id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
182,737 | import pickle
import os
import argparse
import numpy as np
from torch.utils.data import Dataset, DataLoader
from mmpt.processors import PKLJSONStrTextProcessor
from mmpt.utils import ShardedTensor, recursive_config
class TokenizerDataset(Dataset):
def __init__(self, config):
def __getitem__(self, idx):
d... | null |
182,773 | from __future__ import absolute_import, division, print_function, unicode_literals
import re
from collections import deque
from enum import Enum
import numpy as np
class WERTransformer(object):
def __init__(self, hyp_str, ref_str, verbose=True):
def process(self, input):
def report_result(self):
def... | null |
182,811 | from typing import Dict, List, NamedTuple, Optional
import torch
import torch.nn as nn
from examples.simultaneous_translation.modules.monotonic_transformer_layer import (
TransformerMonotonicDecoderLayer,
TransformerMonotonicEncoderLayer,
)
from fairseq.models import (
register_model,
register_model_arc... | null |
182,813 | from typing import Dict, List, NamedTuple, Optional
import torch
import torch.nn as nn
from examples.simultaneous_translation.modules.monotonic_transformer_layer import (
TransformerMonotonicDecoderLayer,
TransformerMonotonicEncoderLayer,
)
from fairseq.models import (
register_model,
register_model_arc... | null |
182,814 | from typing import Dict, List, NamedTuple, Optional
import torch
import torch.nn as nn
from examples.simultaneous_translation.modules.monotonic_transformer_layer import (
TransformerMonotonicDecoderLayer,
TransformerMonotonicEncoderLayer,
)
from fairseq.models import (
register_model,
register_model_arc... | null |
182,832 | import logging
from typing import Dict, List, Optional
from pathlib import Path
import torch.nn as nn
from torch import Tensor
from fairseq import checkpoint_utils
from fairseq.models import register_model, register_model_architecture
from fairseq.utils import safe_hasattr
from fairseq.models.speech_to_text.s2t_transfo... | null |
182,843 | import io
import os
from pathlib import Path
from typing import Optional, List, Dict
import zipfile
import tempfile
from dataclasses import dataclass
from itertools import groupby
import torch
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from examples.speech_to_text.data_utils import load_ts... | null |
182,844 | import io
import os
from pathlib import Path
from typing import Optional, List, Dict
import zipfile
import tempfile
from dataclasses import dataclass
from itertools import groupby
import torch
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from examples.speech_to_text.data_utils import load_ts... | null |
182,845 | import io
import os
from pathlib import Path
from typing import Optional, List, Dict
import zipfile
import tempfile
from dataclasses import dataclass
from itertools import groupby
import torch
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from examples.speech_to_text.data_utils import load_ts... | null |
182,850 | import io
import os
from pathlib import Path
from typing import Optional, List, Dict
import zipfile
import tempfile
from dataclasses import dataclass
from itertools import groupby
import torch
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from examples.speech_to_text.data_utils import load_ts... | null |
182,851 | import ast
import logging
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
import soundfile as sf
import sys
import torch
import torchaudio
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.logging import progress_bar
from fairseq.tasks.text_to_speech import plot_tts_ou... | null |
182,866 | import argparse
import logging
import os
import csv
import tempfile
from collections import defaultdict
from pathlib import Path
import torchaudio
try:
import webrtcvad
except ImportError:
raise ImportError("Please install py-webrtcvad: pip install webrtcvad")
import pandas as pd
from tqdm import tqdm
from exam... | null |
182,872 | import functools
import logging
from contextlib import contextmanager
import inspect
import time
def copy_state(state):
def serialize_model(model):
args, kwargs = model._init_args_kwargs
state = copy_state(model.state_dict())
return {"class": model.__class__, "args": args, "kwargs": kwargs, "state": state} | null |
182,886 | import logging
from collections import namedtuple
import torch
import torch.nn as nn
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.mod... | null |
182,910 | from typing import NamedTuple, List
from urllib.parse import urlparse
import os, sys
import subprocess
from subprocess import check_call, check_output
import glob
import wget
import re
import multiprocessing as mp
from functools import partial
