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
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SyNet | SyNet-master/CenterNet/src/lib/models/networks/DCNv2/setup.py | #!/usr/bin/env python
import os
import glob
import torch
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_extension import CUDAExtension
from setuptools import find_packages
from setuptools import setup
requirements = ["torch", "torchvision"]
... | 2,035 | 27.676056 | 73 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/exdet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import numpy as np
import cv2
import sys
import time
from utils.debugger import Debugger
from models.data_parallel import DataParallel
from models.losses import FocalLoss, RegL1Loss
from models.dec... | 3,605 | 40.930233 | 79 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/ctdet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import numpy as np
from models.losses import FocalLoss
from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss
from models.decode import ctdet_decode
from models.utils impor... | 5,518 | 40.810606 | 78 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/ddd.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import numpy as np
from models.losses import FocalLoss, L1Loss, BinRotLoss
from models.decode import ddd_decode
from models.utils import _sigmoid
from utils.debugger import Debugger
from utils.pos... | 6,919 | 43.645161 | 80 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/multi_pose.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import numpy as np
from models.losses import FocalLoss, RegL1Loss, RegLoss, RegWeightedL1Loss
from models.decode import multi_pose_decode
from models.utils import _sigmoid, flip_tensor, flip_lr_of... | 7,252 | 44.049689 | 82 | py |
SyNet | SyNet-master/CenterNet/src/lib/trains/base_trainer.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import torch
from progress.bar import Bar
from models.data_parallel import DataParallel
from utils.utils import AverageMeter
class ModelWithLoss(torch.nn.Module):
def __init__(self, model, loss)... | 3,913 | 31.890756 | 80 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/sample/exdet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.utils.data as data
import pycocotools.coco as coco
import numpy as np
import torch
import json
import cv2
import os
from utils.image import flip, color_aug
from utils.image import get_affine_transf... | 5,722 | 40.773723 | 81 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/sample/ctdet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.utils.data as data
import numpy as np
import torch
import json
import cv2
import os
from utils.image import flip, color_aug
from utils.image import get_affine_transform, affine_transform
from utils... | 5,803 | 39.027586 | 80 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/sample/ddd.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.utils.data as data
import pycocotools.coco as coco
import numpy as np
import torch
import json
import cv2
import os
import math
from utils.image import flip, color_aug
from utils.image import get_a... | 6,801 | 38.777778 | 90 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/sample/multi_pose.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.utils.data as data
import numpy as np
import torch
import json
import cv2
import os
from utils.image import flip, color_aug
from utils.image import get_affine_transform, affine_transform
from utils... | 7,913 | 42.01087 | 81 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/dataset/kitti.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.utils.data as data
import pycocotools.coco as coco
import numpy as np
import torch
import json
import cv2
import os
import math
import torch.utils.data as data
class KITTI(data.Dataset):
num_c... | 3,058 | 32.988889 | 79 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/dataset/visdrone.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import numpy as np
import json
import os
import torch.utils.data as data
class Visdrone(data.Dataset):
num_classes = 10
default_re... | 4,040 | 35.405405 | 127 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/dataset/coco_hp.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import numpy as np
