.bin
type: split_binary
- features:
- cat_12.bin
files:
- train/cat_12.bin
type: split_binary
- features:
- cat_13.bin
files:
- train/cat_13.bin
type: split_binary
- features:
- cat_14.bin
files:
- train/cat_14.bin
type: split_binary
- features:
- cat_15.bin
files:
- train/cat_15.bin
type: split_binary
- features:
- cat_16.bin
files:
- train/cat_16.bin
type: split_binary
- features:
- cat_17.bin
files:
- train/cat_17.bin
type: split_binary
- features:
- cat_18.bin
files:
- train/cat_18.bin
type: split_binary
- features:
- cat_19.bin
files:
- train/cat_19.bin
type: split_binary
- features:
- cat_20.bin
files:
- train/cat_20.bin
type: split_binary
- features:
- cat_21.bin
files:
- train/cat_21.bin
type: split_binary
- features:
- cat_22.bin
files:
- train/cat_22.bin
type: split_binary
- features:
- cat_23.bin
files:
- train/cat_23.bin
type: split_binary
- features:
- cat_24.bin
files:
- train/cat_24.bin
type: split_binary
- features:
- cat_25.bin
files:
- train/cat_25.bin
type: split_binary
|
TensorFlow/Detection/SSD/models/research/object_detection/predictors/heads | heads | keypoint_head_test | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for object_detection.predictors.heads.keypoint_head."""
import tensorflow as tf
from google.protobuf import text_format
from object_detection.builders import hyperparams_builder
from object_detection.predictors.heads import keypoint_head
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
class MaskRCNNKeypointHeadTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(self,
op_type=hyperparams_pb2.Hyperparams.FC):
hyperparams = hyperparams_pb2.Hyperparams()
hyperparams_text_proto = """
activation: NONE
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
"""
text_format.Merge(hyperparams_text_proto, hyperparams)
hyperparams.op = op_type
return hyperparams_builder.build(hyperparams, is_training=True)
def test_prediction_size(self):
keypoint_prediction_head = keypoint_head.MaskRCNNKeypointHead(
conv_hyperparams_fn=self._build_arg_scope_with_hyperparams())
roi_pooled_features = tf.random_uniform(
[64, 14, 14, 1024], minval=-2.0, maxval=2.0, dtype=tf.float32)
prediction = keypoint_prediction_head.predict(
features=roi_pooled_features, num_predictions_per_location=1)
self.assertAllEqual([64, 1, 17, 56, 56], prediction.get_shape().as_list())
if __name__ == '__main__':
tf.test.main()
|
PyTorch/Classification/GPUNet/triton/runner | runner | configuration | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pathlib
from typing import Any, Dict, Optional
# 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 .task import DataObject
class Configuration(DataObject):
"""
Configuration object - handle single experiment data
"""
def __init__(
self,
parameters: Dict,
checkpoint: Optional[str],
):
"""
Args:
parameters: Configuration parameters
checkpoint: Checkpoint used for experiment
"""
self.parameters = parameters
self.checkpoint = checkpoint
|
TensorFlow/LanguageModeling/BERT/biobert/scripts | scripts | run_pretraining_pubmed_base_phase_1 | #! /bin/bash
echo "Container nvidia build = " $NVIDIA_BUILD_ID
train_batch_size=${1:-128}
learning_rate=${2:-"9.625e-5"}
cased=${3:-false}
precision=${4:-"fp16"}
use_xla=${5:-"true"}
num_gpu=${6:-16}
warmup_steps=${7:-"1953"}
train_steps=${8:-19531}
num_accumulation_steps=${9:-32}
save_checkpoint_steps=${10:-5000}
eval_batch_size=${11:-80}
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_1
mkdir -p ${CHECKPOINTS_DIR}
INIT_CHECKPOINT=/workspace/bert/data/download/google_pretrained_weights/${CASING_DIR_PREFIX}_L-12_H-768_A-12/bert_model.ckpt
INPUT_FILES_DIR="/workspace/bert/data/tfrecord/lower_case_${DO_LOWER_CASE}_seq_len_128_max_pred_20_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/pubmed_baseline/training"
EVAL_FILES_DIR="/workspace/bert/data/tfrecord/lower_case_${DO_LOWER_CASE}_seq_len_128_max_pred_20_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 \* $num_gpus \* num_accumulation_steps)
printf -v TAG "tf_bert_bio_1n_phase1_cased_%s_%s_gbs%d" "$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=$eval_batch_size \
--max_seq_length=128 \
--max_predictions_per_seq=20 \
--num_train_steps=$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_fp16 $use_xla_tag \
--init_checkpoint=$INIT_CHECKPOINT |& tee $LOGFILE |
TensorFlow/Segmentation/UNet_Medical | UNet_Medical | main | # 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.
"""Entry point of the application.
This file serves as entry point to the training of UNet for segmentation of neuronal processes.
Example:
Training can be adjusted by modifying the arguments specified below::
$ python main.py --exec_mode train --model_dir /datasets ...
"""
import os
import horovod.tensorflow as hvd
import math
import numpy as np
import tensorflow as tf
from PIL import Image
from utils.setup import prepare_model_dir, get_logger, build_estimator, set_flags
from utils.cmd_util import PARSER, parse_args
from utils.data_loader import Dataset
from utils.hooks.profiling_hook import ProfilingHook
from utils.hooks.training_hook import TrainingHook
def main(_):
"""
Starting point of the application
"""
hvd.init()
set_flags()
params = parse_args(PARSER.parse_args())
model_dir = prepare_model_dir(params)
logger = get_logger(params)
estimator = build_estimator(params, model_dir)
dataset = Dataset(data_dir=params.data_dir,
batch_size=params.batch_size,
fold=params.crossvalidation_idx,
augment=params.augment,
gpu_id=hvd.rank(),
num_gpus=hvd.size(),
seed=params.seed)
if 'train' in params.exec_mode:
max_steps = params.max_steps // (1 if params.benchmark else hvd.size())
hooks = [hvd.BroadcastGlobalVariablesHook(0),
TrainingHook(logger,
max_steps=max_steps,
log_every=params.log_every)]
if params.benchmark and hvd.rank() == 0:
hooks.append(ProfilingHook(logger,
batch_size=params.batch_size,
log_every=params.log_every,
warmup_steps=params.warmup_steps,
mode='train'))
estimator.train(
input_fn=dataset.train_fn,
steps=max_steps,
hooks=hooks)
if 'evaluate' in params.exec_mode:
if hvd.rank() == 0:
results = estimator.evaluate(input_fn=dataset.eval_fn, steps=dataset.eval_size)
logger.log(step=(),
data={"eval_ce_loss": float(results["eval_ce_loss"]),
"eval_dice_loss": float(results["eval_dice_loss"]),
"eval_total_loss": float(results["eval_total_loss"]),
"eval_dice_score": float(results["eval_dice_score"])})
if 'predict' in params.exec_mode:
if hvd.rank() == 0:
predict_steps = dataset.test_size
hooks = None
if params.benchmark:
hooks = [ProfilingHook(logger,
batch_size=params.batch_size,
log_every=params.log_every,
warmup_steps=params.warmup_steps,
mode="test")]
predict_steps = params.warmup_steps * 2 * params.batch_size
predictions = estimator.predict(
input_fn=lambda: dataset.test_fn(count=math.ceil(predict_steps / dataset.test_size)),
hooks=hooks)
binary_masks = [np.argmax(p['logits'], axis=-1).astype(np.uint8) * 255 for p in predictions]
if not params.benchmark:
multipage_tif = [Image.fromarray(mask).resize(size=(512, 512), resample=Image.BILINEAR)
for mask in binary_masks]
output_dir = os.path.join(params.model_dir, 'pred')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
multipage_tif[0].save(os.path.join(output_dir, 'test-masks.tif'),
compression="tiff_deflate",
save_all=True,
append_images=multipage_tif[1:])
if __name__ == '__main__':
tf.compat.v1.app.run()
|
PyTorch/LanguageModeling/BERT/triton | triton | run_performance_on_triton | #!/usr/bin/env python3
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import csv
import logging
import os
import pathlib
import shutil
from distutils.version import LooseVersion
from enum import Enum
from importlib.metadata import version
from typing import Any, Dict, List
import yaml
# method from PEP-366 to support relative import in executed modules
if __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from .deployment_toolkit.core import BatchingMode, EvaluationMode, MeasurementMode, OfflineMode, PerformanceTool
from .deployment_toolkit.model_analyzer import ModelAnalyzer, ModelAnalyzerConfig, ModelAnalyzerMode
from .deployment_toolkit.perf_analyzer import PerfAnalyzer, PerfAnalyzerConfig
from .deployment_toolkit.report import save_results, show_results, sort_results
from .deployment_toolkit.utils import parse_server_url
from .deployment_toolkit.warmup import performance_evaluation_warmup
LOGGER = logging.getLogger("run_performance_on_triton")
TRITON_CLIENT_VERSION = LooseVersion(version("tritonclient"))
def _log_dict(title: str, dict_: Dict[str, Any]):
LOGGER.info(title)
for key, value in dict_.items():
LOGGER.info(f"\t{key} = {value}")
def _calculate_average_latency(r):
avg_sum_fields = [
"Client Send",
"Network+Server Send/Recv",
"Server Queue",
"Server Compute",
"Server Compute Input",
"Server Compute Infer",
"Server Compute Output",
"Client Recv",
]
avg_latency = sum([int(r.get(f, 0)) for f in avg_sum_fields])
return avg_latency
def _update_performance_data(results: List, batch_size: int, performance_partial_file: str):
row: Dict = {"Batch": batch_size}
with open(performance_partial_file) as csvfile:
reader = csv.DictReader(csvfile)
for r in reader:
avg_latency = _calculate_average_latency(r)
row = {**row, **r, "avg latency": avg_latency}
results.append(row)
def _model_analyzer_evaluation(
server_url: str,
model_name: str,
input_data: str,
input_shapes: List[str],
batch_sizes: List[int],
number_of_triton_instances: int,
number_of_model_instances: int,
measurement_mode: MeasurementMode,
measurement_interval: int,
measurement_request_count: int,
concurrency_steps: int,
batching_mode: BatchingMode,
evaluation_mode: EvaluationMode,
offline_mode: OfflineMode,
model_repository: str,
result_path: str,
output_shared_memory_size: int = 102400,
verbose: bool = False,
):
_log_dict(
"Selected configuration",
{
"server_url": server_url,
"model_name": model_name,
"input_data": input_data,
"input_shapes": input_shapes,
"batch_sizes": batch_sizes,
"number_of_triton_instances": number_of_triton_instances,
"number_of_model_instances": number_of_model_instances,
"measurement_mode": measurement_mode,
"measurement_interval": measurement_interval,
"measurement_request_count": measurement_request_count,
"concurrency_steps": concurrency_steps,
"batching_mode": batching_mode,
"evaluation_mode": evaluation_mode,
"offline_mode": offline_mode,
"output_shared_memory_size": output_shared_memory_size,
"model_repository": model_repository,
"result_path": result_path,
"verbose": verbose,
},
)
perf_analyzer_config = {
"input-data": input_data,
"measurement-interval": measurement_interval,
}
if TRITON_CLIENT_VERSION >= LooseVersion("2.11.0"):
perf_analyzer_config["measurement-mode"] = measurement_mode.value
perf_analyzer_config["measurement-request-count"] = measurement_request_count
if evaluation_mode == EvaluationMode.OFFLINE:
perf_analyzer_config["shared-memory"] = offline_mode.value
perf_analyzer_config["output-shared-memory-size"] = output_shared_memory_size
if input_shapes:
perf_analyzer_config["shape"] = input_shapes[0]
LOGGER.warning("Model Analyzer support only single shape param for Perf Analyzer.")
if batching_mode == BatchingMode.STATIC:
batch_sizes = batch_sizes
concurrency = [number_of_triton_instances]
elif batching_mode == BatchingMode.DYNAMIC:
max_batch_size = max(batch_sizes)
max_total_requests = 2 * max_batch_size * number_of_triton_instances * number_of_model_instances
max_concurrency = min(256, max_total_requests)
step = max(1, max_concurrency // concurrency_steps)
min_concurrency = step
concurrency = {"start": min_concurrency, "stop": max_concurrency, "step": step}
batch_sizes = [max(1, max_total_requests // 256)]
else:
raise ValueError(f"Unsupported batching mode: {batching_mode}")
protocol, host, port = parse_server_url(server_url)
checkpoints = pathlib.Path("./checkpoints")
if checkpoints.is_dir():
shutil.rmtree(checkpoints.as_posix())
checkpoints.mkdir(parents=True, exist_ok=True)
config = {
"model_repository": model_repository,
"triton_launch_mode": "remote",
"run_config_search_disable": True,
"perf_analyzer_flags": perf_analyzer_config,
"perf_analyzer_timeout": 3600, # Workaround for Perf Analyzer timeout - use 1h
"profile_models": [model_name],
"batch_sizes": batch_sizes,
"concurrency": concurrency,
"verbose": verbose,
"checkpoint_directory": checkpoints.as_posix(),
"override_output_model_repository": True,
"client_protocol": protocol,
f"triton_{protocol}_endpoint": f"{host}:{port}",
}
if verbose:
_log_dict("Model Analyzer profiling configuration", config)
with open("config.yaml", "w") as file:
yaml.safe_dump(config, file)
config = ModelAnalyzerConfig()
model_analyzer = ModelAnalyzer(config=config)
model_analyzer.run(mode=ModelAnalyzerMode.PROFILE, verbose=verbose)
result_path = pathlib.Path(result_path)
result_path.mkdir(parents=True, exist_ok=True)
for file in checkpoints.iterdir():
if not file.is_file() or file.suffix != ".ckpt":
continue
LOGGER.info(f"Moving checkpoint {file.name} to {result_path}")
shutil.move(file, result_path / file.name)
inference_output_fields = [
"batch_size",
"concurrency",
"perf_throughput",
"perf_latency",
"perf_client_send_recv",
"perf_client_response_wait",
"perf_server_queue",
"perf_server_compute_input",
"perf_server_compute_infer",
"perf_server_compute_output",
]
gpu_output_fields = [
"gpu_uuid",
"batch_size",
"concurrency",
"gpu_used_memory",
"gpu_free_memory",
"gpu_utilization",
"gpu_power_usage",
]
filename_model_inference = "metrics-model-inference.csv"
filename_model_gpu = "metrics-model-gpu.csv"
config = {
"analysis_models": model_name,
"checkpoint_directory": result_path.as_posix(),
"export_path": "/tmp",
"inference_output_fields": inference_output_fields,
"gpu_output_fields": gpu_output_fields,
"filename_model_inference": filename_model_inference,
"filename_model_gpu": filename_model_gpu,
"summarize": False,
}
if verbose:
_log_dict("Model Analyzer analysis configuration", config)
with open("config.yaml", "w") as file:
yaml.safe_dump(config, file)
config = ModelAnalyzerConfig()
model_analyzer = ModelAnalyzer(config=config)
model_analyzer.run(mode=ModelAnalyzerMode.ANALYZE, verbose=verbose)
inference_metrics_file = pathlib.Path("/tmp") / "results" / filename_model_inference
gpu_metrics_file = pathlib.Path("/tmp") / "results" / filename_model_gpu
for file in [inference_metrics_file, gpu_metrics_file]:
LOGGER.info(f"Moving metrics {file.name} to {result_path}")
shutil.move(file, result_path / file.name)
def _perf_analyzer_evaluation(
server_url: str,
model_name: str,
input_data: str,
input_shapes: List[str],
batch_sizes: List[int],
number_of_triton_instances: int,
number_of_model_instances: int,
measurement_mode: MeasurementMode,
measurement_interval: int,
measurement_request_count: int,
concurrency_steps: int,
batching_mode: BatchingMode,
evaluation_mode: EvaluationMode,
offline_mode: OfflineMode,
result_path: str,
output_shared_memory_size: int = 102400,
verbose: bool = False,
):
protocol, host, port = parse_server_url(server_url)
if batching_mode == BatchingMode.STATIC:
batch_sizes = batch_sizes
max_concurrency = 1
min_concurrency = 1
step = 1
elif batching_mode == BatchingMode.DYNAMIC:
max_batch_size = max(batch_sizes)
max_total_requests = 2 * max_batch_size * number_of_triton_instances * number_of_model_instances
max_concurrency = min(256, max_total_requests)
step = max(1, max_concurrency // concurrency_steps)
min_concurrency = step
batch_sizes = [max(1, max_total_requests // 256)]
else:
raise ValueError(f"Unsupported batching mode: {batching_mode}")
_log_dict(
"Selected configuration",
{
"server_url": server_url,
"model_name": model_name,
"input_data": input_data,
"input_shapes": input_shapes,
"batch_sizes": batch_sizes,
"number_of_triton_instances": number_of_triton_instances,
"number_of_model_instances": number_of_model_instances,
"measurement_mode": measurement_mode,
"measurement_interval": measurement_interval,
"measurement_request_count": measurement_request_count,
"concurrency_steps": concurrency_steps,
"batching_mode": batching_mode,
"evaluation_mode": evaluation_mode,
"offline_mode": offline_mode,
"output_shared_memory_size": output_shared_memory_size,
"result_path": result_path,
"verbose": verbose,
},
)
results: List[Dict] = list()
for batch_size in batch_sizes:
for concurrency in range(min_concurrency, max_concurrency + step, step):
performance_partial_file = f"triton_performance_{evaluation_mode.value.lower()}_{batching_mode.value.lower()}_partial_{batch_size}_{concurrency}.csv"
params = {
"model-name": model_name,
"model-version": 1,
"batch-size": batch_size,
"url": f"{host}:{port}",
"protocol": protocol,
"input-data": input_data,
"measurement-interval": measurement_interval,
"concurrency-range": f"{concurrency}:{concurrency}:1",
"latency-report-file": performance_partial_file,
}
if verbose:
params["extra-verbose"] = True
if TRITON_CLIENT_VERSION >= LooseVersion("2.11.0"):
params["measurement-mode"] = measurement_mode.value
params["measurement-request-count"] = measurement_request_count
if evaluation_mode == EvaluationMode.OFFLINE:
params["shared-memory"] = offline_mode.value
params["output-shared-memory-size"] = output_shared_memory_size
if verbose:
_log_dict(f"Perf Analyzer config for batch_size: {batch_size} and concurrency: {concurrency}", params)
config = PerfAnalyzerConfig()
for param, value in params.items():
config[param] = value
for shape in input_shapes:
config["shape"] = shape
perf_analyzer = PerfAnalyzer(config=config)
perf_analyzer.run()
_update_performance_data(results, batch_size, performance_partial_file)
os.remove(performance_partial_file)
results = sort_results(results=results)
save_results(filename=result_path, data=results)
show_results(results=results)
def _run_performance_analysis(
server_url: str,
model_name: str,
input_data: str,
input_shapes: List[str],
batch_sizes: List[int],
number_of_triton_instances: int,
number_of_model_instances: int,
measurement_mode: MeasurementMode,
measurement_interval: int,
measurement_request_count: int,
concurrency_steps: int,
batching_mode: BatchingMode,
evaluation_mode: EvaluationMode,
offline_mode: OfflineMode,
output_shared_memory_size: int,
performance_tool: PerformanceTool,
model_repository: str,
result_path: str,
warmup: bool,
verbose: bool,
):
log_level = logging.INFO if not verbose else logging.DEBUG
log_format = "%(asctime)s %(levelname)s %(name)s %(message)s"
logging.basicConfig(level=log_level, format=log_format)
if warmup:
LOGGER.info("Running warmup before the main test")
performance_evaluation_warmup(
server_url=server_url,
model_name=model_name,
input_data=input_data,
input_shapes=input_shapes,
batch_sizes=batch_sizes,
number_of_triton_instances=number_of_triton_instances,
number_of_model_instances=number_of_model_instances,
measurement_mode=measurement_mode,
measurement_interval=measurement_interval,
measurement_request_count=measurement_request_count,
batching_mode=batching_mode,
evaluation_mode=evaluation_mode,
offline_mode=offline_mode,
output_shared_memory_size=output_shared_memory_size,
)
if performance_tool == PerformanceTool.MODEL_ANALYZER:
LOGGER.info("Using Model Analyzer for performance evaluation")
_model_analyzer_evaluation(
server_url=server_url,
model_name=model_name,
input_data=input_data,
input_shapes=input_shapes,
batch_sizes=batch_sizes,
number_of_triton_instances=number_of_triton_instances,
number_of_model_instances=number_of_model_instances,
measurement_mode=measurement_mode,
measurement_interval=measurement_interval,
measurement_request_count=measurement_request_count,
concurrency_steps=concurrency_steps,
batching_mode=batching_mode,
evaluation_mode=evaluation_mode,
offline_mode=offline_mode,
output_shared_memory_size=output_shared_memory_size,
model_repository=model_repository,
result_path=result_path,
verbose=verbose,
)
elif performance_tool == PerformanceTool.PERF_ANALYZER:
LOGGER.info("Using Perf Analyzer for performance evaluation")
_perf_analyzer_evaluation(
server_url=server_url,
model_name=model_name,
input_data=input_data,
input_shapes=input_shapes,
batch_sizes=batch_sizes,
number_of_triton_instances=number_of_triton_instances,
number_of_model_instances=number_of_model_instances,
measurement_mode=measurement_mode,
measurement_interval=measurement_interval,
measurement_request_count=measurement_request_count,
concurrency_steps=concurrency_steps,
batching_mode=batching_mode,
evaluation_mode=evaluation_mode,
offline_mode=offline_mode,
output_shared_memory_size=output_shared_memory_size,
result_path=result_path,
verbose=verbose,
)
else:
raise ValueError(f"Unsupported performance tool {performance_tool}")
class MeasurementMode(Enum):
"""
Available measurement stabilization modes
"""
COUNT_WINDOWS = "count_windows"
TIME_WINDOWS = "time_windows"
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--server-url",
type=str,
required=False,
default="grpc://127.0.0.1:8001",
help="Url to Triton server",
)
parser.add_argument(
"--model-name",
type=str,
required=True,
help="Name of the model to test",
)
parser.add_argument(
"--input-data",
type=str,
required=False,
default="random",
help="Input data to perform profiling.",
)
parser.add_argument(
"--input-shapes",
action="append",
required=False,
help="Input data shape in form INPUT_NAME:<full_shape_without_batch_axis>.",
)
parser.add_argument(
"--batch-sizes",
type=str,
required=True,
help="List of batch sizes to tests. Comma separated.",
)
parser.add_argument(
"--number-of-triton-instances",
type=int,
default=1,
help="Number of Triton Server instances",
)
parser.add_argument(
"--number-of-model-instances",
type=int,
default=1,
help="Number of models instances on Triton Server",
)
parser.add_argument(
"--measurement-mode",
choices=[item.value for item in MeasurementMode],
default=MeasurementMode.COUNT_WINDOWS.value,
type=str,
help="Select measurement mode "
"'time_windows' stabilize performance on measurement window. "
"'count_windows' stabilize performance on number of samples.",
)
parser.add_argument(
"--measurement-interval",
required=False,
help="Time window perf_analyzer will wait to stabilize the measurement",
default=5000,
type=int,
)
parser.add_argument(
"--measurement-request-count",
required=False,
help="Number of samples on which perf_analyzer will stabilize the measurement",
default=50,
type=int,
)
parser.add_argument(
"--concurrency-steps",
help="Define number of concurrency steps used for dynamic batching tests",
default=32,
type=int,
)
parser.add_argument(
"--batching-mode",
choices=[item.value for item in BatchingMode],
default=BatchingMode.STATIC.value,
type=str,
help="Select batching mode "
"'static' run static batching scenario. "
"'dynamic' run dynamic batching scenario.",
)
parser.add_argument(
"--evaluation-mode",
choices=[item.value for item in EvaluationMode],
default=EvaluationMode.OFFLINE.value,
type=str,
help="Select evaluation mode "
"'offline' run offline analysis and use GPU memory to pass tensors. "
"'online' run online analysis and use HTTP protocol.",
)
parser.add_argument(
"--offline-mode",
choices=[item.value for item in OfflineMode],
default=OfflineMode.SYSTEM.value,
type=str,
help="Select offline mode "
"'system' pass tensors through CPU RAM memory. "
"'cuda' pass tensors through GPU RAM memory.",
)
parser.add_argument(
"--output-shared-memory-size",
default=100240,
type=int,
help="Size of memory buffer allocated for output with dynamic shapes in bytes. "
"Has to be equal to maximal size of output tensor.",
)
parser.add_argument(
"--performance-tool",
choices=[item.value for item in PerformanceTool],
default=PerformanceTool.MODEL_ANALYZER.value,
type=str,
help="Select performance tool for measurement mode "
"'model_analyzer' use Model Analyzer "
"'perf_analyzer' use Perf Analyzer",
)
parser.add_argument(
"--model-repository",
default=None,
type=str,
help="Path to model repository. Valid when using Model Analyzer",
)
parser.add_argument("--result-path", type=str, required=True, help="Path where results files is stored.")
parser.add_argument(
"--warmup", help="Enable model warmup before performance test", action="store_true", default=False
)
parser.add_argument("-v", "--verbose", help="Verbose logs", action="store_true", default=False)
args = parser.parse_args()
batch_sizes = list(map(lambda x: int(x), args.batch_sizes.split(",")))
_run_performance_analysis(
server_url=args.server_url,
model_name=args.model_name,
input_data=args.input_data,
input_shapes=args.input_shapes or [],
batch_sizes=batch_sizes,
number_of_triton_instances=args.number_of_triton_instances,
number_of_model_instances=args.number_of_model_instances,
measurement_mode=MeasurementMode(args.measurement_mode),
measurement_interval=args.measurement_interval,
measurement_request_count=args.measurement_request_count,
concurrency_steps=args.concurrency_steps,
batching_mode=BatchingMode(args.batching_mode),
evaluation_mode=EvaluationMode(args.evaluation_mode),
offline_mode=OfflineMode(args.offline_mode),
output_shared_memory_size=args.output_shared_memory_size,
performance_tool=PerformanceTool(args.performance_tool),
model_repository=args.model_repository,
result_path=args.result_path,
warmup=args.warmup,
verbose=args.verbose,
)
if __name__ == "__main__":
main()
|
Tools/PyTorch/TimeSeriesPredictionPlatform/models/tft_pyt/triton/runner | runner | pipeline | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pathlib
from typing import Dict, Tuple
# method from PEP-366 to support relative import in executed modules
if __name__ == "__main__" and __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from .stages import (
ConversionStage,
DeployStage,
ExportStage,
ResultsType,
TritonPerformanceOfflineStage,
TritonPerformanceOnlineStage,
TritonPreparePerformanceProfilingDataStage,
)
class Pipeline:
"""
Definition of stages that has to be executed before and during experiments
"""
# Stages to execute as part of single experiment
_experiment_stages = [
ExportStage.label,
ConversionStage.label,
DeployStage.label,
TritonPreparePerformanceProfilingDataStage.label,
TritonPerformanceOfflineStage.label,
TritonPerformanceOnlineStage.label,
]
def __init__(self):
"""
Initialize pipeline
"""
self._stages: Dict = dict()
def model_export(self, commands: Tuple[str, ...]) -> None:
"""
Model export stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = ExportStage(commands=commands)
self._stages[stage.label] = stage
def model_conversion(self, commands: Tuple[str, ...]) -> None:
"""
Model conversion stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = ConversionStage(commands=commands)
self._stages[stage.label] = stage
def model_deploy(self, commands: Tuple[str, ...]) -> None:
"""
Model deployment stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = DeployStage(commands=commands)
self._stages[stage.label] = stage
def triton_prepare_performance_profiling_data(self, commands: Tuple[str, ...]) -> None:
"""
Model profiling data creation stage
Args:
commands: Commands to be executed as part of stage
Returns:
None
"""
stage = TritonPreparePerformanceProfilingDataStage(commands=commands)
self._stages[stage.label] = stage
def triton_performance_offline_tests(self, commands: Tuple[str, ...], result_path: str) -> None:
"""
Model performance offline test stage
Args:
commands: Commands to be executed as part of stage
result_path: Path where results file is stored
Returns:
None
"""
stage = TritonPerformanceOfflineStage(
commands=commands,
result_path=result_path,
result_type=ResultsType.TRITON_PERFORMANCE_OFFLINE,
)
self._stages[stage.label] = stage
def triton_performance_online_tests(self, commands: Tuple[str, ...], result_path: str) -> None:
"""
Model performance online test stage
Args:
commands: Commands to be executed as part of stage
result_path: Path where results file is stored
Returns:
None
"""
stage = TritonPerformanceOnlineStage(
commands=commands,
result_path=result_path,
result_type=ResultsType.TRITON_PERFORMANCE_ONLINE,
)
self._stages[stage.label] = stage
def stages(self):
"""
Generate stages which should be run per experiment
Returns:
Generator with stages object
"""
for stage_name in self._experiment_stages:
stage = self._stages.get(stage_name)
if not stage:
continue
yield stage
|
TensorFlow2/Segmentation/Contrib/UNet3P/data_preparation | data_preparation | README | For data two options are available
- [Train on LiTS Data](#lits-liver-tumor-segmentation-challenge)
- [Train on custom data](#train-on-custom-data)
## LiTS Liver Tumor Segmentation challenge
This dataset consist of 131 Liver CT Scans.
Register [here](https://competitions.codalab.org/competitions/17094) to get dataset access.
Go to participate → Training Data to get dataset link.
Download Training Batch 1 and Training Batch 2 zip files and past them under data folder.
`Training Batch 1` size is 3.97GB and `Training Batch 2` zip file size is 11.5GB.
Inside main directory `/workspace/unet3p` run below command to extract zip files
```shell
bash data_preparation/extract_data.sh
```
After extraction `Training Batch 1` folder size will be 11.4GB and `Training Batch 2` folder size will be 38.5GB.
- `Training Batch 1` consist of 28 scans which are used for testing
- `Training Batch 2` consist of 103 scans which are used for training
Default directory structure looks like this
├── data/
│ ├── Training Batch 1/
├── segmentation-0.nii
├── volume-0.nii
├── ...
├── volume-27.nii
│ ├── Training Batch 2/
├── segmentation-28.nii
├── volume-28.nii
├── ...
├── volume-130.nii
For testing, you can have any number of files in Training Batch 1 and Training Batch 2. But make sure the naming
convention is similar.
To prepare LiTS dataset for training run
```
python data_preparation/preprocess_data.py
```
> Note: Because of the extensive preprocessing, it will take some time, so relax and wait.
#### Final directory
After completion, you will have a directories like this
├── data/
│ ├── train/
├── images
├── image_28_0.png
├── ...
├── mask
├── mask_28_0.png
├── ...
│ ├── val/
├── images
├── image_0_0.png
├── ...
├── mask
├── mask_0_0.png
├── ...
After processing the `train` folder size will be 5GB and `val` folder size will be 1.7GB.
#### Free space (Optional)
At this stage you can delete the intermediate scans files to free space, run below command
```shell
bash data_preparation/delete_extracted_scans_data.sh
```
You can also delete the data zip files using below command, but remember you cannot retrieve them back
```shell
bash data_preparation/delete_zip_data.sh
```
> Note: It is recommended to delete scan files but not zip data because you may need it again.
## Train on custom data
To train on custom dateset it's advised that you follow the same train and val directory structure like
mentioned [above](#final-directory).
In our case image file name can be mapped to it's corresponding mask file name by replacing `image` text with `mask`. If
your data has different mapping then you need to update [image_to_mask_name](./../utils/images_utils.py#L63) function which
is responsible for converting image name to it's corresponding file name.
Each image should be a color image with 3 channels and `RGB` color format. Each mask is considered as a gray scale
image, where each pixel value is the class on which each pixel belongs.
Congratulations, now you can start training and testing on your new dataset! |
PyTorch/LanguageModeling/Transformer-XL | Transformer-XL | .gitignore | **/.DS_Store
__pycache__/
data/
results/
pytorch/LM-TFM/*
*.out
*.log
|
PyTorch/SpeechSynthesis/FastPitch/triton | triton | requirements | # 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.
networkx==2.5
numpy
onnx==1.8.0
onnxruntime==1.5.2
pycuda>=2019.1.2
PyYAML>=5.2
tqdm>=4.44.1
tabulate>=0.8.7
natsort>=7.0.0
# use tags instead of branch names - because there might be docker cache hit causing not fetching most recent changes on branch
model_navigator @ git+https://github.com/triton-inference-server/[email protected]#egg=model_navigator
|
TensorFlow/Detection/SSD/models/research/object_detection/core | core | region_similarity_calculator | # 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.
# ==============================================================================
"""Region Similarity Calculators for BoxLists.
Region Similarity Calculators compare a pairwise measure of similarity
between the boxes in two BoxLists.
"""
from abc import ABCMeta
from abc import abstractmethod
import tensorflow as tf
from object_detection.core import box_list_ops
from object_detection.core import standard_fields as fields
class RegionSimilarityCalculator(object):
"""Abstract base class for region similarity calculator."""
__metaclass__ = ABCMeta
def compare(self, boxlist1, boxlist2, scope=None):
"""Computes matrix of pairwise similarity between BoxLists.
This op (to be overridden) computes a measure of pairwise similarity between
the boxes in the given BoxLists. Higher values indicate more similarity.
Note that this method simply measures similarity and does not explicitly
perform a matching.
Args:
boxlist1: BoxList holding N boxes.
boxlist2: BoxList holding M boxes.
scope: Op scope name. Defaults to 'Compare' if None.
Returns:
a (float32) tensor of shape [N, M] with pairwise similarity score.
"""
with tf.name_scope(scope, 'Compare', [boxlist1, boxlist2]) as scope:
return self._compare(boxlist1, boxlist2)
@abstractmethod
def _compare(self, boxlist1, boxlist2):
pass
class IouSimilarity(RegionSimilarityCalculator):
"""Class to compute similarity based on Intersection over Union (IOU) metric.
This class computes pairwise similarity between two BoxLists based on IOU.
"""
def _compare(self, boxlist1, boxlist2):
"""Compute pairwise IOU similarity between the two BoxLists.
Args:
boxlist1: BoxList holding N boxes.
boxlist2: BoxList holding M boxes.
Returns:
A tensor with shape [N, M] representing pairwise iou scores.
"""
return box_list_ops.iou(boxlist1, boxlist2)
class NegSqDistSimilarity(RegionSimilarityCalculator):
"""Class to compute similarity based on the squared distance metric.
This class computes pairwise similarity between two BoxLists based on the
negative squared distance metric.
"""
def _compare(self, boxlist1, boxlist2):
"""Compute matrix of (negated) sq distances.
Args:
boxlist1: BoxList holding N boxes.
boxlist2: BoxList holding M boxes.
Returns:
A tensor with shape [N, M] representing negated pairwise squared distance.
"""
return -1 * box_list_ops.sq_dist(boxlist1, boxlist2)
class IoaSimilarity(RegionSimilarityCalculator):
"""Class to compute similarity based on Intersection over Area (IOA) metric.
This class computes pairwise similarity between two BoxLists based on their
pairwise intersections divided by the areas of second BoxLists.
"""
def _compare(self, boxlist1, boxlist2):
"""Compute pairwise IOA similarity between the two BoxLists.
Args:
boxlist1: BoxList holding N boxes.
boxlist2: BoxList holding M boxes.
Returns:
A tensor with shape [N, M] representing pairwise IOA scores.
"""
return box_list_ops.ioa(boxlist1, boxlist2)
class ThresholdedIouSimilarity(RegionSimilarityCalculator):
"""Class to compute similarity based on thresholded IOU and score.
This class computes pairwise similarity between two BoxLists based on IOU and
a 'score' present in boxlist1. If IOU > threshold, then the entry in the
output pairwise tensor will contain `score`, otherwise 0.
"""
def __init__(self, iou_threshold=0):
"""Initialize the ThresholdedIouSimilarity.
Args:
iou_threshold: For a given pair of boxes, if the IOU is > iou_threshold,
then the comparison result will be the foreground probability of
the first box, otherwise it will be zero.
"""
self._iou_threshold = iou_threshold
def _compare(self, boxlist1, boxlist2):
"""Compute pairwise IOU similarity between the two BoxLists and score.
Args:
boxlist1: BoxList holding N boxes. Must have a score field.
boxlist2: BoxList holding M boxes.
Returns:
A tensor with shape [N, M] representing scores threholded by pairwise
iou scores.
