id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
165,129 | from typing import Dict,List
import pandas as pd
def tapex_post_prepare():
pass | null |
165,130 | from typing import Dict,List
import pandas as pd
def dic2prompt(dic:Dict):
prompt = ''
pass | null |
165,131 | import os
from tqdm import tqdm
import requests
The provided code snippet includes necessary dependencies for implementing the `download_file` function. Write a Python function `def download_file(url, download_dir=None)` to solve the following problem:
Download file into local file system from url
Here is the functio... | Download file into local file system from url |
165,132 | import pandas as pd
import numpy as np
import argparse
import os
import time
import json
import copy
from typing import List, Dict
import random
import multiprocessing
print(ROOT_DIR)
import sys
from gloc.generation.generator import Generator
from gloc.utils import dict2df
class Generator(object):
"""
CodeX ge... | A worker process for annotating. |
165,134 | import pandas as pd
import numpy as np
import argparse
import os
import time
import json
import copy
from typing import List, Dict
import random
import multiprocessing
print(ROOT_DIR)
import sys
from gloc.generation.generator import Generator
from gloc.utils import dict2df
class Generator(object):
"""
CodeX ge... | A worker process for annotating. |
165,135 | import pandas as pd
import numpy as np
import argparse
import os
import time
import json
import copy
from typing import List, Dict
import random
import multiprocessing
import re
import collections
print(ROOT_DIR)
import sys
from gloc.generation.generator import Generator
from gloc.utils import dict2df
def merge_res(di... | null |
165,136 | import pandas as pd
import numpy as np
import argparse
import os
import time
import json
import copy
from typing import List, Dict
import random
import multiprocessing
import re
import collections
print(ROOT_DIR)
import sys
from gloc.generation.generator import Generator
from gloc.utils import dict2df
class Generator(... | A worker process for annotating. |
165,137 | import pandas as pd
import numpy as np
import argparse
import os
import time
import json
import copy
from typing import List, Dict
import random
import multiprocessing
print(ROOT_DIR)
import sys
from gloc.generation.generator import Generator
from gloc.utils import dict2df
class Generator(object):
"""
CodeX ge... | A worker process for annotating. |
165,139 | import pandas as pd
import numpy as np
import argparse
import os
import time
import json
import copy
from typing import List, Dict
import random
import multiprocessing
print(ROOT_DIR)
import sys
from gloc.generation.generator import Generator
from gloc.utils import dict2df
class Generator(object):
"""
CodeX ge... | A worker process for annotating. |
165,140 | import pandas as pd
import numpy as np
import argparse
import os
import time
import json
import copy
from typing import List, Dict
import random
import multiprocessing
import re
import collections
print(ROOT_DIR)
import sys
from gloc.generation.generator import Generator
from gloc.utils import dict2df
def merge_res(di... | null |
165,141 | import pandas as pd
import numpy as np
import argparse
import os
import time
import json
import copy
from typing import List, Dict
import random
import multiprocessing
import re
import collections
print(ROOT_DIR)
import sys
from gloc.generation.generator import Generator
from gloc.utils import dict2df
class Generator(... | A worker process for annotating. |
165,142 | from curses import meta
import os
from os.path import join, exists
import json
from tqdm import tqdm
import copy
from rich import print
def load_file():
split_list = ['dev', 'train', 'devtest']
output = []
for split in split_list:
file_path = f'../data_dstc11/simmc2.1_dials_dstc11_{sp... | null |
165,143 | import os
from os.path import join, exists
import json
from tqdm import tqdm
from rich import print
import numpy as np
import imagesize
OBJ_BEGIN_TOKEN = '<SOO>'
OBJ_END_TOKEN = '<EOO>'
NOCOREF_TOKEN = '<NOCOREF>'
DISAMBIGUATION_TOKEN = '<DISAM>'
def arrange_object_special_tokens(scene_dir, image_dir, scene_ids, object... | 为VLBert模型的训练准备 |
165,144 | from genericpath import exists
import os
from os.path import join
import json
import argparse
import torch
from torch.optim import AdamW
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import SequentialSampler, DistributedSampler
from tqdm import tqdm, trange
from rich import print
from datetime impor... | 模型方法的评估函数 |
165,145 | import torch
import numpy as np
import random
import random
random.seed(33)
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed) | null |
165,146 | import torch
import numpy as np
import random
import random
random.seed(33)
def set_seed_ddp(args):
seed = args.seed + args.local_rank
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(seed) | null |
165,147 | import torch
import numpy as np
import random
def set_device(args):
if torch.cuda.is_available():
args.device = torch.device("cuda")
torch.cuda.set_device(args.local_rank)
else:
args.device = torch.device('cpu') | null |
