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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SLRT | SLRT-main/CiCo/CLCL/dataloaders/dataloader_ph_retrieval.py | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import torch
import os
from torch.utils.data import Dataset
import numpy as np
import pickle
from dataloaders.rawvideo_util import RawVideoExtractor
import random
class p... | 9,546 | 42.995392 | 141 | py |
GP-Tree | GP-Tree-main/utils.py | import torch
import numpy as np
import random
import logging
import argparse
from contextlib import contextmanager
import os
import json
from pathlib import Path
import sys
import warnings
def set_seed(seed, cudnn_enabled=True):
"""for reproducibility
:param seed:
:return:
"""
np.random.seed(seed... | 6,963 | 27.896266 | 112 | py |
GP-Tree | GP-Tree-main/backbone.py | from torch import nn
from torchvision import models
class ResNet18(nn.Module):
def __init__(self, dims=(512, 100), args=None, pretrained=True):
super().__init__()
self.args = args
# pretrained feature extractor
self.FE = models.resnet18(pretrained=pretrained)
self.FE.fc =... | 558 | 26.95 | 68 | py |
GP-Tree | GP-Tree-main/FSCIL/miniImageNet/dataloader.py | from pathlib import Path
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
"""
Mini-imagenet dataset for learning with episodes
Data available here: https://drive.google.com/u/0/uc?id=0B3Irx3uQNoBMQ1FlNXJsZUdYWEE
Episode index list available here: https://github... | 6,405 | 38.54321 | 118 | py |
GP-Tree | GP-Tree-main/FSCIL/miniImageNet/trainer.py | from torchsummary import summary
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import trange
from utils import *
from FSCIL.miniImageNet.dataloader import MiniImagenetEpisodes
from GP_Tree.Learner import ModelBinaryTree
from io import BytesIO
torch.set_printoptions(profile="full")
par... | 15,280 | 40.865753 | 125 | py |
GP-Tree | GP-Tree-main/FSCIL/cub/dataloader.py | from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import pandas as pd
from sklearn.model_selection import train_test_split
from utils import *
def generate_val_indices_set(root='./dataset/CUB_200_2011', val_pct=.067,
base_classes=Non... | 11,946 | 36.569182 | 118 | py |
GP-Tree | GP-Tree-main/FSCIL/cub/trainer.py | from torchsummary import summary
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import trange
from utils import *
from torchvision import transforms
from FSCIL.cub.dataloader import CUB200_Indexed
from GP_Tree.Learner import ModelBinaryTree
from io import BytesIO
torch.set_printoptions(... | 16,111 | 41.067885 | 117 | py |
GP-Tree | GP-Tree-main/GP_Tree/gp_model.py | from gpytorch.utils.quadrature import GaussHermiteQuadrature1D
from torch import nn
from torch.distributions import MultivariateNormal
from utils import *
from GP_Tree.kernel_class import OneClassGPModel
import torch.nn.functional as F
class Likelihood(nn.Module):
def __init__(self):
super().__init__()
... | 7,065 | 40.810651 | 117 | py |
GP-Tree | GP-Tree-main/GP_Tree/tree.py | from GP_Tree.node import Node_VI, Node_Gibbs
from GP_Tree.class_splits import *
from utils import (detach_to_numpy, pytorch_take, pytorch_take2)
import logging
from torch import nn
import torch
from collections import deque
class BinaryTree(nn.Module):
def __init__(self, args, device):
super(BinaryTree, s... | 16,094 | 39.338346 | 114 | py |
GP-Tree | GP-Tree-main/GP_Tree/gp_model_gibbs.py | from collections import namedtuple
import pypolyagamma
from gpytorch.utils.quadrature import GaussHermiteQuadrature1D
from torch import nn
from GP_Tree.kernel_class import OneClassGPModel
import torch.nn.functional as F
from utils import *
NodeGibbsState = namedtuple("NodeGibbsState", ["omega", "f"])
NodeModelState =... | 10,616 | 35.610345 | 112 | py |
GP-Tree | GP-Tree-main/GP_Tree/node.py | from GP_Tree.gp_model import GP_Model
from GP_Tree.gp_model_gibbs import GP_Model_Gibbs
from torch import nn
from utils import *
class Node(nn.Module):
def __init__(self):
super(Node, self).__init__()
self.left_child = None
self.right_child = None
self.model = None
self.dev... | 4,016 | 33.333333 | 107 | py |
GP-Tree | GP-Tree-main/GP_Tree/Learner.py | import torch.nn as nn
from backbone import ResNet18
from GP_Tree.tree import BinaryTree
from utils import *
from sklearn.cluster import KMeans
class Model(nn.Module):
def __init__(self, args):
super(Model, self).__init__()
self.args = args
self.tree = None
self.NN_classifier = True... | 5,353 | 34.223684 | 105 | py |
GP-Tree | GP-Tree-main/GP_Tree/kernel_class.py | from gpytorch import kernels
from gpytorch import constraints
