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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probdet | probdet-master/src/core/evaluation_tools/scoring_rules.py | import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def sigmoid_compute_cls_scores(input_matches, valid_idxs):
"""
Computes proper scoring rule for multilabel classification results provided by retinanet.
Args:
input_matches (dict): dictionary containing input matc... | 9,408 | 40.632743 | 133 | py |
probdet | probdet-master/src/core/evaluation_tools/evaluation_utils.py | import numpy as np
import os
import tqdm
import torch
import ujson as json
from collections import defaultdict
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.structures import Boxes, pairwise_iou
# Project imports
from core.datasets import metadata
device = torch.device("cuda" if to... | 29,261 | 49.364888 | 139 | py |
probdet | probdet-master/src/core/visualization_tools/results_processing_tools.py | import glob
import itertools
import numpy as np
import os
import pickle
import torch
from collections import defaultdict
# Project imports
from core.setup import setup_config, setup_arg_parser
from probabilistic_inference.inference_utils import get_inference_output_dir
def get_clean_results_dict(config_names,
... | 30,031 | 53.703097 | 161 | py |
probdet | probdet-master/src/probabilistic_inference/probabilistic_retinanet_predictor.py | import numpy as np
import torch
# Detectron Imports
from detectron2.layers import batched_nms, cat
from detectron2.structures import Boxes, Instances, pairwise_iou
# Project Imports
from probabilistic_inference import inference_utils
from probabilistic_inference.inference_core import ProbabilisticPredictor
from prob... | 22,131 | 44.445585 | 138 | py |
probdet | probdet-master/src/probabilistic_inference/probabilistic_rcnn_predictor.py | import numpy as np
import torch
# Detectron Imports
from detectron2.layers import batched_nms
from detectron2.structures import Boxes, Instances, pairwise_iou
# Project Imports
from probabilistic_inference import inference_utils
from probabilistic_inference.inference_core import ProbabilisticPredictor
from probabili... | 19,086 | 43.491841 | 138 | py |
probdet | probdet-master/src/probabilistic_inference/inference_utils.py | import numpy as np
import os
import torch
from PIL import Image
# Detectron imports
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.layers import batched_nms
from detectron2.structures import BoxMode, Boxes, Instances, pairwise_iou
# Project imports
from probabilistic_inference.image_... | 25,812 | 38.896445 | 120 | py |
probdet | probdet-master/src/probabilistic_inference/probabilistic_detr_predictor.py | import numpy as np
import torch
import torch.nn.functional as F
# DETR imports
from detr.util.box_ops import box_cxcywh_to_xyxy
# Detectron Imports
from detectron2.structures import Boxes
# Project Imports
from probabilistic_inference import inference_utils
from probabilistic_inference.inference_core import Probabi... | 8,454 | 41.275 | 119 | py |
probdet | probdet-master/src/probabilistic_modeling/probabilistic_retinanet.py | import logging
import math
from typing import List
import torch
from fvcore.nn import sigmoid_focal_loss_jit, smooth_l1_loss
from torch import nn, distributions
# Detectron Imports
from detectron2.layers import ShapeSpec, cat
from detectron2.utils.events import get_event_storage
from detectron2.modeling.anchor_genera... | 28,509 | 41.362556 | 130 | py |
probdet | probdet-master/src/probabilistic_modeling/modeling_utils.py | import torch
def covariance_output_to_cholesky(pred_bbox_cov):
"""
Transforms output to covariance cholesky decomposition.
Args:
pred_bbox_cov (kx4 or kx10): Output covariance matrix elements.
