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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stellargraph | stellargraph-master/tests/layer/test_cluster_models.py | # -*- coding: utf-8 -*-
#
# Copyright 2020 Data61, CSIRO
#
# 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 law... | 1,978 | 35.648148 | 74 | py |
stellargraph | stellargraph-master/tests/layer/test_attri2vec.py | # -*- coding: utf-8 -*-
#
# Copyright 2018-2020 Data61, CSIRO
#
# 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 applicabl... | 5,167 | 27.086957 | 99 | py |
stellargraph | stellargraph-master/tests/layer/test_cluster_gcn.py | # -*- coding: utf-8 -*-
#
# Copyright 2018-2020 Data61, CSIRO
#
# 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 applicabl... | 5,180 | 28.947977 | 88 | py |
stellargraph | stellargraph-master/tests/layer/test_rgcn.py | # -*- coding: utf-8 -*-
#
# Copyright 2019-2020 Data61, CSIRO
#
# 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 applicabl... | 12,811 | 32.020619 | 88 | py |
stellargraph | stellargraph-master/tests/layer/test_appnp.py | # -*- coding: utf-8 -*-
#
# Copyright 2019-2020 Data61, CSIRO
#
# 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 applicabl... | 10,399 | 32.656958 | 88 | py |
stellargraph | stellargraph-master/tests/layer/test_gcn.py | # -*- coding: utf-8 -*-
#
# Copyright 2018-2020 Data61, CSIRO
#
# 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 applicabl... | 11,000 | 32.336364 | 98 | py |
stellargraph | stellargraph-master/tests/layer/test_node2vec.py | # -*- coding: utf-8 -*-
#
# Copyright 2019-2020 Data61, CSIRO
#
# 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 applicabl... | 3,449 | 29.263158 | 74 | py |
stellargraph | stellargraph-master/tests/layer/test_graphsage.py | # -*- coding: utf-8 -*-
#
# Copyright 2018-2020 Data61, CSIRO
#
# 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 applicabl... | 20,315 | 27.776204 | 94 | py |
stellargraph | stellargraph-master/tests/layer/test_graph_attention.py | # -*- coding: utf-8 -*-
#
# Copyright 2018-2020 Data61, CSIRO
#
# 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 applicabl... | 20,638 | 31.86465 | 85 | py |
stellargraph | stellargraph-master/tests/layer/test_link_inference.py | # -*- coding: utf-8 -*-
#
# Copyright 2018-2020 Data61, CSIRO
#
# 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 applicabl... | 13,763 | 36.606557 | 127 | py |
stellargraph | stellargraph-master/tests/layer/test_knowledge_graph.py | # -*- coding: utf-8 -*-
#
# Copyright 2020 Data61, CSIRO
#
# 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 law... | 18,872 | 36.005882 | 106 | py |
stellargraph | stellargraph-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
import urllib.parse
import docutils
import sphinx
# -- Path setup ---------... | 12,154 | 33.928161 | 296 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/pretrain.py | # -*- encoding: utf-8 -*-
'''
@Time : 2022/06/10 15:51:44
@Author : Chu Xiaokai
@Contact : xiaokaichu@gmail.com
'''
import time
import sys
import os
sys.path.append(os.getcwd())
from dataloader import *
from Transformer4Ranking.model import *
import torch
from torch import nn
from torch.utils.data import Dat... | 5,998 | 35.138554 | 132 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/dataloader.py | # -*- encoding: utf-8 -*-
'''
@Time : 2022/06/10 15:51:44
@Author : Chu Xiaokai
@Contact : xiaokaichu@gmail.com
'''
import math
import torch
import torch.nn.functional as F
import os
import random
from torch.utils.data import Dataset, DataLoader, IterableDataset
import gzip
from functools import reduce
from a... | 8,424 | 37.47032 | 129 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/unbiased_learning.py | # -*- encoding: utf-8 -*-
'''
@Time : 2022/06/12 14:49:28
@Author : Chu Xiaokai
@Contact : xiaokaichu@gmail.com
'''
from baseline_model.utils.sys_tools import find_class
import torch
import numpy as np
import warnings
import sys
from metrics import *
from Transformer4Ranking.model import *
from dataloader im... | 4,451 | 37.713043 | 129 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/submit.py | #!/usr/bin/env python
