python_code stringlengths 0 258k |
|---|
import numpy as np
import cv2, pdb, glob, argparse
MAX_FEATURES = 500
GOOD_MATCH_PERCENT = 0.15
def alignImages(im1, im2,masksDL):
# Convert images to grayscale
im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
akaze = cv2.AKAZE_create()
keypoints1, descriptors1 =... |
from __future__ import print_function
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
from tensorboardX import SummaryWriter
import os
import time
import argparse
from data_loader import AdobeDataAffineHR
from functions import *
from networks import ResnetCondition... |
import numpy as np
import cv2, pdb, glob, argparse
MAX_FEATURES = 500
GOOD_MATCH_PERCENT = 0.15
def alignImages(im1, im2,masksDL):
# Convert images to grayscale
im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
akaze = cv2.AKAZE_create()
keypoints1, descriptors1 =... |
from __future__ import print_function, division
import os
import torch
import pandas as pd
import skimage
from skimage import io
import numpy as np
import matplotlib.pyplot as plt
import pdb, random
from torch.utils.data import Dataset, DataLoader
import random, os, cv2
unknown_code=128
class VideoData(Dataset):
def... |
from __future__ import print_function
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
from tensorboardX import SummaryWriter
import os
import time
import argparse
import numpy as np
from data_loader import VideoData
from functions import *
from networks import Resn... |
import os
import time
from argparse import Namespace
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
from tensorboardX import SummaryWriter
from .data_loader import VideoData
from .functions import compose_image_withshift, write_tb_log
from .networks import ResnetCond... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import numpy as np
class ResnetConditionHR(nn.Module):
def __init__(self, input_nc, output_nc, ngf=64, nf_part=64,norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks1=7, n_blocks2=3, padding_type='reflect'):
assert(n... |
#######################################
# Prepares training data. Takes a path to a directory of videos + captured backgrounds, dumps frames, extracts human
# segmentations. Also takes a path of background videos. Creates a training CSV file with lines of the following format,
# by using all but the last 80 frames of e... |
import subprocess
import sys
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip',
'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirements()
|
from __future__ import print_function
import os, glob, time, argparse, pdb, cv2
#import matplotlib.pyplot as plt
import numpy as np
from skimage.measure import label
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
from functions import *
from networks imp... |
##Copyright 2017 Adobe Systems Inc.
##
##Licensed under the Apache License, Version 2.0 (the "License");
##you 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... |
from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
from torchbenchmark.tasks import COMPUTER_VISION
import torchvision.models as models
class Model(TorchVisionModel):
task = COMPUTER_VISION.CLASSIFICATION
DEFAULT_TRAIN_BSIZE = 32
DEFAULT_EVAL_BSIZE = 32
def __init__(self, ... |
from torchbenchmark.tasks import NLP
from torchbenchmark.util.framework.huggingface.model_factory import HuggingFaceModel
class Model(HuggingFaceModel):
task = NLP.LANGUAGE_MODELING
DEFAULT_TRAIN_BSIZE = 4
DEFAULT_EVAL_BSIZE = 1
def __init__(self, test, device, jit=False, batch_size=None, extra_args=[... |
import subprocess
import sys
import os
from torchbenchmark.util.framework.huggingface.patch_hf import patch_transformers, cache_model
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirem... |
from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
from torchbenchmark.tasks import COMPUTER_VISION
import torchvision.models as models
class Model(TorchVisionModel):
task = COMPUTER_VISION.CLASSIFICATION
DEFAULT_TRAIN_BSIZE = 8
DEFAULT_EVAL_BSIZE = 8
def __init__(self, te... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Description: an implementation of a deep learning recommendation model (DLRM)
# The model input consists of dense and sparse features. The ... |
from __future__ import absolute_import, division, print_function, unicode_literals
# miscellaneous
import builtins
import functools
# import bisect
# import shutil
import time
import json
from typing import Tuple
import sys
# data generation
from . import dlrm_data_pytorch as dp
# numpy
import numpy as np
# pytorch... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Description: generate inputs and targets for the DLRM benchmark
#
# Utility function(s) to download and pre-process public data sets
# - ... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Description: generate inputs and targets for the dlrm benchmark
# The inpts and outputs are generated according to the following three opti... |
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import numpy as np
from torch.util... |
import subprocess
import sys
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirements()
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Description: generate inputs and targets for the dlrm benchmark
# The inpts and outputs are generated according to the following three opti... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Description: an implementation of a deep learning recommendation model (DLRM)
# The model input consists of dense and sparse features. The ... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
#
# This script performs the visualization of the embedding tables created in
# DLRM during the training procedure. We use two popular techni... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Description: compile .so from python code
from __future__ import absolute_import, division, print_function, unicode_literals
from setupto... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Description: run dataset pre-processing in standalone mode
