id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
182,526 | import math
from typing import Dict, List, Optional
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import FairseqEncoder
from fairseq.modules import (
FairseqDropout,
LayerDropModuleList,
LayerNorm,
PositionalEmbedding,
Sinu... | null |
182,535 | import math
import re
from functools import partial
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq.models import (
FairseqEncoder,
)
from fairseq.models.speech_to_text.utils import (
NoOp,
lengths_to_padding_mask,
segments_to_sequence,
)
from fairseq.models.spee... | null |
182,536 | import logging
import math
from typing import Dict, List, Optional, Tuple
from pathlib import Path
import torch
import torch.nn as nn
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
... | null |
182,537 | import logging
import math
from typing import Dict, List, Optional, Tuple
from pathlib import Path
import torch
import torch.nn as nn
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
... | null |
182,538 | import logging
import math
from typing import Dict, List, Optional, Tuple
from pathlib import Path
import torch
import torch.nn as nn
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
... | null |
182,539 | import logging
import math
from typing import Dict, List, Optional, Tuple
from pathlib import Path
import torch
import torch.nn as nn
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
... | null |
182,561 | import logging
import copy
from typing import Dict, List, Optional, Tuple
from fairseq import utils, checkpoint_utils
from fairseq.models import (FairseqEncoderDecoderModel, FairseqEncoder,
register_model, register_model_architecture)
from fairseq.models.transformer import Embedding, Transfo... | null |
182,562 | import logging
import copy
from typing import Dict, List, Optional, Tuple
from fairseq import utils, checkpoint_utils
from fairseq.models import (FairseqEncoderDecoderModel, FairseqEncoder,
register_model, register_model_architecture)
from fairseq.models.transformer import Embedding, Transfo... | null |
182,563 | import logging
import copy
from typing import Dict, List, Optional, Tuple
from fairseq import utils, checkpoint_utils
from fairseq.models import (FairseqEncoderDecoderModel, FairseqEncoder,
register_model, register_model_architecture)
from fairseq.models.transformer import Embedding, Transfo... | null |
182,580 | import logging
import torch
from torch import nn
from fairseq.models import (FairseqEncoder, FairseqEncoderModel, register_model,
register_model_architecture)
from fairseq.modules import (
LayerNorm, PositionalEmbedding, FairseqDropout, MultiheadAttention
)
from fairseq import utils
from... | null |
182,581 | import logging
import torch
from torch import nn
from fairseq.models import (FairseqEncoder, FairseqEncoderModel, register_model,
register_model_architecture)
from fairseq.modules import (
LayerNorm, PositionalEmbedding, FairseqDropout, MultiheadAttention
)
from fairseq import utils
from... | null |
182,582 | import logging
import torch
from torch import nn
from fairseq.models import (FairseqEncoder, FairseqEncoderModel, register_model,
register_model_architecture)
from fairseq.modules import (
LayerNorm, PositionalEmbedding, FairseqDropout, MultiheadAttention
)
from fairseq import utils
from... | null |
182,583 | 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 S2TDataConfig
fr... | null |
182,586 | import logging
from typing import List, Optional
import torch
from torch import nn
from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
FairseqIncrementalDecoder, register_model,
register_model_architecture)
from fairseq.modules import (
Tra... | null |
182,587 | import logging
from typing import List, Optional
import torch
from torch import nn
from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
FairseqIncrementalDecoder, register_model,
register_model_architecture)
from fairseq.modules import (
Tra... | null |
182,588 | import logging
from typing import List, Optional
import torch
from torch import nn
from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
FairseqIncrementalDecoder, register_model,
register_model_architecture)
from fairseq.modules import (
Tra... | null |
182,589 | import logging
from typing import List, Optional
import torch
from torch import nn
from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
FairseqIncrementalDecoder, register_model,
register_model_architecture)
from fairseq.modules import (
Tra... | null |
182,590 | import logging
import torch
from torch import nn
from torch.nn import functional as F
from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
FairseqIncrementalDecoder, register_model,
register_model_architecture)
from fairseq.modules import LSTMCe... | null |
182,591 | import logging
import torch
from torch import nn
from torch.nn import functional as F
from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
FairseqIncrementalDecoder, register_model,
register_model_architecture)
