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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PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from warnings import warn
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, build_conv_layer, build_upsample_layer
from mmcv.ops.carafe import CARAFEPack
from mmcv.runner import BaseModule, ModuleList, ... | 17,394 | 41.118644 | 85 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class FusedSemanticHead(BaseModu... | 4,150 | 34.177966 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/roi_heads/mask_heads/mask_point_head.py | # Copyright (c) OpenMMLab. All rights reserved.
# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
from mmcv.r... | 13,455 | 42.830619 | 126 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/ghm_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)... | 7,923 | 36.028037 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/mse_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def mse_loss(pred, target):
"""Warpper of mse loss."""
return F.mse_loss(pred, target, reduction='none')
@LOSSES.register_module... | 1,905 | 31.862069 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/pisa_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.core import bbox_overlaps
@mmcv.jit(derivate=True, coderize=True)
def isr_p(cls_score,
bbox_pred,
bbox_targets,
rois,
sampling_results,
loss_cls,
bbox_coder,
k=2,
... | 7,216 | 38.010811 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/balanced_l1_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,... | 4,252 | 33.024 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/iou_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import warnings
import mmcv
import torch
import torch.nn as nn
from mmdet.core import bbox_overlaps
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def iou_loss(pred, target, linear=False... | 15,714 | 32.084211 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/smooth_l1_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor)... | 4,635 | 30.537415 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/gfocal_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def quality_focal_loss(pred, target, beta=2.0):
r"""Quality Focal Loss (QFL) is fr... | 7,458 | 38.257895 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/varifocal_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
@mmcv.jit(derivate=True, coderize=True)
def varifocal_loss(pred,
target,
weight=None,
... | 5,365 | 38.748148 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import mmcv
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:
... | 3,103 | 29.431373 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/seesaw_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .accuracy import accuracy
from .cross_entropy_loss import cross_entropy
from .utils import weight_reduce_loss
def seesaw_ce_loss(cls_score,
labels,
... | 10,136 | 37.543726 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/ae_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
@mmcv.jit(derivate=True, coderize=True)
def ae_loss_per_image(tl_preds, br_preds, match):
"""Associative Embedding Loss in one image.
Associative Embedd... | 3,857 | 36.096154 | 143 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/accuracy.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
@mmcv.jit(coderize=True)
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class)
targe... | 2,990 | 36.3875 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/focal_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from ..builder import LOSSES
from .utils import weight_reduce_loss
import ipdb
# This method is only for debugging
def py_sigmoid_focal_loss... | 7,589 | 40.47541 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/cross_entropy_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def cross_entropy(pred,
label,
weight=None,
reduction='mean',
a... | 9,696 | 37.480159 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/gaussian_focal_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
"""`Focal Loss <https://arxiv.org/abs/1708.0... | 3,312 | 34.623656 | 108 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/semi_focal_loss.py | import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.core import reduce_mean
from ..builder import LOSSES
from .utils import weighted_loss, weight_reduce_loss
import ipdb
def diff_focal_loss(pred, target, weight=None, beta=2.0, hard_filter=False,
reduction='mean',... | 8,302 | 39.305825 | 97 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/losses/kd_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
import ipdb
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def knowledge_distillation_kl_div_loss(pred,
... | 4,411 | 31.925373 | 89 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/hrnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule, ModuleList, Sequential
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from .resnet import BasicBlock, Bot... | 23,106 | 38.164407 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/regnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .resnet import ResNet
from .resnext import Bottleneck
@BACKBONES.register_module()
class RegNet(ResNet):
"""RegNet... | 13,605 | 37.112045 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/mobilenet_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import InvertedResidual, make_divisible
