79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
from argparse import ArgumentParser
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import torch
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from torch import nn
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from torch.utils.data import DataLoader
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from dataset import Dataset
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from model import Model
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parser = ArgumentParser()
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parser.add_argument('-d', '--device', help='device to train with', default='cpu')
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parser.add_argument('-i', '--input', help='training data file', default='data')
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parser.add_argument('-e', '--epochs', help='number of epochs to train for', default=100)
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parser.add_argument('-s', '--seq-size', help='sequence size', default=32)
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parser.add_argument('-b', '--batch-size', help='size of each training batch', default=256)
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args = parser.parse_args()
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# Prepare dataloader
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dataset = Dataset(args.input, args.seq_size)
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dataloader = DataLoader(dataset, batch_size=args.batch_size)
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print(len(dataloader))
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# Prepare model
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model = Model(dataset, 512, 512, 3, 0.2).to(args.device)
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print(model)
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loss_fn = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
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for t in range(args.epochs):
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state_h, state_c = model.zero_state(args.batch_size)
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state_h = state_h.to(args.device)
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state_c = state_c.to(args.device)
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iteration = 0
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for batch, (X, y) in enumerate(dataloader):
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model.train()
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iteration += 1
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optimizer.zero_grad()
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X = torch.tensor(X).to(args.device)
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y = torch.tensor(y).to(args.device)
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# Compute prediction error
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logits, (state_h, state_c) = model(X, (state_h, state_c))
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loss = loss_fn(logits.transpose(1, 2), y)
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loss_value = loss.item()
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# Backpropogation
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loss.backward()
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state_h = state_h.detach()
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state_c = state_c.detach()
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_ = torch.nn.utils.clip_grad_norm_(
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model.parameters(), flags.gradients_norm)
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optimizer.step()
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if iteration % 10 == 0:
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print('Epoch: {}/{}'.format(t, args.epochs),
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'Iteration: {}'.format(iteration),
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'Loss: {}'.format(loss_value))
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if iteration % 1000 == 0:
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predict(args.device, net, flags.initial_words, n_vocab,
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vocab_to_int, int_to_vocab, top_k=3)
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torch.save(net.state_dict(),
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'checkpoint/model-{}.pth'.format(iteration))
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