Returns:
predicted_cov_cholesky (kx4x4): cholesky factor matrix
"""
# Embed diagonal... | 1,431 | 32.302326 | 95 | py |
probdet | probdet-master/src/probabilistic_modeling/probabilistic_generalized_rcnn.py | import logging
import numpy as np
import torch
from typing import Dict, List, Union, Optional, Tuple
from torch.nn import functional as F
from torch import nn, distributions
# Detectron imports
import fvcore.nn.weight_init as weight_init
from detectron2.config import configurable
from detectron2.layers import Linea... | 43,022 | 42.326284 | 130 | py |
probdet | probdet-master/src/probabilistic_modeling/probabilistic_detr.py | import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, distributions
# Detectron imports
from detectron2.modeling import META_ARCH_REGISTRY, detector_postprocess
# Detr imports
from models.detr import SetCriterion, MLP, DETR
from util import box_ops
from util.misc import (NestedTensor, ... | 18,746 | 39.403017 | 135 | py |
probdet | probdet-master/src/offline_evaluation/compute_probabilistic_metrics.py | import numpy as np
import os
import torch
import pickle
from prettytable import PrettyTable
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.engine import launch
# Project imports
from core.evaluation_tools import evaluation_utils
from core.evaluation_tools import scoring_rules
from co... | 18,140 | 52.04386 | 129 | py |
probdet | probdet-master/src/offline_evaluation/compute_ood_probabilistic_metrics.py | import itertools
import os
import torch
import ujson as json
import pickle
from prettytable import PrettyTable
# Detectron imports
from detectron2.engine import launch
# Project imports
from core.evaluation_tools import scoring_rules
from core.evaluation_tools.evaluation_utils import eval_predictions_preprocess
from... | 7,146 | 38.486188 | 116 | py |
probdet | probdet-master/src/offline_evaluation/compute_calibration_errors.py | import calibration as cal
import os
import pickle
import torch
from prettytable import PrettyTable
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.engine import launch
# Project imports
from core.evaluation_tools import evaluation_utils
from core.evaluation_tools.evaluation_utils impo... | 14,207 | 45.736842 | 116 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/main.py | import torch
import torch.nn as nn
import argparse
import os
import random
import numpy as np
from tqdm import tqdm
# from torchvision import models
# from transformers import AdamW
from BertModels import BertForMultipleClass, BertForBooleanQuestionYN ,BertForBooleanQuestionFR, BertForQuestionAnswering, BertForBoolean... | 51,033 | 52.327064 | 613 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/PLModels.py | # from transformers import BertPreTrainedModel, BertModel, BertOnlyMLMHead
from transformers import *
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss, MSELoss, BCELoss
from typing import Union, List
import numpy as np
from torch.autograd import Variable
def wei... | 85,919 | 35.176842 | 335 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/Create_LM_input_output.py | # checking with BERT
from unittest.util import _MAX_LENGTH
from torchnlp.nn import attention
from transformers import BertTokenizer, BertTokenizerFast, RobertaTokenizer, RobertaTokenizerFast
import torch
import random
import torch.nn as nn
tokenizer, tokenizerFast = None, None
baseline = None
def initialize_tokeni... | 58,129 | 40.25621 | 239 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/BERT.py |
# checking with BERT
from torchnlp.nn import attention
from transformers import BertTokenizer, BertTokenizerFast
import torch
import random
import torch.nn as nn
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
tokenizerFast = BertTokenizerFast.from_pretrained('bert-base-uncased')
def question_answeri... | 57,750 | 40.398566 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/BertModels.py | # from transformers import BertPreTrainedModel, BertModel, BertOnlyMLMHead
from transformers import *
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss, MSELoss, BCELoss
from typing import Union, List
import numpy
from torch.autograd import Variable
class FocalLo... | 75,165 | 36.009355 | 333 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/test.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from QA.train import question_to_sentence, F1_measure, precision, recall, confusion_matrix, concate_input_components, check_answer_equality
from Create_LM_input_output import tokenizing, boolean_classificatio... | 18,533 | 39.734066 | 241 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/trainold.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from BERT import tokenizing
# from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
# from ALBERT import tokenizing
# from XLNet import tokenizi... | 24,398 | 38.867647 | 237 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/testold.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from QA.train import question_to_sentence, F1_measure, precision, recall, confusion_matrix
from BERT import tokenizing
