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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X-VLM | X-VLM-master/Captioning_scst.py | import argparse
import os
import math
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models import load_pretrained
from models.model_captioning import X... | 11,209 | 38.471831 | 154 | py |
X-VLM | X-VLM-master/Grounding.py | import argparse
import os
import math
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.model_retrieval import XVLM
from models.tokenization_bert ... | 13,266 | 40.984177 | 156 | py |
X-VLM | X-VLM-master/Retrieval.py | import argparse
import os
import sys
import math
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.model_retrieval ... | 15,875 | 40.560209 | 128 | py |
X-VLM | X-VLM-master/Captioning_pretrain.py | import argparse
import copy
import os
import sys
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
import math
import torch
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch.optim import Optimizer
... | 8,365 | 37.376147 | 123 | py |
X-VLM | X-VLM-master/run.py | # Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts (https://arxiv.org/abs/2111.08276)
# Github: https://github.com/zengyan-97/X-VLM
# Copyright (c) 2022, ByteDance Inc.
# All rights reserved.
import os
import sys
import time
import random
import argparse
from utils.hdfs_io import HADOOP... | 14,363 | 37.304 | 149 | py |
X-VLM | X-VLM-master/scheduler.py | from torch.optim.lr_scheduler import LambdaLR
def create_scheduler(args, optimizer):
if 'num_training_steps' not in args:
args['num_training_steps'] = args['epochs'] * args['step_per_epoch']
print("### num_training_steps, ", args['num_training_steps'], flush=True)
if isinstance(args['num_warmup_s... | 1,124 | 37.793103 | 93 | py |
X-VLM | X-VLM-master/Grounding_bbox_pretrain.py | import argparse
import copy
import os
import sys
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
import math
from pathlib import Path
import torch
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torc... | 8,881 | 37.450216 | 122 | py |
X-VLM | X-VLM-master/NLVR_pretrain.py | import argparse
import copy
import os
import sys
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
import math
import torch
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch.optim import Optimizer
... | 8,328 | 37.206422 | 123 | py |
X-VLM | X-VLM-master/Captioning.py | import argparse
import os
import math
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.model_captioning import XVLM
import utils
from utils.checkp... | 10,797 | 38.992593 | 142 | py |
X-VLM | X-VLM-master/Pretrain.py | # Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts (https://arxiv.org/abs/2111.08276)
# Github: https://github.com/zengyan-97/X-VLM
# Copyright (c) 2022, ByteDance Inc.
# All rights reserved.
import argparse
import os
import sys
import ruamel.yaml as yaml
import numpy as np
import rando... | 12,587 | 41.527027 | 148 | py |
X-VLM | X-VLM-master/VQA.py | import argparse
import os
import math
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.model_vqa import XVLM
from models.tokenization_bert import ... | 11,104 | 38.240283 | 145 | py |
X-VLM | X-VLM-master/dataset/nlvr_dataset.py | import json
import os
from torch.utils.data import Dataset
from PIL import Image
from dataset.utils import pre_caption
class nlvr_dataset(Dataset):
def __init__(self, ann_file, transform, image_root):
self.ann = []
for f in ann_file:
self.ann += json.load(open(f, 'r'))
self.tra... | 1,179 | 25.818182 | 69 | py |
X-VLM | X-VLM-master/dataset/pretrain_dataset.py | # Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts (https://arxiv.org/abs/2111.08276)
