python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
import gzip
import json
import os
from typing import Dict, List, Optional, Union
import attr
from habitat.config import Config
from habitat.core.dataset import Dataset
from habitat.core.registry import registry
from habitat.core.utils import not_none_validator
from habitat.datasets.pointnav.pointnav_dataset import ALL... | InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/task.py |
from typing import Dict, List, Optional, Tuple, Union
import networkx as nx
import numpy as np
from habitat.core.simulator import Simulator
from habitat.core.utils import try_cv2_import
from habitat.tasks.vln.vln import VLNEpisode
from habitat.utils.visualizations import maps as habitat_maps
cv2 = try_cv2_import()
A... | InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/maps.py |
from typing import Any, Dict
import numpy as np
from gym import spaces
from habitat.config import Config
from habitat.core.registry import registry
from habitat.core.simulator import Observations, Sensor, SensorTypes, Simulator
from habitat.sims.habitat_simulator.actions import HabitatSimActions
from habitat.tasks.nav... | InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/sensors.py |
from habitat_extensions import measures, obs_transformers, sensors, nav
from habitat_extensions.config.default import get_extended_config
from habitat_extensions.task import VLNCEDatasetV1
from habitat_extensions.habitat_simulator import Simulator
| InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/__init__.py |
import copy
import numbers
from typing import Dict, List, Tuple, Union
import torch
from gym import spaces
from habitat.config import Config
from habitat.core.logging import logger
from habitat_baselines.common.baseline_registry import baseline_registry
from habitat_baselines.common.obs_transformers import Observation... | InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/obs_transformers.py |
import gzip
import json
import pickle
from typing import Any, List, Union
import numpy as np
from dtw import dtw
from fastdtw import fastdtw
from habitat.config import Config
from habitat.core.embodied_task import EmbodiedTask, Measure
from habitat.core.registry import registry
from habitat.core.simulator import Simul... | InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/measures.py |
from typing import Dict
import numpy as np
from habitat.core.utils import try_cv2_import
from habitat.utils.visualizations import maps as habitat_maps
from habitat.utils.visualizations.utils import draw_collision
from habitat_extensions import maps
cv2 = try_cv2_import()
def observations_to_image(observation: Dict... | InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/utils.py |
from turtle import heading
from typing import Any
import math
import numpy as np
from habitat.core.embodied_task import (
SimulatorTaskAction,
)
from habitat.core.registry import registry
from habitat.sims.habitat_simulator.actions import HabitatSimActions
from habitat.tasks.utils import cartesian_to_polar
from h... | InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/nav.py |
#!/usr/bin/env python3
# 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 tree.
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Optional,
Sequence,
Set,
Union,
... | InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/habitat_simulator.py |
