python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
from pkg_resources import packaging
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from .model import build_model
from .simple_tokeni... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/clip.py |
import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corr... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/simple_tokenizer.py |
#!/usr/bin/env python
import warnings
from typing import Tuple, Optional
import torch
from torch import Tensor
from torch.nn.modules.linear import Linear
from torch.nn.init import xavier_uniform_
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.parameter import Parameter
from... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/attention.py |
#!/usr/bin/env python
from collections import OrderedDict
from timm.models.layers import trunc_normal_, DropPath
import torch
import torch.nn as nn
import torch.nn.functional as F
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
def conv_1x1x1(inp, ou... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/evl_module_uniformer_diff_conv_balance.py |
#!/usr/bin/env python
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from .attention import MultiheadAttention
import logging
logger = logging.getLogger(__name__)
MODE... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/clip_vit_only_global.py |
#!/usr/bin/env python
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
import torch.utils.checkpoint as checkpoint
from .attention import MultiheadAttention
MODEL_PATH = '/mnt/lustre/share_data/likunchang.vendor/model'
_MODELS = {
"ViT-B/32... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/clip_vit.py |
from .evl_module import TransformerDecoder
from .evl_module_uniformer_diff_conv_balance import TransformerDecoder_uniformer_diff_conv_balance
from .clip_vit import vit_b32, vit_b16, vit_l14, vit_l14_336
from .clip_vit_2plus1d import vit_2plus1d_b32, vit_2plus1d_b16, vit_2plus1d_l14, vit_2plus1d_l14_336
from .clip_vit_2... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/__init__.py |
r"""Functional interface"""
import warnings
import math
import torch
from torch import _VF
from torch._jit_internal import Optional, Tuple
from torch.overrides import has_torch_function, handle_torch_function
from torch.nn.functional import _pad, linear, softmax, dropout
Tensor = torch.Tensor
pad = _pad
def multi_... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/attention_module.py |
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
from einops import rearrange
from .attention import MultiheadAttention
MODEL_PATH = '/mnt/lustre/share_data/likunchang.vendor/model'
_MODELS = {
"ViT-B/32": os.path.join(MODEL_PATH, "vit_b32.p... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/clip_vit_2plus1d.py |
#!/usr/bin/env python
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from .attention import MultiheadAttention
from ipdb import set_trace
MODEL_PATH = '/mnt/lustre/sha... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/clip_vit_fusion.py |
import warnings
from typing import Tuple, Optional
import torch
from torch import Tensor
from torch.nn.modules.linear import Linear
from torch.nn.init import xavier_uniform_
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.parameter import Parameter
from torch.nn.modules.modul... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/attention_bias.py |
r"""Functional interface"""
import warnings
import math
import torch
from torch import _VF
from torch._jit_internal import Optional, Tuple
from torch.overrides import has_torch_function, handle_torch_function
from torch.nn.functional import _pad, linear, softmax, dropout
Tensor = torch.Tensor
pad = _pad
def multi_... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/attention_module_bias.py |
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
from einops import rearrange
import torch.utils.checkpoint as checkpoint
from .attention_bias import MultiheadAttention
MODEL_PATH = '/mnt/lustre/share_data/likunchang.vendor/model'
_MODELS = {
... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/clip_vit_2plus1d_dw_bias.py |
#!/usr/bin/env python
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
import torch.nn as nn
import torch.nn.functional as F
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualDecoderBlock(nn.Module)... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/evl_utils/evl_module.py |
from .clip import *
| InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/__init__.py |
from collections import OrderedDict
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange
from . import evl_utils
from .evl_utils import TransformerDecoder_uniformer_diff_conv_balance
class LayerNorm(nn.LayerNorm):
"""Sub... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/model.py |
import os
import time
import torch
import torch.nn as nn
from fvcore.nn import FlopCountAnalysis
from fvcore.nn import flop_count_table
import evl_utils
from evl_utils import TransformerDecoder_uniformer_diff_conv_balance
PATH_PREFIX = '/mnt/lustre/share_data/likunchang.vendor/code/EVL/clip_kc/model'