import json
import os
import torch.utils.data as data
class COCOHP(data.Dataset):
num_classes = 13
default... | 4,644 | 39.745614 | 120 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/dataset/pascal.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
import numpy as np
import torch
import json
import os
import torch.utils.data as data
class PascalVOC(data.Dataset):
num_classes = 20
default_resolution = [384, 384]
mean... | 3,032 | 35.542169 | 80 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/dataset/fashion.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import numpy as np
import json
import os
import torch.utils.data as data
class Fashion(data.Dataset):
num_classes = 13
default_res... | 4,151 | 36.405405 | 228 | py |
SyNet | SyNet-master/CenterNet/src/lib/datasets/dataset/coco.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import numpy as np
import json
import os
import torch.utils.data as data
class COCO(data.Dataset):
num_classes = 80
default_resolu... | 5,214 | 39.115385 | 78 | py |
SyNet | SyNet-master/CenterNet/src/lib/utils/utils.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0... | 542 | 22.608696 | 59 | py |
SyNet | SyNet-master/tensorpack/examples/FasterRCNN/config.py | # -*- coding: utf-8 -*-
# File: config.py
import numpy as np
import os
import pprint
import six
from tensorpack.utils import logger
from tensorpack.utils.gpu import get_num_gpu
__all__ = ['config', 'finalize_configs']
class AttrDict():
_freezed = False
""" Avoid accidental creation of new hierarchies. """... | 13,461 | 40.678019 | 176 | py |
SyNet | SyNet-master/tensorpack/tests/benchmark-serializer.py | #!/usr/bin/env python3
import numpy as np
import argparse
import pyarrow as pa
from tabulate import tabulate
import operator
from tensorpack.utils import logger
from tensorpack.utils.serialize import (
MsgpackSerializer,
PyarrowSerializer,
PickleSerializer,
ForkingPickler,
)
from tensorpack.utils.timer... | 3,180 | 31.131313 | 108 | py |
SyNet | SyNet-master/tensorpack/docs/conf.py | # -*- coding: utf-8 -*-
# flake8: noqa
# tensorpack documentation build configuration file, created by
# sphinx-quickstart on Sun Mar 27 01:41:24 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogener... | 15,709 | 32.283898 | 140 | py |
SyNet | SyNet-master/tensorpack/tensorpack/compat/__init__.py | #!/usr/bin/env python
import tensorflow as tf
def backport_tensor_spec():
if hasattr(tf, 'TensorSpec'):
return tf.TensorSpec
try:
# available since 1.7
from tensorflow.python.framework.tensor_spec import TensorSpec
except ImportError:
pass
else:
tf.TensorSpec =... | 917 | 21.390244 | 92 | py |
SyNet | SyNet-master/tensorpack/tensorpack/models/fc.py | # -*- coding: utf-8 -*-
# File: fc.py
import numpy as np
from ..compat import tfv1 as tf # this should be avoided first in model code
from ..tfutils.common import get_tf_version_tuple
from .common import VariableHolder, layer_register
from .tflayer import convert_to_tflayer_args, rename_get_variable
__all__ = ['Fu... | 2,337 | 30.594595 | 110 | py |
SyNet | SyNet-master/tensorpack/tensorpack/models/batch_norm.py | # Copyright (c) Tensorpack Contributors. All Rights Reserved
# -*- coding: utf-8 -*-
# File: batch_norm.py
import re
from ..compat import tfv1 as tf # this should be avoided first in model code
from tensorflow.python.training import moving_averages
from ..tfutils.collection import backup_collection, restore_collect... | 21,444 | 44.530786 | 120 | py |
SyNet | SyNet-master/tensorpack/tensorpack/models/conv2d.py | # -*- coding: utf-8 -*-
# File: conv2d.py
from ..compat import tfv1 as tf # this should be avoided first in model code
from ..tfutils.common import get_tf_version_tuple
from ..utils.argtools import get_data_format, shape2d, shape4d, log_once
from .common import VariableHolder, layer_register
from .tflayer import co... | 10,577 | 38.470149 | 112 | py |
SyNet | SyNet-master/tensorpack/tensorpack/models/tflayer.py | # -*- coding: utf-8 -*-
# File: tflayer.py
import functools
import six
import tensorflow as tf
from ..tfutils.common import get_tf_version_tuple
from ..tfutils.varreplace import custom_getter_scope
from ..utils.argtools import get_data_format
__all__ = []
def map_common_tfargs(kwargs):