from fairseq.modules import LSTMCe... | null |
182,592 | import logging
import torch
from torch import nn
from torch.nn import functional as F
from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
FairseqIncrementalDecoder, register_model,
register_model_architecture)
from fairseq.modules import LSTMCe... | null |
182,593 | import logging
from argparse import Namespace
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.data import Dictionary
from fairseq.dataclass.utils import (
convert_namespace_to_omegaconf,
gen_parser_from_data... | null |
182,595 | from argparse import Namespace
import contextlib
import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass, field
from omegaconf import MISSING, II, open_dict
from typing import Any, Optional
from fairseq import checkpoint_utils, task... | null |
182,596 | from argparse import Namespace
import contextlib
import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass, field
from omegaconf import MISSING, II, open_dict
from typing import Any, Optional
from fairseq import checkpoint_utils, task... | null |
182,598 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
SinusoidalPositionalEmbedding,
... | null |
182,603 | import argparse
import random
import sys
from sacrebleu import extract_ngrams
def get_phrase(words, index, length):
assert index < len(words) - length + 1
phr = " ".join(words[index : index + length])
for i in range(index, index + length):
words.pop(index)
return phr | null |
182,606 | import argparse
import logging
import math
import os
import sys
from typing import Dict, Optional, Any, List, Tuple, Callable
logger = logging.getLogger("fairseq_cli.train")
import numpy as np
import torch
from fairseq import (
checkpoint_utils,
options,
quantization_utils,
tasks,
utils,
)
from fair... | Train the model for one epoch and return validation losses. |
182,607 | import argparse
import logging
import math
import os
import sys
from typing import Dict, Optional, Any, List, Tuple, Callable
logger = logging.getLogger("fairseq_cli.train")
import numpy as np
import torch
from fairseq import (
checkpoint_utils,
options,
quantization_utils,
tasks,
utils,
)
from fair... | null |
182,609 | 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 |
182,610 | 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 |
182,611 | import logging
import os
import sys
from argparse import Namespace
from itertools import chain
import torch
from fairseq import checkpoint_utils, distributed_utils, options, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import metrics, progress_bar
from fairseq.utils impo... | null |
182,612 | import logging
import os
import shutil
import sys
from collections import Counter
from itertools import zip_longest
from multiprocessing import Pool
from fairseq import options, tasks, utils
from fairseq.binarizer import Binarizer
from fairseq.data import indexed_dataset
from fairseq.file_chunker_utils import find_offs... | null |
182,613 | import logging
import os
import shutil
import sys
from collections import Counter
from itertools import zip_longest
from multiprocessing import Pool
from fairseq import options, tasks, utils
from fairseq.binarizer import Binarizer
from fairseq.data import indexed_dataset
from fairseq.file_chunker_utils import find_offs... | null |
182,614 | import logging
import os
import shutil
import sys
from collections import Counter
from itertools import zip_longest
from multiprocessing import Pool
from fairseq import options, tasks, utils
from fairseq.binarizer import Binarizer
from fairseq.data import indexed_dataset
from fairseq.file_chunker_utils import find_offs... | null |
182,616 | import logging
import os
from fairseq.dataclass.initialize import add_defaults, hydra_init
from fairseq_cli.train import main as pre_main
from fairseq import distributed_utils, metrics
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.utils import omegaconf_no_object_check
from fairseq.utils im... | null |
182,617 | import logging
import math
import os
import sys
from argparse import Namespace
from typing import Iterable, List, Optional
import torch
import fairseq
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging im... | Args: models (List[~fairseq.models.FairseqModel]): list of models to evaluate. Models are essentially `nn.Module` instances, but must be compatible with fairseq's `SequenceScorer`. source_dictionary (~fairseq.data.Dictionary): dictionary for applying any relevant post processing or outputing word probs/stats. batch_ite... |
182,619 | 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 fairseq import checkpoint_utils, options, scoring, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import progre... | null |
182,620 | 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 fairseq import checkpoint_utils, options, scoring, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import progre... | null |
182,621 | import math
import warnings
