id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
31,825 | import logging
import json
from typing import Dict
from omegaconf import OmegaConf
from minigpt4.common.registry import registry
class ConfigValidator:
"""
This is a preliminary implementation to centralize and validate the configuration.
May be altered in the future.
A helper class to validate configur... | null |
31,826 | import math
from minigpt4.common.registry import registry
The provided code snippet includes necessary dependencies for implementing the `cosine_lr_schedule` function. Write a Python function `def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr)` to solve the following problem:
Decay the learning rate
... | Decay the learning rate |
31,827 | import math
from minigpt4.common.registry import registry
The provided code snippet includes necessary dependencies for implementing the `warmup_lr_schedule` function. Write a Python function `def warmup_lr_schedule(optimizer, step, max_step, init_lr, max_lr)` to solve the following problem:
Warmup the learning rate
... | Warmup the learning rate |
31,828 | import math
from minigpt4.common.registry import registry
The provided code snippet includes necessary dependencies for implementing the `step_lr_schedule` function. Write a Python function `def step_lr_schedule(optimizer, epoch, init_lr, min_lr, decay_rate)` to solve the following problem:
Decay the learning rate
He... | Decay the learning rate |
31,829 | import datetime
import logging
import time
from collections import defaultdict, deque
import torch
import torch.distributed as dist
from minigpt4.common import dist_utils
def setup_logger():
logging.basicConfig(
level=logging.INFO if dist_utils.is_main_process() else logging.WARN,
format="%(asctime... | null |
31,830 | import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithP... | Make causal mask used for bi-directional self-attention. |
31,831 | import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithP... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
31,832 | import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithP... | null |
31,833 | import os
import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from minigpt4.common.dist_utils import dow... | null |
31,834 | import os
import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from minigpt4.common.dist_utils import dow... | null |
31,835 | import logging
import os
import numpy as np
import torch
import torch.nn as nn
from minigpt4.common.dist_utils import download_cached_file, is_dist_avail_and_initialized
from minigpt4.common.utils import get_abs_path, is_url
from omegaconf import OmegaConf
class GatherLayer(torch.autograd.Function):
"""
Gather ... | Performs all_gather operation on the provided tensors. Graph remains connected for backward grad computation. |
31,836 | import logging
import os
import numpy as np
import torch
import torch.nn as nn
from minigpt4.common.dist_utils import download_cached_file, is_dist_avail_and_initialized
from minigpt4.common.utils import get_abs_path, is_url
from omegaconf import OmegaConf
def is_dist_avail_and_initialized():
if not dist.is_availa... | Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient. |
31,837 | import logging
import os
import numpy as np
import torch
import torch.nn as nn
from minigpt4.common.dist_utils import download_cached_file, is_dist_avail_and_initialized
from minigpt4.common.utils import get_abs_path, is_url
from omegaconf import OmegaConf
def tile(x, dim, n_tile):
init_dim = x.size(dim)
repea... | null |
31,838 | import contextlib
import logging
import os
import time
import datetime
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.nn.functional as F
import minigpt4.common.dist_utils as dist_utils
from minigpt4.common.dist_utils import download_cached_file
from minigpt4.common.utils import is_url
... | Overwrite model.train with this function to make sure train/eval mode does not change anymore. |
31,839 | import contextlib
import logging
import os
import time
import datetime
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.nn.functional as F
import minigpt4.common.dist_utils as dist_utils
from minigpt4.common.dist_utils import download_cached_file
from minigpt4.common.utils import is_url
... | null |
31,840 | from intern_action import intern_action_b16
from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn
import torchvision.transforms as T
import torch.nn.functional as F
import numpy as np
from transforms import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, ToTorchFormatTensor
)
de... | null |
31,841 | from intern_action import intern_action_b16
from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn
import torchvision.transforms as T
import torch.nn.functional as F
import numpy as np
from transforms import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, ToTorchFormatTensor
)
cl... | null |
31,842 | from intern_action import intern_action_b16
from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn
import torchvision.transforms as T
import torch.nn.functional as F
import numpy as np