df = kwargs.pop('data_fo... | 4,159 | 29.814815 | 100 | py |
SyNet | SyNet-master/tensorpack/tensorpack/models/utils.py | # -*- coding: utf-8 -*-
# File: utils.py
import six
class VariableHolder(object):
""" A proxy to access variables defined in a layer. """
def __init__(self, **kwargs):
"""
Args:
kwargs: {name:variable}
"""
self._vars = {}
for k, v in six.iteritems(kwargs):
... | 1,212 | 24.808511 | 88 | py |
SyNet | SyNet-master/tensorpack/tensorpack/models/_old_batch_norm.py | # -*- coding: utf-8 -*-
# File: _old_batch_norm.py
import tensorflow as tf
from tensorflow.contrib.framework import add_model_variable
from tensorflow.python.training import moving_averages
from ..tfutils.common import get_tf_version_tuple
from ..tfutils.tower import get_current_tower_context
from ..utils import logg... | 7,082 | 40.664706 | 114 | py |
SyNet | SyNet-master/tensorpack/tensorpack/models/layer_norm.py | # -*- coding: utf-8 -*-
# File: layer_norm.py
from ..compat import tfv1 as tf # this should be avoided first in model code
from ..utils.argtools import get_data_format
from ..utils.develop import log_deprecated
from .common import VariableHolder, layer_register
from .tflayer import convert_to_tflayer_args
__all__ ... | 4,188 | 30.734848 | 99 | py |
SyNet | SyNet-master/tensorpack/tensorpack/models/pool.py | # -*- coding: utf-8 -*-
# File: pool.py
import numpy as np
from ..compat import tfv1 as tf # this should be avoided first in model code
from ..utils.argtools import get_data_format, shape2d
from .common import layer_register
from .shape_utils import StaticDynamicShape
from .tflayer import convert_to_tflayer_args
__... | 4,686 | 32.719424 | 100 | py |
SyNet | SyNet-master/tensorpack/tensorpack/models/regularize.py | # -*- coding: utf-8 -*-
# File: regularize.py
import re
import tensorflow as tf
from ..compat import tfv1
from ..tfutils.common import get_tf_version_tuple
from ..tfutils.tower import get_current_tower_context
from ..utils import logger
from ..utils.argtools import graph_memoized
from .common import layer_register
... | 6,528 | 36.096591 | 119 | py |
SyNet | SyNet-master/tensorpack/tensorpack/dataflow/format.py | # -*- coding: utf-8 -*-
# File: format.py
import numpy as np
import os
import six
from ..utils import logger
from ..utils.argtools import log_once
from ..utils.serialize import loads
from ..utils.develop import create_dummy_class # noqa
from ..utils.loadcaffe import get_caffe_pb
from ..utils.timer import timed_oper... | 7,910 | 31.690083 | 102 | py |
SyNet | SyNet-master/tensorpack/tensorpack/dataflow/parallel.py | # -*- coding: utf-8 -*-
# File: parallel.py
import atexit
import pickle
import errno
import traceback
import itertools
import multiprocessing as mp
import os
import sys
import uuid
import weakref
from contextlib import contextmanager
import zmq
from six.moves import queue, range
from ..utils import logger
from ..util... | 21,575 | 38.588991 | 123 | py |
SyNet | SyNet-master/tensorpack/tensorpack/dataflow/dataset/ilsvrc.py | # -*- coding: utf-8 -*-
# File: ilsvrc.py
import numpy as np
import os
import tarfile
import tqdm
from ...utils import logger
from ...utils.fs import download, get_dataset_path, mkdir_p
from ...utils.loadcaffe import get_caffe_pb
from ...utils.timer import timed_operation
from ..base import RNGDataFlow
__all__ = ['I... | 10,381 | 32.81759 | 153 | py |
SyNet | SyNet-master/tensorpack/tensorpack/dataflow/imgaug/crop.py | # -*- coding: utf-8 -*-
# File: crop.py
import numpy as np
import cv2
from ...utils.argtools import shape2d
from ...utils.develop import log_deprecated
from .base import ImageAugmentor, ImagePlaceholder
from .transform import CropTransform, TransformList, ResizeTransform, PhotometricTransform
from .misc import Resize... | 6,674 | 36.711864 | 111 | py |
SyNet | SyNet-master/tensorpack/tensorpack/dataflow/imgaug/imgproc.py | # -*- coding: utf-8 -*-
# File: imgproc.py
import numpy as np
import cv2
from ...utils.develop import log_deprecated
from .base import PhotometricAugmentor
__all__ = ['Hue', 'Brightness', 'BrightnessScale', 'Contrast', 'MeanVarianceNormalize',
'GaussianBlur', 'Gamma', 'Clip', 'Saturation', 'Lighting', 'M... | 11,285 | 32.993976 | 114 | py |
SyNet | SyNet-master/tensorpack/tensorpack/tfutils/gradproc.py | # -*- coding: utf-8 -*-