165,148 | import torch
import numpy as np
import random
The provided code snippet includes necessary dependencies for implementing the `set_config` function. Write a Python function `def set_config(args, config)` to solve the following problem:
combine the config and args
Here is the function:
def set_config(args, config):
... | combine the config and args |
165,149 | import json
import torch
from transformers.tokenization_utils import PreTrainedTokenizer
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler, DistributedSampler
from prefetch_generator import BackgroundGenerator
import copy
from .metadata import FASHION_COLOR, FASHION_PATTERN, FASHION_SLE... | 获取Specical Token所对应的id信息 |
165,150 | import json
import os
import re
from typing import List
import attr
from attr.validators import instance_of
DATA_DIR = ''
class FashionMetadata:
name: str = attr.ib(
converter=str,
validator=instance_of(str)
)
asset_type: str = attr.ib(
converter=str,
validator=instance_of(s... | Converts each key from CamelCase to snake_case. Also changes some key names to be more consistent across dataset. |
165,151 | import json
import os
import re
from typing import List
import attr
from attr.validators import instance_of
class FashionMetadata:
name: str = attr.ib(
converter=str,
validator=instance_of(str)
)
asset_type: str = attr.ib(
converter=str,
validator=instance_of(str)
)
c... | Converts each key from CamelCase to snake_case. Also changes some key names to be more consistent across dataset. |
165,152 | 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 fa... | Train the model for one epoch and return validation losses. |
165,153 | 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 fa... | null |
165,154 | import contextlib
import logging
import sys
import time
from argparse import Namespace
from itertools import chain
from typing import Any, Dict, List
import torch
from fairseq import models, optim, utils
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.utils import convert_namespace_to_omegaco... | null |
165,155 | import contextlib
import logging
import sys
import time
from argparse import Namespace
from itertools import chain
from typing import Any, Dict, List
import torch
from fairseq import models, optim, utils
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.utils import convert_namespace_to_omegaco... | null |
165,156 | import contextlib
import logging
import sys
import time
from argparse import Namespace
from itertools import chain
from typing import Any, Dict, List
import torch
from fairseq import models, optim, utils
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.utils import convert_namespace_to_omegaco... | null |
165,157 | from io import BytesIO
import logging
import warnings
import string
import numpy as np
import torch
import base64
from torchvision import transforms
from PIL import Image, ImageFile
from data import data_utils
from data.ofa_dataset import OFADataset
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
... | null |
165,158 | from io import BytesIO
import logging
import warnings
import numpy as np
import torch
import base64
from torchvision import transforms
from PIL import Image, ImageFile
from data import data_utils
from data.ofa_dataset import OFADataset
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
return {}... | null |
165,159 | import logging
import warnings
import string
import numpy as np
import torch
import base64
from torchvision import transforms
from PIL import Image, ImageFile
from data import data_utils
from data.ofa_dataset import OFADataset
from io import BytesIO
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
... | null |
165,160 | from io import BytesIO
import logging
import warnings
import base64
import random
import numpy as np
import torch
from PIL import Image, ImageFile
from itertools import chain
from data.ofa_dataset import OFADataset
from data import data_utils
from PIL import Image
from io import BytesIO
import base64
def collate(
... | null |
165,161 | from io import BytesIO
import logging
import warnings
import base64
import random
import numpy as np
import torch
from PIL import Image, ImageFile
from itertools import chain
from data.ofa_dataset import OFADataset
from data import data_utils
from PIL import Image
from io import BytesIO
import base64
def preprocess_vq... | null |
165,162 | from io import BytesIO
import logging
import warnings
import numpy as np
import torch
import base64
from torchvision import transforms
from PIL import Image, ImageFile
from data import data_utils
from data.ofa_dataset import OFADataset
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
return {}... | null |
165,163 | from io import BytesIO
import logging
import warnings
import numpy as np
import torch
import base64
import utils.transforms as T
from PIL import Image, ImageFile
from data import data_utils
from data.ofa_dataset import OFADataset
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
return {}
... | null |
165,168 | import contextlib
import itertools
import logging
import re
import warnings
from typing import Optional, Tuple
import numpy as np