from transforms import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, ToTorchFormatTensor
)
cla... | null |
31,843 | from intern_action import intern_action_b16
from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn
import torchvision.transforms as T
import torch.nn.functional as F
import numpy as np
from transforms import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, ToTorchFormatTensor
)
de... | null |
31,844 | from decord import VideoReader
from decord import cpu
import numpy as np
import torchvision.transforms as transforms
from transforms import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, ToTorchFormatTensor
)
def loadvideo_decord(sample, sample_rate_scale=1,new_width=384, new_height=384, clip_len=8, fr... | null |
31,845 | from decord import VideoReader
from decord import cpu
import numpy as np
import torchvision.transforms as transforms
from transforms import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, ToTorchFormatTensor
)
def loadvideo_decord_origin(sample, sample_rate_scale=1,new_width=384, new_height=384, clip_le... | null |
31,846 | from langchain.agents.initialize import initialize_agent
from langchain.agents.tools import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.llms.openai import OpenAI
import re
import gradio as gr
import openai
def cut_dialogue_history(history_memory, keep_last_n_words=400)... | null |
31,847 | import os
import numpy as np
import random
import torch
import torchvision.transforms as transforms
from PIL import Image
from models.tag2text import tag2text_caption
from util import *
import gradio as gr
from chatbot import *
from load_internvideo import *
device = torch.device('cuda' if torch.cuda.is_available() els... | null |
31,848 | import os
import numpy as np
import random
import torch
import torchvision.transforms as transforms
from PIL import Image
from models.tag2text import tag2text_caption
from util import *
import gradio as gr
from chatbot import *
from load_internvideo import *
from simplet5 import SimpleT5
from models.grit_model import D... | null |
31,851 | import warnings
from models.vit import VisionTransformer, interpolate_pos_embed
from models.swin_transformer import SwinTransformer, interpolate_relative_pos_embed
from models.med import BertConfig, BertModel, BertLMHeadModel
from transformers import BertTokenizer
import torch
from torch import nn
import torch.nn.funct... | null |
31,852 | import warnings
from models.vit import VisionTransformer, interpolate_pos_embed
from models.swin_transformer import SwinTransformer, interpolate_relative_pos_embed
from models.med import BertConfig, BertModel, BertLMHeadModel
from transformers import BertTokenizer
import torch
from torch import nn
import torch.nn.funct... | null |
31,853 | import warnings
from models.vit import VisionTransformer, interpolate_pos_embed
from models.swin_transformer import SwinTransformer, interpolate_relative_pos_embed
from models.med import BertConfig, BertModel, BertLMHeadModel
from transformers import BertTokenizer
import torch
from torch import nn
import torch.nn.funct... | null |
31,854 | import warnings
from models.vit import VisionTransformer, interpolate_pos_embed
from models.swin_transformer import SwinTransformer, interpolate_relative_pos_embed
from models.med import BertConfig, BertModel, BertLMHeadModel
from transformers import BertTokenizer
import torch
from torch import nn
import torch.nn.funct... | null |
31,873 | import logging
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
from functools import partial
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
from detectron2.layers import ShapeSpec
from centernet.mod... | null |
31,883 | import argparse
import multiprocessing as mp
import os
import time
import cv2
import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from models.grit_src.grit.c... | null |
31,884 | import argparse
import multiprocessing as mp
import os
import time
import cv2
import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from models.grit_src.grit.c... | null |
31,885 | import argparse
import multiprocessing as mp
import os
import time
import cv2
import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from models.grit_src.grit.c... | null |
31,901 | import contextlib
from unittest import mock
import torch
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.modeling.roi_heads import keypoint_head, mask_head
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Caffe... | null |
31,918 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_p... | null |
31,958 | import os
from detectron2.data import DatasetCatalog, MetadataCatalog
from .builtin_meta import ADE20K_SEM_SEG_CATEGORIES, _get_builtin_metadata
from .cityscapes import load_cityscapes_instances, load_cityscapes_semantic
from .cityscapes_panoptic import register_all_cityscapes_panoptic
from .coco import load_sem_seg, r... | null |
32,077 | import cv2
import torch
from torch import nn
from detectron2.utils.comm import get_world_size
from detectron2.structures import pairwise_iou, Boxes
import torch.nn.functional as F
import numpy as np