import gpytorch
import torch
from torch import nn
import torch.nn.functional as F
class GPModel(nn.Module):
def __init__(self, jitter_val=1e-3):
super().__init__()
# mean and cov functions
self.jitter_val = jitter_val
def fo... | 2,709 | 40.060606 | 98 | py |
P-MMF | P-MMF-master/min-regularizer.py | #import main
import pandas as pd
import os
import cvxpy as cp
import numpy as np
import math
from tqdm import tqdm,trange
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import torch
from cvxpylayers.torch import CvxpyLayer
import torch.nn as nn
import argparse
"""
Solve the dual problem to opti... | 4,545 | 31.942029 | 117 | py |
P-MMF | P-MMF-master/P-MMF.py | #import main
import pandas as pd
import os
import cvxpy as cp
import numpy as np
import math
from tqdm import tqdm,trange
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import torch
from cvxpylayers.torch import CvxpyLayer
import torch.nn as nn
import argparse
"""
Solve the dual problem to opti... | 11,638 | 35.716088 | 117 | py |
PARSE | PARSE-main/main.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import copy
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import... | 20,715 | 38.160681 | 168 | py |
PARSE | PARSE-main/eval_example.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import copy
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import... | 8,995 | 32.318519 | 126 | py |
PARSE | PARSE-main/library/optmization.py | '''
Training Loops
'''
import numpy as np
import torch
import math
class Optmization():
def __init__(self, optmization_params):
super(Optmization, self).__init__()
self.params = optmization_params
self.lr = self.params['lr']
self.current_epoch = self.params['current_epoch']
... | 3,728 | 30.336134 | 113 | py |
PARSE | PARSE-main/library/utils.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu June 18 10:16:37 2021
@author: patrick
"""
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
from torch.autograd import Variable
import math
import umap
import matplotlib.pyplot as plt
class WeightClipp... | 3,707 | 26.065693 | 118 | py |
PARSE | PARSE-main/library/model.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import print_function
import numpy as np
import copy
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable
import os,sys,inspect
import yam... | 3,373 | 24.560606 | 100 | py |
PARSE | PARSE-main/library/train_loop.py | '''
###Train Loops###
'''
import torch
import numpy as np
from library.optmization import Optmization
from library.utils import *
from library.optmization import update_ema_variables
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
class TrainLoop():
def __init__(self, training_params):
... | 13,724 | 31.142857 | 142 | py |
DistMIS | DistMIS-main/data_parallel.py | ########################################################################################################################
# @author Oriol Aranda (https://github.com/oriolaranda/)
# @date Oct 2021
########################################################################################################################
from... | 7,938 | 39.299492 | 120 | py |
DistMIS | DistMIS-main/utils.py | ########################################################################################################################
# @author Oriol Aranda (https://github.com/oriolaranda/)
# @date Oct 2021
########################################################################################################################
imp... | 6,647 | 32.407035 | 120 | py |
DistMIS | DistMIS-main/model.py | import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow.keras import Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
def down_block(input_tensor, num_filters, norm='bn', k=3):
conv1 = conv_block(input_tensor, num_filters=num_filters, norm=norm, k=k)
... | 4,247 | 44.191489 | 120 | py |
DistMIS | DistMIS-main/exp_parallel.py | ########################################################################################################################
# @author Oriol Aranda (https://github.com/oriolaranda/)
# @date Oct 2021
########################################################################################################################
imp... | 5,102 | 39.181102 | 120 | py |
DistMIS | DistMIS-main/pytorch/experiment_parallelism/code/model.py | import torch
import torch.nn as nn
from torch.nn import Conv3d, ConvTranspose3d, BatchNorm3d, MaxPool3d, AvgPool1d, Dropout3d
from torch.nn import ReLU, Sigmoid
#############
# UTILS #
#############
def print_model(model):
"""
Print model and parameters per layer to debug.
