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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torchcv | torchcv-master/model/pose/layers/subtree_generator.py | #!/usr/bin/env python
#-*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
# Make proposals that each consists of all possible keypoints.
import os
import math
import time
import numpy as np
import sys
import torch
import cv2
from scipy.spatial.distance import cosine
from scipy.ndimage.filters import gauss... | 9,712 | 42.556054 | 108 | py |
torchcv | torchcv-master/model/pose/loss/mse_loss.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
# Loss function for Pose Estimation.
import torch.nn as nn
class MseLoss(nn.Module):
def __init__(self, configer):
super(MseLoss, self).__init__()
self.configer = configer
self.reduction = self.confige... | 590 | 28.55 | 92 | py |
torchcv | torchcv-master/model/pose/loss/loss.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
# Loss function for Image Classification.
import torch.nn as nn
from model.pose.loss.mse_loss import MseLoss
BASE_LOSS_DICT = dict(
mse_loss=0,
)
class Loss(nn.Module):
def __init__(self, configer):
super(Loss,... | 908 | 23.567568 | 100 | py |
torchcv | torchcv-master/model/gan/tools/image_pool.py | import random
import torch
class ImagePool():
def __init__(self, pool_size):
self.pool_size = pool_size
if self.pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, images):
if self.pool_size == 0:
return images
return_images = ... | 1,046 | 30.727273 | 67 | py |
torchcv | torchcv-master/model/gan/modules/discriminator.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.model.module_helper import ModuleHelper
class NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator"""
def __init__(self, input_nc, ndf=64, n_layers=3, norm_type=None):
"""Construct a PatchGAN discriminator
... | 3,479 | 37.241758 | 124 | py |
torchcv | torchcv-master/model/gan/modules/generator.py | import functools
import torch
import torch.nn as nn
from lib.model.module_helper import ModuleHelper
class ResNetGenerator(nn.Module):
"""Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
We adapt Torch code and idea from Justin Johnson's neural style tra... | 10,344 | 46.237443 | 165 | py |
torchcv | torchcv-master/model/gan/modules/lightcnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d... | 6,959 | 32.301435 | 121 | py |
torchcv | torchcv-master/model/gan/modules/subnet_selector.py | import torch.nn.init as init
from model.gan.modules.generator import ResNetGenerator, UNetGenerator
from model.gan.modules.discriminator import NLayerDiscriminator, FCDiscriminator, PixelDiscriminator
from lib.tools.util.logger import Logger as Log
def init_weights(net, init_type='normal', init_gain=0.02):
"""In... | 4,192 | 49.518072 | 128 | py |
torchcv | torchcv-master/model/gan/nets/pix2pix.py | import torch
import torch.nn as nn
from model.gan.tools.image_pool import ImagePool
from model.gan.modules.subnet_selector import SubNetSelector
from model.gan.loss.gan_modules import GANLoss
class Pix2Pix(nn.Module):
def __init__(self, configer):
super(Pix2Pix, self).__init__()
self.configer = c... | 2,492 | 41.254237 | 118 | py |
torchcv | torchcv-master/model/gan/nets/cycle_gan.py | import torch.nn as nn
from model.gan.tools.image_pool import ImagePool
from model.gan.modules.subnet_selector import SubNetSelector
from model.gan.loss.gan_modules import GANLoss
class CycleGAN(nn.Module):
def __init__(self, configer):
super(CycleGAN, self).__init__()
# load/define networks
... | 4,996 | 47.514563 | 107 | py |
torchcv | torchcv-master/model/gan/loss/gan_modules.py | import torch
import torch.nn as nn
class CMD_K_Loss(nn.Module):
def __init__(self, K=1, p=2):
super(CMD_K_Loss, self).__init__()
assert(K>0)
assert(p>0)
self.K=K
self.p=p
def forward(self, inputA, inputB):
meanA = inputA.mean(0, keepdim=True)
meanB = in... | 4,396 | 33.622047 | 107 | py |
torchcv | torchcv-master/lib/tools/vis/det_visualizer.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
# Visualize the tensor of the detection.
import os
import numpy as np
import cv2
import torch
import time
from PIL import Image
from lib.data.transforms import DeNormalize
from lib.tools.helper.image_helper import ImageHelper
from... | 5,341 | 41.062992 | 119 | py |
torchcv | torchcv-master/lib/tools/helper/dc_helper.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
import torch
import itertools
from lib.parallel.data_container import DataContainer
class DCHelper(object):
@staticmethod
def tolist(dc):
if isinstance(dc, (list, tuple)):
return dc
if isins... | 2,109 | 34.762712 | 129 | py |
torchcv | torchcv-master/lib/tools/helper/dist_helper.py | """
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import pickle
import time
import torch
import torch.distributed as dist
class DistHelper(object):
@staticmethod
def get_world_size():
if not dist.is_available():
return 1
... | 3,932 | 31.237705 | 88 | py |
torchcv | torchcv-master/lib/tools/helper/tensor_helper.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You (youansheng@gmail.com)
