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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wetectron | wetectron-master/wetectron/data/transforms/build.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from . import transform... | 2,340 | 30.635135 | 82 | py |
wetectron | wetectron-master/wetectron/data/transforms/transforms.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import random
import numpy as np
imp... | 4,810 | 30.860927 | 83 | py |
wetectron | wetectron-master/wetectron/modeling/cdb.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
def sample_gumbel(shape, device, eps=1e... | 7,981 | 32.679325 | 112 | py |
wetectron | wetectron-master/wetectron/modeling/matcher.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class Matcher(object):
"""
This class assigns to each predicted "element" (e.g., a box) a ground-truth
element. Each predicted element will have exactly zero or one matches; each
ground-truth element may be assigned t... | 5,129 | 44.39823 | 88 | py |
wetectron | wetectron-master/wetectron/modeling/make_layers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Miscellaneous utility functions
"""
import torch
from torch import nn
from torch.nn import functional as F
from wetectron.config import cfg
from wetectron.layers import Conv2d
from wetectron.modeling.poolers import Pooler
def get_group_gn(di... | 3,530 | 27.707317 | 78 | py |
wetectron | wetectron-master/wetectron/modeling/utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Miscellaneous utility functions
"""
import torch
def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
assert isinstance(tensors, (list, tuple))
if ... | 400 | 22.588235 | 97 | py |
wetectron | wetectron-master/wetectron/modeling/poolers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from wetectron.layers import ROIAlign, ROIPool
from wetectron.config import cfg
from .utils import cat
class LevelMapper(object):
"""Determine which FPN level each RoI in a s... | 4,915 | 33.865248 | 93 | py |
wetectron | wetectron-master/wetectron/modeling/balanced_positive_negative_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class BalancedPositiveNegativeSampler(object):
"""
This class samples batches, ensuring that they contain a fixed proportion of positives
"""
def __init__(self, batch_size_per_image, positive_fraction):
"""
... | 2,748 | 38.84058 | 92 | py |
wetectron | wetectron-master/wetectron/modeling/box_coder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import torch
class BoxCoder(object):
"""
This class encodes and decodes a set of bounding boxes into
the representation used for training the regressors.
"""
def __init__(self, weights, bbox_xform_clip=math.log(1... | 3,367 | 34.083333 | 86 | py |
wetectron | wetectron-master/wetectron/modeling/backbone/resnet.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Variant of the resnet module that takes cfg as an argument.
Example usage. Strings may be specified in the config file.
model = ResNet(
"StemWithFixedBatchNorm",
"BottleneckWithFixedBatchNorm",
"ResNet50StagesTo4",
... | 14,137 | 30.278761 | 85 | py |
wetectron | wetectron-master/wetectron/modeling/backbone/vgg16.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
from __future__ import absolute_import, division, print_function, unicode_literals
from collections import ... | 5,327 | 34.758389 | 114 | py |
wetectron | wetectron-master/wetectron/modeling/backbone/fbnet_builder.py | """
FBNet model builder
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import logging
import math
from collections import OrderedDict
import torch
import torch.nn as nn
from wetectron.layers import (
BatchNorm2d,
Conv2d,
FrozenBatchNorm2d,
interpola... | 24,946 | 29.056627 | 88 | py |
wetectron | wetectron-master/wetectron/modeling/backbone/fbnet.py | from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import json
import logging
from collections import OrderedDict
from . import (
fbnet_builder as mbuilder,
fbnet_modeldef as modeldef,
)
import torch.nn as nn
from wetectron.modeling import registry
from wetectron.mo... | 7,818 | 29.905138 | 83 | py |
wetectron | wetectron-master/wetectron/modeling/backbone/backbone.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict
... | 2,953 | 34.166667 | 81 | py |
wetectron | wetectron-master/wetectron/modeling/backbone/fpn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
class FPN(nn.Module):
"""
Module that adds FPN on top of a list of feature maps.