import pathlib
from collections import OrderedDict
download_to = f'{to_data... | null |
182,934 | import math
import sys
from fractions import Fraction
import warnings
from collections import Counter
from nltk.translate.bleu_score import modified_precision, closest_ref_length, brevity_penalty, SmoothingFunction
def corpus_bleu(
list_of_references,
hypotheses,
weights=(0.25, 0.25, 0.25, 0.25),
smooth... | null |
182,950 | import torch
from examples.textless_nlp.gslm.unit2speech.tacotron2.model import Tacotron2
from examples.textless_nlp.gslm.unit2speech.tacotron2.waveglow_denoiser import (
Denoiser,
)
class Tacotron2(nn.Module):
def __init__(self, hparams):
def parse_batch(self, batch):
def parse_output(self, outputs... | null |
183,011 | import csv
from pathlib import Path
import zipfile
from functools import reduce
from multiprocessing import cpu_count
from typing import Any, Dict, List, Optional, Union
import io
import numpy as np
import pandas as pd
import sentencepiece as sp
from fairseq.data.audio.audio_utils import (
convert_waveform, _get_ka... | null |
183,013 | import csv
from pathlib import Path
import zipfile
from functools import reduce
from multiprocessing import cpu_count
from typing import Any, Dict, List, Optional, Union
import io
import numpy as np
import pandas as pd
import sentencepiece as sp
from fairseq.data.audio.audio_utils import (
convert_waveform, _get_ka... | null |
183,023 | import argparse
import logging
import os
from pathlib import Path
import shutil
from itertools import groupby
from tempfile import NamedTemporaryFile
from typing import Tuple
import pandas as pd
import soundfile as sf
from examples.speech_to_text.data_utils import (
create_zip,
extract_fbank_features,
filte... | null |
183,036 | from fairseq import tasks
import numpy as np
import logging
import random
from fairseq import options
import torch
import os
import soundfile as sf
from fairseq.data.audio.audio_utils import (
get_waveform,
parse_path,
)
def get_waveform(
path_or_fp: Union[str, BinaryIO], normalization: bool = True,
... | Load raw dataset from w2v tsv file. Optionally get waveforms |
183,138 | import torch.nn as nn
from .learned_positional_embedding import LearnedPositionalEmbedding
from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are igno... | null |
183,139 | import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.data import data_utils
from fairseq.dataclass.configs import CheckpointConfig
fro... | Load a checkpoint and restore the training iterator. *passthrough_args* will be passed through to ``trainer.get_train_iterator``. |
183,140 | import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.data import data_utils
from fairseq.dataclass.configs import CheckpointConfig
fro... | Loads an ensemble of models. Args: filenames (List[str]): checkpoint files to load arg_overrides (Dict[str,Any], optional): override model args that were used during model training task (fairseq.tasks.FairseqTask, optional): task to use for loading |
183,141 | import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.data import data_utils
from fairseq.dataclass.configs import CheckpointConfig
fro... | Prune the given state_dict if desired for LayerDrop (https://arxiv.org/abs/1909.11556). Training with LayerDrop allows models to be robust to pruning at inference time. This function prunes state_dict to allow smaller models to be loaded from a larger model and re-maps the existing state_dict for this to occur. It's ca... |
183,142 | import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.data import data_utils
from fairseq.dataclass.configs import CheckpointConfig
fro... | Load a pretrained FairseqEncoder or FairseqDecoder from checkpoint into the provided `component` object. If state_dict fails to load, there may be a mismatch in the architecture of the corresponding `component` found in the `checkpoint` file. |
183,320 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.model_parallel.models.pipeline_parallel_transformer.layers import (
Embedding,
TransformerDecoderEmbedding,
TransformerDecoderLayer,
TransformerDecoderOutputLayer,
TransformerEnco... | null |
183,359 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftma... | null |
183,374 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
183,378 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
183,381 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
183,383 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
183,401 | import torch
from fairseq.utils import new_arange
def new_arange(x, *size):
"""
Return a Tensor of `size` filled with a range function on the device of x.
If size is empty, using the size of the variable x.