# from ALBERT import tokenizing
# from XLNet import tokenizing
def test(model
... | 20,013 | 40.958071 | 241 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/train.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
# from BERT import tokenizing
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
# from ALBERT import tokenizing
# from XLNet import tokenizi... | 24,796 | 37.444961 | 260 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/sprlqa/test.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
from QA.train import check_answer_equality, concate_input_components
# from ALBERT import... | 11,190 | 34.86859 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/sprlqa/train.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
from QA.train import check_answer_equality, concate_input_components
def train(model
... | 17,477 | 36.266525 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/sprlqa/.ipynb_checkpoints/train-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
from QA.train import check_answer_equality, concate_input_components
def train(model
... | 17,477 | 36.266525 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/sprlqa/.ipynb_checkpoints/test-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
from QA.train import check_answer_equality, concate_input_components
# from ALBERT import... | 11,190 | 34.86859 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/StepGame/test.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
from QA.train import check_answer_equality, concate_input_components
# from BERT import to... | 11,232 | 35.235484 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/StepGame/train.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
from QA.train import check_answer_equality, concate_input_components
def train (model
... | 15,959 | 35.521739 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/StepGame/.ipynb_checkpoints/train-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
from QA.train import check_answer_equality, concate_input_components
def train (model
... | 15,774 | 35.431871 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/StepGame/.ipynb_checkpoints/test-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
from QA.train import check_answer_equality, concate_input_components
# from BERT import to... | 11,087 | 35 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/babi/test.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
# from QA.babi.train import question_to_sentence, F1_measure, precision, recall, confusion_matrix
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tok... | 12,331 | 36.256798 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/babi/train.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
from QA.train import check_answer_equality, concate_input_components
# from BERT import to... | 17,496 | 35.835789 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/babi/.ipynb_checkpoints/train-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
from QA.train import check_answer_equality, concate_input_components
# from BERT import to... | 17,481 | 35.959831 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/babi/.ipynb_checkpoints/test-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
# from QA.babi.train import question_to_sentence, F1_measure, precision, recall, confusion_matrix
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tok... | 12,294 | 36.257576 | 215 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/.ipynb_checkpoints/testold-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from QA.train import question_to_sentence, F1_measure, precision, recall, confusion_matrix
from BERT import tokenizing
# from ALBERT import tokenizing
# from XLNet import tokenizing
def test(model
... | 20,013 | 40.958071 | 241 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/.ipynb_checkpoints/train_old-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from BERT import tokenizing
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
# from ALBERT import tokenizing
# from XLNet import tokenizing... | 24,128 | 38.751236 | 237 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/.ipynb_checkpoints/train-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
# from BERT import tokenizing
from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
# from ALBERT import tokenizing
# from XLNet import tokenizi... | 24,362 | 37.246468 | 237 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/.ipynb_checkpoints/test-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from QA.train import question_to_sentence, F1_measure, precision, recall, confusion_matrix, concate_input_components, check_answer_equality
from Create_LM_input_output import tokenizing, boolean_classificatio... | 18,533 | 39.734066 | 241 | py |
