repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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probdet | probdet-master/src/core/setup.py | import numpy as np
import os
import random
import torch
from shutil import copyfile
# Detectron imports
import detectron2.utils.comm as comm
from detectron2.config import get_cfg, CfgNode as CN
from detectron2.engine import default_argument_parser, default_setup
from detectron2.utils.logger import setup_logger
# De... | 8,904 | 33.649805 | 155 | py |
probdet | probdet-master/src/core/__init__.py | import os
def top_dir():
"""
returns project top most directory
:return: (str) Project directory
"""
return os.sep.join(
os.path.dirname(
os.path.realpath(__file__)).split(
os.sep)[
:-2])
def data_dir():
"""
Returns data directory. Data dir... | 609 | 19.333333 | 94 | py |
probdet | probdet-master/src/core/datasets/metadata.py | from collections import ChainMap
# Detectron imports
from detectron2.data import MetadataCatalog
# Useful Dicts for OpenImages Conversion
OPEN_IMAGES_TO_COCO = {'Person': 'person',
'Bicycle': 'bicycle',
'Car': 'car',
'Motorcycle': 'motorcycle',
... | 5,503 | 39.77037 | 114 | py |
probdet | probdet-master/src/core/datasets/convert_openimages_to_coco.py | import argparse
import csv
import cv2
import json
import os
from tqdm import tqdm
# Project imports
import core.datasets.metadata as metadata
def main(args):
dataset_dir = args.dataset_dir
if args.output_dir is None:
output_dir = os.path.expanduser(
os.path.join(dataset_dir, 'COCO-Forma... | 10,246 | 45.789954 | 123 | py |
probdet | probdet-master/src/core/datasets/convert_openimages_odd_to_coco.py | import argparse
import csv
import cv2
import json
import os
from tqdm import tqdm
def main(args):
dataset_dir = args.dataset_dir
if args.output_dir is None:
output_dir = os.path.expanduser(
os.path.join(dataset_dir, 'COCO-Format'))
else:
output_dir = os.path.expanduser(args.o... | 8,433 | 46.117318 | 123 | py |
probdet | probdet-master/src/core/datasets/generate_coco_corrupted_dataset.py | import argparse
import contextlib
import cv2
import joblib
import numpy as np
import os
import random
from joblib import Parallel, delayed
from multiprocessing import Manager, cpu_count
from time import sleep
from tqdm import tqdm
# Project imports
from probabilistic_inference.inference_utils import corrupt
# Fix r... | 4,086 | 27.58042 | 120 | py |
probdet | probdet-master/src/core/datasets/convert_voc_to_coco.py | import argparse
import cv2
import json
import numpy as np
import os
from pascal_voc_tools import XmlParser
def create_coco_lists(ids_list, image_dir, annotations_dir, category_mapper):
"""
Creates lists in coco format to be written to JSON file.
"""
parser = XmlParser()
images_list = []
anno... | 7,734 | 37.869347 | 95 | py |
probdet | probdet-master/src/core/datasets/__init__.py | 0 | 0 | 0 | py | |
probdet | probdet-master/src/core/datasets/setup_datasets.py | import os
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.data.datasets import register_coco_instances
# Project imports
import core.datasets.metadata as metadata
def setup_all_datasets(dataset_dir, image_root_corruption_prefix=None):
"""
Registers all datasets as instances f... | 3,624 | 32.256881 | 147 | py |
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/evaluation_tools/__init__.py | 0 | 0 | 0 | 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/core/visualization_tools/probabilistic_visualizer.py | import numpy as np
import matplotlib as mpl
from scipy.stats import norm, chi2
from detectron2.utils.visualizer import Visualizer, ColorMode, _SMALL_OBJECT_AREA_THRESH
from detectron2.utils.colormap import random_color
class ProbabilisticVisualizer(Visualizer):
"""
Extends detectron2 Visualizer to draw corne... | 13,290 | 36.439437 | 98 | py |
probdet | probdet-master/src/core/visualization_tools/__init__.py | 0 | 0 | 0 | py | |
probdet | probdet-master/src/probabilistic_inference/image_corruptions.py | """
Code for image corruption based on: https://github.com/hendrycks/robustness/tree/master/ImageNet-C/imagenet_c
Code is modified by authors of this paper to support arbitrary image sizes.
