python_code stringlengths 0 679k | repo_name stringlengths 9 41 | file_path stringlengths 6 149 |
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# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import logging
import os
import torch
import torch.utils.data as TD
import torchvision
import torchvision.transforms as transforms
from utils.comm import get_world_size
from . import dataset as D
from . import samplers
from .transforms import... | transformer-ls-master | imagenet/dat/loader.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import os
import base64
from io import BytesIO
import json
from PIL import Image
import torch.utils.data as data
from .utils.tsv_file import TSVFile
from .utils.load_files import load_linelist_file, load_from_yaml_file
from .utils.load_files i... | transformer-ls-master | imagenet/dat/dataset/tsv_dataset.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import base64
from io import BytesIO
import json
from PIL import Image
from .tsv_dataset import TSVYamlDataset
class ClsTsvDataset(TSVYamlDataset):
"""
Generic TSV dataset format for Classification.
"""
def __init__(self, yaml... | transformer-ls-master | imagenet/dat/dataset/cls_tsv.py |
"""
This file is from https://github.com/microsoft/vision-longformer
"""
import os.path as op
from zipfile import ZipFile, BadZipFile
import torch.utils.data as data
from PIL import Image
from io import BytesIO
import multiprocessing
_VALID_IMAGE_TYPES = ['.jpg', '.jpeg', '.tiff', '.bmp', '.png']
class ZipData(data.... | transformer-ls-master | imagenet/dat/dataset/zipdata.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
from .tsv_dataset import TSVDataset, TSVYamlDataset
from .zipdata import ZipData
from .cls_tsv import ClsTsvDataset
__all__ = [
"TSVDataset",
"TSVYamlDataset",
"ZipData",
"ClsTsvDataset",
] | transformer-ls-master | imagenet/dat/dataset/__init__.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import os.path as op
def config_tsv_dataset_args(cfg, dataset_file):
full_yaml_file = op.join(cfg.DATA.PATH, dataset_file)
assert op.isfile(full_yaml_file)
args = dict(
yaml_file=full_yaml_file,
)
tsv_dataset_nam... | transformer-ls-master | imagenet/dat/dataset/utils/config_args.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import base64
import json
import os
import os.path as op
import cv2
import numpy as np
from tqdm import tqdm
from utils.miscellaneous import mkdir
from .tsv_file import TSVFile
def img_from_base64(imagestring):
try:
jpgbytestrin... | transformer-ls-master | imagenet/dat/dataset/utils/tsv_file_ops.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import os
import os.path as op
import errno
import yaml
from collections import OrderedDict
def load_labelmap_file(labelmap_file):
label_dict = None
if labelmap_file is not None and op.isfile(labelmap_file):
label_dict = Order... | transformer-ls-master | imagenet/dat/dataset/utils/load_files.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import logging
import os
import os.path as op
def create_lineidx(filein, idxout):
idxout_tmp = idxout + '.tmp'
with open(filein, 'r') as tsvin, open(idxout_tmp,'w') as tsvout:
fsize = os.fstat(tsvin.fileno()).st_size
f... | transformer-ls-master | imagenet/dat/dataset/utils/tsv_file.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from timm.data import create_transform
from PIL import ImageFilter
import logging
import random
import torchvision.transforms as T
... | transformer-ls-master | imagenet/dat/transforms/build.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
from .build import build_transforms
| transformer-ls-master | imagenet/dat/transforms/__init__.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
from .ra_sampler import RASampler
__all__ = ["RASampler"]
| transformer-ls-master | imagenet/dat/samplers/__init__.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torch.distributed as dist
import math
class RASampler(torch.utils.data.Sampler):
"""Sampler t... | transformer-ls-master | imagenet/dat/samplers/ra_sampler.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right
import torch
import math
# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torc... | transformer-ls-master | imagenet/optim/lr_scheduler.py |
"""
This file is from https://github.com/microsoft/vision-longformer
"""
# Copyright 2018 The Google AI Language Team 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 ... | transformer-ls-master | imagenet/optim/optimization.py |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import logging
from .optimization import AdamW, Lamb
from .lr_scheduler import WarmupMultiStepLR
from .lr_scheduler import WarmupCosineAnnealingLR
from .lr_scheduler import WarmupLinearSchedule
from .qhm import QHM
def get_opt(cfg... | transformer-ls-master | imagenet/optim/__init__.py |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
from torch.optim import Optimizer
class QHM(Optimizer):
r"""
Stochastic gradient method with Quasi-Hyperbolic Momentum (QHM):
h(k) = (1 - \beta) * g(k) + \beta * h(k-1)
