id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
184,392 | import math
from dataclasses import dataclass, field
import torch
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from omegaconf import II
def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce... | null |
184,394 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.model_parallel.modules import ModelParallelTransformerSentenceEncoder
from fairseq.models import FairseqEncoder, register_model, register_model_architecture
from fairseq.models.roberta import (
R... | null |
184,395 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.model_parallel.modules import ModelParallelTransformerSentenceEncoder
from fairseq.models import FairseqEncoder, register_model, register_model_architecture
from fairseq.models.roberta import (
R... | null |
184,398 | 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 |
184,399 | 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 |
184,402 | import logging
from typing import Dict, Any
from hydra.core.config_store import ConfigStore
from fairseq.dataclass.configs import FairseqConfig
logger = logging.getLogger(__name__)
class FairseqConfig(FairseqDataclass):
common: CommonConfig = CommonConfig()
common_eval: CommonEvalConfig = CommonEvalConfig()
... | null |
184,403 | import ast
import inspect
import logging
import os
import re
from argparse import ArgumentError, ArgumentParser, Namespace
from dataclasses import _MISSING_TYPE, MISSING
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Type
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.... | convert a dataclass instance to tailing parser arguments |
184,405 | import ast
import inspect
import logging
import os
import re
from argparse import ArgumentError, ArgumentParser, Namespace
from dataclasses import _MISSING_TYPE, MISSING
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Type
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.... | null |
184,407 | import ast
import inspect
import logging
import os
import re
from argparse import ArgumentError, ArgumentParser, Namespace
from dataclasses import _MISSING_TYPE, MISSING
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Type
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.... | null |
184,415 | from typing import Any, Dict
import torch
def shard_(optimizer, group):
if not _has_fairscale:
raise ImportError(
"\n\nPlease install the fairscale package:" "\n\n pip install fairscale"
)
class FairseqOSS(OSS):
@property
def disable_mem_eff_fp16_loading_hack(self)... | null |
184,422 | 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 |
184,424 | 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 |
184,426 | 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, TransformerSentenceEncoder
from fairseq.modu... | null |
184,427 | 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, TransformerSentenceEncoder
from fairseq.modu... | null |
184,428 | 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, TransformerSentenceEncoder
from fairseq.modu... | null |
184,434 | import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import ... | null |
184,435 | import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import ... | null |
184,436 | import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import ... | null |
184,437 | import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import ... | null |
184,443 | 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 Embeddin... | null |
184,444 | 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 Embeddin... | null |
184,445 | 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 Embeddin... | null |
184,446 | 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 Embeddin... | null |
184,476 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder
from fairseq.models.transformer impo... | null |
184,477 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder
from fairseq.models.transformer impo... | null |
184,478 | import inspect
import logging
import os
import signal
import threading
import torch
import torch.nn as nn
from fairseq import distributed_utils
from fairseq.legacy_distributed_data_parallel import LegacyDistributedDataParallel
logger = logging.getLogger(__name__)
_GOSSIP_DISABLED = False
try:
import gossip
except I... | Wrap a *model* to support distributed data parallel training. This is similar to the built-in DistributedDataParallel, but allows additional configuration of the DistributedDataParallel class to use, and also provides easier access to the wrapped model by forwarding requests for missing attributes to the wrapped model.... |
184,492 | import logging
import math
from typing import Dict, List, Optional, Tuple
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,
register_model,
register_model_... | null |
184,519 | import argparse
import os
import re
import shutil
import sys
def parse_checkpoints(files):
def last_n_checkpoints(files, n):
entries = parse_checkpoints(files)
return [x[1] for x in sorted(entries, reverse=True)[:n]] | null |
184,525 | import argparse
import logging
import math
import os
import sys
from typing import Dict, Optional, Any, List, Tuple, Callable
import numpy as np
import torch
from fairseq import (
checkpoint_utils,
distributed_utils,
options,
quantization_utils,
tasks,
utils,
)
from fairseq.data import iterators... | Train the model for one epoch and return validation losses. |
