id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
32,449 | 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 |
32,450 | import os
import math
import logging
from functools import partial
from collections import OrderedDict
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_
def _cfg(url='', **kwargs):
return... | null |
32,451 | import os
import math
import logging
from functools import partial
from collections import OrderedDict
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_
class VisionTransformer(nn.Module):
... | null |
32,452 | import contextlib
import os
import logging
import torch
import torch.nn as nn
from .Qformer import BertConfig, BertLMHeadModel
from .eva_vit import create_eva_vit_g
from transformers import BertTokenizer
The provided code snippet includes necessary dependencies for implementing the `disabled_train` function. Write a P... | Overwrite model.train with this function to make sure train/eval mode does not change anymore. |
32,453 | import numpy as np
import io
import os
import json
import logging
import random
import time
from collections import defaultdict, deque
import datetime
from pathlib import Path
from typing import List, Union
import torch
import torch.distributed as dist
from .distributed import is_dist_avail_and_initialized
def compute... | null |
32,454 | import numpy as np
import io
import os
import json
import logging
import random
import time
from collections import defaultdict, deque
import datetime
from pathlib import Path
from typing import List, Union
import torch
import torch.distributed as dist
from .distributed import is_dist_avail_and_initialized
def compute... | null |
32,455 | import numpy as np
import io
import os
import json
import logging
import random
import time
from collections import defaultdict, deque
import datetime
from pathlib import Path
from typing import List, Union
import torch
import torch.distributed as dist
from .distributed import is_dist_avail_and_initialized
def setup_s... | null |
32,456 | import numpy as np
import io
import os
import json
import logging
import random
import time
from collections import defaultdict, deque
import datetime
from pathlib import Path
from typing import List, Union
import torch
import torch.distributed as dist
from .distributed import is_dist_avail_and_initialized
def remove_... | null |
32,457 | import numpy as np
import io
import os
import json
import logging
import random
import time
from collections import defaultdict, deque
import datetime
from pathlib import Path
from typing import List, Union
import torch
import torch.distributed as dist
from .distributed import is_dist_avail_and_initialized
def save_js... | null |
32,458 | import numpy as np
import io
import os
import json
import logging
import random
import time
from collections import defaultdict, deque
import datetime
from pathlib import Path
from typing import List, Union
import torch
import torch.distributed as dist
from .distributed import is_dist_avail_and_initialized
def load_js... | null |
32,459 | import numpy as np
import io
import os
import json
import logging
import random
import time
from collections import defaultdict, deque
import datetime
from pathlib import Path
from typing import List, Union
import torch
import torch.distributed as dist
from .distributed import is_dist_avail_and_initialized
def flat_lis... | Args: root: path to the directory to start search files suffix: any str as suffix, or can match multiple such strings when input is List[str]. Example 1, e.g., suffix: `.jpg` or [`.jpg`, `.png`] Example 2, e.g., use a `*` in the `suffix`: `START*.jpg.`. |
32,460 | import numpy as np
import io
import os
import json
import logging
import random
import time
from collections import defaultdict, deque
import datetime
from pathlib import Path
from typing import List, Union
import torch
import torch.distributed as dist
from .distributed import is_dist_avail_and_initialized
def match_k... | null |
32,461 | import numpy as np
import io
import os
import json
import logging
import random
import time
from collections import defaultdict, deque
import datetime
from pathlib import Path
from typing import List, Union
import torch
import torch.distributed as dist
from .distributed import is_dist_avail_and_initialized
def merge_d... | null |
32,462 | import logging
import os
import sys
from os.path import dirname, join
from utils.config import Config
from utils.distributed import init_distributed_mode, is_main_process
from utils.logger import setup_logger
logger = logging.getLogger(__name__)
def setup_config():
"""Conbine yaml config and command line config wit... | Setup config, logger, output_dir, etc. Shared for pretrain and all downstream tasks. |