# Github: https://github.com/zengyan-97/X-VLM
# Copyright (c) 2022, ByteDance Inc.
# All rights reserved.
import json
import copy
import math
import random
import sys
import re
import io
import tracebac... | 19,598 | 39.661826 | 141 | py |
X-VLM | X-VLM-master/dataset/grounding_dataset.py | import json
import os
import math
import random
from random import random as rand
import torch
from torch.utils.data import Dataset
from torchvision.transforms.functional import hflip, resize
from PIL import Image
from dataset.utils import pre_caption
from refTools.refer_python3 import REFER
class grounding_datase... | 4,868 | 31.898649 | 106 | py |
X-VLM | X-VLM-master/dataset/coco_karpathy_dataset.py | import os
import json
import random
from collections import Counter
import torch
from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url
from PIL import Image
from dataset.utils import pre_caption
class coco_karpathy_train(Dataset):
def __init__(self, transform, image_root, ann... | 3,851 | 28.860465 | 101 | py |
X-VLM | X-VLM-master/dataset/utils.py | import re
import json
import os
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
import utils
from tqdm import tqdm
from utils.hdfs_io import hexists, hcopy, hopen
from vqaTools.vqaEval import VQAEval
from refTools.evaluation.refEvaluation import RefEvaluation
def pre... | 11,446 | 29.283069 | 143 | py |
X-VLM | X-VLM-master/dataset/vqa_dataset.py | import os
import json
import random
from random import random as rand
from PIL import Image
from torch.utils.data import Dataset
from dataset.utils import pre_question
from torchvision.transforms.functional import hflip
from transformers import BertTokenizer, RobertaTokenizer
class vqa_dataset(Dataset):
def __... | 3,697 | 29.816667 | 112 | py |
X-VLM | X-VLM-master/dataset/dist_dataset.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts (https://arxiv.org/abs/2111.08276)
# Github: https://github.com/zengyan-97/X-VLM
# Copyright (c) 2022, ByteDance Inc.
# All rights reserved.
import sys
from typing import List, Any
import war... | 3,528 | 36.147368 | 116 | py |
X-VLM | X-VLM-master/dataset/re_dataset.py | import json
import os
from torch.utils.data import Dataset
from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
Image.MAX_IMAGE_PIXELS = None
from dataset.utils import pre_caption
class re_train_dataset(Dataset):
def __init__(self, ann_file, transform, image_root, max_words=3... | 2,206 | 26.5875 | 76 | py |
X-VLM | X-VLM-master/dataset/__init__.py | import os
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from PIL import Image
from dataset.re_dataset import re_train_dataset, re_eval_dataset
from dataset.pretrain_dataset import ImageTextJsonDataset, RegionTextJsonDataset
from dataset.nlvr_dataset import nlvr_dataset
from da... | 10,549 | 47.842593 | 161 | py |
X-VLM | X-VLM-master/models/model_captioning_pretrain.py | import copy
import torch
from transformers import BertTokenizer
from models.xbert import BertLMHeadModel
from models.xroberta import RobertaForCausalLM
from models import XVLMBase, load_pretrained
class XVLM(XVLMBase): # for domain pretrain
def __init__(self, config):
super().__init__(config, load_vis... | 2,298 | 38.637931 | 136 | py |
X-VLM | X-VLM-master/models/model_vqa.py | import copy
from models.xbert import BertLMHeadModel
from models.xroberta import RobertaForCausalLM
from models import XVLMBase, load_pretrained
import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
class XVLM(XVLMBase):
def __init__(self, config):
super().__init__(confi... | 9,751 | 45.218009 | 136 | py |
X-VLM | X-VLM-master/models/model_nlvr_pretrain.py | import torch
from torch import nn
import torch.nn.functional as F
from models import XVLMBase, load_pretrained
from models.xbert import BertConfig
from models.xroberta import RobertaConfig
class XVLM(XVLMBase):
def __init__(self, config):
config_text = RobertaConfig.from_json_file(config['text_config']) ... | 5,299 | 44.299145 | 118 | py |
X-VLM | X-VLM-master/models/swin_transformer.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import numpy as np
from scipy import interpolate
import torch
import to... | 26,827 | 40.021407 | 126 | py |
X-VLM | X-VLM-master/models/xroberta.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | 74,692 | 41.730549 | 213 | py |
X-VLM | X-VLM-master/models/xbert.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | 89,162 | 42.073913 | 213 | py |
X-VLM | X-VLM-master/models/model_captioning.py | import copy
import torch
import torch.nn.functional as F
from transformers import BertTokenizer
from models.xbert import BertLMHeadModel
from models.xroberta import RobertaForCausalLM
from models import XVLMBase, load_pretrained
class XVLM(XVLMBase): # for domain pretrain
def __init__(self, config):
s... | 6,822 | 47.390071 | 146 | py |
X-VLM | X-VLM-master/models/vit.py | import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import _cfg, PatchEmbed
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_, DropPath
class Mlp(nn.Module):
""" MLP as used in ... | 10,198 | 40.125 | 123 | py |
X-VLM | X-VLM-master/models/model_nlvr.py | from models import XVLMBase, build_mlp, load_pretrained
from models.xbert import BertConfig
from models.xroberta import RobertaConfig
import torch
from torch import nn
import torch.nn.functional as F
class XVLM(XVLMBase):
def __init__(self, config):
config_text = RobertaConfig.from_json_file(config['tex... | 4,575 | 48.73913 | 148 | py |
X-VLM | X-VLM-master/models/xvlm.py | # Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts (https://arxiv.org/abs/2111.08276)