Spatial-QA-tasks | Spatial-QA-tasks-main/QA/.ipynb_checkpoints/trainold-checkpoint.py | import json
import re
import random
import torch
from tqdm import tqdm
import numpy as np
import torch.nn as nn
from BERT import tokenizing
# from Create_LM_input_output import tokenizing, boolean_classification, multiple_classification, initialize_tokenizer
# from ALBERT import tokenizing
# from XLNet import tokenizi... | 24,398 | 38.867647 | 237 | py |
FGI-Matting | FGI-Matting-main/main.py | import os
import toml
import argparse
from pprint import pprint
import torch
from torch.utils.data import DataLoader
import utils
from utils import CONFIG
from tester import Tester
import dataloader
def main():
CONFIG.log.logging_path += "_test"
if CONFIG.test.alpha_path is not None:
u... | 1,487 | 23 | 85 | py |
FGI-Matting | FGI-Matting-main/tester.py | import os
import cv2
import logging
import numpy as np
import torch
from time import time
import utils
from utils import CONFIG
import networks
from utils import comput_sad_loss, compute_connectivity_error, \
compute_gradient_loss, compute_mse_loss
class Tester(object):
def __init__(self, test_dataloade... | 4,859 | 36.96875 | 119 | py |
FGI-Matting | FGI-Matting-main/paint_board.py | from PyQt5.QtWidgets import QWidget
from PyQt5.Qt import QPixmap, QPainter, QPoint, QPaintEvent, QMouseEvent, QPen,\
QColor, QSize
from PyQt5.QtCore import Qt
from PIL import Image, ImageQt
# from cv2 import findTransformECC
import numpy as np
# from torch._C import _cuda_resetAccumulatedMemoryStats
import copy
cl... | 6,878 | 27.192623 | 112 | py |
FGI-Matting | FGI-Matting-main/test_one_img.py | import cv2
import numpy as np
import torch
from torch.nn import functional as F
import networks
import utils
import os
from time import time
class Tester_one_image(object):
def __init__(self, test_config):
self.model_config = {'encoder': "res_shortcut_encoder_29_spatial_attn", 'decoder': "res_shortcut... | 4,037 | 26.100671 | 150 | py |
FGI-Matting | FGI-Matting-main/networks/generators.py | import os
import sys
sys.path.append(os.getcwd())
import torch
import torch.nn as nn
from utils import CONFIG
from networks import encoders, decoders
class Generator(nn.Module):
def __init__(self, encoder, decoder):
super(Generator, self).__init__()
if encoder not in encoders.__all__:
... | 1,612 | 28.327273 | 132 | py |
FGI-Matting | FGI-Matting-main/networks/fpemjpu.py | # -*- coding: utf-8 -*-
# @Time : 2019/8/23 21:55
# @Author : zhoujun
import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
from torch.nn import functional as F
from torch.nn import Module, Sequential, Conv2d, ReLU, AdaptiveAvgPool2d, BCELoss, CrossEntropyLoss
from network... | 19,539 | 38.474747 | 128 | py |
FGI-Matting | FGI-Matting-main/networks/ops.py | import torch
from torch import nn
from torch.nn import Parameter
from torch.autograd import Variable
from torch.nn import functional as F
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
"""
Based on https://github.com/heykeetae/Self-Attention-GAN/blob/ma... | 10,696 | 40.785156 | 153 | py |
FGI-Matting | FGI-Matting-main/networks/decoders/res_shortcut_dec_lfm.py | from networks.decoders.resnet_dec import ResNet_D_Dec
from .self_attention import Self_Attn_trimap, Self_Attn
from networks.ops import SpectralNorm
import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_... | 4,263 | 33.387097 | 111 | py |
FGI-Matting | FGI-Matting-main/networks/decoders/self_attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Self_Attn(nn.Module):
def __init__(self, in_dim, with_attention=False):
super (Self_Attn, self).__init__ ()
self.chanel_in = in_dim
# self.activation = activation
self.with_attention = with_attention
self.query_conv = nn.Conv2d (in_... | 6,353 | 34.3 | 114 | py |
FGI-Matting | FGI-Matting-main/networks/decoders/resnet_dec.py | import logging
import torch.nn as nn
from networks.ops import SpectralNorm
def conv5x5(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""5x5 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride,
padding=2, groups=groups, bias=False, d... | 5,600 | 37.895833 | 125 | py |
FGI-Matting | FGI-Matting-main/networks/decoders/res_shortcut_dec_spatial_attn.py | from networks.decoders.resnet_dec import ResNet_D_Dec
from .self_attention import Self_Attn_trimap, Self_Attn
import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3,7)
padd... | 2,206 | 30.528571 | 101 | py |
FGI-Matting | FGI-Matting-main/networks/encoders/res_shortcut_enc.py | import torch.nn as nn
from utils import CONFIG
from networks.encoders.resnet_enc import ResNet_D
from networks.ops import SpectralNorm
from networks.fpemjpu import FPEM_FUSION
class ResShortCut_D(ResNet_D):
def __init__(self, block, layers, norm_layer=None, late_downsample=False):
super(ResShortCu... | 2,240 | 39.017857 | 103 | py |