# Repackage some image operations.
import torch.nn.functional as F
class TensorHelper(object):
@staticmethod
def resize(tensor, target_hw, mode=None, **kwargs):
tensor_type = tensor.type()
dim = len(tens... | 631 | 23.307692 | 78 | py |
torchcv | torchcv-master/lib/tools/helper/det_helper.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
import numpy as np
import torch
from lib.tools.util.logger import Logger as Log
try:
from lib.exts.ops.nms.nms_wrapper import nms
except ImportError:
print('DetHelper NMS ImportError.')
try:
from lib.exts.ops.nms.nms_... | 4,969 | 32.133333 | 117 | py |
torchcv | torchcv-master/lib/exts/ops/dcn/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='deform_conv',
ext_modules=[
CUDAExtension('deform_conv_cuda', [
'src/deform_conv_cuda.cpp',
'src/deform_conv_cuda_kernel.cu',
]),
CUDAExtension('deform_p... | 469 | 28.375 | 72 | py |
torchcv | torchcv-master/lib/exts/ops/dcn/functions/deform_pool.py | import torch
from torch.autograd import Function
from .. import deform_pool_cuda
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(ctx,
data,
rois,
offset,
spatial_scale,
out_size,
out_channels,... | 2,370 | 32.871429 | 78 | py |
torchcv | torchcv-master/lib/exts/ops/dcn/functions/deform_conv.py | import torch
from torch.autograd import Function
from torch.nn.modules.utils import _pair
from .. import deform_conv_cuda
class DeformConvFunction(Function):
@staticmethod
def forward(ctx,
input,
offset,
weight,
stride=1,
paddin... | 7,291 | 39.065934 | 79 | py |
torchcv | torchcv-master/lib/exts/ops/dcn/modules/deform_pool.py | from torch import nn
from ..functions.deform_pool import deform_roi_pooling
class DeformRoIPooling(nn.Module):
def __init__(self,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
... | 7,058 | 39.803468 | 79 | py |
torchcv | torchcv-master/lib/exts/ops/dcn/modules/deform_conv.py | import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from ..functions.deform_conv import deform_conv, modulated_deform_conv
class DeformConv(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
... | 5,198 | 31.905063 | 78 | py |
torchcv | torchcv-master/lib/exts/ops/sigmoid_focal_loss/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='SigmoidFocalLoss',
ext_modules=[
CUDAExtension('sigmoid_focal_loss_cuda', [
'src/sigmoid_focal_loss.cpp',
'src/sigmoid_focal_loss_cuda.cu',
]),
],
cmdcla... | 354 | 26.307692 | 67 | py |
torchcv | torchcv-master/lib/exts/ops/sigmoid_focal_loss/functions/sigmoid_focal_loss.py | import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from .. import sigmoid_focal_loss_cuda
class SigmoidFocalLossFunction(Function):
@staticmethod
def forward(ctx, input, target, gamma=2.0, alpha=0.25, reduction='mean'):
ctx.sav... | 1,388 | 31.302326 | 77 | py |
torchcv | torchcv-master/lib/exts/ops/sigmoid_focal_loss/modules/sigmoid_focal_loss.py | from torch import nn
from ..functions.sigmoid_focal_loss import sigmoid_focal_loss
class SigmoidFocalLoss(nn.Module):
def __init__(self, gamma, alpha):
super(SigmoidFocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
def forward(self, logits, targets):
assert l... | 643 | 25.833333 | 74 | py |
torchcv | torchcv-master/lib/exts/ops/roi_align/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='roi_align_cuda',
ext_modules=[
CUDAExtension('roi_align_cuda', [
'src/roi_align_cuda.cpp',
'src/roi_align_kernel.cu',
]),
],
cmdclass={'build_ext': Build... | 332 | 24.615385 | 67 | py |
torchcv | torchcv-master/lib/exts/ops/roi_align/gradcheck.py | import numpy as np
import torch
from torch.autograd import gradcheck
import os.path as osp
import sys
sys.path.append(osp.abspath(osp.join(__file__, '../../')))
from roi_align import RoIAlign # noqa: E402
feat_size = 15
spatial_scale = 1.0 / 8
img_size = feat_size / spatial_scale
num_imgs = 2
num_rois = 20
batch_in... | 866 | 27.9 | 76 | py |
torchcv | torchcv-master/lib/exts/ops/roi_align/functions/roi_align.py | from torch.autograd import Function
from .. import roi_align_cuda
class RoIAlignFunction(Function):
@staticmethod
def forward(ctx, features, rois, out_size, spatial_scale, sample_num=0):
