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import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import tqdm
from scipy.signal import lfilter
import os
import lossgen
class LossDataset(torch.utils.data.Dataset):
def __init__(self,
loss_file,
sequence_length=997):
self.sequence_length = sequence_length
self.loss = np.loadtxt(loss_file, dtype='float32')
self.nb_sequences = self.loss.shape[0]//self.sequence_length
self.loss = self.loss[:self.nb_sequences*self.sequence_length]
self.perc = lfilter(np.array([.001], dtype='float32'), np.array([1., -.999], dtype='float32'), self.loss)
self.loss = np.reshape(self.loss, (self.nb_sequences, self.sequence_length, 1))
self.perc = np.reshape(self.perc, (self.nb_sequences, self.sequence_length, 1))
def __len__(self):
return self.nb_sequences
def __getitem__(self, index):
r0 = np.random.normal(scale=.02, size=(1,1)).astype('float32')
r1 = np.random.normal(scale=.02, size=(self.sequence_length,1)).astype('float32')
return [self.loss[index, :, :], self.perc[index, :, :]+r0+r1]
adam_betas = [0.8, 0.99]
adam_eps = 1e-8
batch_size=512
lr_decay = 0.0001
lr = 0.001
epsilon = 1e-5
epochs = 20
checkpoint_dir='checkpoint'
os.makedirs(checkpoint_dir, exist_ok=True)
checkpoint = dict()
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
checkpoint['model_args'] = ()
checkpoint['model_kwargs'] = {'gru1_size': 16, 'gru2_size': 48}
model = lossgen.LossGen(*checkpoint['model_args'], **checkpoint['model_kwargs'])
dataset = LossDataset('loss_sorted.txt')
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4)
optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps)
# learning rate scheduler
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x))
if __name__ == '__main__':
model.to(device)
for epoch in range(1, epochs + 1):
running_loss = 0
print(f"training epoch {epoch}...")
with tqdm.tqdm(dataloader, unit='batch') as tepoch:
for i, (loss, perc) in enumerate(tepoch):
optimizer.zero_grad()
loss = loss.to(device)
perc = perc.to(device)
out, _ = model(loss, perc)
out = torch.sigmoid(out[:,:-1,:])
target = loss[:,1:,:]
loss = torch.mean(-target*torch.log(out+epsilon) - (1-target)*torch.log(1-out+epsilon))
loss.backward()
optimizer.step()
scheduler.step()
running_loss += loss.detach().cpu().item()
tepoch.set_postfix(loss=f"{running_loss/(i+1):8.5f}",
)
# save checkpoint
checkpoint_path = os.path.join(checkpoint_dir, f'lossgen_{epoch}.pth')
checkpoint['state_dict'] = model.state_dict()
checkpoint['loss'] = running_loss / len(dataloader)
checkpoint['epoch'] = epoch
torch.save(checkpoint, checkpoint_path)
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