The feature maps are currently supposed to be in increasing depth
order, and must b... | 3,939 | 38.4 | 86 | py |
wetectron | wetectron-master/wetectron/modeling/detector/generalized_rcnn.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Implements the Generalized R-CNN... | 3,729 | 34.188679 | 106 | py |
wetectron | wetectron-master/wetectron/modeling/rpn/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from wetectron.modeling.box_coder import BoxCoder
from wetectron.structures.bounding_box import BoxList
from wetectron.structures.boxlist_ops import cat_boxlist
from wetectron.structures.boxlist_ops import boxlist_nms
from wetectron.s... | 7,713 | 36.2657 | 87 | py |
wetectron | wetectron-master/wetectron/modeling/rpn/anchor_generator.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import numpy as np
import torch
from torch import nn
from wetectron.structures.bounding_box import BoxList
class BufferList(nn.Module):
"""
Similar to nn.ParameterList, but for buffers
"""
def __init__(self, buffers... | 9,938 | 33.272414 | 88 | py |
wetectron | wetectron-master/wetectron/modeling/rpn/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
This file contains specific functions for computing losses on the RPN
file
"""
import torch
from torch.nn import functional as F
from .utils import concat_box_prediction_layers
from ..balanced_positive_negative_sampler import BalancedPositiv... | 5,732 | 35.28481 | 87 | py |
wetectron | wetectron-master/wetectron/modeling/rpn/utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Utility functions minipulating the prediction layers
"""
from ..utils import cat
import torch
def permute_and_flatten(layer, N, A, C, H, W):
layer = layer.view(N, -1, C, H, W)
layer = layer.permute(0, 3, 4, 1, 2)
layer = layer.re... | 1,679 | 35.521739 | 80 | py |
wetectron | wetectron-master/wetectron/modeling/rpn/rpn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from wetectron.modeling import registry
from wetectron.modeling.box_coder import BoxCoder
from wetectron.modeling.rpn.retinanet.retinanet import build_retinanet
from .loss import ma... | 7,580 | 35.447115 | 88 | py |
wetectron | wetectron-master/wetectron/modeling/rpn/retinanet/inference.py | import torch
from ..inference import RPNPostProcessor
from ..utils import permute_and_flatten
from wetectron.modeling.box_coder import BoxCoder
from wetectron.modeling.utils import cat
from wetectron.structures.bounding_box import BoxList
from wetectron.structures.boxlist_ops import cat_boxlist
from wetectron.structu... | 6,869 | 34.230769 | 79 | py |
wetectron | wetectron-master/wetectron/modeling/rpn/retinanet/loss.py | """
This file contains specific functions for computing losses on the RetinaNet
file
"""
import torch
from torch.nn import functional as F
from ..utils import concat_box_prediction_layers
from wetectron.layers import smooth_l1_loss
from wetectron.layers import SigmoidFocalLoss
from wetectron.modeling.matcher import ... | 3,421 | 30.685185 | 83 | py |
wetectron | wetectron-master/wetectron/modeling/rpn/retinanet/retinanet.py | import math
import torch
import torch.nn.functional as F
from torch import nn
from .inference import make_retinanet_postprocessor
from .loss import make_retinanet_loss_evaluator
from ..anchor_generator import make_anchor_generator_retinanet
from wetectron.modeling.box_coder import BoxCoder
class RetinaNetHead(torc... | 5,292 | 33.594771 | 88 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/roi_heads.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .box_head.box_head import build_roi_box_head
from .mask_head.mask_head import build_roi_mask_head
from .keypoint_head.keypoint_head import build_roi_keypoint_head
from .weak_head.weak_head import build_roi_weak_head
class Combi... | 3,515 | 40.364706 | 96 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/weak_head/inference.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
import torch
import torch.nn.functional as F
from torch import nn
from wetectron.structures.bounding_box im... | 5,817 | 37.786667 | 88 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/weak_head/roi_sampler.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
import torch
from torch.nn import functional as F
from wetectron.modeling.box_coder import BoxCoder
from we... | 10,628 | 44.618026 | 108 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/weak_head/roi_weak_predictors.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