@BACKBONES.register_module()... | 7,599 | 37.383838 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/swin.py | import warnings
from collections import OrderedDict
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer, constant_init, trunc_normal_init
from mmcv.cnn.bricks.transformer import FFN, build_dropout
from mm... | 30,173 | 38.443137 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/trident_resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule
from torch.nn.modules.utils import _pair
from mmdet.models.backbones.resnet i... | 11,129 | 36.22408 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/detectors_resnext.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .detectors_resnet import Bottleneck as _Bottleneck
from .detectors_resnet import DetectoRS_ResNet
class Bottleneck(_Bottleneck):
expansion = 4
def __init_... | 3,920 | 30.620968 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
fro... | 23,838 | 34.421991 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/detectors_resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
kaiming_init)
from mmcv.runner import Sequential, load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
fr... | 12,736 | 34.980226 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/ssd_vgg.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import VGG
from mmcv.runner import BaseModule
from ..builder import BACKBONES
from ..necks import ssd_neck
@BACKBONES.register_module()
class SSDVGG(VGG, BaseModule):
"""VGG Backbone network for single-shot-detec... | 4,705 | 35.48062 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/resnext.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
expansion = 4
def __init__... | 5,712 | 35.858065 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/resnest.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule
from ..builder import BACKBONES
from ..utils import ResLayer
fro... | 10,579 | 31.755418 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/csp_darknet.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import CSPLayer
class Focus(n... | 10,544 | 36 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/hourglass.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import BasicBlock
class HourglassModule(BaseModule):
"""Hourglass Modu... | 7,494 | 32.609865 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/res2net.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import Sequential
from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .resnet impor... | 11,659 | 34.54878 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/models/backbones/darknet.py | # Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) 2019 Western Digital Corporation or its affiliates.
import warnings
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
class ResBlo... | 8,233 | 37.476636 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/datasets/custom.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from collections import OrderedDict
import mmcv
import numpy as np
from mmcv.utils import print_log
from terminaltables import AsciiTable
from torch.utils.data import Dataset
from mmdet.core import eval_map, eval_recalls
from .build... | 13,457 | 35.570652 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/datasets/dataset_wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
import collections
import copy
import math
from collections import defaultdict
import numpy as np
from mmcv.utils import build_from_cfg, print_log
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS, PIPELINES... | 15,324 | 37.602015 | 167 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/datasets/builder.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import random
from functools import partial
import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg
from torch.utils.data import DataLoader
from .samplers impo... | 5,629 | 36.284768 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/datasets/samplers/group_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import numpy as np
import torch
from mmcv.runner import get_dist_info
from torch.utils.data import Sampler
class GroupSampler(Sampler):
def __init__(self, dataset, samples_per_gpu=1):
assert hasattr(dataset, 'flag')
self.dataset = datas... | 5,384 | 35.14094 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/datasets/samplers/distributed_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset,
num_replicas=None,
rank=None,
... | 1,358 | 32.146341 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/datasets/pipelines/formating.py | # Copyright (c) OpenMMLab. All rights reserved.
from collections.abc import Sequence
import mmcv
import numpy as np
import torch
from mmcv.parallel import DataContainer as DC
from ..builder import PIPELINES
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported ty... | 12,044 | 31.909836 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/utils/contextmanagers.py | # Copyright (c) OpenMMLab. All rights reserved.
import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
@contextlib.asynccontextmanager
async def comple... | 4,125 | 32.544715 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/mmdet/utils/profiling.py | # Copyright (c) OpenMMLab. All rights reserved.
