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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fine-grained-evals | fine-grained-evals-main/models/ALBEF/optim/novograd.py | """NovoGrad Optimizer.
Original impl by Masashi Kimura (Convergence Lab): https://github.com/convergence-lab/novograd
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
- https://arxiv.org/abs/1905.11286
"""
import torch
from torch.optim.optimizer import Optimizer
i... | 2,925 | 36.512821 | 107 | py |
fine-grained-evals | fine-grained-evals-main/models/ALBEF/optim/sgdp.py | """
SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import ... | 3,231 | 32.319588 | 115 | py |
fine-grained-evals | fine-grained-evals-main/models/ALBEF/optim/lookahead.py | """ Lookahead Optimizer Wrapper.
Implementation modified from: https://github.com/alphadl/lookahead.pytorch
Paper: `Lookahead Optimizer: k steps forward, 1 step back` - https://arxiv.org/abs/1907.08610
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch.optim.optimizer import Optimizer
from c... | 3,815 | 40.032258 | 93 | py |
fine-grained-evals | fine-grained-evals-main/models/ALBEF/optim/optim_factory.py | """ Optimizer Factory w/ Custom Weight Decay
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import optim as optim
from .adafactor import Adafactor
from .adahessian import Adahessian
from .adamp import AdamP
from .lookahead import Lookahead
from .nadam import Nadam
from .novograd import N... | 4,764 | 37.739837 | 100 | py |
fine-grained-evals | fine-grained-evals-main/models/CLIP/SVO.py | import argparse
import os
import time
import datetime
import json
import jsonlines
from tqdm import tqdm
import torch
import clip
from PIL import Image
@torch.no_grad()
def evaluation(model, preprocess, dset, data_dir, device):
model.eval()
start_time = time.time()
scores = []
for example in tqd... | 3,487 | 36.505376 | 120 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/NLVR.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 json
import pickle
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.model_nlvr import XVLM... | 9,554 | 37.22 | 120 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/Grounding_bbox.py | import argparse
import datetime
import json
import math
import os
import random
import time
from pathlib import Path
import numpy as np
import ruamel.yaml as yaml
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import utils
from dataset import create_dataset, create_sampler, create_... | 10,756 | 39.746212 | 135 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 ... | 15,879 | 40.570681 | 128 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/Winoground.py | import argparse
import os
import ruamel.yaml as yaml
import time
import datetime
import json
from pathlib import Path
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
from dataset.utils import pre_caption
from datasets import load_dataset
f... | 5,627 | 41.315789 | 149 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/VSR.py | import argparse
import os
import ruamel.yaml as yaml
import time
import datetime
import json
import jsonlines
from pathlib import Path
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
from dataset.utils import pre_caption
from models.model_p... | 4,495 | 35.852459 | 123 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/VALSE.py | import argparse
import os
import ruamel.yaml as yaml
import time
import datetime
import json
from pathlib import Path
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
from dataset.utils import pre_caption
from models.model_pretrain import XV... | 5,555 | 38.126761 | 148 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/Retrieval_zs.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.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.model_pretrain import XVLM
... | 17,024 | 42.101266 | 148 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/SVO.py | import argparse
import os
import ruamel.yaml as yaml
import time
import datetime
import json
import jsonlines
from pathlib import Path
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
from dataset.utils import pre_caption
from models.model_p... | 5,283 | 39.030303 | 123 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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... | 10,652 | 48.092166 | 161 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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... | 23,954 | 45.245174 | 129 | py |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/X-VLM/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 |
