repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
SSeg | SSeg-master/loss.py | """
Loss.py
"""
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from config import cfg
def get_loss(args):
"""
Get the criterion based on the loss function
args: commandline arguments
return: criterion, criterion_val
"""
if args.img_wt_los... | 6,799 | 34.602094 | 99 | py |
SSeg | SSeg-master/config.py | """
# Code adapted from:
# https://github.com/facebookresearch/Detectron/blob/master/detectron/core/config.py
Source License
# Copyright (c) 2017-present, Facebook, 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 obta... | 4,003 | 31.032 | 90 | py |
SSeg | SSeg-master/demo.py | import os
import sys
import argparse
from PIL import Image
import numpy as np
import cv2
import torch
from torch.backends import cudnn
import torchvision.transforms as transforms
import network
from optimizer import restore_snapshot
from datasets import cityscapes
from config import assert_and_infer_cfg
parser = arg... | 2,091 | 32.741935 | 140 | py |
SSeg | SSeg-master/eval.py | """
Evaluation Script
Support Two Modes: Pooling based inference and sliding based inference
Pooling based inference is simply whole image inference.
"""
import os
import logging
import sys
import argparse
import re
import queue
import threading
from math import ceil
from datetime import datetime
from tqdm import tqdm
... | 21,561 | 34.580858 | 94 | py |
SSeg | SSeg-master/train.py | """
training code
"""
from __future__ import absolute_import
from __future__ import division
import argparse
import logging
import os
import torch
#from apex import amp
from config import cfg, assert_and_infer_cfg
from utils.misc import AverageMeter, prep_experiment, evaluate_eval, fast_hist
import datasets
import los... | 12,946 | 39.713836 | 100 | py |
SSeg | SSeg-master/optimizer.py | """
Pytorch Optimizer and Scheduler Related Task
"""
import math
import logging
import torch
from torch import optim
from config import cfg
def get_optimizer(args, net):
"""
Decide Optimizer (Adam or SGD)
"""
param_groups = net.parameters()
if args.sgd:
optimizer = optim.SGD(param_groups,... | 3,394 | 33.642857 | 92 | py |
SSeg | SSeg-master/datasets/kitti.py | """
KITTI Dataset Loader
http://www.cvlibs.net/datasets/kitti/eval_semseg.php?benchmark=semantics2015
"""
import os
import sys
import numpy as np
from PIL import Image
from torch.utils import data
import logging
import datasets.uniform as uniform
import datasets.cityscapes_labels as cityscapes_labels
import json
from ... | 9,775 | 34.809524 | 133 | py |
SSeg | SSeg-master/datasets/sampler.py | """
# Code adapted from:
# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
#
# BSD 3-Clause License
#
# Copyright (c) 2017,
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions ar... | 4,347 | 38.527273 | 118 | py |
SSeg | SSeg-master/datasets/uavid.py | """
Mapillary Dataset Loader
"""
from PIL import Image
from torch.utils import data
import os
import numpy as np
import json
import datasets.uniform as uniform
from config import cfg
num_classes = 8
ignore_label = 255
root = cfg.DATASET.UAVID_DIR
config_fn = os.path.join(root, 'config.json')
id_to_ignore_or_group = {}... | 6,702 | 33.551546 | 86 | py |
SSeg | SSeg-master/datasets/cityscapes.py | """
Cityscapes Dataset Loader
"""
import logging
import json
import os
import numpy as np
from PIL import Image
from torch.utils import data
import torchvision.transforms as transforms
import datasets.uniform as uniform
import datasets.cityscapes_labels as cityscapes_labels
from config import cfg
trainid_to_name = c... | 19,309 | 38.488753 | 100 | py |
SSeg | SSeg-master/datasets/nullloader.py | """
Null Loader
"""
import numpy as np
import torch
from torch.utils import data
num_classes = 19
ignore_label = 255
class NullLoader(data.Dataset):
"""
Null Dataset for Performance
"""
def __init__(self,crop_size):
self.imgs = range(200)
self.crop_size = crop_size
def __getitem__... | 576 | 23.041667 | 158 | py |
SSeg | SSeg-master/datasets/camvid.py | """
Camvid Dataset Loader
"""
import os
import sys
import numpy as np
from PIL import Image
from torch.utils import data
import logging
import datasets.uniform as uniform
import json
from config import cfg
# trainid_to_name = cityscapes_labels.trainId2name
# id_to_trainid = cityscapes_labels.label2trainid
num_classe... | 10,286 | 35.349823 | 133 | py |
SSeg | SSeg-master/datasets/__init__.py | """
Dataset setup and loaders
"""
from datasets import cityscapes
from datasets import mapillary
from datasets import kitti
from datasets import camvid
from datasets import uavid
import torchvision.transforms as standard_transforms
import transforms.joint_transforms as joint_transforms
import transforms.transforms as ... | 12,145 | 40.172881 | 133 | py |