# coding=utf-8
# File Name: evaluate.py
# Author: Lixin Zou
# Mail: zoulixin15@gmail.com
# Created Time: Tue Sep 13 23:21:03 2022
#### Demo submission to WSDM Cup 2023 #####
# Step 1:
# Get the prediction score of model.
# > ``` python submit.py --emb_dim 768 --nlayer 12 --nhead 12 --dropout 0.1 ... | 2,185 | 32.121212 | 157 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/baseline_model/learning_algorithm/dla.py | """Training and testing the dual learning algorithm for unbiased learning to rank.
See the following paper for more information on the dual learning algorithm.
* Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SI... | 11,065 | 39.985185 | 165 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/baseline_model/learning_algorithm/ipw_rank.py | """Training and testing the inverse propensity weighting algorithm for unbiased learning to rank.
See the following paper for more information on the inverse propensity weighting algorithm.
* Xuanhui Wang, Michael Bendersky, Donald Metzler, Marc Najork. 2016. Learning to Rank with Selection Bias in Personal Searc... | 7,426 | 41.44 | 190 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/baseline_model/learning_algorithm/pairwise_debias.py | """Training and testing the Pairwise Debiasing algorithm for unbiased learning to rank.
See the following paper for more information on the Pairwise Debiasing algorithm.
* Hu, Ziniu, Yang Wang, Qu Peng, and Hang Li. "Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm." In The World Wide Web Conf... | 7,675 | 41.644444 | 181 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/baseline_model/learning_algorithm/base_algorithm.py | """The basic class that contains all the API needed for the implementation of an unbiased learning to rank algorithm.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from matplotlib.cbook import print_cycles
import torch.nn.functional as F
import torc... | 8,278 | 38.802885 | 124 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/baseline_model/learning_algorithm/regression_EM.py | """Training and testing the regression-based EM algorithm for unbiased learning to rank.
See the following paper for more information on the regression-based EM algorithm.
* Wang, Xuanhui, Nadav Golbandi, Michael Bendersky, Donald Metzler, and Marc Najork. "Position bias estimation for unbiased learning to rank i... | 7,819 | 42.687151 | 280 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/baseline_model/learning_algorithm/navie_algorithm.py | """The navie algorithm that directly trains ranking models with clicks.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
from baseline_model.learning_algorithm.base_algorithm import BaseAlgorithm
import baseline_mode... | 4,950 | 37.084615 | 102 | py |
baidu_ultr_dataset | baidu_ultr_dataset-main/Transformer4Ranking/model.py | # -*- encoding: utf-8 -*-
'''
@Time : 2022/06/10 15:51:44
@Author : Chu Xiaokai
@Contact : xiaokaichu@gmail.com
'''
import math
import torch
from torch import nn, Tensor
import torch.nn.functional as F
from torch.nn import TransformerEncoder, TransformerEncoderLayer
from torch.optim.lr_scheduler import Lambda... | 4,708 | 37.284553 | 114 | py |
DaVinci | DaVinci-main/VE.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
import argparse
import os
import ruamel.yaml as yaml
import numpy as np
import random
import tim... | 10,713 | 40.366795 | 136 | py |
DaVinci | DaVinci-main/NLVR.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
import argparse
import os
import ruamel.yaml as yaml
import numpy as np
import random
import tim... | 9,857 | 39.904564 | 136 | py |
DaVinci | DaVinci-main/glue.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. 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
#
# Unless r... | 25,935 | 42.443886 | 136 | py |
DaVinci | DaVinci-main/utils.py | import numpy as np
import io
import os
import time
from collections import defaultdict, deque
import datetime
import torch
import torch.distributed as dist
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
d... | 8,166 | 29.136531 | 94 | py |
DaVinci | DaVinci-main/image_finetune.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
import argparse
import builtins
import os
import random
import shutil
import time
import warning... | 27,204 | 39.665172 | 140 | py |
DaVinci | DaVinci-main/image_sampling.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
import argparse
import os
import sys
import ruamel.yaml as yaml
import numpy as np
import random... | 9,275 | 38.305085 | 172 | py |
DaVinci | DaVinci-main/gen_coco.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
import argparse
import os, sys
import ruamel.yaml as yaml
import numpy as np
import random
impor... | 10,773 | 41.25098 | 166 | py |
DaVinci | DaVinci-main/Pretrain.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
import argparse
import os
import sys
import ruamel.yaml as yaml
import numpy as np
import rando... | 14,283 | 45.832787 | 218 | py |
DaVinci | DaVinci-main/image_linprobe.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
#!/usr/bin/env python
import argparse
import builtins
import os
import random
import shutil
imp... | 25,791 | 39.489796 | 169 | py |
DaVinci | DaVinci-main/VQA.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
import argparse
import os, sys
import ruamel.yaml as yaml
import numpy as np
import random
impor... | 10,600 | 40.901186 | 140 | py |
DaVinci | DaVinci-main/scheduler/plateau_lr.py | """ Plateau Scheduler
Adapts PyTorch plateau scheduler and allows application of noise, warmup.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from .scheduler import Scheduler
class PlateauLRScheduler(Scheduler):
"""Decay the LR by a factor every time the validation loss plateaus."""