32,463 | from __future__ import annotations
import argparse
import ast
import json
import os
import os.path as osp
import re
import shutil
import sys
import tempfile
from copy import deepcopy
from importlib import import_module
import yaml
from .easydict import EasyDict
The provided code snippet includes necessary dependencies... | The values in a will override values in b. Args: a (dict): source dict. b (dict): target dict. Returns: dict. recursively merge dict a into dict b. |
32,464 | from __future__ import annotations
import argparse
import ast
import json
import os
import os.path as osp
import re
import shutil
import sys
import tempfile
from copy import deepcopy
from importlib import import_module
import yaml
from .easydict import EasyDict
def eval_string(string, d):
"""automatically evaluate ... | eval values of dict leaf. Args: d (dict): The dict to eval. Returns: dict. |
32,465 | import os
import torch
import torch.distributed as dist
import logging
def is_main_process():
return get_rank() == 0
def save_on_master(*args, **kwargs):
if is_main_process():
torch.save(*args, **kwargs) | null |
32,466 | import os
import torch
import torch.distributed as dist
import logging
def get_world_size():
if not is_dist_avail_and_initialized():
return 1
return dist.get_world_size()
class GatherLayer(torch.autograd.Function):
"""
Gather tensors from all workers with support for backward propagation:
Th... | null |
32,467 | import os
import torch
import torch.distributed as dist
import logging
def get_world_size():
if not is_dist_avail_and_initialized():
return 1
return dist.get_world_size()
The provided code snippet includes necessary dependencies for implementing the `gather_tensor_along_batch` function. Write a Python ... | Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient. |
32,468 | import functools
import logging
import os
import sys
import time
import wandb
from typing import Any, Dict, Union
import torch
from .distributed import get_rank, is_main_process
from termcolor import colored
def is_main_process():
return get_rank() == 0
def setup_wandb(config):
if not (config.wandb.enable and... | null |
32,469 | import functools
import logging
import os
import sys
import time
import wandb
from typing import Any, Dict, Union
import torch
from .distributed import get_rank, is_main_process
from termcolor import colored
def setup_output_folder(save_dir: str, folder_only: bool = False):
"""Sets up and returns the output file wh... | Initialize the MMF logger and set its verbosity level to "INFO". Outside libraries shouldn't call this in case they have set there own logging handlers and setup. If they do, and don't want to clear handlers, pass clear_handlers options. The initial version of this function was taken from D2 and adapted for MMF. Args: ... |
32,470 | import functools
import logging
import os
import sys
import time
import wandb
from typing import Any, Dict, Union
import torch
from .distributed import get_rank, is_main_process
from termcolor import colored
class ColorfulFormatter(logging.Formatter):
def __init__(self, *args, **kwargs):
super().__init__(*a... | null |
32,471 | 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 dataset.video_transforms import (
GroupNormalize, GroupScale, GroupCenterCrop,
Stack, ToT... | null |
32,473 | 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):
def get_frame_indices(num_frames, vlen, sample='rand', fix_start=None, input_fps=1, max_num_frames=-1):
def read... | null |
32,474 | 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 |
32,475 | 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_decord(
video_path, num_fra... | null |
32,483 | 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
def pre... | null |
32,484 | 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 |
32,485 | 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 |
32,486 | import datetime
import logging
import time
from os.path import join
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import wandb
from dataset import MetaLoader, create_dataset, create_loader, create_sampler
from models.videochat2_pt import VideoChat2_pt
from tasks.shared_utils import ... | null |
32,487 | import datetime
import logging
import time
from os.path import join
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import wandb
from dataset import MetaLoader, create_dataset, create_loader, create_sampler
from models.videochat2_pt import VideoChat2_pt
from tasks.shared_utils import ... | null |
32,488 | 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 models.bert.tokenization_bert import BertTokenizer