if isinstance(out_size, int):
out_h = out_size
out_w = out_size
elif isinstance(o... | 2,113 | 33.096774 | 79 | py |
torchcv | torchcv-master/lib/exts/ops/roi_align/modules/roi_align.py | from torch.nn.modules.module import Module
from ..functions.roi_align import RoIAlignFunction
class RoIAlign(Module):
def __init__(self, out_size, spatial_scale, sample_num=0):
super(RoIAlign, self).__init__()
self.out_size = out_size
self.spatial_scale = float(spatial_scale)
sel... | 535 | 30.529412 | 74 | py |
torchcv | torchcv-master/lib/exts/ops/roi_pool/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='roi_pool',
ext_modules=[
CUDAExtension('roi_pool_cuda', [
'src/roi_pool_cuda.cpp',
'src/roi_pool_kernel.cu',
])
],
cmdclass={'build_ext': BuildExtension}... | 322 | 23.846154 | 67 | py |
torchcv | torchcv-master/lib/exts/ops/roi_pool/gradcheck.py | import torch
from torch.autograd import gradcheck
import os.path as osp
import sys
sys.path.append(osp.abspath(osp.join(__file__, '../../')))
from roi_pool import RoIPool # noqa: E402
feat = torch.randn(4, 16, 15, 15, requires_grad=True).cuda()
rois = torch.Tensor([[0, 0, 0, 50, 50], [0, 10, 30, 43, 55],
... | 500 | 30.3125 | 66 | py |
torchcv | torchcv-master/lib/exts/ops/roi_pool/functions/roi_pool.py | import torch
from torch.autograd import Function
from .. import roi_pool_cuda
class RoIPoolFunction(Function):
@staticmethod
def forward(ctx, features, rois, out_size, spatial_scale):
if isinstance(out_size, int):
out_h = out_size
out_w = out_size
elif isinstance(out_... | 1,815 | 31.428571 | 74 | py |
torchcv | torchcv-master/lib/exts/ops/roi_pool/modules/roi_pool.py | from torch.nn.modules.module import Module
from ..functions.roi_pool import roi_pool
class RoIPool(Module):
def __init__(self, out_size, spatial_scale):
super(RoIPool, self).__init__()
self.out_size = out_size
self.spatial_scale = float(spatial_scale)
def forward(self, features, roi... | 399 | 25.666667 | 74 | py |
torchcv | torchcv-master/lib/exts/ops/nms/setup.py | import os.path as osp
from setuptools import setup, Extension
import numpy as np
from Cython.Build import cythonize
from Cython.Distutils import build_ext
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
ext_args = dict(
include_dirs=[np.get_include()],
language='c++',
extra_compile_arg... | 2,678 | 30.517647 | 79 | py |
torchcv | torchcv-master/lib/exts/ops/nms/nms_wrapper.py | import numpy as np
import torch
from . import nms_cuda, nms_cpu
from .soft_nms_cpu import soft_nms_cpu
def nms(dets, iou_thr, device_id=None):
"""Dispatch to either CPU or GPU NMS implementations.
The input can be either a torch tensor or numpy array. GPU NMS will be used
if the input is a gpu tensor or... | 2,580 | 31.670886 | 79 | py |
torchcv | torchcv-master/lib/parallel/data_parallel.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
# Adapted from: https://github.com/zhanghang1989/PyTorch-Encoding/blob/master/encoding/parallel.py
import functools
import threading
import torch
import torch.cuda.comm as comm
from torch.autograd import Function
from torch.nn.par... | 4,800 | 32.573427 | 105 | py |
torchcv | torchcv-master/lib/parallel/_functions.py | import torch
from torch.nn.parallel._functions import _get_stream
def scatter(input, devices, streams=None):
"""Scatters tensor across multiple GPUs.
"""
if streams is None:
streams = [None] * len(devices)
if isinstance(input, list):
chunk_size = (len(input) - 1) // len(devices) + 1
... | 2,581 | 33.426667 | 76 | py |
torchcv | torchcv-master/lib/parallel/data_container.py | import functools
import torch
def assert_tensor_type(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
if not isinstance(args[0].data, torch.Tensor):
raise AttributeError('{} has no attribute {} for type {}'.format(
args[0].__class__.__name__, func.__name__, arg... | 2,313 | 25.597701 | 115 | py |
torchcv | torchcv-master/lib/parallel/scatter_gather.py | import torch
from torch.nn.parallel._functions import Scatter as OrigScatter
from ._functions import Scatter
from .data_container import DataContainer
def scatter(inputs, target_gpus, dim=0):
"""Scatter inputs to target gpus.