# Github: https://github.com/zengyan-97/X-VLM
# Copyright (c) 2022, ByteDance Inc.
# All rights reserved.
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distri... | 23,481 | 45.133595 | 129 | py |
X-VLM | X-VLM-master/models/model_bbox_pretrain.py | import torch
from models import XVLMBase, load_pretrained
class XVLM(XVLMBase):
def __init__(self, config):
super().__init__(config, load_vision_params=False, load_text_params=False,
use_contrastive_loss=False, use_matching_loss=False, use_mlm_loss=False, use_bbox_loss=True)
... | 1,231 | 48.28 | 117 | py |
X-VLM | X-VLM-master/models/box_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Utilities for bounding box manipulation and GIoU.
"""
import torch
from torchvision.ops.boxes import box_area
def box_cxcywh_to_xyxy(x): # 这个用了
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 *... | 1,523 | 24.4 | 70 | py |
X-VLM | X-VLM-master/models/clip_vit.py | # Copyright 2021 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 15,843 | 42.889197 | 173 | py |
X-VLM | X-VLM-master/models/model_retrieval.py | import torch
from models import XVLMBase, load_pretrained
class XVLM(XVLMBase):
def __init__(self, config):
super().__init__(config, load_vision_params=False, load_text_params=False,
use_contrastive_loss=True, use_matching_loss=True, use_mlm_loss=False, use_bbox_loss=False)
... | 1,349 | 47.214286 | 123 | py |
X-VLM | X-VLM-master/models/model_pretrain.py | import torch
from models import XVLMBase
class XVLM(XVLMBase):
def __init__(self, config):
super().__init__(config, load_vision_params=True, load_text_params=True,
use_contrastive_loss=True, use_matching_loss=True, use_mlm_loss=True, use_bbox_loss=True, config_text=None)
def ... | 1,656 | 43.783784 | 132 | py |
X-VLM | X-VLM-master/accelerators/accelerator.py | # -*- coding: utf-8 -*-
# Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts (https://arxiv.org/abs/2111.08276)
# Github: https://github.com/zengyan-97/X-VLM
# Copyright (c) 2022, ByteDance Inc.
# All rights reserved.
from logging import Logger
import torch
from torch.optim import Optimiz... | 1,062 | 31.212121 | 116 | py |
X-VLM | X-VLM-master/accelerators/apex_ddp_accelerator.py | # -*- coding: utf-8 -*-
# Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts (https://arxiv.org/abs/2111.08276)
# Github: https://github.com/zengyan-97/X-VLM
# Copyright (c) 2022, ByteDance Inc.
# All rights reserved.
import os
import random
import sys
from typing import Tuple, Union, Opti... | 4,068 | 38.504854 | 116 | py |
X-VLM | X-VLM-master/utils/__init__.py | import json
import os
import time
from collections import defaultdict, deque, OrderedDict
import datetime
import numpy as np
import torch
import torch.distributed as dist
from utils.cider.pyciderevalcap.ciderD.ciderD import CiderD
class ScstRewardCriterion(torch.nn.Module):
CIDER_REWARD_WEIGHT = 1
def __... | 11,272 | 29.96978 | 94 | py |
X-VLM | X-VLM-master/utils/checkpointer.py | # Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts (https://arxiv.org/abs/2111.08276)
# Github: https://github.com/zengyan-97/X-VLM
# Copyright (c) 2022, ByteDance Inc.
# All rights reserved.
from typing import Union, Dict, List, Tuple, Any, Callable
import logging
import os
import re
im... | 1,629 | 33.680851 | 116 | py |
X-VLM | X-VLM-master/utils/torch_io.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts (https://arxiv.org/abs/2111.08276)