"""
ious = box_list_ops.iou(boxlist1, boxlist2)
scores = boxlist1.get_field(fields.BoxListFields.scores)
scores = tf.expand_dims(scores, axis=1)
row_replicated_scores = tf.tile(scores, [1, tf.shape(ious)[-1]])
thresholded_ious = tf.where(ious > self._iou_threshold,
row_replicated_scores, tf.zeros_like(ious))
return thresholded_ious
|
PyTorch/Detection/Efficientdet/scripts/D0 | D0 | inference_FP32_V100-32G | #!/bin/bash
rm -rf *.json
python -u -m bind_launch --nproc_per_node=${NUM_PROC:-1} validate.py '/workspace/object_detection/datasets/coco/' --model efficientdet_d0 -b ${BATCH_SIZE:-8} --torchscript --use-ema --checkpoint ${CKPT_PATH:-/checkpoints/Effdet_B0.pth} --inference |
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/waveglow | waveglow | blending | /*
* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "blending.h"
/******************************************************************************
* CONSTANTS ******************************************************************
*****************************************************************************/
namespace
{
constexpr const int BLOCK_SIZE = 1024;
}
/******************************************************************************
* KERNELS ********************************************************************
*****************************************************************************/
__global__ void linearBlendingKernel(const float* const newChunk, float* const base, const int chunkLength,
const int overlapSize, const int spacing, const int offset)
{
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < chunkLength)
{
const float weight
= offset > 0 && idx < overlapSize ? static_cast<float>(idx) / static_cast<float>(overlapSize) : 1.0f;
const int inputIdx = idx + (blockIdx.y * chunkLength);
const int outputIdx = idx + offset + (blockIdx.y * spacing);
float newValue;
if (weight < 1.0f)
{
newValue = (1.0f - weight) * base[outputIdx] + newChunk[inputIdx] * weight;
}
else
{
newValue = newChunk[inputIdx];
}
base[outputIdx] = newValue;
}
}
/******************************************************************************
* HELPER FUNCTIONS ***********************************************************
*****************************************************************************/
namespace
{
static int roundUpBlocks(const int num, const int blockSize)
{
return ((num - 1) / blockSize) + 1;
}
} // namespace
/******************************************************************************
* PUBLIC STATIC METHODS ******************************************************
*****************************************************************************/
void Blending::linear(const int batchSize, const float* const newChunk, float* const base, const int chunkSize,
const int overlapSize, const int outputSequenceSpacing, const int outputSequenceOffset, cudaStream_t stream)
{
const int blocksPerChunk = roundUpBlocks(chunkSize, BLOCK_SIZE);
const dim3 grid(blocksPerChunk, batchSize);
const dim3 block(BLOCK_SIZE);
linearBlendingKernel<<<grid, block, 0, stream>>>(
newChunk, base, chunkSize, overlapSize, outputSequenceSpacing, outputSequenceOffset);
}
|
PyTorch/Detection/Efficientdet/effdet/layers | layers | cond_conv2d | # 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.
# Copyright 2019-2022 Ross Wightman
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from functools import partial
import numpy as np
import torch
from torch import nn as nn
from torch.nn import functional as F
from .helpers import tup_pair
from .conv2d_same import conv2d_same
from .padding import get_padding_value
def get_condconv_initializer(initializer, num_experts, expert_shape):
def condconv_initializer(weight):
"""CondConv initializer function."""
num_params = np.prod(expert_shape)
if (len(weight.shape) != 2 or weight.shape[0] != num_experts or
weight.shape[1] != num_params):
raise (ValueError(
'CondConv variables must have shape [num_experts, num_params]'))
for i in range(num_experts):
initializer(weight[i].view(expert_shape))
return condconv_initializer
class CondConv2d(nn.Module):
""" Conditionally Parameterized Convolution
Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py
Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
https://github.com/pytorch/pytorch/issues/17983
"""
__constants__ = ['in_channels', 'out_channels', 'dynamic_padding']
def __init__(self, in_channels, out_channels, kernel_size=3,
stride=1, padding='', dilation=1, groups=1, bias=False, num_experts=4):
super(CondConv2d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = tup_pair(kernel_size)
self.stride = tup_pair(stride)
padding_val, is_padding_dynamic = get_padding_value(
padding, kernel_size, stride=stride, dilation=dilation)
self.dynamic_padding = is_padding_dynamic # if in forward to work with torchscript
self.padding = tup_pair(padding_val)
self.dilation = tup_pair(dilation)
self.groups = groups
self.num_experts = num_experts
self.weight_shape = (self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight_num_param = 1
for wd in self.weight_shape:
weight_num_param *= wd
self.weight = torch.nn.Parameter(torch.Tensor(self.num_experts, weight_num_param))
if bias:
self.bias_shape = (self.out_channels,)
self.bias = torch.nn.Parameter(torch.Tensor(self.num_experts, self.out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
init_weight = get_condconv_initializer(
partial(nn.init.kaiming_uniform_, a=math.sqrt(5)), self.num_experts, self.weight_shape)
init_weight(self.weight)
if self.bias is not None:
fan_in = np.prod(self.weight_shape[1:])
bound = 1 / math.sqrt(fan_in)
init_bias = get_condconv_initializer(
partial(nn.init.uniform_, a=-bound, b=bound), self.num_experts, self.bias_shape)
init_bias(self.bias)
def forward(self, x, routing_weights):
B, C, H, W = x.shape
weight = torch.matmul(routing_weights, self.weight)
new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight = weight.view(new_weight_shape)
bias = None
if self.bias is not None:
bias = torch.matmul(routing_weights, self.bias)
bias = bias.view(B * self.out_channels)
# move batch elements with channels so each batch element can be efficiently convolved with separate kernel
x = x.view(1, B * C, H, W)
if self.dynamic_padding:
out = conv2d_same(
x, weight, bias, stride=self.stride, padding=self.padding,
dilation=self.dilation, groups=self.groups * B)
else:
out = F.conv2d(
x, weight, bias, stride=self.stride, padding=self.padding,
dilation=self.dilation, groups=self.groups * B)
out = out.permute([1, 0, 2, 3]).view(B, self.out_channels, out.shape[-2], out.shape[-1])
# Literal port (from TF definition)
# x = torch.split(x, 1, 0)
# weight = torch.split(weight, 1, 0)
# if self.bias is not None:
# bias = torch.matmul(routing_weights, self.bias)
# bias = torch.split(bias, 1, 0)
# else:
# bias = [None] * B
# out = []
# for xi, wi, bi in zip(x, weight, bias):
# wi = wi.view(*self.weight_shape)
# if bi is not None:
# bi = bi.view(*self.bias_shape)
# out.append(self.conv_fn(
# xi, wi, bi, stride=self.stride, padding=self.padding,
# dilation=self.dilation, groups=self.groups))
# out = torch.cat(out, 0)
return out |
TensorFlow2/Recommendation/SIM/sim/layers | layers | ctr_classification_mlp | # 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 functools import partial
import tensorflow as tf
class CTRClassificationMLP(tf.keras.layers.Layer):
def __init__(
self,
layer_sizes=(200,),
num_outputs=1,
activation_function=partial(
tf.keras.layers.PReLU, alpha_initializer=tf.keras.initializers.Constant(0.1)
),
use_bn=False,
dropout_rate=-1
):
super().__init__()
self.layer_sizes = layer_sizes
self.activation_function = activation_function
self.use_bn = use_bn
self.dropout_rate = dropout_rate
if self.use_bn:
self.batch_norm = tf.keras.layers.BatchNormalization()
self.layers = []
for layer_size in self.layer_sizes:
# add dense layer and activation
self.layers.append(tf.keras.layers.Dense(layer_size))
self.layers.append(self.activation_function())
if self.dropout_rate > 0.0:
# add dropout between final representation and classification layer
self.layers.append(tf.keras.layers.Dropout(rate=self.dropout_rate))
# add the scoring layer
scoring_layer = tf.keras.layers.Dense(num_outputs, dtype='float32')
self.layers.append(scoring_layer)
def call(self, input, training=False):
if self.use_bn:
input = self.batch_norm(input, training=training)
for layer in self.layers:
input = layer(input, training=training)
return input
|
TensorFlow/Segmentation/UNet_3D_Medical/scripts | scripts | unet3d_infer_benchmark_TF-AMP | # 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 TF-AMP inference benchmark.
# Usage:
# bash examples/unet3d_infer_benchmark_TF-AMP.sh <path/to/dataset> <path/to/results/directory> <batch/size>
python main.py --data_dir $1 --model_dir $2 --exec_mode predict --warmup_steps 20 --fold 0 --batch_size $3 --benchmark --amp --xla |
Tools/PyTorch/TimeSeriesPredictionPlatform/evaluators | evaluators | evaluation_metrics | # 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 sys
import numpy as np
from abc import ABC, abstractmethod
class AbstractMetric(ABC):
@staticmethod
@abstractmethod
def __call__(pred, label, weights):
pass
class SMAPE(AbstractMetric):
name = "SMAPE"
@staticmethod
def __call__(preds, labels, weights):
if not weights.size:
weights = None
return 100 * np.average(2 * np.abs(preds - labels) / (np.abs(labels) + np.abs(preds)), weights=weights)
def normalised_quantile_loss(y_pred, y, quantile, weights=None):
"""Implementation of the q-Risk function from https://arxiv.org/pdf/1912.09363.pdf"""
prediction_underflow = y - y_pred
weighted_errors = quantile * np.maximum(prediction_underflow, 0.0) + (1.0 - quantile) * np.maximum(
-prediction_underflow, 0.0
)
if weights is not None and weights.size:
weighted_errors = weighted_errors * weights
y = y * weights
loss = weighted_errors.sum()
normaliser = abs(y).sum()
return 2 * loss / normaliser
class P50_loss(AbstractMetric):
name = "P50"
selector = 1
@staticmethod
def __call__(labels, preds, weights):
return normalised_quantile_loss(labels, preds, 0.5,weights)
class P90_loss(AbstractMetric):
name = "P90"
selector = 2
@staticmethod
def __call__(labels, preds, weights):
return normalised_quantile_loss(labels, preds, 0.9,weights)
# Normalized Deviation
class ND(AbstractMetric):
name = "ND"
@staticmethod
def __call__(preds, labels, weights):
diff = np.abs(labels - preds)
if not weights.size:
return np.sum(diff) / np.sum(np.abs(labels))
else:
return np.sum(diff * weights) / np.sum(np.abs(labels) * weights)
class MAE(AbstractMetric):
name = "MAE"
@staticmethod
def __call__(preds, labels, weights, return_individual=False):
if not weights.size:
weights = None
if return_individual:
return np.average(np.abs(preds - labels), weights=weights, axis=0)
else:
return np.average(np.abs(preds - labels), weights=weights)
class MSE(AbstractMetric):
name = "MSE"
@staticmethod
def __call__(preds, labels, weights, return_individual=False):
if not weights.size:
weights = None
if return_individual:
return np.average((preds - labels)**2, weights=weights, axis=0)
else:
return np.average((preds - labels)**2, weights=weights)
class RMSE(AbstractMetric):
name = "RMSE"
@staticmethod
def __call__(preds, labels, weights):
if not weights.size:
weights = None
return np.sqrt(np.average((preds - labels)**2, weights=weights))
class R_Squared(AbstractMetric):
name = "R_Squared"
@staticmethod
def __call__(preds, labels, weights, return_individual=False):
if not weights.size:
if return_individual:
return r2_score(preds, labels, multioutput="raw_values")
return r2_score(preds, labels)
else:
values = r2_score(preds, labels, multioutput="raw_values")
if return_individual:
return values * weights
return np.sum(values * weights) / np.sum(weights)
class WMSMAPE(AbstractMetric):
name = "WMSMAPE"
@staticmethod
def __call__(preds, labels, weights, return_individual=False):
if weights.size:
if return_individual:
return 2 * weights * np.abs(preds - labels) / (np.maximum(labels, 1) + np.abs(preds))
else:
return (
100.0
/ np.sum(weights)
* np.sum(2 * weights * np.abs(preds - labels) / (np.maximum(labels, 1) + np.abs(preds)))
)
if return_individual:
return 2 * np.abs(preds - labels) / (np.maximum(labels, 1) + np.abs(preds))
else:
return 100.0 / len(labels) * np.sum(2 * np.abs(preds - labels) / (np.maximum(labels, 1) + np.abs(preds)))
METRICS = {
"SMAPE": SMAPE,
"WMSMAPE": WMSMAPE,
"MSE": MSE,
"MAE": MAE,
"P50": P50_loss,
"P90": P90_loss,
"RMSE": RMSE,
"R_Squared": R_Squared,
"ND": ND,
}
|
TensorFlow/Detection/SSD/models/research/object_detection/utils | utils | per_image_vrd_evaluation | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Evaluates Visual Relations Detection(VRD) result evaluation on an image.
Annotate each VRD result as true positives or false positive according to
a predefined IOU ratio. Multi-class detection is supported by default.
Based on the settings, per image evaluation is performed either on phrase
detection subtask or on relation detection subtask.
"""
import numpy as np
from object_detection.utils import np_box_list
from object_detection.utils import np_box_list_ops
class PerImageVRDEvaluation(object):
"""Evaluate vrd result of a single image."""
def __init__(self, matching_iou_threshold=0.5):
"""Initialized PerImageVRDEvaluation by evaluation parameters.
Args:
matching_iou_threshold: A ratio of area intersection to union, which is
the threshold to consider whether a detection is true positive or not;
in phrase detection subtask.
"""
self.matching_iou_threshold = matching_iou_threshold
def compute_detection_tp_fp(self, detected_box_tuples, detected_scores,
detected_class_tuples, groundtruth_box_tuples,
groundtruth_class_tuples):
"""Evaluates VRD as being tp, fp from a single image.
Args:
detected_box_tuples: A numpy array of structures with shape [N,],
representing N tuples, each tuple containing the same number of named
bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max].
detected_scores: A float numpy array of shape [N,], representing
the confidence scores of the detected N object instances.
detected_class_tuples: A numpy array of structures shape [N,],
representing the class labels of the corresponding bounding boxes and
possibly additional classes.
groundtruth_box_tuples: A float numpy array of structures with the shape
[M,], representing M tuples, each tuple containing the same number
of named bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max].
groundtruth_class_tuples: A numpy array of structures shape [M,],
representing the class labels of the corresponding bounding boxes and
possibly additional classes.
Returns:
scores: A single numpy array with shape [N,], representing N scores
detected with object class, sorted in descentent order.
tp_fp_labels: A single boolean numpy array of shape [N,], representing N
True/False positive label, one label per tuple. The labels are sorted
so that the order of the labels matches the order of the scores.
result_mapping: A numpy array with shape [N,] with original index of each
entry.
"""
scores, tp_fp_labels, result_mapping = self._compute_tp_fp(
detected_box_tuples=detected_box_tuples,
detected_scores=detected_scores,
detected_class_tuples=detected_class_tuples,
groundtruth_box_tuples=groundtruth_box_tuples,
groundtruth_class_tuples=groundtruth_class_tuples)
return scores, tp_fp_labels, result_mapping
def _compute_tp_fp(self, detected_box_tuples, detected_scores,
detected_class_tuples, groundtruth_box_tuples,
groundtruth_class_tuples):
"""Labels as true/false positives detection tuples across all classes.
Args:
detected_box_tuples: A numpy array of structures with shape [N,],
representing N tuples, each tuple containing the same number of named
bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max]
detected_scores: A float numpy array of shape [N,], representing
the confidence scores of the detected N object instances.
detected_class_tuples: A numpy array of structures shape [N,],
representing the class labels of the corresponding bounding boxes and
possibly additional classes.
groundtruth_box_tuples: A float numpy array of structures with the shape
[M,], representing M tuples, each tuple containing the same number
of named bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max]
groundtruth_class_tuples: A numpy array of structures shape [M,],
representing the class labels of the corresponding bounding boxes and
possibly additional classes.
Returns:
scores: A single numpy array with shape [N,], representing N scores
detected with object class, sorted in descentent order.
tp_fp_labels: A single boolean numpy array of shape [N,], representing N
True/False positive label, one label per tuple. The labels are sorted
so that the order of the labels matches the order of the scores.
result_mapping: A numpy array with shape [N,] with original index of each
entry.
"""
unique_gt_tuples = np.unique(
np.concatenate((groundtruth_class_tuples, detected_class_tuples)))
result_scores = []
result_tp_fp_labels = []
result_mapping = []
for unique_tuple in unique_gt_tuples:
detections_selector = (detected_class_tuples == unique_tuple)
gt_selector = (groundtruth_class_tuples == unique_tuple)
selector_mapping = np.where(detections_selector)[0]
detection_scores_per_tuple = detected_scores[detections_selector]
detection_box_per_tuple = detected_box_tuples[detections_selector]
sorted_indices = np.argsort(detection_scores_per_tuple)
sorted_indices = sorted_indices[::-1]
tp_fp_labels = self._compute_tp_fp_for_single_class(
detected_box_tuples=detection_box_per_tuple[sorted_indices],
groundtruth_box_tuples=groundtruth_box_tuples[gt_selector])
result_scores.append(detection_scores_per_tuple[sorted_indices])
result_tp_fp_labels.append(tp_fp_labels)
result_mapping.append(selector_mapping[sorted_indices])
if result_scores:
result_scores = np.concatenate(result_scores)
result_tp_fp_labels = np.concatenate(result_tp_fp_labels)
result_mapping = np.concatenate(result_mapping)
else:
result_scores = np.array([], dtype=float)
result_tp_fp_labels = np.array([], dtype=bool)
result_mapping = np.array([], dtype=int)
sorted_indices = np.argsort(result_scores)
sorted_indices = sorted_indices[::-1]
return result_scores[sorted_indices], result_tp_fp_labels[
sorted_indices], result_mapping[sorted_indices]
def _get_overlaps_and_scores_relation_tuples(self, detected_box_tuples,
groundtruth_box_tuples):
"""Computes overlaps and scores between detected and groundtruth tuples.
Both detections and groundtruth boxes have the same class tuples.
Args:
detected_box_tuples: A numpy array of structures with shape [N,],
representing N tuples, each tuple containing the same number of named
bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max]
groundtruth_box_tuples: A float numpy array of structures with the shape
[M,], representing M tuples, each tuple containing the same number
of named bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max]
Returns:
result_iou: A float numpy array of size
[num_detected_tuples, num_gt_box_tuples].
"""
result_iou = np.ones(
(detected_box_tuples.shape[0], groundtruth_box_tuples.shape[0]),
dtype=float)
for field in detected_box_tuples.dtype.fields:
detected_boxlist_field = np_box_list.BoxList(detected_box_tuples[field])
gt_boxlist_field = np_box_list.BoxList(groundtruth_box_tuples[field])
iou_field = np_box_list_ops.iou(detected_boxlist_field, gt_boxlist_field)
result_iou = np.minimum(iou_field, result_iou)
return result_iou
def _compute_tp_fp_for_single_class(self, detected_box_tuples,
groundtruth_box_tuples):
"""Labels boxes detected with the same class from the same image as tp/fp.
Detection boxes are expected to be already sorted by score.
Args:
detected_box_tuples: A numpy array of structures with shape [N,],
representing N tuples, each tuple containing the same number of named
bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max]
groundtruth_box_tuples: A float numpy array of structures with the shape
[M,], representing M tuples, each tuple containing the same number
of named bounding boxes.
Each box is of the format [y_min, x_min, y_max, x_max]
Returns:
tp_fp_labels: a boolean numpy array indicating whether a detection is a
true positive.
"""
if detected_box_tuples.size == 0:
return np.array([], dtype=bool)
min_iou = self._get_overlaps_and_scores_relation_tuples(
detected_box_tuples, groundtruth_box_tuples)
num_detected_tuples = detected_box_tuples.shape[0]
tp_fp_labels = np.zeros(num_detected_tuples, dtype=bool)
if min_iou.shape[1] > 0:
max_overlap_gt_ids = np.argmax(min_iou, axis=1)
is_gt_tuple_detected = np.zeros(min_iou.shape[1], dtype=bool)
for i in range(num_detected_tuples):
gt_id = max_overlap_gt_ids[i]
if min_iou[i, gt_id] >= self.matching_iou_threshold:
if not is_gt_tuple_detected[gt_id]:
tp_fp_labels[i] = True
is_gt_tuple_detected[gt_id] = True
return tp_fp_labels
|
TensorFlow/Recommendation/VAE-CF/vae/metrics | metrics | ndcg | # 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.
"""
Discounted Cumulative Gain @ R is
DCG@R(u,ω) := Σ_{r=1}^{R} I[ω(r) ∈ I_u] − 1 / log(r + 1) / IDCG@R(u,ω)
IDCG@R(u,ω) := Σ_{r=1}^{|I_u|} 1 / log(r + 1)
https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG
https://arxiv.org/pdf/1802.05814.pdf, chapter 4.2
"""
import numpy as np
from scipy.sparse import csr_matrix
def ndcg(X_true: csr_matrix, X_top_k: np.array, R=100) -> np.array:
""" Calculate ndcg@R for each users in X_true and X_pred matrices
Args:
X_true: Matrix containing True values for user-item interactions
X_top_k: Matrix containing inidices picked by model
R: Number of elements taken into consideration
Returns:
Numpy array containing calculated ndcg@R for each user
"""
penalties = 1. / np.log2(np.arange(2, R + 2))
selected = np.take_along_axis(X_true, X_top_k[:, :R], axis=-1)
DCG = selected * penalties
cpenalties = np.empty(R + 1)
np.cumsum(penalties, out=cpenalties[1:])
cpenalties[0] = 0
maxhit = np.minimum(X_true.getnnz(axis=1), R)
IDCG = cpenalties[maxhit]
return DCG / IDCG
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/preprocessing/datasets | datasets | cora | # 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 logging
import shutil
import subprocess
from typing import List, Union, Optional
import numpy as np
import pandas as pd
from syngen.configuration import SynGenDatasetFeatureSpec
from syngen.preprocessing.base_preprocessing import BasePreprocessing
from syngen.utils.types import MetaData
logger = logging.getLogger(__name__)
log = logger
class CORAPreprocessing(BasePreprocessing):
def __init__(
self,
source_path: str,
destination_path: Optional[str] = None,
download: bool = False,
**kwargs,
):
"""
preprocessing for https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz
"""
super().__init__(source_path, destination_path, download, **kwargs)
def transform(self, gpu=False, use_cache=False):
assert not gpu, "CORA preprocessing does not support cudf preprocessing"
if use_cache and os.path.exists(self.destination_path):
return SynGenDatasetFeatureSpec.instantiate_from_preprocessed(self.destination_path)
tabular_operator = pd
operator = np
examples = {}
with open(os.path.join(self.source_path, 'cora.content'), "r") as cora_content:
for line in cora_content:
entries = line.rstrip("\n").split("\t")
# entries contains [ID, Word1, Word2, ..., Label]; "Words" are 0/1 values.
words = list(map(int, entries[1:-1]))
example_id = int(entries[0])
label = entries[-1]
features = {
"id": example_id,
"label": label,
}
for i, w in enumerate(words):
features[f"w_{i}"] = w
examples[example_id] = features
tabular_data = tabular_operator.DataFrame.from_dict(
examples, orient="index"
).reset_index(drop=True)
node_features = [
{
MetaData.NAME: f"w_{i}",
MetaData.DTYPE: 'int64',
MetaData.FEATURE_TYPE: MetaData.CATEGORICAL,
}
for i in range(len(words))
]
node_features.extend([
{
MetaData.NAME: name,
MetaData.DTYPE: 'int64',
MetaData.FEATURE_TYPE: MetaData.CATEGORICAL,
}
for name in ["label"]
])
for c in tabular_data.columns:
tabular_data[c] = tabular_data[c].astype("category").cat.codes.astype(int)
tabular_data = tabular_data.set_index('id')
structural_data = tabular_operator.read_csv(os.path.join(self.source_path, "cora.cites"))
structural_data.columns = ["src", "dst"]
for c in ["src", "dst"]:
structural_data[c] = structural_data[c].astype(int)
paper_ids = operator.unique(operator.concatenate([
structural_data["src"].values,
structural_data["dst"].values,
]))
mapping = operator.empty(int(paper_ids.max()) + 1, dtype=int)
mapping[paper_ids] = operator.arange(len(paper_ids))
for c in ["src", "dst"]:
structural_data[c] = mapping[structural_data[c]]
graph_metadata = {
MetaData.NODES: [
{
MetaData.NAME: "paper",
MetaData.COUNT: len(tabular_data),
MetaData.FEATURES: node_features,
MetaData.FEATURES_PATH: "paper.parquet",
},
],
MetaData.EDGES: [{
MetaData.NAME: "cite",
MetaData.COUNT: len(structural_data),
MetaData.SRC_NODE_TYPE: "paper",
MetaData.DST_NODE_TYPE: "paper",
MetaData.DIRECTED: False,
MetaData.FEATURES: [],
MetaData.FEATURES_PATH: None,
MetaData.STRUCTURE_PATH: "cite_edge_list.parquet",
}]
}
shutil.rmtree(self.destination_path, ignore_errors=True)
os.makedirs(self.destination_path)
tabular_data.to_parquet(os.path.join(self.destination_path, "paper.parquet"))
structural_data.to_parquet(os.path.join(self.destination_path, "cite_edge_list.parquet"))
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)
def download(self):
log.info("downloading CORA dataset...")
cmds = [
fr"mkdir -p {self.source_path}",
fr"wget 'https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz' -P {self.source_path}",
fr"tar -xf {self.source_path}/cora.tgz -C {self.source_path}",
fr"sed -i 's/\t/,/g' {self.source_path}/cora/cora.cites",
fr"sed -i '1s/^/src,dst\n/' {self.source_path}/cora/cora.cites",
fr"mv {self.source_path}/cora/* {self.source_path}/.",
fr"rm -r {self.source_path}/cora",
]
for cmd in cmds:
try:
subprocess.check_output(cmd, shell=True)
except subprocess.CalledProcessError as e:
raise Exception(e.output)
def _check_files(self):
files = ['cora.cites', 'cora.content']
return all(os.path.exists(os.path.join(self.source_path, file)) for file in files)
|
PyTorch/LanguageModeling/BERT/distillation | distillation | distillation_config_backbone | {"distillation": true,
"distillation_config": {"use_attention_scores": true,
"use_hidden_states": true,
"use_value_states": true,
"use_embedding_states": false,
"use_pred_states": false,
"attention_loss": "kld",
"hidden_state_loss": "cosine",
"embedding_state_loss": "cosine",
"value_state_loss": "kld",
"student_teacher_layer_mapping": "last_layer"}
}
|
TensorFlow2/Segmentation/UNet_Medical/examples | examples | unet_INFER | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script launches U-Net run in FP32 on 1 GPU for inference batch_size 1. Usage:
# bash unet_INFER.sh <path to this repository> <path to dataset> <path to results directory> <fold>
horovodrun -np 1 python main.py --data_dir $1 --model_dir $2 --batch_size 1 --exec_mode predict --xla --fold $3
|
PyTorch/Classification/GPUNet/triton/deployment_toolkit/triton_performance_runner/model_analyzer | model_analyzer | exceptions | # 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.
class ModelAnalyzerException(Exception):
def __init__(self, message: str):
self._message = message
def __str__(self):
"""
Get the exception string representation.
Returns
-------
str
The message associated with this exception, or None if no message.
"""
return self._message
@property
def message(self):
"""
Get the exception message.
Returns
-------
str
The message associated with this exception, or None if no message.
"""
return self._message
|
TensorFlow/Segmentation/UNet_Medical/examples | examples | unet_TRAIN_8GPU | # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script launches U-Net run in FP32 on 8 GPUs and runs 5-fold cross-validation training for 6400 iterations.
# Usage:
# bash unet_TRAIN_FP32_8GPU.sh <path to dataset> <path to results directory> <batch size>
horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --crossvalidation_idx 0 --augment --xla > $2/log_FP32_8GPU_fold0.txt
horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --crossvalidation_idx 1 --augment --xla > $2/log_FP32_8GPU_fold1.txt
horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --crossvalidation_idx 2 --augment --xla > $2/log_FP32_8GPU_fold2.txt
horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --crossvalidation_idx 3 --augment --xla > $2/log_FP32_8GPU_fold3.txt
horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --crossvalidation_idx 4 --augment --xla > $2/log_FP32_8GPU_fold4.txt
python utils/parse_results.py --model_dir $2 --exec_mode convergence --env FP32_8GPU |
PyTorch/Recommendation/DLRM/tests/feature_specs | feature_specs | different_feature_names | channel_spec:
categorical:
- 65ytfg.bin
- dgtwrg.bin
- hmfgd.bin
- 6tyjgh.bin
- 67yu.bin
- l6rtd.bin
- ouikjhfg.bin
- 65ry.bin
- 5yhtrfg.bin
- 65rty.bin
- 34ywesh5rtg.bin
- w4su6js.bin
- 45wyhtr.bin
- u65rhty.bin
- tujy.bin
- tyjdh.bin
- ujtyesh.bin
- 5e7tdyj.bin
- 46rjydh.bin
- 8kiujynrht.bin
- fsgh.bin
- 34eyr.bin
- we5etydj.bin
- fsghfsdgh.bin
- hrthshs.bin
- tujyhfg.bin
label:
- qwer
numerical: &id001
- gadsfgsdfg
- 5yrthf
- 45ryhtf
- u5j6yrhtfd
- u5rtg3qq
- j65ee5he5
- yhe5h
- 4y5e6ru
- 5yfwerf
- g53g6y635
- 42c524
- bge5v6gve5
- jhw5rf
feature_spec:
65ytfg.bin:
cardinality: 100000
dtype: int32
dgtwrg.bin:
cardinality: 100000
dtype: int32
34ywesh5rtg.bin:
cardinality: 100000
dtype: int32
w4su6js.bin:
cardinality: 100000
dtype: int32
45wyhtr.bin:
cardinality: 100000
dtype: int32
u65rhty.bin:
cardinality: 100000
dtype: int32
tujy.bin:
cardinality: 100000
dtype: int32
tyjdh.bin:
cardinality: 100000
dtype: int32
ujtyesh.bin:
cardinality: 100000
dtype: int32
5e7tdyj.bin:
cardinality: 100000
dtype: int32
46rjydh.bin:
cardinality: 100000
dtype: int32
8kiujynrht.bin:
cardinality: 100000
dtype: int32
hmfgd.bin:
cardinality: 100000
dtype: int32
fsgh.bin:
cardinality: 100000
dtype: int32
34eyr.bin:
cardinality: 100000
dtype: int32
we5etydj.bin:
cardinality: 100000
dtype: int32
fsghfsdgh.bin:
cardinality: 100000
dtype: int32
hrthshs.bin:
cardinality: 100000
dtype: int32
tujyhfg.bin:
cardinality: 100000
dtype: int32
6tyjgh.bin:
cardinality: 100000
dtype: int32
67yu.bin:
cardinality: 100000
dtype: int32
l6rtd.bin:
cardinality: 100000
dtype: int32
ouikjhfg.bin:
cardinality: 100000
dtype: int32
65ry.bin:
cardinality: 100000
dtype: int32
5yhtrfg.bin:
cardinality: 100000
dtype: int32
65rty.bin:
cardinality: 100000
dtype: int32
qwer:
dtype: bool
gadsfgsdfg:
dtype: float16
5yrthf:
dtype: float16
42c524:
dtype: float16
bge5v6gve5:
dtype: float16
jhw5rf:
dtype: float16
45ryhtf:
dtype: float16
u5j6yrhtfd:
dtype: float16
u5rtg3qq:
dtype: float16
j65ee5he5:
dtype: float16
yhe5h:
dtype: float16
4y5e6ru:
dtype: float16
5yfwerf:
dtype: float16
g53g6y635:
dtype: float16
metadata: {}
source_spec:
test:
- features: *id001
files:
- test/numerical.bin
type: split_binary
- features:
- qwer
files:
- test/label.bin
type: split_binary
- features:
- 65ytfg.bin
files:
- test/65ytfg.bin
type: split_binary
- features:
- dgtwrg.bin
files:
- test/dgtwrg.bin
type: split_binary
- features:
- hmfgd.bin
files:
- test/hmfgd.bin
type: split_binary
- features:
- 6tyjgh.bin
files:
- test/6tyjgh.bin
type: split_binary
- features:
- 67yu.bin
files:
- test/67yu.bin
type: split_binary
- features:
- l6rtd.bin
files:
- test/l6rtd.bin
type: split_binary
- features:
- ouikjhfg.bin
files:
- test/ouikjhfg.bin
type: split_binary
- features:
- 65ry.bin
files:
- test/65ry.bin
type: split_binary
- features:
- 5yhtrfg.bin
files:
- test/5yhtrfg.bin
type: split_binary
- features:
- 65rty.bin
files:
- test/65rty.bin
type: split_binary
- features:
- 34ywesh5rtg.bin
files:
- test/34ywesh5rtg.bin
type: split_binary
- features:
- w4su6js.bin
files:
- test/w4su6js.bin
type: split_binary
- features:
- 45wyhtr.bin
files:
- test/45wyhtr.bin
type: split_binary
- features:
- u65rhty.bin
files:
- test/u65rhty.bin
type: split_binary
- features:
- tujy.bin
files:
- test/tujy.bin
type: split_binary
- features:
- tyjdh.bin
files:
- test/tyjdh.bin
type: split_binary
- features:
- ujtyesh.bin
files:
- test/ujtyesh.bin
type: split_binary
- features:
- 5e7tdyj.bin
files:
- test/5e7tdyj.bin
type: split_binary
- features:
- 46rjydh.bin
files:
- test/46rjydh.bin
type: split_binary
- features:
- 8kiujynrht.bin
files:
- test/8kiujynrht.bin
type: split_binary
- features:
- fsgh.bin
files:
- test/fsgh.bin
type: split_binary
- features:
- 34eyr.bin
files:
- test/34eyr.bin
type: split_binary
- features:
- we5etydj.bin
files:
- test/we5etydj.bin
type: split_binary
- features:
- fsghfsdgh.bin
files:
- test/fsghfsdgh.bin
type: split_binary
- features:
- hrthshs.bin
files:
- test/hrthshs.bin
type: split_binary
- features:
- tujyhfg.bin
files:
- test/tujyhfg.bin
type: split_binary
train:
- features: *id001
files:
- train/numerical.bin
type: split_binary
- features:
- qwer
files:
- train/label.bin
type: split_binary
- features:
- 65ytfg.bin
files:
- train/65ytfg.bin
type: split_binary
- features:
- dgtwrg.bin
files:
- train/dgtwrg.bin
type: split_binary
- features:
- hmfgd.bin
files:
- train/hmfgd.bin
type: split_binary
- features:
- 6tyjgh.bin
files:
- train/6tyjgh.bin
type: split_binary
- features:
- 67yu.bin
files:
- train/67yu.bin
type: split_binary
- features:
- l6rtd.bin
files:
- train/l6rtd.bin
type: split_binary
- features:
- ouikjhfg.bin
files:
- train/ouikjhfg.bin
type: split_binary
- features:
- 65ry.bin
files:
- train/65ry.bin
type: split_binary
- features:
- 5yhtrfg.bin
files:
- train/5yhtrfg.bin
type: split_binary
- features:
- 65rty.bin
files:
- train/65rty.bin
type: split_binary
- features:
- 34ywesh5rtg.bin
files:
- train/34ywesh5rtg.bin
type: split_binary
- features:
- w4su6js.bin
files:
- train/w4su6js.bin
type: split_binary
- features:
- 45wyhtr.bin
files:
- train/45wyhtr.bin
type: split_binary
- features:
- u65rhty.bin
files:
- train/u65rhty.bin
type: split_binary
- features:
- tujy.bin
files:
- train/tujy.bin
type: split_binary
- features:
- tyjdh.bin
files:
- train/tyjdh.bin
type: split_binary
- features:
- ujtyesh.bin
files:
- train/ujtyesh.bin
type: split_binary
- features:
- 5e7tdyj.bin
files:
- train/5e7tdyj.bin
type: split_binary
- features:
- 46rjydh.bin
files:
- train/46rjydh.bin
type: split_binary
- features:
- 8kiujynrht.bin
files:
- train/8kiujynrht.bin
type: split_binary
- features:
- fsgh.bin
files:
- train/fsgh.bin
type: split_binary
- features:
- 34eyr.bin
files:
- train/34eyr.bin
type: split_binary
- features:
- we5etydj.bin
files:
- train/we5etydj.bin
type: split_binary
- features:
- fsghfsdgh.bin
files:
- train/fsghfsdgh.bin
type: split_binary
- features:
- hrthshs.bin
files:
- train/hrthshs.bin
type: split_binary
- features:
- tujyhfg.bin
files:
- train/tujyhfg.bin
type: split_binary
|
PyTorch/Recommendation/DLRM/tests | tests | test_all_configs | #!/bin/bash
set -e
set -x
NAMES=${1:-'*.yaml'}
TARGET=feature_specs/${NAMES}
OPTIONS=${2-""}
for file in ${TARGET};
do
echo "${file}";
done
for fspec_file in ${TARGET};
do
SYNTH_DATA_DIR=/tmp/generated_data/${fspec_file}
# generate data based on fspec
python -m dlrm.scripts.prepare_synthetic_dataset --feature_spec ${fspec_file} --synthetic_dataset_dir ${SYNTH_DATA_DIR}
# train on the data
for mlp in True False;
do
for graphs in True;
do
for dot in cuda_dot dot;
do
for amp in True False;
do
python -m dlrm.scripts.main --mode train --dataset ${SYNTH_DATA_DIR} --optimized_mlp=${mlp} --cuda_graphs=${graphs} --interaction_op=${dot} --embedding_type=joint_sparse --amp=${amp}
#DGX-2
python -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 bash -c "/workspace/dlrm/bind.sh --cpu=exclusive -- python -m dlrm.scripts.main --dataset ${SYNTH_DATA_DIR} --optimized_mlp=${mlp} --cuda_graphs=${graphs} --interaction_op=${dot} --embedding_type=joint_sparse --amp=${amp}"
#DGX A100
#python -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 bash -c "/workspace/dlrm/bind.sh --cpu=/workspace/dlrm/dgxa100_ccx.sh --mem=/workspace/dlrm/dgxa100_ccx.sh python -m dlrm.scripts.main --dataset ${SYNTH_DATA_DIR} --optimized_mlp=${mlp} --cuda_graphs=${graphs} --interaction_op=${dot} --embedding_type=joint_sparse --amp=${amp}"
done;
done
done
done
# delete the data
rm -r ${SYNTH_DATA_DIR}
done
#
# usage:
# docker build . -t nvidia_dlrm_pyt
# docker run --security-opt seccomp=unconfined --runtime=nvidia -it --rm --ipc=host -v ${PWD}/data:/data nvidia_dlrm_pyt bash
# cd tests
# bash test_custom_dot.sh |
PyTorch/Detection/SSD/ssd | ssd | coco_pipeline | # Copyright (c) 2018-2019, 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 ctypes
import time
import logging
import numpy as np
import torch
# DALI imports
import nvidia.dali as dali
from nvidia.dali.pipeline import Pipeline
from nvidia.dali.types import to_numpy_type
class COCOPipeline(Pipeline):
def __init__(self, batch_size, file_root, annotations_file, default_boxes,
device_id, num_shards,
output_fp16=False, output_nhwc=False, pad_output=False,
num_threads=1, seed=15):
super(COCOPipeline, self).__init__(batch_size=batch_size,
device_id=device_id,
num_threads=num_threads,
seed=seed)
if torch.distributed.is_initialized():
shard_id = torch.distributed.get_rank()
else:
shard_id = 0
# Data loader and image decoder
self.input = dali.ops.readers.COCO(file_root=file_root,
annotations_file=annotations_file,
shard_id=shard_id,
num_shards=num_shards,
ratio=True,
ltrb=True,
shuffle_after_epoch=True,
skip_empty=True)
self.decode_slice = dali.ops.decoders.ImageSlice(device="cpu",
output_type=dali.types.RGB)
# Augumentation techniques
## Random crop
self.crop = dali.ops.RandomBBoxCrop(device="cpu",
aspect_ratio=[0.5, 2.0],
thresholds=[0, 0.1, 0.3, 0.5, 0.7, 0.9],
scaling=[0.3, 1.0],
bbox_layout="xyXY",
allow_no_crop=True,
num_attempts=1)
## Color twist
self.hsv = dali.ops.Hsv(device="gpu",
dtype=dali.types.FLOAT) # use float to avoid clipping and quantizing the intermediate result
self.bc = dali.ops.BrightnessContrast(device="gpu",
contrast_center=128, # input is in the [0, 255] range
dtype=dali.types.UINT8)
## Cropping and normalization
dtype = dali.types.FLOAT16 if output_fp16 else dali.types.FLOAT
output_layout = dali.types.NHWC if output_nhwc else dali.types.NCHW
self.normalize = dali.ops.CropMirrorNormalize(
device="gpu",
crop=(300, 300),
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
mirror=0,
dtype=dtype,
output_layout=output_layout,
pad_output=pad_output)
## Flipping
self.flip = dali.ops.Flip(device="cpu")
self.bbflip = dali.ops.BbFlip(device="cpu", ltrb=True)
# Resize
self.resize = dali.ops.Resize(device="cpu",
resize_x=300,
resize_y=300)
# Random variables
self.rng1 = dali.ops.random.Uniform(range=[0.5, 1.5])
self.rng2 = dali.ops.random.Uniform(range=[0.875, 1.125])
self.rng3 = dali.ops.random.Uniform(range=[-0.5, 0.5])
self.flip_coin = dali.ops.random.CoinFlip(probability=0.5)
# bbox encoder
self.anchors = default_boxes(order='ltrb').cpu().numpy().flatten().tolist()
self.box_encoder = dali.ops.BoxEncoder(device="cpu",
criteria=0.5,
anchors=self.anchors)
def define_graph(self):
saturation = self.rng1()
contrast = self.rng1()
brightness = self.rng2()
hue = self.rng3()
coin_rnd = self.flip_coin()
inputs, bboxes, labels = self.input(name="Reader")
crop_begin, crop_size, bboxes, labels = self.crop(bboxes, labels)
images = self.decode_slice(inputs, crop_begin, crop_size)
images = self.flip(images, horizontal=coin_rnd)
bboxes = self.bbflip(bboxes, horizontal=coin_rnd)
images = self.resize(images)
images = images.gpu()
images = self.hsv(images, hue=hue, saturation=saturation)
images = self.bc(images, brightness=brightness, contrast=contrast)
images = self.normalize(images)
bboxes, labels = self.box_encoder(bboxes, labels)
# bboxes and images and labels on GPU
return (images, bboxes.gpu(), labels.gpu())
to_torch_type = {
np.float32 : torch.float32,
np.float64 : torch.float64,
np.float16 : torch.float16,
np.uint8 : torch.uint8,
np.int8 : torch.int8,
np.int16 : torch.int16,
np.int32 : torch.int32,
np.int64 : torch.int64
}
def feed_ndarray(dali_tensor, arr):
"""
Copy contents of DALI tensor to pyTorch's Tensor.