The only difference from original :func:`scatter` is to add support for
:type:... | 2,076 | 36.089286 | 78 | py |
torchcv | torchcv-master/lib/parallel/distributed.py | import torch
import torch.distributed as dist
import torch.nn as nn
from torch._utils import (_flatten_dense_tensors, _unflatten_dense_tensors,
_take_tensors)
from .scatter_gather import scatter_kwargs
class MMDistributedDataParallel(nn.Module):
def __init__(self, module, dim=0, broadc... | 1,876 | 38.104167 | 78 | py |
torchcv | torchcv-master/lib/runner/runner_helper.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
# Some runner used by main runner.
import math
import os
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn.parallel.scatter_gather import gather as torch_gather
from lib.tools.helper.dist_helper ... | 10,705 | 42.697959 | 115 | py |
torchcv | torchcv-master/lib/runner/blob_helper.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You (youansheng@gmail.com)
# Generate the inputs.
import cv2
import numpy as np
import torch
from lib.data.transforms import DeNormalize, ToTensor, Normalize
from lib.tools.helper.image_helper import ImageHelper
from lib.tools.helper.dc_helper import DCHel... | 2,287 | 35.31746 | 108 | py |
torchcv | torchcv-master/lib/runner/trainer.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
# Some runner used by main runner.
from torch.optim import SGD, Adam, lr_scheduler
from lib.tools.util.logger import Logger as Log
class Trainer(object):
@staticmethod
def init(net_params, solver_dict=None):
opt... | 6,899 | 50.879699 | 122 | py |
torchcv | torchcv-master/lib/data/collate.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You (youansheng@gmail.com)
# Adapted from https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/collate.py
import random
import collections
import torch
import torch.nn.functional as F
from torch.utils.data.dataloader import default_collate
from torc... | 9,830 | 49.415385 | 127 | py |
torchcv | torchcv-master/lib/data/transforms.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You (youansheng@gmail.com)
import numpy as np
import torch
from PIL import Image
class Normalize(object):
"""Normalize a ``torch.tensor``
Args:
inputs (torch.tensor): tensor to be normalized.
mean: (list): the mean of RGB
... | 2,823 | 23.136752 | 84 | py |
torchcv | torchcv-master/lib/model/module_helper.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You (youansheng@gmail.com)
import os
import torch
import torch.nn as nn
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
import lib.model.base as base
from lib.tools.util.logger import Logger as Log
... | 7,153 | 32.745283 | 86 | py |
torchcv | torchcv-master/lib/model/base/shufflenetv2.py | import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
__all__ = [
'ShuffleNetV2', 'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0',
'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0'
]
model_urls = {
'shufflenetv2_x0.5': 'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
... | 7,698 | 35.837321 | 114 | py |
torchcv | torchcv-master/lib/model/base/_utils.py | from collections import OrderedDict
import torch
from torch import nn
from torch.jit.annotations import Dict
class IntermediateLayerGetter(nn.ModuleDict):
"""
Module wrapper that returns intermediate layers from a model
It has a strong assumption that the modules have been registered
into the model ... | 2,604 | 37.308824 | 89 | py |
torchcv | torchcv-master/lib/model/base/inception.py | from __future__ import division
from collections import namedtuple
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.jit.annotations import Optional
from torch import Tensor
from .utils import load_state_dict_from_url
__all__ = ['Inception3', 'inception_v3', 'InceptionOutp... | 16,017 | 35.822989 | 102 | py |
torchcv | torchcv-master/lib/model/base/resnet.py | import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']
model_urls = {
'resnet18': 'https://download.pytor... | 13,737 | 38.251429 | 107 | py |
torchcv | torchcv-master/lib/model/base/squeezenet.py | import torch
import torch.nn as nn
import torch.nn.init as init
from .utils import load_state_dict_from_url
__all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']
model_urls = {