184,526 | import argparse
import logging
import math
import os
import sys
from typing import Dict, Optional, Any, List, Tuple, Callable
import numpy as np
import torch
from fairseq import (
checkpoint_utils,
distributed_utils,
options,
quantization_utils,
tasks,
utils,
)
from fairseq.data import iterators... | null |
184,527 | 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.data import encoders
from fairseq.dataclas... | null |
184,528 | 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.data import encoders
from fairseq.dataclas... | null |
184,529 | 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.data import encoders
from fairseq.dataclas... | null |
184,530 | 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 omegaconf import D... | null |
184,531 | 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
def dataset_dest_file(args, output_prefix, lang,... | null |
184,532 | 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
def dataset_dest_file(args, output_prefix, lang,... | null |
184,533 | 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
class Binarizer:
def binarize(
file... | null |
184,536 | import logging
import os
import sys
from fairseq.dataclass.initialize import hydra_init
from fairseq_cli.train import main as pre_main
from fairseq import distributed_utils, metrics
from fairseq.dataclass.configs import FairseqConfig
import hydra
import torch
from omegaconf import OmegaConf
logger = logging.getLogger("... | null |
184,538 | 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... | null |
184,540 | 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 |
184,541 | import os
import sys
import time
import torch
import logging
import argparse
import copy
from tqdm import tqdm
from torch import Tensor
from omegaconf import open_dict
from typing import Dict, Optional
from fairseq import utils
from fairseq.checkpoint_utils import load_model_ensemble_and_task
def write_result(results,... | null |
184,542 | import os
import sys
import time
import torch
import logging
import argparse
import copy
from tqdm import tqdm
from torch import Tensor
from omegaconf import open_dict
from typing import Dict, Optional
from fairseq import utils
from fairseq.checkpoint_utils import load_model_ensemble_and_task
logger = logging.getLogger... | beam search | greedy decoding implemented by fairseq |
184,543 | import os
import sys
import time
import torch
import logging
import argparse
import copy
from tqdm import tqdm
from torch import Tensor
from omegaconf import open_dict
from typing import Dict, Optional
from fairseq import utils
from fairseq.checkpoint_utils import load_model_ensemble_and_task
logger = logging.getLogger... | batch Implementation |
184,544 | import os
import sys
import time
import torch
import logging
import argparse
import copy
from tqdm import tqdm
from torch import Tensor
from omegaconf import open_dict
from typing import Dict, Optional
from fairseq import utils
from fairseq.checkpoint_utils import load_model_ensemble_and_task
def cut_incremental_state... | null |
184,545 | import os
import sys
import time
import torch
import logging
import argparse
import copy
from tqdm import tqdm
from torch import Tensor
from omegaconf import open_dict
from typing import Dict, Optional
from fairseq import utils
from fairseq.checkpoint_utils import load_model_ensemble_and_task
def forward_decoder(model,... | batch Implementation |
184,546 | import argparse
import glob
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from transformers import (
WEIGHTS_NAM... | Train the model |
184,547 | import argparse
import json
import os
import re
import sys
import time
import openai
import eval_vllm.util as util
from tqdm import tqdm
from multiprocessing import Pool
if os.environ.get("OPENAI_ORGANIZATION") is not None:
openai.organization = os.environ["OPENAI_ORGANIZATION"]
def request_one_example(input_t):
... | null |
184,548 | import argparse
import json
import os
import re
import sys
import time
import openai
import eval_vllm.util as util
from tqdm import tqdm
from multiprocessing import Pool
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--openai_model", type=str, default="gpt-3.5-turbo-0613") # model p... | null |
184,549 | import re
def last_boxed_only_string(string):
idx = string.rfind("\\boxed")
if idx < 0:
idx = string.rfind("\\fbox")
if idx < 0:
return None
i = idx
right_brace_idx = None
num_left_braces_open = 0
while i < len(string):
if string[i] == "{":
num_lef... | null |
184,550 | import re
def only_until_first_boxed_from_tokens(string, tokens):
idx = string.find("\\boxed")
if idx < 0:
idx = string.find("\\fbox")
if idx < 0:
return None
cum_length = 0
for i, t in enumerate(tokens):
cum_length += len(t)
if cum_length >= idx:
... | null |
184,551 | import argparse
import json
import os
import re
import sys
import eval_vllm.util as util
from vllm import LLM, SamplingParams
from tqdm import tqdm
def batch_data(data_list, batch_size=1):
def evaluate_one_task(args, model, sampling_params, prompt_template, task_name, sample):
math_ins = []
math_answers = []
... | null |
184,552 | import argparse
import json
import os
import re
import sys
import eval_vllm.util as util
from vllm import LLM, SamplingParams
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name_or_path", type=str, default=None) # model path
parser.add_argument("--d... | null |
