id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
184,947 | import math
import sys
from typing import Iterable
import torch
import torch.nn as nn
import utils
def train_one_epoch(model: torch.nn.Module, d_vae: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler, max_norm... | null |
184,948 | from math import sqrt
import os
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange
from dall_e import load_model
def top_k(logits, thres = 0.5):
num_logits = logits.shape[-1]
k = max(int((1 - thres) * num_logits), 1)
val, ind = torch.topk(logits, k)
p... | null |
184,949 | from math import sqrt
import os
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange
def exists(val):
return val is not None
from dall_e import load_model
def default(val, d):
return val if exists(val) else d | null |
184,950 | from math import sqrt
import os
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange
from dall_e import load_model
def eval_decorator(fn):
def inner(model, *args, **kwargs):
was_training = model.training
model.eval()
out = fn(model, *args, *... | null |
184,951 | import os
import argparse
import json
from pathlib import Path
import torch
from torch import nn
import torch.distributed as dist
import torch.backends.cudnn as cudnn
from torchvision import datasets
from torchvision import transforms as pth_transforms
import utils
import modeling_finetune
from timm.models import creat... | null |
184,952 | import os
import argparse
import json
from pathlib import Path
import torch
from torch import nn
import torch.distributed as dist
import torch.backends.cudnn as cudnn
from torchvision import datasets
from torchvision import transforms as pth_transforms
import utils
import modeling_finetune
from timm.models import creat... | Parse boolean arguments from the command line. |
184,953 | import os
import copy
import pytorch_lightning as pl
from vlmo.config import ex
from vlmo.modules import VLMo
from vlmo.datamodules.multitask_datamodule import MTDataModule
from pytorch_lightning.plugins import environments as pl_env
from pytorch_lightning.utilities.distributed import rank_zero_info
class OMPIClusterEn... | null |
184,954 | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
import numpy as np
import vlmo.modules.multiway_transformer
from transformers.models.bert.modeling_bert import BertConfig, BertEmbeddings
from vlmo.modules import heads, objectives, vlmo_utils
from pytorch_lightn... | null |
184,955 | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
import numpy as np
import vlmo.modules.multiway_transformer
from transformers.models.bert.modeling_bert import BertConfig, BertEmbeddings
from vlmo.modules import heads, objectives, vlmo_utils
from pytorch_lightn... | null |
184,956 | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
import numpy as np
import vlmo.modules.multiway_transformer
from transformers.models.bert.modeling_bert import BertConfig, BertEmbeddings
from vlmo.modules import heads, objectives, vlmo_utils
from pytorch_lightn... | null |
184,957 | from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from pytorch_lightning.utilities.distributed import rank_zero_info
class MultiW... | null |
184,958 | from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from pytorch_lightning.utilities.distributed import rank_zero_info
class MultiW... | null |
184,959 | from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from pytorch_lightning.utilities.distributed import rank_zero_info
class MultiW... | null |
184,960 | import torch
import random
import json
from transformers.optimization import AdamW
from transformers import (
get_polynomial_decay_schedule_with_warmup,
get_cosine_schedule_with_warmup,
)
from vlmo.modules.dist_utils import all_gather
from vlmo.modules.objectives import compute_irtr_recall, compute_irtr_recall_... | null |
184,961 | import torch
import random
import json
from transformers.optimization import AdamW
from transformers import (
get_polynomial_decay_schedule_with_warmup,
get_cosine_schedule_with_warmup,
)
from vlmo.modules.dist_utils import all_gather
from vlmo.modules.objectives import compute_irtr_recall, compute_irtr_recall_... | null |
184,962 | import torch
import random
import json
from transformers.optimization import AdamW
from transformers import (
get_polynomial_decay_schedule_with_warmup,
get_cosine_schedule_with_warmup,
)
from vlmo.modules.dist_utils import all_gather
from vlmo.modules.objectives import compute_irtr_recall, compute_irtr_recall_... | null |
184,963 | import torch
import random
import json
from transformers.optimization import AdamW
from transformers import (
get_polynomial_decay_schedule_with_warmup,
get_cosine_schedule_with_warmup,
)
from vlmo.modules.dist_utils import all_gather
from vlmo.modules.objectives import compute_irtr_recall, compute_irtr_recall_... | null |
184,964 | import torch
import random
import json
from transformers.optimization import AdamW
from transformers import (
get_polynomial_decay_schedule_with_warmup,
get_cosine_schedule_with_warmup,
)
from vlmo.modules.dist_utils import all_gather
from vlmo.modules.objectives import compute_irtr_recall, compute_irtr_recall_... | null |