d... | 4,140 | 35.324561 | 97 | py |
DaVinci | DaVinci-main/scheduler/tanh_lr.py | """ TanH Scheduler
TanH schedule with warmup, cycle/restarts, noise.
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class TanhLRScheduler(Scheduler):
"""
Hyberbolic-Tan... | 4,045 | 32.438017 | 106 | py |
DaVinci | DaVinci-main/scheduler/cosine_lr.py | """ Cosine Scheduler
Cosine LR schedule with warmup, cycle/restarts, noise.
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from .scheduler import Scheduler
from pdb import set_trace as breakpoint
_logger = logging.getLogger(__name__)
class CosineL... | 4,027 | 33.135593 | 121 | py |
DaVinci | DaVinci-main/scheduler/scheduler.py | from typing import Dict, Any
import torch
class Scheduler:
""" Parameter Scheduler Base Class
A scheduler base class that can be used to schedule any optimizer parameter groups.
Unlike the builtin PyTorch schedulers, this is intended to be consistently called
* At the END of each epoch, before incre... | 4,750 | 43.820755 | 112 | py |
DaVinci | DaVinci-main/scheduler/step_lr.py | """ Step Scheduler
Basic step LR schedule with warmup, noise.
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
import torch
from .scheduler import Scheduler
class StepLRScheduler(Scheduler):
"""
"""
def __init__(self,
optimizer: torch.optim.Optimizer,
... | 1,902 | 28.734375 | 105 | py |
DaVinci | DaVinci-main/scheduler/scheduler_factory.py | """ Scheduler Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from .cosine_lr import CosineLRScheduler
from .tanh_lr import TanhLRScheduler
from .step_lr import StepLRScheduler
from .plateau_lr import PlateauLRScheduler
from torch.optim.lr_scheduler import LambdaLR
def create_scheduler(args, optimizer):... | 4,603 | 39.034783 | 131 | py |
DaVinci | DaVinci-main/dataset/dalle_transforms.py | # --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# B... | 6,719 | 35.923077 | 118 | py |
DaVinci | DaVinci-main/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'))
s... | 1,158 | 27.975 | 82 | py |
DaVinci | DaVinci-main/dataset/utils.py | import re
def pre_question(question,max_ques_words):
question = re.sub(
r"([,.'!?\"()*#:;~])",
'',
question.lower(),
).replace('-', ' ').replace('/', ' ')
question = question.rstrip(' ')
#truncate question
question_words = question.split(' ')
if len(question_words... | 5,225 | 30.865854 | 118 | py |
DaVinci | DaVinci-main/dataset/vqa_dataset.py | import os
import json
import random
from PIL import Image
from torch.utils.data import Dataset
from dataset.utils import pre_question
from collections import Counter
class vqa_dataset(Dataset):
def __init__(self, ann_file, transform, vqa_root, vg_root, eos='[SEP]', split="train", max_ques_words=30, answer_list='./... | 2,702 | 36.541667 | 153 | py |
DaVinci | DaVinci-main/dataset/dist_dataset.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
from typing import List, Any
import warnings
import random
from itertools import cycle
import torch
from torch.utils.data import IterableDataset
from util.hdfs_io import hopen, hlist_files
class DistLineReadingDataset(IterableDataset): # pylint: disable=W0223... | 3,304 | 35.318681 | 113 | py |
DaVinci | DaVinci-main/dataset/ve_dataset.py | import json
import os
from torch.utils.data import Dataset
from PIL import Image
from dataset.utils import pre_caption
class ve_dataset(Dataset):
def __init__(self, ann_file, transform, image_root, max_words=30):
self.ann = json.load(open(ann_file,'r'))
self.transform = transform
s... | 919 | 28.677419 | 80 | py |
DaVinci | DaVinci-main/dataset/caption_dataset.py | import json
import os
import random
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
from dataset.dist_dataset import DistLineReadingDataset
import traceback
from base64 impo... | 8,210 | 31.713147 | 129 | py |
DaVinci | DaVinci-main/dataset/__init__.py | import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from dataset.dalle_transforms import RandomResizedCropAndInterpolationWithTwoPic
from PIL import Image
from timm.data.constants import \
IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_ST... | 7,913 | 49.088608 | 166 | py |
DaVinci | DaVinci-main/dataset/gen_dataset.py | import json