184,553 | from torchvision.datasets.vision import VisionDataset
from PIL import Image
import os
import os.path
import random
import json
from typing import Any, Callable, cast, Dict, List, Optional, Tuple
def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:
"""Checks if a file is an allowed ext... | 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 |
184,554 | from torchvision.datasets.vision import VisionDataset
from PIL import Image
import os
import os.path
import random
import json
from typing import Any, Callable, cast, Dict, List, Optional, Tuple
def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:
"""Checks if a file is an allowed ext... | null |
184,555 | from torchvision.datasets.vision import VisionDataset
from PIL import Image
import os
import os.path
import random
import json
from typing import Any, Callable, cast, Dict, List, Optional, Tuple
def pil_loader(path: str) -> Image.Image:
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/... | null |
184,556 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as distributed
from einops import rearrange, repeat
def ema_inplace(moving_avg, new, decay):
moving_avg.data.mul_(decay).add_(new, alpha = (1 - decay)) | null |
184,557 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as distributed
from einops import rearrange, repeat
def l2norm(t):
return F.normalize(t, p = 2, dim = -1)
def sample_vectors(samples, num):
num_samples, device = samples.shape[0], samples.device
if num_samples >= num... | null |
184,558 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as distributed
from einops import rearrange, repeat
def l2norm(t):
return F.normalize(t, p = 2, dim = -1)
def norm_ema_inplace(moving_avg, new, decay):
moving_avg.data.mul_(decay).add_(new, alpha = (1 - decay))
movi... | null |
184,559 | import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import argparse
import torch
import torch.distributed as dist
from torch._six import inf
from tensorboardX impo... | null |
184,560 | import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import argparse
import torch
import torch.distributed as dist
from torch._six import inf
from tensorboardX impo... | Performs all_gather operation on the provided tensors. |
184,561 | import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import argparse
import torch
import torch.distributed as dist
from torch._six import inf
from tensorboardX impo... | Performs all_gather operation on the provided tensors. Graph remains connected for backward grad computation. |
184,562 | import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import argparse
import torch
import torch.distributed as dist
from torch._six import inf
from tensorboardX impo... | null |
184,563 | import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import argparse
import torch
import torch.distributed as dist
from torch._six import inf
from tensorboardX impo... | null |
184,564 | import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import argparse
import torch
import torch.distributed as dist
from torch._six import inf
from tensorboardX impo... | null |
184,565 | import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import argparse
import torch
import torch.distributed as dist
from torch._six import inf
from tensorboardX impo... | null |
184,566 | import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import argparse
import torch
import torch.distributed as dist
from torch._six import inf
from tensorboardX impo... | null |
184,567 | import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import argparse
import torch
import torch.distributed as dist
from torch._six import inf
from tensorboardX impo... | null |
184,568 | import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from pathlib import Path
import argparse
import torch
import torch.distributed as dist
from torch._six import inf
from tensorboardX impo... | null |
184,570 | 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.novograd import NovoGrad
from timm.optim.nvnovograd impor... | null |
184,571 | import os
import sys
import argparse
import cv2
import random
import colorsys
import requests
from io import BytesIO
import skimage.io
from skimage.measure import find_contours
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import torch
import torch.nn as nn
import torchvision
from torchvision i... | null |
184,572 | 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 |
184,573 | 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 |
184,574 | 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 |
184,575 | 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 |
184,576 | 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 |
184,577 | 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 |
184,578 | 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 |
184,579 | 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 |
184,580 | 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 |
184,581 | import argparse
import datetime
from pyexpat import model
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
import os
from pathlib import Path
from collections import OrderedDict
from timm.data.mixup import Mixup
from timm.models import create_model
from timm.loss import Label... | null |
184,582 | import argparse
import datetime
from pyexpat import model
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
import os
from pathlib import Path