"""
import ctypes
import cv2
import numpy as np
import skimage as sk
from io import BytesIO
from PIL import Image as PILImage
fro... | 16,125 | 30.55773 | 109 | 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/__init__.py | 0 | 0 | 0 | py | |
probdet | probdet-master/src/probabilistic_inference/inference_core.py | import cv2
import os
from abc import ABC, abstractmethod
# Detectron Imports
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.modeling import build_model
from core.visualization_tools.probabilistic_visualizer import ProbabilisticVisualizer
# Project Imports
from probabilistic_inference import ... | 5,275 | 35.386207 | 102 | 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/__init__.py | 0 | 0 | 0 | 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_average_precision.py | import numpy as np
import os
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.engine import launch
# Coco evaluator tools
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
# Project imports
from core.setup import setup_config, setup_arg_parser
from probabilist... | 2,965 | 32.325843 | 90 | 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 |
probdet | probdet-master/src/offline_evaluation/__init__.py | 0 | 0 | 0 | py | |
probdet | probdet-master/src/offline_evaluation/average_metrics_over_iou_thresholds.py | import numpy as np
import os
import pickle
from prettytable import PrettyTable
# Detectron imports
from detectron2.engine import launch
# Project imports
from core.setup import setup_config, setup_arg_parser
from offline_evaluation import compute_probabilistic_metrics, compute_calibration_errors
from probabilistic_i... | 8,797 | 41.095694 | 124 | py |
probdet | probdet-master/visualization/visualize_errors.py | import cv2
import numpy as np
import os
import ujson as json
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.engine import launch
# Project imports
from core.setup import setup_config, setup_arg_parser
from core.evaluation_tools import evaluation_utils
from core.visualization_tools.pro... | 13,513 | 36.643454 | 120 | py |
probdet | probdet-master/visualization/visualize_predictions.py | import cv2
import numpy as np
import os
import ujson as json
from scipy.stats import entropy
from matplotlib import cm
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.engine import launch
# Project imports
from core.setup import setup_config, setup_arg_parser
from core.evaluation_too... | 5,668 | 34.879747 | 131 | 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 |
EEGLearn | EEGLearn-master/setup.py | from distutils.core import setup
setup(
name='EEGLearn',
version='1.11',
packages=['eeglearn'],
install_requires=['numpy==1.13.1', 'scipy==0.19.1', 'scikit-learn==0.18.2', 'theano==0.8',
'lasagne @ git+https://github.com/Lasagne/Lasagne.git#egg=lasagne=0.2.dev1'],
url='https:/... | 477 | 33.142857 | 99 | py |
EEGLearn | EEGLearn-master/eeglearn/utils.py | __author__ = 'Pouya Bashivan'
from __future__ import print_function
import math as m
import numpy as np
np.random.seed(123)
import scipy.io
from sklearn.decomposition import PCA
def cart2sph(x, y, z):
"""
Transform Cartesian coordinates to spherical
:param x: X coordinate
:param y: Y coordinate
:... | 4,902 | 36.715385 | 99 | py |
EEGLearn | EEGLearn-master/eeglearn/__init__.py | 0 | 0 | 0 | py | |
EEGLearn | EEGLearn-master/eeglearn/eeg_cnn_lib.py | from __future__ import print_function
import time
import numpy as np
np.random.seed(1234)
from functools import reduce
import math as m
import scipy.io
import theano
import theano.tensor as T
from scipy.interpolate import griddata
from sklearn.preprocessing import scale
from utils import augment_EEG, cart2sph, pol2c... | 24,363 | 46.308738 | 118 | 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_qt.py | import sys
import os
import copy
from PyQt5 import QtWidgets
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtMultimedia import *
import numpy as np
from PIL import Image, ImageQt
# from trimap_painter import PaintWindow
from paint_board import PaintBoard
############... | 19,932 | 33.970175 | 143 | 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/__init__.py | from .generators import * | 25 | 25 | 25 | 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/res_shortcut_dec.py | from networks.decoders.resnet_dec import ResNet_D_Dec