fine-grained-evals | fine-grained-evals-main/models/CLIP_prefix_caption/parse_conceptual.py | import torch
import clip
from torch.utils.data import DataLoader, Dataset
from PIL import Image
import pickle
from tqdm import tqdm
import os
import csv
import threading
import requests
import shutil
import PIL
import json
from typing import List, Tuple, Optional
import argparse
class ConceptualDS(Dataset):
@sta... | 7,906 | 35.606481 | 137 | py |
fine-grained-evals | fine-grained-evals-main/models/CLIP_prefix_caption/model_utils.py | from typing import Tuple
import torch
import torch.nn as nn
from transformers import GPT2LMHeadModel
class MLP(nn.Module):
def forward(self, x):
return self.model(x)
def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):
super(MLP, self).__init__()
layers = []
... | 2,968 | 38.065789 | 139 | py |
fine-grained-evals | fine-grained-evals-main/models/CLIP_prefix_caption/Retrieval.py | import argparse
import os
import numpy as np
import time
import datetime
import json
from pathlib import Path
from tqdm import tqdm
from PIL import Image
import torch
from torch.nn import functional as nnf
from torch.cuda.amp import autocast
import clip
from transformers import GPT2Tokenizer
from model_utils impor... | 8,202 | 36.117647 | 101 | py |
fine-grained-evals | fine-grained-evals-main/models/CLIP_prefix_caption/Winoground.py | import argparse
import os
import time
import datetime
import json
from pathlib import Path
from tqdm import tqdm
import torch
from torch.nn import functional as nnf
import clip
from datasets import load_dataset
from transformers import GPT2Tokenizer
from model_utils import ClipCaptionModel, pad_tokens
@torch.no_g... | 4,919 | 39.661157 | 120 | py |
fine-grained-evals | fine-grained-evals-main/models/CLIP_prefix_caption/VSR.py | import argparse
import os
import time
import datetime
import json
import jsonlines
from pathlib import Path
from tqdm import tqdm
from PIL import Image
import torch
from torch.nn import functional as nnf
import clip
from transformers import GPT2Tokenizer
from model_utils import ClipCaptionModel, pad_tokens
@torch... | 3,926 | 36.759615 | 112 | py |
fine-grained-evals | fine-grained-evals-main/models/CLIP_prefix_caption/VALSE.py | import argparse
import os
import time
import datetime
import json
from pathlib import Path
from tqdm import tqdm
from PIL import Image
import torch
from torch.nn import functional as nnf
import clip
from transformers import GPT2Tokenizer
from model_utils import ClipCaptionModel, pad_tokens
@torch.no_grad()
def ev... | 4,824 | 37.293651 | 151 | py |
fine-grained-evals | fine-grained-evals-main/models/CLIP_prefix_caption/predict.py | # Prediction interface for Cog ⚙️
# Reference: https://github.com/replicate/cog/blob/main/docs/python.md
import clip
import os
from torch import nn
import numpy as np
import torch
import torch.nn.functional as nnf
import sys
from typing import Tuple, List, Union, Optional
from transformers import (
GPT2Tokenizer,
... | 10,724 | 34.39604 | 88 | py |
fine-grained-evals | fine-grained-evals-main/models/CLIP_prefix_caption/train.py | import torch
import torch.nn as nn
from torch.nn import functional as nnf
from torch.utils.data import Dataset, DataLoader
from enum import Enum
from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup
from tqdm import tqdm
import os
import pickle
import sys
import argparse
import... | 16,025 | 42.196765 | 121 | py |
fine-grained-evals | fine-grained-evals-main/models/CLIP_prefix_caption/SVO.py | import argparse
import os
import time
import datetime
import json
import jsonlines
from pathlib import Path
from tqdm import tqdm
from PIL import Image
import torch
from torch.nn import functional as nnf
import clip
from transformers import GPT2Tokenizer
from model_utils import ClipCaptionModel, pad_tokens
@torch... | 4,423 | 39.587156 | 120 | py |
fine-grained-evals | fine-grained-evals-main/models/CLIP_prefix_caption/parse_coco.py | import torch
import skimage.io as io
import clip
from PIL import Image
import pickle
import json
import os
from tqdm import tqdm
import argparse
def main(clip_model_type: str):
device = torch.device('cuda:0')
clip_model_name = clip_model_type.replace('/', '_')
out_path = f"./data/coco/oscar_split_{clip_mo... | 1,884 | 35.25 | 113 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP2/Retrieval.py | import argparse
import os
import numpy as np
import time
import datetime
import json
from pathlib import Path
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torch.cuda.amp import autocast