import contextlib
import sys
import time
import torch
if sys.version_info >= (3, 7):
@contextlib.contextmanager
def profile_time(trace_name,
name,
enabled=True,
stream=None,
end... | 1,336 | 31.609756 | 73 | py |
PseCo | PseCo-master/configs/supervised_baseline/base.py | mmdet_base = "../../thirdparty/mmdetection/configs/_base_"
_base_ = [
f"{mmdet_base}/models/faster_rcnn_r50_fpn.py",
f"{mmdet_base}/datasets/coco_detection.py",
f"{mmdet_base}/schedules/schedule_1x.py",
f"{mmdet_base}/default_runtime.py",
]
model = dict(
backbone=dict(
norm_cfg=dict(require... | 3,220 | 26.767241 | 96 | py |
PseCo | PseCo-master/configs/PseCo/base.py | mmdet_base = "../../thirdparty/mmdetection/configs/_base_"
_base_ = [
f"{mmdet_base}/models/faster_rcnn_r50_fpn.py",
f"{mmdet_base}/datasets/coco_detection.py",
f"{mmdet_base}/schedules/schedule_1x.py",
f"{mmdet_base}/default_runtime.py",
]
model = dict(
backbone=dict(
norm_cfg=dict(require... | 7,897 | 28.580524 | 96 | py |
RLNLocalization | RLNLocalization-main/statistic_test.py | # ========================================
# Perform alignment based on Prior Library
# ========================================
import os
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from PIL import Image
from scipy.ndimage import center_of_mass
from medpy.metric import dc
from torchvision... | 5,288 | 37.326087 | 120 | py |
RLNLocalization | RLNLocalization-main/utils.py | import os
import torch
import numpy as np
from matplotlib import pyplot as plt
from medpy.metric import dc
from dipy.align import imaffine
from dipy.align import transforms
def check_dir(path):
if not os.path.exists(path):
os.makedirs(path, exist_ok=True)
def set_device(cuda):
"""
Set the torch ... | 4,994 | 26.75 | 129 | py |
RLNLocalization | RLNLocalization-main/prior_localize.py | import os
import numpy as np
from PIL import Image
from matplotlib import pyplot as plt
from torchvision import transforms as T
from scipy.ndimage import center_of_mass
PRIOR_PATH = 'PRIOR_right'
SAVE_PATH = 'Prior_Results_right'
GT_PATH = '../Dataset/Data'
mask_transform = T.Compose([
T.Resize((256, 256), Image.... | 3,704 | 35.683168 | 119 | py |
RLNLocalization | RLNLocalization-main/models/Regress/model.py | import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
from utils import tensor2array
from medpy.metric import dc
class conv_block(nn.Module):
"""
Convolution Block
"""
def __init__(self, in_ch, out_ch):
super(conv_block, self).__init__()
self.conv ... | 5,554 | 26.775 | 85 | py |
RLNLocalization | RLNLocalization-main/models/AutoEncoder/model.py | import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
from utils import tensor2array
from medpy.metric import dc
class conv_block(nn.Module):
"""
Convolution Block
"""
def __init__(self, in_ch, out_ch):
super(conv_block, self).__init__()
self.conv ... | 3,901 | 27.071942 | 85 | py |
RLNLocalization | RLNLocalization-main/op/data_op.py | import os
import torch
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from scipy.ndimage import center_of_mass
from random import uniform
from torch.utils.data import Dataset
from torchvision import transforms as T
from torchvision.transforms.functional import crop, to_tensor
def load_list(t... | 9,157 | 36.076923 | 108 | py |
RLNLocalization | RLNLocalization-main/op/run_op.py | import os
import torch
from PIL import Image
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from op.data_op import RLNDataset, RLNRefineDataset, RLNRriorDataset
from utils import Recorder, set_device, tensor2array
import os
import torch
import numpy as np
from medpy.metric import dc
from dateti... | 13,171 | 34.6 | 112 | py |
Wasserstein2Barycenters | Wasserstein2Barycenters-main/src/distributions.py | import torch
import numpy as np
from scipy.linalg import sqrtm
import sklearn.datasets
import random
def symmetrize(X):
return np.real((X + X.T) / 2)