'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
'squeezenet1_1': 'https://download.pytorch.or... | 5,449 | 38.492754 | 86 | py |
torchcv | torchcv-master/lib/model/base/dfnet.py | #!/usr/bin/env python
#encoding=utf8
#########################################################################
# Author:
# Created Time: Mon Sep 23 14:22:52 2019
# File Name: dfnet_models.py
# Description:
#########################################################################
import math
import torch
import torch.... | 6,421 | 31.434343 | 78 | py |
torchcv | torchcv-master/lib/model/base/vgg.py | import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
__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... | 7,233 | 38.315217 | 113 | py |
torchcv | torchcv-master/lib/model/base/mnasnet.py | import math
import warnings
import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
__all__ = ['MNASNet', 'mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mnasnet1_3']
_MODEL_URLS = {
"mnasnet0_5":
"https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth",
"mnasnet0_75... | 10,620 | 40.007722 | 83 | py |
torchcv | torchcv-master/lib/model/base/utils.py | try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
| 151 | 29.4 | 74 | py |
torchcv | torchcv-master/lib/model/base/densenet.py | import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from .utils import load_state_dict_from_url
from torch import Tensor
from torch.jit.annotations import List
__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densene... | 11,841 | 40.697183 | 112 | py |
torchcv | torchcv-master/lib/model/base/googlenet.py | from __future__ import division
import warnings
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.jit.annotations import Optional, Tuple
from torch import Tensor
from .utils import load_state_dict_from_url
__all__ = ['GoogLeNet', 'googlenet', "GoogLeNetOu... | 10,372 | 34.892734 | 101 | py |
torchcv | torchcv-master/lib/model/base/mobilenet.py | from torch import nn
from .utils import load_state_dict_from_url
__all__ = ['MobileNetV2', 'mobilenet_v2']
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf ... | 6,338 | 34.813559 | 107 | py |
torchcv | torchcv-master/lib/model/base/deepbase_resnet.py | import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
__all__ = ['DeepbaseResNet', 'deepbase_resnet18', 'deepbase_resnet34', 'deepbase_resnet50', 'deepbase_resnet101',
'deepbase_resnet50_d8', 'deepbase_resnet50_d16', 'deepbase_resnet101_d8', 'deepbase_resnet101_d16',
'de... | 16,763 | 39.788321 | 113 | py |
torchcv | torchcv-master/lib/model/base/darknet.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
import torch.nn as nn
import math
from collections import OrderedDict
__all__ = ["DarkNet", 'darknet21', 'darknet53']
model_urls = {
'darknet21': 'https://download.pytorch.org/models/darknet53_weights_pytorch.pth',
'dark... | 3,313 | 28.855856 | 96 | py |
torchcv | torchcv-master/lib/model/base/alexnet.py | import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
__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__... | 2,102 | 30.863636 | 83 | py |
CDAL | CDAL-master/main.py | from __future__ import print_function
import os
import glob
import os.path as osp
import argparse
import sys
import h5py
import time
import datetime
import numpy as np
from tabulate import tabulate
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.optim import lr_scheduler
from torch.d... | 6,513 | 38.478788 | 121 | py |
CDAL | CDAL-master/rewards.py | import torch
import sys
import torch.nn.functional as F
import tqdm
import numpy as np
def KL_classification(a,b):
a=F.softmax(a)
b=F.softmax(b)
kl1=a*torch.log(a/b)
kl2=b*torch.log(b/a)
kl= -0.5*(torch.sum(kl1)) - 0.5*(torch.sum(kl2))
return abs(kl)
def KL_object(a,b):
kl1=a*torch.log(a/b)
kl2=b*torch.log(... | 1,975 | 25.346667 | 90 | py |
CDAL | CDAL-master/utils.py | from __future__ import absolute_import
import os
import sys
import errno
import shutil
import json
import os.path as osp
import torch
def mkdir_if_missing(directory):
if not osp.exists(directory):
try:
os.makedirs(directory)
except OSError as e:
if e.errno != errno.EEXIST:
... | 2,172 | 21.873684 | 100 | py |
CDAL | CDAL-master/models.py | import torch
import torch.nn as nn