# WARNING: These steps are required to work with Cython
# 1. Instal Cython
# > s... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Mixed-Dimensions Trick
#
# Description: Applies mixed dimension trick to embeddings to reduce
# embedding sizes.
#
# References:
# [1] Anto... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Quotient-Remainder Trick
#
# Description: Applies quotient remainder-trick to embeddings to reduce
# embedding sizes.
#
# References:
# [1]... |
import torch
# OSS import
try:
# pyre-ignore[21]
# @manual=//ai_codesign/benchmarks/dlrm/torchrec_dlrm/data:dlrm_dataloader
from .data.dlrm_dataloader import get_dataloader
except ImportError:
pass
import itertools
import os
from pyre_extensions import none_throws
from torch import distributed as dis... |
import argparse
from enum import Enum
from typing import List
class InteractionType(Enum):
ORIGINAL = "original"
DCN = "dcn"
PROJECTION = "projection"
def __str__(self):
return self.value
def parse_args(argv: List[str]) -> argparse.Namespace:
parser = argparse.ArgumentParser(description=... |
import subprocess
import sys
import os
from pathlib import Path
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirements()
|
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
from typing import List
from torch import distributed as dist
from torch.utils.data import... |
from .dataloader import SuperSloMo
from .model_wrapper import Model as ModelWrapper
import torch
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms as transforms
import random
from typing import Tuple
import os
import numpy as np
from argparse import Namespace
from pathlib import... |
from . import slomo_model as model
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
L1_lossFn = nn.L1Loss()
MSE_LossFn = nn.MSELoss()
class Model(torch.nn.Module):
def __init__(self, device='cpu'):
super().__init__()
self.flowComp = model.UNet(6, 4).to(device... |
#!/usr/bin/env python3
import argparse
import os
import os.path
import ctypes
from shutil import rmtree, move
from PIL import Image
import torch
import torchvision.transforms as transforms
import slomo_model as model
import dataloader
import platform
from tqdm import tqdm
# For parsing commandline arguments
parser = a... |
#[Super SloMo]
##High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
import argparse
import torch
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import slomo_model as model
from model_wrapper ... |
import torch.utils.data as data
from PIL import Image
import os
import os.path
import random
def _make_dataset(dir):
"""
Creates a 2D list of all the frames in N clips containing
M frames each.
2D List Structure:
[[frame00, frame01,...frameM] <-- clip0
[frame00, frame01,...frameM] <-- clip... |
import subprocess
import sys
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirements()
|
"""
Converts a Video to SuperSloMo version
"""
from time import time
import click
import cv2
import torch
from PIL import Image
import numpy as np
import slomo_model as model
from torchvision import transforms
import torch.nn.functional as F
torch.set_grad_enabled(False)
device = torch.device("cuda" if torch.cuda.is_... |
import torch
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Le... |
''' Translate input text with trained model. '''
import torch
import argparse
import dill as pickle
from tqdm import tqdm
import transformer.Constants as Constants
from torchbenchmark.util.torchtext_legacy.data import Dataset
from transformer.Models import Transformer
from transformer.Translator import Translator
d... |
''' Handling the data io '''
import contextlib
import os
import pathlib
import argparse
import logging
import dill as pickle
import urllib
from tqdm import tqdm
import json
import sys
import codecs
import spacy
import torch
import tarfile
import torchtext.data
import torchtext.datasets
# Handle torchtext_legacy import... |
from argparse import Namespace
import math
import time
import os
import dill as pickle
from tqdm import tqdm
import torch
import torch.nn.functional as F
import torch.optim as optim
from torchbenchmark.util.torchtext_legacy.field import Field
from torchbenchmark.util.torchtext_legacy.data import Dataset
from torchben... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Rico Sennrich
"""Use operations learned with learn_bpe.py to encode a new text.
The text will not be smaller, but use only a fixed vocabulary, with rare words
encoded as variable-length sequences of subword units.
Reference:
Rico Sennrich, Barry Haddow and Alexa... |
'''
This script handles the training process.
'''
import argparse
import math
import time
import functools
import dill as pickle
from tqdm import tqdm
import torch
import torch.nn.functional as F
import torch.optim as optim
from torchbenchmark.util.torchtext_legacy.field import Field
from torchbenchmark.util.torchte... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Rico Sennrich
"""Use byte pair encoding (BPE) to learn a variable-length encoding of the vocabulary in a text.