class Sampler:
def __init__(
self, device='cuda',
requires_grad=False,
):
self.device = device
self.requires_grad = requires... | 16,356 | 33.006237 | 119 | py |
Wasserstein2Barycenters | Wasserstein2Barycenters-main/src/benchmarks.py | import torch
import torch.nn as nn
import numpy as np
from scipy.stats import ortho_group
from scipy.linalg import sqrtm
from .tools import calculate_frechet_distance
from tqdm import tqdm_notebook as tqdm
from . import distributions
def symmetrize(X):
return np.real((X + X.T) / 2)
def get_barycenter_cov(covs, al... | 10,558 | 40.735178 | 111 | py |
Wasserstein2Barycenters | Wasserstein2Barycenters-main/src/plotters.py | import numpy as np
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import torch
import gc
def plot_rgb_cloud(cloud, ax):
colors = np.clip(cloud, 0, 1)
ax.scatter(cloud[:, 0], cloud[:, 1], cloud[:, 2], c=colors)
ax.set_xlabel('Red'); ax.set_ylabe... | 6,593 | 37.115607 | 112 | py |
Wasserstein2Barycenters | Wasserstein2Barycenters-main/src/tools.py | import os, sys
import torchvision.datasets as datasets
import numpy as np
import pandas as pd
from tqdm import tqdm
from scipy.linalg import sqrtm
import os, sys
import argparse
import collections
from scipy.io import savemat
from tqdm import trange
from torchvision.utils import save_image
from torch.utils.data import... | 5,715 | 34.725 | 106 | py |
Wasserstein2Barycenters | Wasserstein2Barycenters-main/src/layers.py | import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
class ConvexQuadratic(nn.Module):
'''Convex Quadratic Layer'''
__constants__ = ['in_features', 'out_features', 'quadratic_decomposed', 'weight', 'bias']
def __init__(self, in_features, out_features, bias=T... | 2,893 | 33.047059 | 105 | py |
Wasserstein2Barycenters | Wasserstein2Barycenters-main/src/icnn.py | import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
from .layers import ConvexQuadratic, Conv2dConvexQuadratic
class DenseICNN(nn.Module):
'''Fully Conncted ICNN with input-quadratic skip connections'''
def __init__(
self, in_dim,
hidden_la... | 8,460 | 37.990783 | 115 | py |
SSC | SSC-master/main.py | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
python script to train the SSC model
---
Jie Li
jieli_cn@163.com
Nanjing University of Science and Technology
Aug 25, 2019
"""
from utils.seed import seed_torch
import os
import torch
import argparse
import numpy as np
from tqdm import tqdm
from torch.autograd impor... | 10,137 | 39.552 | 142 | py |
SSC | SSC-master/test.py | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
python script to evaluate the SSC model
---
Jie Li
jieli_cn@163.com
Nanjing University of Science and Technology
Aug 25, 2019
"""
import os
import torch
import argparse
import datetime
from dataloaders import make_data_loader
from models import make_model
from main i... | 2,574 | 33.333333 | 120 | py |
SSC | SSC-master/config.py | import numpy as np
import torch
class Path(object):
@staticmethod
def db_root_dir(dataset):
if dataset == 'nyu':
# folder that contains dataset/.
return {'train': '/home/mcheem/data/datasets/NYU_SSC/NYUtrain_npz',
'val': '/home/mcheem/data/datasets/NYU_SSC/NY... | 1,795 | 36.416667 | 93 | py |
SSC | SSC-master/infer_ros.py | #!/usr/bin/env python3
from utils.seed import seed_torch
import os
# Network dependencies
import torch
import argparse
import numpy as np
from torch.autograd import Variable
# ROS dependencies
import rospy
from sensor_msgs.msg import Image
import tf.transformations as tr
import tf
from cv_bridge import CvBridge
# lo... | 6,176 | 35.550296 | 253 | py |
SSC | SSC-master/infer.py | from utils.seed import seed_torch
import os
import torch
import argparse
import numpy as np
from pathlib import Path
import imageio
import glob
from tqdm import tqdm
from torch.autograd import Variable
import datetime
from models import make_model