"""
if len(size) == 0:
size = x.size()
return torch.arange(size[-1], device=x.de... | null |
183,425 | import logging
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoder... | null |
183,444 | import math
import re
from functools import partial
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from torch import device as Device
from fairseq.models import FairseqEncoder
from fairseq.models.speech_to_text.utils import (
NoOp,
attention_suppression,
... | null |
183,447 | import logging
import math
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
Fair... | null |
183,490 | from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.... | null |
183,494 | import logging
import json
from typing import Dict
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from fairseq.data.audio.audio_utils import (
get_window,
get_fourier_basis,
get_mel_filters,
TTSSpectrogram,
)
from fairseq.data.audio.speech_to_text_dataset import S2T... | null |
183,522 | import argparse
import logging
import math
import os
import sys
from typing import Any, Callable, Dict, List, Optional, Tuple
logger = logging.getLogger("fairseq_cli.train")
import numpy as np
import torch
from omegaconf import DictConfig, OmegaConf
from fairseq import checkpoint_utils, options, quantization_utils, tas... | null |
183,524 | import ast
import fileinput
import logging
import math
import os
import sys
import time
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclass.configs import FairseqConfig
fro... | null |
183,526 | import logging
import os
import sys
from argparse import Namespace
from itertools import chain
import torch
from omegaconf import DictConfig
from fairseq import checkpoint_utils, distributed_utils, options, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import metrics, pro... | null |
183,538 | import ast
import logging
import math
import os
import sys
from argparse import Namespace
from itertools import chain
import numpy as np
import torch
from omegaconf import DictConfig
from fairseq import checkpoint_utils, options, scoring, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
f... | null |
183,540 | import sys
import unilm
import ast
import fileinput
import logging
import math
import os
import sys
import time
import re
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclas... | null |
183,541 | import sys
import unilm
import ast
import fileinput
import logging
import math
import os
import sys
import time
import re
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclas... | null |
183,542 | import sys
import unilm
import ast
import fileinput
import logging
import math
import os
import sys
import time
import re
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclas... | null |
183,543 | import sys
import unilm
import ast
import fileinput
import logging
import math
import os
import sys
import time
import re
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclas... | null |
183,544 | import json
from tqdm import tqdm
def cook_data(input_file, image_path, locate_token=None, postfix='.out'):
# read a json file
print(input_file)
dataset = json.load(open(input_file, 'r', encoding='utf-8'))
with open(input_file + postfix, 'w', encoding='utf-8') as f:
for ann in tqdm(dataset[... | null |
183,552 | import sys
import unilm
import ast
import fileinput
import logging
import math
import os
import sys
import time
import re
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclas... | null |
183,553 | import json
from tqdm import tqdm
def cook_data_inline(input_file, image_path, locate_token=None, postfix='.inline.out'):
# read a json file
obj = json.load(open(input_file, 'r', encoding='utf-8'))
with open(input_file + postfix, 'w', encoding='utf-8') as f:
for item in tqdm(obj['images']):
... | null |
183,554 | import xml.etree.ElementTree as ET
from collections import defaultdict
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
from prettytable import PrettyTable
from tqdm import tqdm
from decode_string import decode_bbox_from_caption
import json
The provided c... | Parses a sentence file from the Flickr30K Entities dataset input: filename - full file path to the sentence file to parse output: a list of dictionaries for each sentence with the following fields: sentence - the original sentence phrases - a list of dictionaries for each phrase with the following fields: phrase - the ... |
183,555 | import xml.etree.ElementTree as ET