SSeg | SSeg-master/datasets/mapillary.py | """
Mapillary Dataset Loader
"""
from PIL import Image
from torch.utils import data
import os
import numpy as np
import json
import datasets.uniform as uniform
from config import cfg
num_classes = 65
ignore_label = 65
root = cfg.DATASET.MAPILLARY_DIR
config_fn = os.path.join(root, 'config.json')
id_to_ignore_or_group ... | 6,713 | 33.430769 | 86 | py |
SSeg | SSeg-master/network/Resnet.py | """
# Code Adapted from:
# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
#
# BSD 3-Clause License
#
# Copyright (c) 2017,
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are me... | 8,386 | 31.890196 | 90 | py |
SSeg | SSeg-master/network/wider_resnet.py | """
# Code adapted from:
# https://github.com/mapillary/inplace_abn/
#
# BSD 3-Clause License
#
# Copyright (c) 2017, mapillary
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistribution... | 14,157 | 34.752525 | 87 | py |
SSeg | SSeg-master/network/deepv3.py | """
# Code Adapted from:
# https://github.com/sthalles/deeplab_v3
#
# MIT License
#
# Copyright (c) 2018 Thalles Santos Silva
#
# 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 restric... | 10,730 | 33.728155 | 138 | py |
SSeg | SSeg-master/network/SEresnext.py | """
# Code adapted from:
# https://github.com/Cadene/pretrained-models.pytorch
#
# BSD 3-Clause License
#
# Copyright (c) 2017, Remi Cadene
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Re... | 15,124 | 36.071078 | 98 | py |
SSeg | SSeg-master/network/mynn.py | """
Custom Norm wrappers to enable sync BN, regular BN and for weight initialization
"""
import torch.nn as nn
from config import cfg
from apex import amp
def Norm2d(in_channels):
"""
Custom Norm Function to allow flexible switching
"""
layer = getattr(cfg.MODEL, 'BNFUNC')
normalization_layer = la... | 1,076 | 25.925 | 80 | py |
SSeg | SSeg-master/network/__init__.py | """
Network Initializations
"""
import logging
import importlib
import torch
def get_net(args, criterion):
"""
Get Network Architecture based on arguments provided
"""
net = get_model(network=args.arch, num_classes=args.dataset_cls.num_classes,
criterion=criterion)
num_params... | 1,130 | 23.586957 | 80 | py |
SSeg | SSeg-master/utils/misc.py | """
Miscellanous Functions
"""
import sys
import re
import os
import shutil
import torch
from datetime import datetime
import logging
from subprocess import call
import shlex
from tensorboardX import SummaryWriter
import numpy as np
import torchvision.transforms as standard_transforms
import torchvision.utils as vutil... | 11,777 | 37.616393 | 102 | py |
SSeg | SSeg-master/utils/my_data_parallel.py |
"""
# Code adapted from:
# https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/data_parallel.py
#
# BSD 3-Clause License
#
# Copyright (c) 2017,
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following condition... | 8,606 | 40.985366 | 111 | py |
SSeg | SSeg-master/sdcnet/main.py | #!/usr/bin/env python
import argparse
import os
import numpy as np
import shutil
import torch
import torch.backends.cudnn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from tensorboardX import SummaryWriter
import cv2
from tqdm import tqdm
### masks warning : RuntimeError: Set changed size duri... | 26,977 | 40.062405 | 127 | py |
SSeg | SSeg-master/sdcnet/sdc_aug.py | import os
import sys
import argparse
import cv2
import numpy as np
from PIL import Image
import shutil
import torch
import torch.nn as nn
from torch.autograd import Variable
from models.sdc_net2d import *
parser = argparse.ArgumentParser()
parser.add_argument('--pretrained', default='', type=str, metavar='PATH', he... | 15,478 | 45.623494 | 131 | py |
SSeg | SSeg-master/sdcnet/utility/tools.py | import os
import subprocess
import time
from inspect import isclass
class TimerBlock:
def __init__(self, title):
print(("{}".format(title)))
def __enter__(self):
self.start = time.clock()
return self
def __exit__(self, exc_type, exc_value, traceback):
self.end = time.clock... | 3,701 | 37.164948 | 155 | py |
SSeg | SSeg-master/sdcnet/models/model_utils.py | from __future__ import division
from __future__ import print_function
import torch.nn as nn
def conv2d(channels_in, channels_out, kernel_size=3, stride=1, bias = True):
return nn.Sequential(
nn.Conv2d(channels_in, channels_out, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=bias)... | 649 | 39.625 | 124 | py |
SSeg | SSeg-master/sdcnet/models/sdc_net2d.py | '''
Portions of this code are adapted from:
https://github.com/NVIDIA/flownet2-pytorch/blob/master/networks/FlowNetS.py
https://github.com/ClementPinard/FlowNetPytorch/blob/master/models/FlowNetS.py
'''
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
