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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binet | binet-master/img_tools/ImgTools.py | # imports
import numpy as np
from ipywidgets import *
import matplotlib.pyplot as plt
from PIL import Image as PIL_Image
from torchvision.utils import make_grid
"""
Function: imshow
Function used to display a given Tensor defined image
Args:
img (torch.Tensor): image to be displayed
... | 6,184 | 19.081169 | 100 | py |
binet | binet-master/img_tools/Metrics.py | import numpy as np
from skimage import measure
class EvalMetrics:
class SSIM:
"""
Method: calc
Function used to compute the M-SSIM between two Tensor images
Args:
r_img (torch.Tensor): reference or original images (C,H,W)
... | 2,381 | 28.04878 | 81 | py |
binet | binet-master/layers/soft_attention.py | # imports
import torch
import torch.nn as nn
from .conv_bin import ConvBinarizer
"""
Soft Attention Network
Args:
in_dim (int) : input channel dimension
"""
class SoftAttention(nn.Module):
def __init__(self, in_dim, attn_bnd):
super(SoftAttention, self).__init__()
self.attn... | 1,596 | 20.293333 | 52 | py |
binet | binet-master/layers/conv_gru_cell.py | # imports
import torch
import torch.nn as nn
"""
Class ConvGRU_cell
Creates a single Convolutional Gated Recurrent Unit cell
Args:
input_dim (int) : number of channels in input
hidden_dim (int) : dimension of cell's hidden state
kernel_i (int) ... | 2,368 | 31.452055 | 86 | py |
binet | binet-master/layers/conv_bin.py | # imports
import torch.nn as nn
from functions import binz
"""
Convolutional Binarization Network
Args:
bnd (int) : bottle-neck depth
in_dim (int) : input channel dimension
"""
class ConvBinarizer(nn.Module):
def __init__(self, in_dim, bnd):
super(ConvBinarizer, self).__init__(... | 747 | 18.179487 | 46 | py |
AIApp | AIApp-main/ai_apps_analysis/review_analysis_tfidf.py | import pandas as pd
from nltk.corpus import stopwords
from nltk.tokenize import sent_tokenize, word_tokenize
import pandas as pd
import re
from nltk.corpus import stopwords
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.text import TextCollection
import gensim
from gensim import corpora
pd.set_option... | 9,590 | 35.747126 | 110 | py |
AIApp | AIApp-main/ai_apps_analysis/model_analysis.py | import pandas as pd
from analysis_config import Config
Cf = Config()
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('max_colwidth', 200)
def get_models_update_infor(df_tfmodels_information, df_latest):
df_merge = df_tfmodels_information.merge(df_latest, left_on='... | 4,046 | 43.472527 | 128 | py |
AIApp | AIApp-main/ai_apps_analysis/data.py | dic_year_tflite = {1980: 20144, 1981: 7252, 1985: 4, 2009: 19, 2010: 2, 2012: 1, 2013: 5, 2014: 8, 2015: 4, 2016: 15, 2018: 11, 2019: 201, 2020: 703, 2021: 414, 2107: 1}
dic_year_opencv = {1980: 5474, 1981: 1299, 2008: 124, 2009: 2, 2011: 9, 2012: 93, 2013: 188, 2014: 427, 2015: 387, 2016: 925, 2017: 420, 2018: 293, 20... | 2,500,065 | 44,643.035714 | 1,156,496 | py |
AIApp | AIApp-main/ai_apps_analysis/analysis_config.py | class Config:
def __init__(self):
self.privacy_concerns_keywords = ['name', 'email', 'address', 'birth', 'gender', 'phone number', 'location', 'country',
'photo', 'image', 'user ID', 'audio', 'browsing history', 'video', 'search history',
... | 12,416 | 71.614035 | 238 | py |
AIApp | AIApp-main/ai_apps_analysis/reviews_analysis.py | import pandas as pd
from nltk.corpus import stopwords
from nltk.tokenize import sent_tokenize, word_tokenize
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('max_colwidth', 2000)
path_df_aiapp_review = '/Users/yinghua.li/Documents/Pycharm/AIApps/data/analysis_result/d... | 8,273 | 37.483721 | 108 | py |
AIApp | AIApp-main/ai_apps_analysis/plot.py | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as ticker
from data import *
def get_dic_list_high_low(dic):
"""
return [key1, key2], [value1, value2], high->low.
:param dic: <dict>
:return: <list>, [key1, key2], [value1, value2], high->low.