from detectron2.structures import Boxes, ImageList, Instances
def get_world_size() -> int:
def reduce_sum(tensor):
... | null |
32,083 | import cv2
import numpy as np
import torch
import torch.nn.functional as F
COLORS = ((np.random.rand(1300, 3) * 0.4 + 0.6) * 255).astype(
np.uint8).reshape(1300, 1, 1, 3)
def _imagelist_to_tensor(images):
images = [x for x in images]
image_sizes = [x.shape[-2:] for x in images]
h = max([size[0] for size i... | null |
32,089 | import argparse
import glob
import multiprocessing as mp
import numpy as np
import os
import tempfile
import time
import warnings
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from predictor import Vis... | null |
32,120 | from intern_action import intern_action_b16
from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn
import torchvision.transforms as T
import torch.nn.functional as F
import numpy as np
from transforms import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, ToTorchFormatTensor
)
cl... | null |
32,121 | from intern_action import intern_action_b16
from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn
import torchvision.transforms as T
import torch.nn.functional as F
import numpy as np
from transforms import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, ToTorchFormatTensor
)
cla... | null |
32,125 | import os
import numpy as np
import random
import torch
import torchvision.transforms as transforms
from PIL import Image
from models.tag2text import tag2text_caption
from util import *
import gradio as gr
from stablelm import *
from load_internvideo import *
device = torch.device('cuda' if torch.cuda.is_available() el... | null |
32,126 | import os
import numpy as np
import random
import torch
import torchvision.transforms as transforms
from PIL import Image
from models.tag2text import tag2text_caption
from util import *
import gradio as gr
from stablelm import *
from load_internvideo import *
from models.grit_model import DenseCaptioning
with gr.Blocks... | null |
32,127 | import os
from collections import OrderedDict
from timm.models.layers import DropPath
import torch
from torch import nn
from torch.nn import MultiheadAttention
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
_MODELS = {
"ViT-B/16": os.path.join(MODEL_PATH, "vit_b16.pth"),
"ViT-L/14":... | null |
32,135 | 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
from timm.models.helpers import named_apply, adapt_input_con... | null |
32,138 | import numpy as np
from scipy import interpolate
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def interpolate_relative_pos_embed(rel_pos_bias, dst_num_pos, param_name=''):
# from: https://github.com/microsoft/unilm/... | null |
32,146 | import operator
import torch
import torch.utils.data
from detectron2.utils.comm import get_world_size
from detectron2.config import configurable
from torch.utils.data.sampler import BatchSampler, Sampler
from detectron2.data.common import DatasetFromList, MapDataset
from detectron2.data.dataset_mapper import DatasetMap... | null |
32,165 | import argparse
import multiprocessing as mp
import os
import time
import cv2
import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from models.grit_src.grit.c... | null |
32,166 | import argparse
import multiprocessing as mp
import os
import time
import cv2
import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from models.grit_src.grit.c... | null |
32,167 | import argparse
import multiprocessing as mp
import os
import time
import cv2
import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from models.grit_src.grit.c... | null |
32,192 | import functools
import io
import struct
import types
import torch
from detectron2.modeling import meta_arch
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.roi_heads import keypoint_head
from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
from .c10 impor... | null |
32,213 | from __future__ import absolute_import, division, print_function, unicode_literals
import torch
The provided code snippet includes necessary dependencies for implementing the `pairwise_iou_rotated` function. Write a Python function `def pairwise_iou_rotated(boxes1, boxes2)` to solve the following problem:
Return inter... | Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x_center, y_center, width, height, angle) format. Arguments: boxes1 (Tensor[N, 5]) boxes2 (Tensor[M, 5]) Returns: iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes... |
32,222 | import logging
import numpy as np
from typing import List, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
from detectron2.... | Read an image into the given format. Will apply rotation and flipping if the image has such exif information. Args: file_name (str): image file path format (str): one of the supported image modes in PIL, or "BGR" or "YUV-BT.601". Returns: image (np.ndarray): an HWC image in the given format, which is 0-255, uint8 for s... |
32,241 | import os
from detectron2.data import DatasetCatalog, MetadataCatalog
from .builtin_meta import ADE20K_SEM_SEG_CATEGORIES, _get_builtin_metadata
from .cityscapes import load_cityscapes_instances, load_cityscapes_semantic
