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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ttfnet | ttfnet-master/mmdet/models/anchor_heads/fcos_head.py | import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmdet.core import distance2bbox, force_fp32, multi_apply, multiclass_nms
from ..builder import build_loss
from ..registry import HEADS
from ..utils import ConvModule, Scale, bias_init_with_prob
INF = 1e8
@HEADS.register_module
class FCOSHead(n... | 16,509 | 39.366748 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/anchor_heads/guided_anchor_head.py | from __future__ import division
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmdet.core import (AnchorGenerator, anchor_inside_flags, anchor_target,
delta2bbox, force_fp32, ga_loc_target, ga_shape_target,
multi_apply, multi... | 25,226 | 39.820388 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/anchor_heads/fovea_head.py | import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmdet.core import multi_apply, multiclass_nms
from mmdet.ops import DeformConv
from ..builder import build_loss
from ..registry import HEADS
from ..utils import ConvModule, bias_init_with_prob
INF = 1e8
class FeatureAlign(nn.Module):
def ... | 16,360 | 41.167526 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/bbox_heads/bbox_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from mmdet.core import (auto_fp16, bbox_target, delta2bbox, force_fp32,
multiclass_nms)
from ..builder import build_loss
from ..losses import accuracy
from ..registry import HEADS
@HEAD... | 9,111 | 36.966667 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/bbox_heads/convfc_bbox_head.py | import torch.nn as nn
from ..registry import HEADS
from ..utils import ConvModule
from .bbox_head import BBoxHead
@HEADS.register_module
class ConvFCBBoxHead(BBoxHead):
r"""More general bbox head, with shared conv and fc layers and two optional
separated branches.
/-> cls con... | 6,943 | 36.333333 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/bbox_heads/double_bbox_head.py | import torch.nn as nn
from mmcv.cnn.weight_init import normal_init, xavier_init
from ..backbones.resnet import Bottleneck
from ..registry import HEADS
from ..utils import ConvModule
from .bbox_head import BBoxHead
class BasicResBlock(nn.Module):
"""Basic residual block.
This block is a little different from... | 5,274 | 29.847953 | 78 | py |
ttfnet | ttfnet-master/mmdet/models/shared_heads/res_layer.py | import logging
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from mmdet.core import auto_fp16
from ..backbones import ResNet, make_res_layer
from ..registry import SHARED_HEADS
@SHARED_HEADS.register_module
class ResLayer(nn.Module):
def __init__... | 2,236 | 29.643836 | 74 | py |
ttfnet | ttfnet-master/mmdet/models/utils/weight_init.py | import numpy as np
import torch.nn as nn
def xavier_init(module, gain=1, bias=0, distribution='normal'):
assert distribution in ['uniform', 'normal']
if distribution == 'uniform':
nn.init.xavier_uniform_(module.weight, gain=gain)
else:
nn.init.xavier_normal_(module.weight, gain=gain)
i... | 1,455 | 29.978723 | 71 | py |
ttfnet | ttfnet-master/mmdet/models/utils/norm.py | import torch.nn as nn
norm_cfg = {
# format: layer_type: (abbreviation, module)
'BN': ('bn', nn.BatchNorm2d),
'SyncBN': ('bn', nn.SyncBatchNorm),
'GN': ('gn', nn.GroupNorm),
# and potentially 'SN'
}
def build_norm_layer(cfg, num_features, postfix=''):
""" Build normalization layer
Args:
... | 1,684 | 29.089286 | 74 | py |
ttfnet | ttfnet-master/mmdet/models/utils/scale.py | import torch
import torch.nn as nn
class Scale(nn.Module):
"""
A learnable scale parameter
"""
def __init__(self, scale=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
def forward(self, x):
return x * self.scale
| 314 | 18.6875 | 73 | py |
ttfnet | ttfnet-master/mmdet/models/utils/conv_ws.py | import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input,
weight,
bias=None,
stride=1,
padding=0,
dilation=1,
groups=1,
eps=1e-5):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1... | 1,335 | 27.425532 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/utils/conv_module.py | import warnings
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from .conv_ws import ConvWS2d
from .norm import build_norm_layer
conv_cfg = {
'Conv': nn.Conv2d,
'ConvWS': ConvWS2d,
# TODO: octave conv
}
def build_conv_layer(cfg, *args, **kwargs):
""" Build convolution layer
... | 5,745 | 33.824242 | 78 | py |
ttfnet | ttfnet-master/mmdet/models/losses/ghm_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..registry import LOSSES
def _expand_binary_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(labels >= 1).squeeze()
if inds.numel() > 0:
bin... | 6,304 | 35.656977 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/losses/mse_loss.py | import torch.nn as nn
import torch.nn.functional as F
from ..registry import LOSSES
from .utils import weighted_loss
mse_loss = weighted_loss(F.mse_loss)