from torch.... | 9,288 | 40.842342 | 119 | py |
SSeg | SSeg-master/sdcnet/datasets/frame_loader.py | from __future__ import division
from __future__ import print_function
import os
import natsort
import numpy as np
import cv2
import torch
from torch.utils import data
from datasets.dataset_utils import StaticRandomCrop
class FrameLoader(data.Dataset):
def __init__(self, args, root, is_training = False, transfor... | 3,766 | 35.931373 | 107 | py |
SSeg | SSeg-master/sdcnet/datasets/dataset_utils.py | from __future__ import division
from __future__ import print_function
import torch
class StaticRandomCrop(object):
"""
Helper function for random spatial crop
"""
def __init__(self, size, image_shape):
h, w = image_shape
self.th, self.tw = size
self.h1 = torch.randint(0, h - se... | 519 | 27.888889 | 79 | py |
SSeg | SSeg-master/sdcnet/spatialdisplconv_package/test_spatialdisplconv.py | import torch
import time
from spatialdisplconv import SpatialDisplConv
assert torch.cuda.is_available()
cuda_device = torch.device("cuda") # device object representing GPU
n = 8
h = 224
w = 224
offset = 9 # 11
#input1 = N, 3, H + 11, W + 11
#input2 = N, 11, H, W
#input3 = N, 11, H, W
#input4 = N, 2, H, W
# Note t... | 1,305 | 23.185185 | 98 | py |
SSeg | SSeg-master/sdcnet/spatialdisplconv_package/spatialdisplconv.py | from torch.nn.modules.module import Module
from torch.autograd import Function, Variable
import spatialdisplconv_cuda
class SpatialDisplConvFunction(Function):
@staticmethod
def forward(ctx, input1, input2, input3, input4, kernel_size = 1):
assert input1.is_contiguous(), "spatialdisplconv forward - in... | 2,270 | 32.397059 | 95 | py |
SSeg | SSeg-master/sdcnet/spatialdisplconv_package/setup.py | #!/usr/bin/env python3
import os
import torch
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
cxx_args = ['-std=c++11']
nvcc_args = [
'-gencode', 'arch=compute_50,code=sm_50',
'-gencode', 'arch=compute_52,code=sm_52',
'-gencode', 'arch=compute_60,code=sm_6... | 791 | 25.4 | 67 | py |
SSeg | SSeg-master/transforms/joint_transforms.py | """
# Code borrowded from:
# https://github.com/zijundeng/pytorch-semantic-segmentation/blob/master/utils/joint_transforms.py
#
#
# MIT License
#
# Copyright (c) 2017 ZijunDeng
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "So... | 22,631 | 35.8 | 109 | py |
SSeg | SSeg-master/transforms/transforms.py | """
# Code borrowded from:
# https://github.com/zijundeng/pytorch-semantic-segmentation/blob/master/utils/transforms.py
#
#
# MIT License
#
# Copyright (c) 2017 ZijunDeng
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software... | 11,778 | 32.274011 | 94 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/eval_multipro.py | # System libs
import os
import argparse
from distutils.version import LooseVersion
from multiprocessing import Queue, Process
# Numerical libs
import numpy as np
import math
import torch
import torch.nn as nn
from scipy.io import loadmat
# Our libs
from mit_semseg.config import cfg
from mit_semseg.dataset import ValDat... | 7,059 | 30.517857 | 115 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/test.py | # System libs
import os
import argparse
from distutils.version import LooseVersion
# Numerical libs
import numpy as np
import torch
import torch.nn as nn
from scipy.io import loadmat
import csv
# Our libs
from mit_semseg.dataset import TestDataset
from mit_semseg.models import ModelBuilder, SegmentationModule
from mit_... | 5,870 | 28.502513 | 82 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/setup.py | import setuptools
with open('README.md', 'r') as fh:
long_description = fh.read()
setuptools.setup(
name='mit_semseg',
version='1.0.0',
author='MIT CSAIL',
description='Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset',
long_description=long_description,
... | 817 | 26.266667 | 103 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/eval.py | # System libs
import os
import time
import argparse
from distutils.version import LooseVersion
# Numerical libs
import numpy as np
import torch
import torch.nn as nn
from scipy.io import loadmat
# Our libs
from mit_semseg.config import cfg
from mit_semseg.dataset import ValDataset
from mit_semseg.models import ModelBui... | 5,992 | 29.891753 | 100 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/train.py | # System libs
import os
import time
# import math
import random
import argparse
from distutils.version import LooseVersion
# Numerical libs
import torch
import torch.nn as nn
# Our libs
from mit_semseg.config import cfg
from mit_semseg.dataset import TrainDataset
from mit_semseg.models import ModelBuilder, Segmentation... | 9,224 | 32.667883 | 107 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/dataset.py | import os
import json
import torch