# File: gradproc.py
import inspect
import re
from abc import ABCMeta, abstractmethod
import six
import tensorflow as tf
from ..compat import tfv1
from ..utils import logger
from .summary import add_moving_summary
from .symbolic_functions import print_stat, rms
__all__ = ['GradientProcessor',... | 8,395 | 30.683019 | 107 | py |
SyNet | SyNet-master/tensorpack/tensorpack/utils/argtools.py | # -*- coding: utf-8 -*-
# File: argtools.py
import inspect
import functools
from . import logger
__all__ = ['map_arg', 'memoized', 'memoized_method', 'graph_memoized', 'shape2d', 'shape4d',
'memoized_ignoreargs', 'log_once']
def map_arg(**maps):
"""
Apply a mapping on certain argument before ca... | 5,918 | 24.734783 | 104 | py |
SyNet | SyNet-master/tensorpack/tensorpack/utils/fs.py | # -*- coding: utf-8 -*-
# File: fs.py
import errno
import os
import tqdm
from six.moves import urllib
from . import logger
from .utils import execute_only_once
__all__ = ['mkdir_p', 'download', 'recursive_walk', 'get_dataset_path', 'normpath']
def mkdir_p(dirname):
""" Like "mkdir -p", make a dir recursively,... | 3,592 | 27.744 | 106 | py |
SyNet | SyNet-master/tensorpack/tensorpack/utils/loadcaffe.py | # -*- coding: utf-8 -*-
# File: loadcaffe.py
import numpy as np
import os
import sys
from . import logger
from .concurrency import subproc_call
from .fs import download, get_dataset_path
from .utils import change_env
__all__ = ['load_caffe', 'get_caffe_pb']
CAFFE_PROTO_URL = "https://github.com/BVLC/caffe/raw/mast... | 5,887 | 34.257485 | 92 | py |
SyNet | SyNet-master/tensorpack/tensorpack/contrib/keras.py | # -*- coding: utf-8 -*-
# File: keras.py
from contextlib import contextmanager
import six
import tensorflow as tf
from tensorflow import keras
from ..callbacks import Callback, CallbackToHook, InferenceRunner, InferenceRunnerBase, ScalarStats
from ..models.regularize import regularize_cost_from_collection
from ..tfut... | 12,196 | 40.345763 | 117 | py |
pytorch-playground | pytorch-playground-master/setup.py | from setuptools import setup, find_packages
with open("requirements.txt") as requirements_file:
REQUIREMENTS = requirements_file.readlines()
setup(
name="pytorch-playground",
version="1.0.0",
author='Aaron Chen',
author_email='aaron.xichen@gmail.com',
packages=find_packages(),
entry_points... | 447 | 21.4 | 51 | py |
pytorch-playground | pytorch-playground-master/quantize.py | import argparse
from utee import misc, quant, selector
import torch
import torch.backends.cudnn as cudnn
cudnn.benchmark =True
from collections import OrderedDict
def main():
parser = argparse.ArgumentParser(description='PyTorch SVHN Example')
parser.add_argument('--type', default='cifar10', help='|'.join(sele... | 4,928 | 48.29 | 132 | py |
pytorch-playground | pytorch-playground-master/svhn/model.py | import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import os
from collections import OrderedDict
from utee import misc
print = misc.logger.info
model_urls = {
'svhn': 'http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/svhn-f564f3d8.pth',
}
class SVHN(nn.Module):
def __init__(self, ... | 2,056 | 33.864407 | 122 | py |
pytorch-playground | pytorch-playground-master/svhn/dataset.py | import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import os
def get(batch_size, data_root='/tmp/public_dataset/pytorch', train=True, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'svhn-data'))
num_workers = kwargs.setdefault('num_wor... | 1,565 | 34.590909 | 93 | py |
pytorch-playground | pytorch-playground-master/svhn/train.py | import argparse
import os
import time
from utee import misc
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import dataset
import model
from IPython import embed
parser = argparse.ArgumentParser(description='PyTorch SVHN Example')
parser.add_argument('--ch... | 5,590 | 43.023622 | 125 | py |
pytorch-playground | pytorch-playground-master/stl10/model.py | import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import os
from utee import misc
from collections import OrderedDict
print = misc.logger.info
model_urls = {
'stl10': 'http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/stl10-866321e9.pth',
}
class SVHN(nn.Module):
def __init__(self... | 2,071 | 30.876923 | 89 | py |