@LOSSES.register_module
class MSELoss(nn.Module):
def __init__(self, reduction='mean', loss_weight=1.0):
super().__init__()
self.reduction = ... | 632 | 23.346154 | 66 | py |
ttfnet | ttfnet-master/mmdet/models/losses/balanced_l1_loss.py | import numpy as np
import torch
import torch.nn as nn
from ..registry import LOSSES
from .utils import weighted_loss
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,
alpha=0.5,
gamma=1.5,
reduction='me... | 1,884 | 25.928571 | 73 | py |
ttfnet | ttfnet-master/mmdet/models/losses/iou_loss.py | import torch
import torch.nn as nn
from mmdet.core import bbox_overlaps
from ..registry import LOSSES
from .utils import weighted_loss
@weighted_loss
def iou_loss(pred, target, eps=1e-6):
"""IoU loss.
Computing the IoU loss between a set of predicted bboxes and target bboxes.
The loss is calculated as n... | 5,635 | 32.547619 | 82 | py |
ttfnet | ttfnet-master/mmdet/models/losses/smooth_l1_loss.py | import torch
import torch.nn as nn
from ..registry import LOSSES
from .utils import weighted_loss
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
assert beta > 0
assert pred.size() == target.size() and target.numel() > 0
diff = torch.abs(pred - target)
loss = torch.where(diff < beta, 0.5 *... | 1,288 | 27.021739 | 73 | py |
ttfnet | ttfnet-master/mmdet/models/losses/utils.py | import functools
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
reduction_enum = ... | 3,003 | 29.343434 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/losses/accuracy.py | import torch.nn as nn
def accuracy(pred, target, topk=1):
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
maxk = max(topk)
_, pred_label = pred.topk(maxk, dim=1)
pred_label = pred_label.t(... | 801 | 24.0625 | 69 | py |
ttfnet | ttfnet-master/mmdet/models/losses/focal_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from ..registry import LOSSES
from .utils import weight_reduce_loss
# This method is only for debugging
def py_sigmoid_focal_loss(pred,
target,
... | 3,636 | 31.473214 | 100 | py |
ttfnet | ttfnet-master/mmdet/models/losses/cross_entropy_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..registry import LOSSES
from .utils import weight_reduce_loss
def cross_entropy(pred, label, weight=None, reduction='mean', avg_factor=None):
# element-wise losses
loss = F.cross_entropy(pred, label, reduction='none')
# apply weigh... | 3,386 | 31.567308 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/backbones/dla.py | import os
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from mmdet.models.registry import BACKBONES
import math
import logging
import numpy as np
from os.path import join
import torch
from torch import nn
import torch.nn.functional as F
import torch.u... | 16,847 | 34.770701 | 94 | py |
ttfnet | ttfnet-master/mmdet/models/backbones/hrnet.py | import logging
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
from .resnet import BasicBlock, Bottleneck
class HRM... | 19,868 | 36.773764 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/backbones/resnet.py | import logging
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.plugins import GeneralizedAttention
from mmdet.ops import ContextBlock, DeformConv, Modu... | 18,098 | 32.331492 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/backbones/ssd_vgg.py | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import VGG, constant_init, kaiming_init, normal_init, xavier_init
from mmcv.runner import load_checkpoint
from ..registry import BACKBONES
@BACKBONES.register_module
class SSDVGG(VGG):
"""VGG Backbone network for sin... | 5,335 | 33.425806 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/backbones/resnext.py | import math
import torch.nn as nn
from mmdet.ops import DeformConv, ModulatedDeformConv
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
def __init__(self, inplanes, pl... | 8,336 | 33.882845 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/backbones/darknet.py | import os
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from collections import OrderedDict
from mmdet.models.utils import build_norm_layer
from mmdet.models.registry import BACKBONES
def common_conv2d(inplanes,
planes,
kernel,
padding,
... | 4,949 | 34.611511 | 93 | py |
ttfnet | ttfnet-master/mmdet/models/mask_heads/grid_head.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import kaiming_init, normal_init
from ..builder import build_loss
from ..registry import HEADS
from ..utils import ConvModule
@HEADS.register_module
class GridHead(nn.Module):
def __init__(self,
... | 15,429 | 41.624309 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/mask_heads/maskiou_head.py | import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import kaiming_init, normal_init
from torch.nn.modules.utils import _pair
from mmdet.core import force_fp32
from ..builder import build_loss
from ..registry import HEADS
@HEADS.register_module
class MaskIoUHead(nn.Module):
"""Mask IoU Head.
