python_code stringlengths 0 679k | repo_name stringlengths 9 41 | file_path stringlengths 6 149 |
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# 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 appli... | tao_front_end_services-main | api/handlers/app_handler.py |
# 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 appli... | tao_front_end_services-main | api/handlers/docker_images.py |
# 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 appli... | tao_front_end_services-main | api/handlers/infer_data_sources.py |
# 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 appli... | tao_front_end_services-main | api/handlers/actions.py |
# 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 appli... | tao_front_end_services-main | api/handlers/__init__.py |
# 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 appli... | tao_front_end_services-main | api/handlers/automl_handler.py |
# 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 appli... | tao_front_end_services-main | api/handlers/chaining.py |
# 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 appli... | tao_front_end_services-main | api/handlers/stateless_handlers.py |
# 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 appli... | tao_front_end_services-main | api/handlers/utilities.py |
# 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 appli... | tao_front_end_services-main | api/handlers/infer_params.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/lprnet/preprocess_openalpr_benchmark.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/fpenet/data_utils.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/heartratenet/process_cohface.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/metric_learning_recognition/process_retail_product_checkout_dataset.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/kitti/kitti_to_coco.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/pointpillars/calibration_kitti.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/pointpillars/drop_class.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/pointpillars/gen_lidar_labels.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/pointpillars/kitti_split.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/pointpillars/object3d_kitti.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/pointpillars/gen_lidar_points.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/unet/prepare_data_isbi.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/re_identification/obtain_subset_data.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/pose_classification/select_subset_actions.py |
# 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 appli... | tao_front_end_services-main | notebooks/cli/dataset_prepare/ocrnet/preprocess_label.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/lprnet/preprocess_openalpr_benchmark.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/fpenet/data_utils.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/metric_learning_recognition/process_retail_product_checkout_dataset.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/kitti/kitti_to_coco.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/pointpillars/calibration_kitti.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/pointpillars/drop_class.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/pointpillars/gen_lidar_labels.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/pointpillars/kitti_split.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/pointpillars/object3d_kitti.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/pointpillars/gen_lidar_points.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/unet/prepare_data_isbi.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/re_identification/obtain_subset_data.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/pose_classification/select_subset_actions.py |
# 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 appli... | tao_front_end_services-main | notebooks/api/dataset_prepare/ocrnet/preprocess_label.py |
__author__ = 'Sean Griffin'
__version__ = '1.0.0'
__email__ = 'sean@thoughtbot.com'
import sys
import os.path
import json
import shutil
from pymel.core import *
from maya.OpenMaya import *
from maya.OpenMayaMPx import *
kPluginTranslatorTypeName = 'Three.js'
kOptionScript = 'ThreeJsExportScript'
kDefaultOptionsStri... | three.js-master | utils/exporters/maya/plug-ins/threeJsFileTranslator.py |
# ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distrib... | three.js-master | utils/exporters/blender/2.65/scripts/addons/io_mesh_threejs/import_threejs.py |
# ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distrib... | three.js-master | utils/exporters/blender/2.65/scripts/addons/io_mesh_threejs/export_threejs.py |
# ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distrib... | three.js-master | utils/exporters/blender/2.65/scripts/addons/io_mesh_threejs/__init__.py |
#!/usr/bin/env python
import sys
if sys.version_info < (2, 7):
print("This script requires at least Python 2.7.")
print("Please, update to a newer version: http://www.python.org/download/releases/")
exit()
import argparse
import json
import os
import re
import shutil
import tempfile
def main(argv=None):
parse... | three.js-master | utils/editors/sublime.py |
"""Convert Wavefront OBJ / MTL files into Three.js (JSON model version, to be used with ascii / binary loader)
-------------------------
How to use this converter
-------------------------
python convert_obj_three.py -i infile.obj -o outfile.js [-m "morphfiles*.obj"] [-c "morphcolors*.obj"] [-a center|centerxz|top|bo... | three.js-master | utils/converters/obj/convert_obj_three.py |
"""Split single OBJ model into mutliple OBJ files by materials
-------------------------------------
How to use
-------------------------------------
python split_obj.py -i infile.obj -o outfile
Will generate:
outfile_000.obj
outfile_001.obj
...
outfile_XXX.obj
-------------------------------------
Parser based ... | three.js-master | utils/converters/obj/split_obj.py |
#!/usr/bin/env python
__doc__ = '''
Convert a json file to msgpack.