import torch
from torch import nn
import torch.nn.functional as F
from wetectron.modeling import registry
... | 6,863 | 37.133333 | 87 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/weak_head/pseudo_label_generator.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
import torch
import torch.nn as nn
import numpy as np
import time
from wetectron.config import cfg
from wet... | 6,497 | 47.857143 | 110 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/weak_head/loss.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
import torch
from torch.nn import functional as F
from wetectron.layers import smooth_l1_loss
from wetectro... | 14,241 | 46.0033 | 154 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/weak_head/weak_head.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
import torch
from torch import nn
from ..box_head.roi_box_feature_extractors import make_roi_box_feature_ex... | 6,882 | 47.132867 | 139 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/mask_head/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import torch
from torch import nn
from wetectron.layers.misc import interpolate
from wetectron.structures.bounding_box import BoxList
# TODO check if want to return a single BoxList or a composite
# object
class MaskPostProces... | 6,679 | 30.809524 | 87 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch import nn
from torch.nn import functional as F
from ..box_head.roi_box_feature_extractors import ResNet50Conv5ROIFeatureExtractor
from wetectron.modeling import registry
from wetectron.modeling.poolers import Pooler
from wetectron.model... | 2,475 | 32.917808 | 82 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/mask_head/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F
from wetectron.layers import smooth_l1_loss
from wetectron.modeling.matcher import Matcher
from wetectron.structures.boxlist_ops import boxlist_iou
from wetectron.modeling.utils import cat
def pr... | 5,331 | 36.286713 | 80 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/mask_head/roi_mask_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch import nn
from torch.nn import functional as F
from wetectron.layers import Conv2d
from wetectron.layers import ConvTranspose2d
from wetectron.modeling import registry
@registry.ROI_MASK_PREDICTOR.register("MaskRCNNC4Predictor")
class... | 2,202 | 36.982759 | 83 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/mask_head/mask_head.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from wetectron.structures.bounding_box import BoxList
from .roi_mask_feature_extractors import make_roi_mask_feature_extractor
from .roi_mask_predictors import make_roi_mask_predictor
from .inference import make_... | 3,117 | 36.119048 | 86 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/box_head/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from wetectron.structures.bounding_box import BoxList
from wetectron.structures.boxlist_ops import boxlist_nms
from wetectron.structures.boxlist_ops import cat_boxlist
from wetectro... | 6,791 | 37.590909 | 88 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/box_head/roi_box_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.nn import functional as F
from wetectron.modeling import registry
from wetectron.modeling.backbone import resnet
from wetectron.modeling.poolers import Pooler
from wetectron.modeling.make_layers import ... | 5,359 | 34.263158 | 81 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/box_head/box_head.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from .roi_box_feature_extractors import make_roi_box_feature_extractor
from .roi_box_predictors import make_roi_box_predictor
from .inference import make_roi_box_post_processor
from .loss import make_roi_box_loss_... | 2,831 | 37.794521 | 96 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/box_head/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F
from wetectron.layers import smooth_l1_loss
from wetectron.modeling.box_coder import BoxCoder
from wetectron.modeling.matcher import Matcher
from wetectron.structures.boxlist_ops import boxlist_iou... | 7,278 | 35.762626 | 90 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/box_head/roi_box_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from wetectron.modeling import registry
from torch import nn
@registry.ROI_BOX_PREDICTOR.register("FastRCNNPredictor")
class FastRCNNPredictor(nn.Module):