from collections import defaultdict
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
from prettytable import PrettyTable
from tqdm import tqdm
from decode_string import decode_bbox_from_caption
import json
The provided c... | Parses the xml files in the Flickr30K Entities dataset input: filename - full file path to the annotations file to parse output: dictionary with the following fields: scene - list of identifiers which were annotated as pertaining to the whole scene nobox - list of identifiers which were annotated as not being visible i... |
183,556 | import xml.etree.ElementTree as ET
from collections import defaultdict
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
from prettytable import PrettyTable
from tqdm import tqdm
from decode_string import decode_bbox_from_caption
import json
def _box_inter_... | Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and ``0 <= y1 < y2``. Args: boxes1 (Tensor[N, 4]) boxes2 (Tensor[M, 4]) Returns: iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in ... |
183,557 | import xml.etree.ElementTree as ET
from collections import defaultdict
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
from prettytable import PrettyTable
from tqdm import tqdm
from decode_string import decode_bbox_from_caption
import json
The provided c... | Return the boxes corresponding to the smallest enclosing box containing all the provided boxes The boxes are expected in [x1, y1, x2, y2] format |
183,558 | import re
import numpy as np
def find_patch_index_combinations(s):
# The regular expression pattern for matching the required formats
pattern = r'(?:(<phrase>([^<]+)</phrase>))?<object>((?:<patch_index_\d+><patch_index_\d+></delimiter_of_multi_objects/>)*<patch_index_\d+><patch_index_\d+>)</object>'
#... | null |
183,559 | import base64
import io
import random
import os
from PIL import Image
from tqdm import tqdm
import json
import string
import pdb
def convert_json_to_txt(json_path, image_path, txt_path, answer_path):
json_ann = json.load(open(json_path, 'rb'))
# pdb.set_trace()
with open(txt_path, 'w', encoding='ut... | null |
183,560 | import ast
import json
from tqdm import tqdm, trange
from collections import defaultdict
import re
import os, sys
import pdb
import sys, json
def clean_special_tokens(input_string):
def find_consecutive_int_indices(numbers):
def eval(answer_file, json_file, result_file, split_str='Answer:'):
question_type_dict = j... | null |
183,561 | import os
import gzip
from sre_parse import SPECIAL_CHARS
import numpy as np
from random import Random
from typing import Any, Callable, Dict, Generator, Iterable, Iterator, List, Optional, Tuple, Union
import collections
from infinibatch import iterators
GRD_SYMBOL="<grounding>"
BOP_SYMBOL="<phrase>"
EOP_SYMBOL="</phr... | null |
183,563 | import logging
import math
from argparse import Namespace
from dataclasses import dataclass, field
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
def _get_wds_dataset(wds_args, preprocess_img, is_train, epoch=0, floor=False):
def get_wds_dataset(args, preprocess_img, is_train, epoch=0, floor=False, shard_i... | null |
183,564 | import glob
import os
import numpy as np
import time
import json
import random
import itertools
import hydra
import copy
import ast
from PIL import Image, ImageDraw, ImageFont
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
import base64
import io
def centercrop_norm_bbox(o... | null |
183,565 | import glob
import os
import numpy as np
import time
import json
import random
import itertools
import hydra
import copy
import ast
from PIL import Image, ImageDraw, ImageFont
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
import base64
import io
def plot_boxes_to_image(im... | Args: image_np (_type_): np.array cluster_obj_dict (_type_): {(1, 10): [[(0, 1024)], [0.6991432]]} bin_size (_type_): 32 caption |
183,566 | import json
import os
import multiprocessing
import itertools
import ast
from infinibatch import iterators
from functools import partial
from tiktoken.core import Encoding
import glob
import os
import torch
import numpy as np
import time
import json
import random
import itertools
import hydra
import copy
import torchvi... | null |
183,567 | import torch
import torch.nn as nn
from fairseq.modules import MultiheadAttention
from fairseq import utils
class SimpleConnector(nn.Module):
"""Connector model of GPT and MLM."""