@param model: torch.model
... | 3,330 | 29.281818 | 90 | py |
DistMIS | DistMIS-main/pytorch/experiment_parallelism/code/train.py | import argparse
import json
import time
import cv2
import imageio
import numpy as np
import nibabel as nib
from tqdm import tqdm
from datetime import timedelta, datetime
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader, random_split
from torch.utils.tensorboard import SummaryWri... | 13,011 | 38.075075 | 123 | py |
DistMIS | DistMIS-main/pytorch/data_parallelism/code/model.py | import torch
import torch.nn as nn
from torch.nn import Conv3d, ConvTranspose3d, BatchNorm3d, MaxPool3d, AvgPool1d, Dropout3d
from torch.nn import ReLU, Sigmoid
#############
# UTILS #
#############
def print_model(model):
"""
Print model and parameters per layer to debug.
@param model: torch.model
... | 3,330 | 29.281818 | 90 | py |
DistMIS | DistMIS-main/pytorch/data_parallelism/code/train.py | import argparse
import json
import time
import cv2
import imageio
import numpy as np
import nibabel as nib
from tqdm import tqdm
from datetime import timedelta, datetime
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader, random_split
from torch.utils.tensorboard import SummaryWri... | 13,012 | 37.16129 | 121 | py |
augmix | augmix-master/cifar.py | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 13,852 | 30.412698 | 80 | py |
augmix | augmix-master/imagenet.py | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 15,187 | 29.744939 | 114 | py |
augmix | augmix-master/third_party/WideResNet_pytorch/wideresnet.py | """WideResNet implementation (https://arxiv.org/abs/1605.07146)."""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
"""Basic ResNet block."""
def __init__(self, in_planes, out_planes, stride, drop_rate=0.0):
super(BasicBlock, self).__init__()
se... | 4,017 | 30.637795 | 79 | py |
augmix | augmix-master/third_party/ResNeXt_DenseNet/models/densenet.py | """DenseNet implementation (https://arxiv.org/abs/1608.06993)."""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
"""Bottleneck block for DenseNet."""
def __init__(self, n_channels, growth_rate):
super(Bottleneck, self).__init__()
inter_channels... | 3,988 | 30.65873 | 79 | py |
augmix | augmix-master/third_party/ResNeXt_DenseNet/models/resnext.py | """ResNeXt implementation (https://arxiv.org/abs/1611.05431)."""
import math
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
class ResNeXtBottleneck(nn.Module):
"""ResNeXt Bottleneck Block type C (https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua)."""
expan... | 4,344 | 28.965517 | 117 | py |
augmix | augmix-master/models/cifar/allconv.py | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 2,511 | 31.205128 | 80 | py |
L2CS-Net | L2CS-Net-main/test.py | import os, argparse
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
import torch.backends.cudnn as cudnn
import torchvision
import datasets
from utils import select_devic... | 12,535 | 41.931507 | 206 | py |
L2CS-Net | L2CS-Net-main/utils.py | import numpy as np
import torch
import torch.nn as nn
import os
import scipy.io as sio
import cv2
import math
from math import cos, sin
from pathlib import Path
import subprocess
import re
from model import L2CS
import torchvision
import sys
def atoi(text):
return int(text) if text.isdigit() else text
def natural... | 4,214 | 35.652174 | 118 | py |
L2CS-Net | L2CS-Net-main/model.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import math
import torch.nn.functional as F
class L2CS(nn.Module):
def __init__(self, block, layers, num_bins):
self.inplanes = 64
super(L2CS, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padd... | 2,552 | 33.5 | 84 | py |
L2CS-Net | L2CS-Net-main/datasets.py | import os
import numpy as np
import cv2
import torch
from torch.utils.data.dataset import Dataset
from torchvision import transforms
from PIL import Image, ImageFilter
class Gaze360(Dataset):
def __init__(self, path, root, transform, angle, binwidth, train=True):
self.transform = transform
self.... | 4,837 | 29.620253 | 120 | py |
L2CS-Net | L2CS-Net-main/demo.py | import argparse
import numpy as np
import cv2
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import transforms
import torch.backends.cudnn as cudnn
import torchvision
from PIL import Image
from utils import select_device, draw_gaze
from PIL import Image, ImageOps
... | 5,747 | 34.925 | 132 | py |
L2CS-Net | L2CS-Net-main/train.py | import os
import argparse
import time
import torch.utils.model_zoo as model_zoo
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
import torch.backends.cudnn as cudnn
import torchvision
import datasets
from model import L2... | 15,947 | 40.209302 | 221 | py |
Denise | Denise-main/denise/model_lib_tf.py | """Various models and functions to load them."""