import config
import VoxelUtils as vu
from utils import utils
parser... | 4,225 | 37.072072 | 237 | py |
SSC | SSC-master/models/PALNet.py | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
PALNet
jieli_cn@163.com
"""
import torch
import torch.nn as nn
from torch.nn import functional as F
from .projection_layer import Project2Dto3D
# ----------------------------------------------------------------------
# takes the depth and fTSDF as inputs
class SSC_P... | 6,635 | 32.346734 | 95 | py |
SSC | SSC-master/models/DDR.py | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
DDR
jieli_cn@163.com
"""
import torch
import torch.nn as nn
from torch.nn import functional as F
# ----------------------------------------------------------------------
class BasicDDR2d(nn.Module):
def __init__(self, c, k=3, dilation=1, residual=True):
s... | 5,848 | 36.254777 | 120 | py |
SSC | SSC-master/models/projection_layer.py | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
Project feature tensers of 2D image to 3D space
jieli_cn@163.com
"""
import torch.nn as nn
from torch_scatter import scatter_max
class Project2Dto3D(nn.Module):
def __init__(self, w=240, h=144, d=240):
super(Project2Dto3D, self).__init__()
self.w ... | 922 | 27.84375 | 89 | py |
SSC | SSC-master/models/AICNet.py | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
AICNet
jieli_cn@163.com
"""
import torch
import torch.nn as nn
from torch.nn import functional as F
from .projection_layer import Project2Dto3D
from .DDR import BottleneckDDR2d, BottleneckDDR3d, DownsampleBlock3d
class BasicAIC3d(nn.Module):
def __init__(self, ch... | 8,927 | 40.142857 | 124 | py |
SSC | SSC-master/models/GRFNet.py | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
GRFNet
jieli_cn@163.com
"""
import torch
import torch.nn as nn
from torch.nn import functional as F
from .projection_layer import Project2Dto3D
from .DDR import DDR_ASPP3d
from .DDR import BottleneckDDR2d, BottleneckDDR3d, DownsampleBlock3d
class Conv3dGRUCell(nn.Mod... | 7,381 | 39.119565 | 113 | py |
SSC | SSC-master/models/DDRNet.py | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
DDRNet
jieli_cn@163.com
"""
import torch
import torch.nn as nn
from .projection_layer import Project2Dto3D
from .DDR import DDR_ASPP3d
from .DDR import BottleneckDDR2d, BottleneckDDR3d, DownsampleBlock3d
# DDRNet
# ----------------------------------------------------... | 4,722 | 36.784 | 101 | py |
SSC | SSC-master/dataloaders/dataloader.py | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
Class of pytorch data loader
---
Jie Li
jieli_cn@163.com
Nanjing University of Science and Technology
Aug 10, 2019
"""
import glob
import imageio
import numpy as np
import numpy.matlib
import torch.utils.data
from pathlib import Path
from torchvision import transforms... | 24,291 | 44.920605 | 180 | py |
SSC | SSC-master/dataloaders/__init__.py |
from .dataloader import NYUDataset
from config import Path
from torch.utils.data import DataLoader
def make_data_loader(args, **kwargs):
if args.dataset:
base_dirs = Path.db_root_dir(args.dataset)
print('Training data:{}'.format(base_dirs['train']))
train_loader = DataLoader(
... | 883 | 28.466667 | 88 | py |
SSC | SSC-master/utils/seed.py |
import numpy as np
import scipy.misc
import os
import random
import torch
def seed_torch(seed=3055):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # if you are using mult... | 416 | 18.857143 | 66 | py |
Unilm | Unilm-master/conver_torch_to_tf.py | """
@author: liucong
@contact: logcongcong@gmail.com
@time: 2020/7/27 13:39
"""
from convert_unilm_pytorch_checkpoint_to_original_tf import convert_pytorch_checkpoint_to_tf
from modeling_unilm import UnilmForLM
import os
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = "-1"
def f(... | 626 | 25.125 | 92 | py |
Unilm | Unilm-master/modeling_unilm.py | # coding=utf-8
"""PyTorch UniLM model. """