from utils.optimizer import create_optimizer
from utils.scheduler import create_scheduler
logger = logging.getLo... | null |
32,489 | 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
The provided code snippet includes necessary dependencies for implementing th... | get the media types for for all the dataloaders. Args: datasources (List): List of dataloaders or datasets. Returns: List. The media_types. |
32,491 | import datetime
import logging
import time
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from einops import rearrange
from models.criterions import get_sim
from utils.basic_utils import MetricLogger
from utils.distributed import get_rank, get_world_size
def evaluation(... | null |
32,492 | import datetime
import logging
import time
from os.path import join
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import wandb
from dataset import MetaLoader, create_dataset, create_loader, create_sampler
from models.videochat2_it import VideoChat2_it
from tasks.shared_utils import ... | null |
32,493 | import datetime
import logging
import time
from os.path import join
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import wandb
from dataset import MetaLoader, create_dataset, create_loader, create_sampler
from models.videochat2_it import VideoChat2_it
from tasks.shared_utils import ... | null |
32,494 | 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.videochat2_it import VideoChat2_it
from peft import get_peft_model, LoraConfig, TaskType
class Chat:
def __init__(self,... | null |
32,495 | 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.videochat2_it import VideoChat2_it
from peft import get_peft_model, LoraConfig, TaskType
with gr.Blocks(title="InternVideo-V... | null |
32,496 | 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.videochat2_it import VideoChat2_it
from peft import get_peft_model, LoraConfig, TaskType
with gr.Blocks(title="InternVideo-V... | null |
32,497 | 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.videochat2_it import VideoChat2_it
from peft import get_peft_model, LoraConfig, TaskType
with gr.Blocks(title="InternVideo-V... | null |
32,498 | 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.videochat2_it import VideoChat2_it
from peft import get_peft_model, LoraConfig, TaskType
def gradio_answer(chatbot, chat_st... | null |
32,499 | import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy import interpolate
The provided code snippet includes necessary dependencies for implementing the `_init_transformer_weights` function. Write a Python function `def _init_transformer_weights(module, initiali... | Initialize the weights. Copied from transformers ViT/Bert model init |
32,500 | import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy import interpolate
The provided code snippet includes necessary dependencies for implementing the `interpolate_temporal_pos_embed` function. Write a Python function `def interpolate_temporal_pos_embed(temp_e... | temp_embed_old: (1, num_frames_old, 1, d) Returns: temp_embed_new: (1, num_frames_new, 1, d) |
32,501 | import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy import interpolate
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `interpolate_pos_embed` function. Write a Python function `def interpola... | Args: pos_embed_old: (1, L_old, d), pre-trained pos_embed_new: (1, L_new, d), newly initialized, to be replaced by interpolated weights num_patches_new: |
32,502 | import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy import interpolate
The provided code snippet includes necessary dependencies for implementing the `interpolate_pos_relative_bias_beit` function. Write a Python function `def interpolate_pos_relative_bias_bei... | Args: state_dict_old: loaded state dict state_dict_new: state dict for model with new image size patch_shape_new: new model patch_shape ref: https://github.com/microsoft/unilm/blob/master/beit/run_class_finetuning.py |
32,503 | import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy import interpolate
def tile(x, dim, n_tile):
init_dim = x.size(dim)
repeat_idx = [1] * x.dim()
repeat_idx[dim] = n_tile
x = x.repeat(*repeat_idx)
order_index = torch.LongTensor(
n... | null |
32,504 | import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy import interpolate
def mask_logits(target, mask):
return target * mask + (1 - mask) * (-1e10) | null |
32,505 | import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from torch import Tensor, device, dtype, nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.activatio... | Load tf checkpoints in a pytorch model. |
32,506 | from .xbert import BertConfig, BertForMaskedLM, BertLMHeadModel, BertModel