from typing import Dict, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn import Parameter
import torch.nn.functional as F
def gelu_accurate(x):
if not hasattr(gelu_accurate, "_a"):
gelu_accurate._a = math.sqrt(2 / math.pi)
return (
0.5 * x * (1... | Returns the activation function corresponding to `activation` |
182,622 | import math
import warnings
from typing import Dict, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn import Parameter
import torch.nn.functional as F
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__... | Initialize the weights specific to the BERT Model. This overrides the default initializations depending on the specified arguments. 1. If normal_init_linear_weights is set then weights of linear layer will be initialized using the normal distribution and bais will be set to the specified value. 2. If normal_init_embed_... |
182,623 | import math
import warnings
from typing import Dict, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn import Parameter
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `quant_noise` function. Write a Python function `def quant_noise(m... | Wraps modules and applies quantization noise to the weights for subsequent quantization with Iterative Product Quantization as described in "Training with Quantization Noise for Extreme Model Compression" Args: - module: nn.Module - p: amount of Quantization Noise - block_size: size of the blocks for subsequent quantiz... |
182,624 | import math
import logging
from typing import List, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import LayerNorm
from modules import (
Fp32GroupNorm,
Fp32LayerNorm,
GradMultiply,
MultiheadAttention,
SamePad,
init_bert_params... | Computes random mask spans for a given shape Args: shape: the the shape for which to compute masks. should be of size 2 where first element is batch size and 2nd is timesteps padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements mask_prob: probability for each token t... |
182,625 | import os
from PIL import Image
import xml.etree.ElementTree as ET
import numpy as np
import json
from PIL import Image
from shutil import copyfile
def convert(ROOT, TRACK, SPLIT):
coco_data = {
"images": [],
"annotations": [],
"categories": [{"id": 1, "name": "table"}, ],
}
DATA_DI... | null |
182,626 | import os
from PIL import Image
import xml.etree.ElementTree as ET
import numpy as np
import json
from PIL import Image
from shutil import copyfile
def clean_img(DATA_DIR):
for file in sorted(os.listdir(DATA_DIR)):
if file.endswith(".JPG"):
os.rename(os.path.join(DATA_DIR, file), os.path.join(D... | null |
182,627 | import warnings
import math
import torch
from functools import partial
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 {
'url': url,
'num_classes': ... | null |
182,628 | import warnings
import math
import torch
from functools import partial
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 {
'url': url,
'num_classes': ... | null |
182,629 | import warnings
import math
import torch
from functools import partial
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 {
'url': url,
'num_classes': ... | null |
182,630 | import warnings
import math
import torch
from functools import partial
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 {
'url': url,
'num_classes': ... | null |
182,631 | 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`. |
182,632 | 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
The provided code snippet includes necessary dependencies for implementing the `build_transform_gen` function. Write a Python function `def build_transform_gen(cfg... | Create a list of :class:`TransformGen` from config. Returns: list[TransformGen] |
182,633 | 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. |
182,634 | 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... |
182,635 | 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 parser with some common arguments used by detectron2 users. Args: epilog (str): epilog passed to ArgumentParser describing the usage. Returns: argparse.ArgumentParser: |
182,636 | 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... | 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):... |
182,637 | 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... |
182,638 | 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 |
182,639 | 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 |
182,640 | 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):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224... | null |
182,641 | from collections import Iterable
import numpy as np
from shapely.geometry import Polygon
def flatten(lis):
for item in lis:
if isinstance(item, Iterable) and not isinstance(item, str):
for x in flatten(item):
yield x
else:
yield item | null |
182,642 | from collections import Iterable
import numpy as np
from shapely.geometry import Polygon
def compute_poly_iou(list1, list2):
a1 = np.array(list1, dtype=int).reshape(-1, 2)
poly1 = Polygon(a1)
poly1_clean = poly1.buffer(0)