def __init__(self, input_dim, output_dim):
super().__init__()
self.dense = nn.Linear(input_dim, output_dim)
def ... | null |
183,568 | from dataclasses import dataclass, field
from typing import Optional
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
import logging
import numpy as np
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.da... | null |
183,569 | from dataclasses import dataclass, field
from typing import Optional
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
import logging
import numpy as np
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.da... | null |
183,570 | from dataclasses import dataclass, field
from typing import Optional
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
import logging
import numpy as np
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.da... | null |
183,571 | from dataclasses import dataclass, field
from typing import Optional
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
import logging
import numpy as np
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.da... | null |
183,572 | from dataclasses import dataclass, field
from typing import Optional
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
import logging
import numpy as np
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.da... | null |
183,573 | from dataclasses import dataclass, field
from typing import Optional
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
import logging
import numpy as np
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.da... | null |
183,574 | from dataclasses import dataclass, field
from typing import Optional
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
import logging
import numpy as np
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.da... | null |
183,575 | import logging
import os
import torch
from copy import deepcopy
from typing import Tuple, Union, Callable, Optional
from torch import nn
from torch.nn import functional as F
from open_clip.model import CLIP, CLIPVisionCfg, QuickGELU, TimmModel, ModifiedResNet, VisualTransformer, to_2tuple, LayerNorm, Transformer
from o... | null |
183,576 | import sys
import unilm
import ast
import logging
import math
import os
import sys
import time
import re
import random
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclass.c... | null |
183,577 | import sys
import unilm
import ast
import logging
import math
import os
import sys
import time
import re
import random
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclass.c... | null |
183,578 | import sys
import unilm
import ast
import logging
import math
import os
import sys
import time
import re
import random
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclass.c... | null |
183,579 | import sys
import unilm
import ast
import logging
import math
import os
import sys
import time
import re
import random
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclass.c... | null |
183,580 | import re
import numpy as np
def find_patch_index_combinations(s):
def get_box_coords_from_index(P, ul_idx, lr_idx):
def decode_bbox_from_caption(caption, quantized_size=32, **kwargs):
valid_combinations = find_patch_index_combinations(caption)
entity_names = list(map(lambda x: x[0], valid_combinations)... | null |
183,581 | import os
import sys
from pathlib import Path
import textwrap
import re
import ast
import os
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import cv2
import base64
import io
from decode_string import decode_bbox_... | null |
183,582 | import os
import sys
from pathlib import Path
import textwrap
import re
import ast
import os
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import cv2
import base64
import io
from decode_string import decode_bbox_... | null |
183,590 | import math
import warnings
import torch
import torch.distributed as dist
from fairseq.utils import multi_tensor_l2norm_available, multi_tensor_total_norm
def multi_tensor_total_norm(grads, chunk_size=2048 * 32) -> torch.Tensor:
per_device_grads = {}
norms = []
for grad in grads:
device = grad.devi... | null |
183,593 | import math
import torch
import torch.nn.functional as F
from apex.normalization import FusedLayerNorm as LayerNorm
from torch import nn
from .multiway_network import MultiwayWrapper
from xformers.ops import memory_efficient_attention, LowerTriangularMask, MemoryEfficientAttentionCutlassOp
def rotate_every_two(x):
... | null |
183,595 | import logging
import time
from typing import Any, Tuple, cast
import torch
import torch.distributed as dist
from torch import Tensor
from torch.nn import Module, ModuleList
def _find_my_group_index(grouped_ranks):
my_rank = dist.get_rank()
for i, group in enumerate(grouped_ranks):
if my_rank in group:
... | null |
183,602 | import os
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from fairseq import checkpoint_utils
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerModel,
TransformerDecoderBase,... | null |
183,603 | import os
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from fairseq import checkpoint_utils
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerModel,
TransformerDecoderBase,... | null |
183,604 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
from datasets import ClassLabel, load_dataset, load_metric
import transformers
from layoutlmft.data import DataCollatorForKeyValueExtraction
from layoutlmft.data.xfund import xfund_dataset, XFund_... | null |
183,611 | import torch
from detectron2.layers import (
ShapeSpec,
)
from detectron2.modeling import Backbone, BACKBONE_REGISTRY, FPN
from detectron2.modeling.backbone.fpn import LastLevelP6P7, LastLevelMaxPool
from .beit import beit_base_patch16, dit_base_patch16, dit_large_patch16, beit_large_patch16
from .deit import deit_... | Create a VIT w/ FPN backbone. Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
183,612 | import copy
import logging
import numpy as np
import torch
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from layoutlmft import LayoutLMv3Tokenizer
The provided code snippet includes necessary dependencies for implementing the `build_transform_gen` function. Write a P... | Create a list of :class:`TransformGen` from config. Returns: list[TransformGen] |
183,613 | from detectron2.config import CfgNode as CN
The provided code snippet includes necessary dependencies for implementing the `add_vit_config` function. Write a Python function `def add_vit_config(cfg)` to solve the following problem:
Add config for VIT.