from torch.nn import functional as F
import numpy as np
__all__ = ['DSN']
class DSN(nn.Module):
"""Deep Summarization Network"""
def __init__(self,in_dim =19,hid_dim=256, num_layers=1):
super(DSN, self).__init__()
# in_dim = in_dim*in_dim # for semantic segementation
in_dim = ... | 726 | 28.08 | 90 | py |
CDAL | CDAL-master/preprocess.py | import os
import numpy as np
import glob
import torch
import torch.nn.functional as F
features_path='./features/*'
os.system('rm -r ./features2')
os.system('mkdir ./features2')
features_path2='./features2/'
for idx,f in enumerate(sorted(glob.glob(features_path))):
feature=np.load(f)
feature=F.softmax(torch.tensor(... | 489 | 23.5 | 57 | py |
FRUGAL | FRUGAL-main/SBR/intrinsic_dimensionality/prediction.py | # from __future__ import division
import numpy as np
import glob
import os
import sys
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn import preprocessing
from sklearn import metrics
from sklearn import preprocessing
from sklearn.preprocessing import LabelBinarizer
import time
import war... | 12,802 | 30.612346 | 119 | py |
FRUGAL | FRUGAL-main/SBR/intrinsic_dimensionality/utils.py | import pdb
import os
import sys
# import subprocess, io, threading
import numpy as np
from pprint import pprint
from collections import OrderedDict
from datetime import datetime
class Logger(object):
def __init__(self, opath2logfile):
self.terminal = sys.stdout
self.log = open(opath2logfile, "w")
... | 3,629 | 31.410714 | 110 | py |
FRUGAL | FRUGAL-main/SBR/intrinsic_dimensionality/dataset.py | from __future__ import print_function, division
import os
import torch
import numpy as np
from os.path import join
from utils import ConfigBase, Logger
import pdb
class Config(ConfigBase):
def __init__(self):
super(Config, self).__init__()
self.dataset_config = "====== Simulated dataset ======"
... | 3,360 | 34.010417 | 93 | py |
FRUGAL | FRUGAL-main/static_warnings/prediction.py | # from __future__ import division
import numpy as np
import glob, os, sys
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn import preprocessing
from sklearn import metrics
from sklearn import preprocessing
from sklearn.preprocessing import LabelBinarizer
import time
import warnings
warnin... | 14,142 | 35.830729 | 123 | py |
FRUGAL | FRUGAL-main/static_warnings/utils.py | import pdb
import os
import sys
# import subprocess, io, threading
import numpy as np
from pprint import pprint
from collections import OrderedDict
from datetime import datetime
class Logger(object):
def __init__(self, opath2logfile):
self.terminal = sys.stdout
self.log = open(opath2logfile, "w")
self.write('T... | 3,129 | 27.198198 | 104 | py |
FRUGAL | FRUGAL-main/static_warnings/dataset.py | from __future__ import print_function, division
import os
import torch
import numpy as np
from os.path import join
from utils import ConfigBase, Logger
import pdb
class Config(ConfigBase):
def __init__(self):
super(Config, self).__init__()
self.dataset_config = "====== Simulated dataset ======"
... | 3,402 | 34.821053 | 93 | py |
FRUGAL | FRUGAL-main/static_warnings/dnn_5by5.py | # from __future__ import division
import numpy as np
import glob
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn import metrics
from sklearn import preprocessing
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import LabelEncoder
from statistics import mean
im... | 12,537 | 32.704301 | 116 | py |
FRUGAL | FRUGAL-main/fairness/intrinsic_dimensionality/prediction.py | # from __future__ import division
import numpy as np
import glob
import os
import sys
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn import preprocessing
from sklearn import metrics
from sklearn import preprocessing
from sklearn.preprocessing import LabelBinarizer
import time
import war... | 12,539 | 29.8867 | 119 | py |
FRUGAL | FRUGAL-main/fairness/intrinsic_dimensionality/utils.py | import pdb
import os
import sys
# import subprocess, io, threading
import numpy as np
from pprint import pprint
from collections import OrderedDict
from datetime import datetime
class Logger(object):
def __init__(self, opath2logfile):
self.terminal = sys.stdout
self.log = open(opath2logfile, "w")
... | 3,629 | 31.410714 | 110 | py |
