Datasets:
jblitzar
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github-python-corpus

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from apex import amp
from horovod import torch as hvd
from tqdm import tqdm
from data import (TokenBucketSampler,
MetaLoader, PrefetchLoader, DetectFeatLmdb,
VcrTxtTokLmdb, ImageLmdbGroup, ConcatDatasetWithLens,
MlmDatasetForVCR, mlm_collate_for_vcr,
MrfrDatasetForVCR, mrfr_collate_for_vcr,
MrcDatasetForVCR, mrc_collate_for_vcr)
from model.pretrain_vcr import UniterForPretrainingForVCR
from optim import get_lr_sched
from optim.misc import build_optimizer
from utils.logger import LOGGER, TB_LOGGER, RunningMeter, add_log_to_file
from utils.distributed import (all_reduce_and_rescale_tensors, all_gather_list,
broadcast_tensors)
from utils.save import ModelSaver, save_training_meta
from utils.misc import NoOp, parse_with_config, set_dropout, set_random_seed
from utils.const import IMG_DIM, IMG_LABEL_DIM, BUCKET_SIZE
NUM_SPECIAL_TOKENS = 81
def build_dataloader(dataset, collate_fn, is_train, opts):
if is_train:
batch_size = opts.train_batch_size
else:
batch_size = opts.val_batch_size
sampler = TokenBucketSampler(dataset.lens, bucket_size=BUCKET_SIZE,
batch_size=batch_size, droplast=is_train)
loader = DataLoader(dataset, batch_sampler=sampler,
num_workers=opts.n_workers, pin_memory=opts.pin_mem,
collate_fn=collate_fn)
return loader
def build_mlm_dataset(txt_db, img_db_gt, img_db, is_train, opts):
if is_train:
collate_fn = mlm_collate_for_vcr
datasets = [MlmDatasetForVCR(t, i_gt, i)
for t, i_gt, i in zip(txt_db, img_db_gt, img_db)]
dataset = ConcatDatasetWithLens(datasets)
else:
collate_fn = mlm_collate_for_vcr
dataset = MlmDatasetForVCR(txt_db, img_db_gt, img_db)
return dataset, collate_fn
def build_mrfr_dataset(txt_db, img_db_gt, img_db, is_train, opts):
if is_train:
datasets = [MrfrDatasetForVCR(opts.mrm_prob, t, i_gt, i)
for t, i_gt, i in zip(txt_db, img_db_gt, img_db)]
dataset = ConcatDatasetWithLens(datasets)
else:
dataset = MrfrDatasetForVCR(opts.mrm_prob, txt_db, img_db_gt, img_db)
return dataset, mrfr_collate_for_vcr
def build_mrc_dataset(txt_db, img_db_gt, img_db, is_train, opts):
if is_train:
datasets = [MrcDatasetForVCR(opts.mrm_prob, t, i_gt, i)
for t, i_gt, i in zip(txt_db, img_db_gt, img_db)]
dataset = ConcatDatasetWithLens(datasets)
else:
dataset = MrcDatasetForVCR(opts.mrm_prob, txt_db, img_db_gt, img_db)
return dataset, mrc_collate_for_vcr
def load_img_feat(db_list, all_img_dbs, opts):
db_ = db_list.split(";")
assert len(db_) <= 2, "More than two img_dbs found"
gt_db_path, db_path = "", ""
for d in db_:
if "gt" in d:
gt_db_path = d
else:
db_path = d
if gt_db_path != "":
img_db_gt = DetectFeatLmdb(
gt_db_path, -1, opts.max_bb, opts.min_bb, 100,
opts.compressed_db)
all_img_dbs.path2imgdb[gt_db_path] = img_db_gt
else:
img_db_gt = None
img_db = all_img_dbs[db_path] if db_path != "" else None
all_img_dbs.path2imgdb[db_path] = img_db
return img_db, img_db_gt
def create_dataloaders(datasets, is_train, opts, all_img_dbs=None):
if all_img_dbs is None:
all_img_dbs = ImageLmdbGroup(opts.conf_th, opts.max_bb, opts.min_bb,
opts.num_bb, opts.compressed_db)
dataloaders = {}