FGI-Matting | FGI-Matting-main/networks/encoders/__init__.py | import logging
from .resnet_enc import ResNet_D, BasicBlock
from .res_shortcut_enc import ResShortCut_D
from .res_gca_enc import ResGuidedCxtAtten
from .res_shortcut_enc_spatial_attn import ResShortCut_D_spatial_attn
__all__ = ['res_shortcut_encoder_29', 'resnet_gca_encoder_29','res_shortcut_encoder_29_spatial_at... | 1,501 | 25.821429 | 131 | py |
FGI-Matting | FGI-Matting-main/networks/encoders/res_gca_enc.py | import torch.nn as nn
import torch.nn.functional as F
from utils import CONFIG
from networks.encoders.resnet_enc import ResNet_D
from networks.ops import GuidedCxtAtten, SpectralNorm
class ResGuidedCxtAtten(ResNet_D):
def __init__(self, block, layers, norm_layer=None, late_downsample=False):
super... | 3,850 | 37.89899 | 107 | py |
FGI-Matting | FGI-Matting-main/networks/encoders/resnet_enc.py | import logging
import torch.nn as nn
from utils import CONFIG
from networks.ops import SpectralNorm
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dila... | 5,836 | 36.902597 | 118 | py |
FGI-Matting | FGI-Matting-main/networks/encoders/res_shortcut_enc_spatial_attn.py | import torch.nn as nn
from utils import CONFIG
from networks.encoders.resnet_enc import ResNet_D
from networks.ops import SpectralNorm
from networks.fpemjpu import FPEM_FUSION
class ResShortCut_D_spatial_attn(ResNet_D):
def __init__(self, block, layers, norm_layer=None, late_downsample=False):
sup... | 3,160 | 38.024691 | 116 | py |
FGI-Matting | FGI-Matting-main/utils/logger.py | import os
import cv2
import torch
import logging
import datetime
import numpy as np
from pprint import pprint
from utils import util
from utils.config import CONFIG
LEVELS = {
"DEBUG": logging.DEBUG,
"INFO": logging.INFO,
"WARNING": logging.WARNING,
"ERROR": logging.ERROR,
"CRITICAL": loggi... | 5,334 | 30.382353 | 129 | py |
FGI-Matting | FGI-Matting-main/utils/util.py | import os
import cv2
import torch
import logging
import numpy as np
from utils.config import CONFIG
import torch.distributed as dist
def make_dir(target_dir):
"""
Create dir if not exists
"""
if not os.path.exists(target_dir):
os.makedirs(target_dir)
def print_network(model, name):
"""
... | 7,224 | 31.254464 | 112 | py |
FGI-Matting | FGI-Matting-main/dataloader/Test_dataset/data_generator.py | import cv2
import os
import math
import numbers
import random
import logging
import copy
import numpy as np
import torch
from torch.utils.data import Dataset
from torch.nn import functional as F
from torchvision import transforms
trimap_channel = 1
random_interp = False
crop_size = 512
augmentation = True
rad... | 31,357 | 40.699468 | 155 | py |
FGI-Matting | FGI-Matting-main/dataloader/Test_dataset/prefetcher.py | import torch
class Prefetcher():
"""
Modified from the data_prefetcher in https://github.com/NVIDIA/apex/blob/master/examples/imagenet/main_amp.py
"""
def __init__(self, loader):
self.orig_loader = loader
self.stream = torch.cuda.Stream()
self.next_sample = None
def preloa... | 1,461 | 33 | 113 | py |
FGI-Matting | FGI-Matting-main/dataloader/Test_dataset/Test_dataset.py | import torch
from torch.utils.data import DataLoader
import cv2
import numpy as np
from .image_file import ImageFileTrain, ImageFileTest
from .data_generator import DataGenerator
from .prefetcher import Prefetcher
from utils import CONFIG
def get_Test_dataloader():
test_merged = CONFIG.test.test_mer... | 2,266 | 28.828947 | 85 | py |
arc | arc-master/third_party/cqr/torch_models.py |
import sys
import copy
import torch
import numpy as np
import torch.nn as nn
from cqr import helper
from sklearn.model_selection import train_test_split
if torch.cuda.is_available():
device = "cuda:0"
else:
device = "cpu"
###############################################################################
# Help... | 17,313 | 33.217391 | 215 | py |
arc | arc-master/third_party/cqr/helper.py |
import sys
import torch
import numpy as np
from cqr import torch_models
from functools import partial
from cqr import tune_params_cv
from nonconformist.cp import IcpRegressor
from nonconformist.base import RegressorAdapter
from skgarden import RandomForestQuantileRegressor
if torch.cuda.is_available():
device = "... | 22,414 | 36.927242 | 133 | py |
arc | arc-master/third_party/cqr_comparison/qr_net.py | import numpy as np
import torch
from functools import partial
import pdb
import os, sys
sys.path.insert(0, os.path.abspath("../third_party/"))
from cqr import torch_models
from nonconformist.base import RegressorAdapter
if torch.cuda.is_available():
device = "cuda:0"
else:
device = "cpu"
class NeuralNetworkQ... | 3,909 | 36.961165 | 105 | py |
arc | arc-master/experiments_real_data/run_experiment_real_data.py | import numpy as np
from sklearn.model_selection import train_test_split
import pandas as pd
import os.path
from os import path
from datasets import GetDataset
import random
import torch
import sys
sys.path.insert(0, '..')