from .self_attention import Self_Attn_trimap, Self_Attn
class ResShortCut_D_Dec(ResNet_D_Dec):
def __init__(self, block, layers, norm_layer=None, large_kernel=False, late_downsample=False):
super(ResShortCut_D_Dec, self).__init__(block, layers, norm_... | 1,063 | 32.25 | 98 | 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_gca_dec.py | from networks.ops import GuidedCxtAtten, SpectralNorm
from networks.decoders.res_shortcut_dec import ResShortCut_D_Dec
class ResGuidedCxtAtten_Dec(ResShortCut_D_Dec):
def __init__(self, block, layers, norm_layer=None, large_kernel=False):
super(ResGuidedCxtAtten_Dec, self).__init__(block, layers, nor... | 1,049 | 36.5 | 92 | py |
FGI-Matting | FGI-Matting-main/networks/decoders/__init__.py | from .resnet_dec import ResNet_D_Dec, BasicBlock
from .res_shortcut_dec import ResShortCut_D_Dec
from .res_gca_dec import ResGuidedCxtAtten_Dec
from .res_shortcut_dec_spatial_attn import ResShortCut_D_Dec_spatial_attn
from .res_shortcut_dec_lfm import ResShortCut_D_Dec_lfm
__all__ = ['res_shortcut_decoder_22', 'res_g... | 1,607 | 27.210526 | 129 | 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/evaluate.py | """
Reimplement evaluation.mat provided by Adobe in python
Output of `compute_gradient_loss` is sightly different from the MATLAB version provided by Adobe (less than 0.1%)
Output of `compute_connectivity_error` is smaller than the MATLAB version (~5%, maybe MATLAB has a different algorithm)
So do not report results ca... | 3,256 | 29.157407 | 119 | 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/config.py | from easydict import EasyDict
# Base default config
CONFIG = EasyDict({})
# to indicate this is a default setting, should not be changed by user
CONFIG.is_default = True
CONFIG.version = "baseline"
CONFIG.phase = "train"
# distributed training
CONFIG.dist = False
# global variables which will be assigned in the runtim... | 4,837 | 31.469799 | 128 | 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/utils/__init__.py | from .logger import *
from .config import *
from .util import *
from .evaluate import * | 87 | 21 | 23 | py |
FGI-Matting | FGI-Matting-main/dataloader/__init__.py | from .Test_dataset import get_Test_dataloader
| 157 | 38.5 | 109 | py |
FGI-Matting | FGI-Matting-main/dataloader/Test_dataset/image_file.py | import os
import glob
import logging
import functools
import numpy as np
class ImageFile(object):
def __init__(self, phase='train'):
# self.logger = logging.getLogger("Logger")
self.phase = phase
self.rng = np.random.RandomState(0)#伪随机数生成器
def _get_valid_names(self, *dirs, shuffle=True... | 5,653 | 38.538462 | 118 | 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/__init__.py | from .data_generator import *
from .image_file import *
from .Test_dataset import * | 83 | 27 | 29 | 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/nonconformist/nc.py | #!/usr/bin/env python
"""
Nonconformity functions.
"""
# Authors: Henrik Linusson
# Yaniv Romano modified RegressorNc class to include CQR
from __future__ import division
import abc
import numpy as np
import sklearn.base
from nonconformist.base import ClassifierAdapter, RegressorAdapter
from nonconformist.base impo... | 17,678 | 27.79316 | 79 | py |
arc | arc-master/third_party/nonconformist/base.py | #!/usr/bin/env python
"""
docstring
"""
# Authors: Henrik Linusson
import abc
import numpy as np
from sklearn.base import BaseEstimator
class RegressorMixin(object):
def __init__(self):
super(RegressorMixin, self).__init__()
@classmethod
def get_problem_type(cls):
return 'regression'
class ClassifierMix... | 3,379 | 20.528662 | 63 | py |
arc | arc-master/third_party/nonconformist/icp.py | #!/usr/bin/env python
"""
Inductive conformal predictors.
"""
# Authors: Henrik Linusson
from __future__ import division
from collections import defaultdict
from functools import partial
import numpy as np
from sklearn.base import BaseEstimator
from nonconformist.base import RegressorMixin, ClassifierMixin
from no... | 13,978 | 30.698413 | 79 | py |
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