Here is the function:
def add_vit_config(cfg):
"""
Add co... | Add config for VIT. |
183,614 | import argparse
import logging
import os
import sys
import time
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.transfo... | 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... |
183,615 | import argparse
import logging
import os
import sys
import time
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.transfo... | Create a parser with some common arguments used by detectron2 users. Args: epilog (str): epilog passed to ArgumentParser describing the usage. Returns: argparse.ArgumentParser: |
183,616 | import argparse
import logging
import os
import sys
import time
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.transfo... | Perform some basic common setups at the beginning of a job, including: 1. Set up the detectron2 logger 2. Log basic information about environment, cmdline arguments, and config 3. Backup the config to the output directory Args: cfg (CfgNode or omegaconf.DictConfig): the full config to be used args (argparse.NameSpace):... |
183,617 | import argparse
import logging
import os
import sys
import time
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.transfo... | 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... |
183,618 | from detectron2.checkpoint import DetectionCheckpointer
from typing import Any
import torch
import torch.nn as nn
from fvcore.common.checkpoint import _IncompatibleKeys, _strip_prefix_if_present, TORCH_VERSION, quantization, \
ObserverBase, FakeQuantizeBase
from torch import distributed as dist
from scipy import in... | null |
183,620 | import warnings
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import trunc_normal_, drop_path, to_2tuple
from functools import partial
def _cfg(url='', **kwargs):
class ViT(nn.Module):
def __init__(self,
model_name='vit_base_... | null |
183,623 | import os
import xml.dom.minidom
if os.path.exists("/mnt/localdata/Users/junlongli/projects/datasets/icdar2019"):
PATH = "/mnt/localdata/Users/junlongli/projects/datasets/icdar2019/trackA_modern/test"
else:
PATH = "/mnt/data/data/icdar2019/trackA_modern/test"
reg_gt_path_archival = os.path.abspath(PATH)
import ... | null |
183,626 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
from datasets import ClassLabel, load_dataset, load_metric
import transformers
from layoutlmft.data import DataCollatorForKeyValueExtraction
from transformers import (
AutoConfig,
AutoMode... | null |
183,627 | import json
import os
from pathlib import Path
import datasets
from layoutlmft.data.image_utils import load_image, normalize_bbox
def quad_to_box(quad):
# test 87 is wrongly annotated
box = (
max(0, quad["x1"]),
max(0, quad["y1"]),
quad["x3"],
quad["y3"]
)
if box[3] < bo... | null |
183,628 | import json
import os
from pathlib import Path
import datasets
from layoutlmft.data.image_utils import load_image, normalize_bbox
def _get_drive_url(url):
base_url = 'https://drive.google.com/uc?id='
split_url = url.split('/')
return base_url + split_url[5] | null |
183,629 | import torchvision.transforms.functional as F
import warnings
import math
import random
import numpy as np
from PIL import Image
import torch
from detectron2.data.detection_utils import read_image
from detectron2.data.transforms import ResizeTransform, TransformList
def normalize_bbox(bbox, size):
return [
... | null |
183,630 | import torchvision.transforms.functional as F
import warnings
import math
import random
import numpy as np
from PIL import Image
import torch
from detectron2.data.detection_utils import read_image
from detectron2.data.transforms import ResizeTransform, TransformList
def load_image(image_path):
image = read_image(i... | null |
183,631 | import torchvision.transforms.functional as F
import warnings
import math
import random
import numpy as np
from PIL import Image
import torch
from detectron2.data.detection_utils import read_image
from detectron2.data.transforms import ResizeTransform, TransformList
def clamp(num, min_value, max_value):
return max(... | null |
183,632 | import torchvision.transforms.functional as F
import warnings
import math
import random
import numpy as np
from PIL import Image
import torch
from detectron2.data.detection_utils import read_image
from detectron2.data.transforms import ResizeTransform, TransformList
def resize(image, size, interpolation, boxes=None):
... | null |
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