def __init__(self, config, in_channels):
super(FastRCNNPredictor, self).__init_... | 2,413 | 35.029851 | 87 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/keypoint_head/inference.py | import torch
from torch import nn
class KeypointPostProcessor(nn.Module):
def __init__(self, keypointer=None):
super(KeypointPostProcessor, self).__init__()
self.keypointer = keypointer
def forward(self, x, boxes):
mask_prob = x
scores = None
if self.keypointer:
... | 4,450 | 34.325397 | 102 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/keypoint_head/roi_keypoint_feature_extractors.py | from torch import nn
from torch.nn import functional as F
from wetectron.modeling import registry
from wetectron.modeling.poolers import Pooler
from wetectron.layers import Conv2d
@registry.ROI_KEYPOINT_FEATURE_EXTRACTORS.register("KeypointRCNNFeatureExtractor")
class KeypointRCNNFeatureExtractor(nn.Module):
de... | 1,865 | 35.588235 | 87 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/keypoint_head/loss.py | import torch
from torch.nn import functional as F
from wetectron.modeling.matcher import Matcher
from wetectron.modeling.balanced_positive_negative_sampler import (
BalancedPositiveNegativeSampler,
)
from wetectron.structures.boxlist_ops import boxlist_iou
from wetectron.modeling.utils import cat
from wetectron.l... | 7,040 | 37.266304 | 90 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/keypoint_head/keypoint_head.py | import torch
from .roi_keypoint_feature_extractors import make_roi_keypoint_feature_extractor
from .roi_keypoint_predictors import make_roi_keypoint_predictor
from .inference import make_roi_keypoint_post_processor
from .loss import make_roi_keypoint_loss_evaluator
class ROIKeypointHead(torch.nn.Module):
def __i... | 2,057 | 38.576923 | 86 | py |
wetectron | wetectron-master/wetectron/modeling/roi_heads/keypoint_head/roi_keypoint_predictors.py | from torch import nn
from wetectron import layers
from wetectron.modeling import registry
@registry.ROI_KEYPOINT_PREDICTOR.register("KeypointRCNNPredictor")
class KeypointRCNNPredictor(nn.Module):
def __init__(self, cfg, in_channels):
super(KeypointRCNNPredictor, self).__init__()
input_features =... | 1,255 | 31.205128 | 81 | py |
wetectron | wetectron-master/wetectron/structures/image_list.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from __future__ import division
imp... | 2,697 | 34.038961 | 87 | py |
wetectron | wetectron-master/wetectron/structures/segmentation_mask.py | import cv2
import copy
import torch
import numpy as np
from wetectron.layers.misc import interpolate
from wetectron.utils import cv2_util
import pycocotools.mask as mask_utils
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
""" ABSTRACT
Segmentations come in either:
1) Binary masks
2) Polygons
Binary masks can ... | 18,631 | 31.347222 | 94 | py |
wetectron | wetectron-master/wetectron/structures/bounding_box.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# transpose
FLIP_LEFT_... | 11,860 | 34.195846 | 104 | py |
wetectron | wetectron-master/wetectron/structures/boxlist_ops.py | # --------------------------------------------------------
# Copyright (C) 2020 NVIDIA Corporation. All rights reserved.
# Nvidia Source Code License-NC
# --------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .bounding_box imp... | 7,580 | 31.259574 | 152 | py |
wetectron | wetectron-master/wetectron/structures/keypoint.py | import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class Keypoints(object):
def __init__(self, keypoints, size, mode=None):
# FIXME remove check once we have better integration with device
# in my version this would consistently return a CPU tensor
device = keypoints.device ... | 7,472 | 33.920561 | 97 | py |
WAST | WAST-main/main.py | import os
import sys
from xmlrpc.client import boolean
sys.path.append(os.getcwd())
import time
import logging
import copy
import shutil
import torch
import torch.nn.functional as F
import torch.optim as optim
import torch.backends.cudnn as cudnn
import random
import numpy as np
from test import svm_test
from utils i... | 12,220 | 46.368217 | 287 | py |
WAST | WAST-main/dst.py | from __future__ import print_function
import torch
import numpy as np
import copy
from torch import detach
class dst_FS():
def __init__(self, model, device, alpha, density, hidden_IMP):
self.model = model
self.device = device
self.prune_rate = alpha
self.density = density