import logging
class BertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BertModel`] or a [`TFBertModel`]. It is used to
instantiate a BERT model according to the specified arguments,... | build text encoder. Args: model_config (dict): model config. pretrain (bool): Whether to do pretrain or finetuning. checkpoint (bool): whether to do gradient_checkpointing. Returns: TODO |
32,507 | from .xbert import BertConfig, BertForMaskedLM, BertLMHeadModel, BertModel
import logging
class BertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BertModel`] or a [`TFBertModel`]. It is used to
instantiate a BERT model according to the specified arguments,... | build text decoder the same as the multimodal encoder. Args: model_config (dict): model config. pretrain (bool): Whether to do pretrain or finetuning. checkpoint (bool): whether to do gradient_checkpointing. Returns: TODO |
32,516 | 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
import warnings
from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
from flash_attn.bert_padding import u... | Make causal mask used for bi-directional self-attention. |
32,517 | 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
import warnings
from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
from flash_attn.bert_padding import u... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
32,518 | 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
import warnings
from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
from flash_attn.bert_padding import u... | null |
32,522 | import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from functools import partial
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
logger = logging.getLogger(__name__)
The provided code snippet includes neces... | Sinusoid position encoding table |
32,523 | import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from functools import partial
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
The provided code snippet includes necessary dependencies for implementing th... | Sinusoid position encoding table |
32,524 | import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from functools import partial
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
logger = logging.getLogger(__name__)
def _cfg(url='', **kwargs):
return {
... | null |
32,525 | import torch
import torch.nn as nn
import logging
from .Qformer import BertConfig, BertLMHeadModel
from models.utils import load_temp_embed_with_mismatch
logger = logging.getLogger(__name__)
class BertLMHeadModel(BertPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_mi... | null |
32,526 | import torch
import torch.nn as nn
import logging
from .Qformer import BertConfig, BertLMHeadModel
from models.utils import load_temp_embed_with_mismatch
def load_temp_embed_with_mismatch(temp_embed_old, temp_embed_new, add_zero=True):
"""
Add/Remove extra temporal_embeddings as needed.
https://arxiv.org/a... | null |
32,527 | import contextlib
import os
import logging
import torch
import torch.nn as nn
from .Qformer import BertConfig, BertLMHeadModel
from .vit import build_vit
from transformers import BertTokenizer
The provided code snippet includes necessary dependencies for implementing the `disabled_train` function. Write a Python funct... | Overwrite model.train with this function to make sure train/eval mode does not change anymore. |
32,540 | import re
import torch
from torch import optim as optim
from utils.distributed import is_main_process
import logging
def add_weight_decay(model, weight_decay, no_decay_list=(), filter_bias_and_bn=True):
named_param_tuples = []
for name, param in model.named_parameters():
if not param.requires_grad:
... | null |
32,542 | import os
import torch
import torch.distributed as dist
import logging
logger = logging.getLogger(__name__)
def setup_for_distributed(is_master):
import warnings
builtin_warn = warnings.warn
def warn(*args, **kwargs):
force = kwargs.pop("force", False)
if is_master or force:
buil... | null |
32,545 | from torch.optim import Optimizer
import math
from torch.optim.lr_scheduler import LambdaLR
def get_cosine_schedule_with_warmup(
optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int,
num_cycles: float = 0.5, min_lr_multi: float = 0., last_epoch: int = -1
):
def create_scheduler(args, op... | null |
32,546 | import functools
import logging
import os
import sys
import time
import wandb
from typing import Any, Dict, Union
import torch
from .distributed import get_rank, is_main_process
from termcolor import colored
def is_main_process():
return get_rank() == 0
The provided code snippet includes necessary dependencies fo... | include a separator `/` at the end of `prefix` |