class EVL(nn.... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/model_no_freeze_diff.py |
import os
import time
import torch
import torch.nn as nn
from fvcore.nn import FlopCountAnalysis
from fvcore.nn import flop_count_table
import evl_utils
from evl_utils import TransformerDecoder
PATH_PREFIX = '/mnt/lustre/share_data/likunchang.vendor/code/EVL/clip_kc/model'
class EVL(nn.Module):
def __init__(se... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/model_freeze.py |
import os
import time
import torch
import torch.nn as nn
from fvcore.nn import FlopCountAnalysis
from fvcore.nn import flop_count_table
import evl_utils
from evl_utils import TransformerDecoder
PATH_PREFIX = '/mnt/lustre/share_data/likunchang.vendor/code/EVL/clip_kc/model'
class EVL(nn.Module):
def __init__(se... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/model_no_freeze.py |
import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
from pkg_resources import packaging
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from ipdb import set_trace
from .model import build... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/clip.py |
import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corr... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/simple_tokenizer.py |
#!/usr/bin/env python
import warnings
from typing import Tuple, Optional
import torch
from torch import Tensor
from torch.nn.modules.linear import Linear
from torch.nn.init import xavier_uniform_
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.parameter import Parameter
from... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/evl_utils/attention.py |
#!/usr/bin/env python
from collections import OrderedDict
from timm.models.layers import trunc_normal_, DropPath
import torch
import torch.nn as nn
import torch.nn.functional as F
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
def conv_1x1x1(inp, ou... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/evl_utils/evl_module_uniformer_diff_conv_balance.py |
#!/usr/bin/env python
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
import torch.utils.checkpoint as checkpoint
from .attention import MultiheadAttention
MODEL_PATH = '/mnt/lustre/share_data/likunchang.vendor/model'
_MODELS = {
"ViT-B/32... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/evl_utils/clip_vit.py |
from .evl_module import TransformerDecoder
from .evl_module_uniformer_diff_conv_balance import TransformerDecoder_uniformer_diff_conv_balance
from .clip_vit import vit_b32, vit_b16, vit_l14, vit_l14_336
from .clip_vit_2plus1d import vit_2plus1d_b32, vit_2plus1d_b16, vit_2plus1d_l14, vit_2plus1d_l14_336
from .clip_vit_2... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/evl_utils/__init__.py |
r"""Functional interface"""
import warnings
import math
import torch
from torch import _VF
from torch._jit_internal import Optional, Tuple
from torch.overrides import has_torch_function, handle_torch_function
from torch.nn.functional import _pad, linear, softmax, dropout
Tensor = torch.Tensor
pad = _pad
def multi_... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/evl_utils/attention_module.py |
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
from einops import rearrange
from .attention import MultiheadAttention
MODEL_PATH = '/mnt/lustre/share_data/likunchang.vendor/model'
_MODELS = {
"ViT-B/32": os.path.join(MODEL_PATH, "vit_b32.p... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/evl_utils/clip_vit_2plus1d.py |
import warnings
from typing import Tuple, Optional
import torch
from torch import Tensor
from torch.nn.modules.linear import Linear
from torch.nn.init import xavier_uniform_
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.parameter import Parameter
from torch.nn.modules.modul... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/evl_utils/attention_bias.py |
r"""Functional interface"""
import warnings
import math
import torch
from torch import _VF
from torch._jit_internal import Optional, Tuple
from torch.overrides import has_torch_function, handle_torch_function
from torch.nn.functional import _pad, linear, softmax, dropout
Tensor = torch.Tensor
pad = _pad
def multi_... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/evl_utils/attention_module_bias.py |
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
from einops import rearrange
import torch.utils.checkpoint as checkpoint
from .attention_bias import MultiheadAttention
MODEL_PATH = '/mnt/lustre/share_data/likunchang.vendor/model'
_MODELS = {
... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/evl_utils/clip_vit_2plus1d_dw_bias.py |
#!/usr/bin/env python
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
import torch.nn as nn
import torch.nn.functional as F
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualDecoderBlock(nn.Module)... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc/evl_utils/evl_module.py |
import os
import time
import torch
import torch.nn as nn
# from fvcore.nn import FlopCountAnalysis
# from fvcore.nn import flop_count_table
from modules.clip_kc_new import evl_utils
PATH_PREFIX = '/mnt/lustre/share_data/likunchang.vendor/code/EVL/clip_kc/model'
class EVL(nn.Module):
def __init__(self,
... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/model_no_freeze_only_global.py |
import os
import time
import torch
import torch.nn as nn
from fvcore.nn import FlopCountAnalysis
from fvcore.nn import flop_count_table
from . import evl_utils
PATH_PREFIX = '/mnt/lustre/share_data/likunchang.vendor/code/EVL/clip_kc/model'
class EVL(nn.Module):
def __init__(self,
backbone='vit_b16',
... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/model_no_freeze_uniformer.py |
from .clip import *
from .evl_utils import * | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/__init__.py |
from collections import OrderedDict
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from . import evl_utils
from .evl_utils import TransformerDecoder_uniformer_diff_conv_balance
from einops import rearrange
from ipdb import set_trace
from copy impor... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/model.py |
import os
import time
import torch
import torch.nn as nn
from fvcore.nn import FlopCountAnalysis
from fvcore.nn import flop_count_table
import evl_utils
from evl_utils import TransformerDecoder_uniformer_diff_conv_balance
PATH_PREFIX = '/mnt/lustre/share_data/likunchang.vendor/code/EVL/clip_kc/model'
class EVL(nn.... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/model_no_freeze_diff.py |
import os
import time
import torch
import torch.nn as nn
from fvcore.nn import FlopCountAnalysis
from fvcore.nn import flop_count_table
import evl_utils
from evl_utils import TransformerDecoder
PATH_PREFIX = '/mnt/lustre/share_data/likunchang.vendor/code/EVL/clip_kc/model'
class EVL(nn.Module):
def __init__(se... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/model_freeze.py |
import os
import time
import torch
import torch.nn as nn
from fvcore.nn import FlopCountAnalysis
from fvcore.nn import flop_count_table
import evl_utils
from evl_utils import TransformerDecoder
PATH_PREFIX = '/mnt/lustre/share_data/likunchang.vendor/code/EVL/clip_kc/model'
class EVL(nn.Module):
def __init__(se... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/model_no_freeze.py |
import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
from pkg_resources import packaging
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from .model import build_model
from .simple_tokeni... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/clip.py |
import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corr... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/simple_tokenizer.py |
#!/usr/bin/env python
import warnings
from typing import Tuple, Optional
import torch
from torch import Tensor
from torch.nn.modules.linear import Linear
from torch.nn.init import xavier_uniform_
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.parameter import Parameter
from... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/attention.py |
#!/usr/bin/env python
from collections import OrderedDict
from timm.models.layers import trunc_normal_, DropPath
import torch
import torch.nn as nn
import torch.nn.functional as F
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
def conv_1x1x1(inp, ou... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/evl_module_uniformer_diff_conv_balance.py |
#!/usr/bin/env python
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from .attention import MultiheadAttention
import logging
logger = logging.getLogger(__name__)
MODE... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/clip_vit_only_global.py |
#!/usr/bin/env python
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
import torch.utils.checkpoint as checkpoint
from .attention import MultiheadAttention
MODEL_PATH = '/mnt/lustre/share_data/likunchang.vendor/model'
_MODELS = {
"ViT-B/32... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/clip_vit.py |
from .evl_module import TransformerDecoder
from .evl_module_uniformer_diff_conv_balance import TransformerDecoder_uniformer_diff_conv_balance
from .clip_vit import vit_b32, vit_b16, vit_l14, vit_l14_336
from .clip_vit_2plus1d import vit_2plus1d_b32, vit_2plus1d_b16, vit_2plus1d_l14, vit_2plus1d_l14_336
from .clip_vit_2... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/__init__.py |
r"""Functional interface"""
import warnings
import math
import torch
from torch import _VF
from torch._jit_internal import Optional, Tuple
from torch.overrides import has_torch_function, handle_torch_function
from torch.nn.functional import _pad, linear, softmax, dropout
Tensor = torch.Tensor
pad = _pad
def multi_... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/attention_module.py |
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
from einops import rearrange
from .attention import MultiheadAttention
MODEL_PATH = '/mnt/lustre/share_data/likunchang.vendor/model'
_MODELS = {
"ViT-B/32": os.path.join(MODEL_PATH, "vit_b32.p... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/clip_vit_2plus1d.py |
#!/usr/bin/env python
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from .attention import MultiheadAttention
from ipdb import set_trace
MODEL_PATH = '/mnt/lustre/sha... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/clip_vit_fusion.py |
import warnings