pytorch-playground | pytorch-playground-master/stl10/dataset.py | import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from IPython import embed
import os
def get(batch_size, data_root='/mnt/local0/public_dataset/pytorch/', train=True, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'stl10-data'))
num_w... | 1,678 | 36.311111 | 101 | py |
pytorch-playground | pytorch-playground-master/stl10/train.py | import argparse
import os
import time
from utee import misc
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import dataset
import model
from IPython import embed
parser = argparse.ArgumentParser(description='PyTorch SVHN Example')
parser.add_argument('--ch... | 5,473 | 42.102362 | 124 | py |
pytorch-playground | pytorch-playground-master/imagenet/inception.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from utee import misc
from collections import OrderedDict
__all__ = ['Inception3', 'inception_v3']
model_urls = {
'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
}
def inception_v3(pretrained=Fals... | 11,908 | 34.549254 | 98 | py |
pytorch-playground | pytorch-playground-master/imagenet/resnet.py | import torch.nn as nn
import math
from utee import misc
from collections import OrderedDict
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytor... | 5,916 | 32.055866 | 109 | py |
pytorch-playground | pytorch-playground-master/imagenet/squeezenet.py | import math
import torch
import torch.nn as nn
from utee import misc
from collections import OrderedDict
__all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']
model_urls = {
'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
'squeezenet1_1': 'https://download.pytorch.org... | 5,022 | 35.398551 | 101 | py |
pytorch-playground | pytorch-playground-master/imagenet/vgg.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import math
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://download.pytorch.or... | 4,505 | 32.132353 | 113 | py |
pytorch-playground | pytorch-playground-master/imagenet/dataset.py | from utee import misc
import os
import os.path
import numpy as np
import joblib
def get(batch_size, data_root='/tmp/public_dataset/pytorch', train=False, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'imagenet-data'))
print("Building IMAGENET data loader, 50000 for train, 50000 f... | 1,927 | 29.603175 | 113 | py |
pytorch-playground | pytorch-playground-master/imagenet/alexnet.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['AlexNet', 'alexnet']
model_urls = {
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class AlexNet(nn.Module):
def __init__(self, num_classes=1000):
super(AlexNet, self).__init__()
self... | 1,637 | 29.333333 | 84 | py |
pytorch-playground | pytorch-playground-master/mnist/model.py | import torch.nn as nn
from collections import OrderedDict
import torch.utils.model_zoo as model_zoo
from utee import misc
print = misc.logger.info
model_urls = {
'mnist': 'http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/mnist-b07bb66b.pth'
}
class MLP(nn.Module):
def __init__(self, input_dims, n_hiddens, ... | 1,660 | 34.340426 | 85 | py |
pytorch-playground | pytorch-playground-master/mnist/dataset.py | from torch.utils.data import DataLoader
import torch
from torchvision import datasets, transforms
import os
def get(batch_size, data_root='/tmp/public_dataset/pytorch', train=True, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'mnist-data'))
kwargs.pop('input_size', None)
num_... | 1,398 | 41.393939 | 93 | py |
pytorch-playground | pytorch-playground-master/mnist/train.py | import argparse
import os
import time
from utee import misc
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import dataset
import model
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--wd', type=float, default=0.... | 5,502 | 41.992188 | 125 | py |
pytorch-playground | pytorch-playground-master/cifar/model.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from IPython import embed
from collections import OrderedDict
from utee import misc
print = misc.logger.info
model_urls = {
'cifar10': 'http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/cifar10-d875770b.pth',
'cifar100': 'http://ml.cs.tsinghua.... | 2,809 | 36.972973 | 122 | py |