d(k) = (1 - \nu) * g(k) + \nu * h(k)
... | transformer-ls-master | imagenet/optim/qhm.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import defaultdict
from collections import deque
import os
import torch
from .comm import is_main_process
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or t... | transformer-ls-master | imagenet/utils/metric_logger.py |
"""
This file is from https://github.com/microsoft/vision-longformer
"""
import os
import math
import logging
import shutil
import torch
from collections import OrderedDict
from .comm import is_main_process
import torch.distributed as dist
# def is_dist_avail_and_initialized():
# if not dist.is_available():
# ... | transformer-ls-master | imagenet/utils/checkpoint.py |
"""
This file is from https://github.com/microsoft/vision-longformer
"""
"""
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import pickle
import torch
import torch.distributed as dist
def get_world_size():
if not dist.is_available():
return... | transformer-ls-master | imagenet/utils/comm.py |
transformer-ls-master | imagenet/utils/__init__.py | |
"""
This file is from https://github.com/microsoft/vision-longformer
"""
import errno
import os
import os.path as op
import logging
import numpy as np
import torch
import random
import shutil
from .comm import is_main_process
import yaml
def mkdir(path):
# if it is the current folder, skip.
# otherwise the o... | transformer-ls-master | imagenet/utils/miscellaneous.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
# Written by Pengchuan Zhang, penzhan@microsoft.com
import logging
import torch.nn as nn
import torchvision.models as tvmodels
from .msvit import MsViT
def build_model(cfg):
# ResNet models from torchvision
resnet_model_names = sorted... | transformer-ls-master | imagenet/models/__init__.py |
"""Code for the vision transformer model based on ViL.
Adapted from https://github.com/microsoft/vision-longformer by Chen Zhu (zhuchen.eric@gmail.com)
"""
import math
from functools import partial
import logging
import torch
from torch import nn
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
from .l... | transformer-ls-master | imagenet/models/msvit.py |
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
# Written by Pengchuan Zhang, penzhan@microsoft.com
from torch import nn
import torch
from timm.models.layers import trunc_normal_
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None,
... | transformer-ls-master | imagenet/models/layers/full_attention.py |
# Written by Chen Zhu during an internship at NVIDIA, zhuchen.eric@gmail.com
from .transformer_ls import AttentionLS
from .full_attention import Attention | transformer-ls-master | imagenet/models/layers/__init__.py |
# Copyright (c) 2021 NVIDIA CORPORATION. Licensed under the MIT license.
# Written by Chen Zhu during an internship at NVIDIA, zhuchen.eric@gmail.com
from torch import nn
import torch
from timm.models.layers import trunc_normal_
import torch.nn.functional as F
class AttentionLS(nn.Module):
"""Implementation for ... | transformer-ls-master | imagenet/models/layers/transformer_ls.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | setup.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | tests/test_utils.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | tests/test_container.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | tests/test_pipeline_utils.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | tests/test_topology_sort.py |
clara-pipeline-operator-sizing-tool-main | tests/__init__.py | |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | tests/test_triton_utils.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | tests/test_cli.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | tests/test_main.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | tests/test_clarac_utils.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | src/triton_utils.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | src/clarac_utils.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | src/constants.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | src/topology_sort.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | src/__init__.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | src/container.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | src/cli.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | src/utils.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | src/main.py |
# Copyright 2021 NVIDIA Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writi... | clara-pipeline-operator-sizing-tool-main | src/pipeline_utils.py |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pylab as plt
import numpy as np
def save_figure_to_numpy(fig):
# save it to a numpy array.
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
return data
def... | mellotron-master | plotting_utils.py |
import tensorflow as tf
from text.symbols import symbols
def create_hparams(hparams_string=None, verbose=False):
"""Create model hyperparameters. Parse nondefault from given string."""