... | 7,418 | 37.842932 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/mask_heads/fcn_mask_head.py | import mmcv
import numpy as np
import pycocotools.mask as mask_util
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from mmdet.core import auto_fp16, force_fp32, mask_target
from ..builder import build_loss
from ..registry import HEADS
from ..utils import ConvModule
@HEADS.register_module... | 7,043 | 37.703297 | 79 | py |
ttfnet | ttfnet-master/mmdet/models/mask_heads/fused_semantic_head.py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import kaiming_init
from mmdet.core import auto_fp16, force_fp32
from ..registry import HEADS
from ..utils import ConvModule
@HEADS.register_module
class FusedSemanticHead(nn.Module):
r"""Multi-level fused semantic segmentation head.
in_1 -... | 3,554 | 32.224299 | 79 | py |
ttfnet | ttfnet-master/mmdet/datasets/custom.py | import os.path as osp
import mmcv
import numpy as np
from torch.utils.data import Dataset
from .pipelines import Compose
from .registry import DATASETS
@DATASETS.register_module
class CustomDataset(Dataset):
"""Custom dataset for detection.
Annotation format:
[
{
'filename': 'a.jpg'... | 5,047 | 32.653333 | 75 | py |
ttfnet | ttfnet-master/mmdet/datasets/dataset_wrappers.py | import numpy as np
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .registry import DATASETS
@DATASETS.register_module
class ConcatDataset(_ConcatDataset):
"""A wrapper of concatenated dataset.
Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
concat the group flag for... | 1,639 | 28.285714 | 78 | py |
ttfnet | ttfnet-master/mmdet/datasets/loader/sampler.py | from __future__ import division
import math
import numpy as np
import torch
from mmcv.runner.utils import get_dist_info
from torch.utils.data import DistributedSampler as _DistributedSampler
from torch.utils.data import Sampler
class DistributedSampler(_DistributedSampler):
def __init__(self, dataset, num_repli... | 5,866 | 34.557576 | 78 | py |
ttfnet | ttfnet-master/mmdet/datasets/loader/build_loader.py | import platform
from functools import partial
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from torch.utils.data import DataLoader
from .sampler import DistributedGroupSampler, DistributedSampler, GroupSampler
if platform.system() != 'Windows':
# https://github.com/pytorch/pytorch/issu... | 1,559 | 30.836735 | 78 | py |
ttfnet | ttfnet-master/mmdet/datasets/pipelines/formating.py | from collections.abc import Sequence
import mmcv
import numpy as np
import torch
from mmcv.parallel import DataContainer as DC
from ..registry import PIPELINES
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported types are: :class:`numpy.ndarray`, :class:`torch.... | 5,994 | 31.058824 | 79 | py |
ttfnet | ttfnet-master/mmdet/utils/flops_counter.py | # Modified from flops-counter.pytorch by Vladislav Sovrasov
# original repo: https://github.com/sovrasov/flops-counter.pytorch
# MIT License
# Copyright (c) 2018 Vladislav Sovrasov
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (th... | 14,351 | 32.069124 | 79 | py |
ttfnet | ttfnet-master/mmdet/ops/context_block.py | import torch
from mmcv.cnn import constant_init, kaiming_init
from torch import nn
def last_zero_init(m):
if isinstance(m, nn.Sequential):
constant_init(m[-1], val=0)
else:
constant_init(m, val=0)
class ContextBlock(nn.Module):
def __init__(self,
inplanes,
... | 3,766 | 34.87619 | 76 | py |
ttfnet | ttfnet-master/mmdet/ops/dcn/deform_pool.py | import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from . import deform_pool_cuda
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(ctx,
data,
... | 10,212 | 39.367589 | 79 | py |
ttfnet | ttfnet-master/mmdet/ops/dcn/deform_conv.py | import math
import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from . import deform_conv_cuda