s... | 8,100 | 50.598726 | 259 | py |
WAST | WAST-main/utils.py | import os
from matplotlib import dates
import numpy as np
from tensorflow.keras.datasets import mnist
from tensorflow.keras.datasets import fashion_mnist
import torch
import torch.nn.functional as F
import urllib.request as urllib2
from scipy.io import loadmat
from sklearn.model_selection import train_test_split
from ... | 11,927 | 36.866667 | 148 | py |
WAST | WAST-main/models.py | import torch
import torch.nn as nn
# an autoencoder model with a single hidden layer
class AE(nn.Module):
def __init__(self, dataset, input_size, nhidden):
super(AE, self).__init__()
self.fc1 = nn.Linear(input_size, nhidden, bias=True)
self.fc2 = nn.Linear(nhidden, input_size, bias=True)
... | 540 | 30.823529 | 60 | py |
CMSG | CMSG-main/evaluation/registration_lsq.py | import open3d
import time
import numpy as np
import math
import torch
import os
from torch.utils.tensorboard import SummaryWriter
import cv2
import random
from scipy.spatial.transform import Rotation
import multiprocessing
import matplotlib
matplotlib.use('TkAgg')
from models.multimodal_classifier import MMClassifer
... | 16,981 | 41.243781 | 142 | py |
CMSG | CMSG-main/evaluation/visualize_and_save_data.py | import open3d
import time
import numpy as np
import math
import torch
import os
from torch.utils.tensorboard import SummaryWriter
import cv2
import matplotlib
matplotlib.use('TkAgg')
from models.multimodal_classifier import MMClassifer, MMClassiferCoarse
from data.kitti_pc_img_pose_loader import KittiLoader
from data... | 10,154 | 43.735683 | 140 | py |
CMSG | CMSG-main/evaluation/registration_pnp.py | import open3d
import time
import numpy as np
import math
import torch
import os
from torch.utils.tensorboard import SummaryWriter
import cv2
import random
from scipy.spatial.transform import Rotation
import multiprocessing
import matplotlib
matplotlib.use('TkAgg')
from models.multimodal_classifier import MMClassifer
... | 10,077 | 37.761538 | 142 | py |
CMSG | CMSG-main/evaluation/test_frustum_solver.py | import open3d
import time
import numpy as np
import math
import torch
import os
from torch.utils.tensorboard import SummaryWriter
import cv2
import random
import matplotlib
matplotlib.use('TkAgg')
from models.multimodal_classifier import MMClassifer
from data.kitti_pc_img_pose_loader import KittiLoader
from kitti imp... | 5,398 | 37.021127 | 142 | py |
CMSG | CMSG-main/evaluation/visualization_for_paper.py | import open3d
import numpy as np
import math
import random
from scipy.spatial.transform import Rotation
import os
import torch
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.cm as cm
import kitti.options
import oxford.options
import... | 4,710 | 33.137681 | 106 | py |
CMSG | CMSG-main/evaluation/registration_random.py | import open3d
import time
import numpy as np
import math
import torch
import os
from torch.utils.tensorboard import SummaryWriter
import cv2
import random
from scipy.spatial.transform import Rotation
import multiprocessing
import matplotlib
matplotlib.use('TkAgg')
from models.multimodal_classifier import MMClassifer
... | 8,635 | 35.133891 | 142 | py |
CMSG | CMSG-main/evaluation/gauss_newton_visualization.py | import open3d
import time
import numpy as np
import math
import torch
import os
from torch.utils.tensorboard import SummaryWriter
import cv2
import random
from scipy.spatial.transform import Rotation
import multiprocessing
import matplotlib
matplotlib.use('TkAgg')
from models.multimodal_classifier import MMClassifer
... | 5,592 | 33.73913 | 99 | py |
CMSG | CMSG-main/evaluation/icp/save_depth_map.py | from __future__ import absolute_import, division, print_function
import os
import sys
import glob
import argparse
import numpy as np
import PIL.Image as pil
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import torch
from torchvision import transforms, datasets
import networks
fr... | 4,375 | 30.482014 | 109 | py |
CMSG | CMSG-main/util/pytorch_helper.py | import os
import torch
def model_state_dict_parallel_convert(state_dict, mode):
from collections import OrderedDict
new_state_dict = OrderedDict()
if mode == 'to_single':
for k, v in state_dict.items():
name = k[7:] # remove 'module.' of DataParallel
new_state_dict[name] =... | 1,245 | 35.647059 | 102 | py |
CMSG | CMSG-main/util/som.py | import numpy as np
import torch
def query_topk(node, x, M, k):
'''
:param node: SOM node of BxCxM tensor