# Github: https://github.com/zengyan-97/X-VLM
# Copyright (c) 2022, ByteDance Inc.
# All rights reserved.
import io
import torch
from .hdfs_io import hopen... | 906 | 27.34375 | 116 | py |
sbvqa | sbvqa-master/utils.py | import json
import os
import logging
import h5py
from evalTools.vqaEval import VQAEval
from evalTools.vqa import VQA
def load_questions_answers(filePath):
with h5py.File(filePath, 'r') as hf:
questions_train = hf.get(u'ques_train').value
questions_test = hf.get(u'ques_test').value
ans_tr... | 3,845 | 33.035398 | 100 | py |
sbvqa | sbvqa-master/eval_SpeechMod.py | import json
import sys
import time
import h5py
import numpy as np
from keras.preprocessing.sequence import pad_sequences
from keras.utils.np_utils import to_categorical
import utils as U
from config_speech import *
from models import SpeechMod
try:
loadWeightsFile = sys.argv[1]
except IndexError as e:
print(... | 4,045 | 38.666667 | 107 | py |
sbvqa | sbvqa-master/models.py | import keras.backend as K
class TextMod(object):
""" model initialization """
def __init__(self, fc_dimension, vocab):
self.fc_dimension = fc_dimension
self.vocab = vocab
""" build model """
def build_model(self, max_len):
from keras.models import Model
from keras.la... | 4,978 | 38.204724 | 106 | py |
sbvqa | sbvqa-master/train_SpeechMod.py | import json
import sys
import time
import h5py
import numpy as np
from keras.preprocessing.sequence import pad_sequences
from keras.utils.generic_utils import Progbar
from keras.utils.np_utils import to_categorical
import utils as U
from config_speech import *
from models import SpeechMod
try:
# to start trainin... | 7,599 | 45.91358 | 119 | py |
sbvqa | sbvqa-master/train_TextMod.py | import json
import sys
import time
import h5py
import numpy as np
from keras.utils.generic_utils import Progbar
from keras.utils.np_utils import to_categorical
import utils as U
from config_text import *
from models import TextMod
try:
# to start training from pre-existing model
loadWeightsFile = sys.argv[1]... | 6,646 | 43.019868 | 119 | py |
sbvqa | sbvqa-master/eval_TextMod.py | import json
import sys
import h5py
import numpy as np
from keras.utils.np_utils import to_categorical
import utils as U
from config_text import *
from models import TextMod
try:
loadWeightsFile = sys.argv[1]
except IndexError as e:
print("Must provide as argument path to file containing model weights")
r... | 3,462 | 35.840426 | 107 | py |
FixRes | FixRes-main/hubconf.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from tqdm import tqdm
import torch
import hashlib
import os
import re
import shutil
import sys
import tempfile
try:
f... | 5,269 | 35.597222 | 123 | py |
FixRes | FixRes-main/transforms_v2.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torchvision.transforms.functional as F
from torchvision import transforms
import torch
import math
im... | 6,335 | 30.68 | 111 | py |
FixRes | FixRes-main/imnet_finetune/pnasnet.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
Code from https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
with some... | 18,314 | 43.13253 | 108 | py |
FixRes | FixRes-main/imnet_finetune/resnext_wsl.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Optional list of dependencies required by the package
'''
Code From : https://github.com/facebookresearch/WSL-Image... | 3,386 | 39.321429 | 97 | py |
FixRes | FixRes-main/imnet_finetune/Res.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.mo... | 11,382 | 36.943333 | 106 | py |
FixRes | FixRes-main/imnet_finetune/samplers.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from torch.utils.data.sampler import BatchSampler
import torch
import numpy as np
from torch.utils.data.dataloader import ... | 3,579 | 34.445545 | 138 | py |
FixRes | FixRes-main/imnet_finetune/train.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import os.path as osp
from typing import Optional
import torch
import torch.distributed
import torch.nn as nn
im... | 14,952 | 41.722857 | 207 | py |
FixRes | FixRes-main/imnet_finetune/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torchvision.transforms.functional as F
from torchvision import transforms
import numpy as np
class R... | 2,935 | 33.139535 | 111 | py |
FixRes | FixRes-main/imnet_extract/pnasnet.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
Code from https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
with some... | 18,325 | 43.159036 | 108 | py |
FixRes | FixRes-main/imnet_extract/resnext_wsl.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Optional list of dependencies required by the package