from torchvision import transforms
import numpy as np
from PIL import Image
def imresize(im, size, interp='bilinear'):
if interp == 'nearest':
resample = Image.NEAREST
elif interp == 'bilinear':
resample = Image.BILINEAR
elif interp == 'bicubic':
... | 11,898 | 39.063973 | 108 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/models/hrnet.py | """
This HRNet implementation is modified from the following repository:
https://github.com/HRNet/HRNet-Semantic-Segmentation
"""
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import load_url
from mit_semseg.lib.nn import SynchronizedBatchNorm2d
BatchNorm2d = Synchroniz... | 16,811 | 36.695067 | 164 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/models/resnet.py | import torch.nn as nn
import math
from .utils import load_url
from mit_semseg.lib.nn import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d
__all__ = ['ResNet', 'resnet18', 'resnet50', 'resnet101'] # resnet101 is coming soon!
model_urls = {
'resnet18': 'http://sceneparsing.csail.mit.edu/model/pret... | 6,770 | 30.202765 | 99 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/models/utils.py | import sys
import os
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
import torch
def load_url(url, model_dir='./pretrained', map_location=None):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
filename = url.split('/')[-1]
cached_fil... | 577 | 29.421053 | 78 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/models/resnext.py | import torch.nn as nn
import math
from .utils import load_url
from mit_semseg.lib.nn import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d
__all__ = ['ResNeXt', 'resnext101'] # support resnext 101
model_urls = {
#'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-im... | 5,367 | 31.731707 | 101 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/models/models.py | import torch
import torch.nn as nn
from . import resnet, resnext, mobilenet, hrnet
from mit_semseg.lib.nn import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d
class SegmentationModuleBase(nn.Module):
def __init__(self):
super(SegmentationModuleBase, self).__init__()
def pixel_acc(self... | 21,185 | 35.091993 | 114 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/models/mobilenet.py | """
This MobileNetV2 implementation is modified from the following repository:
https://github.com/tonylins/pytorch-mobilenet-v2
"""
import torch.nn as nn
import math
from .utils import load_url
from mit_semseg.lib.nn import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d
__all__ = ['mobilenetv2']
mo... | 4,938 | 30.864516 | 100 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/nn/modules/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.da... | 3,226 | 32.968421 | 115 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/nn/modules/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import numpy as np
from tor... | 835 | 26.866667 | 157 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/nn/modules/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import torch
import tor... | 13,813 | 40.860606 | 127 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/nn/modules/tests/test_sync_batchnorm.py | # -*- coding: utf-8 -*-
# File : test_sync_batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
import unittest
import torch
import torch.nn as nn
from torch.autograd import Variable
from sync_batchnorm import Synchroniz... | 3,571 | 30.892857 | 109 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/nn/modules/tests/test_numeric_batchnorm.py | # -*- coding: utf-8 -*-
# File : test_numeric_batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
import unittest
import torch
import torch.nn as nn
from torch.autograd import Variable
from sync_batchnorm.unittest impor... | 1,615 | 27.350877 | 85 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/nn/parallel/data_parallel.py | # -*- coding: utf8 -*-
import torch.cuda as cuda
import torch.nn as nn
import torch
import collections
from torch.nn.parallel._functions import Gather
__all__ = ['UserScatteredDataParallel', 'user_scattered_collate', 'async_copy_to']
def async_copy_to(obj, dev, main_stream=None):
if torch.is_tensor(obj):
... | 3,399 | 29.088496 | 82 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/utils/th.py | import torch
from torch.autograd import Variable
import numpy as np
import collections
__all__ = ['as_variable', 'as_numpy', 'mark_volatile']
def as_variable(obj):
if isinstance(obj, Variable):
return obj
if isinstance(obj, collections.Sequence):
return [as_variable(v) for v in obj]
elif i... | 1,237 | 28.47619 | 60 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/utils/data/sampler.py | import torch
class Sampler(object):
"""Base class for all Samplers.