32,547 | import functools
import logging
import os
import sys
import time
import wandb
from typing import Any, Dict, Union
import torch
from .distributed import get_rank, is_main_process
from termcolor import colored
def is_main_process():
def setup_wandb(config):
if not (config.wandb.enable and is_main_process()):
... | null |
32,556 | 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 moss import *
from load_internvideo import *
device = torch.device('cuda' if torch.cuda.is_available() else '... | null |
32,557 | 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 moss import *
from load_internvideo import *
from models.grit_model import DenseCaptioning
with gr.Blocks(css... | null |
32,559 | 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 |
32,561 | 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 |
32,594 | import itertools
from typing import Any, Callable, Dict, Iterable, List, Set, Type, Union
import torch
from detectron2.config import CfgNode
from detectron2.solver.build import maybe_add_gradient_clipping
def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12):
def maybe_add_gradient_clipping(
cfg: CfgNo... | null |
32,674 | 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 |
32,754 | 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():
def get_env_module():
def detect_compute_compatibility(CUDA_HOME, so_file):
def collect_env_info():
... | null |
32,758 | import functools
import numpy as np
import torch
import torch.distributed as dist
def get_rank() -> int:
def is_main_process() -> bool:
return get_rank() == 0 | null |
32,847 | 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 |
32,856 | import logging
import os
from collections import OrderedDict
import torch
from torch.nn.parallel import DistributedDataParallel
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer
from detectron2.config import get_cfg
from detectron2.data import (
Metad... | null |
32,858 | import itertools
import os
import subprocess
from multiprocessing import Process, Queue
DRY_RUN = False
The provided code snippet includes necessary dependencies for implementing the `do_work` function. Write a Python function `def do_work(work: "Queue[str]", gpu_idx: int) -> bool` to solve the following problem:
Proc... | Process for each ID in GPUS. |
32,860 | import itertools
import os
import subprocess
from multiprocessing import Process, Queue
DRY_RUN = True
The provided code snippet includes necessary dependencies for implementing the `do_work` function. Write a Python function `def do_work(work: "Queue[str]", gpu_idx: int) -> bool` to solve the following problem:
Proce... | Process for each ID in GPUS. |
32,863 | import argparse
import csv
import os
from lightning.pytorch import Trainer
from torchgeo.datamodules import ChesapeakeCVPRDataModule
from torchgeo.trainers.chesapeake import SemanticSegmentationTask
The provided code snippet includes necessary dependencies for implementing the `set_up_parser` function. Write a Python ... | Set up the argument parser. Returns: the argument parser |
32,864 | import argparse
import csv
import os
import time
import lightning.pytorch as pl
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.models import resnet34
from torchgeo.datasets import CDL, Landsat8, stack_samples
from torchgeo.samplers import GridGeoS... | Set up the argument parser. Returns: the argument parser |
32,867 | import glob
import json
import os
from collections import defaultdict
from typing import DefaultDict
from tbparse import SummaryReader
The provided code snippet includes necessary dependencies for implementing the `nested_dict` function. Write a Python function `def nested_dict() -> DefaultDict[str, defaultdict]` to s... | Recursive defaultdict. Returns: a nested dictionary |
32,870 | import argparse
import os
import shutil
The provided code snippet includes necessary dependencies for implementing the `delete_scene` function. Write a Python function `def delete_scene(directories: list[str], scene_id: str) -> None` to solve the following problem:
Delete scene_id from all directories. Args: directori... | Delete scene_id from all directories. Args: directories: directories to check scene_id: scene to delete |
32,871 | import argparse
import csv
import json
import os
import time
import warnings
from collections import defaultdict
from datetime import date, timedelta
from multiprocessing.dummy import Lock, Pool
from typing import Any, Optional
import ee
import numpy as np
import rasterio
from rasterio.transform import Affine
def mask... | null |
32,872 | import argparse
import csv
import json
import os
import time
import warnings