:param x: input data BxCxN tensor
:param M: number of SOM nodes
:param k: topk
:return: mask: Nxnode_num
'''
# ensure x, and other stored tensors are in the same device
device =... | 1,678 | 37.159091 | 125 | py |
CMSG | CMSG-main/data/augmentation.py | import random
import numbers
import os
import os.path
import numpy as np
import struct
import math
import torch
import torchvision
import matplotlib.pyplot as plt
def angles2rotation_matrix(angles):
Rx = np.array([[1, 0, 0],
[0, np.cos(angles[0]), -np.sin(angles[0])],
[0, np... | 2,207 | 25.60241 | 91 | py |
CMSG | CMSG-main/data/kitti_semantic_pc_img_pose_loader.py | import open3d
import torch.utils.data as data
import random
import numbers
import os
import os.path
import numpy as np
import struct
import math
import torch
import torchvision
import cv2
from PIL import Image
from torchvision import transforms
import time
import matplotlib
# matplotlib.use('TkAgg')
import matplotlib.... | 40,446 | 41.665612 | 163 | py |
CMSG | CMSG-main/data/oxford_pc_img_pose_loader.py | import open3d
import torch.utils.data as data
import random
import numbers
import os
import os.path
import numpy as np
import struct
import math
import torch
import torchvision
import cv2
from PIL import Image
from torchvision import transforms
import bisect
import matplotlib
# matplotlib.use('TkAgg')
import matplotli... | 16,713 | 41.529262 | 122 | py |
CMSG | CMSG-main/data/nuscenes_pc_img_pose_loader.py | import open3d
import torch.utils.data as data
import random
import numbers
import os
import os.path
import numpy as np
import struct
import math
import torch
import torchvision
import cv2
from PIL import Image
from torchvision import transforms
import pickle
from pyquaternion import Quaternion
import matplotlib
# matp... | 16,532 | 38.270784 | 117 | py |
CMSG | CMSG-main/data/kitti_pc_img_pose_loader.py | import open3d
import torch.utils.data as data
import random
import numbers
import os
import os.path
import numpy as np
import struct
import math
import torch
import torchvision
import cv2
from PIL import Image
from torchvision import transforms
import time
import matplotlib
# matplotlib.use('TkAgg')
import matplotlib.... | 22,620 | 39.980072 | 124 | py |
CMSG | CMSG-main/kitti/test_vis.py | import open3d
import time
import copy
import numpy as np
import math
import os
import shutil
import torch
import sys
from tqdm import tqdm, trange
from torch.utils.tensorboard import SummaryWriter
from sklearn import metrics
import matplotlib
import argparse
matplotlib.use('Agg')
sys.path.append(os.getcwd())
# print... | 6,586 | 51.696 | 144 | py |
CMSG | CMSG-main/kitti/options_clip.py | import numpy as np
import math
import torch
class Options:
def __init__(self):
self.dataroot = 'dataset/kitti'
self.issemantic = True
self.output_path = 'eval/1_10_00'
# self.dataroot = '/data/personal/jiaxin/datasets/kitti'
# self.checkpoints_dir = 'checkpoints'
se... | 2,590 | 28.11236 | 64 | py |
CMSG | CMSG-main/kitti/train_classifier.py | import open3d
import time
import copy
import numpy as np
import math
import os
import shutil
import torch
import sys
from tqdm import tqdm, trange
from torch.utils.tensorboard import SummaryWriter
from sklearn import metrics
import matplotlib
matplotlib.use('Agg')
sys.path.append(os.getcwd())
# print(os.getcwd())
f... | 6,598 | 35.865922 | 114 | py |
CMSG | CMSG-main/kitti/train_seclassifier.py | import open3d
import time
import copy
import numpy as np
import math
import os
import shutil
import torch
import sys
from tqdm import tqdm, trange
from torch.utils.tensorboard import SummaryWriter
from sklearn import metrics
import matplotlib
matplotlib.use('Agg')
sys.path.append(os.getcwd())
# print(os.getcwd())
f... | 8,099 | 37.388626 | 114 | py |
CMSG | CMSG-main/kitti/options_matchnet.py | import numpy as np
import math
import torch
class Options:
def __init__(self):
self.dataroot = 'dataset/kitti'
self.issemantic = True
self.output_path = 'eval/1_10_00'
# self.dataroot = '/data/personal/jiaxin/datasets/kitti'
# self.checkpoints_dir = 'checkpoints'
se... | 2,589 | 28.101124 | 64 | py |
CMSG | CMSG-main/kitti/options.py | import numpy as np
import math
import torch
class Options:
def __init__(self):
self.dataroot = 'dataset/kitti'
# self.dataroot = '/data/personal/jiaxin/datasets/kitti'
self.checkpoints_dir = 'checkpoints'
self.version = '1.27'
self.is_debug = False #True
self.is_fin... | 1,995 | 27.927536 | 64 | py |