Every Sampler subclass has to provide an __iter__ method, providing a way
to iterate over indices of dataset elements, and a __len__ method that
returns the length of the returned iterators.
"""
def __init__(self, data_sourc... | 3,761 | 27.5 | 88 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/utils/data/dataloader.py | import torch
import torch.multiprocessing as multiprocessing
from torch._C import _set_worker_signal_handlers, \
_remove_worker_pids, _error_if_any_worker_fails
try:
from torch._C import _set_worker_pids
except:
from torch._C import _update_worker_pids as _set_worker_pids
from .sampler import SequentialSamp... | 16,207 | 37.046948 | 102 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/utils/data/dataset.py | import bisect
import warnings
from torch._utils import _accumulate
from torch import randperm
class Dataset(object):
"""An abstract class representing a Dataset.
All other datasets should subclass it. All subclasses should override
``__len__``, that provides the size of the dataset, and ``__getitem__``,... | 3,465 | 28.12605 | 118 | py |
semantic-segmentation-pytorch | semantic-segmentation-pytorch-master/mit_semseg/lib/utils/data/distributed.py | import math
import torch
from .sampler import Sampler
from torch.distributed import get_world_size, get_rank
class DistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`... | 1,964 | 32.305085 | 86 | py |
pyhiro | pyhiro-master/test/transformations.py | # -*- coding: utf-8 -*-
# transformations.py
# Copyright (c) 2006-2017, Christoph Gohlke
# Copyright (c) 2006-2017, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modifica... | 66,387 | 33.380114 | 79 | py |
pyhiro | pyhiro-master/trimesh/transformations.py | # -*- coding: utf-8 -*-
# transformations.py
# Copyright (c) 2006-2015, Christoph Gohlke
# Copyright (c) 2006-2015, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modifica... | 64,578 | 33.442133 | 79 | py |
infinite_ae_cf | infinite_ae_cf-main/main.py | import os
os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
os.environ["TF_FORCE_UNIFIED_MEMORY"] = "1"
os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
import time
import copy
import random
import numpy as np
from utils import log_end_epoch, get_item_propensity, get_common_path
def train(hyper_params, dat... | 2,996 | 36.4625 | 133 | py |
infinite_ae_cf | infinite_ae_cf-main/model.py | import jax
import functools
from jax import scipy as sp
from jax import numpy as jnp
from neural_tangents import stax
def make_kernelized_rr_forward(hyper_params):
_, _, kernel_fn = FullyConnectedNetwork(
depth=hyper_params['depth'],
num_classes=hyper_params['num_items']
)
# NOTE: Un-commen... | 1,532 | 36.390244 | 113 | py |
infinite_ae_cf | infinite_ae_cf-main/data.py | from scipy.sparse import csr_matrix
import jax.numpy as jnp
import numpy as np
import copy
import h5py
import gc
class Dataset:
def __init__(self, hyper_params):
self.data = load_raw_dataset(hyper_params['dataset'])
self.set_of_active_users = list(set(self.data['train'][:, 0].tolist())) ... | 4,938 | 35.051095 | 119 | py |
infinite_ae_cf | infinite_ae_cf-main/eval.py | import jax
import numpy as np
import jax.numpy as jnp
from numba import jit, float64
INF = float(1e6)
def evaluate(hyper_params, kernelized_rr_forward, data, item_propensity, train_x, topk = [ 10, 100 ], test_set_eval = False):
preds, y_binary, metrics = [], [], {}
for kind in [ 'HR', 'NDCG', 'PSP' ]:
... | 4,052 | 39.53 | 132 | py |
GCNet | GCNet-main/baseline-cca/dccae.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import os
import cv2
import glob
import time
import tqdm
import random
from functoo... | 23,393 | 36.671498 | 158 | py |
GCNet | GCNet-main/baseline-cca/dcca.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import os
import cv2
import glob
import time
import tqdm
from functools import part... | 21,798 | 36.519793 | 158 | py |