import os
from torch.utils.data import Dataset
from PIL import Image
from dataset.utils import pre_caption
class gen_dataset(Dataset):
def __init__(self, ann_file, transform, image_root, split='train', max_words=30, prompt=''):
self.ann = json.load(open(ann_file,'r'))
self.transform =... | 1,228 | 31.342105 | 97 | py |
DaVinci | DaVinci-main/models/bert.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... | 82,267 | 41.870245 | 213 | py |
DaVinci | DaVinci-main/models/model_vqa.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
from models.davinci_pretrain import DaVinci
import torch
from torch import nn
import torch.nn.f... | 1,496 | 33.813953 | 114 | py |
DaVinci | DaVinci-main/models/resnet.py | from typing import Type, Any, Callable, Union, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
from timm.models.layers import trunc_normal_, DropPath
# from .._internally_replaced_utils import load_state_dict_from_url
try:
from torch.hub import load_state_dict_from_url # noqa: 401
exc... | 25,975 | 39.148377 | 118 | py |
DaVinci | DaVinci-main/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... | 99,022 | 43.029791 | 213 | py |
DaVinci | DaVinci-main/models/vit.py | 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 Vision Trans... | 8,772 | 42.216749 | 120 | py |
DaVinci | DaVinci-main/models/model_nlvr.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
from models.xbert import BertConfig, BertModel
from models.davinci_pretrain import DaVinci
impo... | 2,167 | 38.418182 | 114 | py |
DaVinci | DaVinci-main/models/davinci_pretrain.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
from models.xbert import BertConfig, BertModelImage
from models.bert import BertLMHeadModel
from... | 42,974 | 48.453395 | 302 | py |
DaVinci | DaVinci-main/models/model_linearprobe.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
from models.davinci_pretrain import DaVinci
from torch import nn
class DaVinciLinearProbe(nn.Mo... | 1,617 | 43.944444 | 114 | py |
DaVinci | DaVinci-main/models/model_imageft.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
from models.davinci_pretrain import DaVinci
from torch import nn
class DaVinciImageFT(nn.Module... | 1,713 | 44.105263 | 121 | py |
DaVinci | DaVinci-main/models/dalle_utils.py | # --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# B... | 19,190 | 33.829401 | 128 | py |
DaVinci | DaVinci-main/models/model_glue.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
from models.xbert import BertConfig
from models.davinci_pretrain import DaVinci
from torch impo... | 1,967 | 39.163265 | 114 | py |
DaVinci | DaVinci-main/models/modeling_discrete_vae.py | # --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# B... | 7,573 | 30.823529 | 130 | py |
DaVinci | DaVinci-main/models/model_image_sampling.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
from models.xbert import BertConfig, BertModelImage
from models.bert import BertLMHeadModel
from... | 39,775 | 46.522103 | 309 | py |
DaVinci | DaVinci-main/models/model_ve.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
from models.xbert import BertConfig, BertModel
from models.davinci_pretrain import DaVinci
from... | 1,853 | 39.304348 | 114 | py |
DaVinci | DaVinci-main/models/dall_e/utils.py | import attr
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
logit_laplace_eps: float = 0.1
@attr.s(eq=False)
class Conv2d(nn.Module):
n_in: int = attr.ib(validator=lambda i, a, x: x >= 1)
n_out: int = attr.ib(validator=lambda i, a, x: x >= 1)
kw: int = attr.ib(validator=lambda i... | 1,771 | 29.551724 | 81 | py |
DaVinci | DaVinci-main/models/dall_e/encoder.py | import attr
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from functools import partial
from models.dall_e.utils import Conv2d
@attr.s(eq=False, repr=False)
class EncoderBlock(nn.Module):
n_in: int = attr.ib(validator=lambda i, a, x... | 3,782 | 39.244681 | 117 | py |
DaVinci | DaVinci-main/models/dall_e/decoder.py | import attr
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from functools import partial
from models.dall_e.utils import Conv2d
@attr.s(eq=False, repr=False)
class DecoderBlock(nn.Module):