from collections import defaultdict
from datetime import date, timedelta
from multiprocessing.dummy import Lock, Pool
from typing import Any, Optional
import ee
import numpy as np
import rasterio
from rasterio.transform import Affine
def get_... | null |
32,873 | import argparse
import csv
import json
import os
import time
import warnings
from collections import defaultdict
from datetime import date, timedelta
from multiprocessing.dummy import Lock, Pool
from typing import Any, Optional
import ee
import numpy as np
import rasterio
from rasterio.transform import Affine
def get_r... | null |
32,874 | import argparse
import glob
import os
import numpy as np
import rasterio as rio
from tqdm import tqdm
from tqdm.contrib.concurrent import thread_map
The provided code snippet includes necessary dependencies for implementing the `class_counts` function. Write a Python function `def class_counts(path: str) -> "np.typing... | Calculate the number of values in each class. Args: path: Path to an image file. Returns: Counts of each class. |
32,875 | import argparse
import csv
import os
import time
import numpy as np
import pandas as pd
from rtree import index
from torchvision.datasets.utils import download_and_extract_archive
from tqdm import tqdm
def get_world_cities(
download_root: str = "world_cities", size: int = 10000
) -> pd.DataFrame:
url = "https:... | null |
32,876 | import argparse
import csv
import os
import time
import numpy as np
import pandas as pd
from rtree import index
from torchvision.datasets.utils import download_and_extract_archive
from tqdm import tqdm
def km2deg(kms: float, radius: float = 6371) -> float:
return kms / (2.0 * radius * np.pi / 360.0)
def sample_poi... | null |
32,877 | import argparse
import csv
import os
import time
import numpy as np
import pandas as pd
from rtree import index
from torchvision.datasets.utils import download_and_extract_archive
from tqdm import tqdm
def create_bbox(
coords: tuple[float, float], bbox_size_degree: float
) -> tuple[float, float, float, float]:
... | null |
32,878 | import argparse
import glob
import os
import numpy as np
import rasterio
from rasterio.io import DatasetReader
from rasterio.vrt import WarpedVRT
from rasterio.windows import from_bounds
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `retrieve_mask_chip` function. ... | Retrieve the mask for a given landsat image. Args: img_src: input image for which to find a corresponding chip mask_src: CRS aligned mask from which to retrieve a chip corresponding to img_src Returns: mask array |
32,879 | import argparse
import csv
import os
import random
import fiona
from rtree import index
from sample_ssl4eo import create_bbox, km2deg
from shapely.geometry import MultiPolygon, Point, shape
from shapely.ops import unary_union
from torchvision.datasets.utils import download_and_extract_archive
from tqdm import tqdm
The... | Retrieve CONUS MultiPolygon. Args: download_root: directory where to store usa shape file Returns: MultiPolygon of CONUS |
32,880 | import argparse
import glob
import os
import numpy as np
import rasterio as rio
from tqdm import tqdm
from tqdm.contrib.concurrent import thread_map
The provided code snippet includes necessary dependencies for implementing the `compute` function. Write a Python function `def compute(path: str) -> tuple["np.typing.NDA... | Compute the min, max, mean, and std dev of a single image. Args: path: Path to an image file. Returns: Min, max, mean, and std dev of the image. |
32,881 | import argparse
import glob
import os
import numpy as np
import rasterio as rio
from tqdm import tqdm
from tqdm.contrib.concurrent import thread_map
The provided code snippet includes necessary dependencies for implementing the `compress` function. Write a Python function `def compress(src_path: str) -> None` to solve... | Rescale, convert to uint8, and compress an image. Args: src_path: Path to an image file. |
32,882 | import math
from collections.abc import Iterable
from typing import Any, Callable, Optional, Union
import numpy as np
import torch
from einops import rearrange
from torch import Generator, Tensor
from torch.nn import Module
from torch.utils.data import Subset, TensorDataset, random_split
from ..datasets import NonGeoDa... | Custom collate fn for object detection and instance segmentation. Args: batch: list of sample dicts return by dataset Returns: batch dict output .. versionadded:: 0.6 |
32,883 | import math
from collections.abc import Iterable
from typing import Any, Callable, Optional, Union
import numpy as np
import torch
from einops import rearrange
from torch import Generator, Tensor
from torch.nn import Module