GCNet | GCNet-main/feature_extraction/audio/extract_panns_embedding.py | """
PANNs: https://arxiv.org/abs/1912.10211
official github repo: https://github.com/qiuqiangkong/audioset_tagging_cnn
"""
import os
import numpy as np
import argparse
import librosa
import torch
import glob
import time
import math
from panns.models import *
from panns.pytorch_utils import move_data_to_device
from uti... | 6,800 | 39.482143 | 129 | py |
GCNet | GCNet-main/feature_extraction/audio/extract_wav2vec_embedding.py | # *_*coding:utf-8 *_*
"""
wav2vec: https://arxiv.org/abs/1904.05862
official github repo: https://github.com/pytorch/fairseq/tree/master/examples/wav2vec
"""
import time
import os
import glob
import numpy as np
import pandas as pd
import torch
from fairseq.models.wav2vec import Wav2VecModel # Note: use fairseq version ... | 5,363 | 45.241379 | 192 | py |
GCNet | GCNet-main/feature_extraction/audio/extract_wav2vec2_embedding.py | # *_*coding:utf-8 *_*
"""
wav2vec 2.0: https://arxiv.org/abs/2006.11477
official github repo: https://github.com/pytorch/fairseq/tree/master/examples/wav2vec
"""
import time
import os
import glob
import torch
import numpy as np
from transformers import Wav2Vec2Processor, Wav2Vec2Model
from util import frame_audio, writ... | 6,942 | 46.554795 | 157 | py |
GCNet | GCNet-main/feature_extraction/audio/panns/inference.py | import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
import numpy as np
import argparse
import librosa
import matplotlib.pyplot as plt
import torch
from utilities import create_folder, get_filename
from models import *
from pytorch_utils import move_data_to_device
import config
def audio_ta... | 7,592 | 34.481308 | 101 | py |
GCNet | GCNet-main/feature_extraction/audio/panns/main.py | import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
import numpy as np
import argparse
import time
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from utilities import (create_folder, get_filename, creat... | 13,353 | 37.595376 | 118 | py |
GCNet | GCNet-main/feature_extraction/audio/panns/losses.py | import torch
import torch.nn.functional as F
def clip_bce(output_dict, target_dict):
"""Binary crossentropy loss.
"""
return F.binary_cross_entropy(
output_dict['clipwise_output'], target_dict['target'])
def get_loss_func(loss_type):
if loss_type == 'clip_bce':
return clip_bce | 313 | 21.428571 | 62 | py |
GCNet | GCNet-main/feature_extraction/audio/panns/evaluate.py | from sklearn import metrics
from pytorch_utils import forward
class Evaluator(object):
def __init__(self, model):
"""Evaluator.
Args:
model: object
"""
self.model = model
def evaluate(self, data_loader):
"""Forward evaluation data and calculate stat... | 1,113 | 25.52381 | 87 | py |
GCNet | GCNet-main/feature_extraction/audio/panns/finetune_template.py | import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
import numpy as np
import argparse
import h5py
import math
import time
import logging
import matplotlib.pyplot as plt
import torch
torch.backends.cudnn.benchmark=True
torch.manual_seed(0)
import torch.nn as nn
import torch.nn.functional as ... | 4,049 | 30.889764 | 88 | py |
GCNet | GCNet-main/feature_extraction/audio/panns/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
from torchlibrosa.augmentation import SpecAugmentation
from panns.pytorch_utils import do_mixup, interpolate, pad_framewise_output
def init_layer(layer):
"""Initialize a Linear or Convo... | 121,514 | 35.645054 | 115 | py |
GCNet | GCNet-main/feature_extraction/audio/panns/pytorch_utils.py | import numpy as np
import time
import torch
import torch.nn as nn
def move_data_to_device(x, device):
if 'float' in str(x.dtype):
x = torch.Tensor(x)
elif 'int' in str(x.dtype):
x = torch.LongTensor(x)
else:
return x
return x.to(device)
def do_mixup(x, mixup_lambda):
"""... | 8,309 | 32.10757 | 127 | py |
GCNet | GCNet-main/feature_extraction/visual/extract_emonet_embedding.py | # *_*coding:utf-8 *_*
import os