import torch
from fairseq.file_io import PathManager
from fairseq import utils
import os
The provided code snippet includes necessary dependencies for implementing the `batch_by_size` funct... | Yield mini-batches of indices bucketed by size. Batches may contain sequences of different lengths. Args: indices (List[int]): ordered list of dataset indices num_tokens_fn (callable): function that returns the number of tokens at a given index num_tokens_vec (List[int], optional): precomputed vector of the number of t... |
165,169 | import contextlib
import itertools
import logging
import re
import warnings
from typing import Optional, Tuple
import numpy as np
import torch
from fairseq.file_io import PathManager
from fairseq import utils
import os
The provided code snippet includes necessary dependencies for implementing the `compute_mask_indices... | 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... |
165,172 | import contextlib
import itertools
import logging
import re
import warnings
from typing import Optional, Tuple
import numpy as np
import torch
from fairseq.file_io import PathManager
from fairseq import utils
import os
def get_buckets(sizes, num_buckets):
buckets = np.unique(
np.percentile(
siz... | null |
165,175 | import logging
import warnings
import torch
import numpy as np
from data import data_utils
from data.ofa_dataset import OFADataset
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
return {}
def merge(key):
return data_utils.collate_tokens(
[s[key] for s in samples],
... | null |
165,176 | from io import BytesIO
import logging
import warnings
import functools
import numpy as np
import torch
import base64
from torchvision import transforms
from timm.data import create_transform
from utils.vision_helper import RandomAugment
from PIL import Image, ImageFile
from data import data_utils
from data.ofa_dataset ... | null |
165,177 | from io import BytesIO
import math
import logging
import random
import warnings
import numpy as np
import torch
import base64
from torchvision import transforms
from PIL import Image, ImageFile
from data import data_utils
from data.ofa_dataset import OFADataset
from utils.vision_helper import RandomAugment
import utils... | null |
165,178 | from io import BytesIO
import math
import logging
import random
import warnings
import numpy as np
import torch
import base64
from torchvision import transforms
from PIL import Image, ImageFile
from data import data_utils
from data.ofa_dataset import OFADataset
from utils.vision_helper import RandomAugment
import utils... | null |
165,179 | import logging
import warnings
import torch
import numpy as np
from data import data_utils
from data.ofa_dataset import OFADataset
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
return {}
def merge(key):
return data_utils.collate_tokens(
[s[key] for s in samples],
... | null |
165,186 | from dataclasses import dataclass, field
import json
import logging
import os
import math
import base64
from typing import Optional
from argparse import Namespace
from omegaconf import DictConfig, OmegaConf
from torchvision import transforms
from PIL import Image
from io import BytesIO
import torch
import numpy as np
f... | null |
165,187 | import math
from dataclasses import dataclass, field
from typing import Optional
import torch
import torch.nn.functional as F
import numpy as np
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from omegaconf import II
... | null |
165,188 | import math
from dataclasses import dataclass, field
from typing import Optional
import torch
import torch.nn.functional as F
import numpy as np
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from omegaconf import II
... | null |
165,190 | import math
from dataclasses import dataclass, field
from typing import Optional
import torch
import torch.nn.functional as F
import numpy as np
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from omegaconf import II
... | null |
165,191 | import math
from dataclasses import dataclass, field
from typing import Optional
from PIL import Image
from torchvision import transforms
import torch
import numpy as np
from fairseq import metrics
from fairseq.data import data_utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.datac... | null |
165,192 | import math
from dataclasses import dataclass, field
from typing import Optional
from PIL import Image
from torchvision import transforms
import torch
import numpy as np
from fairseq import metrics
from fairseq.data import data_utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.datac... | null |
165,193 | import math
import string
from dataclasses import dataclass, field
from collections import OrderedDict
from typing import Optional
import torch
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from omegaconf import II
f... | null |
165,194 | from typing import Dict, List, Optional
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.modules import LayerNorm
from fairseq.modules.fairseq_dropout import FairseqDropout
from fairseq.modules.quant_noise import quant_noise
from torch import Tensor