Parameters
----------
`dali_tensor` : nvidia.dali.backend.TensorCPU or nvidia.dali.backend.TensorGPU
Tensor from which to copy
`arr` : torch.Tensor
Destination of the copy
"""
assert dali_tensor.shape() == list(arr.size()), \
("Shapes do not match: DALI tensor has size {0}"
", but PyTorch Tensor has size {1}".format(dali_tensor.shape(), list(arr.size())))
#turn raw int to a c void pointer
c_type_pointer = ctypes.c_void_p(arr.data_ptr())
dali_tensor.copy_to_external(c_type_pointer)
return arr
class DALICOCOIterator(object):
"""
COCO DALI iterator for pyTorch.
Parameters
----------
pipelines : list of nvidia.dali.pipeline.Pipeline
List of pipelines to use
size : int
Epoch size.
"""
def __init__(self, pipelines, size):
if not isinstance(pipelines, list):
pipelines = [pipelines]
self._num_gpus = len(pipelines)
assert pipelines is not None, "Number of provided pipelines has to be at least 1"
self.batch_size = pipelines[0].max_batch_size
self._size = size
self._pipes = pipelines
# Build all pipelines
for p in self._pipes:
p.build()
# Use double-buffering of data batches
self._data_batches = [[None, None, None, None] for i in range(self._num_gpus)]
self._counter = 0
self._current_data_batch = 0
self.output_map = ["image", "bboxes", "labels"]
# We need data about the batches (like shape information),
# so we need to run a single batch as part of setup to get that info
self._first_batch = None
self._first_batch = self.next()
def __next__(self):
if self._first_batch is not None:
batch = self._first_batch
self._first_batch = None
return batch
if self._counter > self._size:
raise StopIteration
# Gather outputs
outputs = []
for p in self._pipes:
p._prefetch()
for p in self._pipes:
outputs.append(p.share_outputs())
for i in range(self._num_gpus):
dev_id = self._pipes[i].device_id
out_images = []
bboxes = []
labels = []
# segregate outputs into image/labels/bboxes entries
for j, out in enumerate(outputs[i]):
if self.output_map[j] == "image":
out_images.append(out)
elif self.output_map[j] == "bboxes":
bboxes.append(out)
elif self.output_map[j] == "labels":
labels.append(out)
# Change DALI TensorLists into Tensors
images = [x.as_tensor() for x in out_images]
images_shape = [x.shape() for x in images]
# Prepare bboxes shapes
bboxes_shape = []
for j in range(len(bboxes)):
bboxes_shape.append([])
for k in range(len(bboxes[j])):
bboxes_shape[j].append(bboxes[j][k].shape())
# Prepare labels shapes and offsets
labels_shape = []
bbox_offsets = []
torch.cuda.synchronize()
for j in range(len(labels)):
labels_shape.append([])
bbox_offsets.append([0])
for k in range(len(labels[j])):
lshape = labels[j][k].shape()
bbox_offsets[j].append(bbox_offsets[j][k] + lshape[0])
labels_shape[j].append(lshape)
# We always need to alocate new memory as bboxes and labels varies in shape
images_torch_type = to_torch_type[to_numpy_type(images[0].dtype)]
bboxes_torch_type = to_torch_type[to_numpy_type(bboxes[0][0].dtype)]
labels_torch_type = to_torch_type[to_numpy_type(labels[0][0].dtype)]
torch_gpu_device = torch.device('cuda', dev_id)
torch_cpu_device = torch.device('cpu')
pyt_images = [torch.zeros(shape, dtype=images_torch_type, device=torch_gpu_device) for shape in images_shape]
pyt_bboxes = [[torch.zeros(shape, dtype=bboxes_torch_type, device=torch_gpu_device) for shape in shape_list] for shape_list in bboxes_shape]
pyt_labels = [[torch.zeros(shape, dtype=labels_torch_type, device=torch_gpu_device) for shape in shape_list] for shape_list in labels_shape]
pyt_offsets = [torch.zeros(len(offset), dtype=torch.int32, device=torch_cpu_device) for offset in bbox_offsets]
self._data_batches[i][self._current_data_batch] = (pyt_images, pyt_bboxes, pyt_labels, pyt_offsets)
# Copy data from DALI Tensors to torch tensors
for j, i_arr in enumerate(images):
feed_ndarray(i_arr, pyt_images[j])
for j, b_list in enumerate(bboxes):
for k in range(len(b_list)):
if (pyt_bboxes[j][k].shape[0] != 0):
feed_ndarray(b_list[k], pyt_bboxes[j][k])
pyt_bboxes[j] = torch.cat(pyt_bboxes[j])
for j, l_list in enumerate(labels):
for k in range(len(l_list)):
if (pyt_labels[j][k].shape[0] != 0):
feed_ndarray(l_list[k], pyt_labels[j][k])
pyt_labels[j] = torch.cat(pyt_labels[j])
for j in range(len(pyt_offsets)):
pyt_offsets[j] = torch.IntTensor(bbox_offsets[j])
for p in self._pipes:
p.release_outputs()
p.schedule_run()
copy_db_index = self._current_data_batch
# Change index for double buffering
self._current_data_batch = (self._current_data_batch + 1) % 2
self._counter += self._num_gpus * self.batch_size
return [db[copy_db_index] for db in self._data_batches]
def next(self):
"""
Returns the next batch of data.
"""
return self.__next__();
def __iter__(self):
return self
def reset(self):
"""
Resets the iterator after the full epoch.
DALI iterators do not support resetting before the end of the epoch
and will ignore such request.
"""
if self._counter > self._size:
self._counter = self._counter % self._size
else:
logging.warning("DALI iterator does not support resetting while epoch is not finished. Ignoring...")
|
PyTorch/SpeechSynthesis/FastPitch/notebooks | notebooks | FastPitch_voice_modification | #!/usr/bin/env python
# coding: utf-8
# In[ ]:
# Copyright 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.
# ==============================================================================
# # FastPitch: Voice Modification with Pre-defined Pitch Transformations
# The [FastPitch](https://arxiv.org/abs/2006.06873) model is based on the [FastSpeech](https://arxiv.org/abs/1905.09263) model. Similarly to [FastSpeech2](https://arxiv.org/abs/2006.04558), which has been developed concurrently, it learns to predict the pitch contour and conditions the generation on such contour.
#
# The simple mechanism of predicting the pitch on grapheme-level (rather than frame-level, as FastSpeech2 does) allows to easily alter the pitch during synthesis. FastPitch can thus change the perceived emotional state of the speaker, or slightly emphasise certain lexical units.
# ## Requirements
# Run the notebook inside the container. By default the container forwards port `8888`.
# ```
# bash scripts/docker/interactive.sh
#
# # inside the container
# cd notebooks
# jupyter notebook --ip='*' --port=8888
# ```
# Please refer the Requirement section in `README.md` for more details and running outside the container.
# In[ ]:
import os
assert os.getcwd().split('/')[-1] == 'notebooks'
# ## Generate audio samples
# Training a FastPitch model from scrath takes 3 to 27 hours depending on the type and number of GPUs, performance numbers can be found in Section "Training performance results" in `README.md`. Therefore, to save the time of running this notebook, we recommend to download the pretrained FastPitch checkpoints on NGC for inference.
#
# You can find FP32 checkpoint at [NGC](https://ngc.nvidia.com/catalog/models/nvidia:fastpitch_pyt_fp32_ckpt_v1/files) , and AMP (Automatic Mixed Precision) checkpoint at [NGC](https://ngc.nvidia.com/catalog/models/nvidia:fastpitch_pyt_amp_ckpt_v1/files).
#
# To synthesize audio, you will need a WaveGlow model, which generates waveforms based on mel-spectrograms generated by FastPitch.You can download a pre-trained WaveGlow AMP model at [NGC](https://ngc.nvidia.com/catalog/models/nvidia:waveglow256pyt_fp16).
# In[ ]:
get_ipython().system(' mkdir -p output')
get_ipython().system(" MODEL_DIR='../pretrained_models' ../scripts/download_fastpitch.sh")
get_ipython().system(" MODEL_DIR='../pretrained_models' ../scripts/download_waveglow.sh")
# You can perform inference using the respective checkpoints that are passed as `--fastpitch` and `--waveglow` arguments. Next, you will use FastPitch model to generate audio samples for input text, including the basic version and the variations i npace, fade out, and pitch transforms, etc.
# In[ ]:
import IPython
# store paths in aux variables
fastp = '../pretrained_models/fastpitch/nvidia_fastpitch_200518.pt'
waveg = '../pretrained_models/waveglow/waveglow_1076430_14000_amp.pt'
flags = f'--cuda --fastpitch {fastp} --waveglow {waveg} --wn-channels 256'
# ### 1. Basic speech synthesis
# You need to create an input file with some text, or just input the text in the below cell:
# In[ ]:
get_ipython().run_cell_magic('writefile', 'text.txt', 'The forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves.\n')
# Run the script below to generate audio from the input text file:
# In[ ]:
# basic systhesis
get_ipython().system('python ../inference.py {flags} -i text.txt -o output/original > /dev/null')
IPython.display.Audio("output/original/audio_0.wav")
# ### 2. Add variations to the generated speech
# FastPitch allows us to exert additional control over the synthesized utterances, the key parameters are the pace, fade out, and pitch transforms in particular.
# ### 2.1 Pace
# FastPitch allows you to linearly adjust the pace of synthesized speech, similar to [FastSpeech](https://arxiv.org/abs/1905.09263) model. For instance, pass --pace 0.5 for a twofold decrease in speed, --pace 1.0 = unchanged.
# In[ ]:
# Change the pace of speech to double with --pace 0.5
# (1.0 = unchanged)
get_ipython().system('python ../inference.py {flags} -i text.txt -o output/pace --pace 0.5 > /dev/null')
IPython.display.Audio("output/pace/audio_0.wav")
# ### 2.2 Raise or lower the pitch
# For every input character, the model predicts a pitch cue - an average pitch over a character in Hz. Pitch can be adjusted by transforming those pitch cues. A few simple examples are provided below.
# In[ ]:
# Raise/lower pitch by --pitch-transform-shift <Hz>
# Synthesize with a -50 Hz shift
get_ipython().system('python ../inference.py {flags} -i text.txt -o output/riselowpitch --pitch-transform-shift -50 > /dev/null')
IPython.display.Audio("output/riselowpitch/audio_0.wav")
# ### 2.3 Flatten the pitch
# In[ ]:
# Flatten the pitch to a constant value with --pitch-transform-flatten
get_ipython().system('python ../inference.py {flags} -i text.txt -o output/flattenpitch --pitch-transform-flatten > /dev/null')
IPython.display.Audio("output/flattenpitch/audio_0.wav")
# ### 2.4 Invert the pitch
# In[ ]:
# Invert pitch wrt. to the mean pitch with --pitch-transform-invert
get_ipython().system('python ../inference.py {flags} -i text.txt -o output/invertpitch --pitch-transform-invert > /dev/null')
IPython.display.Audio("output/invertpitch/audio_0.wav")
# ### 2.5 Amplify the pitch
# In[ ]:
# Amplify pitch wrt. to the mean pitch with --pitch-transform-amplify 2.0
# values in the (1.0, 3.0) range work the best
get_ipython().system('python ../inference.py {flags} -i text.txt -o output/amplifypitch --pitch-transform-amplify 2.0 > /dev/null')
IPython.display.Audio("output/amplifypitch/audio_0.wav")
# ### 2.6 Combine the flags
# The flags can be combined. You can find all the available options by calling python inference.py --help.
# In[ ]:
get_ipython().system('python ../inference.py --help')
# Below example shows how to generate an audio with a combination of the flags --pace --pitch-transform-flatten --pitch-transform-shift --pitch-transform-invert --pitch-transform-amplify
# In[ ]:
# Dobuble the speed and combine multiple transformations
get_ipython().system('python ../inference.py {flags} -i text.txt -o output/combine --pace 2.0 --pitch-transform-flatten --pitch-transform-shift 50 --pitch-transform-invert --pitch-transform-amplify 1.5 > /dev/null')
IPython.display.Audio("output/combine/audio_0.wav")
# ### 3. Inference performance benchmark
# In[ ]:
# Benchmark inference using AMP
get_ipython().system('python ../inference.py {flags} --include-warmup --batch-size 8 --repeats 100 --torchscript --amp -i ../phrases/benchmark_8_128.tsv -o output/benchmark')
# ### 4. Next step
# Now you have learnt how to generate high quality audio from text using FastPitch, as well as add variations to the audio using the flags. You can experiment with more input texts, or change the hyperparameters of the models, such as pitch flags, batch size, different precisions, etc, to see if they could improve the inference results.
#
# If you are interested in learning more about FastPitch, please check more samples (trained with multi-speaker) presented at [samples page](https://fastpitch.github.io/).
|
PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/csrc/cuda | cuda | box_encode | /**
* 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.
*/
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <THC/THC.h>
#include <torch/torch.h>
#include <vector>
#include <iostream>
__global__ void box_encode_kernel(float *targets_dx, float *targets_dy, float *targets_dw, float *targets_dh,
float4 *boxes, float4 *anchors, float wx, float wy, float ww, float wh,
size_t gt, size_t idxJump) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
size_t row_offset;
float anchors_x1, anchors_x2, anchors_y1, anchors_y2,
boxes_x1, boxes_x2, boxes_y1, boxes_y2, ex_w, ex_h,
ex_ctr_x, ex_ctr_y, gt_w, gt_h, gt_ctr_x, gt_ctr_y;
for (int i = idx; i < gt; i += idxJump){
row_offset = i;
anchors_x1 = anchors[row_offset].x;
anchors_y1 = anchors[row_offset].y;
anchors_x2 = anchors[row_offset].z;
anchors_y2 = anchors[row_offset].w;
boxes_x1 = boxes[row_offset].x;
boxes_y1 = boxes[row_offset].y;
boxes_x2 = boxes[row_offset].z;
boxes_y2 = boxes[row_offset].w;
ex_w = anchors_x2 - anchors_x1 + 1;
ex_h = anchors_y2 - anchors_y1 + 1;
ex_ctr_x = anchors_x1 + 0.5 * ex_w;
ex_ctr_y = anchors_y1 + 0.5 * ex_h;
gt_w = boxes_x2 - boxes_x1 + 1;
gt_h = boxes_y2 - boxes_y1 + 1;
gt_ctr_x = boxes_x1 + 0.5 * gt_w;
gt_ctr_y = boxes_y1 + 0.5 * gt_h;
targets_dx[i] = wx * (gt_ctr_x - ex_ctr_x) / ex_w;
targets_dy[i] = wy * (gt_ctr_y - ex_ctr_y) / ex_h;
targets_dw[i] = ww * log(gt_w / ex_w);
targets_dh[i] = wh * log(gt_h / ex_h);
}
}
std::vector<at::Tensor> box_encode_cuda(at::Tensor boxes, at::Tensor anchors, float wx, float wy, float ww, float wh){
int minGridSize;
int blockSize;
cudaOccupancyMaxPotentialBlockSize(&minGridSize,
&blockSize,
(void*) box_encode_kernel,
0, // dynamic memory
0); // maximum utilized threads
long size = boxes.size(0);
auto targets_dx = torch::ones({size}, torch::CUDA(at::kFloat));
auto targets_dy = torch::ones({size}, torch::CUDA(at::kFloat));
auto targets_dw = torch::ones({size}, torch::CUDA(at::kFloat));
auto targets_dh = torch::ones({size}, torch::CUDA(at::kFloat));
dim3 gridDim(minGridSize);
dim3 blockDim(blockSize);
int idxJump = minGridSize * blockSize;
auto stream = at::cuda::getCurrentCUDAStream();
box_encode_kernel<<<gridDim,blockDim,0,stream.stream()>>>(targets_dx.data_ptr<float>(),
targets_dy.data_ptr<float>(),
targets_dw.data_ptr<float>(),
targets_dh.data_ptr<float>(),
(float4*) boxes.data_ptr<float>(),
(float4*) anchors.data_ptr<float>(),
wx, wy, ww, wh,
size, idxJump);
std::vector<at::Tensor> result;
result.push_back(targets_dx);
result.push_back(targets_dy);
result.push_back(targets_dw);
result.push_back(targets_dh);
return result;
}
|
TensorFlow/Segmentation/UNet_Medical/examples | examples | unet_INFER | # 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.
# This script launches U-Net run in FP32 on 1 GPU for inference batch_size 1. Usage:
# bash unet_INFER_FP32.sh <path to this repository> <path to dataset> <path to results directory>
horovodrun -np 1 python main.py --data_dir $1 --model_dir $2 --batch_size 1 --exec_mode predict --xla
|
DGLPyTorch/DrugDiscovery/SE3Transformer/se3_transformer/model/layers | layers | attention | # Copyright (c) 2021-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES
# SPDX-License-Identifier: MIT
import dgl
import numpy as np
import torch
import torch.nn as nn
from dgl import DGLGraph
from dgl.ops import edge_softmax
from torch import Tensor
from typing import Dict, Optional, Union
from se3_transformer.model.fiber import Fiber
from se3_transformer.model.layers.convolution import ConvSE3, ConvSE3FuseLevel
from se3_transformer.model.layers.linear import LinearSE3
from se3_transformer.runtime.utils import degree_to_dim, aggregate_residual, unfuse_features
from torch.cuda.nvtx import range as nvtx_range
class AttentionSE3(nn.Module):
""" Multi-headed sparse graph self-attention (SE(3)-equivariant) """
def __init__(
self,
num_heads: int,
key_fiber: Fiber,
value_fiber: Fiber
):
"""
:param num_heads: Number of attention heads
:param key_fiber: Fiber for the keys (and also for the queries)
:param value_fiber: Fiber for the values
"""
super().__init__()
self.num_heads = num_heads
self.key_fiber = key_fiber
self.value_fiber = value_fiber
def forward(
self,
value: Union[Tensor, Dict[str, Tensor]], # edge features (may be fused)
key: Union[Tensor, Dict[str, Tensor]], # edge features (may be fused)
query: Dict[str, Tensor], # node features
graph: DGLGraph
):
with nvtx_range('AttentionSE3'):
with nvtx_range('reshape keys and queries'):
if isinstance(key, Tensor):
# case where features of all types are fused
key = key.reshape(key.shape[0], self.num_heads, -1)
# need to reshape queries that way to keep the same layout as keys
out = torch.cat([query[str(d)] for d in self.key_fiber.degrees], dim=-1)
query = out.reshape(list(query.values())[0].shape[0], self.num_heads, -1)
else:
# features are not fused, need to fuse and reshape them
key = self.key_fiber.to_attention_heads(key, self.num_heads)
query = self.key_fiber.to_attention_heads(query, self.num_heads)
with nvtx_range('attention dot product + softmax'):
# Compute attention weights (softmax of inner product between key and query)
edge_weights = dgl.ops.e_dot_v(graph, key, query).squeeze(-1)
edge_weights = edge_weights / np.sqrt(self.key_fiber.num_features)
edge_weights = edge_softmax(graph, edge_weights)
edge_weights = edge_weights[..., None, None]
with nvtx_range('weighted sum'):
if isinstance(value, Tensor):
# features of all types are fused
v = value.view(value.shape[0], self.num_heads, -1, value.shape[-1])
weights = edge_weights * v
feat_out = dgl.ops.copy_e_sum(graph, weights)
feat_out = feat_out.view(feat_out.shape[0], -1, feat_out.shape[-1]) # merge heads
out = unfuse_features(feat_out, self.value_fiber.degrees)
else:
out = {}
for degree, channels in self.value_fiber:
v = value[str(degree)].view(-1, self.num_heads, channels // self.num_heads,
degree_to_dim(degree))
weights = edge_weights * v
res = dgl.ops.copy_e_sum(graph, weights)
out[str(degree)] = res.view(-1, channels, degree_to_dim(degree)) # merge heads
return out
class AttentionBlockSE3(nn.Module):
""" Multi-headed sparse graph self-attention block with skip connection, linear projection (SE(3)-equivariant) """
def __init__(
self,
fiber_in: Fiber,
fiber_out: Fiber,
fiber_edge: Optional[Fiber] = None,
num_heads: int = 4,
channels_div: int = 2,
use_layer_norm: bool = False,
max_degree: bool = 4,
fuse_level: ConvSE3FuseLevel = ConvSE3FuseLevel.FULL,
low_memory: bool = False,
**kwargs
):
"""
:param fiber_in: Fiber describing the input features
:param fiber_out: Fiber describing the output features
:param fiber_edge: Fiber describing the edge features (node distances excluded)
:param num_heads: Number of attention heads
:param channels_div: Divide the channels by this integer for computing values
:param use_layer_norm: Apply layer normalization between MLP layers
:param max_degree: Maximum degree used in the bases computation
:param fuse_level: Maximum fuse level to use in TFN convolutions
"""
super().__init__()
if fiber_edge is None:
fiber_edge = Fiber({})
self.fiber_in = fiber_in
# value_fiber has same structure as fiber_out but #channels divided by 'channels_div'
value_fiber = Fiber([(degree, channels // channels_div) for degree, channels in fiber_out])
# key_query_fiber has the same structure as fiber_out, but only degrees which are in in_fiber
# (queries are merely projected, hence degrees have to match input)
key_query_fiber = Fiber([(fe.degree, fe.channels) for fe in value_fiber if fe.degree in fiber_in.degrees])
self.to_key_value = ConvSE3(fiber_in, value_fiber + key_query_fiber, pool=False, fiber_edge=fiber_edge,
use_layer_norm=use_layer_norm, max_degree=max_degree, fuse_level=fuse_level,
allow_fused_output=True, low_memory=low_memory)
self.to_query = LinearSE3(fiber_in, key_query_fiber)
self.attention = AttentionSE3(num_heads, key_query_fiber, value_fiber)
self.project = LinearSE3(value_fiber + fiber_in, fiber_out)
def forward(
self,
node_features: Dict[str, Tensor],
edge_features: Dict[str, Tensor],
graph: DGLGraph,
basis: Dict[str, Tensor]
):
with nvtx_range('AttentionBlockSE3'):
with nvtx_range('keys / values'):
fused_key_value = self.to_key_value(node_features, edge_features, graph, basis)
key, value = self._get_key_value_from_fused(fused_key_value)
with nvtx_range('queries'):
query = self.to_query(node_features)
z = self.attention(value, key, query, graph)
z_concat = aggregate_residual(node_features, z, 'cat')
return self.project(z_concat)
def _get_key_value_from_fused(self, fused_key_value):
# Extract keys and queries features from fused features
if isinstance(fused_key_value, Tensor):
# Previous layer was a fully fused convolution
value, key = torch.chunk(fused_key_value, chunks=2, dim=-2)
else:
key, value = {}, {}
for degree, feat in fused_key_value.items():
if int(degree) in self.fiber_in.degrees:
value[degree], key[degree] = torch.chunk(feat, chunks=2, dim=-2)
else:
value[degree] = feat
return key, value
|
PyTorch/Classification/GPUNet/triton/deployment_toolkit/triton_performance_runner/perf_analyzer | perf_analyzer | warmup | # 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 pathlib
from distutils.version import LooseVersion
from importlib.metadata import version
from typing import List, Optional
# method from PEP-366 to support relative import in executed modules
if __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from ...core import EvaluationMode, MeasurementMode, OfflineMode
from ...utils import parse_server_url
from .perf_analyzer import PerfAnalyzer
from .perf_config import PerfAnalyzerConfig
LOGGER = logging.getLogger("warmup")
TRITON_CLIENT_VERSION = LooseVersion(version("tritonclient"))
class PerfAnalyzerWarmupRunner:
def __init__(
self,
server_url: str,
model_name: str,
batch_sizes: List[int],
concurrency: List[int],
input_data: str,
input_shapes: List[str],
measurement_mode: MeasurementMode,
measurement_interval: int,
measurement_request_count: int,
offline_mode: OfflineMode,
evaluation_mode: EvaluationMode,
output_shared_memory_size: int,
timeout: Optional[int],
):
self._model_name = model_name
self._input_data = input_data
self._input_shapes = input_shapes
self._measurement_mode = measurement_mode
self._offline_mode = offline_mode
self._evaluation_mode = evaluation_mode
self._output_shared_memory_size = output_shared_memory_size
self._protocol, self._host, self._port = parse_server_url(server_url)
self._measurement_interval = 2 * measurement_interval
self._measurement_request_count = 2 * measurement_request_count
self._batch_sizes = [min(batch_sizes)]
self._concurrency = [max(concurrency)]
self._timeout = timeout
def run(self):
for batch_size in self._batch_sizes:
for concurrency in self._concurrency:
params = {
"model-name": self._model_name,
"model-version": 1,
"batch-size": batch_size,
"url": f"{self._host}:{self._port}",
"protocol": self._protocol.value,
"input-data": self._input_data,
"measurement-interval": self._measurement_interval,
"concurrency-range": f"{concurrency}:{concurrency}:1",
"verbose": True,
}
if TRITON_CLIENT_VERSION >= LooseVersion("2.11.0"):
params["measurement-mode"] = self._measurement_mode.value
params["measurement-request-count"] = self._measurement_request_count
if self._evaluation_mode == EvaluationMode.OFFLINE:
params["shared-memory"] = self._offline_mode.value
params["output-shared-memory-size"] = self._output_shared_memory_size
config = PerfAnalyzerConfig()
for param, value in params.items():
config[param] = value
for shape in self._input_shapes:
config["shape"] = shape
perf_analyzer = PerfAnalyzer(config=config, timeout=self._timeout)
perf_analyzer.run()
|
TensorFlow/Classification/ConvNets/resnext101-32x4d/training | training | DGX2_RNxt101-32x4d_FP32_250E | #!/bin/bash
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
WORKSPACE=${1:-"/workspace/rn50v15_tf"}
DATA_DIR=${2:-"/data"}
OTHER=${@:3}
if [[ ! -z "${BIND_TO_SOCKET}" ]]; then
BIND_TO_SOCKET="--bind-to socket"
fi
mpiexec --allow-run-as-root ${BIND_TO_SOCKET} -np 8 python3 main.py --arch=resnext101-32x4d \
--mode=train_and_evaluate --iter_unit=epoch --num_iter=250 --mixup=0.2 \
--batch_size=64 --warmup_steps=100 --cosine_lr --label_smoothing 0.1 \
--lr_init=0.256 --lr_warmup_epochs=8 --momentum=0.875 --weight_decay=6.103515625e-05 \
--data_dir=${DATA_DIR}/tfrecords --data_idx_dir=${DATA_DIR}/dali_idx \
--results_dir=${WORKSPACE}/results --weight_init=fan_in ${OTHER}
|
TensorFlow/Classification/ConvNets/resnet50v1.5/training | training | DGX1_RN50_AMP_250E | #!/bin/bash
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
WORKSPACE=${1:-"/workspace/rn50v15_tf"}
DATA_DIR=${2:-"/data"}
OTHER=${@:3}
if [[ ! -z "${BIND_TO_SOCKET}" ]]; then
BIND_TO_SOCKET="--bind-to socket"
fi
mpiexec --allow-run-as-root ${BIND_TO_SOCKET} -np 8 python3 main.py --arch=resnet50 \
--mode=train_and_evaluate --iter_unit=epoch --num_iter=250 --mixup=0.2 \
--batch_size=256 --warmup_steps=100 --cosine_lr --label_smoothing 0.1 \
--lr_init=0.256 --lr_warmup_epochs=8 --momentum=0.875 --weight_decay=3.0517578125e-05 \
--amp --static_loss_scale 128 \
--data_dir=${DATA_DIR}/tfrecords --data_idx_dir=${DATA_DIR}/dali_idx \
--results_dir=${WORKSPACE}/results --weight_init=fan_in ${OTHER}
|
PyTorch/SpeechRecognition/wav2vec2/common | common | metrics | # 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 time
from collections import defaultdict
from copy import copy
import numpy as np
import torch
from common.utils import all_reduce_cpu_scalars, print_once
def __levenshtein(a, b):
"""Calculates the Levenshtein distance between two sequences."""
n, m = len(a), len(b)
if n > m:
# Make sure n <= m, to use O(min(n,m)) space
a, b = b, a
n, m = m, n
current = list(range(n + 1))
for i in range(1, m + 1):
previous, current = current, [i] + [0] * n
for j in range(1, n + 1):
add, delete = previous[j] + 1, current[j - 1] + 1
change = previous[j - 1]
if a[j - 1] != b[i - 1]:
change = change + 1
current[j] = min(add, delete, change)
return current[n]
def word_error_rate(hypotheses, references):
"""Computes average Word Error Rate (WER) between two text lists."""
scores = 0
words = 0
len_diff = len(references) - len(hypotheses)
if len_diff > 0:
raise ValueError("Uneqal number of hypthoses and references: "
"{0} and {1}".format(len(hypotheses), len(references)))
elif len_diff < 0:
hypotheses = hypotheses[:len_diff]
for h, r in zip(hypotheses, references):
h_list = h.split()
r_list = r.split()
words += len(r_list)
scores += __levenshtein(h_list, r_list)
if words != 0:
wer = 1.0*scores/words
else:
wer = float('inf')
return wer, scores, words
class MetricsAggregator:
def __init__(self, scopes=('train', 'train_avg'),
dllogger_keys=(),
benchmark_keys=(),
benchmark_epochs=0,
reduce_mean=(),
reduce_last=(),
group_tb_entries=False,
cuda=True):
"""
Args:
scopes: possible scopes of metrics accumulation
dll_keys: metrics to log with dllogger
benchmark_keys: metrics to log as benchmark metrics
benchmark_epochs: num of last epochs to benchmark
"""
super().__init__()
self.dll_keys = dllogger_keys
self.partials = defaultdict(float)
self.partial_counts = defaultdict(int)
self.accum_reductions = defaultdict(lambda: 'sum')
self.accum_reductions.update({k: 'mean' for k in reduce_mean})
self.accum_reductions.update({k: 'last' for k in reduce_last})
self.metrics = {scope: defaultdict(float) for scope in scopes}
self.metric_counts = {scope: defaultdict(int) for scope in scopes}
self.start_time = {scope: None for scope in scopes}
self.done_accumulating = {scope: True for scope in scopes}
self.benchmark_epochs = benchmark_epochs
self.metrics['train_benchmark'] = defaultdict(list)
self.benchmark_keys = benchmark_keys
self.scopes = scopes
self.group_tb_entries = group_tb_entries
self.cuda = cuda
def log_scalar(self, key, val, accum_reduction=None):
"""Main primitive for logging partial metrics from single batch.