If fed only an input file the converted will write out a .pack file
of the same base name in the same directory
$ json2msgpack.py -i foo.json
foo.json > foo.pack
Specify an output file path
$ json2msgpack.py -i foo.json -o /bar/tmp/bar.pack
foo.json... | three.js-master | utils/converters/msgpack/json2msgpack.py |
version = (0, 4, 2)
| three.js-master | utils/converters/msgpack/msgpack/_version.py |
# coding: utf-8
from msgpack._version import version
from msgpack.exceptions import *
from collections import namedtuple
class ExtType(namedtuple('ExtType', 'code data')):
"""ExtType represents ext type in msgpack."""
def __new__(cls, code, data):
if not isinstance(code, int):
raise TypeE... | three.js-master | utils/converters/msgpack/msgpack/__init__.py |
class UnpackException(Exception):
pass
class BufferFull(UnpackException):
pass
class OutOfData(UnpackException):
pass
class UnpackValueError(UnpackException, ValueError):
pass
class ExtraData(ValueError):
def __init__(self, unpacked, extra):
self.unpacked = unpacked
self.extr... | three.js-master | utils/converters/msgpack/msgpack/exceptions.py |
"""Fallback pure Python implementation of msgpack"""
import sys
import array
import struct
if sys.version_info[0] == 3:
PY3 = True
int_types = int
Unicode = str
xrange = range
def dict_iteritems(d):
return d.items()
else:
PY3 = False
int_types = (int, long)
Unicode = unicode
... | three.js-master | utils/converters/msgpack/msgpack/fallback.py |
# @author zfedoran / http://github.com/zfedoran
import os
import sys
import math
import operator
import re
import json
import types
import shutil
# #####################################################
# Globals
# #####################################################
option_triangulate = True
option_textures = True
o... | three.js-master | utils/converters/fbx/convert_to_threejs.py |
"""Join multiple binary files into single file and generate JSON snippet with offsets
-------------------------------------
How to use
-------------------------------------
python join_ctm.py -i "part_*.ctm" -o joined.ctm [-j offsets.js]
Will read multiple files following wildcard pattern (ordered lexicographically)... | three.js-master | utils/converters/ctm/join_ctm.py |
#!/usr/bin/env python
import sys
if sys.version_info < (2, 7):
print("This script requires at least Python 2.7.")
print("Please, update to a newer version: http://www.python.org/download/releases/")
# exit()
import argparse
import json
import os
import shutil
import tempfile
def main(argv=None):
parser = argpa... | three.js-master | utils/build/build.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | plotting_utils.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | inference_voice_conversion.py |
# original source takes from https://github.com/jik876/hifi-gan/
# MIT License
#
# Copyright (c) 2020 Jungil Kong
#
# 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, includ... | radtts-main | hifigan_env.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | radtts.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | alignment.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | audio_processing.py |
# Original Source:
# Original Source:
# https://github.com/ndeutschmann/zunis/blob/master/zunis_lib/zunis/models/flows/coupling_cells/piecewise_coupling/piecewise_linear.py
# https://github.com/ndeutschmann/zunis/blob/master/zunis_lib/zunis/models/flows/coupling_cells/piecewise_coupling/piecewise_quadratic.py
# Modific... | radtts-main | splines.py |
# original source takes from https://github.com/jik876/hifi-gan/
# MIT License
#
# Copyright (c) 2020 Jungil Kong
#
# 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, includ... | radtts-main | hifigan_utils.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | loss.py |
# Original source: https://github.com/NVIDIA/waveglow/blob/master/distributed.py
#
# Original license text:
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or wit... | radtts-main | distributed.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | common.py |
# Original source taken from https://github.com/LiyuanLucasLiu/RAdam
#
# Copyright 2019 Liyuan Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licens... | radtts-main | radam.py |
# adapted from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/FastPitch/fastpitch/transformer.py
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http:/... | radtts-main | transformer.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | autoregressive_flow.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | train.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | inference.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | hifigan_denoiser.py |