pytorch-playground | pytorch-playground-master/cifar/dataset.py | import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import os
def get10(batch_size, data_root='/tmp/public_dataset/pytorch', train=True, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'cifar10-data'))
num_workers = kwargs.setdefault('nu... | 2,937 | 40.380282 | 96 | py |
pytorch-playground | pytorch-playground-master/cifar/train.py | import argparse
import os
import time
from utee import misc
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import dataset
import model
from IPython import embed
parser = argparse.ArgumentParser(description='PyTorch CIFAR-X Example')
parser.add_argument('... | 5,777 | 42.119403 | 125 | py |
pytorch-playground | pytorch-playground-master/utee/quant.py | from torch.autograd import Variable
import torch
from torch import nn
from collections import OrderedDict
import math
from IPython import embed
def compute_integral_part(input, overflow_rate):
abs_value = input.abs().view(-1)
sorted_value = abs_value.sort(dim=0, descending=True)[0]
split_idx = int(overflow... | 6,302 | 32.705882 | 124 | py |
pytorch-playground | pytorch-playground-master/utee/misc.py | import cv2
import os
import shutil
import pickle as pkl
import time
import numpy as np
import hashlib
from IPython import embed
class Logger(object):
def __init__(self):
self._logger = None
def init(self, logdir, name='log'):
if self._logger is None:
import logging
if ... | 7,772 | 32.943231 | 114 | py |
pytorch-playground | pytorch-playground-master/utee/selector.py | from utee import misc
import os
from imagenet import dataset
print = misc.logger.info
from IPython import embed
known_models = [
'mnist', 'svhn', # 28x28
'cifar10', 'cifar100', # 32x32
'stl10', # 96x96
'alexnet', # 224x224
'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn', # 224x224
'resnet18', 'resnet3... | 5,245 | 29.5 | 80 | py |
pytorch-playground | pytorch-playground-master/script/convert.py | import os
import numpy as np
import tqdm
from utee import misc
import argparse
import cv2
import joblib
parser = argparse.ArgumentParser(description='Extract the ILSVRC2012 val dataset')
parser.add_argument('--in_file', default='val224_compressed.pkl', help='input file path')
parser.add_argument('--out_root', default=... | 1,337 | 25.76 | 113 | py |
checklist | checklist-master/checklist/text_generation.py | from transformers import AutoTokenizer, AutoModelForMaskedLM
import collections
import itertools
import numpy as np
import re
from transformers import GPT2Config
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from tqdm.auto import tqdm
import torch
import torch.nn.functional as F
from pattern.en import wordnet... | 15,163 | 44.951515 | 175 | py |
checklist | checklist-master/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 4,326 | 30.355072 | 102 | py |
Semi-Online-KD | Semi-Online-KD-master/main.py | import argparse
import yaml
import os
import torch
from trainer import build_trainer
from utils.utils import save_code, save_opts
def main():
parser = argparse.ArgumentParser(description='KnowledgeDistillation')
parser.add_argument('--configs', '-c', dest='params', default='./configs/sokd.yaml')
parser.a... | 1,116 | 31.852941 | 88 | py |
Semi-Online-KD | Semi-Online-KD-master/trainer/vanilla.py | import torch.nn as nn
import torch
from tqdm import tqdm
from trainer.base_trainer import BaseTrainer
from models import model_dict
from utils.utils import count_parameters_in_MB, AverageMeter, accuracy, save_checkpoint
from dataset import get_dataloader
class Vanilla(BaseTrainer):
def __init__(self, params, exp... | 7,150 | 41.820359 | 131 | py |
Semi-Online-KD | Semi-Online-KD-master/trainer/sokd.py | import torch
from trainer.vanilla import Vanilla
from utils.utils import accuracy, AverageMeter, save_checkpoint
from kd_losses import SoftTarget
from models import model_dict
class SemiOnlineKnowledgeDistillation(Vanilla):
def __init__(self, params):
# Model
self.teacher_name = params.get('teach... | 8,214 | 46.212644 | 126 | py |
Semi-Online-KD | Semi-Online-KD-master/dataset/__init__.py | from torchvision import transforms
from torchvision import datasets
import torch
def get_dataset(data_name, data_path):
"""
Get dataset according to data name and data path.