NOTE: Assumption: `log_scalar` cannot be called with different
`accum_reduction` for the same `key`. This results in undefined behavior
Args:
key: metric key
val: metric value
accum_reduction: defines how to accumulate given metric:
- 'sum': sums metrics across grad acc and devices batches
- 'mean': same as 'sum' but with averaging
- 'last': overwrites previous accumulated values. Useful for
logging metric once in a grad acc batch, e.g. learning rate.
If None, a default value is fetched from self.accum_reductions.
If not None, overwrites defaults in self.accum_reductions
"""
if accum_reduction is None:
accum_reduction = self.accum_reductions[key]
else:
self.accum_reductions[key] = accum_reduction
if accum_reduction == 'sum':
self.partials[key] += val
self.partial_counts[key] = 1
elif accum_reduction == 'mean':
self.partials[key] += val
self.partial_counts[key] += 1
elif accum_reduction == 'last':
self.partials[key] = val # overwrite accumulation
self.partial_counts[key] = 1
else:
raise ValueError(accum_reduction)
def log_scalars(self, scalars_dict, accum_reduction=None):
""" Log whole dict of metrics at once """
for k, v in scalars_dict.items():
self.log_scalar(k, v, accum_reduction)
def __setitem__(self, key, val):
""" Convenience logging method. Use sparingly (see NOTE below).
Uses 'last' aggregation and extracts tensors.
Example:
>>> metrics['lr'] = optim.param_groups[0]['lr']
NOTE: `metrics['lr'] = ...` is very different
from `metrics.partial['lr'] = ...`
"""
extract = lambda t: t.item() if type(t) is torch.Tensor else t
if type(val) is dict:
for k, v in val.items():
self.log_scalar(k, extract(v), 'last')
else:
self.log_scalar(key, extract(val), 'last')
def accumulate(self, scopes=None):
""" Accumulates partial metrics in metrics for given scopes.
Defines boundaries of accum_reduction in `log_scalar` method.
Intended to run after each gradient accumulation adjusted iteration.
"""
scopes = scopes if scopes is not None else self.scopes
for scope in scopes:
for k, v in self.partials.items():
self.metrics[scope][k] += v
self.metric_counts[scope][k] += self.partial_counts.get(k, 1)
self.partials.clear()
self.partial_counts.clear()
def all_reduce(self, world_size):
""" Reduce metrics across devices.
Currently assumes that all metrics are float scalars.
After reducing, `log_scalar` method with accumulation other than 'last'
shouldn't be called prior to calling `accumulate`.
"""
if world_size == 1:
return
self.partials = defaultdict(float,
all_reduce_cpu_scalars(self.partials))
for k, v in self.partials.items():
if self.accum_reductions[k] in ('mean', 'last'):
self.partial_counts[k] *= (world_size - self.partials.get('ignore', 0))
if self.partials.get('ignore', 0) > 0:
assert self.accum_reductions[k] == 'mean'
print_once(f'reducing with world size {world_size - self.partials.get("ignore", 0)}')
def start_iter(self, iter):
self._start_accumulating(iter, True, 'train')
def start_epoch(self, epoch):
if self.cuda:
torch.cuda.synchronize()
self._start_accumulating(epoch, True, 'train_avg')
def start_val(self):
if self.cuda:
torch.cuda.synchronize()
self._start_accumulating(None, True, 'val')
def finish_iter(self):
self._accumulate_time('train')
def finish_logging_interval(self):
self._finish_accumulating('train')
def finish_epoch(self):
if self.cuda:
torch.cuda.synchronize()
self._accumulate_time('train_avg')
self._finish_accumulating('train_avg')
metr = self.metrics['train_benchmark']
for k in self.benchmark_keys:
metr[k].append(self.metrics['train_avg'][k])
if len(metr[k]) > self.benchmark_epochs:
metr[k].pop(0)
def finish_val(self, scope='val'):
if self.cuda:
torch.cuda.synchronize()
self._accumulate_time(scope)
self._finish_accumulating(scope)
def get_metrics(self, scope='train', target='dll'):
if scope == 'train_benchmark':
metr = self.metrics[scope]
ret = {'train_avg_' + k: np.mean(v) for k, v in metr.items()}
ret['benchmark_epochs_num'] = len(list(metr.values())[0])
return ret
assert self.done_accumulating[scope]
ret = copy(self.metrics[scope])
if target == 'dll':
ret = {f'{scope}_{k}': v
for k, v in ret.items() if k in self.dll_keys}
elif target == 'tb' and self.group_tb_entries:
# Rename keys so they would group nicely inside TensorBoard
def split_key(k):
pos = k.rfind('_')
return k[:pos] + '/' + k[pos+1:] if pos >= 0 else k
ret = {split_key(k): v for k, v in ret.items()}
return ret
def _start_accumulating(self, step, start_timer=True, scope='train'):
del step # unused
assert not self.partials, 'metrics.accumulate call missed'
assert not self.partial_counts, 'metrics.accumulate call missed'
if self.done_accumulating[scope]:
self.metrics[scope].clear()
self.metric_counts[scope].clear()
if start_timer:
self.start_time[scope] = time.time()
self.done_accumulating[scope] = False
def _finish_accumulating(self, scope='train'):
assert not self.done_accumulating[scope]
metr = self.metrics[scope]
counts = self.metric_counts[scope]
for k, v in metr.items():
metr[k] = v / counts[k]
self.done_accumulating[scope] = True
def _accumulate_time(self, scope='train'):
assert not self.done_accumulating[scope]
took = time.time() - self.start_time[scope]
self.start_time[scope] = None
self.metrics[scope]['took'] += took
self.metric_counts[scope]['took'] = 1 # not +=
|
PyTorch/Segmentation/MaskRCNN/pytorch/tools | tools | test_net | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
import argparse
import os
import torch
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.data import make_data_loader
from maskrcnn_benchmark.engine.inference import inference
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
from maskrcnn_benchmark.utils.collect_env import collect_env_info
from maskrcnn_benchmark.utils.comm import synchronize, get_rank, is_main_process
from maskrcnn_benchmark.utils.logger import setup_logger
from maskrcnn_benchmark.utils.miscellaneous import mkdir
from maskrcnn_benchmark.utils.logger import format_step
import dllogger
def main():
parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
parser.add_argument(
"--config-file",
default="/workspace/object_detection/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument("--local_rank", type=int, default=os.getenv('LOCAL_RANK', 0))
parser.add_argument("--json-summary",
help="Out file for DLLogger",
default="dllogger_inference.out",
type=str)
parser.add_argument(
"--skip-eval",
dest="skip_eval",
help="Do not eval the predictions",
action="store_true",
)
parser.add_argument(
"--fp16",
help="Mixed precision training",
action="store_true",
)
parser.add_argument(
"--amp",
help="Mixed precision training",
action="store_true",
)
parser.add_argument(
"--infer_steps",
help="Total inference steps",
default=-1,
type=int)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
args.fp16 = args.fp16 or args.amp
num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
distributed = num_gpus > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl", init_method="env://"
)
synchronize()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
save_dir = ""
logger = setup_logger("maskrcnn_benchmark", save_dir, get_rank())
if is_main_process():
dllogger.init(backends=[dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE,
filename=args.json_summary),
dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE, step_format=format_step)])
else:
dllogger.init(backends=[])
dllogger.metadata("BBOX_mAP", {"unit": None})
dllogger.metadata("MASK_mAP", {"unit": None})
dllogger.metadata("e2e_infer_time", {"unit": "s"})
dllogger.metadata("inference_perf_fps", {"unit": "images/s"})
dllogger.metadata("latency_avg", {"unit": "s"})
dllogger.metadata("latency_90", {"unit": "s"})
dllogger.metadata("latency_95", {"unit": "s"})
dllogger.metadata("latency_99", {"unit": "s"})
save_dir = ""
dllogger.log(step="PARAMETER", data={"config":cfg})
dllogger.log(step="PARAMETER", data={"gpu_count": num_gpus})
# dllogger.log(step="PARAMETER", data={"env_info": collect_env_info()})
model = build_detection_model(cfg)
model.to(cfg.MODEL.DEVICE)
# Initialize mixed-precision
if args.fp16:
use_mixed_precision = True
else:
use_mixed_precision = cfg.DTYPE == "float16"
output_dir = cfg.OUTPUT_DIR
checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
iou_types = ("bbox",)
if cfg.MODEL.MASK_ON:
iou_types = iou_types + ("segm",)
output_folders = [None] * len(cfg.DATASETS.TEST)
dataset_names = cfg.DATASETS.TEST
if cfg.OUTPUT_DIR:
for idx, dataset_name in enumerate(dataset_names):
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
mkdir(output_folder)
output_folders[idx] = output_folder
data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
results = []
for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
if use_mixed_precision:
with torch.cuda.amp.autocast():
result = inference(
model,
data_loader_val,
dataset_name=dataset_name,
iou_types=iou_types,
box_only=cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
expected_results=cfg.TEST.EXPECTED_RESULTS,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
skip_eval=args.skip_eval,
dllogger=dllogger,
steps=args.infer_steps
)
else:
result = inference(
model,
data_loader_val,
dataset_name=dataset_name,
iou_types=iou_types,
box_only=cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
expected_results=cfg.TEST.EXPECTED_RESULTS,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
skip_eval=args.skip_eval,
dllogger=dllogger,
steps=args.infer_steps
)
synchronize()
results.append(result)
if is_main_process() and not args.skip_eval:
map_results, raw_results = results[0]
bbox_map = map_results.results["bbox"]['AP']
segm_map = map_results.results["segm"]['AP']
dllogger.log(step=tuple(), data={"BBOX_mAP": bbox_map, "MASK_mAP": segm_map})
if __name__ == "__main__":
main()
dllogger.log(step=tuple(), data={})
|
TensorFlow/Detection/SSD/models/research/object_detection/dataset_tools | dataset_tools | create_kitti_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 KITTI detection dataset to TFRecord for object_detection.
Converts KITTI detection dataset to TFRecords with a standard format allowing
to use this dataset to train object detectors. The raw dataset can be
downloaded from:
http://kitti.is.tue.mpg.de/kitti/data_object_image_2.zip.
http://kitti.is.tue.mpg.de/kitti/data_object_label_2.zip
Permission can be requested at the main website.
KITTI detection dataset contains 7481 training images. Using this code with
the default settings will set aside the first 500 images as a validation set.
This can be altered using the flags, see details below.
Example usage:
python object_detection/dataset_tools/create_kitti_tf_record.py \
--data_dir=/home/user/kitti \
--output_path=/home/user/kitti.record
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import hashlib
import io
import os
import numpy as np
import PIL.Image as pil
import tensorflow as tf
from object_detection.utils import dataset_util
from object_detection.utils import label_map_util
from object_detection.utils.np_box_ops import iou
tf.app.flags.DEFINE_string('data_dir', '', 'Location of root directory for the '
'data. Folder structure is assumed to be:'
'<data_dir>/training/label_2 (annotations) and'
'<data_dir>/data_object_image_2/training/image_2'
'(images).')
tf.app.flags.DEFINE_string('output_path', '', 'Path to which TFRecord files'
'will be written. The TFRecord with the training set'
'will be located at: <output_path>_train.tfrecord.'
'And the TFRecord with the validation set will be'
'located at: <output_path>_val.tfrecord')
tf.app.flags.DEFINE_string('classes_to_use', 'car,pedestrian,dontcare',
'Comma separated list of class names that will be'
'used. Adding the dontcare class will remove all'
'bboxs in the dontcare regions.')
tf.app.flags.DEFINE_string('label_map_path', 'data/kitti_label_map.pbtxt',
'Path to label map proto.')
tf.app.flags.DEFINE_integer('validation_set_size', '500', 'Number of images to'
'be used as a validation set.')
FLAGS = tf.app.flags.FLAGS
def convert_kitti_to_tfrecords(data_dir, output_path, classes_to_use,
label_map_path, validation_set_size):
"""Convert the KITTI detection dataset to TFRecords.
Args:
data_dir: The full path to the unzipped folder containing the unzipped data
from data_object_image_2 and data_object_label_2.zip.
Folder structure is assumed to be: data_dir/training/label_2 (annotations)
and data_dir/data_object_image_2/training/image_2 (images).
output_path: The path to which TFRecord files will be written. The TFRecord
with the training set will be located at: <output_path>_train.tfrecord
And the TFRecord with the validation set will be located at:
<output_path>_val.tfrecord
classes_to_use: List of strings naming the classes for which data should be
converted. Use the same names as presented in the KIITI README file.
Adding dontcare class will remove all other bounding boxes that overlap
with areas marked as dontcare regions.
label_map_path: Path to label map proto
validation_set_size: How many images should be left as the validation set.
(Ffirst `validation_set_size` examples are selected to be in the
validation set).
"""
label_map_dict = label_map_util.get_label_map_dict(label_map_path)
train_count = 0
val_count = 0
annotation_dir = os.path.join(data_dir,
'training',
'label_2')
image_dir = os.path.join(data_dir,
'data_object_image_2',
'training',
'image_2')
train_writer = tf.python_io.TFRecordWriter('%s_train.tfrecord'%
output_path)
val_writer = tf.python_io.TFRecordWriter('%s_val.tfrecord'%
output_path)
images = sorted(tf.gfile.ListDirectory(image_dir))
for img_name in images:
img_num = int(img_name.split('.')[0])
is_validation_img = img_num < validation_set_size
img_anno = read_annotation_file(os.path.join(annotation_dir,
str(img_num).zfill(6)+'.txt'))
image_path = os.path.join(image_dir, img_name)
# Filter all bounding boxes of this frame that are of a legal class, and
# don't overlap with a dontcare region.
# TODO(talremez) filter out targets that are truncated or heavily occluded.
annotation_for_image = filter_annotations(img_anno, classes_to_use)
example = prepare_example(image_path, annotation_for_image, label_map_dict)
if is_validation_img:
val_writer.write(example.SerializeToString())
val_count += 1
else:
train_writer.write(example.SerializeToString())
train_count += 1
train_writer.close()
val_writer.close()
def prepare_example(image_path, annotations, label_map_dict):
"""Converts a dictionary with annotations for an image to tf.Example proto.
Args:
image_path: The complete path to image.
annotations: A dictionary representing the annotation of a single object
that appears in the image.
label_map_dict: A map from string label names to integer ids.
Returns:
example: The converted tf.Example.
"""
with tf.gfile.GFile(image_path, 'rb') as fid:
encoded_png = fid.read()
encoded_png_io = io.BytesIO(encoded_png)
image = pil.open(encoded_png_io)
image = np.asarray(image)
key = hashlib.sha256(encoded_png).hexdigest()
width = int(image.shape[1])
height = int(image.shape[0])
xmin_norm = annotations['2d_bbox_left'] / float(width)
ymin_norm = annotations['2d_bbox_top'] / float(height)
xmax_norm = annotations['2d_bbox_right'] / float(width)
ymax_norm = annotations['2d_bbox_bottom'] / float(height)
difficult_obj = [0]*len(xmin_norm)
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(image_path.encode('utf8')),
'image/source_id': dataset_util.bytes_feature(image_path.encode('utf8')),
'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
'image/encoded': dataset_util.bytes_feature(encoded_png),
'image/format': dataset_util.bytes_feature('png'.encode('utf8')),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmin_norm),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmax_norm),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymin_norm),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymax_norm),
'image/object/class/text': dataset_util.bytes_list_feature(
[x.encode('utf8') for x in annotations['type']]),
'image/object/class/label': dataset_util.int64_list_feature(
[label_map_dict[x] for x in annotations['type']]),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.float_list_feature(
annotations['truncated']),
'image/object/alpha': dataset_util.float_list_feature(
annotations['alpha']),
'image/object/3d_bbox/height': dataset_util.float_list_feature(
annotations['3d_bbox_height']),
'image/object/3d_bbox/width': dataset_util.float_list_feature(
annotations['3d_bbox_width']),
'image/object/3d_bbox/length': dataset_util.float_list_feature(
annotations['3d_bbox_length']),
'image/object/3d_bbox/x': dataset_util.float_list_feature(
annotations['3d_bbox_x']),
'image/object/3d_bbox/y': dataset_util.float_list_feature(
annotations['3d_bbox_y']),
'image/object/3d_bbox/z': dataset_util.float_list_feature(
annotations['3d_bbox_z']),
'image/object/3d_bbox/rot_y': dataset_util.float_list_feature(
annotations['3d_bbox_rot_y']),
}))
return example
def filter_annotations(img_all_annotations, used_classes):
"""Filters out annotations from the unused classes and dontcare regions.
Filters out the annotations that belong to classes we do now wish to use and
(optionally) also removes all boxes that overlap with dontcare regions.
Args:
img_all_annotations: A list of annotation dictionaries. See documentation of
read_annotation_file for more details about the format of the annotations.
used_classes: A list of strings listing the classes we want to keep, if the
list contains "dontcare", all bounding boxes with overlapping with dont
care regions will also be filtered out.
Returns:
img_filtered_annotations: A list of annotation dictionaries that have passed
the filtering.
"""
img_filtered_annotations = {}
# Filter the type of the objects.
relevant_annotation_indices = [
i for i, x in enumerate(img_all_annotations['type']) if x in used_classes
]
for key in img_all_annotations.keys():
img_filtered_annotations[key] = (
img_all_annotations[key][relevant_annotation_indices])
if 'dontcare' in used_classes:
dont_care_indices = [i for i,
x in enumerate(img_filtered_annotations['type'])
if x == 'dontcare']
# bounding box format [y_min, x_min, y_max, x_max]
all_boxes = np.stack([img_filtered_annotations['2d_bbox_top'],
img_filtered_annotations['2d_bbox_left'],
img_filtered_annotations['2d_bbox_bottom'],
img_filtered_annotations['2d_bbox_right']],
axis=1)
ious = iou(boxes1=all_boxes,
boxes2=all_boxes[dont_care_indices])
# Remove all bounding boxes that overlap with a dontcare region.
if ious.size > 0:
boxes_to_remove = np.amax(ious, axis=1) > 0.0
for key in img_all_annotations.keys():
img_filtered_annotations[key] = (
img_filtered_annotations[key][np.logical_not(boxes_to_remove)])
return img_filtered_annotations
def read_annotation_file(filename):
"""Reads a KITTI annotation file.
Converts a KITTI annotation file into a dictionary containing all the
relevant information.
Args:
filename: the path to the annotataion text file.
Returns:
anno: A dictionary with the converted annotation information. See annotation
README file for details on the different fields.
"""
with open(filename) as f:
content = f.readlines()
content = [x.strip().split(' ') for x in content]
anno = {}
anno['type'] = np.array([x[0].lower() for x in content])
anno['truncated'] = np.array([float(x[1]) for x in content])
anno['occluded'] = np.array([int(x[2]) for x in content])
anno['alpha'] = np.array([float(x[3]) for x in content])
anno['2d_bbox_left'] = np.array([float(x[4]) for x in content])
anno['2d_bbox_top'] = np.array([float(x[5]) for x in content])
anno['2d_bbox_right'] = np.array([float(x[6]) for x in content])
anno['2d_bbox_bottom'] = np.array([float(x[7]) for x in content])
anno['3d_bbox_height'] = np.array([float(x[8]) for x in content])
anno['3d_bbox_width'] = np.array([float(x[9]) for x in content])
anno['3d_bbox_length'] = np.array([float(x[10]) for x in content])
anno['3d_bbox_x'] = np.array([float(x[11]) for x in content])
anno['3d_bbox_y'] = np.array([float(x[12]) for x in content])
anno['3d_bbox_z'] = np.array([float(x[13]) for x in content])
anno['3d_bbox_rot_y'] = np.array([float(x[14]) for x in content])
return anno
def main(_):
convert_kitti_to_tfrecords(
data_dir=FLAGS.data_dir,
output_path=FLAGS.output_path,
classes_to_use=FLAGS.classes_to_use.split(','),
label_map_path=FLAGS.label_map_path,
validation_set_size=FLAGS.validation_set_size)
if __name__ == '__main__':
tf.app.run()
|
TensorFlow/LanguageModeling/BERT | BERT | run_squad | # coding=utf-8
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.
"""Run BERT on SQuAD 1.1 and SQuAD 2.0."""
from __future__ import absolute_import, division, print_function
import collections
import json
import math
import os
import random
import shutil
import time
import horovod.tensorflow as hvd
import numpy as np
import six
import tensorflow as tf
from tensorflow.python.client import device_lib
import modeling
import optimization
import tokenization
from utils.create_squad_data import *
from utils.utils import LogEvalRunHook, LogTrainRunHook, setup_xla_flags
from utils.gpu_affinity import set_affinity
import utils.dllogger_class
from dllogger import Verbosity
flags = tf.flags
FLAGS = None
def extract_run_squad_flags():
## Required parameters
flags.DEFINE_string(
"bert_config_file", None,
"The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
flags.DEFINE_string("vocab_file", None,
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_string(
"output_dir", None,
"The output directory where the model checkpoints will be written.")
## Other parameters
flags.DEFINE_string(
"dllog_path", "/results/bert_dllog.json",
"filename where dllogger writes to")
flags.DEFINE_string("train_file", None,
"SQuAD json for training. E.g., train-v1.1.json")
flags.DEFINE_string(
"predict_file", None,
"SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
flags.DEFINE_string(
"eval_script", None,
"SQuAD evaluate.py file to compute f1 and exact_match E.g., evaluate-v1.1.py")
flags.DEFINE_string(
"init_checkpoint", None,
"Initial checkpoint (usually from a pre-trained BERT model).")
flags.DEFINE_bool(
"do_lower_case", True,
"Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
flags.DEFINE_integer(
"max_seq_length", 384,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded.")
flags.DEFINE_integer(
"doc_stride", 128,
"When splitting up a long document into chunks, how much stride to "
"take between chunks.")
flags.DEFINE_integer(
"max_query_length", 64,
"The maximum number of tokens for the question. Questions longer than "
"this will be truncated to this length.")
flags.DEFINE_bool("do_train", False, "Whether to run training.")
flags.DEFINE_bool("do_predict", False, "Whether to run eval on the dev set.")
flags.DEFINE_integer("train_batch_size", 8, "Total batch size for training.")
flags.DEFINE_integer("predict_batch_size", 8,
"Total batch size for predictions.")
flags.DEFINE_float("learning_rate", 5e-6, "The initial learning rate for Adam.")
flags.DEFINE_bool("use_trt", False, "Whether to use TF-TRT")
flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs")
flags.DEFINE_float("num_train_epochs", 3.0,
"Total number of training epochs to perform.")
flags.DEFINE_float(
"warmup_proportion", 0.1,
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10% of training.")
flags.DEFINE_integer("save_checkpoints_steps", 5000,
"How often to save the model checkpoint.")
flags.DEFINE_integer("display_loss_steps", 10,
"How often to print loss from estimator")
flags.DEFINE_integer("iterations_per_loop", 1000,
"How many steps to make in each estimator call.")
flags.DEFINE_integer("num_accumulation_steps", 1,
"Number of accumulation steps before gradient update"
"Global batch size = num_accumulation_steps * train_batch_size")
flags.DEFINE_integer(
"n_best_size", 20,
"The total number of n-best predictions to generate in the "
"nbest_predictions.json output file.")
flags.DEFINE_integer(
"max_answer_length", 30,
"The maximum length of an answer that can be generated. This is needed "
"because the start and end predictions are not conditioned on one another.")
flags.DEFINE_bool(
"verbose_logging", False,
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
flags.DEFINE_bool(
"version_2_with_negative", False,
"If true, the SQuAD examples contain some that do not have an answer.")
flags.DEFINE_float(
"null_score_diff_threshold", 0.0,
"If null_score - best_non_null is greater than the threshold predict null.")
flags.DEFINE_bool("amp", True, "Whether to enable AMP ops. When false, uses TF32 on A100 and FP32 on V100 GPUS.")
flags.DEFINE_bool("use_xla", True, "Whether to enable XLA JIT compilation.")
flags.DEFINE_integer("num_eval_iterations", None,
"How many eval iterations to run - performs inference on subset")
# Triton Specific flags
flags.DEFINE_bool("export_triton", False, "Whether to export saved model or run inference with Triton")
flags.DEFINE_string("triton_model_name", "bert", "exports to appropriate directory for Triton")
flags.DEFINE_integer("triton_model_version", 1, "exports to appropriate directory for Triton")
flags.DEFINE_string("triton_server_url", "localhost:8001", "exports to appropriate directory for Triton")
flags.DEFINE_bool("triton_model_overwrite", False, "If True, will overwrite an existing directory with the specified 'model_name' and 'version_name'")
flags.DEFINE_integer("triton_max_batch_size", 8, "Specifies the 'max_batch_size' in the Triton model config. See the Triton documentation for more info.")
flags.DEFINE_float("triton_dyn_batching_delay", 0, "Determines the dynamic_batching queue delay in milliseconds(ms) for the Triton model config. Use '0' or '-1' to specify static batching. See the Triton documentation for more info.")
flags.DEFINE_integer("triton_engine_count", 1, "Specifies the 'instance_group' count value in the Triton model config. See the Triton documentation for more info.")
flags.mark_flag_as_required("vocab_file")
flags.mark_flag_as_required("bert_config_file")
flags.mark_flag_as_required("output_dir")
return flags.FLAGS
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
use_one_hot_embeddings):
"""Creates a classification model."""
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
compute_type=tf.float32)
final_hidden = model.get_sequence_output()
final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
batch_size = final_hidden_shape[0]
seq_length = final_hidden_shape[1]
hidden_size = final_hidden_shape[2]
output_weights = tf.get_variable(
"cls/squad/output_weights", [2, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
final_hidden_matrix = tf.reshape(final_hidden,
[batch_size * seq_length, hidden_size])
logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
logits = tf.reshape(logits, [batch_size, seq_length, 2])
logits = tf.transpose(logits, [2, 0, 1])
unstacked_logits = tf.unstack(logits, axis=0, name='unstack')
(start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
return (start_logits, end_logits)
def get_frozen_tftrt_model(bert_config, shape, use_one_hot_embeddings, init_checkpoint):
tf_config = tf.compat.v1.ConfigProto()
tf_config.gpu_options.allow_growth = True
output_node_names = ['unstack']
with tf.Session(config=tf_config) as tf_sess:
input_ids = tf.placeholder(tf.int32, shape, 'input_ids')
input_mask = tf.placeholder(tf.int32, shape, 'input_mask')
segment_ids = tf.placeholder(tf.int32, shape, 'segment_ids')
(start_logits, end_logits) = create_model(bert_config=bert_config,
is_training=False,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
tvars = tf.trainable_variables()
(assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf_sess.run(tf.global_variables_initializer())
print("LOADED!")
tf.compat.v1.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
else:
init_string = ", *NOTTTTTTTTTTTTTTTTTTTTT"
tf.compat.v1.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string)
frozen_graph = tf.graph_util.convert_variables_to_constants(tf_sess,
tf_sess.graph.as_graph_def(), output_node_names)
num_nodes = len(frozen_graph.node)
print('Converting graph using TensorFlow-TensorRT...')
from tensorflow.python.compiler.tensorrt import trt_convert as trt
converter = trt.TrtGraphConverter(
input_graph_def=frozen_graph,
nodes_blacklist=output_node_names,
max_workspace_size_bytes=(4096 << 20) - 1000,
precision_mode = "FP16" if FLAGS.amp else "FP32",
minimum_segment_size=4,
is_dynamic_op=True,
maximum_cached_engines=1000
)
frozen_graph = converter.convert()
print('Total node count before and after TF-TRT conversion:',
num_nodes, '->', len(frozen_graph.node))
print('TRT node count:',
len([1 for n in frozen_graph.node if str(n.op) == 'TRTEngineOp']))
with tf.io.gfile.GFile("frozen_modelTRT.pb", "wb") as f:
f.write(frozen_graph.SerializeToString())
return frozen_graph
def model_fn_builder(bert_config, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps,
hvd=None, amp=False, use_one_hot_embeddings=False):
"""Returns `model_fn` closure for Estimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for Estimator."""
if FLAGS.verbose_logging:
tf.compat.v1.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.compat.v1.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
unique_ids = features["unique_ids"]
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
if not is_training and FLAGS.use_trt:
trt_graph = get_frozen_tftrt_model(bert_config, input_ids.shape, use_one_hot_embeddings, init_checkpoint)
(start_logits, end_logits) = tf.import_graph_def(trt_graph,
input_map={'input_ids':input_ids, 'input_mask':input_mask, 'segment_ids':segment_ids},
return_elements=['unstack:0', 'unstack:1'],
name='')
predictions = {
"unique_ids": unique_ids,
"start_logits": start_logits,
"end_logits": end_logits,
}
output_spec = tf.estimator.EstimatorSpec(
mode=mode, predictions=predictions)
return output_spec
(start_logits, end_logits) = create_model(
bert_config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
tvars = tf.trainable_variables()
initialized_variable_names = {}
if init_checkpoint and (hvd is None or hvd.rank() == 0):
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
if FLAGS.verbose_logging:
tf.compat.v1.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.compat.v1.logging.info(" %d name = %s, shape = %s%s", 0 if hvd is None else hvd.rank(), var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
seq_length = modeling.get_shape_list(input_ids)[1]
def compute_loss(logits, positions):
one_hot_positions = tf.one_hot(
positions, depth=seq_length, dtype=tf.float32)
log_probs = tf.nn.log_softmax(logits, axis=-1)
loss = -tf.reduce_mean(
tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
return loss
start_positions = features["start_positions"]
end_positions = features["end_positions"]
start_loss = compute_loss(start_logits, start_positions)
end_loss = compute_loss(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2.0
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps, hvd, False, amp, FLAGS.num_accumulation_steps)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op)
elif mode == tf.estimator.ModeKeys.PREDICT:
dummy_op = tf.no_op()
# Need to call mixed precision graph rewrite if fp16 to enable graph rewrite
if amp:
loss_scaler = tf.train.experimental.FixedLossScale(1)
dummy_op = tf.train.experimental.enable_mixed_precision_graph_rewrite(
optimization.LAMBOptimizer(learning_rate=0.0), loss_scaler)
predictions = {
"unique_ids": tf.identity(unique_ids),
"start_logits": start_logits,
"end_logits": end_logits,
}
output_spec = tf.estimator.EstimatorSpec(
mode=mode, predictions=predictions)
else:
raise ValueError(
"Only TRAIN and PREDICT modes are supported: %s" % (mode))
return output_spec
return model_fn
def input_fn_builder(input_file, batch_size, seq_length, is_training, drop_remainder, hvd=None):
"""Creates an `input_fn` closure to be passed to Estimator."""
name_to_features = {
"unique_ids": tf.io.FixedLenFeature([], tf.int64),
"input_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
"input_mask": tf.io.FixedLenFeature([seq_length], tf.int64),
"segment_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
}
if is_training:
name_to_features["start_positions"] = tf.io.FixedLenFeature([], tf.int64)
name_to_features["end_positions"] = tf.io.FixedLenFeature([], tf.int64)
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
def input_fn():
"""The actual input function."""
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
if is_training:
d = tf.data.TFRecordDataset(input_file, num_parallel_reads=4)
if hvd is not None: d = d.shard(hvd.size(), hvd.rank())
d = d.apply(tf.data.experimental.ignore_errors())
d = d.shuffle(buffer_size=100)
d = d.repeat()
else:
d = tf.data.TFRecordDataset(input_file)
d = d.apply(
tf.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
return input_fn
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_logits", "end_logits"])
def get_predictions(all_examples, all_features, all_results, n_best_size, max_answer_length,
do_lower_case, version_2_with_negative, verbose_logging):
"""Get final predictions"""
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction",
["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min mull score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
feature_null_score = result.start_logits[0] + result.end_logits[0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= len(feature.tokens):
continue
if end_index >= len(feature.tokens):
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index]))
if version_2_with_negative:
prelim_predictions.append(
_PrelimPrediction(
feature_index=min_null_feature_index,
start_index=0,
end_index=0,
start_logit=null_start_logit,
end_logit=null_end_logit))
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_logit + x.end_logit),
reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"])
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
tok_text = " ".join(tok_tokens)
# De-tokenize WordPieces that have been split off.
tok_text = tok_text.replace(" ##", "")
tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(
text=final_text,
start_logit=pred.start_logit,
end_logit=pred.end_logit))
# if we didn't inlude the empty option in the n-best, inlcude it
if version_2_with_negative:
if "" not in seen_predictions:
nbest.append(
_NbestPrediction(
text="", start_logit=null_start_logit,
end_logit=null_end_logit))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
assert len(nbest) >= 1
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1
if not version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null - best_non_null_entry.start_logit - (
best_non_null_entry.end_logit)
scores_diff_json[example.qas_id] = score_diff
try:
null_score_diff_threshold = FLAGS.null_score_diff_threshold
except:
null_score_diff_threshold = 0.0
if score_diff > null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
return all_predictions, all_nbest_json, scores_diff_json
def write_predictions(all_examples, all_features, all_results, n_best_size,
max_answer_length, do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file,
version_2_with_negative, verbose_logging):
"""Write final predictions to the json file and log-odds of null if needed."""
tf.compat.v1.logging.info("Writing predictions to: %s" % (output_prediction_file))
tf.compat.v1.logging.info("Writing nbest to: %s" % (output_nbest_file))
all_predictions, all_nbest_json, scores_diff_json = get_predictions(all_examples, all_features,
all_results, n_best_size, max_answer_length, do_lower_case, version_2_with_negative, verbose_logging)
with tf.io.gfile.GFile(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with tf.io.gfile.GFile(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
with tf.io.gfile.GFile(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heruistic between
# `pred_text` and `orig_text` to get a character-to-charcter alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for (i, c) in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose_logging:
tf.compat.v1.logging.info(
"Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose_logging:
tf.compat.v1.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
orig_ns_text, tok_ns_text)
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose_logging:
tf.compat.v1.logging.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose_logging:
tf.compat.v1.logging.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
return output_text
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
def validate_flags_or_throw(bert_config):
"""Validate the input FLAGS or throw an exception."""
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_train and not FLAGS.do_predict and not FLAGS.export_triton:
raise ValueError("At least one of `do_train` or `do_predict` or `export_SavedModel` must be True.")
if FLAGS.do_train:
if not FLAGS.train_file:
raise ValueError(
"If `do_train` is True, then `train_file` must be specified.")
if FLAGS.do_predict:
if not FLAGS.predict_file:
raise ValueError(
"If `do_predict` is True, then `predict_file` must be specified.")
