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main_label_feature.py
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main_label_feature.py
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import torch
import argparse
import numpy as np
from modules.tokenizers import Tokenizer
from modules.dataloaders import LADataLoader
from modules.metrics import compute_scores
from modules.optimizers import build_optimizer, build_lr_scheduler
from modules.trainer import Trainer
from modules.loss import compute_loss
from models.LabelFeature import LAMRGModel
def parse_agrs():
parser = argparse.ArgumentParser()
# Data input settings
parser.add_argument('--image_dir', type=str, default='data/iu_xray/images/', help='the path to the directory containing the data.')
parser.add_argument('--ann_path', type=str, default='data/iu_xray/annotation.json', help='the path to the directory containing the data.')
parser.add_argument('--label_path', type=str, default='/home/shuxinyang/data/mimic/finding/chexpert_labeled.csv', help='the path to the directory containing the label.')
# Data loader settings
parser.add_argument('--dataset_name', type=str, default='iu_xray', choices=['iu_xray', 'mimic_cxr'], help='the dataset to be used.')
parser.add_argument('--max_seq_length', type=int, default=60, help='the maximum sequence length of the reports.')
parser.add_argument('--threshold', type=int, default=3, help='the cut off frequency for the words.')
parser.add_argument('--num_workers', type=int, default=2, help='the number of workers for dataloader.')
parser.add_argument('--batch_size', type=int, default=16, help='the number of samples for a batch')
# Model settings (for visual extractor)
parser.add_argument('--visual_extractor', type=str, default='resnet101', help='the visual extractor to be used.')
parser.add_argument('--visual_extractor_pretrained', type=bool, default=True, help='whether to load the pretrained visual extractor')
parser.add_argument('--pretrain_cnn_file', type=str,
default='/home/shuxinyang/code/tencent/EfficientNet-PyTorch/runs/chexnet-mimic-imagenet-pretrain/model_best.pth.tar',
help='the visual extractor to be used.')
# Model settings (for Transformer)
parser.add_argument('--d_model', type=int, default=512, help='the dimension of Transformer.')
parser.add_argument('--d_ff', type=int, default=512, help='the dimension of FFN.')
parser.add_argument('--d_vf', type=int, default=1024, help='the dimension of the patch features.')
parser.add_argument('--num_heads', type=int, default=8, help='the number of heads in Transformer.')
parser.add_argument('--num_layers', type=int, default=6, help='the number of layers of Transformer.')
parser.add_argument('--num_labels', type=int, default=14, help='the number of labels.')
parser.add_argument('--dropout', type=float, default=0.1, help='the dropout rate of Transformer.')
parser.add_argument('--logit_layers', type=int, default=1, help='the number of the logit layer.')
parser.add_argument('--bos_idx', type=int, default=0, help='the index of <bos>.')
parser.add_argument('--eos_idx', type=int, default=0, help='the index of <eos>.')
parser.add_argument('--pad_idx', type=int, default=0, help='the index of <pad>.')
parser.add_argument('--use_bn', type=int, default=0, help='whether to use batch normalization.')
parser.add_argument('--drop_prob_lm', type=float, default=0.5, help='the dropout rate of the output layer.')
# Sample related
parser.add_argument('--sample_method', type=str, default='beam_search', help='the sample methods to sample a report.')
parser.add_argument('--beam_size', type=int, default=3, help='the beam size when beam searching.')
parser.add_argument('--temperature', type=float, default=1.0, help='the temperature when sampling.')
parser.add_argument('--sample_n', type=int, default=1, help='the sample number per image.')
parser.add_argument('--group_size', type=int, default=1, help='the group size.')
parser.add_argument('--output_logsoftmax', type=int, default=1, help='whether to output the probabilities.')
parser.add_argument('--decoding_constraint', type=int, default=0, help='whether decoding constraint.')
parser.add_argument('--block_trigrams', type=int, default=1, help='whether to use block trigrams.')
# Trainer settings
parser.add_argument('--n_gpu', type=int, default=1, help='the number of gpus to be used.')
parser.add_argument('--epochs', type=int, default=100, help='the number of training epochs.')
parser.add_argument('--save_dir', type=str, default='results/iu_xray', help='the patch to save the models.')
parser.add_argument('--record_dir', type=str, default='records/', help='the patch to save the results of experiments')
parser.add_argument('--save_period', type=int, default=1, help='the saving period.')
parser.add_argument('--monitor_mode', type=str, default='max', choices=['min', 'max'], help='whether to max or min the metric.')
parser.add_argument('--monitor_metric', type=str, default='BLEU_4', help='the metric to be monitored.')
parser.add_argument('--early_stop', type=int, default=50, help='the patience of training.')
parser.add_argument('--label_smoothing', type=float, default=0.0)
# Optimization
parser.add_argument('--optim', type=str, default='Adam', help='the type of the optimizer.')
parser.add_argument('--lr_ve', type=float, default=5e-5, help='the learning rate for the visual extractor.')
parser.add_argument('--lr_ed', type=float, default=1e-4, help='the learning rate for the remaining parameters.')
parser.add_argument('--weight_decay', type=float, default=5e-5, help='the weight decay.')
parser.add_argument('--amsgrad', type=bool, default=True, help='.')
# Learning Rate Scheduler
parser.add_argument('--lr_scheduler', type=str, default='StepLR', help='the type of the learning rate scheduler.')
parser.add_argument('--step_size', type=int, default=50, help='the step size of the learning rate scheduler.')
parser.add_argument('--gamma', type=float, default=0.1, help='the gamma of the learning rate scheduler.')
# Others
parser.add_argument('--seed', type=int, default=9233, help='.')
parser.add_argument('--gpu', type=int, default=0, help='')
parser.add_argument('--resume', type=str, help='whether to resume the training from existing checkpoints.')
args = parser.parse_args()
return args
def main():
# parse arguments
args = parse_agrs()
# fix random seeds
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(args.seed)
# create tokenizer
tokenizer = Tokenizer(args)
# create data loader
train_dataloader = LADataLoader(args, tokenizer, split='train', shuffle=True)
val_dataloader = LADataLoader(args, tokenizer, split='val', shuffle=False)
test_dataloader = LADataLoader(args, tokenizer, split='test', shuffle=False)
# build model architecture
model = LAMRGModel(args, tokenizer)
# get function handles of loss and metrics
criterion = compute_loss
metrics = compute_scores
# build optimizer, learning rate scheduler
optimizer = build_optimizer(args, model)
lr_scheduler = build_lr_scheduler(args, optimizer)
# build trainer and start to train
trainer = Trainer(model, criterion, metrics, optimizer, args, lr_scheduler, train_dataloader, val_dataloader, test_dataloader)
trainer.train()
if __name__ == '__main__':
main()