# pylint: disable=invalid-name
import math
import os
import tempfile
from absl import flags
import numpy as np
import tensorflow as tf
from tensorflow import keras as k
flags.DEFINE_string(
'weights_dir_path', None,
'Path to directory where to load weights fro... | 11,643 | 40.437722 | 88 | py |
Denise | Denise-main/denise/algo_tf.py | """Defines the training procedure and loss functions for Denise."""
import tempfile
from absl import flags
import tensorflow as tf
from tensorflow import keras as k
from denise import model_lib_tf
FLAGS = flags.FLAGS
flags.DEFINE_string('tb_logdir', None, 'Path for tensorboard logs.')
flags.DEFINE_string('master',... | 6,345 | 32.4 | 84 | py |
Denise | Denise-main/denise/retrain_market.py | """Retrain already trained models on market data"""
import sys
import shutil
import time
from absl import app
from absl import flags
from denise import algo_tf
from denise import evaluation
from denise import market_matrices # pylint: disable=unused-import
from denise import positive_semidefinite_matrices # pylint: ... | 5,647 | 35.205128 | 95 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/main.py | """
main.py:
Start functions
- Read json/jsonnet config files
- Parse args and override parameters in config files
- Find selected data loader and initialize
- Run Trainer to perform training and testing
"""
import os
import argparse
from tabnanny import verbose
import torch
impor... | 14,484 | 36.721354 | 156 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/tools/prepare_faiss_index.py | import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import torch
from datasets import Features, Sequence, Value, load_dataset
import faiss
from transformers import DPRContextE... | 7,864 | 40.17801 | 192 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/models/T5_batch_knowledge.py |
import copy
import math
import os
import warnings
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.utils.checkpoint import checkpoint
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Co... | 43,512 | 50.252061 | 151 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/models/rag/rag_model.py |
import copy
import math
import os
from turtle import forward
import warnings
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from torch.utils.checkpoint import checkpoint
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
from collec... | 33,592 | 42.234234 | 156 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/models/retriever/retriever_dpr.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
import pytorch_lightning as pl
from transformers import T5EncoderModel, T5Config
from transformers import DPRQuestionEncoder, DPRContextEncoder, DPRConfig
from transformers import BertModel, BertConfig
from eas... | 9,036 | 41.627358 | 145 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/data_loader_okvqa_with_knowledge.py | import os
import re
import sys
import time
import json
import copy
from tqdm import tqdm
import csv
import json
import torch
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import random
import cv2
import base64
from copy import deepcopy
from time import time
from datetime import datetime... | 20,033 | 46.813842 | 153 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/module_parser.py | from ast import Raise
from typing import Optional
from easydict import EasyDict
import torch
class ModuleParser():
"""
This is a module inherited by the dataset class
This class is used to parse the sample to form input/output/decoder_input data
It should be able to process both text-based features an... | 12,034 | 40.5 | 149 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/data_loader_okvqa.py | import os
import re
import sys
import time
import json
import copy
from tqdm import tqdm
import csv
import json
import torch
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import random
import cv2
import base64
from copy import deepcopy
from pprint import pprint
from easydict import Eas... | 20,972 | 42.512448 | 141 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/data_loader_fvqa.py | import os
import re
import sys
import time
import json
import copy
from tqdm import tqdm
import csv
import json
import torch
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import random
import cv2
import base64
from fuzzywuzzy import fuzz
from copy import deepcopy
from pprint import ppr... | 11,660 | 37.87 | 121 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/data_loader_wrapper.py | import sys
import time
import json
import copy
import numpy as np
import json
import torch
from tqdm import tqdm
from copy import deepcopy
from easydict import EasyDict
import logging
logger = logging.getLogger(__name__)
from transformers import T5Tokenizer, T5ForConditionalGeneration
from transformers import BertTo... | 3,110 | 39.934211 | 178 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/data_loader_fvqa_with_knowledge.py | import os
import re
import sys
import time
import json
import copy
from tqdm import tqdm
import csv
import json
import torch
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import random
import cv2
import base64
from copy import deepcopy
from time import time
from datetime import datetime... | 21,683 | 45.234542 | 153 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/datasets/okvqa_datasets.py | import os
import re
import sys
import time
import json
import copy
from tqdm import tqdm
import csv
import json
import torch