'''
Code From : https://github.com/facebookresearch/WSL-Image... | 3,386 | 39.321429 | 97 | py |
FixRes | FixRes-main/imnet_extract/Res.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
Code from : https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
'''
import torch.nn as nn
... | 11,486 | 36.910891 | 106 | py |
FixRes | FixRes-main/imnet_extract/samplers.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from torch.utils.data.sampler import BatchSampler
import torch
import numpy as np
from torch.utils.data.dataloader import ... | 3,579 | 34.445545 | 138 | py |
FixRes | FixRes-main/imnet_extract/train.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import os.path as osp
from typing import Optional
import torch
import torch.distributed
import torch.nn as nn
im... | 7,624 | 38.102564 | 192 | py |
FixRes | FixRes-main/imnet_extract/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torchvision.transforms.functional as F
from torchvision import transforms
class Resize(transforms.Res... | 2,904 | 33.176471 | 111 | py |
FixRes | FixRes-main/imnet_evaluate/pnasnet.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
Code from https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
with some... | 18,314 | 43.13253 | 108 | py |
FixRes | FixRes-main/imnet_evaluate/resnext_wsl.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Optional list of dependencies required by the package
'''
Code From : https://github.com/facebookresearch/WSL-Image... | 3,386 | 39.321429 | 97 | py |
FixRes | FixRes-main/imnet_evaluate/Res.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
Code from : https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
'''
import torch.nn as nn
... | 11,481 | 36.894389 | 106 | py |
FixRes | FixRes-main/imnet_evaluate/samplers.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from torch.utils.data.sampler import BatchSampler
import torch
import numpy as np
from torch.utils.data.dataloader import ... | 3,579 | 34.445545 | 138 | py |
FixRes | FixRes-main/imnet_evaluate/train.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import os.path as osp
from typing import Optional
import torch
import torch.distributed
import torch.nn as nn
im... | 7,892 | 33.021552 | 192 | py |
FixRes | FixRes-main/imnet_evaluate/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torchvision.transforms.functional as F
from torchvision import transforms
class Resize(transforms.Re... | 2,905 | 32.790698 | 111 | py |
FixRes | FixRes-main/imnet_resnet50_scratch/samplers.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from torch.utils.data.sampler import BatchSampler
import torch
import numpy as np
from torch.utils.data.dataloader import ... | 3,579 | 34.445545 | 138 | py |
FixRes | FixRes-main/imnet_resnet50_scratch/train.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import os.path as osp
from typing import Optional
import torch
import torch.distributed
import torch.nn as nn
im... | 9,202 | 40.642534 | 180 | py |
FixRes | FixRes-main/imnet_resnet50_scratch/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torchvision.transforms.functional as F
from torchvision import transforms
import numpy as np
class ... | 2,883 | 31.772727 | 111 | py |
isbi2017-part3 | isbi2017-part3-master/nets/resnet_utils.py | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 10,613 | 40.623529 | 80 | py |
pyshoe | pyshoe-master/ins_tools/geometry_helpers.py | ### borrowed from https://github.com/matthew-brett/transforms3d/blob/master/transforms3d/taitbryan.py
import numpy as np
import math
_MAX_FLOAT = np.maximum_sctype(np.float)
_FLOAT_EPS = np.finfo(np.float).eps
def quat2mat(q):
''' Calculate rotation matrix corresponding to quaternion
Parameters
---------... | 11,293 | 27.811224 | 101 | py |
pyshoe | pyshoe-master/ins_tools/EKF.py | import numpy as np
from numpy import linalg as LA
import ins_tools.LSTM as lstm #remove if there is no pytorch installation
import ins_tools.SVM as SVM #remove if there is no sci-kit-learn installation
from ins_tools.util import *
from ins_tools.geometry_helpers import quat2mat, mat2quat, euler2quat, quat2euler
from sk... | 9,143 | 36.170732 | 150 | py |
pyshoe | pyshoe-master/ins_tools/LSTM.py | import torch
import numpy as np
device = torch.device("cpu")
class LSTM(torch.nn.Module):
def __init__(self):
super(LSTM, self).__init__()
self.lstm = torch.nn.LSTM(
input_size=6,
hidden_size=90,
num_layers=4,
batch_first=True,