from torch.utils.data import Subset, TensorDataset, random_split
from ..datasets import NonGeoDa... | Split a torch Dataset into train/val/test sets. If ``test_pct`` is not set then only train and validation splits are returned. .. deprecated:: 0.4 Use :func:`torch.utils.data.random_split` instead, ``random_split`` now supports percentages as of PyTorch 1.13. Args: dataset: dataset to be split into train/val or train/v... |
32,884 | import math
from collections.abc import Iterable
from typing import Any, Callable, Optional, Union
import numpy as np
import torch
from einops import rearrange
from torch import Generator, Tensor
from torch.nn import Module
from torch.utils.data import Subset, TensorDataset, random_split
from ..datasets import NonGeoDa... | Method for performing a single group-wise shuffle split of data. Loosely based off of :class:`sklearn.model_selection.GroupShuffleSplit`. Args: groups: a sequence of group values used to split. Should be in the same order as the data you want to split. train_size: the proportion of groups to include in the train split.... |
32,885 | from typing import Any
import torch
from torch import Tensor
from ..datasets import FAIR1M
from .geo import NonGeoDataModule
The provided code snippet includes necessary dependencies for implementing the `collate_fn` function. Write a Python function `def collate_fn(batch: list[dict[str, Tensor]]) -> dict[str, Any]` t... | Custom object detection collate fn to handle variable boxes. Args: batch: list of sample dicts return by dataset Returns: batch dict output .. versionadded:: 0.5 |
32,886 | import math
from typing import Optional, Union, overload
import torch
from ..datasets import BoundingBox
def _to_tuple(value: Union[tuple[int, int], int]) -> tuple[int, int]: ...
def _to_tuple(value: Union[tuple[float, float], float]) -> tuple[float, float]: ...
def _to_tuple(value: Union[tuple[float, float], float]) -... | Returns a random bounding box within a given bounding box. The ``size`` argument can either be: * a single ``float`` - in which case the same value is used for the height and width dimension * a ``tuple`` of two floats - in which case, the first *float* is used for the height dimension, and the second *float* for the w... |
32,887 | import math
from typing import Optional, Union, overload
import torch
from ..datasets import BoundingBox
The provided code snippet includes necessary dependencies for implementing the `tile_to_chips` function. Write a Python function `def tile_to_chips( bounds: BoundingBox, size: tuple[float, float], strid... | r"""Compute number of :term:`chips <chip>` that can be sampled from a :term:`tile`. Let :math:`i` be the size of the input tile. Let :math:`k` be the requested size of the output patch. Let :math:`s` be the requested stride. Let :math:`o` be the number of output chips sampled from each tile. :math:`o` can then be compu... |
32,888 | import os
import warnings
from typing import Any, Optional, Union
import kornia.augmentation as K
import lightning
import timm
import torch
import torch.nn as nn
import torch.nn.functional as F
from lightly.loss import NTXentLoss
from lightly.models.modules import SimCLRProjectionHead
from torch import Tensor
from torc... | Data augmentation used by SimCLR. Args: size: Size of patch to crop. weights: Weight vector for grayscale computation. Returns: Data augmentation pipeline. |
32,889 | import warnings
from collections import OrderedDict
from typing import Optional, Union, cast
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.modules import Conv2d, Module
The provided code snippet includes necessary dependencies for implementing the `extract_backbone` function. Write a Python... | Extracts a backbone from a lightning checkpoint file. Args: path: path to checkpoint file (.ckpt) Returns: tuple containing model name and state dict Raises: ValueError: if 'model' or 'backbone' not in checkpoint['hyper_parameters'] .. versionchanged:: 0.4 Renamed from *extract_encoder* to *extract_backbone* |
32,890 | import warnings
from collections import OrderedDict
from typing import Optional, Union, cast
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.modules import Conv2d, Module
def _get_input_layer_name_and_module(model: Module) -> tuple[str, Module]:
"""Retrieve the input layer name and module ... | Load pretrained resnet weights to a model. Args: model: model to load the pretrained weights to state_dict: dict containing tensor parameters Returns: The missing and unexpected keys Warns: If input channels in model != pretrained model input channels If num output classes in model != pretrained model num classes |
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