class DeformConvFunction(Function):
@staticmethod
def forward(ctx,
input,
... | 12,468 | 35.890533 | 79 | py |
ttfnet | ttfnet-master/mmdet/ops/masked_conv/masked_conv.py | import math
import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from . import masked_conv2d_cuda
class MaskedConv2dFunction(Function):
@staticmethod
def forward(ctx, features, mask, weight, b... | 3,375 | 36.511111 | 79 | py |
ttfnet | ttfnet-master/mmdet/ops/sigmoid_focal_loss/sigmoid_focal_loss.py | import torch.nn as nn
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):
ctx.save_for_backward(input, target)... | 1,637 | 28.781818 | 77 | py |
ttfnet | ttfnet-master/mmdet/ops/roi_align/roi_align.py | import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from . import roi_align_cuda
class RoIAlignFunction(Function):
@staticmethod
def forward(ctx, features, rois, out_size, spatial_scale, sample_num=0):
... | 3,068 | 33.875 | 79 | py |
ttfnet | ttfnet-master/mmdet/ops/roi_align/gradcheck.py | import os.path as osp
import sys
import numpy as np
import torch
from torch.autograd import gradcheck
sys.path.append(osp.abspath(osp.join(__file__, '../../')))
from roi_align import RoIAlign # noqa: E402, isort:skip
feat_size = 15
spatial_scale = 1.0 / 8
img_size = feat_size / spatial_scale
num_imgs = 2
num_rois =... | 879 | 27.387097 | 76 | py |
ttfnet | ttfnet-master/mmdet/ops/roi_pool/roi_pool.py | import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from . import roi_pool_cuda
class RoIPoolFunction(Function):
@staticmethod
def forward(ctx, features, rois, out_size, spatial_scale):
... | 2,544 | 32.486842 | 78 | py |
ttfnet | ttfnet-master/mmdet/ops/roi_pool/gradcheck.py | import os.path as osp
import sys
import torch
from torch.autograd import gradcheck
sys.path.append(osp.abspath(osp.join(__file__, '../../')))
from roi_pool import RoIPool # noqa: E402, isort:skip
feat = torch.randn(4, 16, 15, 15, requires_grad=True).cuda()
rois = torch.Tensor([[0, 0, 0, 50, 50], [0, 10, 30, 43, 55]... | 513 | 29.235294 | 66 | py |
ttfnet | ttfnet-master/mmdet/ops/nms/nms_wrapper.py | import numpy as np
import torch
import torch.nn as nn
from . import nms_cpu, nms_cuda
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 in... | 3,959 | 34.675676 | 82 | py |
transitional-cGAN | transitional-cGAN-main/legacy.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 16,502 | 50.411215 | 154 | py |
transitional-cGAN | transitional-cGAN-main/style_mixing.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 4,891 | 40.109244 | 132 | py |
transitional-cGAN | transitional-cGAN-main/projector.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 8,990 | 41.211268 | 136 | py |
transitional-cGAN | transitional-cGAN-main/generate.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 5,338 | 40.069231 | 132 | py |
transitional-cGAN | transitional-cGAN-main/train.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 25,202 | 43.371479 | 192 | py |
transitional-cGAN | transitional-cGAN-main/calc_metrics.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 8,336 | 42.649215 | 142 | py |
transitional-cGAN | transitional-cGAN-main/training/loss.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 9,760 | 49.314433 | 160 | py |
transitional-cGAN | transitional-cGAN-main/training/augment.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 26,373 | 60.050926 | 366 | py |
transitional-cGAN | transitional-cGAN-main/training/dataset.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 8,551 | 35.084388 | 158 | py |
transitional-cGAN | transitional-cGAN-main/training/networks.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 38,364 | 50.56586 | 166 | py |