from typing import Tuple, Optional
import torch
from torch import Tensor
from torch.nn.modules.linear import Linear
from torch.nn.init import xavier_uniform_
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.parameter import Parameter
from torch.nn.modules.modul... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/attention_bias.py |
r"""Functional interface"""
import warnings
import math
import torch
from torch import _VF
from torch._jit_internal import Optional, Tuple
from torch.overrides import has_torch_function, handle_torch_function
from torch.nn.functional import _pad, linear, softmax, dropout
Tensor = torch.Tensor
pad = _pad
def multi_... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/attention_module_bias.py |
import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
from einops import rearrange
import torch.utils.checkpoint as checkpoint
from .attention_bias import MultiheadAttention
MODEL_PATH = '/mnt/lustre/share_data/likunchang.vendor/model'
_MODELS = {
... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/clip_vit_2plus1d_dw_bias.py |
#!/usr/bin/env python
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
import torch.nn as nn
import torch.nn.functional as F
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualDecoderBlock(nn.Module)... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc_new/evl_utils/evl_module.py |
import warnings
from sklearn import ensemble
# This ignore the scheduler warning, see https://github.com/Lightning-AI/lightning/issues/5558
warnings.filterwarnings("ignore", "Detected call of", UserWarning)
import os
import copy
import pytorch_lightning as pl
from CoTrain.config import ex
from CoTrain.modules import ... | InternVideo-main | Downstream/multi-modalities-downstream/run.py |
from sacred import Experiment
ex = Experiment("CoTrain", save_git_info=False)
def _loss_names(d):
ret = {
# pretrain
"vtm": 0,
"mlm": 0,
"mpp": 0,
"vtc": 0,
"vcop": 0,
"dino": 0,
# downstream
"vqa": 0,
"openend_vqa": 0,
"mc_v... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/config.py |
InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/__init__.py | |
try:
from petrel_client.client import Client
client = Client()
# Disable boto logger
import logging
logging.getLogger('boto3').setLevel(logging.WARNING)
logging.getLogger('botocore').setLevel(logging.WARNING)
logging.getLogger('nose').setLevel(logging.WARNING)
except:
client = None
# ... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/__init__.py |
from .video_base_dataset import BaseDataset, read_large_frames_decord, get_video_len
import os
import pandas as pd
class EGO4DChoiceDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
if self.split == "train":
... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/ego4d_choice.py |
import numpy as np
from .video_base_dataset import BaseDataset, read_frames_gif
import os
import json
import pandas as pd
import random
# 2022.1.28 read gif is too slow, may be need to speedup by convert gif -> video
# https://stackify.dev/833655-python-convert-gif-to-videomp4
class TGIFDataset(BaseDataset):
def... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/tgif.py |
from .video_base_dataset import BaseDataset, sample_frames, video_clip_reader, clean_subtitles, align_using_dtw
import torch as th
import pandas as pd
import os
import numpy as np
import random
import ffmpeg
import json
import ftfy
class YTTemporalDataset(BaseDataset):
"""YTTemporal Video-Text loader."""
def... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/yttemporal.py |
import numpy as np
from .video_base_dataset import BaseDataset
import os
import json
import pandas as pd
class MSRVTTQADataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
# if split == "test":
# split = "val"
... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/msrvttqa.py |
from .video_base_dataset import BaseDataset
import torch as th
import pandas as pd
import os
import numpy as np
import random
import ffmpeg
import io
import decord
decord.bridge.set_bridge('torch')
from CoTrain.datasets import client
class HT100MDataset(BaseDataset):
"""HowTo100M Video-Text loader."""
def _... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/howto100m.py |
from .video_base_dataset import BaseDataset
import os
import pandas as pd
import cv2
import torch
from CoTrain.datasets.video.video_base_dataset import sample_frames
# each sample: https://tvqa.cs.unc.edu/download_tvqa_plus.html
# {
# "answer_idx": "1",
# "qid": 134094,
# "ts": [5.99, 11.98],
# "a1": "Howard i... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/tvqaplus.py |
from .video_base_dataset import BaseDataset
import random
import os
import pandas as pd
class LSMDCDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
self.metadata = None
if split == "train":
... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/lsmdc_dataset.py |
from .video_base_dataset import BaseDataset
import torch as th
import pandas as pd
import os
import numpy as np
import random
import ffmpeg
import io
import decord
import re
decord.bridge.set_bridge('torch')
from CoTrain.datasets import client
class YOUTUBEDataset(BaseDataset):
"""Youtube Video-Text loader."""
... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/youtube.py |
import numpy as np
from .video_base_dataset import BaseDataset
import os
import json
import pandas as pd
class ACTIVITYNETQADataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
# if split == "test":
# split = "val... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/activitynetqa.py |
from .video_base_dataset import BaseDataset, read_frames_decord
import random
import os
import pandas as pd
from .pack_meta import pack_metadata, unpack_metadata
class WEBVID10MDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split ... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/webvid10m.py |
from .video_base_dataset import BaseDataset
import random
import os
import pandas as pd
class MSVDDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
self.metadata = None
if split == "train":
n... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/msvd.py |
import pandas as pd
import numpy as np
from typing import Union
SEP = "<<<sep>>>"
class DummyMeta(object):
def __init__(self, l):
self._len = l
def __len__(self):
return self._len
def string_to_sequence(s: Union[str, list], dtype=np.int32) -> np.ndarray:
if isinstance(s, list):
... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/pack_meta.py |
from .video_base_dataset import BaseDataset
import os
import pandas as pd
import random
from .pack_meta import pack_metadata, unpack_metadata
class LSMDCChoiceDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
se... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/lsmdc_choice.py |
import random
import torch
import io
import os
import cv2
import numpy as np
from PIL import Image
from CoTrain.transforms import keys_to_transforms
import decord
from decord import cpu
import imageio
# add for ytt asr clean
import ftfy
import regex as re
import demoji
import editdistance
import tslearn.metrics
import ... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/video_base_dataset.py |
import numpy as np
from .video_base_dataset import BaseDataset
import os
import random
from CoTrain.transforms.video.videoaug import VideoTransform
class K400Dataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
sel... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/k400.py |
import numpy as np
from .video_base_dataset import BaseDataset
import os
import json
import pandas as pd
class MSVDQADataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
self.metadata = None
self.ans_lab_di... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/msvdqa.py |
InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/__init__.py | |
from .video_base_dataset import BaseDataset
import os
import pandas as pd
# some videos are missed, for better results, do IO exception.
class DIDEMODataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
self.metada... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/didemo.py |
from .video_base_dataset import BaseDataset, read_frames_gif
import random
import os
import pandas as pd
# action and transition: {
# "gif_name": "tumblr_nk172bbdPI1u1lr18o1_250",
# "question": "What does the butterfly do 10 or more than 10 times ?",
# "options": ["stuff marshmallow", "holds a phone toward... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/tgifqa.py |
import numpy as np
from .video_base_dataset import BaseDataset
import os
class UCF101Dataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
self.metadata = None
self.ans_lab_dict = dict()
if split == ... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/ucf101.py |
from .video_base_dataset import BaseDataset
import random
import os
import pandas as pd
import cv2
import torch
from CoTrain.datasets.video.video_base_dataset import sample_frames
# each sample: https://tvqa.cs.unc.edu/download_tvqa.html
# {
# "a0": "A martini glass",
# "a1": "Nachos",
# "a2": "Her purse",
# "a3": "Ma... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/tvqa.py |
from .video_base_dataset import BaseDataset, read_frames_decord
import random
import os
import pandas as pd
class WEBVIDDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
self.metadata = None
self.cut = "... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/webvid_old.py |
from .video_base_dataset import BaseDataset
import torch as th
import os
import numpy as np
import random
import ffmpeg
import json
from transforms.video.videoaug import VideoTransform
import subprocess
# {'timestamp_sec': 221.29666, 'narration_text': '#C C walks on the ground'}
class Ego4DDataset(BaseDataset):
... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/ego4d_v2.py |
from docutils import DataError
from importlib_metadata import metadata
from .video_base_dataset import BaseDataset, read_frames_decord
import os
import pandas as pd
class K400VideoDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.spl... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/k400_video.py |
from .video_base_dataset import BaseDataset
import random
import os
import pandas as pd
import json
import numpy as np
class MSRVTTDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
self.metadata = None
s... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/msrvtt.py |
import numpy as np
from .video_base_dataset import BaseDataset
import os
class HMDB51Dataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
self.metadata = None
self.ans_lab_dict = dict()
if split == ... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/hmdb51.py |
from .video_base_dataset import BaseDataset
import os
import pandas as pd
from .pack_meta import pack_metadata, unpack_metadata
class MSRVTTChoiceDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
if self.split =... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/msrvtt_choice.py |
from .video_base_dataset import BaseDataset, read_large_frames_decord
import pandas as pd
import os
# {'timestamp_sec': 221.29666, 'narration_text': '#C C walks on the ground'}
class Ego4DDataset(BaseDataset):
"""EGO4D Video-Text loader."""