a2 = np.array(list2, dtype=int).reshape(-1, 2)
poly2 = Polygon(a2)
poly2... | null |
182,643 | import os
import xml.dom.minidom
reg_gt_path_archival = os.path.abspath("data/test")
import xml.dom.minidom
from os.path import join as osj
from .data_structure import *
class eval:
STR = "-str"
REG = "-reg"
DEFAULT_ENCODING = "UTF-8"
# reg_gt_path = "./annotations/trackA/"
# str_gt_path = "./annota... | null |
182,644 | import os
import itertools
import torch
from typing import Any, Dict, List, Set
from detectron2.data import build_detection_train_loader
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, lau... | Create configs and perform basic setups. |
182,645 | import argparse
import os
import cv2
import tqdm
def convert(fn):
# given a file name, convert it into binary and store at the same position
img = cv2.imread(fn)
gim = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gim = cv2.adaptiveThreshold(gim, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 45, 11)
... | null |
182,646 | import os
import os.path
import random
from typing import Any, Callable, cast, Dict, List, Optional, Tuple
from PIL import Image
from torchvision.datasets.vision import VisionDataset
def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:
"""Checks if a file is an allowed extension.
... | Checks if a file is an allowed image extension. Args: filename (string): path to a file Returns: bool: True if the filename ends with a known image extension |
182,647 | import os
import os.path
import random
from typing import Any, Callable, cast, Dict, List, Optional, Tuple
from PIL import Image
from torchvision.datasets.vision import VisionDataset
def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:
"""Checks if a file is an allowed extension.
... | null |
182,648 | import os
import os.path
import random
from typing import Any, Callable, cast, Dict, List, Optional, Tuple
from PIL import Image
from torchvision.datasets.vision import VisionDataset
def pil_loader(path: str) -> Image.Image:
def accimage_loader(path: str) -> Any:
def default_loader(path: str) -> Any:
from torchvis... | null |
182,649 | import datetime
import io
import os
import math
import time
import json
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import torch
import torch.distributed as dist
from torch._six import inf
from torch.utils.tensorboard impor... | null |
182,650 | import datetime
import io
import os
import math
import time
import json
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import torch
import torch.distributed as dist
from torch._six import inf
from torch.utils.tensorboard impor... | null |
182,651 | import datetime
import io
import os
import math
import time
import json
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import torch
import torch.distributed as dist
from torch._six import inf
from torch.utils.tensorboard impor... | null |
182,652 | import datetime
import io
import os
import math
import time
import json
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import torch
import torch.distributed as dist
from torch._six import inf
from torch.utils.tensorboard impor... | null |
182,653 | import datetime
import io
import os
import math
import time
import json
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import torch
import torch.distributed as dist
from torch._six import inf
from torch.utils.tensorboard impor... | null |
182,654 | import datetime
import io
import os
import math
import time
import json
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import torch
import torch.distributed as dist
from torch._six import inf
from torch.utils.tensorboard impor... | null |
182,656 | import torch
from torch import optim as optim
from timm.optim.adafactor import Adafactor
from timm.optim.adahessian import Adahessian
from timm.optim.adamp import AdamP
from timm.optim.lookahead import Lookahead
from timm.optim.nadam import Nadam
from timm.optim.nvnovograd import NvNovoGrad
from timm.optim.radam import... | null |
182,657 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from timm.models.registry import register_model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input... | null |
182,658 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from timm.models.registry import register_model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input... | null |
182,659 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from timm.models.registry import register_model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input... | null |
182,660 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from timm.models.registry import register_model
def _cfg(url='', **kwargs):
class VisionTransformer(nn.Module):
def __init__(self, img_size... | null |
182,661 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from timm.models.registry import register_model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input... | null |