transitional-cGAN | transitional-cGAN-main/training/training_loop.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 23,347 | 50.314286 | 174 | py |
transitional-cGAN | transitional-cGAN-main/torch_utils/custom_ops.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 5,644 | 43.448819 | 146 | py |
transitional-cGAN | transitional-cGAN-main/torch_utils/training_stats.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 10,707 | 38.806691 | 118 | py |
transitional-cGAN | transitional-cGAN-main/torch_utils/persistence.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 9,708 | 37.527778 | 144 | py |
transitional-cGAN | transitional-cGAN-main/torch_utils/misc.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 10,995 | 40.338346 | 133 | py |
transitional-cGAN | transitional-cGAN-main/torch_utils/ops/bias_act.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 10,047 | 46.173709 | 185 | py |
transitional-cGAN | transitional-cGAN-main/torch_utils/ops/grid_sample_gradfix.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 3,299 | 38.285714 | 138 | py |
transitional-cGAN | transitional-cGAN-main/torch_utils/ops/conv2d_gradfix.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 7,677 | 43.900585 | 197 | py |
transitional-cGAN | transitional-cGAN-main/torch_utils/ops/upfirdn2d.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 16,287 | 41.306494 | 157 | py |
transitional-cGAN | transitional-cGAN-main/torch_utils/ops/conv2d_resample.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 7,591 | 47.356688 | 130 | py |
transitional-cGAN | transitional-cGAN-main/torch_utils/ops/fma.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 2,034 | 32.360656 | 105 | py |
transitional-cGAN | transitional-cGAN-main/metrics/metric_utils.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 12,034 | 42.136201 | 167 | py |
transitional-cGAN | transitional-cGAN-main/metrics/kernel_inception_distance.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 2,302 | 48 | 118 | py |
transitional-cGAN | transitional-cGAN-main/metrics/frechet_inception_distance.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 2,312 | 50.4 | 118 | py |
transitional-cGAN | transitional-cGAN-main/metrics/perceptual_path_length.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 5,538 | 40.962121 | 131 | py |
transitional-cGAN | transitional-cGAN-main/metrics/inception_score.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 1,874 | 47.076923 | 126 | py |
transitional-cGAN | transitional-cGAN-main/metrics/metric_main.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 5,714 | 36.598684 | 147 | py |
transitional-cGAN | transitional-cGAN-main/metrics/precision_recall.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 3,617 | 56.428571 | 159 | py |
ANTsPy | ANTsPy-master/docs/source/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# ANTsPy documentation build configuration file, created by
# sphinx-quickstart on Fri Dec 23 13:31:47 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
# aut... | 7,708 | 31.944444 | 84 | py |
T2NER | T2NER-master/t2ner/base.py | # -*- coding: utf-8 -*-
import os
import random
import logging
import numpy as np
import torch
import dataclasses
import json
from dataclasses import dataclass
from transformers.file_utils import cached_property
logger = logging.getLogger(__name__)
class TrainInferenceUtils:
@cached_property
def _set... | 5,967 | 28.98995 | 88 | py |
T2NER | T2NER-master/t2ner/evaluate.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import numpy as np
import os
import logging
from seqeval.metrics import (
f1_score,
precision_score,
recall_score,
classification_report
)
from tqdm import tqdm
from .base import TrainInferenceUtils
from .data import utils, IGNORE_INDEX
log... | 9,698 | 36.886719 | 87 | py |
T2NER | T2NER-master/t2ner/train.py | # -*- coding: utf-8 -*-