from PIL import Image
from lavis.models import load_model_and_preprocess
@torch.no_grad()
def evaluat... | 7,981 | 35.117647 | 101 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP2/Winoground.py | import argparse
import os
import time
import datetime
import json
from pathlib import Path
from tqdm import tqdm
import torch
from datasets import load_dataset
from lavis.models import load_model_and_preprocess
@torch.no_grad()
def evaluation(model, dset, vis_processors, text_processors, device):
start_time =... | 3,705 | 39.282609 | 123 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP2/utils.py | import math
import os
import time
from collections import defaultdict, deque
import datetime
import torch
import torch.distributed as dist
# =========================================================================== #
# Learning rate schedules #
# ================... | 9,377 | 31.116438 | 94 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP2/VSR.py | import argparse
import os
import time
import datetime
import json
import jsonlines
from pathlib import Path
from tqdm import tqdm
import torch
from PIL import Image
from lavis.models import load_model_and_preprocess
@torch.no_grad()
def evaluation(model, data, vis_processors, text_processors, device, data_dir):
... | 3,263 | 36.090909 | 112 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP2/VALSE.py | import argparse
import os
import time
import datetime
import json
from pathlib import Path
from tqdm import tqdm
import torch
from PIL import Image
from lavis.models import load_model_and_preprocess
@torch.no_grad()
def evaluation(model, instrument2data, instrument2piece, vis_processors, text_processors, device, ... | 3,941 | 36.542857 | 148 | py |
fine-grained-evals | fine-grained-evals-main/models/BLIP2/SVO.py | import argparse
import os
import time
import datetime
import json
import jsonlines
from pathlib import Path
from tqdm import tqdm
import torch
from PIL import Image
from lavis.models import load_model_and_preprocess
@torch.no_grad()
def evaluation(model, dset, vis_processors, text_processors, device, data_dir):
... | 3,709 | 38.892473 | 120 | py |
fine-grained-evals | fine-grained-evals-main/data/winoground/statistics/compute_statistics.py | import json
import statistics
from tabulate import tabulate
import numpy as np
import random
import scipy.stats
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from tqdm import tqdm
from tags_with_examples import tags_with_examples
import matplotlib.pyplot as plt
from matplotlib.offsetbox impo... | 21,656 | 51.9511 | 219 | py |
GLN | GLN-master/setup.py | from setuptools import setup
from torch.utils.cpp_extension import CppExtension, BuildExtension, CUDAExtension
from distutils.command.build import build
from setuptools.command.install import install
from setuptools.command.develop import develop
import os
import subprocess
import platform
BASEPATH = os.path.dirname... | 1,532 | 25.431034 | 127 | py |
GLN | GLN-master/gln/graph_logic/graph_feat.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import numpy as np
import os
import rdkit
from rdkit import Chem
import csv
from gln.mods.mol_gnn.torch_util import MLP
from gln.mods.mol_gnn.gnn_family.utils import get_agg
from gln.mods.mol_gnn.mol_utils impo... | 3,314 | 32.826531 | 102 | py |
GLN | GLN-master/gln/graph_logic/logic_net.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import numpy as np
import os
import rdkit
from rdkit import Chem
import csv
from gln.mods.mol_gnn.mol_utils import SmartsMols, SmilesMols
from gln.common.consts import DEVICE, t_float
from torch_scatter import ... | 2,319 | 34.151515 | 103 | py |
GLN | GLN-master/gln/graph_logic/soft_logic.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import numpy as np
import os
import rdkit
from rdkit import Chem
import csv
from gln.mods.mol_gnn.mol_utils import SmartsMols
from gln.mods.mol_gnn.torch_util import MLP, glorot_uniform
from gln.common.consts ... | 8,879 | 40.302326 | 119 | py |
GLN | GLN-master/gln/mods/mol_gnn/torch_util.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import torch
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
class Lambda(nn.M... | 3,239 | 26.692308 | 104 | py |
GLN | GLN-master/gln/mods/mol_gnn/gnn_family/mean_field.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from gln.mods.mol_gnn.gnn_family.utils import GNNEmbedding, prepare_gnn, get_agg, ReadoutNet
from torch_geometric.nn.conv import MessagePassin... | 3,539 | 40.647059 | 173 | py |