"""
transform_train, transform_test = data_transform(data_name)
if data_name.lower() == 'cifar100':
train_dataset = ... | 1,750 | 38.795455 | 113 | py |
Semi-Online-KD | Semi-Online-KD-master/models/wrn.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from copy import deepcopy
__all__ = ['wrn']
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
... | 5,436 | 33.411392 | 116 | py |
Semi-Online-KD | Semi-Online-KD-master/kd_losses/st.py | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftTarget(nn.Module):
'''
Distilling the Knowledge in a Neural Network
https://arxiv.org/pdf/1503.02531.pdf
'''
def __init__(self,... | 563 | 23.521739 | 53 | py |
Semi-Online-KD | Semi-Online-KD-master/utils/utils.py | import logging
import colorlog
import os
import time
import shutil
import torch
import random
import numpy as np
from shutil import copyfile
def create_logger():
"""
Setup the logging environment
"""
log = logging.getLogger() # root logger
log.setLevel(logging.DEBUG)
format_str = '%(ascti... | 4,987 | 27.340909 | 85 | py |
Simplified_DMC | Simplified_DMC-master/location_dmc.py | import argparse
import os
import torch
from torch.utils.data import DataLoader
from torch import optim
import numpy as np
from data.MUSIC_dataset import MUSIC_Dataset, MUSIC_AV_Classify
from model.base_model import resnet18
from model.dmc_model import DMC_NET
from sklearn import cluster, metrics
import numpy as np
f... | 8,957 | 41.254717 | 138 | py |
Simplified_DMC | Simplified_DMC-master/data/MUSIC_dataset.py | import numpy as np
import librosa
from PIL import Image, ImageEnhance
import pickle
import random
import os
import torchvision.transforms as transforms
import json
import torch
def augment_image(image):
if(random.random() < 0.5):
image = image.transpose(Image.FLIP_LEFT_RIGHT)
enhancer = ImageEnhance.Br... | 9,784 | 42.29646 | 117 | py |
Simplified_DMC | Simplified_DMC-master/data/base_sampler.py | import torch
from torch.utils.data.sampler import Sampler
Class BaseSampler(Sampler):
def __init__(self):
super(BaseSampler,self).__init__()
def __len__(self):
def __iter__(self):
| 203 | 17.545455 | 44 | py |
Simplified_DMC | Simplified_DMC-master/model/base_model.py | import torch
import torch.nn as nn
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'r... | 9,147 | 38.261803 | 106 | py |
Simplified_DMC | Simplified_DMC-master/model/audio_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Unet(nn.Module):
def __init__(self, fc_dim=64, num_downs=5, ngf=64, use_dropout=False):
super(Unet, self).__init__()
# construct unet structure
unet_block = UnetBlock(
ngf * 8, ngf * 8, input_nc=None,
... | 3,744 | 33.675926 | 74 | py |
Simplified_DMC | Simplified_DMC-master/model/vision_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Resnet(nn.Module):
def __init__(self, original_resnet):
super(Resnet, self).__init__()
self.features = nn.Sequential(
*list(original_resnet.children())[:-1])
# for param in self.features.parameters():
... | 4,152 | 27.445205 | 69 | py |
Simplified_DMC | Simplified_DMC-master/model/base_model_v1.py | import torch
import torch.nn as nn
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'r... | 9,008 | 38.169565 | 106 | py |
Simplified_DMC | Simplified_DMC-master/model/dmc_model.py | import torch
import torch.nn as nn
import random
class Cluster_layer(nn.Module):
def __init__(self, input_dim = 512, num_cluster=2, iters=4, beta=-30, **kwargs):
super(Cluster_layer, self).__init__()
self.input_dim = input_dim
self.num_cluster = num_cluster
self.iters = iters
... | 3,338 | 35.293478 | 152 | py |
synfeal | synfeal-main/utils.py | import numpy as np
import os
import cv2
import torch
import torch
import math
import yaml
from sklearn.metrics import mean_squared_error
from torchsummary import summary
from yaml.loader import SafeLoader
from colorama import Fore
from scipy.spatial.transform import Rotation as R
from models.loss_functions import Bet... | 7,993 | 29.51145 | 146 | py |
synfeal | synfeal-main/dataset.py | import cv2
import torch.utils.data as data
import numpy as np
import torch
import os
import yaml
from PIL import Image
from yaml.loader import SafeLoader
from utils import read_pcd, matrixToXYZ, matrixToQuaternion, normalize_quat
# pytorch datasets: https://pytorch.org/tutorials/beginner/basics/data_tutorial.html
c... | 4,547 | 34.53125 | 137 | py |
synfeal | synfeal-main/models/pointnet.py | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
# this is a regularization to avoid overfitting! It adds another term to the cost function to penalize the complexity of the models.