hparams = tf.contrib.training.HParams(
################################
# Experiment Parameters #
... | mellotron-master | hparams.py |
import re
import numpy as np
import music21 as m21
import torch
import torch.nn.functional as F
from text import text_to_sequence, get_arpabet, cmudict
CMUDICT_PATH = "data/cmu_dictionary"
CMUDICT = cmudict.CMUDict(CMUDICT_PATH)
PHONEME2GRAPHEME = {
'AA': ['a', 'o', 'ah'],
'AE': ['a', 'e'],
'AH': ['u', 'e... | mellotron-master | mellotron_utils.py |
import torch
import numpy as np
from scipy.signal import get_window
import librosa.util as librosa_util
def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
n_fft=800, dtype=np.float32, norm=None):
"""
# from librosa 0.6
Compute the sum-square envelope of a window fu... | mellotron-master | audio_processing.py |
import random
import torch
from tensorboardX import SummaryWriter
from plotting_utils import plot_alignment_to_numpy, plot_spectrogram_to_numpy
from plotting_utils import plot_gate_outputs_to_numpy
class Tacotron2Logger(SummaryWriter):
def __init__(self, logdir):
super(Tacotron2Logger, self).__init__(logd... | mellotron-master | logger.py |
import torch
from torch import nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from loss_scaler import DynamicLossScaler, LossScaler
FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
HALF_TYPES = (torch.H... | mellotron-master | fp16_optimizer.py |
from math import sqrt
import numpy as np
from numpy import finfo
import torch
from torch.autograd import Variable
from torch import nn
from torch.nn import functional as F
from layers import ConvNorm, LinearNorm
from utils import to_gpu, get_mask_from_lengths
from modules import GST
drop_rate = 0.5
def load_model(hpa... | mellotron-master | model.py |
"""
BSD 3-Clause License
Copyright (c) 2017, Prem Seetharaman
All rights reserved.
* Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of... | mellotron-master | stft.py |
import torch
import torch.distributed as dist
from torch.nn.modules import Module
from torch.autograd import Variable
def _flatten_dense_tensors(tensors):
"""Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
same dense type.
Since inputs are dense, the resulting tensor will be a conc... | mellotron-master | distributed.py |
import random
import os
import re
import numpy as np
import torch
import torch.utils.data
import librosa
import layers
from utils import load_wav_to_torch, load_filepaths_and_text
from text import text_to_sequence, cmudict
from yin import compute_yin
class TextMelLoader(torch.utils.data.Dataset):
"""
1) ... | mellotron-master | data_utils.py |
from torch import nn
class Tacotron2Loss(nn.Module):
def __init__(self):
super(Tacotron2Loss, self).__init__()
def forward(self, model_output, targets):
mel_target, gate_target = targets[0], targets[1]
mel_target.requires_grad = False
gate_target.requires_grad = False
... | mellotron-master | loss_function.py |
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):
sa... | mellotron-master | utils.py |
import os
import time
import argparse
import math
from numpy import finfo
import torch
from distributed import apply_gradient_allreduce
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from model import load_model
from data_utils impo... | mellotron-master | train.py |
# adapted from https://github.com/patriceguyot/Yin
import numpy as np
def differenceFunction(x, N, tau_max):
"""
Compute difference function of data x. This corresponds to equation (6) in [1]
This solution is implemented directly with Numpy fft.
:param x: audio data
:param N: length of data
... | mellotron-master | yin.py |
# adapted from https://github.com/KinglittleQ/GST-Tacotron/blob/master/GST.py
# MIT License
#
# Copyright (c) 2018 MagicGirl Sakura
#
# 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 r... | mellotron-master | modules.py |
import torch
from librosa.filters import mel as librosa_mel_fn
from audio_processing import dynamic_range_compression, dynamic_range_decompression
from stft import STFT
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(LinearNorm, self).__init__... | mellotron-master | layers.py |
import time
import torch
import sys
import subprocess
argslist = list(sys.argv)[1:]
num_gpus = torch.cuda.device_count()
argslist.append('--n_gpus={}'.format(num_gpus))
workers = []
job_id = time.strftime("%Y_%m_%d-%H%M%S")
argslist.append("--group_name=group_{}".format(job_id))
for i in range(num_gpus):
argslist... | mellotron-master | multiproc.py |
import torch
class LossScaler:
def __init__(self, scale=1):
self.cur_scale = scale
# `params` is a list / generator of torch.Variable
def has_overflow(self, params):
return False
# `x` is a torch.Tensor
def _has_inf_or_nan(x):
return False
# `overflow` is boolean ind... | mellotron-master | loss_scaler.py |
""" 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', 'ER1', 'E... | mellotron-master | text/cmudict.py |
""" from https://github.com/keithito/tacotron """
import re
import random
from text import cleaners
from text.symbols import symbols
# Mappings from symbol to numeric ID and vice versa:
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
# Regular expression ma... | mellotron-master | text/__init__.py |
""" from https://github.com/keithito/tacotron """
import inflect
import re
_inflect = inflect.engine()
_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
_ordinal_r... | mellotron-master | text/numbers.py |
""" 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 tex... | mellotron-master | text/symbols.py |
""" from https://github.com/keithito/tacotron """
'''
Cleaners are transformations that run over the input text at both training and eval time.