def __init__(self, *args, split="", **kwargs):
assert spli... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/ego4d.py |
from .video_base_dataset import BaseDataset, read_frames_decord
import random
import os
import pandas as pd
from .pack_meta import pack_metadata, unpack_metadata
class WEBVIDDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = s... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/webvid.py |
from .video_base_dataset import BaseDataset, read_frames_from_img_dir
import random
import os
import pandas as pd
class ActivityNetDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
self.metadata = None
i... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/video/activitynet.py |
from .base_dataset import BaseDataset
class F30KCaptionKarpathyDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
if split == "train":
names = ["f30k_caption_karpathy_train", "f30k_caption_karpathy_val"]
elif split == ... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/f30k_caption_karpathy_dataset.py |
from .base_dataset import BaseDataset
class VisualGenomeCaptionDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
if split == "test":
split = "val"
if split == "train":
names = ["vg_train"]
elif spl... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/vg_caption_dataset.py |
import json
from .base_dataset import BaseDataset
import random
import os
import pandas as pd
import io
from PIL import Image
from CoTrain.datasets import client
class LAION400MDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split ... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/laion400m.py |
import random
import torch
import io
import pyarrow as pa
import os
import cv2
import numpy as np
from PIL import Image
from CoTrain.transforms import keys_to_transforms
import decord
from CoTrain.transforms.image.imageaug import image_aug
import CoTrain.modules.InternVideo as internvideo
class BaseDataset(torch.util... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/base_dataset.py |
from .base_dataset import BaseDataset
class CocoCaptionKarpathyDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
if split == "train":
names = ["coco_caption_karpathy_train"] # , "coco_caption_karpa... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/coco_caption_karpathy_dataset.py |
import json
from .base_dataset import BaseDataset
import random
import os
import pandas as pd
import io
from PIL import Image
from CoTrain.datasets import client
class YFCC15MDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = ... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/yfcc15m.py |
InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/__init__.py | |
from glob import glob
from .base_dataset import BaseDataset
class SBUCaptionDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
if split == "test":
split = "val"
if split == "train":
names = [f"sbu_{i}" for ... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/sbu_caption_dataset.py |
from CoTrain.datasets.video.video_base_dataset import BaseDataset, color_img
import random
import os
import pandas as pd
import cv2
from CoTrain.transforms.video.videoaug import VideoTransform
import torch
## from https://github.com/rowanz/r2c/blob/master/dataloaders/vcr.py
# Here's an example jsonl
# {
# "movie": "30... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/vcr.py |
from .base_dataset import BaseDataset
import sys
import random
class NLVR2Dataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
if split == "train":
names = ["nlvr2_train"]
elif split == "val":
... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/nlvr2_dataset.py |
import json
from .base_dataset import BaseDataset
import random
import os
import pandas as pd
import io
from PIL import Image
from CoTrain.datasets import client
class CC12MDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = sp... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/cc12m.py |
from .base_dataset import BaseDataset
class VQAv2Dataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
if split == "train":
names = ["vqav2_train", "vqav2_trainable_val"]
elif split == "val":
... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/vqav2_dataset.py |
import json
from .base_dataset import BaseDataset
import random
import os
import pandas as pd
import numpy as np
import io
import torch
from PIL import Image
from CoTrain.datasets import client
import CoTrain.modules.dist_utils as du
class MIX100MDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs)... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/mix100m.py |
from glob import glob
from .base_dataset import BaseDataset
class ConceptualCaptionDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
if split == "test":
split = "val"
if split == "train":
names = [f"concep... | InternVideo-main | Downstream/multi-modalities-downstream/CoTrain/datasets/image/conceptual_caption_dataset.py |
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