dropout=0.0... | 1,482 | 36.075 | 93 | py |
AGI | AGI-master/IG_quantitative.py | # %%
# from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torchvision import models
# from models.resnet import resnet20
import numpy as np
import matplotlib.pyplot as plt
import argparse
... | 4,125 | 28.683453 | 110 | py |
AGI | AGI-master/saliency_quantitative.py | # %%
# from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torchvision import models
# from models.resnet import resnet20
import numpy as np
import matplotlib.pyplot as plt
import argparse
... | 4,072 | 28.302158 | 110 | py |
AGI | AGI-master/AGI_main.py |
# %%
# from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torchvision import models
import numpy as np
import matplotlib.pyplot as plt
import argparse
import os
import cv2
from utils impo... | 7,205 | 29.533898 | 118 | py |
AGI | AGI-master/utils.py | import torch
import torch.nn.functional as F
import torch.nn as nn
import cv2
import numpy as np
from matplotlib import pyplot as plt
from torch.utils.data.sampler import Sampler
from torchvision import transforms, datasets
from PIL import Image
import json
class Normalize(nn.Module) :
def __init__(self, mean, ... | 4,607 | 29.315789 | 88 | py |
AGI | AGI-master/evaluator.py |
#%%
# from evaluation import CausalMetric, auc, gkern
import pickle
import torch
import torch.nn as nn
import argparse
from torchvision import models
from evaluation import CausalMetric, auc, gkern
from utils import Normalize, pre_processing, pgd_step
import pandas as pd
#%%
parser = argparse.ArgumentParser(descriptio... | 2,773 | 32.02381 | 110 | py |
AGI | AGI-master/AGI_quantitive.py |
# %%
# from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torchvision import models
import numpy as np
import matplotlib.pyplot as plt
import argparse
import os
import cv2
from utils impo... | 5,712 | 30.39011 | 118 | py |
AGI | AGI-master/evaluator_gpu1.py |
#%%
# from evaluation import CausalMetric, auc, gkern
import pickle
import torch
import torch.nn as nn
import argparse
from torchvision import models
from evaluation import CausalMetric, auc, gkern
from utils import Normalize, pre_processing, pgd_step
import pandas as pd
#%%
parser = argparse.ArgumentParser(descriptio... | 2,767 | 31.952381 | 110 | py |
AGI | AGI-master/evaluator_demo.py |
#%%
# from evaluation import CausalMetric, auc, gkern
import pickle
import torch
import torch.nn as nn
import argparse
from torchvision import models
from evaluation import CausalMetric, auc, gkern
from utils import Normalize, pre_processing, pgd_step
import pandas as pd
#%%
parser = argparse.ArgumentParser(descriptio... | 2,444 | 30.753247 | 110 | py |
AGI | AGI-master/evaluation.py | from torch import nn
from tqdm import tqdm
from scipy.ndimage.filters import gaussian_filter
from utils import *
HW = 224 * 224 # image area
n_classes = 1000
def gkern(klen, nsig):
"""Returns a Gaussian kernel array.
Convolution with it results in image blurring."""
# create nxn zeros
inp = np.zeros(... | 6,741 | 40.617284 | 134 | py |
AGI | AGI-master/AGI_quantitive_gpu0.py |
# %%
# from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torchvision import models
import numpy as np
import matplotlib.pyplot as plt
import argparse
import os
import cv2
from utils impo... | 5,712 | 30.39011 | 118 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/test_network.py | import torch
from util import util
import models
from models import dist_model as dm
from IPython import embed
use_gpu = False # Whether to use GPU
spatial = True # Return a spatial map of perceptual distance.
# Linearly calibrated models (LPIPS)
model = models.PerceptualLoss(model='net-lin', net='ale... | 1,774 | 33.803922 | 150 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/train.py | import torch.backends.cudnn as cudnn
cudnn.benchmark=False
sys.path.insert(0, './')
import numpy as np
import time
import os
from models import dist_model as dm
from data import data_loader as dl
import argparse
from util.visualizer import Visualizer
from IPython import embed
parser = argparse.ArgumentParser()
parser.... | 5,530 | 52.182692 | 269 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/perceptual_loss.py |
from __future__ import absolute_import
import sys
import scipy
import scipy.misc
import numpy as np
import torch
from torch.autograd import Variable
import models
use_gpu = True
ref_path = './imgs/ex_ref.png'
pred_path = './imgs/ex_p1.png'
ref_img = scipy.misc.imread(ref_path).transpose(2, 0, 1) / 255.