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You'll typically want to use... | mellotron-master | text/cleaners.py |
#!/usr/bin/env python2
# Copyright (c) 2013, NVIDIA CORPORATION. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# th... | tegra-uboot-scripts-master | gen-uboot-script.py |
#!/usr/bin/env python2
# Copyright (c) 2011-2013, NVIDIA CORPORATION. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation... | tegra-uboot-scripts-master | part-uuid.py |
from yum.plugins import PluginYumExit, TYPE_CORE, TYPE_INTERACTIVE
from yum.packages import YumInstalledPackage
from yum.constants import *
from rpmUtils.miscutils import compareEVR
import sys
import os
import re
sys.path.insert(0,'/usr/share/yum-cli/')
import yum
from yum.Errors import *
from utils import YumUtilBa... | yum-packaging-nvidia-plugin-main | nvidia-yum.py |
from __future__ import absolute_import
from __future__ import unicode_literals
import os
import shutil
from functools import cmp_to_key
from dnf.cli.option_parser import OptionParser
import dnf
import dnf.cli
import dnf.sack
import libdnf.transaction
DRIVER_PKG_NAME = 'nvidia-driver'
KERNEL_PKG_NAME = 'kernel'
KERNE... | yum-packaging-nvidia-plugin-main | nvidia-dnf.py |
# Copyright (c) 2013, NVIDIA CORPORATION. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, m... | tegra-uboot-flasher-scripts-master | tegraboardconfigs.py |
# ali_funcs.py 3/27/2018
#
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Alibaba (ali) Cloud Service Provider specific functions
#
# HELPTEXT: "Alibaba Cloud Service Provider"
#
import json
import time
import subprocess
from cspbaseclass import CSPBas... | ngc-examples-master | ncsp/ali_funcs.py |
# gcp_funcs.py 3/27/2018
#
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Google Cloud Service Provider specific functions
#
# HELPTEXT: "Google Cloud Service Provider"
#
# See: https://cloud.google.com/sdk/docs/scripting-gcloud
#
import json
import ti... | ngc-examples-master | ncsp/gcp_funcs.py |
# template_funcs.py 3/23/2018
#
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# <CSP> specific class Template functions - starting point for new <CSP> development
# Copy this file to your CSP specific name, and fill in functions
#
# The following line is use ... | ngc-examples-master | ncsp/template_funcs.py |
#!/usr/bin/python
# csp 3/22/2018
#
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Top level generic CSP (Cloud Service Provider) interface
#
# Demonstrates how to use python to create a consistent interface
# across multiple different c... | ngc-examples-master | ncsp/ncsp.py |
# aws_funcs.py 3/23/2018
#
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Amazon (aws) Cloud Service Provider specific functions
#
# HELPTEXT: "Amazon Cloud Service Provider"
#
import json
import time
import sys
from cspbaseclass import CSPBaseClass
fro... | ngc-examples-master | ncsp/aws_funcs.py |
# cspbaseclass.py 3/23/2018
#
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Cloud Service Provider base class
#
import os
import sys
import time
import subprocess
import json
g_trace_level = 0 # global trace level, see trace_do and debug funcs
... | ngc-examples-master | ncsp/cspbaseclass.py |
# SPDX-FileCopyrightText: Copyright (c) 2021 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... | ProViz-AI-Samples-master | inference_partner_training/TensorRT/Models/fetch_model.py |
# SPDX-FileCopyrightText: Copyright (c) 2021 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... | ProViz-AI-Samples-master | inference_partner_training/TensorRT/TRTPluginSample/replace_bn.py |
# Copyright (c) 2017 NVIDIA Corporation
import argparse
from math import sqrt
parser = argparse.ArgumentParser(description='RMSE_calculator')
parser.add_argument('--path_to_predictions', type=str, default="", metavar='N',
help='Path file with actual ratings and predictions')
parser.add_argument('-... | DeepRecommender-master | compute_RMSE.py |
# Copyright (c) 2017 NVIDIA Corporation
import torch
import argparse
from reco_encoder.data import input_layer
from reco_encoder.model import model
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR
import torch.nn as nn
from torch.autograd import Variable
import copy
import time
from pathlib ... | DeepRecommender-master | run.py |