# Copied from https://github.com/facebookresearch/habitat-lab/blob/v0.1.4/habitat/tasks/nav/shortest_path_follower.py
# Use the Habitat v0.1.4 ShortestPathFollower for compatibility with
# the dataset generation oracle.
from typing import Optional, Union
import habitat_sim
import numpy as np
from habitat.sims.habitat... | InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/shortest_path_follower.py |
InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/config/__init__.py | |
from typing import List, Optional, Union
from habitat.config.default import Config as CN
from habitat.config.default import get_config
_C = get_config()
_C.defrost()
# ----------------------------------------------------------------------------
# CUSTOM ACTION: HIGHTOLOWINFERENCE ACTION
# ---------------------------... | InternVideo-main | Downstream/Visual-Language-Navigation/habitat_extensions/config/default.py |
import gc
import os
import io
import sys
import random
import warnings
from collections import defaultdict
from typing import Dict, List
import jsonlines
import lmdb
import msgpack_numpy
import numpy as np
import math
import time
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from tor... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/trainer_HAMT.py |
from vlnce_baselines import trainer_HAMT
from vlnce_baselines.common import environments
from vlnce_baselines.models import (
Policy_ViewSelection_CMA,
Policy_ViewSelection_HAMT,
)
| InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/__init__.py |
import torch
import torch.distributed as dist
import numpy as np
import math
import copy
class ARGS():
def __init__(self):
self.local_rank = 0
def reduce_loss(tensor, rank, world_size):
with torch.no_grad():
dist.reduce(tensor, dst=0)
if rank == 0:
# print(tensor)
... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/utils.py |
InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/config/__init__.py | |
from typing import List, Optional, Union
import habitat_baselines.config.default
from habitat.config.default import CONFIG_FILE_SEPARATOR
from habitat.config.default import Config as CN
from habitat_extensions.config.default import (
get_extended_config as get_task_config,
)
# -----------------------------------... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/config/default.py |
import abc
from typing import Any
from habitat_baselines.rl.ppo.policy import Policy
from habitat_baselines.utils.common import (
CategoricalNet,
CustomFixedCategorical,
)
from torch.distributions import Categorical
class ILPolicy(Policy, metaclass=abc.ABCMeta):
def __init__(self, net, dim_actions):
... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/policy.py |
from copy import deepcopy
import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from gym import Space
from habitat import Config
from habitat_baselines.common.baseline_registry import baseline_registry
from habitat_baselines.rl.models.rnn_state_encoder import (
build_rnn... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/Policy_ViewSelection_HAMT.py |
InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/__init__.py | |
import math
from turtle import heading
import torch
def angle_feature(headings, device=None):
# twopi = math.pi * 2
# heading = (heading + twopi) % twopi # From 0 ~ 2pi
# It will be the same
heading_enc = torch.zeros(len(headings), 64, dtype=torch.float32)
for i, head in enumerate(headings):
... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/utils.py |
import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from gym import Space
from habitat import Config
from habitat_baselines.common.baseline_registry import baseline_registry
from habitat_baselines.rl.models.rnn_state_encoder import (
build_rnn_state_encoder,
)
from hab... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/Policy_ViewSelection_CMA.py |
import argparse
def get_args():
parser = argparse.ArgumentParser('VideoMAE fine-tuning and evaluation script for video classification', add_help=False)
parser.add_argument('--batch_size', default=64, type=int)
parser.add_argument('--epochs', default=30, type=int)
parser.add_argument('--update_freq', de... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/videomae/get_args.py |
"""
This implementation is based on
https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/auto_augment.py
pulished under an Apache License 2.0.
COMMENT FROM ORIGINAL:
AutoAugment, RandAugment, and AugMix for PyTorch
This code implements the searched ImageNet policies with various tweaks and
improveme... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/videomae/rand_augment.py |
import numpy as np
from PIL import Image
import torch
def convert_img(img):
"""Converts (H, W, C) numpy.ndarray to (C, W, H) format
"""
if len(img.shape) == 3:
img = img.transpose(2, 0, 1)
if len(img.shape) == 2:
img = np.expand_dims(img, 0)
return img
class ClipToTensor(object):... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/videomae/volume_transforms.py |
import numbers
import cv2
import numpy as np
import PIL
import torch
def _is_tensor_clip(clip):
return torch.is_tensor(clip) and clip.ndimension() == 4
def crop_clip(clip, min_h, min_w, h, w):
if isinstance(clip[0], np.ndarray):
cropped = [img[min_h:min_h + h, min_w:min_w + w, :] for img in clip]
... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/videomae/functional.py |
import io
import os
import math
import time
import json
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from torch.utils.data._utils.collate import default_collate
from pathlib import Path
import subprocess
import torch
import torch.distributed as dist... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/videomae/utils.py |
"""
This implementation is based on
https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/random_erasing.py
pulished under an Apache License 2.0.