CMSG | CMSG-main/kitti/train_seclassifier copy.py | import open3d
import time
import copy
import numpy as np
import math
import os
import shutil
import torch
import sys
from tqdm import tqdm, trange
from torch.utils.tensorboard import SummaryWriter
from sklearn import metrics
import matplotlib
matplotlib.use('Agg')
sys.path.append(os.getcwd())
# print(os.getcwd())
f... | 8,015 | 37.171429 | 114 | py |
CMSG | CMSG-main/kitti/eval_batch.py | import open3d
import time
import copy
import numpy as np
import math
import os
import shutil
import torch
import sys
from tqdm import tqdm, trange
from torch.utils.tensorboard import SummaryWriter
from matplotlib import pyplot as plt
from sklearn import metrics
import matplotlib
matplotlib.use('Agg')
sys.path.append(... | 4,602 | 28.88961 | 108 | py |
CMSG | CMSG-main/kitti/options_ace.py | import numpy as np
import math
import torch
class Options:
def __init__(self):
self.dataroot = 'dataset/kitti'
# self.dataroot = '/data/personal/jiaxin/datasets/kitti'
self.checkpoints_dir = 'checkpoints'
self.version = '1.27'
self.is_debug = False #True
self.is_fin... | 6,134 | 52.815789 | 144 | py |
CMSG | CMSG-main/kitti/train_pointnetVlad.py | import open3d
import time
import copy
import numpy as np
import math
import os
import shutil
import torch
import sys
from tqdm import tqdm, trange
from torch.utils.tensorboard import SummaryWriter
from sklearn import metrics
import matplotlib
matplotlib.use('Agg')
sys.path.append(os.getcwd())
# print(os.getcwd())
f... | 7,841 | 36.701923 | 129 | py |
CMSG | CMSG-main/kitti/model_test.py | import torch
import torch.nn as nn
import torch.optim as optim
from torch.nn import init
import torchvision.models as models
import torch.backends.cudnn as cudnn
from torch.nn.utils.clip_grad import clip_grad_norm
import numpy as np
from collections import OrderedDict
import torch.nn.functional as F
import math
from m... | 2,574 | 34.763889 | 95 | py |
CMSG | CMSG-main/kitti/train_matchnet.py | import open3d
import time
import copy
import numpy as np
import math
import os
import shutil
import torch
import sys
from tqdm import tqdm, trange
from torch.utils.tensorboard import SummaryWriter
from sklearn import metrics
import matplotlib
matplotlib.use('Agg')
sys.path.append(os.getcwd())
# print(os.getcwd())
f... | 8,068 | 37.241706 | 128 | py |
catvrnn | catvrnn-main/generate.py | import torch
from catvrnn import CatVRNN
import dataset
import os
import argparse
def breakmodelname(modelpath):
name = os.path.split(modelpath)[-1]
name = os.path.splitext(name)[0]
corpus, mode, tag, w, ts = name.split('_')
if w != 'True':
w = float(w)
else:
w = False
return mo... | 3,223 | 37.843373 | 143 | py |
catvrnn | catvrnn-main/catvrnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class VRNNCell(nn.Module):
def __init__(self, x_dim, h_dim, z_dim):
super(VRNNCell, self).__init__()
self.x_dim = x_dim
self.h_dim = h_dim
self.z_dim = z_dim
#encoder
self.enc = nn.Sequential(
... | 5,224 | 30.859756 | 95 | py |
catvrnn | catvrnn-main/train.py | import torch.optim as optim
import torch
from catvrnn import CatVRNN
import dataset
from tqdm import tqdm
import pickle
import argparse
import datetime
import math
import os
def outpath(name, mode, w, tag):
root = os.path.split(os.path.realpath(__file__))[0]
now = datetime.datetime.now().strftime('%m%d')
f... | 5,509 | 38.357143 | 175 | py |
catvrnn | catvrnn-main/tools/CNNClassifier.py | # -*- coding: utf-8 -*-
# @Author : William
# @Project : TextGAN-william
# @FileName : config.py
# @Time : Created at 2019-03-18
# @Blog : http://zhiweil.ml/
# @Description :
# Copyrights (C) 2018. All Rights Reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
i... | 3,028 | 39.386667 | 139 | py |
Relay-FL | Relay-FL-main/main.py | from typing import Optional, Any, Callable
import numpy as np
import argparse
import math
import time
import torch
import copy
import learning_flow
import train_script
from Nets import CNNMnist, MLP
import scipy.io as sio
def initial():
# network parameters
setup = argparse.ArgumentParser()
setup.add_arg... | 11,994 | 47.760163 | 125 | py |
Relay-FL | Relay-FL-main/Nets.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
# import torch
from torch import nn
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self, dim_in, dim_hidden, dim_out):
super(MLP, self).__init__()
self.layer_input = nn.Linear(dim_in, dim_hidden)
self... | 1,499 | 29.612245 | 68 | py |