from .cityscapes_panoptic import register_all_cityscapes_panoptic
from .coco import load_sem_seg, r... | null |
32,245 | import itertools
import logging
import numpy as np
import operator
import pickle
from typing import Any, Callable, Dict, List, Optional, Union
import torch
import torch.utils.data as torchdata
from tabulate import tabulate
from termcolor import colored
from detectron2.config import configurable
from detectron2.structur... | null |
32,246 | import itertools
import logging
import numpy as np
import operator
import pickle
from typing import Any, Callable, Dict, List, Optional, Union
import torch
import torch.utils.data as torchdata
from tabulate import tabulate
from termcolor import colored
from detectron2.config import configurable
from detectron2.structur... | Build a dataloader for object detection with some default features. Args: dataset (list or torch.utils.data.Dataset): a list of dataset dicts, or a pytorch dataset (either map-style or iterable). It can be obtained by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. mapper (callable): a callable... |
32,248 | import itertools
import logging
import numpy as np
import operator
import pickle
from typing import Any, Callable, Dict, List, Optional, Union
import torch
import torch.utils.data as torchdata
from tabulate import tabulate
from termcolor import colored
from detectron2.config import configurable
from detectron2.structur... | Similar to `build_detection_train_loader`, with default batch size = 1, and sampler = :class:`InferenceSampler`. This sampler coordinates all workers to produce the exact set of all samples. Args: dataset: a list of dataset dicts, or a pytorch dataset (either map-style or iterable). They can be obtained by using :func:... |
32,255 | import torch
from torch.nn import functional as F
from detectron2.structures import Instances, ROIMasks
The provided code snippet includes necessary dependencies for implementing the `detector_postprocess` function. Write a Python function `def detector_postprocess( results: Instances, output_height: int, output_w... | Resize the output instances. The input images are often resized when entering an object detector. As a result, we often need the outputs of the detector in a different resolution from its inputs. This function will resize the raw outputs of an R-CNN detector to produce outputs according to the desired output resolution... |
32,272 | import itertools
import logging
import numpy as np
from collections import OrderedDict
from collections.abc import Mapping
from typing import Dict, List, Optional, Tuple, Union
import torch
from omegaconf import DictConfig, OmegaConf
from torch import Tensor, nn
from detectron2.layers import ShapeSpec
from detectron2.s... | null |
32,282 | from typing import List
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d, ConvTranspose2d, cat, interpolate
from detectron2.structures import Instances, heatmaps_to_keypoints
from detectron2.utils.events import get_eve... | Post process each predicted keypoint heatmap in `pred_keypoint_logits` into (x, y, score) and add it to the `pred_instances` as a `pred_keypoints` field. Args: pred_keypoint_logits (Tensor): A tensor of shape (R, K, S, S) where R is the total number of instances in the batch, K is the number of keypoints, and S is the ... |
32,307 | import dataclasses
import logging
from collections import abc
from typing import Any
from detectron2.utils.registry import _convert_target_to_string, locate
def locate(name: str) -> Any:
"""
Locate and return an object ``x`` using an input string ``{x.__module__}.{x.__qualname__}``,
such as "module.submodu... | Recursively instantiate objects defined in dictionaries by "_target_" and arguments. Args: cfg: a dict-like object with "_target_" that defines the caller, and other keys that define the arguments Returns: object instantiated by cfg |
32,315 | import argparse
import logging
import os
import sys
import weakref
from collections import OrderedDict
from typing import Optional
import torch
from fvcore.nn.precise_bn import get_bn_modules
from omegaconf import OmegaConf
from torch.nn.parallel import DistributedDataParallel
import detectron2.data.transforms as T
fro... | Create a DistributedDataParallel model if there are >1 processes. Args: model: a torch.nn.Module fp16_compression: add fp16 compression hooks to the ddp object. See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook kwargs: other argu... |
32,318 | import argparse
import logging
import os
import sys
import weakref
from collections import OrderedDict
from typing import Optional
import torch
from fvcore.nn.precise_bn import get_bn_modules
from omegaconf import OmegaConf
from torch.nn.parallel import DistributedDataParallel
import detectron2.data.transforms as T
fro... | Build a list of :class:`EventWriter` to be used. It now consists of a :class:`CommonMetricPrinter`, :class:`TensorboardXWriter` and :class:`JSONWriter`. Args: output_dir: directory to store JSON metrics and tensorboard events max_iter: the total number of iterations Returns: list[EventWriter]: a list of :class:`EventWr... |