Unlike the original BPE, it does not compress the plain text, but can be used to reduce the vocabulary
of a text to a configurable number of symbols, wi... |
import os
import sys
import subprocess
from pathlib import Path
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
def spacy_download(language):
subprocess.check_call([sys.executable, '-m', 'spacy', 'download', language])
def prepro... |
''' Define the Transformer model '''
import torch
import torch.nn as nn
import numpy as np
from .Layers import EncoderLayer, DecoderLayer
__author__ = "Yu-Hsiang Huang"
def get_pad_mask(seq, pad_idx : int):
return (seq != pad_idx).unsqueeze(-2)
def get_subsequent_mask(seq):
''' For masking out the subsequ... |
PAD_WORD = '<blank>'
UNK_WORD = '<unk>'
BOS_WORD = '<s>'
EOS_WORD = '</s>'
|
from . import Constants, Modules, Layers, SubLayers, Models, Translator, Optim
|
''' Define the sublayers in encoder/decoder layer '''
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .Modules import ScaledDotProductAttention
from typing import Optional
__author__ = "Yu-Hsiang Huang"
class MultiHeadAttention(nn.Module):
''' Multi-Head Attention module... |
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
__author__ = "Yu-Hsiang Huang"
class ScaledDotProductAttention(nn.Module):
''' Scaled Dot-Product Attention '''
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature... |
''' This module will handle the text generation with beam search. '''
import torch
import torch.nn as nn
import torch.nn.functional as F
from .Models import Transformer, get_pad_mask, get_subsequent_mask
class Translator(nn.Module):
''' Load a trained model and translate in beam search fashion. '''
def __in... |
''' Define the Layers '''
import torch.nn as nn
import torch
from .SubLayers import MultiHeadAttention, PositionwiseFeedForward
from typing import Optional
__author__ = "Yu-Hsiang Huang"
class EncoderLayer(nn.Module):
''' Compose with two layers '''
def __init__(self, d_model, d_inner, n_head, d_k, d_v, dr... |
'''A wrapper class for scheduled optimizer '''
import numpy as np
class ScheduledOptim():
'''A simple wrapper class for learning rate scheduling'''
def __init__(self, optimizer, init_lr, d_model, n_warmup_steps):
self._optimizer = optimizer
self.init_lr = init_lr
self.d_model = d_model... |
# This file was adapted from
# https://github.com/facebookresearch/higher/blob/master/examples/maml-omniglot.py
# It comes with the following license.
#
# Copyright (c) Facebook, Inc. and its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance... |
import subprocess
import sys
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirements()
|
from torchbenchmark.tasks import NLP
from torchbenchmark.util.framework.huggingface.model_factory import HuggingFaceModel
class Model(HuggingFaceModel):
task = NLP.LANGUAGE_MODELING
DEFAULT_TRAIN_BSIZE = 8
DEFAULT_EVAL_BSIZE = 1
def __init__(self, test, device, jit=False, batch_size=None, extra_args=[... |
import subprocess
import sys
import os
from torchbenchmark.util.framework.huggingface.patch_hf import patch_transformers, cache_model
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirem... |
import os
from torchbenchmark.tasks import COMPUTER_VISION
from torchbenchmark.util.framework.detectron2.model_factory import Detectron2Model
MODEL_NAME = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
MODEL_DIR = os.path.abspath(os.path.dirname(__file__))
class Model(Detectron2Model):
task = COMPU... |
import os
from torchbenchmark.util.framework.detectron2 import install_detectron2
MODEL_NAME = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
MODEL_DIR = os.path.abspath(os.path.dirname(__file__))
if __name__ == '__main__':
install_detectron2(MODEL_NAME, MODEL_DIR)
|
"Doctr recognition model"
from doctr.models import ocr_predictor
import numpy as np
import torch
# TorchBench imports
from torchbenchmark.util.model import BenchmarkModel
from torchbenchmark.tasks import COMPUTER_VISION
from typing import Tuple
class Model(BenchmarkModel):
task = COMPUTER_VISION.DETECTION
DE... |
import os
import warnings
import subprocess
import sys
def pip_install_requirements():
try:
subprocess.check_call(["conda", "install", "-y", "expecttest", "libglib", "pango", "-c", "conda-forge"])
except:
warnings.warn("The doctr_reco_predictor model requires conda binary libaries to be install... |
import os
from torchbenchmark.tasks import COMPUTER_VISION
from torchbenchmark.util.framework.detectron2.model_factory import Detectron2Model
MODEL_NAME = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
MODEL_DIR = os.path.abspath(os.path.dirname(__file__))
class Model(Detectron2Model):
task = COMPUT... |
import os
from torchbenchmark.util.framework.detectron2 import install_detectron2
MODEL_NAME = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
MODEL_DIR = os.path.abspath(os.path.dirname(__file__))
if __name__ == '__main__':
install_detectron2(MODEL_NAME, MODEL_DIR)
|
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Dora the Explorer, special thank to @pierrestock.