n_in: int = attr.ib(validator=lambda i, a, x... | 3,943 | 40.515789 | 117 | py |
DaVinci | DaVinci-main/models/dall_e/__init__.py | import io, requests
import torch
import torch.nn as nn
from models.dall_e.encoder import Encoder
from models.dall_e.decoder import Decoder
from models.dall_e.utils import map_pixels, unmap_pixels
def load_model(path: str, device: torch.device = None) -> nn.Module:
if path.startswith('http://') or path.startswit... | 616 | 31.473684 | 68 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/reversible.py | import torch
import torch.nn as nn
from operator import itemgetter
from torch.autograd.function import Function
from torch.utils.checkpoint import get_device_states, set_device_states
# for routing arguments into the functions of the reversible layer
def route_args(router, args, depth):
routed_args = [(dict(), dic... | 5,390 | 33.120253 | 165 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/dalle_pytorch.py | from math import log2, sqrt
import torch
from torch import nn, einsum
import torch.nn.functional as F
import numpy as np
from axial_positional_embedding import AxialPositionalEmbedding
from einops import rearrange
from models.dalle_pytorch import distributed_utils
from models.dalle_pytorch.vae import OpenAIDiscreteVA... | 21,183 | 33.501629 | 170 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/vae.py | import io
import sys
import os
import requests
import PIL
import warnings
import hashlib
import urllib
import yaml
from pathlib import Path
from tqdm import tqdm
from math import sqrt, log
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel, GumbelVQ
import importlib
import torch
from torch import ... | 7,302 | 32.045249 | 111 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/distributed_utils.py | """
Utility functions for optional distributed execution.
To use,
1. set the `BACKENDS` to the ones you want to make available,
2. in the script, wrap the argument parser with `wrap_arg_parser`,
3. in the script, set and use the backend by calling
`set_backend_from_args`.
You can check whether a backend is in use ... | 2,846 | 28.350515 | 79 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/transformer.py | from functools import partial
from itertools import islice, cycle
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange
from models.dalle_pytorch.reversible import ReversibleSequence, SequentialSequence
from models.dalle_pytorch.attention import Attention, SparseAttent... | 8,295 | 34.758621 | 180 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/tokenizer.py | # take from https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
# to give users a quick easy start to training DALL-E without doing BPE
import torch
import youtokentome as yttm
from tokenizers import Tokenizer
from tokenizers.processors import ByteLevel
from transformers import BertTokenizer
import htm... | 9,420 | 34.284644 | 120 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/__init__.py | from models.dalle_pytorch.dalle_pytorch import DALLE, CLIP, DiscreteVAE
from models.dalle_pytorch.vae import OpenAIDiscreteVAE, VQGanVAE
from pkg_resources import get_distribution
# __version__ = get_distribution('dalle_pytorch').version
| 239 | 39 | 71 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/attention.py | from inspect import isfunction
from math import ceil
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from rotary_embedding_torch import apply_rotary_emb
# helpers
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in ... | 13,544 | 34.181818 | 165 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/loader.py | from pathlib import Path
from random import randint, choice
import PIL
from torch.utils.data import Dataset
from torchvision import transforms as T
class TextImageDataset(Dataset):
def __init__(self,
folder,
text_len=256,
image_size=128,
trunca... | 3,456 | 33.57 | 112 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/distributed_backends/deepspeed_backend.py | import json
import os
import torch
from .distributed_backend import DistributedBackend
class DeepSpeedBackend(DistributedBackend):
"""Distributed backend using the DeepSpeed engine."""
