dalle.load_state_dict(torch.load("dalle-small.pth"))
"""
text = torch.randint(0, NUM_TOKENS, (BATCH_SIZE, TEXTSEQLEN))
images = torch.randn(BATCH_SIZE, 3, IMAGE_SIZE, IMAGE_SIZE)
mask = torch.ones_like(text).bool()
"""
tokenDset = token_dataset('./coco/merged-smallsample.txt')
# do the above for a long time with a lot of data ... then
num_pics = 30
def denorm(img: torch.Tensor):
mean = torch.mean(img)
min_maxrange = (torch.max(img) - torch.min(img))
return (((img - mean) / (min_maxrange) + 0.5) * 255)
for i in range(30):
test_text = "犬が地面に寝そべっている写真"
textToken, mask = fixlen([tokenDset.tokenizeList(test_text)])
textToken = textToken.cuda()
mask = mask.cuda()
images = dalle.generate_images(textToken, mask=mask)
#print(images[0][0])
print(images.size())
optimizerDALLE = torch.optim.Adam(dalle.parameters(), lr=learning_rate)
DALLEloss = []
for epoch in range(EPOCHS):
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"