"""
import argparse
import json
import logging
import shlex
import sub... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Run training locally on all visible GPUs. Start only
one task per node as this script will spawn ... |
import torch
import sys
a = torch.load(sys.argv[1])
b = torch.load(sys.argv[2])
torch.testing.assert_allclose(a,b, rtol=0.01, atol=0.01)
|
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Quantize a pre-trained model. Just pass the path to the model to this script
and it will save a gzipped compressed versi... |
import json
import torch
import random
import numpy as np
from fractions import Fraction
from .demucs.model import Demucs
from .demucs.parser import get_name, get_parser
from .demucs.augment import FlipChannels, FlipSign, Remix, Shift
from .demucs.utils import capture_init, center_trim
from ...util.model import Benchm... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import gzip
import json
import sys
from collections import defaultdict
from pathlib import Path
import num... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
from collections import defaultdict
from pathlib import Path
import numpy as np
import treetab... |
import subprocess
import sys
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
def spacy_download(language):
pass
def preprocess():
pass
if __name__ == '__main__':
pip_install_requirements()
spacy_download('')
pre... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Run training from Slurm on all visible GPUs. Start only
one task per node as this script will spa... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
from collections import defaultdict, namedtuple
from pathlib import Path
import musdb
import num... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
from concurrent import futures
import musdb
from .audio import AudioFile
def get_musdb_tracks(root, *args, ... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Created on 2018/12
# Author: Kaituo XU
# Modified on 2019/11 by Alexandre Defossez, added support for multiple output ch... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
|
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import hashlib
import sys
from pathlib import Path
import requests
import torch as th
import tqdm
from sci... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import gzip
import sys
from concurrent import futures
import musdb
import museval
import torch as th
import tqdm
from scip... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch as th
from torch import nn
class Shift(nn.Module):
"""
Randomly shift audio in time by ... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch as th
from torch import Tensor, nn
from .utils import capture_init, center_trim
fro... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
from pathlib import Path
def get_parser():
parser = argparse.ArgumentParser("demucs", descr... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import errno
import functools
import gzip
import os
import random
import socket
import tempfile
import warnings
from contex... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sys
import tqdm
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import subprocess as sp
from pathlib import Path
import numpy as np
import torch
from .utils import temp_filen... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import sys
import time
from dataclasses import dataclass, field
from fractions import Fraction
impor... |
from torchbenchmark.tasks import NLP
from torchbenchmark.util.framework.huggingface.model_factory import HuggingFaceModel
class Model(HuggingFaceModel):
task = NLP.LANGUAGE_MODELING
DEFAULT_TRAIN_BSIZE = 8
DEFAULT_EVAL_BSIZE = 1
def __init__(self, test, device, jit=False, batch_size=None, extra_args=[... |
import subprocess
import sys
import os
from torchbenchmark.util.framework.huggingface.patch_hf import patch_transformers, cache_model
def pip_install_requirements():
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
if __name__ == '__main__':
pip_install_requirem... |
import torch
import sys
a = torch.load(sys.argv[1])
b = torch.load(sys.argv[2])
torch.testing.assert_allclose(a,b, rtol=0.01, atol=0.01)
|
import argparse
import torch.distributed as dist
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.tensorboard import SummaryWriter
from .test import test # import test.py to get mAP after each epoch
from .yolo_models import *
from .yolo_utils.datasets import *
from .yolo_u... |
#!/usr/bin/env python
# Make all randomness deterministic
import random
import argparse
import torch
import os
import numpy as np
from contextlib import nullcontext
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
from shlex import split
from .yolo_train import prepare_training_loop
f... |
import argparse
import json
from torch.utils.data import DataLoader
from .yolo_models import *
from .yolo_utils.datasets import *
from .yolo_utils.utils import *
import os.path
def test(cfg,
data,
weights=None,
batch_size=16,
imgsz=416,
conf_thres=0.001,
iou_thr... |
import subprocess
import sys
import os
from pathlib import Path
def setup_data_dir():
current_dir = Path(os.path.dirname(os.path.realpath(__file__)))
coco128_data_dir = os.path.join(current_dir.parent.parent, "data", ".data", "coco128")
assert os.path.exists(coco128_data_dir), "Couldn't find coco128 data d... |
from .yolo_utils.google_utils import *
from .yolo_utils.layers import *
from .yolo_utils.parse_config import *
ONNX_EXPORT = False
def create_modules(module_defs, img_size, cfg):
# Constructs module list of layer blocks from module configuration in module_defs
img_size = [img_size] * 2 if isinstance(img_siz... |
import argparse
from models import * # set ONNX_EXPORT in models.py
from utils.datasets import *
from utils.utils import *
def detect(save_img=False):
imgsz = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
out, source, weights, half, view_img, save_... |
import glob
import math
import os
import random
import shutil
import time
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import Dataset
from .utils import xyxy2xywh, xywh2xyxy
help_url = 'https://github.com/ultral... |
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