import argparse
from tqdm import tqdm
import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import numpy as np
from emonet.models.emonet import EmoNet
from dataset import FaceDatas... | 3,336 | 35.67033 | 123 | py |
GCNet | GCNet-main/feature_extraction/visual/dataset.py | # *_*coding:utf-8 *_*
import os
import glob
from PIL import Image
from skimage import io
import torch.utils.data as data
class FaceDataset(data.Dataset):
def __init__(self, vid, face_dir, transform=None):
super(FaceDataset, self).__init__()
self.vid = vid
self.path = os.path.join(face_dir,... | 2,209 | 31.985075 | 95 | py |
GCNet | GCNet-main/feature_extraction/visual/extract_manet_embedding.py | # *_*coding:utf-8 *_*
import os
import argparse
from tqdm import tqdm
import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt
from manet.model.manet import manet
f... | 5,923 | 36.974359 | 123 | py |
GCNet | GCNet-main/feature_extraction/visual/extract_ferplus_embedding.py | # *_*coding:utf-8 *_*
from __future__ import division
import os
import time
import six
import sys
from tqdm import tqdm
import argparse
import pickle
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import torch.utils.data
from os.path import join as pjoin
import torch.backends.cudnn as cudnn
... | 8,918 | 37.61039 | 144 | py |
GCNet | GCNet-main/feature_extraction/visual/emonet/evaluation.py | import numpy as np
import torch
def evaluate_metrics(ground_truth, predictions, metrics, verbose=True, print_tex=True):
results = {}
for name, metric in metrics.items():
results[name] = metric(ground_truth, predictions)
if verbose:
print(', '.join(f'{name}={results[name]:.2f}' for name in m... | 7,930 | 37.31401 | 171 | py |
GCNet | GCNet-main/feature_extraction/visual/emonet/models/emonet.py | #########################################################
# #
# Authors: Jean Kossaifi, Antoine Toisoul, Adrian Bulat #
# #
#########################################################
import torch
import torch.nn ... | 8,683 | 35.64135 | 145 | py |
GCNet | GCNet-main/feature_extraction/visual/emonet/data/affecnet.py | from pathlib import Path
import pickle
import numpy as np
import torch
import math
from torch.utils.data import Dataset
from skimage import io
class AffectNet(Dataset):
_expressions = {0: 'neutral', 1:'happy', 2:'sad', 3:'surprise', 4:'fear', 5:'disgust', 6:'anger', 7:'contempt', 8:'none'}
_expressions_indic... | 5,964 | 43.185185 | 141 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/run_imagenet_benchmarks.py | # -*- coding: utf-8 -*-
"""This script evaluates imported PyTorch models on the
ImageNet validation set
e.g.
python run_imagenet_benchmarks.py --model_subset pt_tpu --gpus 2
"""
import os
import argparse
from torchvision.models import densenet
from imagenet.evaluation import imagenet_benchmark
from pathlib import Pat... | 6,947 | 35.761905 | 88 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/run_fer_benchmarks.py | # -*- coding: utf-8 -*-
"""This module evaluates imported PyTorch models on fer2013
"""
import os
import argparse
from os.path import join as pjoin
from fer2013.fer import fer2013_benchmark
from utils.benchmark_helpers import load_module_2or3
# MODEL_DIR = os.path.expanduser('~/data/models/pytorch/mcn_imports')
# FER... | 2,973 | 33.183908 | 73 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/lfw_eval.py | # -*- coding: utf-8 -*-
"""LFW benchmark for face verification. This is designed to be used as a
sanity check for imported models.
Example Invocation:
ipy lfw_eval.py
ipy lfw_eval.py -- --limit 200 --model_name vgg_face_dag
ipy lfw_eval.py -- --model_name vgg_m_face_bn_dag
This code is primarily based on the code of ... | 10,780 | 31.375375 | 79 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/imagenet/evaluation.py | # -*- coding: utf-8 -*-
"""Imagenet validation set benchmark
The module evaluates the performance of a pytorch model on the ILSVRC 2012
validation set.