# Modified partialconv source code based on implementation from
# https://github.com/NVIDIA/partialconv/blob/master/models/partialconv2d.py
###############################################################################
# BSD 3-Clause License
#
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Author &... | radtts-main | partialconv1d.py |
# original source takes from https://github.com/jik876/hifi-gan/
# MIT License
#
# Copyright (c) 2020 Jungil Kong
#
# 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, includ... | radtts-main | hifigan_models.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | attribute_prediction_model.py |
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# 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 res... | radtts-main | data.py |
""" adapted from https://github.com/keithito/tacotron """
import re
_letters_and_numbers_re = re.compile(
r"((?:[a-zA-Z]+[0-9]|[0-9]+[a-zA-Z])[a-zA-Z0-9']*)", re.IGNORECASE)
_hardware_re = re.compile(
'([0-9]+(?:[.,][0-9]+)?)(?:\s?)(tb|gb|mb|kb|ghz|mhz|khz|hz|mm)', re.IGNORECASE)
_hardware_key = {'tb': 'terab... | radtts-main | tts_text_processing/letters_and_numbers.py |
""" adapted from https://github.com/keithito/tacotron """
import re
valid_symbols = [
'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', '... | radtts-main | tts_text_processing/cmudict.py |
import re
_no_period_re = re.compile(r'(No[.])(?=[ ]?[0-9])')
_percent_re = re.compile(r'([ ]?[%])')
_half_re = re.compile('([0-9]½)|(½)')
# List of (regular expression, replacement) pairs for abbreviations:
_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
('mrs', 'misess'),
... | radtts-main | tts_text_processing/abbreviations.py |
""" adapted from https://github.com/keithito/tacotron """
import re
import numpy as np
from . import cleaners
from .cleaners import Cleaner
from .symbols import get_symbols
from .grapheme_dictionary import Grapheme2PhonemeDictionary
#########
# REGEX #
#########
# Regular expression matching text enclosed in curly ... | radtts-main | tts_text_processing/text_processing.py |
""" adapted from https://github.com/keithito/tacotron """
import inflect
import re
_magnitudes = ['trillion', 'billion', 'million', 'thousand', 'hundred', 'm', 'b', 't']
_magnitudes_key = {'m': 'million', 'b': 'billion', 't': 'trillion'}
_measurements = '(f|c|k|d|m)'
_measurements_key = {'f': 'fahrenheit',
... | radtts-main | tts_text_processing/numerical.py |
""" adapted from https://github.com/keithito/tacotron """
import re
_alt_re = re.compile(r'\([0-9]+\)')
class Grapheme2PhonemeDictionary:
"""Thin wrapper around g2p data."""
def __init__(self, file_or_path, keep_ambiguous=True, encoding='latin-1'):
with open(file_or_path, encoding=encoding) as f:
... | radtts-main | tts_text_processing/grapheme_dictionary.py |
""" adapted 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.'''
arpabet = [
'AA', 'AA0', 'AA1... | radtts-main | tts_text_processing/symbols.py |
""" adapted from https://github.com/keithito/tacotron """
'''
Cleaners are transformations that run over the input text at both training and eval time.
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You'll typically wan... | radtts-main | tts_text_processing/cleaners.py |
""" adapted from https://github.com/keithito/tacotron """
import re
_ampm_re = re.compile(
r'([0-9]|0[0-9]|1[0-9]|2[0-3]):?([0-5][0-9])?\s*([AaPp][Mm]\b)')
def _expand_ampm(m):
matches = list(m.groups(0))
txt = matches[0]
txt = txt if int(matches[1]) == 0 else txt + ' ' + matches[1]
if matches[2... | radtts-main | tts_text_processing/datestime.py |
import re
from .cmudict import CMUDict
_letter_to_arpabet = {
'A': 'EY1',
'B': 'B IY1',
'C': 'S IY1',
'D': 'D IY1',
'E': 'IY1',
'F': 'EH1 F',
'G': 'JH IY1',
'H': 'EY1 CH',
'I': 'AY1',
'J': 'JH EY1',
'K': 'K EY1',
'L': 'EH1 L',
'M': 'EH1 M',
'N': 'EH1 N',
'O':... | radtts-main | tts_text_processing/acronyms.py |
""" Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
This script is for dumping the gaussian landmark predictions,
provided the dataset and trained landmark model checkpoint.