# THIS FILE IS COPY-PASTED FROM HERE: https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/04-utils/tensorboard
# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
import tensorflow as tf
import numpy as np
import scipy.misc
try:
from StringIO import StringIO # Python... | DeepRecommender-master | logger.py |
# Copyright (c) 2017 NVIDIA Corporation
import torch
import argparse
import copy
from reco_encoder.data import input_layer
from reco_encoder.model import model
from torch.autograd import Variable
from pathlib import Path
parser = argparse.ArgumentParser(description='RecoEncoder')
parser.add_argument('--drop_prob', ty... | DeepRecommender-master | infer.py |
# Copyright (c) 2017 NVIDIA Corporation
import unittest
import sys
import torch.optim as optim
from torch.autograd import Variable
from reco_encoder.data.input_layer import UserItemRecDataProvider
from reco_encoder.model.model import AutoEncoder, MSEloss
sys.path.append('data')
sys.path.append('model')
class iRecAutoE... | DeepRecommender-master | test/test_model.py |
# Copyright (c) 2017 NVIDIA Corporation | DeepRecommender-master | test/__init__.py |
# Copyright (c) 2017 NVIDIA Corporation
import unittest
from reco_encoder.data.input_layer import UserItemRecDataProvider
class UserItemRecDataProviderTest(unittest.TestCase):
def test_1(self):
print("Test 1 started")
params = {}
params['batch_size'] = 64
params['data_dir'] = 'test/testData_iRec'
... | DeepRecommender-master | test/data_layer_tests.py |
# Copyright (c) 2017 NVIDIA Corporation
from os import listdir, path, makedirs
import random
import sys
import time
import datetime
def print_stats(data):
total_ratings = 0
print("STATS")
for user in data:
total_ratings += len(data[user])
print("Total Ratings: {}".format(total_ratings))
print("Total User... | DeepRecommender-master | data_utils/netflix_data_convert.py |
# Copyright (c) 2017 NVIDIA Corporation
import sys
import datetime
import random
from math import floor
def print_stats(data):
total_ratings = 0
print("STATS")
for user in data:
total_ratings += len(data[user])
print("Total Ratings: {}".format(total_ratings))
print("Total User count: {}".format(len(data.... | DeepRecommender-master | data_utils/movie_lense_data_converter.py |
# Copyright (c) 2017 NVIDIA Corporation
| DeepRecommender-master | reco_encoder/__init__.py |
# Copyright (c) 2017 NVIDIA Corporation
| DeepRecommender-master | reco_encoder/model/__init__.py |
# Copyright (c) 2017 NVIDIA Corporation
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as weight_init
from torch.autograd import Variable
def activation(input, kind):
#print("Activation: {}".format(kind))
if kind == 'selu':
return F.selu(input)
elif kind == 'relu':
... | DeepRecommender-master | reco_encoder/model/model.py |
# Copyright (c) 2017 NVIDIA Corporation
| DeepRecommender-master | reco_encoder/data/__init__.py |
# Copyright (c) 2017 NVIDIA Corporation
"""Data Layer Classes"""
from os import listdir, path
from random import shuffle
import torch
class UserItemRecDataProvider:
def __init__(self, params, user_id_map=None, item_id_map=None):
self._params = params
self._data_dir = self.params['data_dir']
self._extensi... | DeepRecommender-master | reco_encoder/data/input_layer.py |
# Copyright (c) 2022-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
import ctypes
from functools import lru_cache
import os
from pathlib import Path
import re
import shutil
import subprocess
from subprocess import CalledProcessError
import sys
import tempfile
from ... | TransformerEngine-main | setup.py |
# Copyright (c) 2022-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
"""Test TE Paddle Layer-level APIs"""
import math
import os
import pytest
from utils import assert_allclose
import paddle
import transformer_engine.paddle as te
from transformer_engine.paddle.fp8... | TransformerEngine-main | tests/paddle/test_layers.py |
# Copyright (c) 2022-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
"""Test TE operators"""
import struct
import numpy as np
import pytest
import paddle
import paddle.nn.functional as F
from utils import assert_allclose, create_fp8_meta
import transformer_engine ... | TransformerEngine-main | tests/paddle/test_operators.py |
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