"""
import math
import random
import torch
def _get_pixels(
per_pixel, rand_color, patch_size, dtype=torch.float32, device="cuda"
):
# NOTE I've s... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/videomae/random_erasing.py |
#!/usr/bin/env python3
import math
import numpy as np
import random
import torch
import torchvision.transforms.functional as F
from PIL import Image
from torchvision import transforms
from .rand_augment import rand_augment_transform
from .random_erasing import RandomErasing
import numbers
import PIL
import torchvisi... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/videomae/video_transforms.py |
from functools import partial
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from timm.models.registry import register_model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 400... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/videomae/modeling_finetune.py |
# PREVALENT, 2020, weituo.hao@duke.edu
# Modified in Recurrent VLN-BERT, 2020, Yicong.Hong@anu.edu.au
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import math
import os
import sys
from io import open
import torch
from torch import nn
from torch.nn impo... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/vlnbert/vlnbert_PREVALENT.py |
# Recurrent VLN-BERT, 2020, by Yicong.Hong@anu.edu.au
from pytorch_transformers import (BertConfig, BertTokenizer)
def get_vlnbert_models(config=None):
config_class = BertConfig
from vlnce_baselines.models.vlnbert.vlnbert_PREVALENT import VLNBert
model_class = VLNBert
# model_name_or_path = 'data/myd... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/vlnbert/vlnbert_init.py |
import json
import jsonlines
import os
import sys
import time
import glob
import warnings
from collections import defaultdict
from typing import Dict, List
import torch
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as distr
import torch.multiproce... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/hamt/base_il_trainer.py |
import json
import logging
import math
import os
import sys
from io import open
from typing import Callable, List, Tuple
import numpy as np
import copy
import torch
from torch import nn
from torch import Tensor, device, dtype
from transformers import BertPreTrainedModel
logger = logging.getLogger(__name__)
BertLaye... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/hamt/vilmodel_cmt.py |
import torch
def get_tokenizer(args):
from transformers import AutoTokenizer
if args.dataset == 'rxr' or args.tokenizer == 'xlm':
cfg_name = 'xlm-roberta-base'
else:
cfg_name = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(cfg_name)
return tokenizer
def get_vlnbert... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/hamt/vlnbert_init.py |
from vlnce_baselines.models.videomae import volume_transforms, video_transforms, modeling_finetune, utils
from vlnce_baselines.models.videomae.get_args import get_args
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm import create_model
from habitat import logger
from collections import Or... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/encoders/video_encoder.py |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from gym import spaces
from habitat import logger
from habitat_baselines.rl.ddppo.policy import resnet
from habitat_baselines.rl.ddppo.policy.resnet_policy import ResNetEncoder
import clip
import to... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/encoders/image_encoders.py |
import gzip
import json
import torch
import torch.nn as nn
from habitat import Config
class InstructionEncoder(nn.Module):
def __init__(self, config: Config):
r"""An encoder that uses RNN to encode an instruction. Returns
the final hidden state after processing the instruction sequence.
... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/models/encoders/instruction_encoder.py |
import torch
import numpy as np
import sys
import glob
import json
def neighborhoods(mu, x_range, y_range, sigma, circular_x=True, gaussian=False):
""" Generate masks centered at mu of the given x and y range with the
origin in the centre of the output
Inputs:
mu: tensor (N, 2)
Outputs:
... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/waypoint_pred/utils.py |
import torch
import torch.nn as nn
import numpy as np
import vlnce_baselines.waypoint_pred.utils as utils
from .transformer.waypoint_bert import WaypointBert
from pytorch_transformers import BertConfig
class BinaryDistPredictor_TRM(nn.Module):
def __init__(self, hidden_dim=768, n_classes=12, device=None):
... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/waypoint_pred/TRM_net.py |