pred_img =... | 1,553 | 27.254545 | 85 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/models/base_model.py | import os
import torch
from torch.autograd import Variable
from pdb import set_trace as st
from IPython import embed
class BaseModel():
def __init__(self):
pass;
def name(self):
return 'BaseModel'
def initialize(self, use_gpu=True, gpu_ids=[0]):
self.use_gpu = use_gpu
... | 1,622 | 26.508475 | 77 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/models/pretrained_networks.py | from collections import namedtuple
import torch
from torchvision import models as tv
from IPython import embed
class squeezenet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(squeezenet, self).__init__()
pretrained_features = tv.squeezenet1_1(pretrained=pretrained... | 6,533 | 34.901099 | 109 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/models/util.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from skimage.metrics import structural_similarity as compare_ssim
import torch
from torch.autograd import Variable
from . import dist_model
class PerceptualLoss(torch.nn.Module):
def _... | 5,767 | 34.826087 | 172 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/models/networks_basic.py |
from __future__ import absolute_import
import sys
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable
import numpy as np
from pdb import set_trace as st
from skimage import color
from IPython import embed
import PerceptualSimilarity.models.pretrained_networks as pn
fro... | 7,536 | 39.090426 | 134 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/models/dist_model.py |
from __future__ import absolute_import
import sys
import numpy as np
import torch
from torch import nn
import os
from collections import OrderedDict
from torch.autograd import Variable
import itertools
from .base_model import BaseModel
from scipy.ndimage import zoom
import fractions
import functools
import skimage.tr... | 11,819 | 40.328671 | 177 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/util/util.py | from __future__ import print_function
import numpy as np
from PIL import Image
import numpy as np
import os
import matplotlib.pyplot as plt
import torch
def load_image(path):
if(path[-3:] == 'dng'):
import rawpy
with rawpy.imread(path) as raw:
img = raw.postprocess()
elif(path[-3:]... | 1,433 | 28.265306 | 72 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/data/custom_dataset_data_loader.py | import torch.utils.data
from data.base_data_loader import BaseDataLoader
import os
def CreateDataset(dataroots,dataset_mode='2afc',load_size=64,):
dataset = None
if dataset_mode=='2afc': # human judgements
from dataset.twoafc_dataset import TwoAFCDataset
dataset = TwoAFCDataset()
elif datas... | 1,482 | 36.075 | 138 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/data/image_folder.py | ################################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
# Modified the original code so that it also loads images from the current
# directory as well as the subdirectories
###############################... | 2,261 | 30.416667 | 94 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/data/dataset/twoafc_dataset.py | import os.path
import torchvision.transforms as transforms
from data.dataset.base_dataset import BaseDataset
from data.image_folder import make_dataset
from PIL import Image
import numpy as np
import torch
# from IPython import embed
class TwoAFCDataset(BaseDataset):
def initialize(self, dataroots, load_size=64):
... | 2,411 | 35.545455 | 99 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/data/dataset/base_dataset.py | import torch.utils.data as data
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
def name(self):
return 'BaseDataset'
def initialize(self):
pass
| 237 | 17.307692 | 43 | py |
MSSR | MSSR-main/degradation/PerceptualSimilarity/data/dataset/jnd_dataset.py | import os.path
import torchvision.transforms as transforms
from data.dataset.base_dataset import BaseDataset
from data.image_folder import make_dataset
from PIL import Image
import numpy as np
import torch
from IPython import embed
class JNDDataset(BaseDataset):
def initialize(self, dataroot, load_size=64):
... | 1,792 | 32.203704 | 75 | py |
MSSR | MSSR-main/degradation/codes/patch_inference.py | import torch
from utils import *
import model
import torchvision.transforms.functional as TF
import argparse
parser = argparse.ArgumentParser(description='Apply the trained model to create a dataset')
parser.add_argument('--gpu', type=str, help='gpu num')
parser.add_argument('--n_GPUs', default=1, type=int, help='ngpu'... | 3,104 | 56.5 | 170 | py |
MSSR | MSSR-main/degradation/codes/test.py | import argparse
import os
import torch.optim as optim
import torch.utils.data
import torchvision.utils as tvutils
import data_loader as loader
import yaml
import loss
import model
from receptive_cal import *
import utils
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from tqdm import tqd... | 14,982 | 59.906504 | 129 | py |
MSSR | MSSR-main/degradation/codes/crop.py | import utils
# std_list = [0,0.02,0.04,0.06,0.08,0.1]
# paths = [f"/mnt/workspace/DASR/codes/DSN/noise_ablation/{x}.png" for x in std_list]
# for std,path in zip(std_list,paths):
# img = utils.pil_loader(path)
# img_cropped = img.crop((120,10,200,90))
# img_cropped.save(f"/mnt/workspace/DASR/codes/DSN/nois... | 5,084 | 41.375 | 150 | py |
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