drop_last=True,
sampler=data_sampler)
# initialize DALL-E
dalle = DALLE(vae=vae, **dalle_params)
if args.fp16:
dalle = dalle.half()
dalle = dalle.cuda()
if RESUME:
dalle.load_state_dict(weights)
# optimizer
opt = Adam(dalle.parameters(), lr=LEARNING_RATE)
if LR_DECAY:
scheduler = ReduceLROnPlateau(
opt,
mode="min",
factor=0.5,
patience=10,
cooldown=10,
min_lr=1e-6,
verbose=True,
)
if distr_backend.is_root_worker():
# experiment tracker
assert len(ds) > 0, 'dataset is empty'
print(f'{len(ds)} image-text pairs found for training')
dl = DataLoader(ds, batch_size = BATCH_SIZE, shuffle = True, drop_last = True)
# initialize DALL-E
dalle = DALLE(**dalle_params).cuda()
if exists(args.dalle_path):
dalle.load_state_dict(weights)
# optimizer
opt = Adam(dalle.parameters(), lr = LEARNING_RATE)
# experiment tracker
import wandb
wandb.config.depth = DEPTH
wandb.config.heads = HEADS
wandb.config.dim_head = DIM_HEAD
wandb.init(project = 'dalle_train_transformer', resume = exists(args.dalle_path))
# training
for epoch in range(EPOCHS):
for i, (text, images, mask) in enumerate(dl):
torch.save(vae.state_dict(), "Vae-small.pth")
dalle = DALLE(
dim=1024,
vae=
vae, # automatically infer (1) image sequence length and (2) number of image tokens
num_text_tokens=NUM_TOKENS, # vocab size for text
text_seq_len=TEXTSEQLEN, # text sequence length
depth=12, # should aim to be 64
heads=16, # attention heads
dim_head=64, # attention head dimension
attn_dropout=0.1, # attention dropout
ff_dropout=0.1 # feedforward dropout
).cuda()
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:
dl = DataLoader(ds, batch_size = BATCH_SIZE, shuffle = True, drop_last = True)
# initialize DALL-E
dalle = DALLE(**dalle_params).cuda()
if RESUME:
dalle.load_state_dict(weights)
# optimizer
opt = Adam(dalle.parameters(), lr = LEARNING_RATE)
# experiment tracker
import wandb
wandb.config.depth = DEPTH
wandb.config.heads = HEADS
wandb.config.dim_head = DIM_HEAD
wandb.init(project = 'dalle_train_transformer', resume = RESUME)
# training
for epoch in range(epoch_start, EPOCHS):
for i, (text, images, mask) in enumerate(dl):
sampler=data_sampler)
# initialize DALL-E
dalle = DALLE(vae=vae, **dalle_params)
if not using_deepspeed:
if args.fp16:
dalle = dalle.half()
dalle = dalle.cuda()
if RESUME:
dalle.load_state_dict(weights)
# optimizer
opt = AdamW(dalle.parameters(),
lr=LEARNING_RATE,
betas=(0.9, 0.96),
weight_decay=4.5e-2,
amsgrad=True)
if LR_DECAY:
scheduler = ReduceLROnPlateau(
opt,
mode="min",
factor=0.5,
patience=10,
cooldown=10,
min_lr=1e-6,
verbose=True,
)
i_data = []
c_data = []
for i in range(0, self.batchsize):
i_data.append(self.data[self.index][0])
c_tokens = [0] * 256 # fill to match text_seq_len
c_tokens_ = self.data[self.index][1]
c_tokens[:len(c_tokens_)] = c_tokens_
c_data.append(c_tokens)
self.index += 1
if self.index == self.len:
self.end = True
break
return i_data, c_data
optimizer = optim.Adam(dalle.parameters(), lr=lr)
for epoch in range(start_epoch, start_epoch + n_epochs):
batch_idx = 0
train_loss = 0
dset = ImageCaptions(data, batchsize=batchSize) # initialize iterator
for i, c in dset: # loop through dataset by minibatch
text = torch.LongTensor(c) # a minibatch of text (numerical tokens)
images = torch.zeros(len(i), 3, 256, 256) # placeholder for images
text = text.to(device)
#print(text)
# fetch images into tensor based on paths given in minibatch
ix = 0