Relay-FL | Relay-FL-main/train_script.py | # -*- coding: utf-8 -*-
import numpy as np
np.set_printoptions(precision=6, threshold=1e3)
import torch
from torchvision import datasets, transforms
import copy
import torch.nn as nn
from torch.utils.data import DataLoader
def mnist_iid(dataset, K, M):
dict_users, all_idxs = {}, [i for i in range(len(dataset))... | 6,432 | 37.520958 | 116 | py |
Relay-FL | Relay-FL-main/learning_flow.py | import numpy as np
import copy
import torch
import train_script
import AirComp
def FedAvg_grad(w_glob, grad, device):
ind = 0
w_return = copy.deepcopy(w_glob)
for item in w_return.keys():
a = np.array(w_return[item].size())
if len(a):
b = np.prod(a)
w_return[item] ... | 3,468 | 32.038095 | 109 | py |
PaperNotebooks | PaperNotebooks-main/custom_traceELBO.py | #Supplement of paper titled:
#Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis- prepared by Arpan Biswas
######### We extend the standard trace ELBO class to incorporate the inclusion of the physics driven loss functions
###########and augmenting the physics driven loss with... | 12,396 | 42.960993 | 158 | py |
PaperNotebooks | PaperNotebooks-main/customsvitrainer.py | #Supplement of paper titled:
#Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis- prepared by Arpan Biswas
######### This standard svi trainner class is extended to incorporate physics defined loss functions along with VAE loss
import matplotlib.pyplot as plt
import math
impor... | 15,392 | 40.048 | 185 | py |
PaperNotebooks | PaperNotebooks-main/customsvi.py | #Supplement of paper titled:
#Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis- prepared by Arpan Biswas
######### This class is modified to pass additional arguments in "model", "data", "weight" as stated in comments under (new modification)
## SVI class is extended to inco... | 6,853 | 36.867403 | 137 | py |
iglu | iglu-main/makedata/makedata_proteins.py | import torch
import torch.nn.functional as F
import torch_geometric.transforms as T
from torch_geometric.nn import GCNConv, SAGEConv
from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
import numpy as np
import scipy.sparse as sp
dataset = PygNodePropPredDataset(name='ogbn-proteins',
... | 1,099 | 24 | 62 | py |
xcfun | xcfun-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document wi... | 4,169 | 32.902439 | 88 | py |
Bone_MRI | Bone_MRI-main/scripts_notebooks/segmentation_disease_region/db_load_pytable.py | import torch.utils.data
import tables
class LoadPyTable(torch.utils.data.Dataset):
'''
Purpose: load image and mask from a database.
Input: path to the database file.
Output:
- image, 3D numpy array of size [1,H,W]
- mask, 3D numpy array of size [1,H,W]
Methods:
__len__(): give... | 1,786 | 34.039216 | 98 | py |
Bone_MRI | Bone_MRI-main/scripts_notebooks/segmentation_disease_region/augmentation.py | import numpy as np
import scipy.linalg as la
import scipy.ndimage as nd
import elasticdeform
import torch.nn.functional as F
import torch
class Transform():
'''
Purpose: perform the following transformations on images and corresponding masks
- affine (rotation, shearing, scaling);
- elastic def... | 13,901 | 41.513761 | 99 | py |
Bone_MRI | Bone_MRI-main/scripts_notebooks/segmentation_disease_region/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class UNet(nn.Module):
'''
Implementation of 2D Unet, "U-Net: Convolutional Networks for Biomedical Image Segmentation"
by O.Ronneberger et al., 2015;
This implementation is taken from https://github.com/jvanvugt/pytorch-unet;
... | 15,213 | 41.37883 | 101 | py |
Bone_MRI | Bone_MRI-main/scripts_notebooks/segmentation_disease_region/db_modify_array.py | import numpy as np
def modify_array(array):
'''
Purpose: data processing
- rotate 3D numpy array by 90d counterclockwise in the plane specified by axes;
- add one dimension and modify dimensions order;
- change data type to float32;
Explanation:
- given data is stored in a specific wa... | 1,567 | 39.205128 | 99 | py |
Bone_MRI | Bone_MRI-main/scripts_notebooks/segmentation_disease_region/dice_generalization_test.py | import unittest
from dice_generalization import *
## Create Dice class object;
dice = Dice()
class SoftDiceTest(unittest.TestCase):
def setUp(self):
'Purpose: generate inputs and the expected outputs to test Dice class methods;'
## Create 4D torch tensor of size [2,1,3,3], that repre... | 2,675 | 37.228571 | 94 | py |
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