"""
L... | 6,532 | 43.746575 | 235 | py |
AIApp | AIApp-main/identification/identification_config.py | class Config:
def __init__(self):
self.google_category = ['Art & Design', 'Augmented reality', 'Auto & Vehicles', 'Beauty', 'Books & Reference',
'Business', 'Comics', 'Communication', 'Dating', 'Daydream', 'Education', 'Entertainment',
'Events... | 10,203 | 68.414966 | 238 | py |
lazy-training-CNN | lazy-training-CNN-master/cnn/extract_kernel.py | '''Train CIFAR10 with PyTorch.'''
from __future__ import print_function
import torch
torch.manual_seed(58)
import numpy as np
np.random.seed(58)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
import copy
import torch.nn as nn
import torchvision
import torchvision.transforms as transf... | 5,198 | 32.980392 | 117 | py |
lazy-training-CNN | lazy-training-CNN-master/cnn/models.py | '''VGG11/13/16/19 in Pytorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M','M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 128, 128, 'M'... | 3,692 | 35.205882 | 130 | py |
lazy-training-CNN | lazy-training-CNN-master/cnn/train.py | '''Train CIFAR10 with PyTorch.'''
from __future__ import print_function
import torch
# /!\ THOSE LINES MAKE THE WHOLE PROCESS DETERMINISTIC!
torch.manual_seed(58)
import numpy as np
np.random.seed(58)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
import torch.nn as nn
import torch... | 14,242 | 32.355972 | 193 | py |
SegForestNet | SegForestNet-main/core/__init__.py | import types
import argparse
from pathlib import Path
import re
import yaml
import os
import sys
import subprocess
# IMPORTANT: Do not add any imports here otherwise you may break the code below that limits the number of threads! Some libraries, e.g., NumPy, must not be imported before certain environment variables are... | 9,453 | 40.464912 | 210 | py |
SegForestNet | SegForestNet-main/models/classification/ConvNeXt.py | # https://arxiv.org/abs/2201.03545
import core
import models
import torch.nn as nn
class ConvNeXt(nn.Module):
def __init__(self, config, params):
super().__init__()
self.backbone = core.create_object(models.backbones, config.backbone, **params.__dict__)
self.model_name ("ConvNeXt"... | 737 | 32.545455 | 96 | py |
SegForestNet | SegForestNet-main/models/classification/Xception.py | # https://arxiv.org/abs/1610.02357
import core
import models
import torch.nn as nn
class Xception(nn.Module):
def __init__(self, config, params):
super().__init__()
self.backbone = core.create_object(models.backbones, config.backbone, **params.__dict__)
self.model_name ("Xception"... | 640 | 29.52381 | 96 | py |
SegForestNet | SegForestNet-main/models/classification/MobileNetv2.py | # https://arxiv.org/abs/1801.04381
import core
import models
import torch.nn as nn
class MobileNetv2(nn.Module):
def __init__(self, config, params):
super().__init__()
self.backbone = core.create_object(models.backbones, config.backbone, **params.__dict__)
self.model_name ("Mobile... | 775 | 31.333333 | 96 | py |
SegForestNet | SegForestNet-main/models/segmentation/SegForestNet.py | import core
import numpy as np
import torch
import torch.nn as nn
import models
import functools
import copy
from .SegForestTree import *
class SegForestNetEncoder(nn.Module):
def __init__(self, config, input_shape, num_output_features):
super().__init__()
config.features.skips = getattr(... | 13,172 | 49.665385 | 137 | py |
SegForestNet | SegForestNet-main/models/segmentation/PFNet.py | # https://openaccess.thecvf.com/content/CVPR2021/html/Li_PointFlow_Flowing_Semantics_Through_Points_for_Aerial_Image_Segmentation_CVPR_2021_paper.html
import core
import torch
import torch.nn as nn
import functools
from .FCN import FCNEncoder
class PPModule(nn.Module):
def __init__(self, config, params, encoder):... | 13,767 | 48.34767 | 150 | py |
SegForestNet | SegForestNet-main/models/segmentation/SegForestTree.py | import core
import numpy as np
import torch
import torch.nn as nn
import types
from .SegForestComponents import *
class TreeFeatureDecoder(nn.ModuleList):
def __init__(self, config, encoder, num_input_features, num_output_features, effective_num_input_features = -1):
super().__init__()
self.use_re... | 10,431 | 43.016878 | 159 | py |
SegForestNet | SegForestNet-main/models/segmentation/FarSeg.py | # https://arxiv.org/abs/2011.09766
# https://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Foreground-Aware_Relation_Network_for_Geospatial_Object_Segmentation_in_High_Spatial_CVPR_2020_paper.pdf
import core
import torch
import torch.nn as nn
import functools
from .FCN import FCNEncoder
class FarSeg(nn.Module)... | 4,009 | 41.659574 | 167 | py |