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import random
import cv2
import base64
from copy import deepcopy
from pprint import pprint
from easydict import Eas... | 6,757 | 37.617143 | 129 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/datasets/fvqa_datasets_with_passages.py | import os
import re
import sys
import time
import json
import copy
from tqdm import tqdm
import csv
import json
import torch
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import random
import cv2
import base64
from copy import deepcopy
from pprint import pprint
from easydict import Eas... | 7,464 | 36.893401 | 129 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/datasets/okvqa_datasets_with_passages.py | import os
import re
import sys
import time
import json
import copy
from tqdm import tqdm
import csv
import json
import torch
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import random
import cv2
import base64
from copy import deepcopy
from pprint import pprint
from easydict import Eas... | 7,475 | 36.949239 | 129 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/datasets/okvqa_datasets_for_DPR.py | import os
import re
import sys
import time
import json
import copy
from tqdm import tqdm
import csv
import json
import torch
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import random
import cv2
import base64
from copy import deepcopy
from pprint import pprint
from easydict import Eas... | 8,123 | 40.030303 | 129 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/datasets/fvqa_datasets.py | import os
import re
import sys
import time
import json
import copy
from tqdm import tqdm
import csv
import json
import torch
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import random
import cv2
import base64
from copy import deepcopy
from pprint import pprint
from easydict import Eas... | 6,779 | 37.742857 | 129 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/data_loader_manager/datasets/fvqa_datasets_for_DPR.py | import os
import re
import sys
import time
import json
import copy
from tqdm import tqdm
import csv
import json
import torch
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
import random
import cv2
import base64
from copy import deepcopy
from pprint import pprint
from easydict import Eas... | 7,581 | 38.905263 | 129 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/utils/retrieval_metrics.py | import numpy as np
from sklearn.metrics import roc_auc_score, log_loss, mean_squared_error
import torch
def recall(rank, ground_truth, N):
return len(set(rank[:N]) & set(ground_truth)) / float(len(set(ground_truth)))
def precision_at_k(r, k):
"""Score is precision @ k
Relevance is binary (nonzero is rel... | 3,464 | 24.477941 | 82 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/utils/cuda_stats.py | import sys
from subprocess import call
import torch
import logging
logger = logging.getLogger(__name__)
def print_cuda_statistics():
logger.info('__Python VERSION: {}'.format(sys.version))
logger.info('__pyTorch VERSION: {}'.format(torch.__version__))
logger.info('__CUDA VERSION')
call(["nvcc", "--v... | 947 | 40.217391 | 90 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/utils/seed.py | import random
import numpy as np
import torch
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False | 268 | 21.416667 | 45 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/utils/collect_env.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import PIL
from torch.utils.collect_env import get_pretty_env_info
def get_pil_version():
return "\n Pillow ({})".format(PIL.__version__)
def collect_env_info():
env_str = get_pretty_env_info()
env_str += get_pil_version()
... | 338 | 21.6 | 71 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/utils/evaluation.py | from utils.retrieval_metrics import *
import torch
import numpy as np
import multiprocessing
import heapq
from time import time
def ranklist_by_heapq(user_pos_test, test_items, rating, Ks):
item_score = {}
for i in test_items:
item_score[i] = rating[i]
K_max = max(Ks)
K_max_item_score = heap... | 1,934 | 26.253521 | 81 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/utils/metrics_log_callback.py | import collections
from pytorch_lightning.loggers import LightningLoggerBase
from pytorch_lightning.loggers.base import rank_zero_experiment
from pytorch_lightning.utilities import rank_zero_only
class MetricsHistoryLogger(LightningLoggerBase):
"""
This is a logger that logs the metrics history, since PyTorch... | 2,111 | 38.849057 | 143 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/trainers/trig_executor.py | import math
import time
import os
import sys
import scipy
import datetime
import numpy as np
import json
import operator
from trainers.base_executor import BaseExecutor
import wandb
import logging
logger = logging.getLogger(__name__)
from pprint import pprint
from tqdm import tqdm
from easydict import EasyDict
from fu... | 12,304 | 38.313099 | 131 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/trainers/t5_executor_with_knowledge.py | import math
import time
import os
import sys
import scipy
import datetime
import numpy as np
import json
import operator
from trainers.base_executor import BaseExecutor
import wandb
import logging
logger = logging.getLogger(__name__)
from pprint import pprint
from tqdm import tqdm
from easydict import EasyDict
from fu... | 15,008 | 40.233516 | 135 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/trainers/t5_executor.py | import math