import logging
import os
import sys
import random
import json
import time
from abc import ABC, abstractmethod
from tqdm import tqdm, trange
from dataclasses import dataclass, field
from collections import OrderedDict
import torch
import torch.nn as nn
import numpy as np
from .base import Tra... | 25,359 | 35.647399 | 97 | py |
T2NER | T2NER-master/t2ner/modules/pooling.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
class Pooling(nn.Module):
def __init__(self, mode="mean", hidden_dim=None):
super().__init__()
self.mode = mode
if mode == "attn":
self.linear = nn.Linear(hidden_dim, 1, bias=False)
def forward(self, x, m... | 1,972 | 34.872727 | 75 | py |
T2NER | T2NER-master/t2ner/modules/tagger.py | # -*- coding: utf-8 -*-
import torch.nn as nn
from .crf import ChainCRF
from ..losses import MaskedSeqCrossEntropyLoss, MaskedSeqFocalLoss, MaskedSeqLDAMLoss
from ..data import IGNORE_INDEX
class Tagger(nn.Module):
def __init__(
self,
input_dim,
num_labels,
use_crf=False,
... | 2,550 | 31.291139 | 86 | py |
T2NER | T2NER-master/t2ner/modules/lstm.py | # -*- coding: utf-8 -*-
"""
Modified from https://github.com/thespectrewithin/joint_align/blob/master/feature_ner.py
"""
import torch
import torch.nn as nn
import numpy as np
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
class LSTM(nn.Module):
def __init__(
self,
... | 1,426 | 27.54 | 151 | py |
T2NER | T2NER-master/t2ner/modules/multicell_lstm.py | # -*- coding: utf-8 -*-
"""
Taken from https://github.com/jiachenwestlake/Multi-Cell_LSTM/blob/master/UDA/model/MultiCell_LSTM_compos_UDA.py
"""
import copy
import math
import torch
from torch import nn
from torch.nn import functional, init
import torch.nn.functional as F
import numpy as np
class Compute_Gate(nn.Mo... | 10,493 | 40.976 | 123 | py |
T2NER | T2NER-master/t2ner/modules/mmd.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from torch.nn import functional as F
class MaximumMeanDiscrepancy(nn.Module):
def __init__(self, kernel_type="rbf", normalize=False):
super(MaximumMeanDiscrepancy, self).__init__()
self.kernel_type = kernel_type
self.normali... | 2,900 | 33.129412 | 75 | py |
T2NER | T2NER-master/t2ner/modules/language_modeling.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from ..losses import CausalLanguageModelingLoss
class TransformersCLM(nn.Module):
def __init__(self, config):
super().__init__()
self.net = nn.Sequential(
nn.Dropout(config.hidden_dropout_prob),
nn.Linear(con... | 2,413 | 36.138462 | 88 | py |
T2NER | T2NER-master/t2ner/modules/classifier.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from .tagger import Tagger
from .grl import GradientReverseLayer, WarmStartGradientReverseLayer
class SoftmaxClassifier(nn.Module):
def __init__(self, input_dim, num_labels):
super().__init__()
self.linear = nn.Linear(input_dim, num... | 4,553 | 29.15894 | 80 | py |
T2NER | T2NER-master/t2ner/modules/transformer.py | # -*- coding: utf-8 -*-
import torch.nn as nn
class Transformer(nn.Module):
def __init__(
self,
input_dim,
hidden_dim=768,
num_layers=1,
num_heads=8,
dropout=0.1,
activation="relu"
):
super().__init__()
transformer_encoder_layer = n... | 847 | 24.69697 | 87 | py |
T2NER | T2NER-master/t2ner/modules/grl.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Function
class GradientReverseFunction(Function):
@staticmethod
def forward(ctx, input, coeff=1.):
ctx.coeff = coeff
output = input * 1.0
return output
@staticmethod... | 1,406 | 21.693548 | 100 | py |
T2NER | T2NER-master/t2ner/modules/crf.py | # -*- coding: utf-8 -*-
"""
Taken from https://github.com/shijie-wu/crosslingual-nlp/blob/master/src/crf.py
Based on https://github.com/thespectrewithin/joint_align/blob/master/crf.py with better
support of masking, allowing masking in the middle of a sentence.