SegForestNet | SegForestNet-main/models/segmentation/DeepLabv3p.py | # https://arxiv.org/abs/1802.02611
import core
import torch
import torch.nn as nn
import models
import types
class AtrousSpatialPyramidPooling(nn.ModuleList):
def __init__(self, config, input_features, feature_space_size, backbone):
super().__init__()
# global average pooling
self.append(n... | 3,653 | 36.285714 | 150 | py |
SegForestNet | SegForestNet-main/models/segmentation/UNetpp.py | # https://arxiv.org/abs/1807.10165
import core
import torch
import torch.nn as nn
import utils
from .UNet import ConvBlock, UNetEncoder
class UNetpp(nn.Module):
def __init__(self, config, params):
super().__init__()
if params.input_shape[1] != params.input_shape[2]:
raise Runt... | 3,765 | 45.493827 | 104 | py |
SegForestNet | SegForestNet-main/models/segmentation/FCN.py | # https://arxiv.org/abs/1411.4038
import core
import torch.nn as nn
import models
def FCNEncoder(config, params):
encoder = core.create_object(models.backbones, config.backbone, **params.__dict__)
assert encoder.downsampling == 5
return encoder.to(core.device)
class FCN(nn.Module):
def __init__(self... | 1,676 | 43.131579 | 125 | py |
SegForestNet | SegForestNet-main/models/segmentation/RAFCN.py | # https://arxiv.org/abs/1904.05730
# https://openaccess.thecvf.com/content_CVPR_2019/papers/Mou_A_Relation-Augmented_Fully_Convolutional_Network_for_Semantic_Segmentation_in_Aerial_CVPR_2019_paper.pdf
import core
import torch
import torch.nn as nn
from .FCN import FCNEncoder
class RelationModule(nn.Module):
def _... | 2,940 | 38.743243 | 165 | py |
SegForestNet | SegForestNet-main/models/segmentation/SegForestComponents.py | import torch
from torch.nn.functional import relu
class Line():
num_params = 3
@staticmethod
def compute(x, sample_points):
return (x[:,:2] * sample_points).sum(1) - x[:,-1]
class Square():
num_params = 3
@staticmethod
def compute(x, sample_points):
t = x[:,:2] -... | 7,498 | 35.759804 | 121 | py |
SegForestNet | SegForestNet-main/models/segmentation/UNet.py | # https://arxiv.org/abs/1505.04597
import core
import torch
import torch.nn as nn
import utils
class ConvBlock(nn.Module):
def __init__(self, parent, channels, pooling=False, upsampling=False):
super().__init__()
if pooling:
block = [nn.MaxPool2d(2)]
else:
... | 3,685 | 38.212766 | 124 | py |
SegForestNet | SegForestNet-main/models/backbones/ConvNeXt.py | # https://arxiv.org/abs/2201.03545
import core
import torch
import torch.nn as nn
import utils
class ConvNeXtBlock(nn.Module):
def __init__(self, features, padding, normalization):
super().__init__()
self.block = nn.Sequential(
padding(3),
nn.Conv2d(features, features, 7, g... | 3,206 | 33.117021 | 149 | py |
SegForestNet | SegForestNet-main/models/backbones/Xception.py | # https://arxiv.org/abs/1610.02357
import core
import torch
import torch.nn as nn
import utils
class EntryFlow(nn.ModuleList):
def __init__(self, parent, in_filters, out_filters, additional_activation=True, stride=2, involution=False):
super().__init__()
block = [parent.activation(in_filters)] if ... | 6,292 | 41.234899 | 139 | py |
SegForestNet | SegForestNet-main/models/backbones/MobileNetv2.py | # https://arxiv.org/abs/1801.04381
import core
import torch
import torch.nn as nn
import utils
class InvBottleneck(nn.ModuleList):
def __init__(self, prev_filters, padding, normalization, activation, t, c, n, s, initial_dilation=1, dilation=1, involution=False):
super().__init__()
for sub_index in... | 4,875 | 50.326316 | 186 | py |
SegForestNet | SegForestNet-main/datasets/SegmentationDataset.py | import core
import numpy as np
import rust
import datasets
import types
import PIL.Image
import torch
import itertools
import utils
from .SparseInstanceImage import SparseInstanceImage
from .SegmentationDatasetPatchProviders import *
class SegmentationDataset():
def __init__(self, config, params):
print(f... | 23,323 | 47.794979 | 152 | py |
SegForestNet | SegForestNet-main/datasets/ToyDataset.py | import core
import numpy as np
import torch
import types
import rust
class ToyDataset():
def __init__(self, config, params):
print(f"generating toy dataset ...")
generator = getattr(ToyDataset, f"{config.func.name}_generator")
rng = np.random.RandomState(core.random_seeds[config.r... | 4,415 | 36.423729 | 109 | py |
SegForestNet | SegForestNet-main/datasets/PseudoClassificationDataset.py | import core
import numpy as np
import torch
import PIL.Image
import PIL.ImageDraw
import itertools
import types
class PseudoClassificationDataset():
def __init__(self, config, params):
print("generating dataset ...")