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
if FLAGS.max_seq_length <= FLAGS.max_query_length + 3:
raise ValueError(
"The max_seq_length (%d) must be greater than max_query_length "
"(%d) + 3" % (FLAGS.max_seq_length, FLAGS.max_query_length))
def export_model(estimator, export_dir, init_checkpoint):
"""Exports a checkpoint in SavedModel format in a directory structure compatible with Triton."""
def serving_input_fn():
label_ids = tf.placeholder(tf.int32, [None,], name='unique_ids')
input_ids = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_ids')
input_mask = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_mask')
segment_ids = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='segment_ids')
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
'unique_ids': label_ids,
'input_ids': input_ids,
'input_mask': input_mask,
'segment_ids': segment_ids,
})()
return input_fn
saved_dir = estimator.export_savedmodel(
export_dir,
serving_input_fn,
assets_extra=None,
as_text=False,
checkpoint_path=init_checkpoint,
strip_default_attrs=False)
model_name = FLAGS.triton_model_name
model_folder = export_dir + "/triton_models/" + model_name
version_folder = model_folder + "/" + str(FLAGS.triton_model_version)
final_model_folder = version_folder + "/model.savedmodel"
if not os.path.exists(version_folder):
os.makedirs(version_folder)
if (not os.path.exists(final_model_folder)):
os.rename(saved_dir, final_model_folder)
print("Model saved to dir", final_model_folder)
else:
if (FLAGS.triton_model_overwrite):
shutil.rmtree(final_model_folder)
os.rename(saved_dir, final_model_folder)
print("WARNING: Existing model was overwritten. Model dir: {}".format(final_model_folder))
else:
print("ERROR: Could not save Triton model. Folder already exists. Use '--triton_model_overwrite=True' if you would like to overwrite an existing model. Model dir: {}".format(final_model_folder))
return
# Now build the config for Triton. Check to make sure we can overwrite it, if it exists
config_filename = os.path.join(model_folder, "config.pbtxt")
optimization_str = ""
if FLAGS.amp:
optimization_str = r"""
optimization {
execution_accelerators
{
gpu_execution_accelerator :
[ {
name : "auto_mixed_precision"
} ]
}
}"""
if (os.path.exists(config_filename) and not FLAGS.triton_model_overwrite):
print("ERROR: Could not save Triton model config. Config file already exists. Use '--triton_model_overwrite=True' if you would like to overwrite an existing model config. Model config: {}".format(config_filename))
return
config_template = r"""
name: "{model_name}"
platform: "tensorflow_savedmodel"
max_batch_size: {max_batch_size}
{optimization_str}
input [
{{
name: "unique_ids"
data_type: TYPE_INT32
dims: [ 1 ]
reshape: {{ shape: [ ] }}
}},
{{
name: "segment_ids"
data_type: TYPE_INT32
dims: {seq_length}
}},
{{
name: "input_ids"
data_type: TYPE_INT32
dims: {seq_length}
}},
{{
name: "input_mask"
data_type: TYPE_INT32
dims: {seq_length}
}}
]
output [
{{
name: "end_logits"
data_type: TYPE_FP32
dims: {seq_length}
}},
{{
name: "start_logits"
data_type: TYPE_FP32
dims: {seq_length}
}}
]
{dynamic_batching}
instance_group [
{{
count: {engine_count}
}}
]"""
batching_str = ""
max_batch_size = FLAGS.triton_max_batch_size
if (FLAGS.triton_dyn_batching_delay > 0):
# Use only full and half full batches
pref_batch_size = [int(max_batch_size / 2.0), max_batch_size]
batching_str = r"""
dynamic_batching {{
preferred_batch_size: [{0}]
max_queue_delay_microseconds: {1}
}}""".format(", ".join([str(x) for x in pref_batch_size]), int(FLAGS.triton_dyn_batching_delay * 1000.0))
config_values = {
"model_name": model_name,
"max_batch_size": max_batch_size,
"seq_length": FLAGS.max_seq_length,
"dynamic_batching": batching_str,
"engine_count": FLAGS.triton_engine_count,
"optimization_str":optimization_str,
}
with open(model_folder + "/config.pbtxt", "w") as file:
final_config_str = config_template.format_map(config_values)
file.write(final_config_str)
def main(_):
setup_xla_flags()
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
dllogging = utils.dllogger_class.dllogger_class(FLAGS.dllog_path)
if FLAGS.horovod:
hvd.init()
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
validate_flags_or_throw(bert_config)
tf.io.gfile.makedirs(FLAGS.output_dir)
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
master_process = True
training_hooks = []
global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps
hvd_rank = 0
config = tf.compat.v1.ConfigProto()
learning_rate = FLAGS.learning_rate
if FLAGS.horovod:
tf.compat.v1.logging.info("Multi-GPU training with TF Horovod")
tf.compat.v1.logging.info("hvd.size() = %d hvd.rank() = %d", hvd.size(), hvd.rank())
global_batch_size = FLAGS.train_batch_size * hvd.size() * FLAGS.num_accumulation_steps
learning_rate = learning_rate * hvd.size()
master_process = (hvd.rank() == 0)
hvd_rank = hvd.rank()
config.gpu_options.visible_device_list = str(hvd.local_rank())
set_affinity(hvd.local_rank())
if hvd.size() > 1:
training_hooks.append(hvd.BroadcastGlobalVariablesHook(0))
if FLAGS.use_xla:
config.graph_options.optimizer_options.global_jit_level = tf.compat.v1.OptimizerOptions.ON_1
if FLAGS.amp:
tf.enable_resource_variables()
run_config = tf.estimator.RunConfig(
model_dir=FLAGS.output_dir if master_process else None,
session_config=config,
save_checkpoints_steps=FLAGS.save_checkpoints_steps if master_process else None,
save_summary_steps=FLAGS.save_checkpoints_steps if master_process else None,
log_step_count_steps=FLAGS.display_loss_steps,
keep_checkpoint_max=1)
if master_process:
tf.compat.v1.logging.info("***** Configuaration *****")
for key in FLAGS.__flags.keys():
tf.compat.v1.logging.info(' {}: {}'.format(key, getattr(FLAGS, key)))
tf.compat.v1.logging.info("**************************")
train_examples = None
num_train_steps = None
num_warmup_steps = None
training_hooks.append(LogTrainRunHook(global_batch_size, hvd_rank, FLAGS.save_checkpoints_steps))
# Prepare Training Data
if FLAGS.do_train:
train_examples = read_squad_examples(
input_file=FLAGS.train_file, is_training=True,
version_2_with_negative=FLAGS.version_2_with_negative)
num_train_steps = int(
len(train_examples) / global_batch_size * FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
# Pre-shuffle the input to avoid having to make a very large shuffle
# buffer in in the `input_fn`.
rng = random.Random(12345)
rng.shuffle(train_examples)
start_index = 0
end_index = len(train_examples)
tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record")]
if FLAGS.horovod:
tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record{}".format(i)) for i in range(hvd.size())]
num_examples_per_rank = len(train_examples) // hvd.size()
remainder = len(train_examples) % hvd.size()
if hvd.rank() < remainder:
start_index = hvd.rank() * (num_examples_per_rank+1)
end_index = start_index + num_examples_per_rank + 1
else:
start_index = hvd.rank() * num_examples_per_rank + remainder
end_index = start_index + (num_examples_per_rank)
model_fn = model_fn_builder(
bert_config=bert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
hvd=None if not FLAGS.horovod else hvd,
amp=FLAGS.amp)
estimator = tf.estimator.Estimator(
model_fn=model_fn,
config=run_config)
if FLAGS.do_train:
# We write to a temporary file to avoid storing very large constant tensors
# in memory.
train_writer = FeatureWriter(
filename=tmp_filenames[hvd_rank],
is_training=True)
convert_examples_to_features(
examples=train_examples[start_index:end_index],
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=True,
output_fn=train_writer.process_feature,
verbose_logging=FLAGS.verbose_logging)
train_writer.close()
tf.compat.v1.logging.info("***** Running training *****")
tf.compat.v1.logging.info(" Num orig examples = %d", end_index - start_index)
tf.compat.v1.logging.info(" Num split examples = %d", train_writer.num_features)
tf.compat.v1.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.compat.v1.logging.info(" Num steps = %d", num_train_steps)
tf.compat.v1.logging.info(" LR = %f", learning_rate)
del train_examples
train_input_fn = input_fn_builder(
input_file=tmp_filenames,
batch_size=FLAGS.train_batch_size,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
hvd=None if not FLAGS.horovod else hvd)
train_start_time = time.time()
estimator.train(input_fn=train_input_fn, hooks=training_hooks, max_steps=num_train_steps)
train_time_elapsed = time.time() - train_start_time
train_time_wo_overhead = training_hooks[-1].total_time
avg_sentences_per_second = num_train_steps * global_batch_size * 1.0 / train_time_elapsed
ss_sentences_per_second = (num_train_steps - training_hooks[-1].skipped) * global_batch_size * 1.0 / train_time_wo_overhead
if master_process:
tf.compat.v1.logging.info("-----------------------------")
tf.compat.v1.logging.info("Total Training Time = %0.2f for Sentences = %d", train_time_elapsed,
num_train_steps * global_batch_size)
tf.compat.v1.logging.info("Total Training Time W/O Overhead = %0.2f for Sentences = %d", train_time_wo_overhead,
(num_train_steps - training_hooks[-1].skipped) * global_batch_size)
tf.compat.v1.logging.info("Throughput Average (sentences/sec) with overhead = %0.2f", avg_sentences_per_second)
tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second)
dllogging.logger.log(step=(), data={"throughput_train": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT)
tf.compat.v1.logging.info("-----------------------------")
if FLAGS.export_triton and master_process:
export_model(estimator, FLAGS.output_dir, FLAGS.init_checkpoint)
if FLAGS.do_predict and master_process:
eval_examples = read_squad_examples(
input_file=FLAGS.predict_file, is_training=False,
version_2_with_negative=FLAGS.version_2_with_negative)
# Perform evaluation on subset, useful for profiling
if FLAGS.num_eval_iterations is not None:
eval_examples = eval_examples[:FLAGS.num_eval_iterations*FLAGS.predict_batch_size]
eval_writer = FeatureWriter(
filename=os.path.join(FLAGS.output_dir, "eval.tf_record"),
is_training=False)
eval_features = []
def append_feature(feature):
eval_features.append(feature)
eval_writer.process_feature(feature)
convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=False,
output_fn=append_feature,
verbose_logging=FLAGS.verbose_logging)
eval_writer.close()
tf.compat.v1.logging.info("***** Running predictions *****")
tf.compat.v1.logging.info(" Num orig examples = %d", len(eval_examples))
tf.compat.v1.logging.info(" Num split examples = %d", len(eval_features))
tf.compat.v1.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_input_fn = input_fn_builder(
input_file=eval_writer.filename,
batch_size=FLAGS.predict_batch_size,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=False)
all_results = []
eval_hooks = [LogEvalRunHook(FLAGS.predict_batch_size)]
eval_start_time = time.time()
for result in estimator.predict(
predict_input_fn, yield_single_examples=True, hooks=eval_hooks):
if len(all_results) % 1000 == 0:
tf.compat.v1.logging.info("Processing example: %d" % (len(all_results)))
unique_id = int(result["unique_ids"])
start_logits = [float(x) for x in result["start_logits"].flat]
end_logits = [float(x) for x in result["end_logits"].flat]
all_results.append(
RawResult(
unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
eval_time_elapsed = time.time() - eval_start_time
time_list = eval_hooks[-1].time_list
time_list.sort()
# Removing outliers (init/warmup) in throughput computation.
eval_time_wo_overhead = sum(time_list[:int(len(time_list) * 0.99)])
num_sentences = (int(len(time_list) * 0.99)) * FLAGS.predict_batch_size
avg = np.mean(time_list)
cf_50 = max(time_list[:int(len(time_list) * 0.50)])
cf_90 = max(time_list[:int(len(time_list) * 0.90)])
cf_95 = max(time_list[:int(len(time_list) * 0.95)])
cf_99 = max(time_list[:int(len(time_list) * 0.99)])
cf_100 = max(time_list[:int(len(time_list) * 1)])
ss_sentences_per_second = num_sentences * 1.0 / eval_time_wo_overhead
tf.compat.v1.logging.info("-----------------------------")
tf.compat.v1.logging.info("Total Inference Time = %0.2f for Sentences = %d", eval_time_elapsed,
eval_hooks[-1].count * FLAGS.predict_batch_size)
tf.compat.v1.logging.info("Total Inference Time W/O Overhead = %0.2f for Sentences = %d", eval_time_wo_overhead,
num_sentences)
tf.compat.v1.logging.info("Summary Inference Statistics")
tf.compat.v1.logging.info("Batch size = %d", FLAGS.predict_batch_size)
tf.compat.v1.logging.info("Sequence Length = %d", FLAGS.max_seq_length)
tf.compat.v1.logging.info("Precision = %s", "fp16" if FLAGS.amp else "fp32")
tf.compat.v1.logging.info("Latency Confidence Level 50 (ms) = %0.2f", cf_50 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 90 (ms) = %0.2f", cf_90 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 95 (ms) = %0.2f", cf_95 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 99 (ms) = %0.2f", cf_99 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 100 (ms) = %0.2f", cf_100 * 1000)
tf.compat.v1.logging.info("Latency Average (ms) = %0.2f", avg * 1000)
tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second)
dllogging.logger.log(step=(), data={"throughput_val": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT)
tf.compat.v1.logging.info("-----------------------------")
output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json")
output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json")
output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json")
write_predictions(eval_examples, eval_features, all_results,
FLAGS.n_best_size, FLAGS.max_answer_length,
FLAGS.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file,
FLAGS.version_2_with_negative, FLAGS.verbose_logging)
if FLAGS.eval_script:
import sys
import subprocess
eval_out = subprocess.check_output([sys.executable, FLAGS.eval_script,
FLAGS.predict_file, output_prediction_file])
scores = str(eval_out).strip()
exact_match = float(scores.split(":")[1].split(",")[0])
f1 = float(scores.split(":")[2].split("}")[0])
dllogging.logger.log(step=(), data={"f1": f1}, verbosity=Verbosity.DEFAULT)
dllogging.logger.log(step=(), data={"exact_match": exact_match}, verbosity=Verbosity.DEFAULT)
print(str(eval_out))
if __name__ == "__main__":
FLAGS = extract_run_squad_flags()
tf.app.run() |
CUDA-Optimized/FastSpeech/fastspeech/utils | utils | tensorboard | # 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.
import matplotlib.pyplot as plt
import numpy as np
import cv2
import data as global_data
plt.switch_backend('Agg')
def image_plot(x, name='image'):
fig, ax = plt.subplots()
ax.imshow(x, cmap='magma', aspect='auto')
fig.canvas.draw()
buf = np.array(fig.canvas.renderer._renderer)
plt.clf()
plt.close('all')
cv2.imshow(name, buf)
cv2.waitKey(0)
def plot_to_buf(x, align=True):
fig, ax = plt.subplots()
ax.plot(x)
if align:
ax.set_ylim([-1, 1])
fig.canvas.draw()
im = np.array(fig.canvas.renderer._renderer)
plt.clf()
plt.close('all')
return np.rollaxis(im[..., :3], 2)
def imshow_to_buf(x, scale01=False):
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
return np.exp(x) / np.sum(np.exp(x), axis=0)
if scale01:
x = (x - x.min()) / (x.max() - x.min())
if x.max() > 1.:
x = softmax(x)
if len(x.shape) == 3:
x = x[0]
fig, ax = plt.subplots()
ax.imshow(x, cmap='magma', aspect='auto')
fig.canvas.draw()
im = np.array(fig.canvas.renderer._renderer)
plt.clf()
plt.close('all')
return np.rollaxis(im[..., :3], 2)
def origin_to_chrs(target):
results = []
for t in target:
idx = t - 1 if t - 1 >= 0 else 0
if idx < len(global_data.idx2chr):
results.append(global_data.idx2chr[idx])
else:
break
return ''.join(results) |
PyTorch/DrugDiscovery/MoFlow/scripts | scripts | predict | #!/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.
bs=${1:-512}
prec=${2:-amp}
flags="${@:3}"
cmd="python \
/workspace/moflow_pyt/moflow/runtime/generate.py \
--batch_size ${bs} \
--jit \
--correct_validity \
${flags} \
"
if [ $prec == "amp" ]; then
cmd="${cmd} --amp"
fi
set -x
bash -c "${cmd}"
|
PaddlePaddle/LanguageModeling/BERT/vocab | vocab | bert-base-uncased-vocab | [PAD]
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owned
older
via
heavy
1st
makes
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africa
##ri
stated
length
ended
fingers
command
staff
skin
foreign
opening
governor
okay
medal
kill
sun
cover
job
1985
introduced
chest
hell
feeling
##ies
success
meet
reason
standard
meeting
novel
1984
trade
source
buildings
##land
rose
guy
goal
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chapter
native
husband
previously
unit
limited
entered
weeks
producer
operations
mountain
takes
covered
forced
related
roman
complete
successful
key
texas
cold
##ya
channel
1980
traditional
films
dance
clear
approximately
500
nine
van
prince
question
active
tracks
ireland
regional
silver
author
personal
sense
operation
##ine
economic
1983
holding
twenty
isbn
additional
speed
hour
edition
regular
historic
places
whom
shook
movie
km²
secretary
prior
report
chicago
read
foundation
view
engine
scored
1982
units
ask
airport
property
ready
immediately
lady
month
listed
contract
##de
manager
themselves
lines
##ki
navy
writer
meant
##ts
runs
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practice
championships
singer
glass
commission
required
forest
starting
culture
generally
giving
access
attended
test
couple
stand
catholic
martin
caught
executive
##less
eye
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thinking
chair
quite
shoulder
1979
hope
decision
plays
defeated
municipality
whether
structure
offered
slowly
pain
ice
direction
##ion
paper
mission
1981
mostly
200
noted
individual
managed
nature
lives
plant
##ha
helped
except
studied
computer
figure
relationship
issue
significant
loss
die
smiled
gun
ago
highest
1972
##am
male
bring
goals
mexico
problem
distance
commercial
completely
location
annual
famous
drive
1976
neck
1978
surface
caused
italy
understand
greek
highway
wrong
hotel
comes
appearance
joseph
double
issues
musical
companies
castle
income
review
assembly
bass
initially
parliament
artists
experience
1974
particular
walk
foot
engineering
talking
window
dropped
##ter
miss
baby
boys
break
1975
stars
edge
remember
policy
carried
train
stadium
bar
sex
angeles
evidence
##ge
becoming
assistant
soviet
1977
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step
wing
1970
youth
financial
reach
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actor
numerous
##se
##st
nodded
arrived
##ation
minute
##nt
believed
sorry
complex
beautiful
victory
associated
temple
1968
1973
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metal
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1945
bishop
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lee
launched
particularly
tree
le
retired
subject
prize
contains
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terms
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champion
1971
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pass
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names
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port
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tom
corner
represented
watch
legal
anti
justice
cause
watched
brothers
45
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changes
simply
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louis
fast
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answer
60
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1969
stories
straight
create
feature
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rate
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virginia
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activities
cultural
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programs
basketball
legs
guard
beyond
cast
doctor
mm
flight
results
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cost
effect
winter
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larger
islands
problems
chairman
grew
commander
isn
1967
pay
failed
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fort
box
regiment
majority
journal
35
edward
plans
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##ni
shown
pretty
irish
characters
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scene
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operated
allow
spring
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houses
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beach
marriage
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rules
oil
65
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expected
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congress
sam
peace
recent
iii
wait
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variety
serving
agreed
please
poor
joe
pacific
attempt
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piece
prime
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mile
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1964
1966
soldiers
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1965
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fighting
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score
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editor
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filled
genus
specific
sitting
super
mom
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supported
status
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cases
store
meaning
wales
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vice
frank
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horse
fifth
remaining
branch
32
presented
stared
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uses
secret
forms
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baseball
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truth
russia
ball
color
kiss
dad
wind
continue
ring
referred
numbers
digital
greater
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metres
slightly
direct
increase
1960
responsible
crew
rule
trees
troops
##no
broke
goes
individuals
hundred
weight
creek
sleep
memory
defense
provides
ordered
code
value
jewish
windows
1944
safe
judge
whatever
corps
realized
growing
pre
##ga
cities
alexander
gaze
lies
spread
scott
letter
showed
situation
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transport
watching
workers
extended
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chart
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border
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host
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walls
piano
##ko
heat
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products
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seasons
join
grade
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machine
1963
territory
mainly
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stations
squadron
1962
stepped
iron
19th
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serve
appear
sky
speak
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charge
knowledge
kilometres
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ships
article
campus
simple
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boston
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poland
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quality
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presence
planned
nations
mass
broadcast
jean
share
image
influence
wild
offer
emperor
electric
reading
headed
ability
promoted
yellow
ministry
1942
throat
smaller
politician
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latin
spoke
cars
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males
lack
pop
80
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acting
seeing
consists
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estate
1961
pressure
johnson
newspaper
jr
chris
olympics
online
conditions
beat
elements
walking
vote
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needs
carolina
text
featuring
global
block
shirt
levels
francisco
purpose
females
et
dutch
duke
ahead
gas
twice
safety
serious
turning
highly
lieutenant
firm
maria
amount
mixed
daniel
proposed
perfect
agreement
affairs
3rd
seconds
contemporary
paid
1943
prison
save
kitchen
label
administrative
intended
constructed
academic
nice
teacher
races
1956
formerly
corporation
ben
nation
issued
shut
1958
drums
housing
victoria
seems
opera
1959
graduated
function
von
mentioned
picked
build
recognized
shortly
protection
picture
notable
exchange
elections
1980s
loved
percent
racing
fish
elizabeth
garden
volume
hockey
1941
beside
settled
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1940
competed
replied
drew
1948
actress
marine
scotland
steel
glanced
farm
steve
1957
risk
tonight
positive
magic
singles
effects
gray
screen
dog
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bus
sides
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literature
polish
destroyed
flying
founder
households
1939
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gallery
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1946
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approach
appearances
urban
ones
1950
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avenue
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projects
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revealed
basic
90
infantry
pair
equipment
visit
33
evening
search
grant
effort
solo
treatment
buried
republican
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bottom
owner
1970s
israel
gives
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dream
bob
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70
notes
produce
champions
contact
ed
soul
accepted
ways
del
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split
price
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basis
trial
questions
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1955
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officially
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initial
##ization
whispered
median
engineer
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sydney
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strength
300
1952
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00
card
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agent
1947
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44
draw
warm
supposed
com
pro
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transferred
leaned
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mountains
asia
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activity
entertainment
seem
traffic
jackson
murder
36
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product
orchestra
haven
agency
bbc
taught
website
comedy
unable
storm
planning
albums
rugby
environment
scientific
grabbed
protect
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boat
typically
1954
1953
damage
principal
divided
dedicated
mount
ohio
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pick
fought
driver
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shoulders
sort
thank
berlin
prominent
account
freedom
necessary
efforts
alex
headquarters
follows
alongside
des
simon
andrew
suggested
operating
learning
steps
1949
sweet
technical
begin
easily
34
teeth
speaking
settlement
scale
##sh
renamed
ray
max
enemy
semi
joint
compared
##rd
scottish
leadership
analysis
offers
georgia
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organized
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combined
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challenge
1960s
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telling
path
1951
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1990s
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assigned
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pull
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conducted
wearing
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fleet
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64
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iran
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parties
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generation
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quick
fields
brigade
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clothes
impact
weapons
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flat
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supreme
1938
37
relations
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plants
suffered
1936
wilson
kids
begins
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1918
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models
worth
laws
400
communities
classes
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knows
thanks
quarter
reaching
humans
carry
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75
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disease
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possibly
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harry
doors
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1937
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42
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vision
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1935
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entry
1920
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claims
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labor
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intelligence
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39
sunday
48
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fame
hunter
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1930
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figures
davis
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faculty
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cemetery
1919
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1933
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explained
rain
composer
injury
apartment
municipal
educational
occupied
netherlands
clean
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1914
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1934
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1917
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41
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1929
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1932
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52
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1927
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77
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73
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hp
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1892
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1891
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83
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1888
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1864
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1865
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op
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gates
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1887
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1862
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1863
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1886
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forcing
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discussed
substantial
ranks
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1870
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switch
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1884
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resort
demolished
hans
lucy
1881
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writes
craig
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beth
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ss
viewed
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sixteen
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hampshire
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1882
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scientist
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lanka
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outdoor
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1883
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1878
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tube
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110
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1876
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1879
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1875
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hampton
diagnosis
1824
throws
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rita
disputed
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nobility
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organisms
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chasing
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1810
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230
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190
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128
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visions
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kingdoms
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laboratories
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metric
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mound
brett
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1818
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113
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1827
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1793
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114
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1806
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1823
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clip
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witches
117
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rey
hut
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parkway
trauma
gran
malay
121
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2020
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210
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116
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118
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127
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1808
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122
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123
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1794
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119
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124
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280
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colombian
comparing
dolphins
perkins
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reprinted
unanimous
bounced
hairs
underworld
midwest
semester
bucket
paperback
miniseries
coventry
demise
##leigh
demonstrations
sensor
rotating
yan
##hler
arrange
soils
##idge
hyderabad
labs
##dr
brakes
grandchildren
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negotiated
rover
ferrari
continuation
directorate
augusta
stevenson
counterpart
gore
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nursery
rican
ave
collectively
broadly
pastoral
repertoire
asserted
discovering
nordic
styled
fiba
cunningham
harley
middlesex
survives
tumor
tempo
zack
aiming
lok
urgent
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devils
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contractor
turin
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bliss
repaired
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moan
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cr
negotiate
lyric
1890s
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bred
clad
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posed
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hernandez
possessions
elbows
psychiatric
strokes
confluence
electorate
lifts
campuses
lava
alps
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##ution
##date
physicist
woody
##page
##ographic
##itis
juliet
reformation
sparhawk
320
complement
suppressed
jewel
##½
floated
##kas
continuity
sadly
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inability
melting
scanning
paula
flour
judaism
safer
vague
##lm
solving
curb
##stown
financially
gable
bees
expired
miserable
cassidy
dominion
1789
cupped
145
robbery
facto
amos
warden
resume
tallest
marvin
ing
pounded
usd
declaring
gasoline
##aux
darkened
270
650
sophomore
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erection
gossip
televised
risen
dial
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pillars
##link
passages
profound
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ashton
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nail
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fleming
firearms
ducked
circuits
blows
waterloo
titans
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atom
fireplace
cheshire
financed
activation
algorithms
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constituent
catcher
cherokee
partnerships
sexuality
platoon
tragic
vivian
guarded
whiskey
meditation
poetic
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porto
listeners
dominance
kendra
mona
chandler
factions
22nd
salisbury
attitudes
derivative
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intake
paced
javier
illustrator
barrels
bias
cockpit
burnett
dreamed
ensuing
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receptors
someday
hawkins
mattered
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slavic
1799
jesuit
cameroon
wasted
tai
wax
lowering
victorious
freaking
outright
hancock
librarian
sensing
bald
calcium
myers
tablet
announcing
barack
shipyard
pharmaceutical
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greenwich
flush
medley
patches
wolfgang
pt
speeches
acquiring
exams
nikolai
##gg
hayden
kannada
##type
reilly
##pt
waitress
abdomen
devastated
capped
pseudonym
pharmacy
fulfill
paraguay
1796
clicked
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archipelago
syndicated
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lumber
orgasm
rejection
clifford
lorraine
advent
mafia
rodney
brock
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cassette
chamberlain
despair
mongolia
sensors
developmental
upstream
##eg
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spanning
165
trombone
basque
seeded
interred
renewable
rhys
leapt
revision
molecule
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chord
vicious
nord
shivered
23rd
arlington
debts
corpus
sunrise
bays
blackburn
centimetres
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shuddered
gm
strangely
gripping
cartoons
isabelle
orbital
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seals
proving
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refusal
strengthened
bust
assisting
baghdad
batsman
portrayal
mara
pushes
spears
og
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reside
nathaniel
brennan
1776
confirmation
caucus
##worthy
markings
yemen
nobles
ku
lazy
viewer
catalan
encompasses
sawyer
##fall
sparked
substances
patents
braves
arranger
evacuation
sergio
persuade
dover
tolerance
penguin
cum
jockey
insufficient
townships
occupying
declining
plural
processed
projection
puppet
flanders
introduces
liability
##yon
gymnastics
antwerp
taipei
hobart
candles
jeep
wes
observers
126
chaplain
bundle
glorious
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hazel
flung
sol
excavations
dumped
stares
sh
bangalore
triangular
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intervals
expressing
turbine
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songwriting
crafts
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jasmine
ditch
rite
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entertaining
comply
sorrow
wrestlers
basel
emirates
marian
rivera
helpful
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caution
downward
networking
##atory
##tered
darted
genocide
emergence
replies
specializing
spokesman
convenient
unlocked
fading
augustine
concentrations
resemblance
elijah
investigator
andhra
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promotes
bean
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fleeing
wan
simone
announcer
##ame
##bby
lydia
weaver
132
residency
modification
##fest
stretches
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alternatively
nat
lowe
lacks
##ented
pam
tile
concealed
inferior
abdullah
residences
tissues
vengeance
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moisture
peculiar
groove
zip
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jennings
ninja
oversaw
zombies
pumping
batch
livingston
emerald
installations
1797
peel
nitrogen
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schooling
strands
responding
werner
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lime
casa
accurately
targeting
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underway
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lester
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occupies
2d
griffith
angrily
reorganized
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courtney
deposited
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dunn
exiled
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checks
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##о
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unexpectedly
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assessed
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observing
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spiders
kn
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nodes
prosperity
audrey
divisional
155
broncos
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adjust
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surf
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admiralty
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gt
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transcription
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violently
clutching
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cyclist
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ragged
unnecessary
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stubborn
coordinated
harriet
baba
disqualified
330
insect
wolfe
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reinforcements
rocked
duel
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bricks
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pending
brightly
jealousy
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stein
kidney
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underwear
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habits
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uranium
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disguise
bundesliga
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dundee
erotic
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collectors
float
individually
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behavioral
bucharest
ser
hare
valerie
corporal
nutrition
proportional
##isa
immense
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pavement
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sutherland
crouched
1775
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suzuki
trades
endurance
operas
crosby
prayed
priory
rory
socially
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gujarat
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walton
cube
pasha
privilege
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floods
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waterfall
nipple
scouting
approve
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confessed
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investor
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pagan
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confront
bert
embarrassment
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aston
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mortar
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selfish
exports
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unfinished
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canoe
solitary
buchanan
delays
magistrate
fk
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motivation
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recruiting
assess
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malik
antique
1791
pius
rahman
reich
tub
zhou
smashed
airs
galway
xii
conditioning
honduras
discharged
dexter
##pf
lionel
129
debates
lemon
tiffany
volunteered
dom
dioxide
procession
devi
sic
tremendous
advertisements
colts
transferring
verdict
hanover
decommissioned
utter
relate
pac
racism
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beacon
limp
similarity
terra
occurrence
ant
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becky
capt
updates
armament
richie
pal
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halloween
mayo
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cara
serena
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obligations
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violated
lafayette
jakarta
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iconic
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kitty
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dread
spill
crystals
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modeled
bluff
equilibrium
separating
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ordnance
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onset
cosmic
attachment
sammy
expose
privy
anchored
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abbott
admits
bending
baritone
emmanuel
policeman
vaughan
winged
climax
dresses
denny
polytechnic
mohamed
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authentic
nikki
genetics
grandparents
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dissertation
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monaco
##ي
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prominently
dispatched
podium
##mir
uci
##uation
133
fortifications
birthplace
kendall
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preacher
rack
goodman
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persistent
##ott
countless
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recorder
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persecution
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renewal
wagons
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choral
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lawson
tailed
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turf
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rebuild
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td
rowan
audition
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fortunately
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harp
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illustration
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presses
practitioners
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recognizing
relates
misery
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potatoes
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manners
hbo
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ours
bullshit
ernie
perennial
sensitivity
illuminated
rupert
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haul
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solidarity
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igor
pedestrian
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tenants
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medication
unlimited
guiding
impacts
diabetes
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sasha
pas
clive
extraction
131
continually
constraints
##bilities
sonata
hunted
sixteenth
chu
planting
quote
mayer
pretended
abs
spat
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pitching
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playground
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mates
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desires
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drought
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mai
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shove
posting
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pilgrimage
humorous
packing
fry
pencil
wines
smells
144
marilyn
aching
newest
clung
bon
neighbours
sanctioned
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drowning
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lilly
investigators
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compulsory
packet
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depressed
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heroic
importantly
theresa
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saturn
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rae
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royce
pact
jasper
explosives
packaging
aluminium
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denotes
rhythmic
spans
assignments
hereditary
outlined
originating
sundays
lad
reissued
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plots
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extract
masculine
blur
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trembled
owens
hymn
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reacted
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talbot
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1200
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creativity
billed
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deacon
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identifies
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gunner
hogan
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reconstructed
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clearance
darius
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fascist
neville
exceeded
upwards
viable
ducks
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possesses
1600
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disturbing
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1870s
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ren
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ct
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26th
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aggression
liaison
cooler
cares
headache
plantations
gorge
dots
impulse
thickness
ashamed
averaging
kathy
obligation
precursor
137
fowler
symmetry
thee
225
hears
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damaging
friction
drifting
expeditions
hardened
prostitution
152
fauna
blankets
claw
tossing
snarled
butterflies
recruits
investigative
coated
healed
138
communal
hai
xiii
academics
boone
psychologist
restless
lahore
stephens
mba
brendan
foreigners
printer
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ached
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27th
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scratched
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1780
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hercules
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noun
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kira
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misty
rails
sustainability
134
unused
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1783
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185
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muddy
backgrounds
136
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huang
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141
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usc
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castles
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1788
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sprang
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administrators
rotterdam
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ions
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340
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mans
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kindness
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distribute
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seeming
143
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1777
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acceleration
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enforce
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alvarez
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bremen
translates
ensured
waterfront
29th
forrest
manny
malone
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cookies
simpler
absorption
205
engraved
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1778
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146
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crossover
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suppression
fatigue
cutter
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201
wurttemberg
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defendants
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1900s
killings
textbook
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cinematography
wharf
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setup
creed
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inequality
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signatures
fallon
370
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laurie
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excavation
threads
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148
frantically
icc
utilize
determines
respiratory
thoughtful
receptions
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chandra
seine
147
builders
builds
diagnostic
dev
visibility
goddamn
analyses
dhaka
cho
proves
chancel
concurrent
curiously
canadians
pumped
restoring
1850s
turtles
jaguar
sinister
spinal
traction
declan
vows
1784
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capitalism
swirling
install
universidad
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coincidence
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combustion
infectious
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conclude
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jew
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surreal
toni
##wc
harmonica
151
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fishermen
intending
bravery
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klaus
titan
wembley
taiwanese
ransom
40th
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eyelids
jp
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utilities
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granada
lana
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openings
concord
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bethany
connie
morality
sega
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earnings
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communes
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severed
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##17
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analyzed
warlord
hubbard
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behave
slovenian
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trance
caldwell
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dukes
instinctively
conan
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accessories
gradual
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sioux
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undertake
intercepted
sparkling
compressed
139
fungus
tombs
haley
imposing
rests
degradation
lincolnshire
retailers
wetlands
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distributor
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convey
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oman
dresser
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digits
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juniors
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cents
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une
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nan
technician
fibers
liking
royalty
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154
persia
terribly
magician
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vance
cafeteria
booker
camille
warmer
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consume
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chromosome
dwellings
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mole
153
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relics
tenant
mandated
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fridge
hats
honesty
patented
raul
heap
cruisers
accusing
enlightenment
infants
wherein
chatham
contractors
zen
affinity
hc
osborne
piston
156
traps
maturity
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protocols
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moldova
italians
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expresses
thumbs
167
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coca
1781
cheating
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skipped
sensory
heidelberg
spies
satan
dangers
semifinal
202
bohemia
whitish
confusing
shipbuilding
relies
surgeons
landings
ravi
baku
moor
suffix
alejandro
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litre
upheld
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rajasthan
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coaster
insists
posture
scenarios
etienne
favoured
appoint
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defences
fulfilled
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somali
1758
chalk
potent
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migrants
wink
assistants
nos
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activism
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colon
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magnet
playable
xvi
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overthrow
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newcomer
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farmhouse
interrogation
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committing
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forthcoming
strawberry
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macau
ponds
shocking
siberia
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chant
contributors
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sped
absorb
hail
1782
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karate
opus
originates
saul
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correlation
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weekday
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tracing
brig
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pathways
utilizing
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disturbance
kneeling
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100th
pune
##thy
decreasing
168
manipulation
miriam
academia
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occupational
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racist
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wikipedia
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zu
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choreography
divinity
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apocalypse
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elves
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programmed
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cancellation
flexibility
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stronghold
nets
specializes
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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
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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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tramway
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staffordshire
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watkins
sleepy
candidacy
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timeline
throbbing
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uzbekistan
vanderbilt
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ballots
gem
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ang
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saloon
stab
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terrifying
rom
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coil
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satisfying
vendors
31st
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tugging
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unionist
rites
foley
morse
motives
creepy
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tory
reorganization
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depict
biographer
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unopposed
manifesto
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kapoor
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kilkenny
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lively
##13
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cautious
proposes
sanctions
trenton
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tor
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elf
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reissue
intro
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brace
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dem
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cows
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northward
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biodiversity
cora
310
roaring
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connell
theologian
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compose
pathetic
unmarried
dynamo
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az
calculation
toulouse
deserves
humour
nr
forgiveness
tam
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martyr
pamela
myths
whore
counselor
hicks
290
heavens
battleship
electromagnetic
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stellar
establishments
presley
hopped
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temptation
90s
wills
nas
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seminars
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satirical
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characterised
correspond
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participates
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eruption
skate
therapeutic
1785
canals
wholesale
defaulted
sac
460
petit
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virgil
leak
ravens
256
portraying
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ghetto
creators
dams
portray
vicente
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fae
namesake
bounty
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joachim
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aforementioned
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480
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earnest
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inflicted
objections
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gritted
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jericho
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flick
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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
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businessmen
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ul
nationale
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scenery
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161
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constitutes
cpu
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jurisdictions
sd
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algebraic
marquess
bahn
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paperwork
bestowed
natasha
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oceans
purchases
157
vaccine
215
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fixtures
playhouse
integrate
jai
oswald
intellectuals
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booked
nests
mortimer