"""
import torch
import torch.nn as nn
from torch.nn.p... | 7,716 | 35.923445 | 88 | py |
T2NER | T2NER-master/t2ner/modules/hyper_multi_head_attention.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
class MHAParameterGenetrator(nn.Module):
def __init__(self, input_dim, hidden_dim):
super().__init__()
self.hidden_dim = hidden_dim
self.in_hparam_w = nn.Parameter(torch.empty(input_di... | 3,129 | 31.947368 | 91 | py |
T2NER | T2NER-master/t2ner/modules/optimal_transport.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from torch.nn import functional as F
class OptimalTransport(nn.Module):
@staticmethod
def distance(batch1, batch2, dist_metric="cosine"):
if dist_metric == "cosine":
batch1 = F.normalize(batch1, p=2, dim=1)
batch2... | 3,342 | 32.767677 | 85 | py |
T2NER | T2NER-master/t2ner/modules/masked_softmax.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
class MaskedSoftmax(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits, mask=None, dim=-1, epsilon=1e-5):
# cf. https://discuss.pytorch.org/t/apply-mask-softmax/14212/15
exps = torch.exp(logi... | 566 | 26 | 71 | py |
T2NER | T2NER-master/t2ner/nets/nets.py | # -*- coding: utf-8 -*-
import torch.nn as nn
from ..modules import LSTM, Transformer
class XNetBase(nn.Module):
def __init__(self, config):
super().__init__()
def forward(self, encoded, mask=None):
raise NotImplementedError
class LSTMNet(XNetBase):
def __init__(self, co... | 1,504 | 26.87037 | 90 | py |
T2NER | T2NER-master/t2ner/models/base.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import dataclasses
from dataclasses import dataclass, field
from .. import modules
from .. import nets
from ..base import ArgumentsBase
class TransformersUtils(nn.Module):
def __init__(self):
super().__init__()
@classmethod
d... | 8,942 | 34.070588 | 100 | py |
T2NER | T2NER-master/t2ner/models/multi_token_classification.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import os
from . import base
from .. import modules
from . import HF_MODELS, bert, xlm, xlmr
from transformers import AutoConfig, PretrainedConfig
from collections import OrderedDict
class MultiTokenClassificationUtils(base.TransformersUtils):
def... | 16,162 | 35.567873 | 103 | py |
T2NER | T2NER-master/t2ner/models/token_classification.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import os
from . import base
from .. import modules
from . import HF_MODELS, bert, xlm, xlmr
from transformers import AutoConfig, PretrainedConfig
from collections import OrderedDict
class TokenClassificationUtils(base.TransformersUtils):
def __in... | 7,196 | 35.907692 | 103 | py |
T2NER | T2NER-master/t2ner/optim/lr_scheduler.py | """
Modified from https://github.com/KaiyangZhou/deep-person-reid
"""
import torch
from transformers import optimization as optim
AVAI_SCHEDS = ["constant", "linear", "cosine", "cosinehr"]
def build_lr_scheduler(optimizer, lr_scheduler="linear", **kwargs):
"""A function wrapper for building a learning rate sche... | 2,280 | 34.092308 | 88 | py |
T2NER | T2NER-master/t2ner/optim/radam.py | """
Imported from: https://github.com/LiyuanLucasLiu/RAdam
https://arxiv.org/abs/1908.03265
@article{liu2019radam,
title={On the Variance of the Adaptive Learning Rate and Beyond},
author={Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei},
jou... | 11,564 | 34.045455 | 129 | py |
T2NER | T2NER-master/t2ner/optim/optimizer.py | """
Modified from https://github.com/KaiyangZhou/deep-person-reid
"""
import warnings
import torch
import torch.nn as nn
from .radam import RAdam
from transformers.optimization import AdamW
AVAI_OPTIMS = ["adam", "adamw", "amsgrad", "sgd", "rmsprop", "radam"]
def build_optimizer(model, optim="adamw", **kwargs):
... | 4,631 | 28.503185 | 87 | py |
T2NER | T2NER-master/t2ner/data/ner.py | # -*- coding: utf-8 -*-
import os
import logging
import torch
import random
import collections
import functools
from torch.utils.data import Dataset, DataLoader
from torch.utils.data import RandomSampler, SequentialSampler
from transformers import XLMTokenizer
from . import utils
logging.basicConfig(level=logging.I... | 25,485 | 35.82948 | 110 | py |
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