layoutlmv2
Thu Apr 13 2023 08:14:53 GMT+0000 (Coordinated Universal Time)
Saved by @mehla99_shubham #python
from layoutlm import FunsdDataset, LayoutlmConfig, LayoutlmForTokenClassification
from transformers import BertTokenizer,AdamW
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
import torch
from tqdm import tqdm, trange
MODEL_CLASSES = { "layoutlm": (LayoutlmConfig, LayoutlmForTokenClassification, BertTokenizer), }
def train( train_dataset, model, tokenizer, labels, pad_token_label_id):
""" Train the model """
if torch.cuda.is_available():
device = torch.device("cuda")
print("GPU is available")
else:
device = torch.device("cpu")
print("GPU is not available, using CPU instead")
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=None )
no_decay = ["bias", "LayerNorm.weight"]
optimizer = AdamW( lr=learning_rate, eps=adam_epsilon)
model = torch.nn.Module(model, find_unused_parameters=True)
global_step, tr_loss = 0, 0.0
model.zero_grad()
train_iterator = trange(num_train_epochs, desc="Epoch")
for in trainiterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration")
for step, batch in enumerate(epoch_iterator):
model.train()
inputs = {"input_ids": batch[0].to(device), "attention_mask": batch[1].to(device), "labels": batch[3].to(device)}
inputs["bbox"] = batch[4].to(device)
inputs["token_type_ids"] = batch[2].to(device)
outputs = model(**inputs)
loss = outputs[0]
loss.backward()
tr_loss += loss.item()
optimizer.step() # scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
return global_step, tr_loss / global_step
def main():
if torch.cuda.is_available():
device = torch.device("cuda")
print("GPU is available")
else:
device = torch.device("cpu")
print("GPU is not available, using CPU instead")
labels = get_labels(labels) # in our case labels will be x-axis,y-axis,title
num_labels = len(labels) # Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
pad_token_label_id = CrossEntropyLoss().ignore_index
config = config_class.from_pretrained( "layoutlm-base-uncased/", num_labels=num_labels, force_download = True, ignore_mismatched_sizes=True, cache_dir= cache_dir_path else None, )
tokenizer = tokenizer_class.from_pretrained( "microsoft/layoutlm-base-uncased", do_lower_case=True, force_download = True, ignore_mismatched_sizes=True, cache_dir= cache_dir_path else None, )
model = model_class.from_pretrained( "layoutlm-base-uncased/", config=config, )
model.to(args.device)
train_dataset = FunsdDataset( args, tokenizer, labels, pad_token_label_id, mode="train" )
global_step, tr_loss = train( args, train_dataset, model, tokenizer, labels, pad_token_label_id )
tokenizer = tokenizer_class.from_pretrained( "microsoft/layoutlm-base-uncased",force_download = True, do_lower_case=args.do_lower_case,ignore_mismatched_sizes=True)
model = model_class.from_pretrained(args.output_dir)
model.to(args.device)
result, predictions = evaluate( args, model, tokenizer, labels, pad_token_label_id, mode="test" )
return result,predictions



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