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obsession
sept
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##sum
440
scrutiny
simultaneous
squinted
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collects
oven
shankar
penned
remarkably
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luggage
spectral
1786
collaborations
louie
consolidation
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420
hoover
blackpool
harness
ignition
vest
tails
belmont
mongol
skinner
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visually
mage
derry
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stevie
transitional
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redskins
drying
prep
prospective
##21
annoyance
oversee
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fills
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announces
fda
scowled
respects
prasad
mystic
tucson
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revue
springer
bankrupt
1772
aristotle
salvatore
habsburg
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dal
natal
nut
pod
chewing
darts
moroccan
walkover
rosario
lenin
punjabi
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grossed
scattering
wired
invasive
hui
polynomial
corridors
wakes
gina
portrays
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erich
irwin
sniper
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lindsey
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butch
shutting
socio
bounce
commemorative
postseason
jeremiah
pines
275
mystical
beads
bp
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furnace
bidding
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rubble
enclosure
sob
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detailing
grayson
riga
sloane
raging
##yah
herbs
bravo
##athlon
alloy
giggle
imminent
suffers
assumptions
waltz
##itate
accomplishments
##ited
bathing
remixed
deception
prefix
##emia
deepest
##tier
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balkan
frogs
##rong
slab
##pate
philosophers
peterborough
grains
imports
dickinson
rwanda
##atics
1774
dirk
lan
tablets
##rove
clone
##rice
caretaker
hostilities
mclean
##gre
regimental
treasures
norms
impose
tsar
tango
diplomacy
variously
complain
192
recognise
arrests
1779
celestial
pulitzer
##dus
bing
libretto
##moor
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
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chiang
darcy
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mandolin
spice
statesman
babylon
182
walled
forgetting
afro
##cap
158
giorgio
buffer
##polis
planetary
##gis
overlap
terminals
kinda
centenary
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arising
manipulate
elm
ke
1770
ak
##tad
chrysler
mapped
moose
pomeranian
quad
macarthur
assemblies
shoreline
recalls
stratford
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noticeable
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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
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maddie
apologized
luton
plagued
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nana
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sway
fanny
łodz
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psi
suspicions
hanged
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initiate
charlton
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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
##max
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
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accountant
analyze
flicker
foil
priesthood
voluntarily
decreases
##80
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slater
sv
charting
mcgill
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moreno
##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
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mauritius
salle
sincere
beers
hooks
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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
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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
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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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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
##get
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
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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
##comb
trait
transporting
##ddin
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##yna
depart
gregg
mold
ledge
hangar
oldham
playboy
termination
analysts
gmbh
romero
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insist
cradle
filthy
brightness
slash
shootout
deposed
bordering
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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
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nike
salzburg
swell
yen
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idle
ligue
samson
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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
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kitten
priorities
addison
pseudo
##guard
dusk
icons
sermon
##psis
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bt
##lift
##xt
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
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minimize
hydro
wrecked
wrought
palazzo
##gling
cans
vernacular
friedman
nobleman
shale
walnut
danielle
##ection
##tley
sears
##kumar
chords
lend
flipping
streamed
por
dracula
gallons
sacrifices
gamble
orphanage
##iman
mckenzie
##gible
boxers
daly
##balls
##ان
208
##ific
##rative
##iq
exploited
slated
##uity
circling
hillary
pinched
goldberg
provost
campaigning
lim
piles
ironically
jong
mohan
successors
usaf
##tem
##ught
autobiographical
haute
preserves
##ending
acquitted
comparisons
203
hydroelectric
gangs
cypriot
torpedoes
rushes
chrome
derive
bumps
instability
fiat
pets
##mbe
silas
dye
reckless
settler
##itation
info
heats
##writing
176
canonical
maltese
fins
mushroom
stacy
aspen
avid
##kur
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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
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pomerania
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ronan
seizure
pavel
problematic
##zaki
domenico
##ulin
catering
penelope
dependence
parental
emilio
ministerial
atkinson
##bolic
clarkson
chargers
colby
grill
peeked
arises
summon
##aged
fools
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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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##eve
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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##ssi
pg
##ometer
##lington
sanitation
##tility
intercontinental
apps
##adt
¹⁄₂
cylinders
economies
favourable
unison
croix
gertrude
odyssey
vanity
dangling
##logists
upgrades
dice
middleweight
practitioner
##ight
206
henrik
parlor
orion
angered
lac
python
blurted
##rri
sensual
intends
swings
angled
##phs
husky
attain
peerage
precinct
textiles
cheltenham
shuffled
dai
confess
tasting
bhutan
##riation
tyrone
segregation
abrupt
ruiz
##rish
smirked
blackwell
confidential
browning
amounted
##put
vase
scarce
fabulous
raided
staple
guyana
unemployed
glider
shay
##tow
carmine
troll
intervene
squash
superstar
##uce
cylindrical
len
roadway
researched
handy
##rium
##jana
meta
lao
declares
##rring
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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
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aperture
orioles
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doncaster
intrigued
bombed
coating
otis
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cocktail
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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
##mot
##db
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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
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disliked
lublin
salesman
##ulates
spherical
whatsoever
sweating
avalon
contention
punt
severity
alderman
atari
##dina
##grant
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scarf
seville
vertices
annexation
fairfield
fascination
inspiring
launches
palatinate
regretted
##rca
feral
##iom
elk
nap
olsen
reddy
yong
##leader
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garment
transports
feng
gracie
outrage
viceroy
insides
##esis
breakup
grady
organizer
softer
grimaced
222
murals
galicia
arranging
vectors
##rsten
bas
##sb
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sloan
##eka
bitten
ara
fender
nausea
bumped
kris
banquet
comrades
detector
persisted
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adjustment
endowed
cinemas
##shot
sellers
##uman
peek
epa
kindly
neglect
simpsons
talon
mausoleum
runaway
hangul
lookout
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rewards
coughed
acquainted
chloride
##ald
quicker
accordion
neolithic
##qa
artemis
coefficient
lenny
pandora
tx
##xed
ecstasy
litter
segunda
chairperson
gemma
hiss
rumor
vow
nasal
antioch
compensate
patiently
transformers
##eded
judo
morrow
penis
posthumous
philips
bandits
husbands
denote
flaming
##any
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langley
yorker
1760
walters
##uo
##kle
gubernatorial
fatty
samsung
leroy
outlaw
##nine
unpublished
poole
jakob
##ᵢ
##ₙ
crete
distorted
superiority
##dhi
intercept
crust
mig
claus
crashes
positioning
188
stallion
301
frontal
armistice
##estinal
elton
aj
encompassing
camel
commemorated
malaria
woodward
calf
cigar
penetrate
##oso
willard
##rno
##uche
illustrate
amusing
convergence
noteworthy
##lma
##rva
journeys
realise
manfred
##sable
410
##vocation
hearings
fiance
##posed
educators
provoked
adjusting
##cturing
modular
stockton
paterson
vlad
rejects
electors
selena
maureen
##tres
uber
##rce
swirled
##num
proportions
nanny
pawn
naturalist
parma
apostles
awoke
ethel
wen
##bey
monsoon
overview
##inating
mccain
rendition
risky
adorned
##ih
equestrian
germain
nj
conspicuous
confirming
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shivering
##imeter
milestone
rumours
flinched
bounds
smacked
token
##bei
lectured
automobiles
##shore
impacted
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nouns
nero
##leaf
ismail
prostitute
trams
##lace
bridget
sud
stimulus
impressions
reins
revolves
##oud
##gned
giro
honeymoon
##swell
criterion
##sms
##uil
libyan
prefers
##osition
211
preview
sucks
accusation
bursts
metaphor
diffusion
tolerate
faye
betting
cinematographer
liturgical
specials
bitterly
humboldt
##ckle
flux
rattled
##itzer
archaeologists
odor
authorised
marshes
discretion
##ов
alarmed
archaic
inverse
##leton
explorers
##pine
drummond
tsunami
woodlands
##minate
##tland
booklet
insanity
owning
insert
crafted
calculus
##tore
receivers
##bt
stung
##eca
##nched
prevailing
travellers
eyeing
lila
graphs
##borne
178
julien
##won
morale
adaptive
therapist
erica
cw
libertarian
bowman
pitches
vita
##ional
crook
##ads
##entation
caledonia
mutiny
##sible
1840s
automation
##ß
flock
##pia
ironic
pathology
##imus
remarried
##22
joker
withstand
energies
##att
shropshire
hostages
madeleine
tentatively
conflicting
mateo
recipes
euros
ol
mercenaries
nico
##ndon
albuquerque
augmented
mythical
bel
freud
##child
cough
##lica
365
freddy
lillian
genetically
nuremberg
calder
209
bonn
outdoors
paste
suns
urgency
vin
restraint
tyson
##cera
##selle
barrage
bethlehem
kahn
##par
mounts
nippon
barony
happier
ryu
makeshift
sheldon
blushed
castillo
barking
listener
taped
bethel
fluent
headlines
pornography
rum
disclosure
sighing
mace
doubling
gunther
manly
##plex
rt
interventions
physiological
forwards
emerges
##tooth
##gny
compliment
rib
recession
visibly
barge
faults
connector
exquisite
prefect
##rlin
patio
##cured
elevators
brandt
italics
pena
173
wasp
satin
ea
botswana
graceful
respectable
##jima
##rter
##oic
franciscan
generates
##dl
alfredo
disgusting
##olate
##iously
sherwood
warns
cod
promo
cheryl
sino
##ة
##escu
twitch
##zhi
brownish
thom
ortiz
##dron
densely
##beat
carmel
reinforce
##bana
187
anastasia
downhill
vertex
contaminated
remembrance
harmonic
homework
##sol
fiancee
gears
olds
angelica
loft
ramsay
quiz
colliery
sevens
##cape
autism
##hil
walkway
##boats
ruben
abnormal
ounce
khmer
##bbe
zachary
bedside
morphology
punching
##olar
sparrow
convinces
##35
hewitt
queer
remastered
rods
mabel
solemn
notified
lyricist
symmetric
##xide
174
encore
passports
wildcats
##uni
baja
##pac
mildly
##ease
bleed
commodity
mounds
glossy
orchestras
##omo
damian
prelude
ambitions
##vet
awhile
remotely
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asserts
imply
##iques
distinctly
modelling
remedy
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windshield
dani
xiao
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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
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gaius
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captures
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dorm
claudio
##sic
benches
knighted
blackness
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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
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curran
##west
bois
325
induce
surveying
emperors
sax
unpopular
##kk
cartoonist
fused
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unto
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localities
##cko
##ln
darlington
slain
academie
lobbying
sediment
puzzles
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defiance
dickens
manifest
tongues
alumnus
arbor
coincide
184
appalachian
mustafa
examiner
cabaret
traumatic
yves
bracelet
draining
heroin
magnum
baths
odessa
consonants
mitsubishi
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kellan
vaudeville
##fr
joked
null
straps
probation
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ceded
interfaces
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##zawa
blinding
viet
224
rothschild
museo
640
huddersfield
##vr
tactic
##storm
brackets
dazed
incorrectly
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reg
glazed
fearful
manifold
benefited
irony
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stumbling
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willingness
balkans
mei
wraps
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injected
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gu
syed
harmless
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takeoff
poppy
timor
cardboard
astronaut
purdue
weeping
southbound
cursing
stalls
diagonal
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lamar
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comte
weekdays
harrington
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negatively
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lays
grouping
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affirmed
halle
modernist
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smelling
aristocratic
baptized
dismiss
justification
oilers
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coupling
qin
snack
healer
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gardener
layla
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formulated
stephenson
gravitational
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1768
granny
coordinating
suites
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monarchs
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sep
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apr
barrister
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fia
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paranoid
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churchyard
covert
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creep
abandoning
tr
transmit
conceal
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understands
readiness
spire
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##enia
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610
startling
unlock
vida
bowled
slots
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##islav
spaced
trusting
admire
rig
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##70
mv
207
casualty
##wei
classmates
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##rar
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furnished
evolve
foundry
menace
mead
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flu
wesleyan
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monterey
webber
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wil
##mith
##на
bartholomew
justices
restrained
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amenities
191
mediated
sewage
trenches
ml
mainz
##thus
1800s
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caine
bonding
213
converts
spheres
superseded
marianne
crypt
sweaty
ensign
historia
##br
spruce
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forks
thoughtfully
yukon
pamphlet
ames
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karma
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bryn
negotiation
sighs
incapable
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actresses
taft
##mill
luce
prevailed
##amine
1773
motionless
envoy
testify
investing
sculpted
instructors
provence
kali
cullen
horseback
##while
goodwin
##jos
gaa
norte
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modify
wavelength
abd
214
skinned
sprinter
forecast
scheduling
marries
squared
tentative
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boer
##isch
bolts
swap
fisherman
assyrian
impatiently
guthrie
martins
murdoch
194
tanya
nicely
dolly
lacy
med
##45
syn
decks
fashionable
millionaire
##ust
surfing
##ml
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heaved
tammy
consulate
attendees
routinely
197
fuse
saxophonist
backseat
malaya
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scowl
tau
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193
sighted
steaming
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303
911
##holes
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ching
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bless
conserved
jurassic
stacey
unix
zion
chunk
rigorous
blaine
198
peabody
slayer
dismay
brewers
nz
##jer
det
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glover
postwar
int
penetration
sylvester
imitation
vertically
airlift
heiress
knoxville
viva
##uin
390
macon
##rim
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janice
##orescence
##wari
marius
belongings
leicestershire
196
blanco
inverted
preseason
sanity
sobbing
##due
##elt
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collingwood
regeneration
flickering
shortest
##mount
##osi
feminism
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sherlock
cabinets
fumbled
northbound
precedent
snaps
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guillaume
insights
manipulated
vapor
neighbour
sap
gangster
frey
f1
stalking
scarcely
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barnett
tendencies
audi
doomed
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slung
panchayat
ambiguous
bartlett
##etto
distributing
violating
wolverhampton
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swami
histoire
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liable
pounder
groin
hussain
larsen
popping
surprises
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relocate
musicals
authorization
richter
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tna
bombings
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deteriorated
yiddish
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robbed
colchester
cs
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oxfordshire
retention
nottinghamshire
contender
judd
invitational
shrimp
uhf
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longitudinal
tanker
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acronym
broker
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sundance
suppliers
##gil
4000
clipped
fuels
petite
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landslide
helene
diversion
populous
landowners
auspices
melville
quantitative
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ferries
nicky
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haunting
roche
carver
downed
unavailable
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approximation
hiroshima
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garfield
valle
comparatively
keyboardist
traveler
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congestion
calculating
subsidiaries
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serb
modernization
fairies
deepened
ville
averages
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inflammatory
tonga
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co₂
squads
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gigantic
serum
enjoyment
retailer
verona
35th
cis
##phobic
magna
technicians
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arithmetic
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levin
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amtrak
chow
sienna
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backstage
entrepreneurship
##otic
learnt
tao
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worcestershire
formulation
baggage
hesitant
bali
sabotage
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enhancing
murmur
pl
freshly
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syntax
aces
medicines
resentment
bandwidth
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chili
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pertaining
admissions
geo
thorpe
proliferation
sato
bela
analyzing
parting
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awakened
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huddled
secrecy
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hush
gentry
540
dungeons
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coasts
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sacrificed
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landowner
mutually
prevalence
programmer
adolescent
disrupted
seaside
gee
trusts
vamp
georgie
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schedules
sindh
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etched
hm
sparse
bey
beaux
scratching
gliding
unidentified
216
collaborating
gems
jesuits
oro
accumulation
shaping
mbe
anal
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231
enthusiasts
newscast
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janata
dewey
parkinson
179
ankara
biennial
towering
dd
inconsistent
950
##chet
thriving
terminate
cabins
furiously
eats
advocating
donkey
marley
muster
phyllis
leiden
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grassland
glittering
iucn
loneliness
217
memorandum
armenians
##ddle
popularized
rhodesia
60s
lame
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sans
bikini
header
orbits
##xx
##finger
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sharif
spines
biotechnology
strolled
naughty
yates
##wire
fremantle
milo
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abducted
removes
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wonderland
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hume
pivotal
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armand
grams
believers
elector
rte
apron
bis
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endorsement
initials
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eps
dotted
hints
buzzing
emigration
nearer
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indicators
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coarse
neutron
protectorate
##uze
directional
exploits
pains
loire
1830s
proponents
guggenheim
rabbits
ritchie
305
hectare
inputs
hutton
##raz
verify
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boilers
longitude
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skeletal
yer
emilia
citrus
compromised
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pokemon
prescription
paragraph
eduard
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attire
categorized
kenyan
weddings
charley
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monmouth
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nutrients
davey
mesh
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kemp
subdued
overheard
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bodily
maxim
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apprenticeship
ursula
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lodged
rug
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unconstitutional
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inspected
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unbeaten
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34th
disruption
convict
parcel
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collier
implicated
mallory
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winkler
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phelps
sediments
graphical
robotic
##sner
adulthood
mart
smoked
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kathryn
clarified
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divides
convictions
oppression
pausing
burying
##mt
federico
mathias
eileen
##tana
kite
hunched
##acies
189
##atz
disadvantage
liza
kinetic
greedy
paradox
yokohama
dowager
trunks
ventured
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gupta
vilnius
olaf
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hopper
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progressively
arturo
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simulcast
oceania
##orum
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busiest
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navigator
cary
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rowe
rowland
posing
insurgents
shafts
lawsuits
activate
conor
inward
culturally
garlic
265
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##hui
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furrowed
vargas
meteorological
rendezvous
##aus
culinary
commencement
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salaries
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sums
wentworth
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mainline
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plaintiff
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propagation
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vineyards
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beckett
optimization
golfer
songwriters
seminal
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thud
volatile
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ridley
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trivial
distributions
scandinavia
jiang
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wrestled
insistence
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emphasizes
napkin
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adjunct
rhyme
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hopeless
surrounds
tremble
32nd
smoky
##ntly
oils
medicinal
padded
steer
wilkes
219
255
concessions
hue
uniquely
blinded
landon
yahoo
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hendrix
commemorating
dex
specify
chicks
##ggio
intercity
1400
morley
##torm
highlighting
##oting
pang
oblique
stalled
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flirting
newborn
1769
bishopric
shaved
232
currie
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dharma
spartan
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favorites
smug
novella
sirens
abusive
creations
espana
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semiconductor
sheen
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nrl
renew
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marches
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weimar
grunt
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lordship
plotting
expenditure
infringement
lbs
refrain
av
mimi
mistakenly
postmaster
1771
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ras
motorsports
tito
199
subjective
##zza
bully
stew
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prescott
1a
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##zam
bids
styling
paranormal
reeve
sneaking
exploding
katz
akbar
migrant
syllables
indefinitely
##ogical
destroys
replaces
applause
##phine
pest
##fide
218
articulated
bertie
##thing
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courtroom
crowley
aesthetics
cummings
tehsil
hormones
titanic
dangerously
##ibe
stadion
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auguste
ciudad
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partisans
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philipp
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debating
henley
interiors
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homecoming
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prepares
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ely
plucked
##pire
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luxurious
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artifact
password
pasture
juno
maddy
minsk
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##ologies
##rone
assessments
martian
royalist
1765
examines
##mani
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nino
223
parry
scooped
relativity
##eli
##uting
##cao
congregational
noisy
traverse
##agawa
strikeouts
nickelodeon
obituary
transylvania
binds
depictions
polk
trolley
##yed
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breeders
##under
dryly
hokkaido
1762
strengths
stacks
bonaparte
connectivity
neared
prostitutes
stamped
anaheim
gutierrez
sinai
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bram
fresno
madhya
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proton
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wanda
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distinguishing
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grasping
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tomato
bland
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avenues
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distortion
fatalities
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correctional
pricing
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prom
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welterweight
33rd
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multitude
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federally
intimacy
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functioned
ud
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netflix
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predictions
fuji
bu
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clocks
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babu
onion
xml
forcibly
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petra
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hartley
peeled
storytelling
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fig
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greenville
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cemeteries
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660
corrections
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ambassadors
blacksmith
fielded
fluids
hardcover
turnover
medications
melvin
academies
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ro
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absorbing
spaniards
colton
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outsider
espionage
kelsey
245
edible
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dora
establishes
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cinematic
costello
nesting
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connolly
duff
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mma
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sexes
gi
optics
spectator
woodstock
banning
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differentiate
outfielder
refinery
226
312
gerhard
horde
lair
drastically
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landfall
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motorsport
odi
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predominant
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skins
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harshly
complementary
murdering
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wreckage
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ono
outstretched
lennox
munitions
galen
reconcile
470
scalp
bicycles
gillespie
questionable
rosenberg
guillermo
hostel
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kabul
volvo
opium
yd
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abuses
decca
outpost
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sensible
neutrality
##64
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turrets
inadvertently
disagree
libre
vodka
reassuring
weighs
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mixes
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manually
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efficacy
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capacities
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novelty
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drafting
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humiliation
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liturgy
spectacle
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pont
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auf
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illumination
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festivities
bows
govern
vibe
vp
333
sprawled
larson
pilgrim
bwf
leaping
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greyhound
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craven
fantasies
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supervising
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commentaries
unnatural
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spirituality
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protesting
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controllers
dodd
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squire
steiner
whisky
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560
inevitably
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alliances
jailed
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czechoslovak
narration
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guangdong
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hose
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deploy
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battleships
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shortlisted
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af
christi
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1761
assemble
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northamptonshire
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42nd
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reproduce
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1766
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boroughs
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1755
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streetcar
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christoph
feeder
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twists
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manson
vuelta
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peanut
confessions
pouch
unpredictable
allowance
theodor
vascular
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bala
authenticity
metabolic
coughing
nanjing
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pembroke
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splendid
36th
ff
hourly
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elmer
handel
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awarding
thrusting
dl
experimentation
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##46
caressed
entertained
steak
##rangle
biologist
orphans
baroness
oyster
stepfather
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mirage
reefs
speeding
##31
barons
1764
227
inhabit
preached
repealed
##tral
honoring
boogie
captives
administer
johanna
##imate
gel
suspiciously
1767
sobs
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backbone
hayward
garry
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maxi
##oof
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ellison
galileo
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crimea
frenzy
amour
bumper
matrices
natalia
baking
garth
palestinians
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smack
conveyed
ensembles
gardening
##manship
##rup
##stituting
1640
harvesting
topography
jing
shifters
dormitory
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ist
skulls
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dolores
jewellery
sarawak
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fences
christy
confinement
tumbling
credibility
fir
stench
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##plication
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virtues
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marjorie
pba
##eem
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celebrates
schooner
agitated
barley
fulfilling
anthropologist
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restrict
novi
regulating
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padres
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loyola
tabitha
milky
olson
proprietor
crambidae
guarantees
intercollegiate
ljubljana
hilda
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ignorant
hooded
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sardinia
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frontman
privileged
witchcraft
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jammed
laude
poking
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bracket
amazement
yunnan
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maharaja
linnaeus
264
commissioning
milano
peacefully
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akira
rani
regulator
##36
grasses
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luzon
crows
compiler
gretchen
seaman
edouard
tab
buccaneers
ellington
hamlets
whig
socialists
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directorial
easton
mythological
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rhineland
semantic
taut
dune
inventions
succeeds
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replication
branched
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jul
prosecuted
kangaroo
penetrated
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middlesbrough
doses
bleak
madam
predatory
relentless
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reluctance
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hailey
crore
silvery
1759
monstrous
swimmers
transmissions
hawthorn
informing
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toilets
caracas
crouch
kb
##sett
295
cartel
hadley
##aling
alexia
yvonne
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cinderella
eton
superb
blizzard
stabbing
industrialist
maximus
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groves
maud
clade
oversized
comedic
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rosen
nomadic
fulham
montane
beverages
galaxies
redundant
swarm
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buckinghamshire
fen
bearings
bahadur
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gilles
phased
dynamite
faber
benoit
vip
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booking
fractured
tailored
anya
spices
westwood
cairns
auditions
inflammation
steamed
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skyla
thereof
watford
torment
archdeacon
transforms
lulu
demeanor
fucked
serge
##sor
mckenna
minas
entertainer
##icide
caress
originate
residue
##sty
1740
##ilised
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beech
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subsidies
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emptied
gladstone
ru
firefighters
voodoo
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het
nightingale
tamara
edmond
ingredient
weaknesses
silhouette
285
compatibility
withdrawing
hampson
##mona
anguish
giggling
##mber
bookstore
##jiang
southernmost
tilting
##vance
bai
economical
rf
briefcase
dreadful
hinted
projections
shattering
totaling
##rogate
analogue
indicted
periodical
fullback
##dman
haynes
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##ffs
##ishment
1745
thirst
stumble
penang
vigorous
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##lium
octave
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siberian
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cbn
repeal
swaying
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khalid
tanaka
unicorn
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plastered
lobe
riddle
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perch
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croydon
filtered
graeme
tripoli
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crocodile
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sufi
mined
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lsu
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swelled
utilizes
£2
cale
periodicals
styx
hike
informally
coop
lund
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ala
hen
qui
transformations
disposed
sheath
chickens
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fitzroy
sas
silesia
unacceptable
odisha
1650
sabrina
pe
spokane
ratios
athena
massage
shen
dilemma
##drum
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corona
doubtful
niall
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fines
cite
acknowledging
bangor
ballard
bathurst
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huron
mustered
alzheimer
garments
kinase
tyre
warship
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flashback
pulmonary
braun
cheat
kamal
cyclists
constructions
grenades
ndp
traveller
excuses
stomped
signalling
trimmed
futsal
mosques
relevance
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wta
##23
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hoc
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optimistic
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sim
interacting
rejecting
moniker
waterways
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mayors
gdansk
outnumbered
pearls
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fairs
totals
dominating
262
notions
stairway
compiling
pursed
commodities
grease
yeast
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carthage
griffiths
residual
amc
contraction
laird
sapphire
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amalgamation
dissolve
inclination
lyle
packaged
altitudes
suez
canons
graded
lurched
narrowing
boasts
guise
wed
enrico
##ovsky
rower
scarred
bree
cub
iberian
protagonists
bargaining
proposing
trainers
voyages
vans
fishes
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##ivist
##verance
encryption
artworks
kazan
sabre
cleopatra
hepburn
rotting
supremacy
mecklenburg
##brate
burrows
hazards
outgoing
flair
organizes
##ctions
scorpion
##usions
boo
234
chevalier
dunedin
slapping
##34
ineligible
pensions
##38
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manufactures
emails
bismarck
238
weakening
blackish
ding
mcgee
quo
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northernmost
xx
manpower
greed
sampson
clicking
##ange
##horpe
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##roving
torre
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symbolism
38th
asshole
meritorious
outfits
splashed
biographies
sprung
astros
##tale
302
737
filly
raoul
nw
tokugawa
linden
clubhouse
##apa
tracts
romano
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putin
tags
##note
chained
dickson
gunshot
moe
gunn
rashid
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zipper
##bas
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contrasted
##ply
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plum
pharaoh
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aw
comedies
ingrid
sandwiches
subdivisions
1100
mariana
nokia
kamen
hz
delaney
veto
herring
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possessive
outlines
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siemens
stairwell
rc
gallantry
messiah
palais
yells
233
zeppelin
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bolivar
##cede
smackdown
mckinley
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muted
geologic
finely
unitary
avatar
hamas
maynard
rees
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contrasting
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liv
chico
disposition
pixel
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becca
dmitry
yeshiva
narratives
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mercenary
sharpe
tempered
navigate
stealth
amassed
keynes
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untouched
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havoc
lithium
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abyss
graf
southward
wolverine
balloons
implements
ngos
transitions
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ambushed
concacaf
dormant
economists
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costing
csi
rana
universite
boulders
verity
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collin
mellon
misses
cypress
fluorescent
lifeless
spence
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crewe
shepard
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revelations
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jolly
gibbons
paw
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freeing
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shack
fries
palatine
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accompaniment
cruising
recycled
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erwin
sorting
synthesizers
dyke
realities
sg
strides
enslaved
wetland
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competence
gunpowder
grassy
maroon
reactors
objection
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carlson
gearbox
macintosh
radios
shelton
##sho
clergyman
prakash
254
mongols
trophies
oricon
228
stimuli
twenty20
cantonese
cortes
mirrored
##saurus
bhp
cristina
melancholy
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enjoyable
nuevo
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downfall
schumacher
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banging
lausanne
rumbled
paramilitary
reflex
ax
amplitude
migratory
##gall
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midi
barnard
lastly
sherry
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keystone
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carleton
slippery
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coloring
foe
socket
otter
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mats
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consultants
bafta
bison
topping
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490
primal
abandonment
transplant
atoll
hideous
mort
pained
reproduced
tae
howling
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unlawful
billionaire
hotter
poised
lansing
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dinamo
retro
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nfc
domesday
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blitz
timed
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ascending
gesturing
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signaled
tis
chinatown
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savanna
jameson
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catalina
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cochrane
cy
chatting
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alerted
computation
mused
noelle
majestic
mohawk
campo
octagonal
##sant
##hend
241
aspiring
##mart
comprehend
iona
paralyzed
shimmering
swindon
rhone
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reputed
configurations
pitchfork
agitation
francais
gillian
lipstick
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outsiders
pontifical
resisting
bitterness
sewer
rockies
##edd
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misleading
1756
exiting
galloway
##nging
risked
##heart
246
commemoration
schultz
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integrating
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poses
shrieked
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guineas
gladys
jerking
owls
goldsmith
nightly
penetrating
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lia
##33
ignited
betsy
##aring
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follower
vigorously
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coded
kiran
knit
zoology
tbilisi
##28
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repository
govt
deciduous
dino
growling
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enhancement
unleashed
chanting
pussy
biochemistry
##eric
kettle
repression
toxicity
nrhp
##arth
##kko
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ernesto
commended
outspoken
242
mca
parchment
sms
kristen
##aton
bisexual
raked
glamour
navajo
a2
conditioned
showcased
##hma
spacious
youthful
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usl
appliances
junta
brest
layne
conglomerate
enchanted
chao
loosened
picasso
circulating
inspect
montevideo
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piazza
spurred
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bari
freedoms
poultry
stamford
lieu
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indigo
sarcastic
bahia
stump
attach
dvds
frankenstein
lille
approx
scriptures
pollen
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nmi
overseen
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tides
proponent
newmarket
inherit
milling
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centralized
##rou
distributors
credentials
drawers
abbreviation
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downing
uncomfortably
ripe
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erase
franchises
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populace
##bery
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decomposition
pleas
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daryl
sabah
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fearless
genie
lesions
annette
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oboe
appendix
nair
dripped
petitioned
maclean
mosquito
parrot
rpg
hampered
1648
operatic
reservoirs
##tham
irrelevant
jolt
summarized
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medallion
##taff
##−
clawed
harlow
narrower
goddard
marcia
bodied
fremont
suarez
altering
tempest
mussolini
porn
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sweetly
oversees
walkers
solitude
grimly
shrines
hk
ich
supervisors
hostess
dietrich
legitimacy
brushes
expressive
##yp
dissipated
##rse
localized
systemic
##nikov
gettysburg
##js
##uaries
dialogues
muttering
251
housekeeper
sicilian
discouraged
##frey
beamed
kaladin
halftime
kidnap
##amo
##llet
1754
synonymous
depleted
instituto
insulin
reprised
##opsis
clashed
##ctric
interrupting
radcliffe
insisting
medici
1715
ejected
playfully
turbulent
##47
starvation
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shipment
rebellious
petersen
verification
merits
##rified
cakes
##charged
1757
milford
shortages
spying
fidelity
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emitted
storylines
harvested
seismic
##iform
cheung
kilda
theoretically
barbie
lynx
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goblin
mata
poisonous
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reactive
residues
obedience
##евич
conjecture
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401
hating
sixties
kicker
moaning
motown
##bha
emancipation
neoclassical
##hering
consoles
ebert
professorship
##tures
sustaining
assaults
obeyed
affluent
incurred
tornadoes
##eber
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emphasizing
highlanders
cheated
helmets
##ctus
internship
terence
bony
executions
legislators
berries
peninsular
tinged
##aco
1689
amplifier
corvette
ribbons
lavish
pennant
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worthless
##chfield
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mariano
pyrenees
expenditures
##icides
chesterfield
mandir
tailor
39th
sergey
nestled
willed
aristocracy
devotees
goodnight
raaf
rumored
weaponry
remy
appropriations
harcourt
burr
riaa
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limitation
unnoticed
guo
soaking
swamps
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collapsing
tatiana
descriptive
brigham
psalm
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maddox
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patti
caliph
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akron
injuring
serra
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basins
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astonished
launcher
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hilary
wilkins
sewing
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stinging
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underwood
startup
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compilations
vibrations
embankment
jurist
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bard
juventus
groundwater
kern
palaces
helium
boca
cramped
marissa
soto
##worm
jae
princely
##ggy
faso
bazaar
warmly
##voking
229
pairing
##lite
##grate
##nets
wien
freaked
ulysses
rebirth
##alia
##rent
mummy
guzman
jimenez
stilled
##nitz
trajectory
tha
woken
archival
professions
##pts
##pta
hilly
shadowy
shrink
##bolt
norwood
glued
migrate
stereotypes
devoid
##pheus
625
evacuate
horrors
infancy
gotham
knowles
optic
downloaded
sachs
kingsley
parramatta
darryl
mor
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shady
commence
confesses
kan
##meter
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marlborough
roundabout
regents
frigates
io
##imating
gothenburg
revoked
carvings
clockwise
convertible
intruder
##sche
banged
##ogo
vicky
bourgeois
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dupont
footing
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pd
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buckle
yun
penthouse
sane
720
serviced
stakeholders
neumann
bb
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comb
##gam
catchment
pinning
rallies
typing
##elles
forefront
freiburg
sweetie
giacomo
widowed
goodwill
worshipped
aspirations
midday
##vat
fishery
##trick
bournemouth
turk
243
hearth
ethanol
guadalajara
murmurs
sl
##uge
afforded
scripted
##hta
wah
##jn
coroner
translucent
252
memorials
puck
progresses
clumsy
##race
315
candace
recounted
##27
##slin
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filtering
##mac
howl
strata
heron
leveled
##ays
dubious
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##т
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citations
exhibiting
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##mics
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turkic
##lberg
injunction
##ennial
##mit
antibodies
##44
organise
##rigues
cardiovascular
cushion
inverness
##zquez
dia
cocoa
sibling
##tman
##roid
expanse
feasible
tunisian
algiers
##relli
rus
bloomberg
dso
westphalia
bro
tacoma
281
downloads
##ours
konrad
duran
##hdi
continuum
jett
compares
legislator
secession
##nable
##gues
##zuka
translating
reacher
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##ła
aleppo
##agi
tc
orchards
trapping
linguist
versatile
drumming
postage
calhoun
superiors
##mx
barefoot
leary
##cis
ignacio
alfa
kaplan
##rogen
bratislava
mori
##vot
disturb
haas
313
cartridges
gilmore
radiated
salford
tunic
hades
##ulsive
archeological
delilah
magistrates
auditioned
brewster
charters
empowerment
blogs
cappella
dynasties
iroquois
whipping
##krishna
raceway
truths
myra
weaken
judah
mcgregor
##horse
mic
refueling
37th
burnley
bosses
markus
premio
query
##gga
dunbar
##economic
darkest
lyndon
sealing
commendation
reappeared
##mun
addicted
ezio
slaughtered
satisfactory
shuffle
##eves
##thic
##uj
fortification
warrington
##otto
resurrected
fargo
mane
##utable
##lei
##space
foreword
ox
##aris
##vern
abrams
hua
##mento
sakura
##alo
uv
sentimental
##skaya
midfield
##eses
sturdy
scrolls
macleod
##kyu
entropy
##lance
mitochondrial
cicero
excelled
thinner
convoys
perceive
##oslav
##urable
systematically
grind
burkina
287
##tagram
ops
##aman
guantanamo
##cloth
##tite
forcefully
wavy
##jou
pointless
##linger
##tze
layton
portico
superficial
clerical
outlaws
##hism
burials
muir
##inn
creditors
hauling
rattle
##leg
calais
monde
archers
reclaimed
dwell
wexford
hellenic
falsely
remorse
##tek
dough
furnishings
##uttered
gabon
neurological
novice
##igraphy
contemplated
pulpit
nightstand
saratoga
##istan
documenting
pulsing
taluk
##firmed
busted
marital
##rien
disagreements
wasps
##yes
hodge
mcdonnell
mimic
fran
pendant
dhabi
musa
##nington
congratulations
argent
darrell
concussion
losers
regrets
thessaloniki
reversal
donaldson
hardwood
thence
achilles
ritter
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demonic
jurgen
prophets
goethe
eki
classmate
buff
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yank
irrational
##inging
perished
seductive
qur
sourced
##crat
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mustard
ravine
barre
horizontally
characterization
phylogenetic
boise
##dit
##runner
##tower
brutally
intercourse
seduce
##bbing
fay
ferris
ogden
amar
nik
unarmed
##inator
evaluating
kyrgyzstan
sweetness
##lford
##oki
mccormick
meiji
notoriety
stimulate
disrupt
figuring
instructional
mcgrath
##zoo
groundbreaking
##lto
flinch
khorasan
agrarian
bengals
mixer
radiating
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ingram
pitchers
nad
tariff
##cript
tata
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##emi
##ungen
appellate
lehigh
##bled
##giri
brawl
duct
texans
##ciation
##ropolis
skipper
speculative
vomit
doctrines
stresses
253
davy
graders
whitehead
jozef
timely
cumulative
haryana
paints
appropriately
boon
cactus
##ales
##pid
dow
legions
##pit
perceptions
1730
picturesque
##yse
periphery
rune
wr
##aha
celtics
sentencing
whoa
##erin
confirms
variance
425
moines
mathews
spade
rave
m1
fronted
fx
blending
alleging
reared
##gl
237
##paper
grassroots
eroded
##free
##physical
directs
ordeal
##sław
accelerate
hacker
rooftop
##inia
lev
buys
cebu
devote
##lce
specialising
##ulsion
choreographed
repetition
warehouses
##ryl
paisley
tuscany
analogy
sorcerer
hash
huts
shards
descends
exclude
nix
chaplin
gaga
ito
vane
##drich
causeway
misconduct
limo
orchestrated
glands
jana
##kot
u2
##mple
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branching
contrasts
scoop
longed
##virus
chattanooga
##75
syrup
cornerstone
##tized
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##iaceae
careless
precedence
frescoes
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chilled
consult
modelled
snatch
peat
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caucasian
humane
relaxation
spins
temperance
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occupations
lambda
hybrids
moons
mp3
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247
rolf
societal
yerevan
ness
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befriended
mechanized
nominate
trough
boasted
cues
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emptiness
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tian
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anxiously
lark
propellers
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jock
ev
2a
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recounts
tori
loyalist
abduction
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tempting
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steered
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dipping
laborers
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looming
titanium
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tensor
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rash
denies
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lombard
showers
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welles
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trillion
joanne
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sinks
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uruguayan
clasp
sirius
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accelerator
prohibit
sunken
byu
chronological
diplomats
ochreous
510
symmetrical
1644
maia
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salts
reigns
atrocities
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rollins
atheist
ominous
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herr
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martina
strung
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erased
ptolemy
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analytic
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530
navarro
beak
gorilla
herrera
broom
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raiding
sykes
311
bsc
deliveries
1720
invasions
carmichael
tajikistan
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ecumenical
sentiments
onstage
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catastrophic
flanks
molten
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waller
aimee
terminating
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outraged
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empires
template
banners
repetitive
za
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opt
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lucknow
synthesized
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finalized
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fictitious
mayoral
unreliable
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embracing
peppers
rbis
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inhibition
slashed
togo
orderly
embroidered
safari
salty
236
barron
benito
totaled
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pubs
simulated
caden
devin
tolkien
momma
welding
sesame
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gottingen
hardness
630
shaman
temeraire
620
adequately
pediatric
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ck
assertion
radicals
composure
cadence
seafood
beaufort
lazarus
mani
warily
cunning
kurdistan
249
cantata
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ares
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nape
townland
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insulted
flutter
boating
violate
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dumping
malmo
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exceeds
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accessibility
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bouts
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waterway
mindanao
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halves
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liter
maternity
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bragg
electrification
mcc
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roxy
schizophrenia
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munoz
kaye
whaling
239
mil
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tolerant
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unconventional
volcanoes
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robson
kaufman
neuroscience
wai
deportation
masovian
scraping
converse
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hacking
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administratively
yao
580
amp
mammoth
booster
claremont
hooper
nomenclature
pursuits
mclaughlin
melinda
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catfish
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substrates
taxa
zee
originals
kimberly
packets
padma
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ostensibly
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322
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345
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1753
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recaptured
timetable
vila
1710
