for i in range(DATASET_SIZE):
#print(i,":",tokenDset.getRand(i),img.size())
optimizerDALLE.zero_grad()
img, strs = cap[i]
#print(img.size())
img = img.unsqueeze(0).cuda()
if i % 10 == 0:
print("DALLE epoch {} / {}".format(i + epoch * DATASET_SIZE,
EPOCHS * DATASET_SIZE))
try:
textToken, mask = fixlen([tokenDset.getRand(i)])
except KeyError:
continue
loss = dalle(textToken.cuda(), img, mask=mask.cuda(), return_loss=True)
DALLEloss.append(loss.detach().cpu().numpy())
loss.backward()
optimizerDALLE.step()
np.savetxt("dalleloss.csv", np.asarray(DALLEloss), delimiter=",")
# do the above for a long time with a lot of data ... then
torch.save(dalle.state_dict(), "dalle-small.pth")
test_text = "犬が地面に寝そべっている写真"
textToken, mask = fixlen([tokenDset.tokenizeList(test_text)])
images = dalle.generate_images(textToken.cuda(), mask=mask)
print(images.shape) # (2, 3, 256, 256)
images = images.to(device)
mask = torch.ones_like(text).bool().to(device)
# train and optimize a single minibatch
optimizer.zero_grad()
loss = dalle(text, images, mask=mask, return_loss=True)
train_loss += loss.item()
loss.backward()
optimizer.step()
if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(i), len(data),
100. * batch_idx / int(round(len(data) / batchSize)),
loss.item() / len(i)))
batch_idx += 1
print('====> Epoch: {} Average loss: {:.4f}'.format(
epoch, train_loss / len(data)))
torch.save(dalle.state_dict(),
"./models/" + name + "_dalle_" + str(epoch) + ".pth")
# generate a test sample from the captions in the last minibatch
oimgs = dalle.generate_images(text, mask=mask)
save_image(oimgs,
'results/' + name + '_dalle_epoch_' + str(epoch) + '.png',
normalize=True)