184,972 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,973 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,974 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,975 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,976 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,977 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,978 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,979 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,980 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,981 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,982 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,983 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from pytorch_lightning.utilities.distributed import rank_zero_info
f... | null |
184,985 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,986 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,987 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,988 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,989 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,990 | from sacred import Experiment
def _loss_names(d):
def task_mlm_itm_itc_large():
exp_name = "mlm_itm_itc_large"
datasets = ["coco", "vg", "sbu", "gcc"]
loss_names = _loss_names({"itm": 1, "mlm": 1, "itc": 1})
batch_size = 1024
whole_word_masking = True
learning_rate = 5e-5
train_transform_ke... | null |
184,991 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,992 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,993 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,994 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,995 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,996 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,997 | from sacred import Experiment
def _loss_names(d):
def task_finetune_vqa_base_image480():
exp_name = "finetune_vqa_base_image480"
datasets = ["vqa"]
train_transform_keys = ["square_transform_randaug"]
loss_names = _loss_names({"vqa": 1})
batch_size = 128
max_epoch = 10
max_steps = None
w... | null |
184,998 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
184,999 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
185,000 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
185,001 | from sacred import Experiment
def _loss_names(d):
def task_finetune_irtr_f30k_base_image384():
exp_name = "finetune_irtr_f30k_base_image384"
datasets = ["f30k"]
train_transform_keys = ["square_transform_randaug"]
val_transform_keys = ["square_transform"]
loss_names = _loss_names({"irtr": 1.0})
... | null |
185,002 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
185,003 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
185,004 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
185,005 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
185,006 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0, # image-text matching loss
"itc": 0, # image-text contrastive loss
"mlm": 0, # masked language modeling loss
"textmlm": 0, # text-only masked language modeling
"vqa": 0,
"nlvr2": 0,
"irtr": 0,... | null |
185,007 | from sacred import Experiment
def step1_5k():
max_epoch = 100
warmup_steps = 150
max_steps = 1500 | null |
185,008 | from sacred import Experiment
def step3k():
max_epoch = 100
warmup_steps = 300
max_steps = 3000 | null |
185,009 | from sacred import Experiment
def step200k():
max_epoch = 200
warmup_steps = 2500
max_steps = 200000 | null |
185,010 | from sacred import Experiment
def step500k():
max_epoch = 500
warmup_steps = 2500
max_steps = 500000 | null |
185,018 | import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def ShearX(img, v):
def ShearY(img, v):
def TranslateXabs(img, v):
def TranslateYabs(img, v):
def Rotate(img, v):
def AutoContrast(img, _):
def Equalize(img, _):
def Solarize(img, v):
def Solari... | null |
185,044 | from .utils import (
inception_normalize,
)
from torchvision import transforms
from .randaugment import RandomAugment
from PIL import Image
inception_normalize = transforms.Compose(
[transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])]
)
def square_transform(size=224):
return transforms.Compos... | null |
185,045 | from .utils import (
inception_normalize,
)
from torchvision import transforms
from .randaugment import RandomAugment
from PIL import Image
inception_normalize = transforms.Compose(
[transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])]
)
class RandomAugment(object):
def __init__(self, N=2, M=... | null |
185,047 | import json
import pandas as pd
import pyarrow as pa
import random
import os
from tqdm import tqdm
from glob import glob
from collections import defaultdict
def path2rest(path, iid2captions, iid2split):
def make_arrow(root, dataset_root):
with open(f"{root}/karpathy/dataset_flickr30k.json", "r") as fp:
cap... | null |
185,049 | import json
import pandas as pd
import pyarrow as pa
import gc
import random
import os
from tqdm import tqdm
from glob import glob
def path2rest(line):
return [
"None",
[line],
"wikibk",
"train",
]
def make_arrow(root, dataset_root):
for index in range(0, 50):
file_p... | null |
185,052 | import json
import pandas as pd
import pyarrow as pa
import gc
import random
import os
from tqdm import tqdm
from glob import glob
def path2rest(path, iid2captions):
split, _, name = path.split("/")[-3:]
split = split.split("_")[-1]
iid = name
with open(path, "rb") as fp:
binary = fp.read()
... | null |
185,053 | import json
import pandas as pd
import pyarrow as pa
import gc
import random
import os
from tqdm import tqdm
from glob import glob
def path2rest(path, iid2captions):
split, _, name = path.split("/")[-3:]
split = split.split("_")[-1]
iid = name
with open(path, "rb") as fp:
binary = fp.read()
... | null |
185,054 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import logging
import math
import os
import pickle
import random
from time import sleep
import numpy as np
import torch
from nltk.translate.bleu_score import sentence_bleu
from tqdm import tqdm
f... | null |
185,055 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import logging
import math
import os
import pickle
import random
from time import sleep
import numpy as np
import torch
from nltk.translate.bleu_score import sentence_bleu
from tqdm import tqdm
f... | null |
185,056 | from __future__ import absolute_import, division, print_function
import argparse
import json
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import (DataLoader, SequentialSampler)
from torch.utils.data.distributed import DistributedSampler
try:
from torch.utils.tensorboa... | Train the model |
185,057 | from __future__ import absolute_import, division, print_function
import argparse
import json
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import (DataLoader, SequentialSampler)
from torch.utils.data.distributed import DistributedSampler
import tqdm
from s2s_ft.modeling im... | null |
185,058 | from __future__ import absolute_import, division, print_function
import argparse
import json
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import (DataLoader, SequentialSampler)
from torch.utils.data.distributed import DistributedSampler
try:
from torch.utils.tensorboa... | null |
185,059 | from __future__ import absolute_import, division, print_function
import argparse
import json
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import (DataLoader, SequentialSampler)
from torch.utils.data.distributed import DistributedSampler
import tqdm
from s2s_ft.modeling im... | null |
185,062 | import torch
import logging
from transformers.modeling_utils import cached_path, WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME
logger = logging.getLogger(__name__)
def hf_distilbert_to_hf_bert(state_dict):
logger.info(" * Convert Huggingface DistilBERT format to Huggingface BERT format * ")
new_state_dict =... | null |
185,063 | import torch
import logging
from transformers.modeling_utils import cached_path, WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME
def hf_bert_to_hf_bert(state_dict):
# NOTE: all cls states are used for prediction,
# we predict the index so omit all pretrained states for prediction.
new_state_dict = {}
... | null |
185,064 | import torch
import logging
from transformers.modeling_utils import cached_path, WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME
logger = logging.getLogger(__name__)
def hf_layoutlm_to_hf_bert(state_dict):
logger.info(" * Convert Huggingface LayoutLM format to Huggingface BERT format * ")
new_state_dict = {}
... | null |
185,065 | from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
def batch_list_to_batch_tensors(batch):
batch_tensors = []
for x in zip(*batch):
if isinstance(x[0], torch.Tensor):
... | null |
185,066 | from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
def get_max_epoch_model(output_dir):
fn_model_list = glob.glob(os.path.join(output_dir, "model.*.bin"))
fn_optim_list = glob... | null |
185,067 | from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
logger = logging.getLogger(__name__)
def load_and_cache_examples(
example_file, tokenizer, local_rank, cached_features_file,... | null |
185,068 | from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
logger = logging.getLogger(__name__)
def load_and_cache_line_order_examples(
example_path, tokenizer, local_rank, cached_fea... | null |
185,069 | from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
logger = logging.getLogger(__name__)
def load_and_cache_layoutlm_examples(
example_path, tokenizer, local_rank, cached_featu... | null |
185,070 | from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
def convert_src_layout_inputs_to_tokens(inputs, converter, max_src_length, layout_flag=True):
ret = []
if not layout_flag:
... | null |
185,071 | from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
def convert_tgt_layout_inputs_to_tokens(inputs, converter, max_tgt_length, layout_flag=True):
ret = []
if not layout_flag:
... | null |
185,072 | from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
def get_tokens_from_src_and_index(src, index, modifier=None):
result = []
for i in index:
i = modifier(i)
i ... | null |
185,073 | from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
def get_layout_from_src_and_index(src, index, modifier=None):
result = []
s = set()
for i in index:
i = modifier... | null |
185,074 | from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
def get_everything_from_src_and_index(src, index, modifier=None):
result = []
for i in index:
i = modifier(i)
... | null |
185,079 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import json
import logging
import math
import os
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.modules.loss import _Loss