FRUGAL | FRUGAL-main/fairness/intrinsic_dimensionality/dataset.py | from __future__ import print_function, division
import os
import torch
import numpy as np
from os.path import join
from utils import ConfigBase, Logger
import pdb
class Config(ConfigBase):
def __init__(self):
super(Config, self).__init__()
self.dataset_config = "====== Simulated dataset ======"
... | 3,360 | 34.010417 | 93 | py |
FRUGAL | FRUGAL-main/issue_close_time/utils.py | import pdb
import os
import sys
# import subprocess, io, threading
import numpy as np
from pprint import pprint
from collections import OrderedDict
from datetime import datetime
class Logger(object):
def __init__(self, opath2logfile):
self.terminal = sys.stdout
self.log = open(opath2logfile, "w")
... | 3,628 | 31.693694 | 110 | py |
FRUGAL | FRUGAL-main/issue_close_time/dataset.py | from __future__ import print_function, division
import os
import torch
import numpy as np
from os.path import join
from utils import ConfigBase, Logger
import pdb
class Config(ConfigBase):
def __init__(self):
super(Config, self).__init__()
self.dataset_config = "====== Simulated dataset ======"
... | 3,359 | 34.368421 | 93 | py |
FRUGAL | FRUGAL-main/issue_close_time/simple_bugzilla.py | from data import Data
from tensorflow.keras.utils import to_categorical
# from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import sys
import pandas as pd
import numpy as np
def preprocess(df, filename):
df.drop(['Unnamed: 0', 'bugID'], axis=1, inplace=True)
_df = df[... | 4,550 | 40 | 152 | py |
ML-Argument-Based-Computational-Persuasion | ML-Argument-Based-Computational-Persuasion-main/meat_example_experiments.py | import utils
import os
from sklearn.metrics import mean_absolute_error, accuracy_score, fowlkes_mallows_score
from sklearn.model_selection import KFold
import numpy as np
from sklearn.cluster import KMeans
from sklearn.model_selection import train_test_split
import pdb
import pandas as pd
from PlotResults import PlotRe... | 10,587 | 54.434555 | 184 | py |
ML-Argument-Based-Computational-Persuasion | ML-Argument-Based-Computational-Persuasion-main/experiments_util_prediction_parallel.py | """
Make classification experiments
"""
from DT_simulation import Simulations
import multiprocessing
from sklearn.neural_network import MLPRegressor
from multiprocessing import Process
import os
import numpy as np
import getopt, sys
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
from sklearn.cl... | 16,221 | 53.254181 | 182 | py |
PAP-FZS3D | PAP-FZS3D-main/models/dgcnn.py | """DGCNN as Backbone to extract point-level features
Adapted from https://github.com/WangYueFt/dgcnn/blob/master/pytorch/model.py
"""
import os
import sys
import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def knn(x, k):
inner = -2 * torch.matmul... | 4,193 | 31.511628 | 93 | py |
PAP-FZS3D | PAP-FZS3D-main/models/mpti_learner.py | """ MPTI with/without attention Learner for Few-shot 3D Point Cloud Semantic Segmentation
"""
import os
import torch
from torch import optim
from torch.nn import functional as F
from models.mpti import MultiPrototypeTransductiveInference
from utils.checkpoint_util import load_pretrain_checkpoint, load_model_checkpoi... | 4,115 | 41.875 | 111 | py |
PAP-FZS3D | PAP-FZS3D-main/models/proto_learner_FZ.py | """ ProtoNet with/without attention learner for Few-shot 3D Point Cloud Semantic Segmentation
"""
import torch
from torch import optim
from torch.nn import functional as F
from models.protonet_FZ import ProtoNetAlignFZ
from utils.checkpoint_util import load_pretrain_checkpoint, load_model_checkpoint
from models.gmmn... | 6,515 | 46.562044 | 149 | py |
PAP-FZS3D | PAP-FZS3D-main/models/dgcnn_new.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: Yue Wang
@Contact: yuewangx@mit.edu
@File: model.py
@Time: 2018/10/13 6:35 PM
Modified by
@Author: An Tao
@Contact: ta19@mails.tsinghua.edu.cn
@Time: 2020/3/9 9:32 PM
"""
import os
import sys
import copy
import math
import numpy as np
import torch
import to... | 18,933 | 49.625668 | 181 | py |
PAP-FZS3D | PAP-FZS3D-main/models/protonet.py | """ Prototypical Network
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.dgcnn import DGCNN
from models.attention import SelfAttention
class BaseLearner(nn.Module):
"""The class for inner loop."""