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aerodrome
mortals
scientology
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mag
convection
unpaid
insertion
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lego
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endeavor
kota
pereira
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304
bwv
glamorgan
insults
agatha
fey
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mahogany
protruding
steamship
zeta
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urges
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stalk
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802
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replacements
hawker
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insurgency
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casts
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680
ives
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woodrow
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lasers
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wand
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strategically
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bile
handkerchief
pere
storing
christophe
insulting
316
nakamura
romani
asiatic
magdalena
palma
cruises
stripping
405
konstantin
soaring
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colloquially
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parasites
sincerity
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ornaments
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feasibility
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morphological
usable
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jaguars
keepers
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assigns
ewing
bacterium
hurled
tricky
hungarians
integers
wallis
321
yamaha
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oblivion
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evangelist
friars
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monograph
ode
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airplanes
labourers
charms
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1661
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tnt
rudder
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transcript
dorothea
ska
inhibitor
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retorted
raining
encompassed
clauses
menacing
1642
lineman
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dealings
easing
seekers
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adjustments
1688
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horne
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pulsed
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saddam
banda
kobe
blaming
buddies
dissent
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corbett
jaya
delle
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lexie
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435
amiga
hermes
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chapels
gospels
jamal
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compute
revolving
warp
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handwriting
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estimation
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newcomers
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staten
communicated
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mcgraw
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hannibal
mein
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lucifer
wo
badger
boar
cher
christensen
greenberg
interruption
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jem
244
mocked
bottoms
cambridgeshire
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sprawling
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eastwood
ghent
synth
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advisers
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nominally
hapoel
qu
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estranged
fabricated
towels
vinnie
wcw
misunderstanding
anglia
nothin
unmistakable
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chilly
marquette
truss
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reece
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272
308
41st
bash
raion
waterfalls
##ump
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labyrinth
queue
theorist
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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
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vivo
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antonia
expects
indoors
kildare
harlan
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forgiven
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childish
tavi
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grimm
successively
scooted
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azure
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topological
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drilled
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footprint
petitions
ufo
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mcpherson
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ashland
cuffs
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pendleton
whistling
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deviation
revisited
constituents
rallied
rotate
loomed
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amateurs
augsburg
auschwitz
crowns
skeletons
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bonnet
257
dummy
globalization
simeon
sleeper
mandal
differentiated
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milne
bundled
exasperated
talmud
owes
segregated
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dentist
piracy
props
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devlin
##torium
malicious
paws
##laid
dependency
##ergy
##fers
##enna
258
pistons
rourke
jed
grammatical
tres
maha
wig
512
ghostly
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designate
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cambodian
clones
showdown
throttle
twain
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metz
nagoya
335
braking
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385
roaming
##minster
amin
crippled
##37
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indifferent
hoffmann
idols
intimidating
1751
261
influenza
memo
onions
1748
bandage
consciously
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clandestine
observes
swiped
tangle
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hugs
congresses
josiah
spirited
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humanist
managerial
filmmaking
inmate
rhymes
debuting
grimsby
ur
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duplicate
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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
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bmg
kerman
res
fairbanks
jarrett
abstraction
saharan
##zek
1746
procedural
clearer
kincaid
sash
luciano
##ffey
crunch
helmut
##vara
revolutionaries
##tute
creamy
leach
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1747
permitting
nes
plight
wendell
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contra
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clancy
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mach
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autopsy
disturbances
nueva
karin
pontiac
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haunt
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bergman
expands
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wal
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canning
celine
cords
obesity
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intrusion
planner
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reasoned
sequencing
307
harrow
##chon
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marred
mcintyre
repay
tarzan
darting
248
harrisburg
margarita
repulsed
##hur
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belinda
hamburger
novo
compliant
runways
bingham
registrar
skyscraper
ic
cuthbert
improvisation
livelihood
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admiring
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believer
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popcorn
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rectory
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guerre
bathed
upbringing
vaulted
sandals
blessings
##naut
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1680
306
foxes
pia
corrosion
hesitantly
confederates
crystalline
footprints
shapiro
tirana
valentin
drones
45th
microscope
shipments
texted
inquisition
wry
guernsey
unauthorized
resigning
760
ripple
schubert
stu
reassure
felony
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brittle
koreans
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tyres
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magnolia
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fei
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familiarity
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fundamentally
jimmie
overrun
395
anchors
moans
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britannia
armagh
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purposely
seizing
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mundane
avoidance
cosmopolitan
geometridae
quartermaster
caf
415
chatter
engulfed
gleam
purge
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juliette
jurisprudence
guerra
revisions
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casimir
brew
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1749
clapton
cloudy
conde
hermitage
278
simulations
torches
vincenzo
matteo
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hidalgo
booming
westbound
accomplishment
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unaffected
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sloping
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dreamer
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vu
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consecration
copying
messaging
breaker
climates
hospitalized
1752
torino
afternoons
winfield
witnessing
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breakers
choirs
sawmill
coldly
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conical
bibliography
pamphlets
severn
edict
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sis
thinkers
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1690
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civilizations
paces
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pharmaceuticals
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yearbook
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hamid
moors
pastures
summons
jihad
nectar
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bayou
untitled
pleasing
vastly
republics
intellect
##η
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crumbling
stylistic
sb
##ی
consolation
frequented
h₂o
walden
widows
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404
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chunks
improves
288
grit
recited
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snarl
sociological
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inquired
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bruise
clube
consultancy
homogeneous
hornets
multiplication
pasta
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bn
distillery
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dockyard
massif
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voss
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illusions
workings
floats
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kv
annapolis
madden
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noctuidae
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avenge
steamboat
forte
illustrates
erika
##trip
570
dew
nationalities
bran
manifested
thirsty
diversified
muscled
reborn
##standing
arson
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willoughby
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286
alsace
dashboard
yuki
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granville
myspace
publicized
tricked
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reorganisation
enthusiastically
indications
saxe
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consolidate
iec
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ramps
renaming
regulars
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convicts
inaccurate
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mana
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marrow
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rei
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ns
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lew
seamus
1630
cocky
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methyl
kimberley
mcdowell
reductions
barbed
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condensed
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invincible
missy
pious
balthazar
ci
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myriad
1707
silenced
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netball
yourselves
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clarify
heller
peg
durban
etudes
offender
roast
blackmail
curvature
##woods
vile
309
illicit
suriname
##linson
overture
1685
bubbling
gymnast
tucking
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maldives
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gurney
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backside
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tending
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cupping
flawed
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puddle
swearing
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educating
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75th
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respondents
analytics
cavaliers
papacy
raju
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##tip
funnel
271
disneyland
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sociologist
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2500
faulkner
louvre
menon
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276
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afterlife
mannheim
peptide
referees
comedians
meaningless
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fabrics
hurley
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unsafe
redesign
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firth
barnsley
portage
reset
narrows
268
commandos
expansive
speechless
tubular
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eyelashes
smashwords
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craved
sprinted
chet
somme
astor
wrocław
orton
266
bane
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mischief
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terre
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impairment
offenses
undermine
moi
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arcadia
inuit
seam
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590
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frs
uniformed
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259
powerless
priscilla
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980
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regimes
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kickoff
gallon
rockwell
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crusader
glue
revolutions
scrambling
1714
grover
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englishman
aztec
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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strewn
clarissa
rejoin
environmentally
glint
banded
drenched
moat
albanians
johor
rr
maestro
malley
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taxonomy
v6
adhere
bunk
airfields
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1741
encompass
remington
tran
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mazda
friar
morals
passions
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breadth
vis
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argus
burnham
caressing
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rudd
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murderous
textual
wainwright
armada
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weave
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ardent
salazar
taps
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gland
philanthropic
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implication
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judas
noticeably
wynn
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inched
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crises
villiers
bandit
royalties
patterned
cupboard
interspersed
accessory
isla
kendrick
entourage
stitches
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headwaters
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interlude
distraught
draught
1727
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biased
sy
transient
triad
subgenus
adapting
kidd
shortstop
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dimly
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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bender
nantes
serene
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mmm
323
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cmll
coinage
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juarez
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smeared
sprayed
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juveniles
enveloped
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dq
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mercantile
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psychologists
stryker
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screenings
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interpreting
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phosphorus
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mammal
unitarian
urgently
chopin
sanitary
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tipping
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impoverished
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267
epoch
nonstop
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aromatic
centrally
cerro
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billions
modulation
sedimentary
283
facilitating
outrageous
goldstein
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maitland
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fleets
spaceship
vertebrae
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alcoholism
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
bloomfield
takahashi
274
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presenters
ringo
blazed
witty
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devastation
haydn
harmed
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001
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1743
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indefinite
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525
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1603
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ligament
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spawn
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filmfare
rochelle
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1725
circumstance
saber
supplements
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crowe
wellesley
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sincerely
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smarter
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undo
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announcements
anthologies
barrio
nebula
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bodyguards
2021
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gorman
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writ
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alla
invertebrates
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1641
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1742
align
healy
redistribution
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adherents
hezbollah
vitro
ferns
yanking
269
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registering
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confines
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tully
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49th
docked
roam
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prompt
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blindly
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craftsmen
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katharine
scramble
shaggy
sponge
helix
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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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artefacts
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searing
smallpox
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litres
musique
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benevolent
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blackened
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mccall
racers
tingle
##ocene
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introductions
radically
292
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1610
1739
munchen
plead
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scissors
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apprehension
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formulas
sequels
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commencing
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prohibits
marne
vedic
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euclidean
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blasts
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740
conversions
hierarchical
rios
simulator
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thrusts
shadowed
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maximize
1744
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influencing
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412
ammonia
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incompatible
violins
cornered
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grooves
astronauts
columbian
rampant
fabrication
kyushu
mahmud
vanish
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ict
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pitted
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999
grimace
roanoke
tchaikovsky
twinned
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xinjiang
arias
clemson
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sizable
1662
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plunge
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vittorio
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bracing
indifference
projectile
superliga
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realises
upgrading
299
porte
retribution
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nk
stil
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ama
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blackberry
bosch
testosterone
collapses
greer
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detecting
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fledgling
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gan
telecommunication
mingled
sobbed
6000
controversies
outdated
taxis
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fright
slams
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sheng
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restrictive
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faltered
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51st
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ticked
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recommends
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stagecoach
wilfred
trickle
pronouns
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nighttime
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stitch
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strikers
appleton
kitchens
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disclose
metis
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1701
tesla
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fitch
1735
blooded
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cyclones
eun
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peas
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crabs
boil
lanterns
partridge
roofed
1645
necks
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opined
patting
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volta
whereupon
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degraded
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astonishing
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generosity
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frankish
plastics
accommodations
zoological
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motorized
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tackled
canucks
budgets
situ
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dail
gabled
grasslands
whimpered
writhing
judgments
##65
minnie
pv
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bananas
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monique
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markham
tierney
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notwithstanding
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luz
supplementary
tianjin
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croats
transcribed
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cv
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357
seventies
staggering
alam
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hs
regression
timbers
blasting
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montagu
manipulating
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catalytic
1550
troopers
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condemnation
fitzpatrick
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inexperienced
1670
castes
##lative
outing
314
dubois
flicking
quarrel
ste
learners
1625
iq
whistled
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282
classify
tariffs
temperament
355
folly
liszt
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ceasefire
apparel
extras
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barbecue
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delightful
smartphone
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hissing
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44th
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provocative
1724
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utopia
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purcell
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swallows
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respectful
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alyssa
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empathy
stigma
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taxpayers
gui
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planck
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kayla
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mortally
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cognition
radha
319
liechtenstein
meade
richly
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harpsichord
liberalism
trumpets
lauded
tyrant
salsa
tiled
lear
promoters
reused
slicing
trident
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checkpoint
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mammalian
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nirvana
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headlining
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jug
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1649
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intersections
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brentford
frazier
pleasures
dunne
potsdam
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dentistry
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relinquished
sutra
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flaps
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poly
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linger
womb
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doorstep
orthodoxy
threaded
westfield
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fridays
subsided
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loyalists
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letterman
lunatic
prelate
tenderly
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standalone
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transatlantic
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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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equator
overdose
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sonora
treacherous
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responsive
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820
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designations
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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
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lest
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fulbright
valkyrie
sikhs
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swans
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merritt
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redhead
clerks
leasing
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greenfield
servicemen
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dt
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pods
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stale
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asteroids
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buds
flea
hive
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1737
classifications
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olympus
scala
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churning
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lodges
riches
sima
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socrates
regulates
mueller
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1702
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solids
himalayas
nutrient
pup
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servicing
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rayon
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promenade
stubble
2010s
artisans
axial
liquids
mora
sho
yoo
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bundles
oldies
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notification
bastion
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sparkle
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1728
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pathogen
highs
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immature
880
gonzaga
ignatius
mansions
monterrey
sweets
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brightest
pei
rosy
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payroll
addict
meath
cornerback
heaviest
lodging
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capcom
rippled
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barnet
mayhem
ymca
snuggled
rousseau
##cute
blanchard
284
fragmented
leighton
chromosomes
risking
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corinne
coyotes
cynical
hiroshi
yeomanry
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ebook
grading
mandela
plume
agustin
magdalene
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femme
trafford
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52nd
fourier
upton
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camilla
gust
iihf
islamabad
longevity
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feldman
netting
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foraging
mfa
orr
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contradiction
graz
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marlene
tweed
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spp
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miocene
pri
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cooks
pluto
cozy
pornographic
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70th
fairness
glided
jonny
lynne
rounding
sired
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remade
uncover
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complied
lei
newsweek
##jured
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##enting
##pg
293
finer
guerrillas
athenian
deng
disused
stepmother
accuse
gingerly
seduction
521
confronting
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gora
nostalgia
sabres
virginity
wrenched
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syndication
wielding
eyre
##56
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behaved
taxpayer
sweeps
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childless
gallant
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amplified
geraldine
scrape
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babylonian
fresco
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1718
restricting
tack
fukuoka
osborn
selector
partnering
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318
gnu
kia
tak
whitley
gables
##54
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mri
softness
immersion
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1713
chilling
insignificant
pcs
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elites
lina
purported
supplemental
teaming
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proficient
rouen
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niccolo
selects
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fluffy
1621
gruff
knotted
mukherjee
polgara
thrash
nicholls
secluded
smoothing
thru
corsica
loaf
whitaker
inquiries
##rrier
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indochina
289
marlins
myles
peking
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extracts
pastry
superhuman
connacht
vogel
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gloss
quarries
refit
teaser
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20s
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camped
pickering
tung
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pursuant
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soc
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workout
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stains
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surpassing
joyah
triggers
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grandmaster
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clapping
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nagasaki
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tun
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dax
heroism
ina
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unsigned
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moshe
galley
winnie
droplets
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praises
watered
noodles
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stink
bingo
comets
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constraint
domestically
chemotherapy
featherweight
stifled
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darkly
irresistible
refreshing
hasty
isolate
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kitchener
planners
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cages
yarn
implant
toulon
elects
childbirth
yue
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cn
rightful
sportsman
junctions
remodeled
specifies
##rgh
291
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resented
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tyrol
1675
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296
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persians
1733
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324
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298
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1672
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racks
unicode
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pic
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reacts
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dorsey
lass
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arching
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1708
researches
tia
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validation
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felicity
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zulu
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1734
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ratification
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heartland
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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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406
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seychelles
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1654
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arrays
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carnage
optimism
puebla
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quieter
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townsville
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1638
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297
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straightening
356
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411
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417
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331
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neighbourhoods
1540
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1732
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ow
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1667
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ported
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875
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710
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1665
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1721
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holiness
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dispose
paxton
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1704
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engraver
hounds
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rutland
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disqualification
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48th
rein
scribe
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exemplary
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traversed
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interviewing
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greaves
wealthiest
343
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jogged
£5
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illuminating
knicks
respecting
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strife
kinship
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354
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1629
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ob
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bromwich
egan
paranoia
utilization
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provoke
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338
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1679
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sonya
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wiring
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nyu
skepticism
np
townspeople
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336
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umar
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46th
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00pm
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statehood
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730
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1738
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441
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robbers
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reunification
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loco
robyn
402
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newell
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restraints
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chopper
1609
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investigates
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690
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840
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1622
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294
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housemates
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451
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326
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1736
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1086
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475
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1580
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1659
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1726
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341
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whitehall
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peacekeeping
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1703
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1656
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variability
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1655
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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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327
pancakes
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351
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337
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1634
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guitarists
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1624
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780
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padre
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mis
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1692
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astro
oskar
1628
bulldog
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yucatan
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1604
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1646
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1626
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coveted
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lucie
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marvelous
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1652
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870
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1570
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fractures
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eastenders
inference
messengers
bf
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407
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1658
flashbacks
ideally
misunderstood
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mendez
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505
uncanny
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1709
friendships
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sacrament
accommodated
reiterated
logistical
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thumped
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administering
decrees
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picket
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winthrop
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interfered
339
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francoise
morally
uta
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croft
dwarfs
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welded
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1663
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1618
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1623
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inducing
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geek
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495
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1653
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signage
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|
TensorFlow/Detection/SSD/models/research/slim/scripts | scripts | export_mobilenet | #!/bin/bash
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# This script prepares the various different versions of MobileNet models for
# use in a mobile application. If you don't specify your own trained checkpoint
# file, it will download pretrained checkpoints for ImageNet. You'll also need
# to have a copy of the TensorFlow source code to run some of the commands,
# by default it will be looked for in ./tensorflow, but you can set the
# TENSORFLOW_PATH environment variable before calling the script if your source
# is in a different location.
# The main slim/nets/mobilenet_v1.md description has more details about the
# model, but the main points are that it comes in four size versions, 1.0, 0.75,
# 0.50, and 0.25, which controls the number of parameters and so the file size
# of the model, and the input image size, which can be 224, 192, 160, or 128
# pixels, and affects the amount of computation needed, and the latency.
# Here's an example generating a frozen model from pretrained weights:
#
set -e
print_usage () {
echo "Creates a frozen mobilenet model suitable for mobile use"
echo "Usage:"
echo "$0 <mobilenet version> <input size> [checkpoint path]"
}
MOBILENET_VERSION=$1
IMAGE_SIZE=$2
CHECKPOINT=$3
if [[ ${MOBILENET_VERSION} = "1.0" ]]; then
SLIM_NAME=mobilenet_v1
elif [[ ${MOBILENET_VERSION} = "0.75" ]]; then
SLIM_NAME=mobilenet_v1_075
elif [[ ${MOBILENET_VERSION} = "0.50" ]]; then
SLIM_NAME=mobilenet_v1_050
elif [[ ${MOBILENET_VERSION} = "0.25" ]]; then
SLIM_NAME=mobilenet_v1_025
else
echo "Bad mobilenet version, should be one of 1.0, 0.75, 0.50, or 0.25"
print_usage
exit 1
fi
if [[ ${IMAGE_SIZE} -ne "224" ]] && [[ ${IMAGE_SIZE} -ne "192" ]] && [[ ${IMAGE_SIZE} -ne "160" ]] && [[ ${IMAGE_SIZE} -ne "128" ]]; then
echo "Bad input image size, should be one of 224, 192, 160, or 128"
print_usage
exit 1
fi
if [[ ${TENSORFLOW_PATH} -eq "" ]]; then
TENSORFLOW_PATH=../tensorflow
fi
if [[ ! -d ${TENSORFLOW_PATH} ]]; then
echo "TensorFlow source folder not found. You should download the source and then set"
echo "the TENSORFLOW_PATH environment variable to point to it, like this:"
echo "export TENSORFLOW_PATH=/my/path/to/tensorflow"
print_usage
exit 1
fi
MODEL_FOLDER=/tmp/mobilenet_v1_${MOBILENET_VERSION}_${IMAGE_SIZE}
if [[ -d ${MODEL_FOLDER} ]]; then
echo "Model folder ${MODEL_FOLDER} already exists!"
echo "If you want to overwrite it, then 'rm -rf ${MODEL_FOLDER}' first."
print_usage
exit 1
fi
mkdir ${MODEL_FOLDER}
if [[ ${CHECKPOINT} = "" ]]; then
echo "*******"
echo "Downloading pretrained weights"
echo "*******"
curl "http://download.tensorflow.org/models/mobilenet_v1_${MOBILENET_VERSION}_${IMAGE_SIZE}_2017_06_14.tar.gz" \
-o ${MODEL_FOLDER}/checkpoints.tar.gz
tar xzf ${MODEL_FOLDER}/checkpoints.tar.gz --directory ${MODEL_FOLDER}
CHECKPOINT=${MODEL_FOLDER}/mobilenet_v1_${MOBILENET_VERSION}_${IMAGE_SIZE}.ckpt
fi
echo "*******"
echo "Exporting graph architecture to ${MODEL_FOLDER}/unfrozen_graph.pb"
echo "*******"
bazel run slim:export_inference_graph -- \
--model_name=${SLIM_NAME} --image_size=${IMAGE_SIZE} --logtostderr \
--output_file=${MODEL_FOLDER}/unfrozen_graph.pb --dataset_dir=${MODEL_FOLDER}
cd ../tensorflow
echo "*******"
echo "Freezing graph to ${MODEL_FOLDER}/frozen_graph.pb"
echo "*******"
bazel run tensorflow/python/tools:freeze_graph -- \
--input_graph=${MODEL_FOLDER}/unfrozen_graph.pb \
--input_checkpoint=${CHECKPOINT} \
--input_binary=true --output_graph=${MODEL_FOLDER}/frozen_graph.pb \
--output_node_names=MobilenetV1/Predictions/Reshape_1
echo "Quantizing weights to ${MODEL_FOLDER}/quantized_graph.pb"
bazel run tensorflow/tools/graph_transforms:transform_graph -- \
--in_graph=${MODEL_FOLDER}/frozen_graph.pb \
--out_graph=${MODEL_FOLDER}/quantized_graph.pb \
--inputs=input --outputs=MobilenetV1/Predictions/Reshape_1 \
--transforms='fold_constants fold_batch_norms quantize_weights'
echo "*******"
echo "Running label_image using the graph"
echo "*******"
bazel build tensorflow/examples/label_image:label_image
bazel-bin/tensorflow/examples/label_image/label_image \
--input_layer=input --output_layer=MobilenetV1/Predictions/Reshape_1 \
--graph=${MODEL_FOLDER}/quantized_graph.pb --input_mean=-127 --input_std=127 \
--image=tensorflow/examples/label_image/data/grace_hopper.jpg \
--input_width=${IMAGE_SIZE} --input_height=${IMAGE_SIZE} --labels=${MODEL_FOLDER}/labels.txt
echo "*******"
echo "Saved graphs to ${MODEL_FOLDER}/frozen_graph.pb and ${MODEL_FOLDER}/quantized_graph.pb"
echo "*******"
|
CUDA-Optimized/FastSpeech | FastSpeech | .gitignore | .idea
__pycache__
.DS_Store
*.egg-info
.vscode
|
TensorFlow2/Segmentation/UNet_Medical/data_loading | data_loading | data_loader | # 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.
""" Dataset class encapsulates the data loading"""
import multiprocessing
import os
from collections import deque
import numpy as np
import tensorflow as tf
from PIL import Image, ImageSequence
class Dataset:
"""Load, separate and prepare the data for training and prediction"""
def __init__(self, data_dir, batch_size, fold, augment=False, gpu_id=0, num_gpus=1, seed=0, amp=False):
if not os.path.exists(data_dir):
raise FileNotFoundError('Cannot find data dir: {}'.format(data_dir))
self._data_dir = data_dir
self._batch_size = batch_size
self._augment = augment
self.precision = tf.float16 if amp else tf.float32
self._seed = seed
images = self._load_multipage_tiff(os.path.join(self._data_dir, 'train-volume.tif'))
masks = self._load_multipage_tiff(os.path.join(self._data_dir, 'train-labels.tif'))
self._test_images = \
self._load_multipage_tiff(os.path.join(self._data_dir, 'test-volume.tif'))
train_indices, val_indices = self._get_val_train_indices(len(images), fold)
self._train_images = images[train_indices]
self._train_masks = masks[train_indices]
self._val_images = images[val_indices]
self._val_masks = masks[val_indices]
self._num_gpus = num_gpus
self._gpu_id = gpu_id
@property
def train_size(self):
return len(self._train_images)
@property
def eval_size(self):
return len(self._val_images)
@property
def test_size(self):
return len(self._test_images)
def _load_multipage_tiff(self, path):
"""Load tiff images containing many images in the channel dimension"""
return np.array([np.array(p) for p in ImageSequence.Iterator(Image.open(path))])
def _get_val_train_indices(self, length, fold, ratio=0.8):
assert 0 < ratio <= 1, "Train/total data ratio must be in range (0.0, 1.0]"
np.random.seed(self._seed)
indices = np.arange(0, length, 1, dtype=np.int)
np.random.shuffle(indices)
if fold is not None:
indices = deque(indices)
indices.rotate(fold * int((1.0 - ratio) * length))
indices = np.array(indices)
train_indices = indices[:int(ratio * len(indices))]
val_indices = indices[int(ratio * len(indices)):]
else:
train_indices = indices
val_indices = []
return train_indices, val_indices
def _normalize_inputs(self, inputs):
"""Normalize inputs"""
inputs = tf.expand_dims(tf.cast(inputs, tf.float32), -1)
# Center around zero
inputs = tf.divide(inputs, 127.5) - 1
# Resize to match output size
inputs = tf.image.resize(inputs, (388, 388))
return tf.image.resize_with_crop_or_pad(inputs, 572, 572)
def _normalize_labels(self, labels):
"""Normalize labels"""
labels = tf.expand_dims(tf.cast(labels, tf.float32), -1)
labels = tf.divide(labels, 255)
# Resize to match output size
labels = tf.image.resize(labels, (388, 388))
labels = tf.image.resize_with_crop_or_pad(labels, 572, 572)
cond = tf.less(labels, 0.5 * tf.ones(tf.shape(input=labels)))
labels = tf.where(cond, tf.zeros(tf.shape(input=labels)), tf.ones(tf.shape(input=labels)))
return tf.one_hot(tf.squeeze(tf.cast(labels, tf.int32)), 2)
@tf.function
def _preproc_samples(self, inputs, labels, augment=True):
"""Preprocess samples and perform random augmentations"""
inputs = self._normalize_inputs(inputs)
labels = self._normalize_labels(labels)
if self._augment and augment:
# Horizontal flip
h_flip = tf.random.uniform([]) > 0.5
inputs = tf.cond(pred=h_flip, true_fn=lambda: tf.image.flip_left_right(inputs), false_fn=lambda: inputs)
labels = tf.cond(pred=h_flip, true_fn=lambda: tf.image.flip_left_right(labels), false_fn=lambda: labels)
# Vertical flip
v_flip = tf.random.uniform([]) > 0.5
inputs = tf.cond(pred=v_flip, true_fn=lambda: tf.image.flip_up_down(inputs), false_fn=lambda: inputs)
labels = tf.cond(pred=v_flip, true_fn=lambda: tf.image.flip_up_down(labels), false_fn=lambda: labels)
# Prepare for batched transforms
inputs = tf.expand_dims(inputs, 0)
labels = tf.expand_dims(labels, 0)
# Random crop and resize
left = tf.random.uniform([]) * 0.3
right = 1 - tf.random.uniform([]) * 0.3
top = tf.random.uniform([]) * 0.3
bottom = 1 - tf.random.uniform([]) * 0.3
inputs = tf.image.crop_and_resize(inputs, [[top, left, bottom, right]], [0], (572, 572))
labels = tf.image.crop_and_resize(labels, [[top, left, bottom, right]], [0], (572, 572))
# Gray value variations
# Adjust brightness and keep values in range
inputs = tf.image.random_brightness(inputs, max_delta=0.2)
inputs = tf.clip_by_value(inputs, clip_value_min=-1, clip_value_max=1)
inputs = tf.squeeze(inputs, 0)
labels = tf.squeeze(labels, 0)
# Bring back labels to network's output size and remove interpolation artifacts
labels = tf.image.resize_with_crop_or_pad(labels, target_width=388, target_height=388)
cond = tf.less(labels, 0.5 * tf.ones(tf.shape(input=labels)))
labels = tf.where(cond, tf.zeros(tf.shape(input=labels)), tf.ones(tf.shape(input=labels)))
return tf.cast(inputs, self.precision), labels
@tf.function
def _preproc_eval_samples(self, inputs, labels):
"""Preprocess samples and perform random augmentations"""
inputs = self._normalize_inputs(inputs)
labels = self._normalize_labels(labels)
# Bring back labels to network's output size and remove interpolation artifacts
labels = tf.image.resize_with_crop_or_pad(labels, target_width=388, target_height=388)
cond = tf.less(labels, 0.5 * tf.ones(tf.shape(input=labels)))
labels = tf.where(cond, tf.zeros(tf.shape(input=labels)), tf.ones(tf.shape(input=labels)))
return tf.cast(inputs, self.precision), labels
@tf.function
def _preproc_test_samples(self, inputs):
inputs = self._normalize_inputs(inputs)
return tf.cast(inputs, self.precision)
def train_fn(self, drop_remainder=False):
"""Input function for training"""
dataset = tf.data.Dataset.from_tensor_slices(
(self._train_images, self._train_masks))
dataset = dataset.shard(self._num_gpus, self._gpu_id)
dataset = dataset.repeat()
dataset = dataset.shuffle(self._batch_size * 3)
dataset = dataset.map(self._preproc_samples,
num_parallel_calls=multiprocessing.cpu_count()//self._num_gpus)
dataset = dataset.batch(self._batch_size, drop_remainder=drop_remainder)
dataset = dataset.prefetch(self._batch_size)
return dataset
def eval_fn(self, count, drop_remainder=False):
"""Input function for validation"""
dataset = tf.data.Dataset.from_tensor_slices(
(self._val_images, self._val_masks))
dataset = dataset.repeat(count=count)
dataset = dataset.map(self._preproc_eval_samples,
num_parallel_calls=multiprocessing.cpu_count())
dataset = dataset.batch(self._batch_size, drop_remainder=drop_remainder)
dataset = dataset.prefetch(self._batch_size)
return dataset
def test_fn(self, count, drop_remainder=False):
"""Input function for testing"""
dataset = tf.data.Dataset.from_tensor_slices(
self._test_images)
dataset = dataset.repeat(count=count)
dataset = dataset.map(self._preproc_test_samples)
dataset = dataset.batch(self._batch_size, drop_remainder=drop_remainder)
dataset = dataset.prefetch(self._batch_size)
return dataset
def synth_fn(self):
"""Synthetic data function for testing"""
inputs = tf.random.truncated_normal((572, 572, 1), dtype=tf.float32, mean=127.5, stddev=1, seed=self._seed,
name='synth_inputs')
masks = tf.random.truncated_normal((388, 388, 2), dtype=tf.float32, mean=0.01, stddev=0.1, seed=self._seed,
name='synth_masks')
dataset = tf.data.Dataset.from_tensors((inputs, masks))
dataset = dataset.cache()
dataset = dataset.repeat()
dataset = dataset.batch(self._batch_size)
dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return dataset
|
PyTorch/Classification/GPUNet/triton | triton | dataloader | # 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 numpy as np
import json
from timm.data import create_dataset, create_loader
import torch
def update_argparser(parser):
parser.add_argument(
"--config", type=str, required=True, help="Network to deploy")
parser.add_argument("--val-path", type=str, help="Path to dataset to be used", required=True)
parser.add_argument("--batch-size", type=int, help="Batch size to use", default=10)
parser.add_argument("--precision", type=str, default="fp32",
choices=["fp32", "fp16"], help="Inference precision")
parser.add_argument(
"--is-prunet", type=bool, required=True, help="Bool on whether network is a prunet")
def get_dataloader_fn(config, val_path, batch_size, precision, is_prunet):
imagenet_val_path = val_path
dataset = create_dataset( root=imagenet_val_path, name='', split='validation', load_bytes=False, class_map='')
with open(config) as configFile:
modelJSON = json.load(configFile)
configFile.close()
config = modelJSON
assert len(config) > 0
dataLayer = config[0]
assert dataLayer['layer_type'] == 'data'
assert dataLayer['img_resolution'] > 0
imgRes = dataLayer['img_resolution']
crop_pct = 1.0
if is_prunet == "True":
crop_pct = 0.875
data_config = {'input_size': (3, imgRes, imgRes), 'interpolation': 'bicubic', 'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225), 'crop_pct': crop_pct}
batch_size = int(batch_size)
loader = create_loader(
dataset,
input_size=data_config['input_size'],
batch_size=batch_size,
use_prefetcher=True,
interpolation=data_config['interpolation'],
mean=data_config['mean'],
std=data_config['std'],
num_workers=1,
crop_pct=data_config['crop_pct'],
pin_memory=False,
tf_preprocessing=False)
dtype = precision
if dtype == 'fp16':
dtype = torch.float16
elif dtype == 'fp32':
dtype = torch.float32
else:
raise NotImplementedError
def _get_dataloader():
for batch_idx, (input, target) in enumerate(loader):
x = {"INPUT__0": input.to(dtype).cpu().numpy()}
y_real = {"OUTPUT__0": np.tile(target.to(dtype).cpu().numpy()[:, np.newaxis], (1, 1000))}
ids = np.tile(batch_idx, target.shape[0])
yield (ids, x, y_real)
return _get_dataloader |
PyTorch/Classification/GPUNet/triton | triton | run_performance_on_triton | #!/usr/bin/env python3
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
import pathlib
# method from PEP-366 to support relative import in executed modules
if __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from .deployment_toolkit.core import EvaluationMode, MeasurementMode, OfflineMode, PerformanceTool
from .deployment_toolkit.triton_performance_runner import TritonPerformanceRunner
LOGGER = logging.getLogger("run_performance_on_triton")
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model-name",
type=str,
required=True,
help="Name of the model to test",
)
parser.add_argument(
"--result-path",
type=pathlib.Path,
required=True,
help="Path where results files is stored.",
)
parser.add_argument(
"--server-url",
type=str,
default="http://127.0.0.1:8000",
help="Url to Triton server",
)
parser.add_argument(
"--model-version",
type=str,
default=1,
help="Version of model",
)
parser.add_argument(
"--input-data",
type=str,
default="random",
help="Input data to perform profiling.",
)
parser.add_argument(
"--input-shapes",
action="append",
help="Input data shape in form INPUT_NAME:<full_shape_without_batch_axis>.",
)
parser.add_argument(
"--batch-sizes",
type=int,
default=[1],
help="List of batch sizes to tests.",
nargs="*",
)
parser.add_argument(
"--concurrency",
type=int,
default=[1],
help="List of concurrency modes.",
nargs="*",
)
parser.add_argument(
"--measurement-mode",
choices=[item.value for item in MeasurementMode],
default=MeasurementMode.COUNT_WINDOWS.value,
type=str,
help="Select measurement mode "
"'time_windows' stabilize performance on measurement window. "
"'count_windows' stabilize performance on number of samples.",
)
parser.add_argument(
"--measurement-interval",
help="Time window perf_analyzer will wait to stabilize the measurement",
default=5000,
type=int,
)
parser.add_argument(
"--measurement-request-count",
help="Number of samples on which perf_analyzer will stabilize the measurement",
default=50,
type=int,
)
parser.add_argument(
"--evaluation-mode",
choices=[item.value for item in EvaluationMode],
default=EvaluationMode.OFFLINE.value,
type=str,
help="Select evaluation mode "
"'offline' run offline analysis and use GPU memory to pass tensors. "
"'online' run online analysis and use HTTP protocol.",
)
parser.add_argument(
"--offline-mode",
choices=[item.value for item in OfflineMode],
default=OfflineMode.SYSTEM.value,
type=str,
help="Select offline mode "
"'system' pass tensors through CPU RAM memory. "
"'cuda' pass tensors through GPU RAM memory.",
)
parser.add_argument(
"--output-shared-memory-size",
default=102400,
type=int,
help="Size of memory buffer allocated for output with dynamic shapes in bytes. "
"Has to be equal to maximal size of output tensor.",
)
parser.add_argument(
"--performance-tool",
choices=[item.value for item in PerformanceTool],
default=PerformanceTool.MODEL_ANALYZER.value,
type=str,
help="Select performance tool for measurement mode "
"'model_analyzer' use Model Analyzer "
"'perf_analyzer' use Perf Analyzer",
)
parser.add_argument(
"--model-repository",
default=None,
type=str,
help="Path to model repository. Valid when using Model Analyzer",
)
parser.add_argument(
"--warmup",
help="Enable model warmup before performance test",
action="store_true",
default=False,
)
parser.add_argument(
"--timeout",
help="Timeout for performance analysis",
type=int,
default=None,
required=False,
)
parser.add_argument(
"-v",
"--verbose",
help="Verbose logs",
action="store_true",
default=False,
)
args = parser.parse_args()
log_level = logging.INFO if not args.verbose else logging.DEBUG
log_format = "%(asctime)s %(levelname)s %(name)s %(message)s"
logging.basicConfig(level=log_level, format=log_format)
runner = TritonPerformanceRunner(
server_url=args.server_url,
model_name=args.model_name,
input_data=args.input_data,
input_shapes=args.input_shapes or [],
batch_sizes=args.batch_sizes,
measurement_mode=MeasurementMode(args.measurement_mode),
measurement_interval=args.measurement_interval,
measurement_request_count=args.measurement_request_count,
concurrency=args.concurrency,
evaluation_mode=EvaluationMode(args.evaluation_mode),
offline_mode=OfflineMode(args.offline_mode),
output_shared_memory_size=args.output_shared_memory_size,
performance_tool=PerformanceTool(args.performance_tool),
model_repository=args.model_repository,
result_path=args.result_path,
warmup=args.warmup,
timeout=args.timeout,
verbose=args.verbose,
)
runner.run()
if __name__ == "__main__":
main()
|
TensorFlow2/Detection/Efficientdet/object_detection | object_detection | argmax_matcher | # Copyright 2020 Google Research. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Argmax matcher implementation.