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import math
import logging
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
fr... | 31,656 | 44.095442 | 238 | py |
Unilm | Unilm-master/run_seq2seq.py | # coding=utf-8
import os
import logging
import glob
import math
import json
import argparse
import random
from pathlib import Path
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import RandomSampler
from torch.utils.data.distributed import DistributedSampler
import torch.distribute... | 19,869 | 50.343669 | 330 | py |
Unilm | Unilm-master/decode_seq2seq.py | # coding=utf-8
# The MIT License (MIT)
# Copyright (c) Microsoft Corporation
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# t... | 12,437 | 46.473282 | 225 | py |
Unilm | Unilm-master/convert_unilm_pytorch_checkpoint_to_original_tf.py | """
@author: liucong
@contact: logcongcong@gmail.com
@time: 2020/7/27 13:53
"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from modeling_unilm import UnilmForLM
def convert_pytorch_checkpoint_to_tf(model: UnilmForLM, ckpt_dir: str, model_name: str):
tensors_to_transpose =... | 3,081 | 35.258824 | 118 | py |
Unilm | Unilm-master/utils_seq2seq.py | # coding=utf-8
from random import randint, shuffle, choice
from random import random as rand
import math
import numpy as np
import torch
import torch.utils.data
def get_random_word(vocab_words):
i = randint(0, len(vocab_words)-1)
return vocab_words[i]
def batch_list_to_batch_tensors(batch):
batch_ten... | 33,772 | 38.134415 | 175 | py |
DoTra | DoTra-main/latAEModels.py | #Source code for 'Domain Transformer: Predicting Samples of Unseen, Future Domains' by Johannes Schneider, IJCNN, 2022, https://arxiv.org/abs/2106.06057; Github; https://github.com/JohnTailor/DoTra
#Licence: Use it however you like, but cite the paper :-)
#Models for Cycle-GAN on encoded data
import torch.nn as nn
i... | 2,462 | 31.84 | 198 | py |
DoTra | DoTra-main/main.py | # Source code for 'Domain Transformer: Predicting Samples of Unseen, Future Domains' by Johannes Schneider, IJCNN, 2022, https://arxiv.org/abs/2106.06057; Github; https://github.com/JohnTailor/DoTra
# Licence: Use it however you like, but cite the paper :-)
#Main routine to train models
import sklearn
import torch
... | 7,497 | 48.006536 | 366 | py |
DoTra | DoTra-main/optCycEncoded.py | #Source code for 'Domain Transformer: Predicting Samples of Unseen, Future Domains' by Johannes Schneider, IJCNN, 2022, https://arxiv.org/abs/2106.06057; Github; https://github.com/JohnTailor/DoTra
#Licence: Use it however you like, but cite the paper :-)
#Based on https://github.com/yunjey/mnist-svhn-transfer/
imp... | 14,096 | 47.947917 | 264 | py |
DoTra | DoTra-main/doTraModel.py | #Source code for 'Domain Transformer: Predicting Samples of Unseen, Future Domains' by Johannes Schneider, IJCNN, 2022, https://arxiv.org/abs/2106.06057; Github; https://github.com/JohnTailor/DoTra
#Licence: Use it however you like, but cite the paper :-)
import torch.nn as nn
import torch.nn.functional as F
import t... | 5,990 | 41.792857 | 198 | py |
DoTra | DoTra-main/classifierModels.py | #Source code for 'Domain Transformer: Predicting Samples of Unseen, Future Domains' by Johannes Schneider, IJCNN, 2022, https://arxiv.org/abs/2106.06057; Github; https://github.com/JohnTailor/DoTra
#Licence: Use it however you like, but cite the paper :-)
#Classifier models
import numpy as np
import torch
import tor... | 4,359 | 43.948454 | 246 | py |
DoTra | DoTra-main/AEModels.py | #Source code for 'Domain Transformer: Predicting Samples of Unseen, Future Domains' by Johannes Schneider, IJCNN, 2022, https://arxiv.org/abs/2106.06057; Github; https://github.com/JohnTailor/DoTra
#Licence: Use it however you like, but cite the paper :-)
#Autoencoder models and training
import numpy as np
import pi... | 10,633 | 49.398104 | 203 | py |