BACKEND_MODULE_NAME = 'deepspeed'
BACKEND_NAME = 'DeepSpeed'
def wrap_arg_parser(self, parser):
if not se... | 5,987 | 33.813953 | 78 | py |
DaVinci | DaVinci-main/models/dalle_pytorch/distributed_backends/horovod_backend.py | import torch
from .distributed_backend import DistributedBackend
class HorovodBackend(DistributedBackend):
"""Distributed backend using Horovod."""
BACKEND_MODULE_NAME = 'horovod.torch'
BACKEND_NAME = 'Horovod'
def wrap_arg_parser(self, parser):
return parser
def check_batch_size(self,... | 1,703 | 27.881356 | 71 | py |
DaVinci | DaVinci-main/models/DALLE-pytorch/train_dalle.py | import argparse
from pathlib import Path
import time
from glob import glob
import os
import shutil
import torch
import wandb # Quit early if user doesn't have wandb installed.
from torch.nn.utils import clip_grad_norm_
from torch.optim import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.util... | 22,831 | 33.333835 | 199 | py |
DaVinci | DaVinci-main/models/DALLE-pytorch/setup.py | from setuptools import setup, find_packages
setup(
name = 'dalle-pytorch',
packages = find_packages(),
include_package_data = True,
version = '1.2.1',
license='MIT',
description = 'DALL-E - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/dal... | 1,052 | 22.4 | 65 | py |
DaVinci | DaVinci-main/models/DALLE-pytorch/generate.py | import argparse
from pathlib import Path
from tqdm import tqdm
# torch
import torch
from einops import repeat
# vision imports
from PIL import Image
from torchvision.utils import make_grid, save_image
# dalle related classes and utils
from dalle_pytorch import DiscreteVAE, OpenAIDiscreteVAE, VQGanVAE, DALLE
from... | 4,657 | 31.573427 | 286 | py |
DaVinci | DaVinci-main/models/DALLE-pytorch/train_vae.py | import math
from math import sqrt
import argparse
from pathlib import Path
# torch
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import ExponentialLR
# vision imports
from torchvision import transforms as T
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolde... | 9,491 | 29.037975 | 168 | py |
DaVinci | DaVinci-main/util/checkpointer.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
from typing import Union, Dict, List, Tuple, Any, Callable
import logging
import os
import re
im... | 7,516 | 44.283133 | 114 | py |
DaVinci | DaVinci-main/util/torch_io.py | # Write and Paint: Generative Vision-Language Models are Unified Modal Learners (https://arxiv.org/abs/2206.07699)
# Github: https://github.com/shizhediao/DaVinci
# Copyright (c) 2023, ByteDance Inc.
# All rights reserved.
#!/usr/bin/env python
# -*- coding: utf-8 -*-
''' torch model hdfs io warpper '''
import io
imp... | 943 | 26.764706 | 114 | py |
DaVinci | DaVinci-main/optim/adahessian.py | """ AdaHessian Optimizer
Lifted from https://github.com/davda54/ada-hessian/blob/master/ada_hessian.py
Originally licensed MIT, Copyright 2020, David Samuel
"""
import torch
class Adahessian(torch.optim.Optimizer):
"""
Implements the AdaHessian algorithm from "ADAHESSIAN: An Adaptive Second OrderOptimizer fo... | 6,535 | 40.630573 | 129 | py |
DaVinci | DaVinci-main/optim/radam.py | """RAdam Optimizer.
Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam
Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265
"""
import math
import torch
from torch.optim.optimizer import Optimizer, required
class RAdam(Optimizer):
def __init__(self... | 5,924 | 37.72549 | 111 | py |
DaVinci | DaVinci-main/optim/nvnovograd.py | """ Nvidia NovoGrad Optimizer.