The provided code snippe... | Implementation of the gelu activation function. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) |
185,080 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import json
import logging
import math
import os
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.modules.loss import _Loss
def swish(x):
return... | null |
185,081 | import math
import numpy as np
from typing import Dict, Optional, Tuple
import torch
from torch import Tensor, nn
import torch.nn.functional as F
from torch.nn import LayerNorm, Parameter
from modules import (
GradMultiply,
SamePad,
get_activation_fn,
GLU_Linear,
quant_noise,
)
class MultiheadAttent... | 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_... |
185,082 | import math
import warnings
import torch
from torch import Tensor, nn
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 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
... | Returns the activation function corresponding to `activation` |
185,083 | import math
import warnings
import torch
from torch import Tensor, nn
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(module, p, block_size)` to solve the following problem:
Wraps modules and... | 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... |
185,084 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as distributed
def ema_inplace(moving_avg, new, decay):
moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) | null |
185,085 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as distributed
def l2norm(t):
def sample_vectors(samples, num):
def kmeans(samples, num_clusters, num_iters=10, use_cosine_sim=False):
dim, dtype, device = samples.shape[-1], samples.dtype, samples.device
means = sampl... | null |
185,086 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as distributed
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))
moving_avg.data.copy_(l2norm(moving_avg.data)) | null |
185,087 | import itertools
import logging
import os
from typing import Any, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset import FairseqDataset
logger = logging.getLogger(__name__)
def load_audio(manifest_path, max... | null |
185,088 | import itertools
import logging
import os
from typing import Any, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset import FairseqDataset
def load_label(label_path, inds, tot):
with open(label_path) as f:... | null |
185,089 | import itertools
import logging
import os
from typing import Any, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset import FairseqDataset
def load_label_offset(label_path, inds, tot):
with open(label_path... | null |
185,090 | import logging
import os
from typing import Any, List, Optional
import librosa
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data.fairseq_dataset import FairseqDataset
The provided code snippet includes necessary dependencies for implementing the `_collate_frames` function. Write a Pytho... | Convert a list of 2D frames into a padded 3D tensor Args: frames (list): list of 2D frames of size L[i]*f_dim. Where L[i] is length of i-th frame and f_dim is static dimension of features Returns: 3D tensor of size len(frames)*len_max*f_dim where len_max is max of L[i] |
185,091 | import logging
import os
from typing import Any, List, Optional
import librosa
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data.fairseq_dataset import FairseqDataset
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `loa... | manifest tsv: src_wav, src_nframe, tgt_wav, tgt_nframe, tgt_spkemb |
185,092 | import logging
import os
from typing import Any, List, Optional
import librosa
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data.fairseq_dataset import FairseqDataset
The provided code snippet includes necessary dependencies for implementing the `logmelfilterbank` function. Write a Pyth... | Compute log-Mel filterbank feature. (https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/parallel_wavegan/bin/preprocess.py) Args: audio (ndarray): Audio signal (T,). sampling_rate (int): Sampling rate. fft_size (int): FFT size. hop_size (int): Hop size. win_length (int): Window length. If set to None, it will b... |
185,093 | import itertools
import logging
import os
import sys
from typing import Any, List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils
from fairseq.data.fairseq_d... | Convert a list of 2D frames into a padded 3D tensor Args: frames (list): list of 2D frames of size L[i]*f_dim. Where L[i] is length of i-th frame and f_dim is static dimension of features Returns: 3D tensor of size len(frames)*len_max*f_dim where len_max is max of L[i] |
185,094 | import itertools
import logging
import os
import sys
from typing import Any, List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils
from fairseq.data.fairseq_d... | null |
185,095 | import itertools
import logging
import os
import sys
from typing import Any, List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils
from fairseq.data.fairseq_d... | null |
185,096 | import itertools
import logging
import os
import sys
from typing import Any, List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils
from fairseq.data.fairseq_d... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.