def __init__(self, in_channels, params):
super(BaseLearner, self).... | 8,795 | 42.117647 | 116 | py |
PAP-FZS3D | PAP-FZS3D-main/models/proto_learner.py | """ ProtoNet with/without attention learner for Few-shot 3D Point Cloud Semantic Segmentation
"""
import torch
from torch import optim
from torch.nn import functional as F
from models.protonet_QGPA import ProtoNetAlignQGPASR
from models.protonet import ProtoNet
from utils.checkpoint_util import load_pretrain_checkpo... | 4,405 | 42.623762 | 111 | py |
PAP-FZS3D | PAP-FZS3D-main/models/mpti.py | """ Multi-prototype transductive inference
"""
import numpy as np
import faiss
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_cluster import fps
from models.dgcnn import DGCNN
from models.attention import SelfAttention
class BaseLearner(nn.Module):
"""The class for inner loop.""... | 12,367 | 41.648276 | 124 | py |
PAP-FZS3D | PAP-FZS3D-main/models/protonet_QGPA.py | """ Prototypical Network
"""
import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.dgcnn import DGCNN
from models.dgcnn_new import DGCNN_semseg
from models.attention import SelfAttention, QGPA
from models.gmmn import GMMNnetwork
class BaseLearner(nn.Module):
"""The class f... | 15,782 | 46.396396 | 141 | py |
PAP-FZS3D | PAP-FZS3D-main/models/protonet_FZ.py | """ Prototypical Network
"""
import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.dgcnn import DGCNN
from models.dgcnn_new import DGCNN_semseg
from models.attention import SelfAttention, QGPA
from models.gmmn import GMMNnetwork
class BaseLearner(nn.Module):
"""The class f... | 16,051 | 46.916418 | 141 | py |
PAP-FZS3D | PAP-FZS3D-main/models/attention.py | """Self Attention Module
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class SelfAttention(nn.Module):
def __init__(self, in_channel, out_channel=None, attn_dropout=0.1):
"""
:param in_channel: previous layer's output feature dimension
:param out_channel: size of... | 3,228 | 34.483516 | 88 | py |
PAP-FZS3D | PAP-FZS3D-main/models/gmmn.py | import torch
from torch import nn
class GMMNnetwork(nn.Module):
def __init__(
self,
noise_dim,
embed_dim,
hidden_size,
feature_dim,
drop_out_gmm,
semantic_reconstruction=False,
):
super().__init__()
def block(in_feat, out_feat):
... | 2,456 | 27.569767 | 70 | py |
PAP-FZS3D | PAP-FZS3D-main/dataloaders/loader.py | """ Data Loader for Generating Tasks
"""
import os
import random
import math
import glob
import numpy as np
import h5py as h5
import transforms3d
from itertools import combinations
import torch
from torch.utils.data import Dataset
def sample_K_pointclouds(data_path, num_point, pc_attribs, pc_augm, pc_augm_config,
... | 17,439 | 45.506667 | 129 | py |
PAP-FZS3D | PAP-FZS3D-main/utils/checkpoint_util.py | """ Util functions for loading and saving checkpoints
"""
import os
import torch
def load_pretrain_checkpoint(model, pretrain_checkpoint_path):
# load pretrained model for point cloud encoding
model_dict = model.state_dict()
if pretrain_checkpoint_path is not None:
print('Load encoder module fro... | 1,858 | 38.553191 | 104 | py |
PAP-FZS3D | PAP-FZS3D-main/runs/fine_tune.py | """ Finetune Baseline for Few-shot 3D Point Cloud Semantic Segmentation
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from runs.eval import evaluate_metric
from runs.pre_train im... | 5,552 | 35.294118 | 121 | py |
PAP-FZS3D | PAP-FZS3D-main/runs/eval.py | """Evaluating functions for Few-shot 3D Point Cloud Semantic Segmentation
"""
import os
import numpy as np
from datetime import datetime
import torch
from torch.utils.data import DataLoader
from dataloaders.loader import MyTestDataset, batch_test_task_collate
from models.proto_learner import ProtoLearner
from model... | 4,901 | 37 | 123 | py |
PAP-FZS3D | PAP-FZS3D-main/runs/proto_train.py | """ Prototypical Network for Few-shot 3D Point Cloud Semantic Segmentation [Baseline]
"""
import os
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from runs.eval import test_few_shot
from dataloaders.loader import MyDataset, MyTestDataset, batch_test_task_colla... | 3,576 | 40.593023 | 114 | py |
PAP-FZS3D | PAP-FZS3D-main/runs/mpti_train.py | """ Attetion-aware Multi-Prototype Transductive Inference for Few-shot 3D Point Cloud Semantic Segmentation [Our method]
"""
import os
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from runs.eval import test_few_shot
from dataloaders.loader import MyDataset, M... | 3,676 | 43.841463 | 120 | py |
PAP-FZS3D | PAP-FZS3D-main/runs/pre_train.py | """ Pre-train phase
"""
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from dataloaders.loader import MyPretrainDataset
from models.dgcnn import DGCNN
f... | 8,273 | 39.165049 | 139 | py |
sporco | sporco-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# SPORCO documentation build configuration file, created by
# sphinx-quickstart on Tue Apr 7 06:02:44 2015.
#
# 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
# autogenerated file.
#
# Al... | 15,894 | 29.27619 | 134 | py |
CARLCS-CNN | CARLCS-CNN-master/CARLCS-CNN/main.py | from __future__ import print_function
import os
import tensorflow as tf
import keras.backend.tensorflow_backend as K
import sys
import random
import traceback
import pickle
from keras.optimizers import RMSprop, Adam,SGD
from scipy.stats import rankdata
import math
from math import log
from models import *
import argpa... | 15,271 | 43.524781 | 201 | py |
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