Original impl by Nvidia from Jasper example:
- https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
- https://arxiv.org/abs/1905.11286
"""
im... | 4,795 | 39.302521 | 99 | py |
DaVinci | DaVinci-main/optim/adamp.py | """
AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.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
"""
impor... | 3,689 | 33.166667 | 123 | py |
DaVinci | DaVinci-main/optim/nadam.py | import torch
from torch.optim import Optimizer
class Nadam(Optimizer):
"""Implements Nadam algorithm (a variant of Adam based on Nesterov momentum).
It has been proposed in `Incorporating Nesterov Momentum into Adam`__.
Arguments:
params (iterable): iterable of parameters to optimize or dicts de... | 3,758 | 41.235955 | 108 | py |
DaVinci | DaVinci-main/optim/adamw.py | """ AdamW Optimizer
Impl copied from PyTorch master
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class AdamW(Optimizer):
r"""Implements AdamW algorithm.
The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
The AdamW variant was proposed in... | 4,965 | 41.084746 | 116 | py |
DaVinci | DaVinci-main/optim/adafactor.py | """ Adafactor Optimizer
Lifted from https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
Original header/copyright below.
"""
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source... | 8,126 | 45.706897 | 114 | py |
DaVinci | DaVinci-main/optim/rmsprop_tf.py | """ RMSProp modified to behave like Tensorflow impl
Originally cut & paste from PyTorch RMSProp
https://github.com/pytorch/pytorch/blob/063946d2b3f3f1e953a2a3b54e0b34f1393de295/torch/optim/rmsprop.py
Licensed under BSD-Clause 3 (ish), https://github.com/pytorch/pytorch/blob/master/LICENSE
Modifications Copyright 2020... | 6,127 | 43.729927 | 117 | py |
DaVinci | DaVinci-main/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 |
DaVinci | DaVinci-main/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 |
DaVinci | DaVinci-main/optim/lars.py | # Copyright (c) Meta Platforms, Inc. and 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.
# --------------------------------------------------------
# LARS optimizer, implementation from MoCo v3:
# https://github.... | 1,851 | 38.404255 | 113 | py |
DaVinci | DaVinci-main/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 |
DaVinci | DaVinci-main/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 |
DaVinci | DaVinci-main/accelerators/accelerator.py | # -*- coding: utf-8 -*-
'''
Created on Feb-19-21 16:36
accelerator.py
Description: accelerators的基类，便于后续其他加速方案的接入。
'''
from logging import Logger
import torch
from torch.optim import Optimizer
Net = torch.nn.Module
class Accelerator:
"""
Accelerator是所有accelerators的基类，新添加的accelerator需要继承该类。
"""
def ... | 1,012 | 24.974359 | 98 | py |
DaVinci | DaVinci-main/accelerators/apex_ddp_accelerator.py | # -*- coding: utf-8 -*-
'''
Created on Nov-18-20 15:21
ddp_accelerator.py
@author: liuzhen.nlp
Description:
'''
import os
import random
import sys
from typing import Tuple, Union, Optional, Any
import numpy as np
import torch
import torch.distributed as distributed
from torch.optim import Optimizer
from torch.optim.l... | 4,241 | 34.35 | 110 | py |
DaVinci | DaVinci-main/taming/main.py | import argparse, os, sys, datetime, glob, importlib
from omegaconf import OmegaConf
import numpy as np
from PIL import Image
import torch
import torchvision
from torch.utils.data import random_split, DataLoader, Dataset
import pytorch_lightning as pl
from pytorch_lightning import seed_everything
from pytorch_lightning.... | 21,114 | 35.217839 | 138 | py |
DaVinci | DaVinci-main/taming/modules/util.py | import torch
import torch.nn as nn
def count_params(model):
total_params = sum(p.numel() for p in model.parameters())
return total_params
class ActNorm(nn.Module):
def __init__(self, num_features, logdet=False, affine=True,
allow_reverse_init=False):
assert affine
super(... | 3,847 | 28.374046 | 85 | py |
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