This class takes a similarity matrix and matches columns to rows based on the
maximum value per column. One can specify matched_thresholds and
to prevent columns from matching to rows (generally resulting in a negative
training example) and unmatched_theshold to ignore the match (generally
resulting in neither a positive or negative training example).
This matcher is used in Fast(er)-RCNN.
Note: matchers are used in TargetAssigners. There is a create_target_assigner
factory function for popular implementations.
"""
import tensorflow.compat.v1 as tf
from object_detection import matcher
from object_detection import shape_utils
class ArgMaxMatcher(matcher.Matcher):
"""Matcher based on highest value.
This class computes matches from a similarity matrix. Each column is matched
to a single row.
To support object detection target assignment this class enables setting both
matched_threshold (upper threshold) and unmatched_threshold (lower thresholds)
defining three categories of similarity which define whether examples are
positive, negative, or ignored:
(1) similarity >= matched_threshold: Highest similarity. Matched/Positive!
(2) matched_threshold > similarity >= unmatched_threshold: Medium similarity.
Depending on negatives_lower_than_unmatched, this is either
Unmatched/Negative OR Ignore.
(3) unmatched_threshold > similarity: Lowest similarity. Depending on flag
negatives_lower_than_unmatched, either Unmatched/Negative OR Ignore.
For ignored matches this class sets the values in the Match object to -2.
"""
def __init__(self,
matched_threshold,
unmatched_threshold=None,
negatives_lower_than_unmatched=True,
force_match_for_each_row=False):
"""Construct ArgMaxMatcher.
Args:
matched_threshold: Threshold for positive matches. Positive if
sim >= matched_threshold, where sim is the maximum value of the
similarity matrix for a given column. Set to None for no threshold.
unmatched_threshold: Threshold for negative matches. Negative if
sim < unmatched_threshold. Defaults to matched_threshold
when set to None.
negatives_lower_than_unmatched: Boolean which defaults to True. If True
then negative matches are the ones below the unmatched_threshold,
whereas ignored matches are in between the matched and unmatched
threshold. If False, then negative matches are in between the matched
and unmatched threshold, and everything lower than unmatched is ignored.
force_match_for_each_row: If True, ensures that each row is matched to
at least one column (which is not guaranteed otherwise if the
matched_threshold is high). Defaults to False. See
argmax_matcher_test.testMatcherForceMatch() for an example.
Raises:
ValueError: if unmatched_threshold is set but matched_threshold is not set
or if unmatched_threshold > matched_threshold.
"""
if (matched_threshold is None) and (unmatched_threshold is not None):
raise ValueError('Need to also define matched_threshold when'
'unmatched_threshold is defined')
self._matched_threshold = matched_threshold
if unmatched_threshold is None:
self._unmatched_threshold = matched_threshold
else:
if unmatched_threshold > matched_threshold:
raise ValueError('unmatched_threshold needs to be smaller or equal'
'to matched_threshold')
self._unmatched_threshold = unmatched_threshold
if not negatives_lower_than_unmatched:
if self._unmatched_threshold == self._matched_threshold:
raise ValueError('When negatives are in between matched and '
'unmatched thresholds, these cannot be of equal '
'value. matched: %s, unmatched: %s',
self._matched_threshold, self._unmatched_threshold)
self._force_match_for_each_row = force_match_for_each_row
self._negatives_lower_than_unmatched = negatives_lower_than_unmatched
def _match(self, similarity_matrix):
"""Tries to match each column of the similarity matrix to a row.
Args:
similarity_matrix: tensor of shape [N, M] representing any similarity
metric.
Returns:
Match object with corresponding matches for each of M columns.
"""
def _match_when_rows_are_empty():
"""Performs matching when the rows of similarity matrix are empty.
When the rows are empty, all detections are false positives. So we return
a tensor of -1's to indicate that the columns do not match to any rows.
Returns:
matches: int32 tensor indicating the row each column matches to.
"""
similarity_matrix_shape = shape_utils.combined_static_and_dynamic_shape(
similarity_matrix)
return -1 * tf.ones([similarity_matrix_shape[1]], dtype=tf.int32)
def _match_when_rows_are_non_empty():
"""Performs matching when the rows of similarity matrix are non empty.
Returns:
matches: int32 tensor indicating the row each column matches to.
"""
# Matches for each column
matches = tf.argmax(similarity_matrix, 0, output_type=tf.int32)
# Deal with matched and unmatched threshold
if self._matched_threshold is not None:
# Get logical indices of ignored and unmatched columns as tf.int64
matched_vals = tf.reduce_max(similarity_matrix, 0)
below_unmatched_threshold = tf.greater(self._unmatched_threshold,
matched_vals)
between_thresholds = tf.logical_and(
tf.greater_equal(matched_vals, self._unmatched_threshold),
tf.greater(self._matched_threshold, matched_vals))
if self._negatives_lower_than_unmatched:
matches = self._set_values_using_indicator(matches,
below_unmatched_threshold,
-1)
matches = self._set_values_using_indicator(matches,
between_thresholds,
-2)
else:
matches = self._set_values_using_indicator(matches,
below_unmatched_threshold,
-2)
matches = self._set_values_using_indicator(matches,
between_thresholds,
-1)
if self._force_match_for_each_row:
similarity_matrix_shape = shape_utils.combined_static_and_dynamic_shape(
similarity_matrix)
force_match_column_ids = tf.argmax(similarity_matrix, 1,
output_type=tf.int32)
force_match_column_indicators = tf.one_hot(
force_match_column_ids, depth=similarity_matrix_shape[1])
force_match_row_ids = tf.argmax(force_match_column_indicators, 0,
output_type=tf.int32)
force_match_column_mask = tf.cast(
tf.reduce_max(force_match_column_indicators, 0), tf.bool)
final_matches = tf.where(force_match_column_mask,
force_match_row_ids, matches)
return final_matches
else:
return matches
if similarity_matrix.shape.is_fully_defined():
if similarity_matrix.shape[0] == 0:
return _match_when_rows_are_empty()
else:
return _match_when_rows_are_non_empty()
else:
return tf.cond(
tf.greater(tf.shape(similarity_matrix)[0], 0),
_match_when_rows_are_non_empty, _match_when_rows_are_empty)
def _set_values_using_indicator(self, x, indicator, val):
"""Set the indicated fields of x to val.
Args:
x: tensor.
indicator: boolean with same shape as x.
val: scalar with value to set.
Returns:
modified tensor.
"""
indicator = tf.cast(indicator, x.dtype)
return x * (1 - indicator) + val * indicator
|
PyTorch/SpeechSynthesis/Tacotron2/tacotron2_common | tacotron2_common | utils | # *****************************************************************************
# 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 numpy as np
from scipy.io.wavfile import read
import torch
import os
import argparse
import json
class ParseFromConfigFile(argparse.Action):
def __init__(self, option_strings, type, dest, help=None, required=False):
super(ParseFromConfigFile, self).__init__(option_strings=option_strings, type=type, dest=dest, help=help, required=required)
def __call__(self, parser, namespace, values, option_string):
with open(values, 'r') as f:
data = json.load(f)
for group in data.keys():
for k,v in data[group].items():
underscore_k = k.replace('-', '_')
setattr(namespace, underscore_k, v)
def get_mask_from_lengths(lengths):
max_len = torch.max(lengths).item()
ids = torch.arange(0, max_len, device=lengths.device, dtype=lengths.dtype)
mask = (ids < lengths.unsqueeze(1)).byte()
mask = torch.le(mask, 0)
return mask
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
def load_filepaths_and_text(dataset_path, filename, split="|"):
with open(filename, encoding='utf-8') as f:
def split_line(root, line):
parts = line.strip().split(split)
if len(parts) > 2:
raise Exception(
"incorrect line format for file: {}".format(filename))
path = os.path.join(root, parts[0])
text = parts[1]
return path,text
filepaths_and_text = [split_line(dataset_path, line) for line in f]
return filepaths_and_text
def to_gpu(x):
x = x.contiguous()
if torch.cuda.is_available():
x = x.cuda(non_blocking=True)
return x
|
PyTorch/Classification/GPUNet/triton/085ms/runner | runner | pipeline_impl | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pathlib
if __name__ == "__main__" and __package__ is None:
__package__ = pathlib.Path(__file__).parent.name
from ...runner.pipeline import Pipeline
pipeline = Pipeline()
pipeline.model_export(
commands=(
r"""
if [[ "${EXPORT_FORMAT}" == "torchscript" ]]; then
export FORMAT_SUFFIX="pt"
else
export FORMAT_SUFFIX="${EXPORT_FORMAT}"
fi
python3 triton/export_model.py \
--input-path triton/model.py \
--input-type pyt \
--output-path ${SHARED_DIR}/exported_model.${FORMAT_SUFFIX} \
--output-type ${EXPORT_FORMAT} \
--ignore-unknown-parameters \
--onnx-opset 13 \
--torch-jit ${TORCH_JIT} \
\
--config /workspace/gpunet/configs/batch1/GV100/0.85ms.json \
--checkpoint ${CHECKPOINT_DIR}/0.85ms.pth.tar \
--precision ${EXPORT_PRECISION} \
\
--dataloader triton/dataloader.py \
--val-path ${DATASETS_DIR}/ \
--is-prunet False \
--batch-size 1
""",
)
)
pipeline.model_conversion(
commands=(
r"""
if [[ "${EXPORT_FORMAT}" == "torchscript" ]]; then
export FORMAT_SUFFIX="pt"
else
export FORMAT_SUFFIX="${EXPORT_FORMAT}"
fi
model-navigator convert \
--model-name ${MODEL_NAME} \
--model-path ${SHARED_DIR}/exported_model.${FORMAT_SUFFIX} \
--output-path ${SHARED_DIR}/converted_model \
--target-formats ${FORMAT} \
--target-precisions ${PRECISION} \
--launch-mode local \
--override-workspace \
--verbose \
\
--onnx-opsets 13 \
--max-batch-size ${MAX_BATCH_SIZE} \
--container-version 21.12 \
--max-workspace-size 10000000000 \
--atol OUTPUT__0=100 \
--rtol OUTPUT__0=100
""",
)
)
pipeline.model_deploy(
commands=(
r"""
model-navigator triton-config-model \
--model-repository ${MODEL_REPOSITORY_PATH} \
--model-name ${MODEL_NAME} \
--model-version 1 \
--model-path ${SHARED_DIR}/converted_model \
--model-format ${FORMAT} \
--model-control-mode explicit \
--load-model \
--load-model-timeout-s 100 \
--verbose \
\
--backend-accelerator ${BACKEND_ACCELERATOR} \
--tensorrt-precision ${PRECISION} \
--tensorrt-capture-cuda-graph \
--tensorrt-max-workspace-size 10000000000 \
--max-batch-size ${MAX_BATCH_SIZE} \
--batching ${MODEL_BATCHING} \
--preferred-batch-sizes ${MAX_BATCH_SIZE} \
--engine-count-per-device gpu=${NUMBER_OF_MODEL_INSTANCES}
""",
)
)
pipeline.triton_performance_offline_tests(
commands=(
r"""
python triton/run_performance_on_triton.py \
--model-repository ${MODEL_REPOSITORY_PATH} \
--model-name ${MODEL_NAME} \
--input-data random \
--batch-sizes 1 2 4 8 16 32 64 \
--concurrency 1 \
--evaluation-mode offline \
--measurement-request-count 10 \
--warmup \
--performance-tool perf_analyzer \
--result-path ${SHARED_DIR}/triton_performance_offline.csv
""",
),
result_path="${SHARED_DIR}/triton_performance_offline.csv",
)
pipeline.triton_performance_online_tests(
commands=(
r"""
python triton/run_performance_on_triton.py \
--model-repository ${MODEL_REPOSITORY_PATH} \
--model-name ${MODEL_NAME} \
--input-data random \
--batch-sizes 1 \
--concurrency 8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 128 136 144 152 160 168 176 184 192 200 208 216 224 232 240 248 256 \
--evaluation-mode online \
--measurement-request-count 500 \
--warmup \
--performance-tool perf_analyzer \
--result-path ${SHARED_DIR}/triton_performance_online.csv
""",
),
result_path="${SHARED_DIR}/triton_performance_online.csv",
) |
TensorFlow/Detection/SSD/models/research/object_detection/core | core | model | # 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.
# ==============================================================================
"""Abstract detection model.
This file defines a generic base class for detection models. Programs that are
designed to work with arbitrary detection models should only depend on this
class. We intend for the functions in this class to follow tensor-in/tensor-out
design, thus all functions have tensors or lists/dictionaries holding tensors as
inputs and outputs.
Abstractly, detection models predict output tensors given input images
which can be passed to a loss function at training time or passed to a
postprocessing function at eval time. The computation graphs at a high level
consequently look as follows:
Training time:
inputs (images tensor) -> preprocess -> predict -> loss -> outputs (loss tensor)
Evaluation time:
inputs (images tensor) -> preprocess -> predict -> postprocess
-> outputs (boxes tensor, scores tensor, classes tensor, num_detections tensor)
DetectionModels must thus implement four functions (1) preprocess, (2) predict,
(3) postprocess and (4) loss. DetectionModels should make no assumptions about
the input size or aspect ratio --- they are responsible for doing any
resize/reshaping necessary (see docstring for the preprocess function).
Output classes are always integers in the range [0, num_classes). Any mapping
of these integers to semantic labels is to be handled outside of this class.
Images are resized in the `preprocess` method. All of `preprocess`, `predict`,
and `postprocess` should be reentrant.
The `preprocess` method runs `image_resizer_fn` that returns resized_images and
`true_image_shapes`. Since `image_resizer_fn` can pad the images with zeros,
true_image_shapes indicate the slices that contain the image without padding.
This is useful for padding images to be a fixed size for batching.
The `postprocess` method uses the true image shapes to clip predictions that lie
outside of images.
By default, DetectionModels produce bounding box detections; However, we support
a handful of auxiliary annotations associated with each bounding box, namely,
instance masks and keypoints.
"""
from abc import ABCMeta
from abc import abstractmethod
from object_detection.core import standard_fields as fields
class DetectionModel(object):
"""Abstract base class for detection models."""
__metaclass__ = ABCMeta
def __init__(self, num_classes):
"""Constructor.
Args:
num_classes: number of classes. Note that num_classes *does not* include
background categories that might be implicitly predicted in various
implementations.
"""
self._num_classes = num_classes
self._groundtruth_lists = {}
@property
def num_classes(self):
return self._num_classes
def groundtruth_lists(self, field):
"""Access list of groundtruth tensors.
Args:
field: a string key, options are
fields.BoxListFields.{boxes,classes,masks,keypoints} or
fields.InputDataFields.is_annotated.
Returns:
a list of tensors holding groundtruth information (see also
provide_groundtruth function below), with one entry for each image in the
batch.
Raises:
RuntimeError: if the field has not been provided via provide_groundtruth.
"""
if field not in self._groundtruth_lists:
raise RuntimeError('Groundtruth tensor {} has not been provided'.format(
field))
return self._groundtruth_lists[field]
def groundtruth_has_field(self, field):
"""Determines whether the groundtruth includes the given field.
Args:
field: a string key, options are
fields.BoxListFields.{boxes,classes,masks,keypoints} or
fields.InputDataFields.is_annotated.
Returns:
True if the groundtruth includes the given field, False otherwise.
"""
return field in self._groundtruth_lists
@abstractmethod
def preprocess(self, inputs):
"""Input preprocessing.
To be overridden by implementations.
This function is responsible for any scaling/shifting of input values that
is necessary prior to running the detector on an input image.
It is also responsible for any resizing, padding that might be necessary
as images are assumed to arrive in arbitrary sizes. While this function
could conceivably be part of the predict method (below), it is often
convenient to keep these separate --- for example, we may want to preprocess
on one device, place onto a queue, and let another device (e.g., the GPU)
handle prediction.
A few important notes about the preprocess function:
+ We assume that this operation does not have any trainable variables nor
does it affect the groundtruth annotations in any way (thus data
augmentation operations such as random cropping should be performed
externally).
+ There is no assumption that the batchsize in this function is the same as
the batch size in the predict function. In fact, we recommend calling the
preprocess function prior to calling any batching operations (which should
happen outside of the model) and thus assuming that batch sizes are equal
to 1 in the preprocess function.
+ There is also no explicit assumption that the output resolutions
must be fixed across inputs --- this is to support "fully convolutional"
settings in which input images can have different shapes/resolutions.
Args:
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.
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.
"""
pass
@abstractmethod
def predict(self, preprocessed_inputs, true_image_shapes):
"""Predict prediction tensors from inputs tensor.
Outputs of this function can be passed to loss or postprocess functions.
Args:
preprocessed_inputs: a [batch, height, width, channels] float32 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 prediction tensors to be
passed to the Loss or Postprocess functions.
"""
pass
@abstractmethod
def postprocess(self, prediction_dict, true_image_shapes, **params):
"""Convert predicted output tensors to final detections.
Outputs adhere to the following conventions:
* Classes are integers in [0, num_classes); background classes are removed
and the first non-background class is mapped to 0. If the model produces
class-agnostic detections, then no output is produced for classes.
* Boxes are to be interpreted as being in [y_min, x_min, y_max, x_max]
format and normalized relative to the image window.
* `num_detections` is provided for settings where detections are padded to a
fixed number of boxes.
* We do not specifically assume any kind of probabilistic interpretation
of the scores --- the only important thing is their relative ordering.
Thus implementations of the postprocess function are free to output
logits, probabilities, calibrated probabilities, or anything else.
Args:
prediction_dict: a dictionary holding prediction tensors.
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.
**params: Additional keyword arguments for specific implementations of
DetectionModel.
Returns:
detections: a dictionary containing the following fields
detection_boxes: [batch, max_detections, 4]
detection_scores: [batch, max_detections]
detection_classes: [batch, max_detections]
(If a model is producing class-agnostic detections, this field may be
missing)
instance_masks: [batch, max_detections, image_height, image_width]
(optional)
keypoints: [batch, max_detections, num_keypoints, 2] (optional)
num_detections: [batch]
"""
pass
@abstractmethod
def loss(self, prediction_dict, true_image_shapes):
"""Compute scalar loss tensors with respect to provided groundtruth.
Calling this function requires that groundtruth tensors have been
provided via the provide_groundtruth function.
Args:
prediction_dict: a dictionary holding predicted tensors
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:
a dictionary mapping strings (loss names) to scalar tensors representing
loss values.
"""
pass
def provide_groundtruth(self,
groundtruth_boxes_list,
groundtruth_classes_list,
groundtruth_masks_list=None,
groundtruth_keypoints_list=None,
groundtruth_weights_list=None,
groundtruth_confidences_list=None,
groundtruth_is_crowd_list=None,
is_annotated_list=None):
"""Provide groundtruth tensors.
Args:
groundtruth_boxes_list: a list of 2-D tf.float32 tensors of shape
[num_boxes, 4] containing coordinates of the groundtruth boxes.
Groundtruth boxes are provided in [y_min, x_min, y_max, x_max]
format and assumed to be normalized and clipped
relative to the image window with y_min <= y_max and x_min <= x_max.
groundtruth_classes_list: a list of 2-D tf.float32 one-hot (or k-hot)
tensors of shape [num_boxes, num_classes] containing the class targets
with the 0th index assumed to map to the first non-background class.
groundtruth_masks_list: a list of 3-D tf.float32 tensors of
shape [num_boxes, height_in, width_in] containing instance
masks with values in {0, 1}. If None, no masks are provided.
Mask resolution `height_in`x`width_in` must agree with the resolution
of the input image tensor provided to the `preprocess` function.
groundtruth_keypoints_list: a list of 3-D tf.float32 tensors of
shape [num_boxes, num_keypoints, 2] containing keypoints.
Keypoints are assumed to be provided in normalized coordinates and
missing keypoints should be encoded as NaN.
groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape
[num_boxes] containing weights for groundtruth boxes.
groundtruth_confidences_list: A list of 2-D tf.float32 tensors of shape
[num_boxes, num_classes] containing class confidences for groundtruth
boxes.
groundtruth_is_crowd_list: A list of 1-D tf.bool tensors of shape
[num_boxes] containing is_crowd annotations
is_annotated_list: A list of scalar tf.bool tensors indicating whether
images have been labeled or not.
"""
self._groundtruth_lists[fields.BoxListFields.boxes] = groundtruth_boxes_list
self._groundtruth_lists[
fields.BoxListFields.classes] = groundtruth_classes_list
if groundtruth_weights_list:
self._groundtruth_lists[fields.BoxListFields.
weights] = groundtruth_weights_list
if groundtruth_confidences_list:
self._groundtruth_lists[fields.BoxListFields.
confidences] = groundtruth_confidences_list
if groundtruth_masks_list:
self._groundtruth_lists[
fields.BoxListFields.masks] = groundtruth_masks_list
if groundtruth_keypoints_list:
self._groundtruth_lists[
fields.BoxListFields.keypoints] = groundtruth_keypoints_list
if groundtruth_is_crowd_list:
self._groundtruth_lists[
fields.BoxListFields.is_crowd] = groundtruth_is_crowd_list
if is_annotated_list:
self._groundtruth_lists[
fields.InputDataFields.is_annotated] = is_annotated_list
@abstractmethod
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.
"""
pass
@abstractmethod
def restore_map(self, fine_tune_checkpoint_type='detection'):
"""Returns a map of variables to load from a foreign checkpoint.
Returns a map of variable names to load from a checkpoint to variables in
the model graph. This enables the model to initialize based on weights from
another task. For example, the feature extractor variables from a
classification model can be used to bootstrap training of an object
detector. When loading from an object detection model, the checkpoint model
should have the same parameters as this detection model with exception of
the num_classes parameter.
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'.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
"""
pass
@abstractmethod
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.
"""
pass
|
PyTorch/SpeechSynthesis/Tacotron2/platform | platform | DGX1_waveglow_AMP_4NGPU_train | mkdir -p output
python -m multiproc train.py -m WaveGlow -o output/ --amp -lr 1e-4 --epochs 1001 -bs 10 --segment-length 8000 --weight-decay 0 --grad-clip-thresh 65504.0 --cudnn-benchmark --cudnn-enabled --log-file nvlog.json
|
TensorFlow2/Recommendation/DLRM_and_DCNv2/deployment | deployment | evaluate_accuracy | # 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.
#
# author: Tomasz Grel ([email protected])
import dataloading.feature_spec
import os
import numpy as np
import argparse
import dllogger
from dataloading.dataloader import create_input_pipelines
from nn.evaluator import Evaluator
from utils.logging import IterTimer, init_logging
import deployment.tf.triton_ensemble_wrapper
import deployment.hps.triton_ensemble_wrapper
def log_results(auc, test_loss, latencies, batch_size, compute_latencies=False, warmup_steps=10):
# don't benchmark the first few warmup steps
latencies = latencies[warmup_steps:]
result_data = {
'mean_inference_throughput': batch_size / np.mean(latencies),
'mean_inference_latency': np.mean(latencies)
}
if compute_latencies:
for percentile in [90, 95, 99]:
result_data[f'p{percentile}_inference_latency'] = np.percentile(latencies, percentile)
result_data['auc'] = auc
result_data['test_loss'] = test_loss
dllogger.log(data=result_data, step=tuple())
def parse_args():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--dataset_path', type=str, required=True, help='')
parser.add_argument('--dataset_type', default='tf_raw', type=str, help='')
parser.add_argument('--feature_spec', default='feature_spec.yaml', type=str, help='')
parser.add_argument('--batch_size', type=int, default=32768, help='Batch size')
parser.add_argument('--auc_thresholds', type=int, default=8000, help='')
parser.add_argument('--max_steps', type=int, default=None, help='')
parser.add_argument('--print_freq', type=int, default=10, help='')
parser.add_argument('--log_path', type=str, default='dlrm_tf_log.json', help='triton_inference_log.json')
parser.add_argument('--verbose', action='store_true', default=False, help='')
parser.add_argument('--test_on_train', action='store_true', default=False,
help='Run validation on the training set.')
parser.add_argument('--fused_embedding', action='store_true', default=False,
help='Fuse the embedding table together for better GPU utilization.')
parser.add_argument("--model_name", type=str, help="The name of the model used for inference.", required=True)
parser.add_argument("--sparse_input_format", type=str, choices=["tf-savedmodel", "hps"],
required=True, default="tf-savedmodel")
args = parser.parse_args()
return args
def main():
args = parse_args()
init_logging(log_path=args.log_path, params_dict=args.__dict__)
fspec = dataloading.feature_spec.FeatureSpec.from_yaml(os.path.join(args.dataset_path, args.feature_spec))
num_tables = len(fspec.get_categorical_sizes())
table_ids = list(range(num_tables)) # possibly wrong ordering, to be tested
train_pipeline, validation_pipeline = create_input_pipelines(dataset_type=args.dataset_type,
dataset_path=args.dataset_path,
train_batch_size=args.batch_size,
test_batch_size=args.batch_size,
table_ids=table_ids,
feature_spec=args.feature_spec,
rank=0, world_size=1)
if args.test_on_train:
validation_pipeline = train_pipeline
if args.sparse_input_format == 'hps':
wrapper_cls = deployment.hps.triton_ensemble_wrapper.RecsysTritonEnsemble
else:
wrapper_cls = deployment.tf.triton_ensemble_wrapper.RecsysTritonEnsemble
model = wrapper_cls(model_name=args.model_name, num_tables=num_tables, verbose=args.verbose,
categorical_sizes=fspec.get_categorical_sizes(), fused_embedding=args.fused_embedding)
timer = IterTimer(train_batch_size=args.batch_size, test_batch_size=args.batch_size,
optimizer=None, print_freq=args.print_freq, enabled=True)
evaluator = Evaluator(model=model, timer=timer, auc_thresholds=args.auc_thresholds,
max_steps=args.max_steps, cast_dtype=None)
auc, test_loss, latencies = evaluator(validation_pipeline=validation_pipeline)
log_results(auc, test_loss, latencies, batch_size=args.batch_size)
print('DONE')
if __name__ == '__main__':
main() |
TensorFlow/Recommendation/WideAndDeep/scripts | scripts | DGX1_benchmark_training_fp32_8gpu | #!/bin/bash
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -x
set -e
mpiexec --allow-run-as-root --bind-to socket -np 8 \
python -m trainer.task \
--hvd \
--benchmark_warmup_steps 500 \
--benchmark_steps 1000 \
--gpu \
--benchmark
|
Tools/DGLPyTorch/SyntheticGraphGeneration/syngen/cli/commands | commands | __init__ | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
|
TensorFlow/Classification/ConvNets/triton | triton | calculate_metrics | #!/usr/bin/env python3
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""
Using `calculate_metrics.py` script, you can obtain model accuracy/error metrics using defined `MetricsCalculator` class.
Data provided to `MetricsCalculator` are obtained from dump files
stored in directory pointed by `--dump-dir` argument.
Above files are prepared by `run_inference_on_fw.py` and `run_inference_on_triton.py` scripts.
Output data is stored in csv file pointed by `--csv` argument.
Example call:
```shell script
python ./triton/calculate_metrics.py \
--dump-dir /results/dump_triton \
--csv /results/accuracy_results.csv \
--metrics metrics.py \
--metric-class-param1 value
```
"""
import argparse
import csv
import logging
import string
from pathlib import Path
# method from PEP-366 to support relative import in executed modules
if __package__ is None:
__package__ = Path(__file__).parent.name
from .deployment_toolkit.args import ArgParserGenerator
from .deployment_toolkit.core import BaseMetricsCalculator, load_from_file
from .deployment_toolkit.dump import JsonDumpReader
LOGGER = logging.getLogger("calculate_metrics")
TOTAL_COLUMN_NAME = "_total_"
def main():
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser(description="Run models with given dataloader", allow_abbrev=False)
parser.add_argument("--metrics", help="Path to python module containing metrics calculator", required=True)
parser.add_argument("--csv", help="Path to csv file", required=True)
parser.add_argument("--dump-dir", help="Path to directory with dumped outputs (and labels)", required=True)
args, *_ = parser.parse_known_args()
MetricsCalculator = load_from_file(args.metrics, "metrics", "MetricsCalculator")
ArgParserGenerator(MetricsCalculator).update_argparser(parser)
args = parser.parse_args()
LOGGER.info("args:")
for key, value in vars(args).items():
LOGGER.info(f" {key} = {value}")
MetricsCalculator = load_from_file(args.metrics, "metrics", "MetricsCalculator")
metrics_calculator: BaseMetricsCalculator = ArgParserGenerator(MetricsCalculator).from_args(args)
reader = JsonDumpReader(args.dump_dir)
for ids, x, y_true, y_pred in reader.iterate_over(["ids", "inputs", "labels", "outputs"]):
ids = list(ids["ids"]) if ids is not None else None
metrics_calculator.update(ids=ids, x=x, y_pred=y_pred, y_real=y_true)
metrics = metrics_calculator.metrics
metric_names_with_space = [name for name in metrics if any([c in string.whitespace for c in name])]
if metric_names_with_space:
raise ValueError(f"Metric names shall have no spaces; Incorrect names: {', '.join(metric_names_with_space)}")
csv_path = Path(args.csv)
csv_path.parent.mkdir(parents=True, exist_ok=True)
with csv_path.open("w") as csv_file:
writer = csv.DictWriter(csv_file, fieldnames=list(metrics.keys()))
writer.writeheader()
writer.writerow(metrics)
if __name__ == "__main__":
main()
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