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Corentin 2021-08-26 16:48:09 +09:00
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from argparse import ArgumentParser
from pathlib import Path
import shutil
import time
import numpy as np
import torch
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from src.torch_networks import LSTMModel, StackedLSTMModel
from src.torch_utils.train import parameter_summary
def generate_data(batch_size: int, sequence_length: int, dim: int) -> np.ndarray:
return np.random.uniform(0, dim, (batch_size, sequence_length)).astype(np.int64)
def main():
parser = ArgumentParser()
parser.add_argument('--output', type=Path, default=Path('output', 'recorder'), help='Output dir')
parser.add_argument('--batch', type=int, default=32, help='Batch size')
parser.add_argument('--sequence', type=int, default=8, help='Max sequence length')
parser.add_argument('--dimension', type=int, default=15, help='Input dimension')
parser.add_argument('--step', type=int, default=20_000, help='Number of steps to train')
parser.add_argument('--model', help='Model to train')
arguments = parser.parse_args()
output_dir: Path = arguments.output
batch_size: int = arguments.batch
sequence_size: int = arguments.sequence
input_dim: int = arguments.dimension
max_step: int = arguments.step
model: str = arguments.model
if not output_dir.exists():
output_dir.mkdir(parents=True)
if (output_dir / 'train').exists():
shutil.rmtree(output_dir / 'train')
writer_train = SummaryWriter(log_dir=str(output_dir / 'train'), flush_secs=20)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
if model == 'cell':
network = StackedLSTMModel(input_dim + 1).to(device)
else:
network = LSTMModel(input_dim + 1).to(device)
# Save parameters info
with open(output_dir / 'parameters.csv', 'w') as param_file:
param_summary = parameter_summary(network)
names = [len(name) for name, _, _ in param_summary]
shapes = [len(str(shape)) for _, shape, _ in param_summary]
param_file.write(
'\n'.join(
[f'{name: <{max(names)}} {str(shape): <{max(shapes)}} {size}'
for name, shape, size in param_summary]))
optimizer = torch.optim.Adam(network.parameters(), lr=1e-3)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.996)
criterion = nn.CrossEntropyLoss()
zero_data = torch.zeros((batch_size, sequence_size, input_dim + 1)).to(device)
# writer_train.add_graph(network, (zero_data[:, :5],))
if model == 'cell':
state = [(torch.zeros((batch_size, (input_dim + 1) * 2)).to(device),
torch.zeros((batch_size, (input_dim + 1) * 2)).to(device))] * network.NUM_LAYERS
else:
state = None
running_loss = 0.0
running_accuracy = 0.0
running_count = 0
summary_period = max_step // 100
np.set_printoptions(precision=2)
try:
start_time = time.time()
for step in range(1, max_step + 1):
label_np = generate_data(batch_size, int(np.random.uniform(4, sequence_size + 1)), input_dim)
label = torch.from_numpy(label_np).to(device)
data = nn.functional.one_hot(label, input_dim + 1).float()
data[:, :, input_dim] = 1.0
optimizer.zero_grad()
outputs, _state = network(torch.cat([data, zero_data[:, :label_np.shape[1]]], dim=1).transpose(1, 0), state)
outputs = outputs[label_np.shape[1]:, :, :-1]
# state = (state[0].detach(), state[1].detach())
# state = (state[0][:, :1].detach().expand(state[0].shape[0], batch_size, *state[0].shape[2:]),
# state[1][:, :1].detach().expand(state[1].shape[0], batch_size, *state[1].shape[2:]))
data[:, :, input_dim] = 0.0
loss = criterion(outputs[:, 0], label[0])
for i in range(1, batch_size):
loss += criterion(outputs[:, i], label[i])
loss /= batch_size
running_loss += loss.item()
running_accuracy += (
torch.sum((torch.argmax(outputs, 2).transpose(1, 0) == label).long(),
1) == label.size(1)).float().mean().item()
running_count += 1
if step % summary_period == 0:
writer_train.add_scalar('metric/loss', running_loss / running_count, global_step=step)
writer_train.add_scalar('metric/error', 1 - (running_accuracy / running_count), global_step=step)
writer_train.add_scalar('optimizer/lr', scheduler.get_last_lr()[0], global_step=step)
scheduler.step()
speed = summary_period / (time.time() - start_time)
print(f'Step {step}, loss: {running_loss / running_count:.03e}'
f', acc: {running_accuracy / running_count:.03e}, speed: {speed:0.3f}step/s')
start_time = time.time()
running_loss = 0.0
running_accuracy = 0.0
running_count = 0
loss.backward()
optimizer.step()
except KeyboardInterrupt:
print('\r ', end='\r')
writer_train.close()
network.eval()
test_label = [
np.asarray([[0, 0, 0, 0]], dtype=np.int64),
np.asarray([[2, 2, 2, 2]], dtype=np.int64),
np.asarray([[8, 1, 10, 5, 6, 13]], dtype=np.int64),
generate_data(1, 4, input_dim),
np.asarray([[0, 0, 0, 0, 0, 0]], dtype=np.int64),
np.asarray([[5, 5, 5, 5, 5, 5]], dtype=np.int64),
np.asarray([[11, 0, 2, 8, 5, 1]], dtype=np.int64),
generate_data(1, sequence_size, input_dim)
]
zero_data = torch.zeros((1, sequence_size, input_dim + 1)).to(device)
if model == 'cell':
state = [(torch.zeros((1, (input_dim + 1) * 2)).to(device),
torch.zeros((1, (input_dim + 1) * 2)).to(device))] * 3
running_accuracy = 0.0
running_count = 0
for label_np in test_label:
label = torch.from_numpy(label_np).to(device)
data = nn.functional.one_hot(label, input_dim + 1).float()
data[:, :, input_dim] = 1.0
outputs, _state = network(torch.cat([data, zero_data[:, :label_np.shape[1]]], dim=1).transpose(1, 0), state)
outputs = outputs[label_np.shape[1]:, :, :-1]
# state = (state[0].detach(), state[1].detach())
running_accuracy += (
torch.sum(
(torch.argmax(outputs, 2).transpose(1, 0) == label).long(), 1) == label.size(1)).float().mean().item()
running_count += 1
print(f'{len(label_np)} label: {label_np}, output: {torch.argmax(outputs, 2)[:, 0].detach().cpu().numpy()}')
print(f'Test accuracy: {running_accuracy / running_count:.03f}')
if __name__ == '__main__':
main()