import arc
def assess_predictions(S, X, y):
# Marginal coverage
coverage = np.mean([y[i]... | 6,950 | 37.192308 | 109 | py |
arc | arc-master/experiments_real_data/datasets.py |
import numpy as np
import pandas as pd
import torch
from torchvision import transforms
import torchvision.datasets as datasets
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
def GetDataset(name, base_path):
""" Load a dataset
Parameters
----------
name... | 4,903 | 31.263158 | 116 | py |
arc | arc-master/arc/others.py | import numpy as np
from sklearn.model_selection import train_test_split
from scipy.stats.mstats import mquantiles
# Note: skgarden has recent compatibility issues
#from skgarden import RandomForestQuantileRegressor
# Note: skgarden has recent compatibility issues
#import sys
#sys.path.insert(0, '../third_party')
#fr... | 9,005 | 40.311927 | 132 | py |
RefVAE | RefVAE-main/main_GAN.py | from __future__ import print_function
import argparse
from math import log10
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from laploss import LapLoss
from torch.utils.data import DataLoader
import torch.nn.functional as F
from model import *
from network... | 10,971 | 37.633803 | 147 | py |
RefVAE | RefVAE-main/test.py | from __future__ import print_function
import argparse
import os
import torch
import cv2
from model import *
import torchvision.transforms as transforms
from collections import OrderedDict
import numpy as np
from os.path import join
import time
from network import encoder4, decoder4
import numpy
from dataset import is_... | 8,446 | 35.5671 | 102 | py |
RefVAE | RefVAE-main/image_utils.py | import torch
import numpy as np
from PIL import Image
import math
import cv2
class TVLoss(torch.nn.Module):
def __init__(self):
super(TVLoss,self).__init__()
def forward(self,x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = self._tensor_size(x[... | 9,144 | 37.104167 | 184 | py |
RefVAE | RefVAE-main/network.py | import torch
import torch.nn as nn
class encoder3(nn.Module):
def __init__(self):
super(encoder3,self).__init__()
# vgg
# 224 x 224
self.conv1 = nn.Conv2d(3,3,1,1,0)
self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1))
# 226 x 226
self.conv2 = nn.Conv2d(3,64,3,1,... | 31,991 | 31.611621 | 125 | py |
RefVAE | RefVAE-main/model.py | import torch
import torch.nn as nn
from torch.nn import functional as F
import math
from torchvision import models
class ncc_test(nn.Module):
"""Residual Channel Attention Networks.
Paper: Image Super-Resolution Using Very Deep Residual Channel Attention
Networks
Ref git repo: https://github.com/... | 47,234 | 33.129335 | 116 | py |
RefVAE | RefVAE-main/dataset.py | import torch.utils.data as data
import torch
import numpy as np
import os
from os import listdir
from os.path import join
from PIL import Image, ImageOps, ImageEnhance
import random
from torchvision import transforms
from glob import glob
from imresize import imresize
def is_image_file(filename):
return any(filen... | 8,504 | 32.093385 | 108 | py |
RefVAE | RefVAE-main/data.py | from os.path import join
from torchvision import transforms
from dataset import DatasetFromFolderEval, DatasetFromFolder
def transform():
return transforms.Compose([
transforms.ToTensor(),
# Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# def transform(fineSize):
# return transforms.Comp... | 887 | 26.75 | 84 | py |
RefVAE | RefVAE-main/laploss.py | import numpy as np
from PIL import Image
import torch
from torch import nn
import torch.nn.functional as fnn
from torch.autograd import Variable
def build_gauss_kernel(size=5, sigma=1.0, n_channels=1, cuda=False):
if size % 2 != 1:
raise ValueError("kernel size must be uneven")
grid = np.float32(np.m... | 3,025 | 35.457831 | 91 | py |
RefVAE | RefVAE-main/eval_4x.py | from __future__ import print_function
import argparse
import os
import torch
import cv2
from model import *
import torchvision.transforms as transforms
from collections import OrderedDict
import numpy as np
from os.path import join
import time
from network import encoder4, decoder4
import numpy
from dataset import is_... | 7,250 | 33.528571 | 109 | py |
RefVAE | RefVAE-main/eval_8x.py | from __future__ import print_function
import argparse
import os
import torch
import cv2
from model import *
import torchvision.transforms as transforms