32,322 | import datetime
import json
import logging
import os
import time
from collections import defaultdict
from contextlib import contextmanager
from typing import Optional
import torch
from fvcore.common.history_buffer import HistoryBuffer
from detectron2.utils.file_io import PathManager
_CURRENT_STORAGE_STACK = []
The pro... | Returns: The :class:`EventStorage` object that's currently being used. Throws an error if no :class:`EventStorage` is currently enabled. |
32,323 | import importlib
import numpy as np
import os
import re
import subprocess
import sys
from collections import defaultdict
import PIL
import torch
import torchvision
from tabulate import tabulate
def collect_torch_env():
try:
import torch.__config__
return torch.__config__.show()
except ImportErro... | null |
32,325 | import functools
import numpy as np
import torch
import torch.distributed as dist
_LOCAL_PROCESS_GROUP = None
def get_rank() -> int:
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
return dist.get_rank()
The provided code snippet includes necessary dependencies f... | Returns: The rank of the current process within the local (per-machine) process group. |
32,327 | import functools
import numpy as np
import torch
import torch.distributed as dist
def get_rank() -> int:
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
return dist.get_rank()
def is_main_process() -> bool:
return get_rank() == 0 | null |
32,328 | import functools
import numpy as np
import torch
import torch.distributed as dist
def get_world_size() -> int:
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
return dist.get_world_size()
The provided code snippet includes necessary dependencies for implementing ... | Helper function to synchronize (barrier) among all processes when using distributed training |
32,331 | import functools
import numpy as np
import torch
import torch.distributed as dist
def get_world_size() -> int:
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
return dist.get_world_size()
def get_rank() -> int:
if not dist.is_available():
return 0
... | Reduce the values in the dictionary from all processes so that process with rank 0 has the reduced results. Args: input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor. average (bool): whether to do average or sum Returns: a dict with the same keys as input_dict, after reduction. |
32,332 | from typing import Any
import pydoc
from fvcore.common.registry import Registry
def locate(name: str) -> Any:
"""
Locate and return an object ``x`` using an input string ``{x.__module__}.{x.__qualname__}``,
such as "module.submodule.class_name".
Raise Exception if it cannot be found.
"""
obj = ... | Inverse of ``locate()``. Args: t: any object with ``__module__`` and ``__qualname__`` |
32,337 | import typing
from typing import Any, List
import fvcore
from fvcore.nn import activation_count, flop_count, parameter_count, parameter_count_table
from torch import nn
from detectron2.export import TracingAdapter
ACTIVATIONS_MODE = "activations"
def _wrapper_count_operators(
model: nn.Module, inputs: list, mode: s... | Implement operator-level activations counting using jit. This is a wrapper of fvcore.nn.activation_count, that supports standard detection models in detectron2. Note: The function runs the input through the model to compute activations. The activations of a detection model is often input-dependent, for example, the act... |
32,340 | import atexit
import functools
import logging
import os
import sys
import time
from collections import Counter
import torch
from tabulate import tabulate
from termcolor import colored
from detectron2.utils.file_io import PathManager
class _ColorfulFormatter(logging.Formatter):
def __init__(self, *args, **kwargs):
... | Initialize the detectron2 logger and set its verbosity level to "DEBUG". Args: output (str): a file name or a directory to save log. If None, will not save log file. If ends with ".txt" or ".log", assumed to be a file name. Otherwise, logs will be saved to `output/log.txt`. name (str): the root module name of this logg... |
32,341 | import atexit
import functools
import logging
import os
import sys
import time
from collections import Counter
import torch
from tabulate import tabulate
from termcolor import colored
from detectron2.utils.file_io import PathManager
def _find_caller():
"""
Returns:
str: module name of the caller
... | Log only for the first n times. Args: lvl (int): the logging level msg (str): n (int): name (str): name of the logger to use. Will use the caller's module by default. key (str or tuple[str]): the string(s) can be one of "caller" or "message", which defines how to identify duplicated logs. For example, if called with `n... |
32,343 | import atexit
import functools
import logging
import os
import sys
import time
from collections import Counter
import torch
from tabulate import tabulate
from termcolor import colored
from detectron2.utils.file_io import PathManager
def _find_caller():
"""
Returns:
str: module name of the caller