n = config.samples_per_class.training + config.samples_per_class.validat... | 3,261 | 40.820513 | 118 | py |
SegForestNet | SegForestNet-main/utils/involution.py | import types
import torch.nn as nn
# implementation of algorithm 1 from v2 (11 Apr 2021) of https://arxiv.org/abs/2103.06255
class Involution(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, r=1):
super().__init__()
... | 2,419 | 40.724138 | 122 | py |
SegForestNet | SegForestNet-main/utils/modelfitter.py | import core
import os
import time
import datetime
import numpy as np
import json
import concurrent.futures
import queue
import numbers
import traceback
import types
import torch
class Queue(queue.Queue):
def put(self, item):
try:
super().put(item, timeout=2)
return False
ex... | 9,601 | 40.034188 | 256 | py |
SegForestNet | SegForestNet-main/utils/lrscheduler.py | import torch
import numpy as np
class LearningRateScheduler():
def __init__(self, optimizer, min_learning_rate, num_cycles, cycle_length_factor, num_iterations):
self.index = 0
self.optimizer = optimizer
self.min_learning_rate = min_learning_rate
self.cycles = LearningRate... | 1,466 | 38.648649 | 121 | py |
SegForestNet | SegForestNet-main/utils/__init__.py | from .modelfitter import ModelFitter
from .lrscheduler import LearningRateScheduler
from .confusionmatrix import ConfusionMatrix
from .imagegrid import ImageGrid
from .involution import Involution
from .experiments import *
import core
import torch
import torch.nn as nn
import os
import http.client
import urllib
def ... | 2,979 | 30.368421 | 77 | py |
SegForestNet | SegForestNet-main/tasks/classification.py | import core
import datasets
import models
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import utils
import time
class Classification(utils.ModelFitter):
def __init__(self, config, params):
super().__init__(config)
self.config.smoothing = getattr(se... | 6,583 | 43.187919 | 133 | py |
SegForestNet | SegForestNet-main/tasks/semanticsegmentation.py | import core
import datasets
import models
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import utils
import time
import tasks
class SemanticSegmentation(utils.ModelFitter):
def __init__(self, config, params):
super().__init__(config)
prev_task = par... | 12,306 | 53.455752 | 181 | py |
pero-ocr | pero-ocr-master/pero_ocr/layout_engines/cnn_layout_engine.py | import numpy as np
from copy import deepcopy
import time
import cv2
from scipy import ndimage
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import connected_components
import skimage.draw
import shapely.geometry as sg
from pero_ocr.layout_engines import layout_helpers as helpers
from pero_ocr.layout_e... | 16,880 | 42.960938 | 163 | py |
pero-ocr | pero-ocr-master/pero_ocr/layout_engines/torch_parsenet.py | import numpy as np
import cv2
import torch
class Net(object):
def __init__(self, model_path, device, max_mp=5):
self.max_megapixels = max_mp if max_mp is not None else 5
self.device = device
if self.device.type == "cpu":
model_path += ".cpu"
if model_path is not None:... | 5,077 | 38.671875 | 118 | py |
pero-ocr | pero-ocr-master/pero_ocr/ocr_engine/pytorch_ocr_engine.py | # -*- coding: utf-8 -*-
from __future__ import print_function
import torch
from torch import nn
import numpy as np
from functools import partial
from .line_ocr_engine import BaseEngineLineOCR
import sys
# scores_probs should be N,C,T, blank is last class
def greedy_decode_ctc(scores_probs, chars):
if len(scores_... | 2,421 | 31.293333 | 103 | py |
pero-ocr | pero-ocr-master/pero_ocr/decoding/lm_wrapper.py | import numpy as np
import torch
class HiddenState:
def __init__(self, h):
self._h = h
def __getitem__(self, indices):
h = self._for_every(lambda h: h[:, indices])
return HiddenState(h)
def _for_every(self, op):
if isinstance(self._h, tuple):
return tuple(op(pa... | 3,918 | 29.617188 | 128 | py |
pero-ocr | pero-ocr-master/pero_ocr/decoding/decoding_itf.py | import json
import logging
import sys
import time
import torch
from torch.nn import functional as F
from brnolm.language_models import language_model
from pero_ocr.utils import compose_path
from .decoders import GreedyDecoder, CTCPrefixLogRawNumpyDecoder, BLANK_SYMBOL
from .lm_wrapper import LMWrapper
ZERO_LOGITS =... | 4,370 | 30.905109 | 136 | py |
pero-ocr | pero-ocr-master/pero_ocr/document_ocr/page_parser.py | import numpy as np
import logging
from multiprocessing import Pool
import math
import time
import torch.cuda
from pero_ocr.utils import compose_path
from .layout import PageLayout, RegionLayout, TextLine
from pero_ocr.document_ocr import crop_engine as cropper