import time
import os
import sys
import scipy
import datetime
import numpy as np
import json
import operator
from trainers.base_executor import BaseExecutor
import wandb
import logging
logger = logging.getLogger(__name__)
from pprint import pprint
from tqdm import tqdm
from easydict import EasyDict
from fu... | 11,961 | 37.837662 | 131 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/trainers/dpr_executor.py | import math
import time
import os
import sys
import scipy
import datetime
import numpy as np
import json
import operator
from trainers.base_executor import BaseExecutor
import wandb
import logging
logger = logging.getLogger(__name__)
from pprint import pprint
from tqdm import tqdm
from easydict import EasyDict
from fu... | 25,959 | 40.403509 | 148 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/trainers/metrics_processors.py |
import math
import time
import os
import sys
import scipy
import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from easydict import EasyDict
from tqdm import tqdm
import wandb
import logging
logger = logging.getLogger(__name__)
from utils.vqaEval import VQAEval
from u... | 14,545 | 39.293629 | 162 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/trainers/rag_executor.py | import math
import time
import os
import sys
import scipy
import datetime
import numpy as np
import json
import operator
from trainers.base_executor import BaseExecutor
import wandb
import logging
logger = logging.getLogger(__name__)
from pprint import pprint
from tqdm import tqdm
from easydict import EasyDict
from fu... | 14,535 | 39.946479 | 173 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/src/trainers/base_executor.py | import math
import time
import os
import sys
import scipy
import datetime
import numpy as np
import json
import operator
import wandb
import logging
logger = logging.getLogger(__name__)
from pprint import pprint
from tqdm import tqdm
from easydict import EasyDict
from functools import partial
import torch
import torc... | 2,860 | 33.059524 | 108 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#!/usr/bin/env python
import glob
import os
import torch
from setuptools import find_packages
from setuptools import setup
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_ext... | 2,084 | 28.785714 | 100 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/scene_parser.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
"""
Implements the Scene Parser framework
"""
import numpy as np
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.image_list import to_image_list
from maskrcnn_benchmark.modeling.d... | 18,604 | 51.855114 | 142 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/AttrRCNN.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
"""
Implements the FRCNN with Attribute Head
"""
import numpy as np
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.image_list import to_image_list
from maskrcnn_benchmark.modelin... | 4,502 | 39.567568 | 93 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/attribute_head/inference.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from maskrcnn_benchmark.structures.bounding_box import BoxList
# TODO check if want to return a single BoxList or a composite
# object
class AttributePostPr... | 3,575 | 37.869565 | 88 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/attribute_head/loss.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import torch
from torch.nn import functional as F
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
class A... | 4,334 | 46.119565 | 114 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/attribute_head/attribute_head.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import os
import torch
from maskrcnn_benchmark.modeling.roi_heads.mask_head.mask_head import keep_only_positive_boxes
from .roi_attribute_feature_extractors import make_roi_attribute_feature_extractor
from .roi_attribute_predictors import mak... | 3,103 | 42.71831 | 103 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/attribute_head/roi_attribute_predictors.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import torch
from torch import nn
from torch.nn import functional as F
from .. import registry
@registry.ROI_ATTRIBUTE_PREDICTOR.register("AttributeRCNNPredictor")
class AttributeRCNNPredictor(nn.Module):
def __init__(self, cfg, in_chann... | 2,979 | 39.27027 | 83 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import torch
import torch.nn.functional as F
from torch import nn
class PostProcessor(nn.Module):
"""
From a set of classification scores, box regression and pro... | 2,962 | 33.453488 | 76 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/roi_relation_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import torch
from torch import nn
from torch.nn import functional as F
from .. import registry
from maskrcnn_benchmark.modeling.backbone import resnet
from maskrcnn_bench... | 8,032 | 37.806763 | 92 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/pair_matcher.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import torch
class PairMatcher(object):
"""
This class assigns to each predicted "element" (e.g., a box) a ground-truth
element. Each predicted element will ... | 5,253 | 45.087719 | 88 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/roi_relation_box_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
from .. import registry
from torch import nn
@registry.ROI_RELATION_BOX_PREDICTOR.register("FastRCNNPredictor")