from collections import OrderedDict
import numpy as np
from os.path import join
import time
from network import encoder4, decoder4
import numpy
from dataset import is_... | 7,250 | 33.528571 | 109 | py |
TrianFlow | TrianFlow-master/test.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from core.dataset import KITTI_2012, KITTI_2015
from core.evaluation import eval_flow_avg, load_gt_flow_kitti
from core.evaluation import eval_depth
from core.visualize import Visualizer_debug
from core.networks import Model_depth_pose, Model_fl... | 10,749 | 40.030534 | 144 | py |
TrianFlow | TrianFlow-master/train.py | import os, sys
import yaml
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from core.dataset import KITTI_RAW, KITTI_Prepared, NYU_Prepare, NYU_v2, KITTI_Odo
from core.networks import get_model
from core.config import generate_loss_weights_dict
from core.visualize import Visualizer
from core.evaluation impo... | 10,881 | 49.37963 | 169 | py |
TrianFlow | TrianFlow-master/infer_vo.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from core.networks.model_depth_pose import Model_depth_pose
from core.networks.model_flow import Model_flow
from visualizer import *
from profiler import Profiler
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as... | 13,467 | 38.964392 | 223 | py |
TrianFlow | TrianFlow-master/core/networks/model_flow.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from structures import *
from pytorch_ssim import SSIM
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pdb
import cv2
def transformerFwd(U,
flo,
out_size,
... | 18,005 | 46.384211 | 201 | py |
TrianFlow | TrianFlow-master/core/networks/model_flowposenet.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from structures import *
from pytorch_ssim import SSIM
from model_flow import Model_flow
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'visualize'))
from visualizer import *
from profiler import Profiler
import t... | 6,517 | 35.824859 | 178 | py |
TrianFlow | TrianFlow-master/core/networks/model_triangulate_pose.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import torch
import torch.nn as nn
import numpy as np
from structures import *
from model_flow import Model_flow
import pdb
import cv2
class Model_triangulate_pose(nn.Module):
def __init__(self, cfg):
super(Model_triangulate_pose, s... | 5,969 | 47.536585 | 182 | py |
TrianFlow | TrianFlow-master/core/networks/model_depth_pose.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from structures import *
from model_triangulate_pose import Model_triangulate_pose
from pytorch_ssim import SSIM
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'visualize'))
from visualizer import *
import torch
i... | 30,237 | 53.978182 | 188 | py |
TrianFlow | TrianFlow-master/core/networks/pytorch_ssim/ssim.py | import torch
import torch.nn as nn
def SSIM(x, y):
C1 = 0.01 ** 2
C2 = 0.03 ** 2
mu_x = nn.AvgPool2d(3, 1, padding=1)(x)
mu_y = nn.AvgPool2d(3, 1, padding=1)(y)
sigma_x = nn.AvgPool2d(3, 1, padding=1)(x**2) - mu_x**2
sigma_y = nn.AvgPool2d(3, 1, padding=1)(y**2) - mu_y**2
sigma_xy = nn.Av... | 535 | 24.52381 | 65 | py |
TrianFlow | TrianFlow-master/core/networks/structures/ransac.py | import torch
import numpy as np
import os, sys
import torch.nn as nn
import pdb
import cv2
class reduced_ransac(nn.Module):
def __init__(self, check_num, thres, dataset):
super(reduced_ransac, self).__init__()
self.check_num = check_num
self.thres = thres
self.dataset = dataset
... | 3,145 | 45.955224 | 189 | py |
TrianFlow | TrianFlow-master/core/networks/structures/depth_model.py | '''
This code was ported from existing repos
[LINK] https://github.com/nianticlabs/monodepth2
'''
from __future__ import absolute_import, division, print_function
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional a... | 7,964 | 36.394366 | 92 | py |
TrianFlow | TrianFlow-master/core/networks/structures/flowposenet.py | import torch
import torch.nn as nn
from torch import sigmoid
from torch.nn.init import xavier_uniform_, zeros_
def conv(in_planes, out_planes, kernel_size=3):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, padding=(kernel_size-1)//2, stride=2),
nn.ReLU(inplace=True... | 1,951 | 30.483871 | 104 | py |
TrianFlow | TrianFlow-master/core/networks/structures/feature_pyramid.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from net_utils import conv