from .unify_multihead_attention import Multih... | Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discu... |
165,195 | import math
import random
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDeco... | null |
165,196 | import math
import random
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDeco... | null |
165,197 | import math
import random
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDeco... | null |
165,198 | import math
import random
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDeco... | null |
165,199 | import math
import random
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDeco... | null |
165,200 | import math
import random
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDeco... | null |
165,201 | from typing import Optional
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import register_model, register_model_architecture
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .unify_transformer import Transfor... | null |
165,202 | from typing import Optional
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import register_model, register_model_architecture
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .unify_transformer import Transfor... | null |
165,203 | from typing import Optional
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import register_model, register_model_architecture
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .unify_transformer import Transfor... | null |
165,204 | from typing import Optional
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import register_model, register_model_architecture
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .unify_transformer import Transfor... | null |
165,205 | import torch
import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `drop_path` function. Write a Python function `def drop_path(x, drop_prob: float = 0., training: bool = False)` to solve the following problem:
Drop paths (Stochastic Depth) per sample (when applied in mai... | Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a.sh different form of dropout in a.sh separate paper... See discussion: https://githu... |
165,208 | import torch
import torch.nn as nn
import torch.nn.functional as F
from models.taming.modules.losses.lpips import LPIPS
from models.taming.modules.discriminator.model import NLayerDiscriminator, weights_init
def adopt_weight(weight, global_step, threshold=0, value=0.):
if global_step < threshold:
weight = ... | null |
165,209 | import torch
import torch.nn as nn
import torch.nn.functional as F
from models.taming.modules.losses.lpips import LPIPS
from models.taming.modules.discriminator.model import NLayerDiscriminator, weights_init
def hinge_d_loss(logits_real, logits_fake):
loss_real = torch.mean(F.relu(1. - logits_real))
loss_fake ... | null |
165,210 | import torch
import torch.nn as nn
import torch.nn.functional as F
from models.taming.modules.losses.lpips import LPIPS
from models.taming.modules.discriminator.model import NLayerDiscriminator, weights_init
def vanilla_d_loss(logits_real, logits_fake):
d_loss = 0.5 * (
torch.mean(torch.nn.functional.softp... | null |
165,211 | import torch
import torch.nn as nn
from torchvision import models
from collections import namedtuple
from models.taming.util import get_ckpt_path
def normalize_tensor(x,eps=1e-10):
norm_factor = torch.sqrt(torch.sum(x**2,dim=1,keepdim=True))
return x/(norm_factor+eps) | null |
165,212 | import torch
import torch.nn as nn
from torchvision import models
from collections import namedtuple
from models.taming.util import get_ckpt_path
def spatial_average(x, keepdim=True):
return x.mean([2,3],keepdim=keepdim) | null |
165,217 | import functools
import torch.nn as nn
from models.taming.modules.util import ActNorm
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, ... | null |
165,218 | import os, hashlib
import requests
from tqdm import tqdm
import importlib
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, packag... | null |
165,219 | import os, hashlib
import requests
from tqdm import tqdm
import importlib
URL_MAP = {
"vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"
}
CKPT_MAP = {
"vgg_lpips": "vgg.pth"
}
MD5_MAP = {
"vgg_lpips": "d507d7349b931f0638a25a48a722f98a"
}
def download(url, local_path, chunk_size=10... | null |
165,220 | import os, hashlib
import requests
from tqdm import tqdm
import importlib
class KeyNotFoundError(Exception):
def __init__(self, cause, keys=None, visited=None):
self.cause = cause
self.keys = keys
self.visited = visited
messages = list()
if keys is not None:
messa... | Given a nested list or dict return the desired value at key expanding callable nodes if necessary and :attr:`expand` is ``True``. The expansion is done in-place. Parameters ---------- list_or_dict : list or dict Possibly nested list or dictionary. key : str key/to/value, path like string describing all keys necessary t... |
165,221 | 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... |
165,228 | import logging
import os
import sys
import numpy as np
import torch
from fairseq import distributed_utils, options, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import progress_bar