from pero_ocr.ocr_engine.pytorch_ocr_engine import Pytorc... | 22,222 | 42.151456 | 175 | py |
pero-ocr | pero-ocr-master/user_scripts/parse_folder.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import os
import configparser
import argparse
import cv2
import logging
import logging.handlers
import re
from typing import Set, List, Optional
import traceback
import sys
import time
from multiprocessing import Pool
import torch
from safe_gpu import ... | 15,119 | 41.591549 | 204 | py |
pero-ocr | pero-ocr-master/test/test_decoding/test_lm_wrapper.py | import unittest
import torch
import numpy as np
import os
from pero_ocr.decoding.lm_wrapper import LMWrapper, HiddenState
class DummyModel(torch.nn.Module):
def __init__(self):
super().__init__()
self._model_i = torch.nn.Embedding(4, 1)
self._model_i.weight[0, 0] = 0
self._model_i... | 6,240 | 33.865922 | 103 | py |
pero-ocr | pero-ocr-master/test/test_decoding/test_decoders.py | import unittest
import numpy as np
import torch
from pero_ocr.decoding.decoders import BLANK_SYMBOL
from pero_ocr.decoding.decoders import find_new_prefixes
from pero_ocr.decoding.decoders import GreedyDecoder
from pero_ocr.decoding.decoders import CTCPrefixLogRawNumpyDecoder
from pero_ocr.decoding.decoders import g... | 17,774 | 31.318182 | 124 | py |
RFIB-Code | RFIB-Code-main/src/main.py | import torch
import numpy as np
import time
import network
import dataset
import cost_functions
import evaluations
from early_stopping import EarlyStopping
from tqdm import tqdm
from torch.utils.data import SubsetRandomSampler, random_split
import pandas as pd
import umap_functions
start_time = time.time()
seed = 2026... | 12,032 | 40.493103 | 262 | py |
RFIB-Code | RFIB-Code-main/src/evaluations.py | import torch
from tqdm import tqdm
import numpy as np
import metrics
import cost_functions
import sklearn.ensemble, sklearn.linear_model, sklearn.dummy
from sklearn import preprocessing
import pandas as pd
def evaluate_logistic_regression(model, trainset, testset, device, debugging, numworkers):
model.eval()
... | 9,422 | 34.02974 | 116 | py |
RFIB-Code | RFIB-Code-main/src/umap_functions.py | import umap
import matplotlib.pyplot as plt
import torch
from tqdm import tqdm
from sklearn.preprocessing import StandardScaler
import seaborn as sns
# UMAP plots
def plot(embedding, a_train, y_train, alpha, dataset_type, representation=False):
datasets = ["CelebA_race", "EyePACS", 'fairface_race']
dataset =... | 3,116 | 28.130841 | 91 | py |
RFIB-Code | RFIB-Code-main/src/cost_functions.py | import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Calculate Renyi divergence between two multivariate Gaussians
# mu is mean of 1st distribution, mean of 2nd distribution is 0
# var is variance of 1st distribution, gamma is variance of 2nd distribution
def renyi_divergence(mu, var, alpha, gamma=1... | 3,408 | 34.510417 | 99 | py |
RFIB-Code | RFIB-Code-main/src/network.py | import torch
from torch import nn
from torchvision import models
import torch.nn.functional as F
# Baseline model
class Baseline(nn.Module):
def __init__(self):
super().__init__()
self.resnet = models.resnet50(pretrained=True) # Pretrained on resnet, 50 layers
self.avgpool = nn.AdaptiveAv... | 9,015 | 27.531646 | 112 | py |
RFIB-Code | RFIB-Code-main/src/dataset.py | import torch
import torchvision
from torchvision import transforms
from skimage import io
import numpy as np
import pandas as pd
import os
import main
from sklearn import preprocessing
torch.manual_seed(2022)
np.random.seed(2022)
private_sensitive_equal = main.privateSensitiveEqual
swapVariables = main.swapVariables... | 17,660 | 37.730263 | 128 | py |
RFIB-Code | RFIB-Code-main/src/early_stopping.py | import torch
class EarlyStopping():
"""
Early stopping to stop the training when the loss does not improve after
certain epochs.
"""
def __init__(self, patience=5, min_delta=0):
"""
:param patience: how many epochs to wait before stopping when loss is
not improving
... | 2,235 | 35.064516 | 84 | py |
RFIB-Code | RFIB-Code-main/src/metrics.py | import numpy as np
import torch
def get_accuracy(predictions,y):
accuracy = np.sum(predictions == y) / len(y)
return accuracy
def get_min_accuracy(predictions, y, s):
predictions_0 = predictions[s == 0]
y_0 = y[s == 0]
accuracy_0 = np.sum(predictions_0 == y_0) / (len(y_0) + 1e-10)
predicti... | 3,582 | 24.963768 | 102 | py |
Multi-Grid-Deep-Homography | Multi-Grid-Deep-Homography-main/CCL_pytorch.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# A pytorch implement of CCL as described in [1]
#[1] Nie et al. Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation. TCSVT, 2021.
