mirror of
https://github.com/saymrwulf/onnxruntime.git
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85 lines
3.6 KiB
Python
85 lines
3.6 KiB
Python
import argparse
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import math
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import torch
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import onnxruntime
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from utils import prepare_data, get_batch
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from ort_utils import my_loss, transformer_model_description_dynamic_axes
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from pt_model import TransformerModel
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def train(trainer, data_source, device, epoch, args, bptt=35):
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total_loss = 0.
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for batch, i in enumerate(range(0, data_source.size(0) - 1, bptt)):
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data, targets = get_batch(data_source, i)
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loss, pred = trainer.train_step(data, targets)
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total_loss += loss.item()
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if batch % args.log_interval == 0 and batch > 0:
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cur_loss = total_loss / args.log_interval
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print('epoch {:3d} | {:5d}/{:5d} batches | loss {:5.2f}'.format(epoch,
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batch,
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len(data_source) // bptt,
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cur_loss))
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total_loss = 0
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def evaluate(trainer, data_source, bptt=35):
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total_loss = 0.
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with torch.no_grad():
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for i in range(0, data_source.size(0) - 1, bptt):
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data, targets = get_batch(data_source, i)
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loss, pred = trainer.eval_step(data, targets)
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total_loss += len(data) * loss.item()
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return total_loss / (len(data_source) - 1)
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if __name__ == "__main__":
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# Training settings
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parser = argparse.ArgumentParser(description='PyTorch TransformerModel example')
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parser.add_argument('--batch-size', type=int, default=20, metavar='N',
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help='input batch size for training (default: 20)')
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parser.add_argument('--test-batch-size', type=int, default=20, metavar='N',
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help='input batch size for testing (default: 20)')
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parser.add_argument('--epochs', type=int, default=2, metavar='N',
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help='number of epochs to train (default: 2)')
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parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
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help='learning rate (default: 0.001)')
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parser.add_argument('--no-cuda', action='store_true', default=False,
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help='disables CUDA training')
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parser.add_argument('--seed', type=int, default=1, metavar='S',
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help='random seed (default: 1)')
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parser.add_argument('--log-interval', type=int, default=200, metavar='N',
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help='how many batches to wait before logging training status (default: 200)')
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# Basic setup
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args = parser.parse_args()
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if not args.no_cuda and torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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torch.manual_seed(args.seed)
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onnxruntime.set_seed(args.seed)
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# Model
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optim_config = onnxruntime.training.optim.SGDConfig(lr=args.lr)
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model_desc = transformer_model_description_dynamic_axes()
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model = TransformerModel(28785, 200, 2, 200, 2, 0.2).to(device)
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# Preparing data
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train_data, val_data, test_data = prepare_data(device, args.batch_size, args.test_batch_size)
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trainer = onnxruntime.training.ORTTrainer(model, model_desc, optim_config, loss_fn=my_loss)
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# Train
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for epoch in range(1, args.epochs + 1):
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train(trainer, train_data, device, epoch, args)
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val_loss = evaluate(trainer, val_data)
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print('-' * 89)
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print('| end of epoch {:3d} | valid loss {:5.2f} | '.format(epoch, val_loss))
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print('-' * 89)
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# Evaluate
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test_loss = evaluate(trainer, test_data)
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print('=' * 89)
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print('| End of training | test loss {:5.2f}'.format(test_loss))
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print('=' * 89)
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