from fairseq.utils import reset_logging
from omegaconf import DictConfig
import tor... | null |
165,229 | import logging
import os
import sys
import numpy as np
import torch
from fairseq import distributed_utils, options, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import progress_bar
from fairseq.utils import reset_logging
from omegaconf import DictConfig
import tor... | null |
165,230 | import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
import math
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.dataclass.configs import CheckpointConfig
from fairseq.dataclass.util... | Load a checkpoint and restore the training iterator. *passthrough_args* will be passed through to ``trainer.get_train_iterator``. |
165,231 | import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
import math
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.dataclass.configs import CheckpointConfig
from fairseq.dataclass.util... | 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 |
165,232 | import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
import math
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.dataclass.configs import CheckpointConfig
from fairseq.dataclass.util... | 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... |
165,233 | import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
import math
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.dataclass.configs import CheckpointConfig
from fairseq.dataclass.util... | 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. |
165,234 | import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
import math
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.dataclass.configs import CheckpointConfig
from fairseq.dataclass.util... | null |
165,235 | import ast
import collections
import contextlib
import logging
import numpy as np
import os
import re
import time
import traceback
import math
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.dataclass.configs import CheckpointConfig
from fairseq.dataclass.util... | Loads exponential moving averaged (EMA) checkpoint from input and returns a model with ema weights. Args: fpath: A string path of checkpoint to load from. Returns: A dict of string keys mapping to various values. The 'model' key from the returned dict should correspond to an OrderedDict mapping string parameter names t... |
165,236 | import random
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
import numpy as np
from PIL import Image
def crop(image, target, region, delete=True):
cropped_image = F.crop(image, *region)
target = target.copy()
i, j, h, w = region
# should we do something... | null |
165,237 | import random
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
import numpy as np
from PIL import Image
def hflip(image, target):
flipped_image = F.hflip(image)
w, h = image.size
target = target.copy()
if "boxes" in target:
boxes = target["boxes"]
... | null |
165,238 | import random
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
import numpy as np
from PIL import Image
def resize(image, target, size, max_size=None):
# size can be min_size (scalar) or (w, h) tuple
def get_size_with_aspect_ratio(image_size, size, max_size=None):
... | null |
165,262 | import string
import math
import torch
from data import data_utils
def eval_vqa_gen(task, generator, models, sample, **kwargs):
hypos = task.inference_step(generator, models, sample)
results = []
for i, sample_id in enumerate(sample["id"].tolist()):
detok_hypo_str = decode_fn(hypos[i][0]["tokens"], ... | null |
165,263 | import string
import math
import json
from itertools import chain
import os
import torch
import torch.distributed as dist
from data import data_utils
from tasks.nlg_tasks.gigaword import fix_tokenization
import random
def eval_simmc2(task, generator, models, sample, **kwargs):
hypos = task.inference_step(generator,... | null |
165,264 | import string
import math
import json
from itertools import chain
import os
import torch
import torch.distributed as dist
from data import data_utils
from tasks.nlg_tasks.gigaword import fix_tokenization
import random
def merge_results(task, cfg, logger, score_cnt, score_sum, results):
if task.cfg._name == 'image_... | null |
165,265 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import six
from six.moves import cPickle
from collections import defaultdict
import numpy as np
import math
import os
def precook(s, n=4, out=False):
"""
Takes a string as input and returns a... | Takes a list of reference sentences for a single segment and returns an object that encapsulates everything that BLEU needs to know about them. :param refs: list of string : reference sentences for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (list of dict) |
165,266 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import six
from six.moves import cPickle
from collections import defaultdict
import numpy as np
import math
import os
def precook(s, n=4, out=False):
"""