############ usage ############
# feature_1: bs, c, h, w --- feature maps encoded from i... | 2,660 | 33.558442 | 121 | py |
AirObject | AirObject-main/eval_seq.py | import os
import pickle
import yaml
import argparse
import torch
import numpy as np
from tqdm import tqdm
from model.build_model import build_netvlad, build_seqnet, build_airobj
def get_neighbor(vertex_id,tri):
# get neighbor vertexes of a vertex
helper = tri.vertex_neighbor_vertices
index_pointers = hel... | 9,141 | 34.85098 | 126 | py |
AirObject | AirObject-main/superpoint_extraction.py | import os
import argparse
import yaml
from tqdm import tqdm
import torch
import cv2
from model.build_model import build_superpoint_model
from model.inference import superpoint_inference
from cocoapi.PythonAPI.pycocotools.ytvos import YTVOS
def find(lst, key, value):
ind = []
id = []
for i, dic in enumerat... | 2,834 | 28.842105 | 115 | py |
AirObject | AirObject-main/eval.py | import os
import pickle
import yaml
import argparse
import torch
from torch.utils import data
import numpy as np
from scipy.spatial import Delaunay
from tqdm import tqdm
from model.build_model import build_gcn, build_netvlad, build_seqnet, build_airobj
from datasets.vis.vis import YouTubeVIS
from datasets.uvo.uvo imp... | 14,127 | 39.83237 | 212 | py |
AirObject | AirObject-main/datasets/uvo/uvo.py | from __future__ import print_function
import sys
sys.path.append('.')
import os
from typing import Optional, Union
import cv2
import numpy as np
import pickle
import torch
from torch.utils import data
from cocoapi.PythonAPI.pycocotools.ytvos import YTVOS
__all__ = ["UVO"]
def find(lst, key, value):
indices = []... | 11,057 | 39.065217 | 148 | py |
AirObject | AirObject-main/datasets/ovis/ovis.py | from __future__ import print_function
import sys
sys.path.append('.')
import os
from typing import Optional, Union
import cv2
import numpy as np
import pickle
import torch
from torch.utils import data
from cocoapi.PythonAPI.pycocotools.ytvos import YTVOS
__all__ = ["OVIS"]
def find(lst, key, value):
indices = [... | 10,691 | 39.195489 | 144 | py |
AirObject | AirObject-main/datasets/vis/vis.py | from __future__ import print_function
import sys
sys.path.append('.')
import os
from typing import Optional, Union
import cv2
import numpy as np
import pickle
import torch
from torch.utils import data
from cocoapi.PythonAPI.pycocotools.ytvos import YTVOS
__all__ = ["YouTubeVIS"]
def find(lst, key, value):
indic... | 12,273 | 38.850649 | 145 | py |
AirObject | AirObject-main/datasets/tao/tao.py | from __future__ import print_function
import sys
sys.path.append('.')
import os
from typing import Optional, Union
import cv2
import numpy as np
import PIL.Image as Image
import pickle
import torch
from torch.utils import data
__all__ = ["TAO"]
class TAO(data.Dataset):
r"""A torch Dataset for loading in `the TA... | 10,280 | 40.792683 | 135 | py |
AirObject | AirObject-main/datasets/utils/batch_collator.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import torch
import re
import collections
from torch._six import string_classes
class BatchCollator(object):
'''
pack dict batch
'''
def __init__(self):
super(BatchCollator,self).__init__()
def __call__(self, batch):
data= {}... | 3,470 | 25.496183 | 83 | py |
AirObject | AirObject-main/datasets/utils/preprocess.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import sys
sys.path.append('.')
import torch
from datasets.utils import transforms as T
def unify_size(size_list):
v, _ = size_list.max(0)
max_H, max_W = v[0].item(), v[1].item()
new_H = (1 + (max_H - 1) // 32) * 32
new_W... | 1,757 | 26.046154 | 97 | py |
AirObject | AirObject-main/datasets/utils/postprocess.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import sys
sys.path.append('.')
import os
import torch
import pickle
import numpy as np
def nms_fast(in_corners, H, W, dist_thresh):
"""
Run a faster approximate Non-Max-Suppression on numpy corners shaped:
3xN [x_i,y_i,conf_... | 8,716 | 32.656371 | 133 | py |
AirObject | AirObject-main/datasets/utils/transforms.py | import math
import torch
from torch import nn, Tensor
from torch.nn import functional as F
import torchvision
from typing import List, Tuple, Dict, Optional
@torch.jit.unused
def _resize_image_and_masks_onnx(image, self_min_size, self_max_size, target):
# type: (Tensor, float, float, Optional[Dict[str, Tensor]]) -> ... | 5,222 | 36.847826 | 107 | py |
AirObject | AirObject-main/train/train_netvlad.py | import os
import sys
sys.path.append('.')
import yaml
import argparse
from datetime import datetime
from tqdm import tqdm
import torch
from torch import nn
from torch.utils import data
from torch.utils.tensorboard import SummaryWriter
from datasets.vis.vis import YouTubeVIS
from datasets.utils.batch_collator import vi... | 6,826 | 35.704301 | 147 | py |
AirObject | AirObject-main/train/train_seqnet.py | import os
import sys
sys.path.append('.')
import yaml
import argparse
from datetime import datetime
from tqdm import tqdm
import torch
from torch import nn
from torch.utils import data
from torch.utils.tensorboard import SummaryWriter
from datasets.vis.vis import YouTubeVIS
from datasets.utils.batch_collator import vi... | 7,103 | 35.244898 | 147 | py |
AirObject | AirObject-main/train/train_gcn.py | import os
import sys
sys.path.append('.')
