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test_data_loader.py
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executable file
·46 lines (43 loc) · 1.93 KB
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from data_loader.data_loaders import *
from data_loader.TR_data_loaders import *
from data_loader.CT_Wiki_data_loaders import *
from data_loader.hybrid_data_loaders import *
import pdb
import torch
import numpy as np
from utils.util import *
if __name__ == "__main__":
torch.manual_seed(0)
np.random.seed(0)
mode = 0
data_dir = "./data/wikitables_v2"
entity_vocab = load_entity_vocab(data_dir, ignore_bad_title=True, min_ent_count=2)
dev_dataset = WikiHybridTableDataset(data_dir,\
entity_vocab,max_cell=100, max_input_tok=350, max_input_ent=150, src="dev", max_length = [50, 10, 10], force_new=True, tokenizer = None, mode = mode)
train_dataset = WikiHybridTableDataset(data_dir,\
entity_vocab,max_cell=100, max_input_tok=350, max_input_ent=150, src="train", max_length = [50, 10, 10], force_new=True, tokenizer = None, mode = mode)
pdb.set_trace()
dev_data_generator = HybridTableLoader(dev_dataset,10,num_workers=0,mlm_probability=0.5,ent_mlm_probability=0.5,is_train=False,use_cand=True, mode = mode)
train_data_generator = HybridTableLoader(train_dataset,10,num_workers=0,mlm_probability=0.5,ent_mlm_probability=0.5,is_train=True,use_cand=False, mode = mode)
for x in train_data_generator:
pass
pdb.set_trace()
break
for x in dev_data_generator:
pass
pdb.set_trace()
break
# data_dir = "data/WebQueryTable_Dataset"
# train_dataset = WebQueryTableDataset(data_dir, src="train", force_new=True)
# train_dataloader = TRLoader(train_dataset, 10, is_train=True)
# for x in train_dataloader:
# pass
# pdb.set_trace()
# break
# data_dir = "data/T2D_IO"
# train_dataset = SemColDataset(data_dir,entity_vocab, src="train", max_cell_length=10, force_new=True)
# train_dataloader = CTLoader(train_dataset, 10, is_train=True)
# for x in train_dataloader:
# pass
# pdb.set_trace()
# break