... | Log no more than once per n seconds. Args: lvl (int): the logging level msg (str): n (int): name (str): name of the logger to use. Will use the caller's module by default. |
32,344 | import atexit
import functools
import logging
import os
import sys
import time
from collections import Counter
import torch
from tabulate import tabulate
from termcolor import colored
from detectron2.utils.file_io import PathManager
The provided code snippet includes necessary dependencies for implementing the `create... | Create a small table using the keys of small_dict as headers. This is only suitable for small dictionaries. Args: small_dict (dict): a result dictionary of only a few items. Returns: str: the table as a string. |
32,345 | import atexit
import functools
import logging
import os
import sys
import time
from collections import Counter
import torch
from tabulate import tabulate
from termcolor import colored
from detectron2.utils.file_io import PathManager
The provided code snippet includes necessary dependencies for implementing the `_log_a... | Internal function used to log the usage of different detectron2 components inside facebook's infra. |
32,350 | import logging
import numpy as np
import pycocotools.mask as mask_util
import torch
from fvcore.common.file_io import PathManager
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
f... | Create a list of default :class:`Augmentation` from config. Now it includes resizing and flipping. Returns: list[Augmentation] |
32,354 | import copy
import logging
import numpy as np
import operator
import torch
import torch.utils.data
import json
from detectron2.utils.comm import get_world_size
from detectron2.data import samplers
from torch.utils.data.sampler import BatchSampler, Sampler
from detectron2.data.common import DatasetFromList, MapDataset
f... | Modified from detectron2.data.build.build_custom_train_loader, but supports different samplers |
32,391 | from detectron2.config import CfgNode as CN
def add_centernet_config(cfg):
_C = cfg
_C.MODEL.CENTERNET = CN()
_C.MODEL.CENTERNET.NUM_CLASSES = 80
_C.MODEL.CENTERNET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"]
_C.MODEL.CENTERNET.FPN_STRIDES = [8, 16, 32, 64, 128]
_C.MODEL.CENTERNET.PRIOR_PROB ... | null |
32,414 | import logging
import numpy as np
from collections import Counter
import tqdm
from fvcore.nn import flop_count_table
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
from detectron2.data import build_detection_test_loader
from detectron2.en... | null |
32,424 | import itertools
import logging
import psutil
import torch
import tqdm
from fvcore.common.timer import Timer
from torch.nn.parallel import DistributedDataParallel
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import LazyConfig, get_cfg, instantiate
from detectron2.data import (
Data... | null |
32,427 | from PIL import Image
import torch
from transformers import StoppingCriteria, StoppingCriteriaList
from enum import auto, Enum
import numpy as np
from decord import VideoReader, cpu
import torchvision.transforms as T
from models.video_transformers import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, To... | null |
32,428 | import random
import io
import av
import cv2
import decord
import imageio
from decord import VideoReader
import torch
import numpy as np
import math
import logging
def get_frame_indices_by_fps():
pass | null |
32,429 | import random
import io
import av
import cv2
import decord
import imageio
from decord import VideoReader
import torch
import numpy as np
import math
import logging
def get_pyav_video_duration(video_reader):
video_stream = video_reader.streams.video[0]
video_duration = pts_to_secs(
video_stream.duration,... | null |
32,430 | import random
import io
import av
import cv2
import decord
import imageio
from decord import VideoReader
import torch
import numpy as np
import math
import logging
def get_frame_indices(num_frames, vlen, sample='rand', fix_start=None, input_fps=1, max_num_frames=-1):
def read_frames_gif(
video_path, num_frames... | null |
32,431 | import random
import io
import av
import cv2
import decord
import imageio
from decord import VideoReader
import torch
import numpy as np
import math
import logging
def get_frame_indices(num_frames, vlen, sample='rand', fix_start=None, input_fps=1, max_num_frames=-1):
if sample in ["rand", "middle"]: # uniform sampl... | null |
32,432 | from utils.distributed import is_main_process, get_rank, get_world_size
import logging
import torch.distributed as dist
import torch
import io
import os
import json
import re
import numpy as np
from os.path import join
from tqdm import trange
from PIL import Image
from PIL import ImageFile
from torchvision.transforms i... | null |
32,433 | from utils.distributed import is_main_process, get_rank, get_world_size
import logging
import torch.distributed as dist
import torch
import io
import os
import json
import re
import numpy as np
from os.path import join
from tqdm import trange
from PIL import Image
from PIL import ImageFile