This is necessary for evaluating the landmark quality (on BBC)
as well as for performing the video manipulation tasks.
"""
import to... | UnsupervisedLandmarkLearning-master | dump_preds.py |
"""Main training script. Currently only supports the BBCPose dataset
"""
from apex.parallel import DistributedDataParallel as DDP
from utils.visualizer import dump_image, project_heatmaps_colorized
from models.losses import Vgg19PerceptualLoss, GANLoss
from torch.utils.data import DataLoader
from tensorboardX import Su... | UnsupervisedLandmarkLearning-master | train.py |
"""Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
Dataset classes for handling the BBCPose data
"""
from torch.utils.data import Dataset
import torch
import os
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
import scipy.io as sio
from .base_datasets import BaseVideo... | UnsupervisedLandmarkLearning-master | dataloaders/bbc_pose_dataset.py |
"""
Custom transformation functions for image augmentation
"""
import random
import numpy as np
from numpy.random import random_sample
import cv2 # for TPS
import torch
import torchvision.transforms as transforms_t
import torchvision.transforms.functional as F
class TPSWarp(object):
"""
TPS param for non-li... | UnsupervisedLandmarkLearning-master | dataloaders/transforms.py |
""" Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
Base class for our video dataset
"""
from torch.utils.data import Dataset
import numpy as np
import torchvision.transforms as transforms
from .transforms import TPSWarp, PairedColorJitter
class BaseVideoDataset(Dataset):
"""
Base dataset class... | UnsupervisedLandmarkLearning-master | dataloaders/base_datasets.py |
UnsupervisedLandmarkLearning-master | dataloaders/__init__.py | |
import torch
import torch.distributed
import yaml
import os
from models.part_factorized_model import PartFactorizedModel
def denormalize_batch(batch, div_factor=1):
"""denormalize for visualization"""
# normalize using imagenet mean and std
mean = batch.data.new(batch.data.size())
std = batch.data.ne... | UnsupervisedLandmarkLearning-master | utils/utils.py |
"""
Utility functions for visualization and image dumping
"""
from utils.utils import denormalize_batch
from PIL import Image
from PIL import ImageDraw
import numpy as np
def uint82bin(n, count=8):
"""adapted from https://github.com/ycszen/pytorch-segmentation/blob/master/transform.py
returns the binary of i... | UnsupervisedLandmarkLearning-master | utils/visualizer.py |
""" Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
This file contains subroutines for our training pipeline
"""
import torch
import torch.nn as nn
def conv_ReLU(in_channels, out_channels, kernel_size, stride=1, padding=0,
use_norm=True, norm=nn.InstanceNorm2d):
"""Returns a 2D Conv f... | UnsupervisedLandmarkLearning-master | models/submodules.py |
"""Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
Parameterized Unet module that we use to construct our shape and appearance encoders
"""
import torch.nn as nn
from .submodules import conv_ReLU, encoder_block, decoder_block
class Unet(nn.Module):
def __init__(self, num_input_channels, decoder_ou... | UnsupervisedLandmarkLearning-master | models/unet.py |
UnsupervisedLandmarkLearning-master | models/__init__.py | |
"""
Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
Network definition for our shape and appearance encoder model.
Heavily inspired by the network architecture described in https://arxiv.org/pdf/1903.06946.pdf
"""
import torch
import torch.nn as nn
import torch.nn.functional
from collections import named... | UnsupervisedLandmarkLearning-master | models/part_factorized_model.py |
"""
Copyright (C) 2019,2020 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
Modifications made to adapt the SPADE code to this work.
"""
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
i... | UnsupervisedLandmarkLearning-master | models/generator.py |
"""Implementation for various loss modules
GAN loss adapted from pix2pixHD (see comment below)
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision.models as models
class PerceptualLoss(nn.Module):
def __init__(self):
super(PerceptualLoss, self).__init__()
... | UnsupervisedLandmarkLearning-master | models/losses.py |
"""
Original source: https://github.com/NVlabs/SPADE/blob/master/models/networks/normalization.py
Modifications made to adapt to this work
Copyright (C) 2019,2020 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
impo... | UnsupervisedLandmarkLearning-master | models/normalization.py |
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