class FastRCNNPredictor(nn.Module):
def __init__(self... | 2,426 | 35.772727 | 89 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import numpy as np
import torch
from torch.nn import functional as F
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.modeling.matcher i... | 40,355 | 57.066187 | 156 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/balanced_positive_negative_pair_sampler.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import torch
class BalancedPositiveNegativePairSampler(object):
"""
This class samples batches, ensuring that they contain a fixed proportion of positives
"""
def __init__(self, batch_size_per_image, positive_fraction):
... | 2,756 | 39.544118 | 92 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/roi_relation_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
from .. import registry
from torch import nn
@registry.ROI_RELATION_PREDICTOR.register("FastRCNNRelationPredictor")
class FastRCNNPredictor(nn.Module):
def __init__(... | 2,382 | 32.56338 | 81 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/contrastive_loss_sample_pairs.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import torch
from torch.nn import functional as F
import scipy
from scipy import sparse
import numpy as np
import numpy.random as npr
def add_rel_blobs(cfg, blobs, propo... | 25,523 | 61.10219 | 144 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/roi_relation_box_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import torch
from torch import nn
from torch.nn import functional as F
from .. import registry
from maskrcnn_benchmark.modeling.backbone import resnet
from maskrcnn_bench... | 7,744 | 35.880952 | 92 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/sparse_targets.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import torch
import torch.nn as nn
class FrequencyBias(nn.Module):
"""
The goal of this is to provide a simplified way of computing
P(predicate | obj1, obj2, img).
"""
def __init__(self, pred_dist, store_cpu=False):
... | 4,393 | 35.92437 | 87 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/relation_head.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
"""
Relation head for predicting relationship between object pairs.
"""
import os.path as op
import numpy as np
import torch
from maskrcnn_benchmark.structures.bounding_... | 16,574 | 52.990228 | 150 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/imp/imp.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
# Reimplemtened by Jianwei Yang (jianwyan@microsoft.com)
# Adapted from https://github.com/jwyang/graph-rcnn.pytorch (Jianwei Yang) and https://github.com/danfeiX/scene-graph-TF-release (Danfei Xu)
"""
Scene Graph Generation by Iterative Messa... | 6,441 | 49.724409 | 140 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/baseline/baseline.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
# Written by Jianwei Yang (jianwyan@microsoft.com)
"""
Baseline (vanilla) model for scene graph generation
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch... | 1,710 | 40.731707 | 101 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/grcnn/grcnn.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
# Reimplemtened by Jianwei Yang (jianwyan@microsoft.com)
# Adapted from https://github.com/jwyang/graph-rcnn.pytorch (Jianwei Yang)
"""
Graph R-CNN for scene graph generation
"""
import numpy as np
import torch
import torch.nn as nn
import to... | 8,685 | 46.206522 | 101 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/grcnn/agcn/agcn.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
# Reimplemtened by Jianwei Yang (jianwyan@microsoft.com)
# Adapted from https://github.com/jwyang/graph-rcnn.pytorch (Jianwei Yang)
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
import time
... | 3,788 | 44.650602 | 110 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/neural_motif/decoder_rnn.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils.rnn import PackedSequence
from typing import Optional, Tuple
from .word_vectors import obj_edge_vectors
# from .highway... | 16,112 | 46.530973 | 109 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/neural_motif/roi_sorter.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import numpy as np
import torch
def enumerate_by_image(im_inds):
im_inds_np = im_inds.cpu().numpy()
initial_ind = int(im_inds_np[0])
s = 0
for i, val in enumerate(im_inds_np):
if val != initial_ind:
yield i... | 5,543 | 38.319149 | 107 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/neural_motif/neuralmotif.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
# Reimnplemetned by Pengchuan Zhang (penzhan@microsoft.com)
"""
Scene Graph Generation by Neural Motif
"""
import math
import json
import os.path as op
import torch
import torch.nn as nn
import torch.nn.functional as F
from .context_encoder ... | 7,767 | 40.763441 | 99 | py |
Retrieval-Augmented-Visual-Question-Answering | Retrieval-Augmented-Visual-Question-Answering-main/materials/scene_graph_benchmark/scene_graph_benchmark/relation_head/neural_motif/context_encoder.py | # Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
# written by Chu Wang during internship at Bing MM team.
import math
import os.path as op
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.utils.rnn import PackedSequence
from torch.autograd import Variable
... | 14,197 | 42.820988 | 115 | py |
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