182,662 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from timm.models.registry import register_model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input... | null |
182,663 | import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
import os
from pathlib import Path
from timm.data.mixup import Mixup
from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils ... | null |
182,664 | from timm.data import create_transform
from timm.data.constants import \
IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.data.transforms import str_to_interp_mode
from torchvision import transforms
from dataset_folder import RvlcdipImageFolder
def build_transfo... | null |
182,665 | import math
import sys
from typing import Iterable, Optional
import torch
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
import utils
def train_class_batch(model, samples, target, criterion):
outputs = model(samples)
if not isinstance(outputs, torch.Tensor):
# assume that the mode... | null |
182,667 | import warnings
import math
import torch
from functools import partial
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):
class BEiT(nn.Module):
def __init__(self,
... | null |
182,668 | import warnings
import math
import torch
from functools import partial
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):
class BEiT(nn.Module):
def __init__(self,
... | null |
182,669 | import warnings
import math
import torch
from functools import partial
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):
class BEiT(nn.Module):
def __init__(self,
... | null |
182,681 | 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... |
182,682 | 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 parser with some common arguments used by detectron2 users. Args: epilog (str): epilog passed to ArgumentParser describing the usage. Returns: argparse.ArgumentParser: |
182,683 | 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... | 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):... |
182,684 | 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... |
182,685 | 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
from torch import distributed as dist
from scipy import interpolate
import numpy as np
import torch.nn.functio... | null |
182,686 | 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):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224... | null |
182,688 | from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.engine import default_argument_parser, default_setup, launch
from detectron2.data.datasets import register_coco_instances
from ditod import MyTrainer, add_vit_config
The provided code snippet includes necessar... | Create configs and perform basic setups. |
182,692 | import json
import hashlib
import io
import os
import base64
from PIL import Image
from tqdm import tqdm
def calculate_md5(image):
md5_hash = hashlib.md5()
with io.BytesIO() as output:
image.save(output, format='JPEG')
image_data = output.getvalue()
md5_hash.update(... | null |
182,693 | import json
import hashlib
import io
import os
import base64
from PIL import Image
from tqdm import tqdm
def write_tsv(tsv_data, output_file):
with open(output_file, 'w') as file:
file.write('\n'.join(tsv_data)) | null |
182,694 | import json
import os
from glob import glob
def grit():
json_files = glob(f"/path/to/grit/*.tsv")
source_files = []
for json_file_name in json_files:
basename = os.path.basename(json_file_name)
source_files.append(f"../grit/{basename}")
file_list = {
... | null |
182,695 | import json
import os
import requests
from urllib.parse import urlparse
from requests.exceptions import HTTPError
import sys
from pathlib import Path
import textwrap
import ast
import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import cv2
import... | null |
182,696 | import json
import os
import requests
from urllib.parse import urlparse
from requests.exceptions import HTTPError
import sys
from pathlib import Path
import textwrap
import ast
import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import cv2
import... | null |
182,719 | import ast
import json
import logging
import math
import os
import random
import sys
import time
from dataclasses import dataclass
from multiprocessing import Value
import braceexpand
import numpy as np
import pandas as pd
import torch
import torchvision.datasets as datasets
import webdataset as wds
from PIL import Ima... | null |
182,724 | import unilm
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, quantizati... | Train the model for one epoch and return validation losses. |
182,725 | import unilm
import argparse
import logging
import math
import os
import sys
from typing import Any, Callable, Dict, List, Optional, Tuple
import numpy as np
import torch
from omegaconf import DictConfig, OmegaConf
from fairseq import checkpoint_utils, options, quantization_utils, tasks, utils
from fairseq.data import ... | null |
182,726 | import unilm
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, quantizati... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.