Takes a string as input and returns a... | Takes a test sentence and returns an object that encapsulates everything that BLEU needs to know about it. :param test: list of string : hypothesis sentence for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (dict) |
165,267 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
from collections import defaultdict
import numpy as np
import pdb
import math
import six
from six.moves import cPickle
import os
def precook(s, n=4, out=False):
"""
Takes a string as input an... | Takes a list of reference sentences for a single segment and returns an object that encapsulates everything that BLEU needs to know about them. :param refs: list of string : reference sentences for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (list of dict) |
165,268 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
from collections import defaultdict
import numpy as np
import pdb
import math
import six
from six.moves import cPickle
import os
def precook(s, n=4, out=False):
"""
Takes a string as input an... | Takes a test sentence and returns an object that encapsulates everything that BLEU needs to know about it. :param test: list of string : hypothesis sentence for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (dict) |
165,269 | import os
from os.path import join, exists
import json
from tqdm import tqdm
from rich import print
import numpy as np
import imagesize
OBJ_BEGIN_TOKEN = '<SOO>'
OBJ_END_TOKEN = '<EOO>'
NOCOREF_TOKEN = '<NOCOREF>'
DISAMBIGUATION_TOKEN = '<DISAM>'
FASHION_DST = "<INTENT><FAS_TYPE><FAS_PRICE><FAS_CUSTOMER_REVIEW><FAS_BRA... | 为VLBert模型的训练准备 |
165,271 | from genericpath import exists
import os
from os.path import join
import json
import argparse
import torch
from torch.optim import AdamW
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import SequentialSampler, DistributedSampler
from tqdm import tqdm, trange
from rich import print
from datetime impor... | 模型方法的评估函数 |
165,277 | import json
import torch
from transformers.tokenization_utils import PreTrainedTokenizer
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler, DistributedSampler
from prefetch_generator import BackgroundGenerator
import copy
from .metadata import FASHION_COLOR, FASHION_PATTERN, FASHION_SLE... | null |
165,280 | import os
from os.path import join, exists
import json
from tqdm import tqdm
from rich import print
import numpy as np
import imagesize
The provided code snippet includes necessary dependencies for implementing the `process_metadata_dict` function. Write a Python function `def process_metadata_dict(scene_dir, scene_id... | 根据scene ids 生成对应的 object dict |
165,281 | import os
from os.path import join, exists
import json
from tqdm import tqdm
from rich import print
import numpy as np
import imagesize
OBJ_BEGIN_TOKEN = '<SOO>'
OBJ_END_TOKEN = '<EOO>'
NOCOREF_TOKEN = '<NOCOREF>'
def arrange_object_special_tokens(scene_dir, image_dir, scene_ids, object_item2id, insert_bbox_coords):
... | 为VLBert模型的训练准备 |
165,283 | from genericpath import exists
import os
from os.path import join
import json
import argparse
import torch
from torch.optim import AdamW
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import SequentialSampler, DistributedSampler
from tqdm import tqdm, trange
from rich import print
from datetime impor... | null |
165,288 | import json
import torch
from os.path import join, exists
from transformers.tokenization_utils import PreTrainedTokenizer
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler, DistributedSampler
from prefetch_generator import BackgroundGenerator
import copy
from .metadata import FASHION_CO... | 获取Specical Token所对应的id信息 |
165,289 | import json
import torch
from os.path import join, exists
from transformers.tokenization_utils import PreTrainedTokenizer
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler, DistributedSampler
from prefetch_generator import BackgroundGenerator
import copy
from .metadata import FASHION_CO... | null |
165,290 | import json
import torch
from os.path import join, exists
from transformers.tokenization_utils import PreTrainedTokenizer
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler, DistributedSampler
from prefetch_generator import BackgroundGenerator
import copy
from .metadata import FASHION_CO... | null |
165,291 | import json
import torch
from os.path import join, exists
from transformers.tokenization_utils import PreTrainedTokenizer
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler, DistributedSampler
from prefetch_generator import BackgroundGenerator
import copy
from .metadata import FASHION_CO... | null |
165,294 | import torch
def preprocess(utterances, sql, tokenizer):
text = ""
for u in utterances:
for t in u.split(' '):
text = text + ' ' + t.strip()
sql = sql.strip()
sql = sql.replace(".", " ")
sql = sql.replace("_", " ")
l = []
for char in sql:
if char.isupper():
... | null |
165,295 | import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import StepLR
import numpy as np
import logging
import argparse
import os
from .dataset import NL2SQL_Dataset
from .model import ReRanker
from sklearn.metrics import confusion_matrix, accuracy_score
def parser_arg(... | null |
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