# Copyright (c) 2020 Microsoft Corporation. Licensed under the MIT license.
# Modified in Recurrent VLN-BERT, 2020, Yicong.Hong@anu.edu.au
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import math
import torch
from torch import nn
import torch.nn.functional as F
from... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/waypoint_pred/transformer/waypoint_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... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/waypoint_pred/transformer/pytorch_transformer/modeling_utils.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... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/waypoint_pred/transformer/pytorch_transformer/modeling_bert.py |
"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import sys
import json
import logging
import os
impor... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/waypoint_pred/transformer/pytorch_transformer/file_utils.py |
import os
import random
import sys
from typing import List, Optional, Type, Union
import habitat
from habitat import logger
from habitat import Config, Env, RLEnv, VectorEnv, make_dataset
from habitat_baselines.utils.env_utils import make_env_fn
random.seed(0)
SLURM_JOBID = os.environ.get("SLURM_JOB_ID", None)
def... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/common/env_utils.py |
import json
import jsonlines
import os
import sys
import time
import glob
import warnings
from collections import defaultdict
from typing import Dict, List
import torch
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as distr
import torch.multiproce... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/common/base_il_trainer.py |
import gzip
import json
from collections import defaultdict, deque
import numpy as np
import torch
import tqdm
from gym import Space
from habitat.config.default import Config
from habitat.sims.habitat_simulator.actions import HabitatSimActions
from habitat_baselines.common.environments import get_env_class
from habita... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/common/recollection_dataset.py |
from typing import Any, Dict, List
import torch
import torch.distributed as dist
import numpy as np
import copy
import math
def extract_instruction_tokens(
observations: List[Dict],
instruction_sensor_uuid: str,
tokens_uuid: str = "tokens",
max_length: int = 512,
pad_id: int = 0,
) -> Dict[str, Any... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/common/utils.py |
from collections import defaultdict
from typing import Any, Dict, Optional, Tuple, List, Union
import habitat
import numpy as np
from habitat import Config, Dataset
from habitat.core.simulator import Observations
from habitat.tasks.utils import cartesian_to_polar
from habitat.utils.geometry_utils import quaternion_rot... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/common/environments.py |
import torch
class _AuxLosses:
def __init__(self):
self._losses = {}
self._loss_alphas = {}
self._is_active = False
def clear(self):
self._losses.clear()
self._loss_alphas.clear()
def register_loss(self, name, loss, alpha=1.0):
assert self.is_active()
... | InternVideo-main | Downstream/Visual-Language-Navigation/vlnce_baselines/common/aux_losses.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import torch
import numpy as np
import random
import os
from metrics import compute_metrics, tensor_text_to_video_metrics, tensor_video_to_text_sim
import time
import arg... | InternVideo-main | Downstream/Video-Text-Retrieval/main_task_retrieval.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import numpy as np
import torch
def compute_metrics(x):
sx = np.sort(-x, axis=1)
d = np.diag(-x)
d = d[:, np.newaxis]
ind = sx - d
ind = np.where(ind... | InternVideo-main | Downstream/Video-Text-Retrieval/metrics.py |
import torch
import torch.nn as nn
import threading
from torch._utils import ExceptionWrapper
import logging
def get_a_var(obj):
if isinstance(obj, torch.Tensor):
return obj
if isinstance(obj, list) or isinstance(obj, tuple):
for result in map(get_a_var, obj):
if isinstance(result,... | InternVideo-main | Downstream/Video-Text-Retrieval/util.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import torch
import numpy as np
import random
import os
from metrics import compute_metrics, tensor_text_to_video_metrics, tensor_video_to_text_sim
import time
import arg... | InternVideo-main | Downstream/Video-Text-Retrieval/inference.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import os
from torch.utils.data import Dataset
import numpy as np
import pickle
import json
from dataloaders.rawvideo_util import RawVideoExtractor
import io
from decord ... | InternVideo-main | Downstream/Video-Text-Retrieval/dataloaders/dataloader_vatex_retrieval.py |
import torch
from torch.utils.data import DataLoader
from dataloaders.dataloader_msrvtt_retrieval import MSRVTT_DataLoader
from dataloaders.dataloader_msrvtt_retrieval import MSRVTT_TrainDataLoader
from dataloaders.dataloader_msvd_retrieval import MSVD_DataLoader
from dataloaders.dataloader_lsmdc_retrieval import LSMDC... | InternVideo-main | Downstream/Video-Text-Retrieval/dataloaders/data_dataloaders.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import os
import io
from torch.utils.data import Dataset
import numpy as np
import pandas as pd
from collections import defaultdict
import json
import random
from dataloa... | InternVideo-main | Downstream/Video-Text-Retrieval/dataloaders/dataloader_msrvtt_retrieval.py |
import torch as th
import numpy as np
from PIL import Image
# pytorch=1.7.1
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
# pip install opencv-python
import cv2
import io
try:
from petrel_client.client import Client
client = Client()
# Disable boto logger
import l... | InternVideo-main | Downstream/Video-Text-Retrieval/dataloaders/rawvideo_util.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import os
from torch.utils.data import Dataset
import numpy as np
import json
from dataloaders.rawvideo_util import RawVideoExtractor
import io
from decord import VideoRe... | InternVideo-main | Downstream/Video-Text-Retrieval/dataloaders/dataloader_didemo_retrieval.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import os
import io
from torch.utils.data import Dataset
import numpy as np
import pickle
from dataloaders.rawvideo_util import RawVideoExtractor