Based on PyTorch imagenet example:
https://github.com/pytorch/examples/tree/master/imagenet
"""
from __future__ import division
import os
import time
from PIL ... | 4,937 | 31.486842 | 86 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/utils/benchmark_helpers.py | # -*- coding: utf-8 -*-
"""Utilties shared among the benchmarking protocols
"""
import os
import sys
import six
import torchvision.transforms as transforms
def compose_transforms(meta, resize=256, center_crop=True,
override_meta_imsize=False):
"""Compose preprocessing transforms for model
... | 2,669 | 37.142857 | 81 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/model/vgg_m_face_bn_fer_dag.py | # *_*coding:utf-8 *_*
import torch
import torch.nn as nn
class Vgg_m_face_bn_fer_dag(nn.Module):
def __init__(self):
super(Vgg_m_face_bn_fer_dag, self).__init__()
self.meta = {'mean': [131.45376586914062, 103.98748016357422, 91.46234893798828],
'std': [1, 1, 1],
... | 3,509 | 42.875 | 112 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/model/senet50_ferplus_dag.py | # *_*coding:utf-8 *_*
import torch
import torch.nn as nn
class Senet50_ferplus_dag(nn.Module):
def __init__(self):
super(Senet50_ferplus_dag, self).__init__()
self.meta = {'mean': [131.0912, 103.8827, 91.4953],
'std': [1, 1, 1],
'imageSize': [224, 224, 3... | 39,468 | 71.955638 | 123 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/model/alexnet_face_fer_bn_dag.py | # *_*coding:utf-8 *_*
import torch
import torch.nn as nn
class Alexnet_face_fer_bn_dag(nn.Module):
def __init__(self):
super(Alexnet_face_fer_bn_dag, self).__init__()
self.meta = {'mean': [131.09375, 103.88607788085938, 91.47599792480469],
'std': [1, 1, 1],
... | 3,522 | 43.0375 | 108 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/model/vgg_vd_face_fer_dag.py | # *_*coding:utf-8 *_*
import torch
import torch.nn as nn
class Vgg_vd_face_fer_dag(nn.Module):
def __init__(self):
super(Vgg_vd_face_fer_dag, self).__init__()
self.meta = {'mean': [129.186279296875, 104.76238250732422, 93.59396362304688],
'std': [1, 1, 1],
... | 4,424 | 42.382353 | 108 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/model/resnet50_ferplus_dag.py | # *_*coding:utf-8 *_*
import torch
import torch.nn as nn
class Resnet50_ferplus_dag(nn.Module):
def __init__(self):
super(Resnet50_ferplus_dag, self).__init__()
self.meta = {'mean': [131.0912, 103.8827, 91.4953],
'std': [1, 1, 1],
'imageSize': [224, 224,... | 52,441 | 71.035714 | 123 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/fer2013/fer_loader.py | # -*- coding: utf-8 -*-
"""Contains two data loaders. One is for the Fer 2013 emotion dataset
described in the paper:
Goodfellow, Ian J., et al. "Challenges in representation learning:
A report on three machine learning contests." International Conference on
Neural Information Processing. Springer, Berlin, Heidelberg,... | 8,345 | 39.712195 | 79 | py |
GCNet | GCNet-main/feature_extraction/visual/pytorch-benchmarks/fer2013/fer.py | # -*- coding: utf-8 -*-
"""Fer2013 benchmark
The module evaluates the performance of a pytorch model on the FER2013
benchmark.
"""
from __future__ import division
import os
import time
import torch
import numpy as np
import torch.utils.data
import torch.backends.cudnn as cudnn
from fer2013.fer_loader import Fer2013... | 4,347 | 31.691729 | 72 | py |
GCNet | GCNet-main/feature_extraction/visual/manet/main.py | import argparse
import os
import time
import shutil
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import matplotlib.pyplot as plt
import torchvision.datasets as datasets
import torchvision.t... | 14,215 | 38.709497 | 119 | py |
GCNet | GCNet-main/feature_extraction/visual/manet/model/manet.py | from manet.model.attention import *
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in... | 9,989 | 34.425532 | 114 | py |
GCNet | GCNet-main/feature_extraction/visual/manet/model/attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):
super(BasicConv, self).__init__()
self.out_channels = out_planes
... | 3,163 | 35.790698 | 154 | py |
GCNet | GCNet-main/feature_extraction/text/extract_text_embedding_LZ.py | # *_*coding:utf-8 *_*
import os
import glob
import math
import pandas as pd
import numpy as np
import torch
import time
from tqdm import tqdm
import itertools
from transformers import AutoModel, AutoTokenizer # version: 4.5.1, pip install transformers
import re
import argparse
from util import write_feature_to_csv, lo... | 15,582 | 46.947692 | 185 | py |
GCNet | GCNet-main/feature_extraction/text/extract_text_embedding.py | # *_*coding:utf-8 *_*
import os
import glob
import math
import pandas as pd
import numpy as np
import torch
import time
from tqdm import tqdm
import itertools
from transformers import AutoModel, AutoTokenizer # version: 4.5.1, pip install transformers
import re
import argparse
from util import write_feature_to_csv, loa... | 17,177 | 47.66289 | 185 | py |
GCNet | GCNet-main/gcnet/dataloader_iemocap.py | import os
import time
import glob
import tqdm
import pickle
import random
import argparse
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from torch.nn.utils.rnn import pad_sequence
## gain name2features
def read_data(label_path, feature_root):
## gain (names, speakers)
... | 6,858 | 38.877907 | 166 | py |
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