GLN | GLN-master/gln/mods/mol_gnn/gnn_family/ggnn.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from gln.mods.mol_gnn.gnn_family.utils import GNNEmbedding, prepare_gnn, get_agg
from torch_geometric.nn import NNConv, Set2Set
from gln.mods.... | 5,233 | 40.212598 | 105 | py |
GLN | GLN-master/gln/mods/mol_gnn/gnn_family/s2v.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from gln.mods.mol_gnn.gnn_family.utils import GNNEmbedding, prepare_gnn, get_agg, ReadoutNet
from torch_geometric.nn.conv import MessagePassin... | 4,520 | 46.09375 | 182 | py |
GLN | GLN-master/gln/mods/mol_gnn/gnn_family/utils.py | from __future__ import print_function
import os
import sys
import numpy as np
import torch
import random
from functools import partial
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
fro... | 5,676 | 34.48125 | 108 | py |
GLN | GLN-master/gln/mods/mol_gnn/gnn_family/mpnn.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from gln.mods.mol_gnn.gnn_family.utils import GNNEmbedding, prepare_gnn
from torch_geometric.nn import NNConv, Set2Set
from gln.mods.mol_gnn.t... | 1,978 | 38.58 | 105 | py |
GLN | GLN-master/gln/mods/mol_gnn/gnn_family/morganfp.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from gln.mods.mol_gnn.gnn_family.utils import GNNEmbedding
from gln.mods.mol_gnn.torch_util import MLP, NONLINEARITIES
from torch_scatter impo... | 2,632 | 37.15942 | 110 | py |
GLN | GLN-master/gln/mods/mol_gnn/mg_clib/mg_lib.py | import ctypes
import numpy as np
import os
import sys
try:
import torch
except:
print('no torch loaded')
class _mg_lib(object):
def __init__(self, sys_args):
dir_path = os.path.dirname(os.path.realpath(__file__))
self.lib = ctypes.CDLL('%s/build/dll/libmolgnn.so' % dir_path)
atom_... | 4,603 | 41.238532 | 135 | py |
GLN | GLN-master/gln/mods/torchext/jagged_ops.py | import torch
import extlib
try:
import extlib_cuda
except:
print('not loading cuda jagged ops')
from torch.autograd import Function
from torch.nn import Module
import numpy as np
#----------------------
# jagged_log_softmax
#----------------------
class JaggedLogSoftmaxFunc(Function):
@staticmethod
d... | 1,277 | 29.428571 | 120 | py |
GLN | GLN-master/gln/common/consts.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import argparse
import logging
import numpy as np
import torch
import torch.nn as nn
t_float = torch.float32
np_float = np.float32
str_float = "float32"
opts = argparse.ArgumentParser(description='gpu option... | 653 | 23.222222 | 90 | py |
GLN | GLN-master/gln/common/torch_util.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import torch
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from gln.mods.mol_gnn.torch_util imp... | 953 | 21.186047 | 51 | py |
GLN | GLN-master/gln/training/main.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import numpy as np
import os
import sys
import rdkit
from rdkit import Chem
import random
import pickle as cp
import csv
from gln.common.cmd_args import cmd_args
from gln.common.consts import t_float, DEVICE
fr... | 2,462 | 33.208333 | 110 | py |
GLN | GLN-master/gln/test/model_inference.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import rdkit
from rdkit import Chem
import os
import numpy as np
import torch
import pickle as cp
import math
from scipy.special import softmax
from gln.data_process.data_info import DataInfo, load_bin_feats
f... | 6,860 | 39.839286 | 113 | py |
GLN | GLN-master/gln/test/main_test.py | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import numpy as np
import os
import sys
import rdkit
from rdkit import Chem
import random
import csv
from gln.common.cmd_args import cmd_args
from gln.data_process.data_info import DataInfo, load_center_maps
fr... | 4,985 | 33.867133 | 188 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/experiments.py | import os
import shutil
import piq
import torch
import mlflow
import tempfile
from glob import glob
from scipy import ndimage
from typing import Any, Dict, Optional, Union, Tuple, List
import cv2
import numpy as np
from iquaflow.datasets import DSWrapper, DSModifier
from iquaflow.experiments import ExperimentInfo, E... | 8,828 | 30.308511 | 127 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/lowresgen.py | import torch
import numpy as np
import torch.nn.functional as F
from typing import List, Optional, Tuple, Union
class LRSimulator(object):
""" This class degradates an image and generates its lower res """
def __init__(self,img_array,zoom):
self.img_array = img_array
self.... | 18,463 | 43.172249 | 123 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/swd.py |