import yaml
import argparse
from datetime import datetime
from tqdm import tqdm
import torch
from torch import nn
from torch.utils import data
from torch.utils.tensorboard import SummaryWriter
from datasets.vis.vis import YouTubeVIS
from datasets.utils.batch_collator import vi... | 8,487 | 37.40724 | 147 | py |
AirObject | AirObject-main/train/train_airobj.py | import os
import sys
sys.path.append('.')
import yaml
import argparse
from datetime import datetime
from tqdm import tqdm
import torch
from torch import nn
from torch.utils import data
from torch.utils.tensorboard import SummaryWriter
from datasets.vis.vis import YouTubeVIS
from datasets.utils.batch_collator import vi... | 7,838 | 35.124424 | 147 | py |
AirObject | AirObject-main/utils/viz.py | import os
import cv2
import copy
import torch
import numpy as np
from utils import cv2_util
### write result to image
def compute_colors_for_labels(labels):
"""
Simple function that adds fixed colors depending on the class
"""
palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
colors = labels[:... | 4,473 | 30.069444 | 118 | py |
AirObject | AirObject-main/utils/imports.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
if torch._six.PY3:
import importlib
import importlib.util
import sys
# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path?utm_medium=organic&utm_source=google_rich_qa&utm_campai... | 843 | 34.166667 | 168 | py |
AirObject | AirObject-main/model/inference.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import sys
sys.path.append('.')
import torch
from datasets.utils.preprocess import preprocess_data
from datasets.utils import postprocess as post
def superpoint_inference(model, batch, data_config, detection_threshold, save_dir=None... | 877 | 26.4375 | 99 | py |
AirObject | AirObject-main/model/build_model.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.nn.functional as F
from model.backbone.resnet_fpn import resnet_fpn_backbone
from model.mask_rcnn.mask_rcnn import MaskRCNN
from model.backbone.fcn import VGGNet
from model.superpoint.vgg_like import VggLike
from model.graph_models.object_descrip... | 7,931 | 36.592417 | 113 | py |
AirObject | AirObject-main/model/netvlad.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
class NetVLADDescriptor(nn.Module):
def __init__(self, config):
super(NetVLADDescriptor, self).__init__()
descriptor_dim = config['descriptor_dim']
vlad_numc = config['vlad_numc']
nfeat ... | 2,780 | 30.602273 | 103 | py |
AirObject | AirObject-main/model/seqnet.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
class SeqNet(nn.Module):
'''SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition IEEE RA-L & ICRA 2021'''
def __init__(self, inDims, outDims, w=1):
super(SeqNet, self).__init__()
self.in... | 717 | 26.615385 | 110 | py |
AirObject | AirObject-main/model/airobject.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from model.graph_models.attention import GraphAtten
class AirObject(nn.Module):
def __init__(self, config):
super(AirObject, self).__init__()
points_encoder_dims = config['points_encoder_dims']
descriptor_dim = config['des... | 4,264 | 29.905797 | 122 | py |
AirObject | AirObject-main/model/backbone/fcn.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models
from torchvision.models.vgg import VGG
class FCNs(nn.Module):
def __init__(self, pretrained_net):
super(FCNs,self).__init__()
self.pretrained_net = pretrained_net
... | 6,262 | 43.41844 | 127 | py |
AirObject | AirObject-main/model/backbone/resnet_fpn.py | import warnings
from collections import OrderedDict
from torch import nn
from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool
from torchvision.ops import misc as misc_nn_ops
from torchvision.models._utils import IntermediateLayerGetter
from torchvision.models import resnet
class... | 5,541 | 43.336 | 118 | py |
AirObject | AirObject-main/model/graph_models/descriptor_loss.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import sys
sys.path.append('.')
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class DescriptorLoss(nn.Module):
'''
loss for object descriptor
'''
def __init__(self, config):
super().... | 1,610 | 29.980769 | 89 | py |
AirObject | AirObject-main/model/graph_models/object_descriptor.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from model.graph_models.attention import GraphAtten
class ObjectDescriptor(nn.Module):
def __init__(self, config):
super(ObjectDescriptor, self).__init__()
points_encoder_dims = config['points_encoder_dims']
descriptor_dim... | 3,678 | 31.27193 | 108 | py |
AirObject | AirObject-main/model/graph_models/attention.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
class GraphAtten(nn.Module):
def __init__(self, nfeat, nhid, nout, alpha=0.2, nheads=8):
super(GraphAtten, self).__init__()
self.attns = [Attention(nfeat, nhid, alpha) for _ in range(nheads)]
for i, attention in... | 1,787 | 29.827586 | 71 | py |
AirObject | AirObject-main/model/mask_rcnn/mask_rcnn.py | from collections import OrderedDict
import torch
from torch import nn
from torchvision.ops import MultiScaleRoIAlign
from torchvision.models.utils import load_state_dict_from_url
from torchvision.models.detection.faster_rcnn import FasterRCNN
from torchvision.models.detection.backbone_utils import resnet_fpn_backbone... | 17,229 | 46.205479 | 114 | py |
AirObject | AirObject-main/model/mask_rcnn/transform.py | import math
import torch
from torch import nn, Tensor
from torch.nn import functional as F
import torchvision
from typing import List, Tuple, Dict, Optional
from .image_list import ImageList
from .roi_heads import paste_masks_in_image
@torch.jit.unused
def _resize_image_and_masks_onnx(image, self_min_size, self_max_... | 11,521 | 40.15 | 115 | py |
AirObject | AirObject-main/model/superpoint/vgg_like.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models
class VggLike(nn.Module):
def __init__(self, pretrained_net):
super(VggLike, self).__init__()
self.pretrained_net = pretrained_net
self.relu = nn.ReLU(inplace=True)
... | 1,414 | 31.159091 | 79 | py |
AirObject | AirObject-main/tracking/track.py | import os
import sys
sys.path.append('.')