def feature_t... | 5,796 | 34.564417 | 143 | py |
synfeal | synfeal-main/models/pointnet_classification.py | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
class STN3d(nn.Module):
def __init__(self):
super(STN3d, self).__init__()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.co... | 4,884 | 32.006757 | 128 | py |
synfeal | synfeal-main/models/loss_functions.py | import torch
from torch import nn
class BetaLoss(nn.Module):
def __init__(self, beta= 512):
super(BetaLoss, self).__init__()
self.beta = beta
#self.loss_fn = torch.nn.L1Loss() # PoseNet said that L1 was the best
self.loss_fn = torch.nn.MSELoss()
def forward(self, pred, targ):
... | 1,656 | 39.414634 | 261 | py |
synfeal | synfeal-main/models/poselstm.py |
from turtle import forward
from unicodedata import bidirectional
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
from torchvision import transforms, models
# based on: https://github.com/hazirbas... | 6,396 | 33.766304 | 120 | py |
synfeal | synfeal-main/models/posenet.py |
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
from torchvision import transforms, models
#https://github.com/youngguncho/PoseNet-Pytorch/blob/6c583a345a20ba17f67b76e54a26cf78e2811604/posenet_si... | 7,521 | 34.314554 | 116 | py |
synfeal | synfeal-main/models/depthnet.py | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import numpy as np
import torch.nn.functional as F
class CNNDepth(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepth, self).__init__() # call th... | 45,692 | 44.784569 | 152 | py |
synfeal | synfeal-main/models/hourglass.py |
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
from torchvision import transforms, models
# paper: https://arxiv.org/abs/1703.07971
# github: https://github.com/youngguncho/HourglassPose-Pytorch/blob/master/model.py
class HourglassBatch(nn.Module):... | 4,639 | 36.723577 | 120 | py |
PT-M2 | PT-M2-main/evaluate.py | import argparse
import torch
import os
from utils import load_file, load_dir, write_to_csv
from metrics import PTM2
def main():
parser = argparse.ArgumentParser("PT-M2")
parser.add_argument("--source", type=str, default="source file path")
parser.add_argument("--reference", type=str, default="reference f... | 2,143 | 43.666667 | 124 | py |
PT-M2 | PT-M2-main/utils.py | import os
import sys
import csv
import random
import numpy as np
import torch
sys.path.append("m2scorer")
def load_file(src_file):
sources = []
with open(src_file, "r", encoding="utf8") as fr:
for line in fr:
sources.append(line.strip("\n"))
return sources
def load_dir(ref_dir):
... | 945 | 23.25641 | 59 | py |
PT-M2 | PT-M2-main/bart_score.py | # %%
import torch
import torch.nn as nn
import traceback
from transformers import BartTokenizer, BartForConditionalGeneration
from typing import List
import numpy as np
class BARTScorer:
def __init__(self, device='cuda:0', max_length=1024, checkpoint='facebook/bart-large-cnn'):
# Set up model
self... | 4,219 | 36.678571 | 97 | py |
PT-M2 | PT-M2-main/bert_score/score.py | import os
import sys
import time
import pathlib
import torch
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import pandas as pd
from collections import defaultdict
from transformers import AutoTokenizer
from .utils import (
get_model,
get_tokenizer,
... | 11,254 | 35.781046 | 112 | py |
PT-M2 | PT-M2-main/bert_score/scorer.py | import os
import sys
import time
import pathlib
import torch
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import pandas as pd
import warnings
from collections import defaultdict
from transformers import AutoTokenizer
from .utils import (
get_model,
... | 11,730 | 35.095385 | 133 | py |
PT-M2 | PT-M2-main/bert_score/utils.py | import sys
import os
import torch
from math import log
from itertools import chain
from collections import defaultdict, Counter
from multiprocessing import Pool
from functools import partial
from tqdm.auto import tqdm
from torch.nn.utils.rnn import pad_sequence
from distutils.version import LooseVersion
from transform... | 28,789 | 44.553797 | 173 | py |
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