from torchvision.transforms i... | [summary] Args: ann_file_list (List[List[str, str]] or List[str, str]): the latter will be automatically converted to the former. Each sublist contains [anno_path, image_root], (or [anno_path, video_root, 'video']) which specifies the data type, video or image Returns: List(dict): each dict is { image: str or List[str]... |
32,434 | from utils.distributed import is_main_process, get_rank, get_world_size
import logging
import torch.distributed as dist
import torch
import io
import os
import json
import re
import numpy as np
from os.path import join
from tqdm import trange
from PIL import Image
from PIL import ImageFile
from torchvision.transforms i... | null |
32,435 | from utils.distributed import is_main_process, get_rank, get_world_size
import logging
import torch.distributed as dist
import torch
import io
import os
import json
import re
import numpy as np
from os.path import join
from tqdm import trange
from PIL import Image
from PIL import ImageFile
from torchvision.transforms i... | null |
32,436 | from utils.distributed import is_main_process, get_rank, get_world_size
import logging
import torch.distributed as dist
import torch
import io
import os
import json
import re
import numpy as np
from os.path import join
from tqdm import trange
from PIL import Image
from PIL import ImageFile
from torchvision.transforms i... | gather results from multiple GPUs |
32,437 | from utils.distributed import is_main_process, get_rank, get_world_size
import logging
import torch.distributed as dist
import torch
import io
import os
import json
import re
import numpy as np
from os.path import join
from tqdm import trange
from PIL import Image
from PIL import ImageFile
from torchvision.transforms i... | Pad a single-nested list or a sequence of n-d array (torch.tensor or np.ndarray) into a (n+1)-d array, only allow the first dim has variable lengths. Args: sequences: list(n-d tensor or list) dtype: np.dtype or torch.dtype device: fixed_length: pad all seq in sequences to fixed length. All seq should have a length <= f... |
32,438 | import logging
import os
import json
import sqlite3
import random
from os.path import basename
import numpy as np
from dataset.base_dataset import ImageVideoBaseDataset
from dataset.utils import load_anno, pre_text
from dataset.video_utils import VIDEO_READER_FUNCS
from utils.distributed import is_main_process
The pro... | TODO: Docstring for get_anno_by_id. Args: cur (sqlite3.Cursor): The dataset cursor. id (int): The annotation id. Returns: |
32,439 | import datetime
import logging
import time
from os.path import join
import pandas as pd
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import wandb
from torch.utils.data import ConcatDataset
from dataset import MetaLoader, create_dataset, create_loader, create_sampler
from models.vid... | null |
32,440 | import datetime
import logging
import time
from os.path import join
import pandas as pd
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import wandb
from torch.utils.data import ConcatDataset
from dataset import MetaLoader, create_dataset, create_loader, create_sampler
from models.vid... | null |
32,441 | import copy
import logging
import os
import os.path as osp
from os.path import join
import torch
from torch.utils.data import ConcatDataset, DataLoader
from utils.optimizer import create_optimizer
from utils.scheduler import create_scheduler
logger = logging.getLogger(__name__)
def create_optimizer(args, model, filter... | null |
32,442 | import torch
import gradio as gr
from gradio.themes.utils import colors, fonts, sizes
from conversation import Chat
from utils.config import Config
from utils.easydict import EasyDict
from models.videochat import VideoChat
class Chat:
def __init__(self, model, device='cuda:0'):
def ask(self,text,conv):
... | null |
32,443 | import torch
import gradio as gr
from gradio.themes.utils import colors, fonts, sizes
from conversation import Chat
from utils.config import Config
from utils.easydict import EasyDict
from models.videochat import VideoChat
with gr.Blocks(title="InternVideo-VideoChat!",theme=gvlabtheme,css="#chatbot {overflow:auto; heig... | null |
32,444 | import torch
import gradio as gr
from gradio.themes.utils import colors, fonts, sizes
from conversation import Chat
from utils.config import Config
from utils.easydict import EasyDict
from models.videochat import VideoChat
with gr.Blocks(title="InternVideo-VideoChat!",theme=gvlabtheme,css="#chatbot {overflow:auto; heig... | null |
32,445 | import torch
import gradio as gr
from gradio.themes.utils import colors, fonts, sizes
from conversation import Chat
from utils.config import Config
from utils.easydict import EasyDict
from models.videochat import VideoChat
with gr.Blocks(title="InternVideo-VideoChat!",theme=gvlabtheme,css="#chatbot {overflow:auto; heig... | null |
32,446 | import torch
import gradio as gr
from gradio.themes.utils import colors, fonts, sizes
from conversation import Chat
from utils.config import Config
from utils.easydict import EasyDict
from models.videochat import VideoChat
def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
llm_message,llm_me... | null |
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