import copy
import pickle
import yaml
import argparse
import cv2
import torch
import numpy as np
from scipy.spatial import Delaunay
from tqdm import tqdm
from scipy import optimize
from model.build_model import build_airobj
from utils import viz
def get_neighbor(vertex_id,t... | 7,681 | 35.235849 | 134 | py |
AirObject | AirObject-main/tracking/superpoint_inference.py | import os
import sys
sys.path.append('.')
import argparse
import yaml
from tqdm import tqdm
import torch
import cv2
from model.build_model import build_superpoint_model
from model.inference import superpoint_inference
def validate(configs):
# read configs
## command line config
use_gpu = configs['use_gpu'... | 2,254 | 26.5 | 111 | py |
AirObject | AirObject-main/tracking/maskrcnn_inference.py | import os
import sys
sys.path.append('.')
import argparse
import yaml
from tqdm import tqdm
import torch
import cv2
from model.build_model import build_maskrcnn
from utils.tools import tensor_to_numpy
from datasets.utils import postprocess as post
from datasets.utils.preprocess import preprocess_data
def maskrcnn_in... | 3,038 | 27.138889 | 98 | py |
CRFP | CRFP-main/main.py | #### take reference from
from option import args
from utils import mkExpDir
from dataset import dataloader
from model import CRFP
from loss.loss import get_loss_dict
from trainer import Trainer
import math
import os
import time
import torch
import torch.nn as nn
import warnings
from tqdm import tqdm
warnings.filterw... | 2,971 | 42.705882 | 198 | py |
CRFP | CRFP-main/test_runtime.py | from model import MRCF_runtime as MRCF
import os
import time
import numpy as np
import torch
import torch.nn as nn
from torch.profiler import profile, record_function, ProfilerActivity
from pytorch_memlab import LineProfiler
from pytorch_memlab import MemReporter
from dcn_v2 import DCNv2
def conv_identify(weight, bia... | 8,163 | 42.195767 | 210 | py |
CRFP | CRFP-main/test_video.py | from torch import save
from model import MRCF_test as MRCF
from model import LTE
from utils import flow_to_color
from dataset import dataloader
from utils import calc_psnr_and_ssim_cuda, bgr2ycbcr
import os
import numpy as np
import PIL
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.util... | 31,077 | 55.815356 | 213 | py |
CRFP | CRFP-main/utils.py | import math
import numpy as np
import logging
import cv2
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import pytorch_ssim as pytorch_ssim
class Logger(object):
def __init__(self, log_file_name, logger_name, log_level=logging.DEBUG):... | 16,307 | 35.159645 | 133 | py |
CRFP | CRFP-main/png2mp4.py |
import os
import numpy as np
from PIL import Image
import cv2
from torchaudio import save_encinfo
if __name__ == '__main__':
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
model_code = 15
hr_dcn = True
offset_prop = True
split_ratio = 3
model_name = 'FVSR_x8_simple_v{}_hrdcn_{}_offsetprop_{}_fnet{}_... | 1,904 | 36.352941 | 149 | py |
CRFP | CRFP-main/trainer.py | from utils import calc_psnr_and_ssim_cuda, bgr2ycbcr
import os
import numpy as np
from imageio import imread, imsave, get_writer
from PIL import Image
import random
import time
from math import cos, pi
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as opti... | 34,951 | 49.074499 | 201 | py |
CRFP | CRFP-main/dataset/dataloader.py | from torch.utils.data import DataLoader
from importlib import import_module
def get_dataloader(args):
### import module
m = import_module('dataset.' + args.dataset.lower())
if (args.dataset == 'Vimeo7'):
print('Processing Vimeo7 dataset...')
data_train = getattr(m, 'TrainSet')(args)
... | 1,543 | 50.466667 | 121 | py |
CRFP | CRFP-main/dataset/vimeo7.py |
import os
import random
import pickle
import logging
import numpy as np
import PIL
import pdb
import math
import torch
import torch.utils.data as data
import torchvision.transforms.functional as F
import torch.nn.functional as nnF
from torchvision.transforms import Compose, ToTensor
logger = logging.getLogger('base'... | 16,885 | 37.640732 | 125 | py |
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