from collections import OrderedDict
from timm.data.mixup import Mixup
from timm.models import create_model
from timm.loss import Label... | null |
184,583 | import argparse
import copy
import os
import os.path as osp
import time
import mmcv
import mmcv_custom
import torch
from mmcv.runner import init_dist
from mmcv.utils import Config, DictAction, get_git_hash
from mmseg import __version__
from mmseg.apis import set_random_seed
from mmcv_custom import train_segmentor
from ... | null |
184,586 | import random
import warnings
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import build_optimizer, build_runner
from mmseg.core import DistEvalHook, EvalHook
from mmseg.datasets import build_dataloader, build_dataset
from mmseg.utils import get_roo... | Launch segmentor training. |
184,591 | 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.models import create_model
from optim_factory import create_optimizer
from datasets import build_vqkd_dataset
from engine_for_vqkd import evaluate, tr... | null |
184,592 | 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.models import create_model
from optim_factory import create_optimizer
from datasets import build_vqkd_dataset
from engine_for_vqkd import evaluate, tr... | null |
184,593 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial, reduce
from collections import OrderedDict
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
import pdb
def drop_path(x, drop_prob: float = 0., training: bool = F... | null |
184,594 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial, reduce
from collections import OrderedDict
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
import pdb
class VisionTransformer(nn.Module):
""" Vision Transfo... | null |
184,595 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial, reduce
from collections import OrderedDict
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
import pdb
class VisionTransformer(nn.Module):
""" Vision Transfo... | null |
184,596 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial, reduce
from collections import OrderedDict
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
import pdb
def vit_base(patch_size=16, **kwargs):
model = VisionT... | null |
184,597 | import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
from pkg_resources import packaging
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from .model import build_model
from .simple_tokenize... | Load a CLIP model Parameters ---------- name : str A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict device : Union[str, torch.device] The device to put the loaded model jit : bool Whether to load the optimized JIT model or more hackable non-JIT model (default... |
184,604 | import argparse
import os
import torch
import random
from torchvision import datasets, transforms
from timm.data.constants import \
IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from transforms import RandomResizedCropAndInterpolationWithTwoPic, _pil_interp
from timm.d... | null |
184,605 | import argparse
import os
import torch
import random
from torchvision import datasets, transforms
from timm.data.constants import \
IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from transforms import RandomResizedCropAndInterpolationWithTwoPic, _pil_interp
from timm.d... | null |
184,606 | import argparse
import os
import torch
import random
from torchvision import datasets, transforms
from timm.data.constants import \
IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from transforms import RandomResizedCropAndInterpolationWithTwoPic, _pil_interp
from timm.d... | null |
184,607 | 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)
loss = criterion(outputs, target)
return loss, outputs
def get_loss_... | null |
184,608 | 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 evaluate(data_loader, model, device):
criterion = torch.nn.CrossEntropyLoss()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'... | null |
184,609 | import math
import torch
import torch.nn as nn
from functools import partial
from modeling_finetune import Block, _cfg, PatchEmbed, RelativePositionBias
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
def trunc_normal_(tensor, mean=0., std=1.):
_... | null |
184,610 | import math
import torch
import torch.nn as nn
from functools import partial
from modeling_finetune import Block, _cfg, PatchEmbed, RelativePositionBias
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
class VisionTransformerForMaskedImageModelingCLS(V... | null |
184,611 | import math
import torch
import torch.nn as nn
from functools import partial
from modeling_finetune import Block, _cfg, PatchEmbed, RelativePositionBias
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
class VisionTransformerForMaskedImageModeling(nn.M... | null |
184,612 | import math
import torch
import torch.nn as nn
from functools import partial
from modeling_finetune import Block, _cfg, PatchEmbed, RelativePositionBias
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
class VisionTransformerForMaskedImageModeling(nn.M... | null |
184,613 | import math
import torch
import torch.nn as nn
from functools import partial
from modeling_finetune import Block, _cfg, PatchEmbed, RelativePositionBias
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
class VisionTransformerForMaskedImageModeling(nn.M... | null |
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