from decord import Video... | InternVideo-main | Downstream/Video-Text-Retrieval/dataloaders/dataloader_msvd_retrieval.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import io
import os
from torch.utils.data import Dataset
import numpy as np
import json
import math
from dataloaders.rawvideo_util import RawVideoExtractor
from decord im... | InternVideo-main | Downstream/Video-Text-Retrieval/dataloaders/dataloader_activitynet_retrieval.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import os
from torch.utils.data import Dataset
import numpy as np
import json
import math
from dataloaders.rawvideo_util import RawVideoExtractor
import io
from decord im... | InternVideo-main | Downstream/Video-Text-Retrieval/dataloaders/dataloader_lsmdc_retrieval.py |
"""
Used to compress video in: https://github.com/ArrowLuo/CLIP4Clip
Author: ArrowLuo
"""
import os
import argparse
import ffmpeg
import subprocess
import time
import multiprocessing
from multiprocessing import Pool
import shutil
try:
from psutil import cpu_count
except:
from multiprocessing import cpu_count
# ... | InternVideo-main | Downstream/Video-Text-Retrieval/preprocess/compress_video.py |
"""
Adapted from: https://github.com/openai/CLIP/blob/main/clip/clip.py
"""
from collections import OrderedDict
from typing import Tuple, Union
import hashlib
import os
import urllib
import warnings
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torch import nn
_MODELS = {
"RN50": "http... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/module_clip.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import torch
from torch import nn
import torch.nn.functional as F
from modules.until_module import PreTrainedModel, AllGather, CrossEn
from modules.module_cross import CrossModel, CrossConfig,... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/modeling_backup.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import copy
import json
import math
import logging
import tarfile
import tempfile
import shutil
import torch
from torch import nn
import torch.nn.functional as F
from .file_utils import cached_path
f... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/module_cross.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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 copy... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/until_module.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# 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/LICENS... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/optimization.py |
InternVideo-main | Downstream/Video-Text-Retrieval/modules/__init__.py | |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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 copy... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/until_config.py |
"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
import os
import logging
import shutil
import tempfile
import json
from urllib.parse import urlparse
from pathlib import Path
from typing ... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/file_utils.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/tokenization_clip.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import torch
from torch import nn
import torch.nn.functional as F
from modules.until_module import PreTrainedModel, AllGather, CrossEn
from modules.module_cross import CrossModel, CrossConfig,... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/modeling.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import torch
from torch import nn
import torch.nn.functional as F
from modules.until_module import PreTrainedModel, AllGather, CrossEn
from modules.module_cross import CrossModel, CrossConfig,... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/modeling_raw.py |
# Modified from https://github.com/lucidrains/CoCa-pytorch/blob/main/coca_pytorch/coca_pytorch.py
from turtle import forward
import torch
from torch import einsum, nn
import torch.nn.functional as F
from einops import rearrange, repeat
# helper functions
def exists(val):
return val is not None
def default(val, ... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc2/coca.py |
from .clip import *
| InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc2/__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_kc2/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_kc2/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_kc2/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_kc2/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_kc2/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_kc2/simple_tokenizer.py |
import torch
import torch.nn as nn
from .evl_utils.evl_module import ResidualDecoderBlock
from .coca import Residual, ParallelTransformerBlock, CrossAttention
from einops import repeat
class CaptionDecoder(nn.Module):
def __init__(
self,
n_layers,
transformer_width,
vision_width,
... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc2/clip_decoders.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_kc2/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_kc2/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_kc2/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_kc2/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_kc2/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_kc2/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_kc2/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_kc2/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
from ipdb import set_trace
MODEL_PATH = '/mnt/lustre/share_data/likunchang.ve... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_kc2/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_kc2/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_evl import evl_utils
PATH_PREFIX = '/mnt/lustre/share_data/likunchang.vendor/code/EVL/clip_kc/model'
class EVL(nn.Module):
def __init__(self,
ba... | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/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_evl/model_no_freeze_uniformer.py |
from .clip import *
from .evl_utils import * | InternVideo-main | Downstream/Video-Text-Retrieval/modules/clip_evl/__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_evl/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_evl/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_evl/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_evl/model_no_freeze.py |
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