DoTra | DoTra-main/trainClassifiers.py | #Source code for 'Domain Transformer: Predicting Samples of Unseen, Future Domains' by Johannes Schneider, IJCNN, 2022, https://arxiv.org/abs/2106.06057; Github; https://github.com/JohnTailor/DoTra
#Licence: Use it however you like, but cite the paper :-)
#Training of classifiers (and also DoTra on paired samples)
i... | 11,382 | 45.461224 | 258 | py |
DoTra | DoTra-main/dutils.py | #Source code for 'Domain Transformer: Predicting Samples of Unseen, Future Domains' by Johannes Schneider, IJCNN, 2022, https://arxiv.org/abs/2106.06057; Github; https://github.com/JohnTailor/DoTra
#Licence: Use it however you like, but cite the paper :-)
from scipy import ndimage
from torch.utils.data import Dataset... | 2,472 | 44.796296 | 198 | py |
OpenFWI | OpenFWI-main/pytorch_ssim.py | # From https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) fo... | 2,722 | 35.306667 | 104 | py |
OpenFWI | OpenFWI-main/test.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 10,383 | 42.814346 | 156 | py |
OpenFWI | OpenFWI-main/gan_train.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 16,662 | 43.553476 | 128 | py |
OpenFWI | OpenFWI-main/network.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 14,861 | 45.15528 | 167 | py |
OpenFWI | OpenFWI-main/vis.py | import os
import torch
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
# Load colormap for velocity map visualization
rainbow_cmap = ListedColormap(np.load('rainbow256.npy'))
def plot_velocity(output, target, path, vmin=None, vmax... | 4,324 | 38.318182 | 89 | py |
OpenFWI | OpenFWI-main/utils.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 17,006 | 34.804211 | 105 | py |
OpenFWI | OpenFWI-main/dataset.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 3,920 | 37.441176 | 129 | py |
OpenFWI | OpenFWI-main/scheduler.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 2,380 | 35.075758 | 105 | py |
OpenFWI | OpenFWI-main/train.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 14,469 | 41.558824 | 122 | py |
OpenFWI | OpenFWI-main/transforms.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 8,236 | 29.394834 | 105 | py |
hurricast | hurricast-master/utils/data_processing.py | from __future__ import print_function
import pandas as pd
import math
import torch
import numpy as np
import warnings
warnings.filterwarnings('ignore')
dtype = torch.float
device = torch.device("cpu")
#allows to keep only specific columns
def select_data(data):
return data[['SID', 'NUMBER', 'ISO_TIME', 'LAT',... | 12,412 | 30.585242 | 190 | py |
DialogID | DialogID-main/src/auto_text_classifier/atc/models/hf_base.py |
import torch
import torch.nn.functional as F
import torch.nn as nn
import os
import copy
import numpy as np
import pandas as pd
import random
import datetime
from tqdm import tqdm, trange
from transformers import BertConfig
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from t... | 25,472 | 38.493023 | 131 | py |
DialogID | DialogID-main/src/auto_text_classifier/atc/models/base_model.py | import numpy as np
from atc.utils.data_utils import init_dir, load_df, DataGet
from atc.utils.metrics_utils import get_model_metrics, get_multi_class_report,refit_map
import torch
import random
import os
import pandas as pd
import traceback
from tqdm import tqdm
import time
class BaseModel():
def __init__(self, ... | 6,316 | 32.247368 | 87 | py |
DialogID | DialogID-main/src/auto_text_classifier/atc/models/aml.py | import os
import copy
import time
import pandas as pd
import numpy as np
from tqdm import tqdm
from keras.layers import Lambda, Dense
from atc.utils.data_utils import init_dir
from atc.models.base_model import BaseModel
from atc.utils.metrics_utils import get_model_metrics,get_multi_class_report
from atc.utils.data_uti... | 7,288 | 38.61413 | 98 | py |
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