import torch
import torch.nn as nn
class FeaturePyramid(nn.Module):
def __init__(self):
super(FeaturePyramid, self).__init__()
self.conv1 = conv(3, 16, kernel_size=3, stride=2)
self.conv2... | 1,586 | 40.763158 | 77 | py |
TrianFlow | TrianFlow-master/core/networks/structures/inverse_warp.py | from __future__ import division
import torch
import torch.nn.functional as F
pixel_coords = None
def set_id_grid(depth):
global pixel_coords
b, h, w = depth.size()
i_range = torch.arange(0, h).view(1, h, 1).expand(
1, h, w).type_as(depth) # [1, H, W]
j_range = torch.arange(0, w).view(1, 1, w... | 10,018 | 36.107407 | 119 | py |
TrianFlow | TrianFlow-master/core/networks/structures/pwc_tf.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from net_utils import conv, deconv, warp_flow
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'external'))
# from correlation_package.correlation import Correlation
# from spatial_correlation_sampler import S... | 8,423 | 45.541436 | 97 | py |
TrianFlow | TrianFlow-master/core/networks/structures/net_utils.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import pdb
import numpy as np
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
pa... | 2,088 | 32.693548 | 119 | py |
TrianFlow | TrianFlow-master/core/dataset/kitti_raw.py | import os, sys
import numpy as np
import imageio
from tqdm import tqdm
import torch.multiprocessing as mp
import pdb
def process_folder(q, static_frames, test_scenes, data_dir, output_dir, stride=1):
while True:
if q.empty():
break
folder = q.get()
if folder in static_frames.key... | 8,568 | 40.8 | 150 | py |
TrianFlow | TrianFlow-master/core/dataset/nyu_v2.py | import os, sys
import numpy as np
import imageio
import cv2
import copy
import h5py
import scipy.io as sio
import torch
import torch.utils.data
import pdb
from tqdm import tqdm
import torch.multiprocessing as mp
def collect_image_list(path):
# Get ppm images list of a folder.
files = os.listdir(path)
sorte... | 13,298 | 36.997143 | 150 | py |
TrianFlow | TrianFlow-master/core/dataset/kitti_2012.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from kitti_prepared import KITTI_Prepared
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'evaluation'))
from evaluate_flow import get_scaled_intrinsic_matrix, eval_flow_avg
import numpy as np
import cv2
import cop... | 2,288 | 35.333333 | 110 | py |
TrianFlow | TrianFlow-master/core/dataset/kitti_odo.py | import os, sys
import numpy as np
import imageio
from tqdm import tqdm
import torch.multiprocessing as mp
def process_folder(q, data_dir, output_dir, stride=1):
while True:
if q.empty():
break
folder = q.get()
image_path = os.path.join(data_dir, folder, 'image_2/')
dump_... | 3,745 | 37.22449 | 122 | py |
TrianFlow | TrianFlow-master/core/dataset/kitti_prepared.py | import os, sys
import numpy as np
import cv2
import copy
import torch
import torch.utils.data
import pdb
class KITTI_Prepared(torch.utils.data.Dataset):
def __init__(self, data_dir, num_scales=3, img_hw=(256, 832), num_iterations=None):
super(KITTI_Prepared, self).__init__()
self.data_dir = data_d... | 4,630 | 33.559701 | 128 | py |
TrianFlow | TrianFlow-master/core/visualize/profiler.py | import os
import time
import torch
import pdb
class Profiler(object):
def __init__(self, silent=False):
self.silent = silent
torch.cuda.synchronize()
self.start = time.time()
self.cache_time = self.start
def reset(self, silent=None):
if silent is None:
silen... | 887 | 25.117647 | 76 | py |
AlignShift | AlignShift-master/setup.py | import os
import platform
import subprocess
import time
from setuptools import Extension, dist, find_packages, setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
dist.Distribution().fetch_build_eggs(['Cython', 'numpy>=1.11.1'])
import numpy as np # noqa: E402, isort:skip
from Cython.Build impo... | 6,587 | 30.222749 | 79 | py |
AlignShift | AlignShift-master/nn/utiles.py | from torch._six import container_abcs
import collections.abc
from itertools import repeat
def as_triple(x, d_value=1):
if isinstance(x, container_abcs.Iterable):
x = list(x)
if len(x)==2:
x = [d_value] + x
return x
else:
return [d_value] + [x] * 2
def _ntuple_same... | 1,076 | 25.268293 | 86 | py |
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