import numpy as np
import torch
import torch.nn.functional as F
import torchvision
from PIL import Image
from typing import Any, Dict, Optional, List
class SlicedWassersteinDistance:
def __init__(
self,
n_pyramids: Optional[int] = None,
slice_size: int = 7,
n_descriptors: int... | 7,676 | 37.772727 | 110 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/iqf-usecase.py | import os
import shutil
import piq
import torch
from glob import glob
from scipy import ndimage
from typing import Any, Dict, Optional, Union, Tuple, List
import cv2
import mlflow
import numpy as np
from iquaflow.datasets import DSWrapper
from iquaflow.experiments import ExperimentInfo, ExperimentSetup
from iquaflow... | 4,096 | 27.255172 | 98 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/custom_iqf.py | import os
import tempfile
import sys
import json
import cv2
import piq
import torch
import yaml
import signal
import time
import math
import numpy as np
import PIL.Image as pil_image
from glob import glob
from torch.utils.data import DataLoader
from typing import Any, Dict, Optional, List,Union,Tuple
from iquaflow.da... | 34,355 | 31.971209 | 126 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/esrgan.py | import os
import tempfile
import os.path as osp
import cv2
import numpy as np
import torch
import math
import kornia
import PIL.Image as pil_image
from glob import glob
from torchvision import transforms
from models.esrgan import RRDBNet_arch as arch
from lowresgen import LRSimulator
# from custom_iqf import ModelConf... | 3,617 | 33.788462 | 109 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/models/model_fsrcnn.py | import math
from torch import nn
class FSRCNN(nn.Module):
def __init__(self, scale_factor, num_channels=1, d=56, s=12, m=4):
super(FSRCNN, self).__init__()
self.first_part = nn.Sequential(
nn.Conv2d(num_channels, d, kernel_size=5, padding=5//2),
nn.PReLU(d)
)
... | 1,624 | 41.763158 | 119 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/models/msrn/msrn.py | import torch
from torch import nn
import math
# init ICNR to start from NN interpolation
def ICNR(tensor, scale_factor=2, initializer=nn.init.kaiming_normal_):
print('Tensor shape: ' + str(tensor.shape))
OUT, IN, H, W = tensor.shape
sub = torch.zeros(math.ceil(OUT/scale_factor**2), IN, H, W)
sub = init... | 3,452 | 33.878788 | 119 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/models/msrn/perceptual_loss.py | import torch
import torchvision
def gram_matrix(y):
(b, ch, h, w) = y.size()
features = y.view(b, ch, w * h)
features_t = features.transpose(1, 2)
gram = features.bmm(features_t) / (ch * h * w)
return gram
def criterion(a, b):
return torch.mean(torch.abs((a-b)**2).view(-1))
class VGGPerceptu... | 2,033 | 38.115385 | 98 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/models/esrgan/lr_scheduler.py | import math
from collections import Counter
from torch.optim.lr_scheduler import _LRScheduler
class MultiStepRestartLR(_LRScheduler):
""" MultiStep with restarts learning rate scheme.
Args:
optimizer (torch.nn.optimizer): Torch optimizer.
milestones (list): Iterations that will decrease learni... | 3,948 | 43.370787 | 116 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/models/esrgan/base_model.py | import logging
import os
import torch
from collections import OrderedDict
from copy import deepcopy
from torch.nn.parallel import DataParallel, DistributedDataParallel
from models.esrgan import lr_scheduler as lr_scheduler
from utils.esrgan.dist_util import master_only
logger = logging.getLogger('basicsr')
class Ba... | 12,521 | 39.393548 | 114 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/models/esrgan/sr_model.py | import torch
from collections import OrderedDict
from os import path as osp
from tqdm import tqdm
from archs.esrgan import build_network
from losses.esrgan import build_loss
from metrics.esrgan import calculate_metric
from utils.esrgan import get_root_logger, imwrite, tensor2img
from utils.esrgan.registry import MODEL... | 8,259 | 37.962264 | 119 | py |
iquaflow-sisr-use-case | iquaflow-sisr-use-case-main/models/esrgan/esrgan_model.py | import torch
torch.cuda.empty_cache()
from collections import OrderedDict
from utils.esrgan.registry import MODEL_REGISTRY
from .srgan_model import SRGANModel
@MODEL_REGISTRY.register()
class ESRGANModel(SRGANModel):
"""ESRGAN model for single image super-resolution."""
def optimize_parameters(self, current... | 3,197 | 37.071429 | 106 | py |
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