parser.add_argument('--log_filename',
type = str, default = 'train.log')
-parser.add_argument('--download',
- action='store_true', default = False)
-
parser.add_argument('--seed',
type = int, default = 0)
##############################
# picoclvr options
-parser.add_argument('--picoclvr_many_colors',
- action='store_true', default = False)
+parser.add_argument('--picoclvr_nb_colors',
+ type = int, default = 5)
parser.add_argument('--picoclvr_height',
type = int, default = 12)
######################################################################
-def produce_results(
- self,
- model, nb_samples, nb_tokens_to_generate, starting_input = None,
- device = 'cpu'
+def autoregression(
+ model,
+ nb_samples, nb_tokens_to_generate, starting_input = None,
+ device = torch.device('cpu')
):
- results = torch.zeros(nb_samples, nb_tokens_to_generate, dtype = torch.int64, device = device)
- for input in results.split(self.batch_size):
- for s in tqdm.tqdm(range(input.size(1) - 1), desc = 'synth'):
+ results = torch.zeros(
+ nb_samples, nb_tokens_to_generate,
+ dtype = torch.int64, device = device
+ )
+
+ if starting_input is None:
+ first = 0
+ else:
+ first = starting_input.size(1)
+ results = torch.cat((starting_input, results), 1)
+
+ for input in results.split(args.batch_size):
+ for s in tqdm.tqdm(range(first, input.size(1)), desc = 'synth'):
output = model(input)
logits = output[:, s]
if args.synthesis_sampling:
dist = torch.distributions.categorical.Categorical(logits = logits)
- t = dist.sample()
+ t_next = dist.sample()
else:
- t = logits.argmax(1)
- input[:, s + 1] = t
+ t_next = logits.argmax(1)
+ input[:, s] = t_next
+
+ return results
######################################################################
class TaskPicoCLVR(Task):
def __init__(self, batch_size,
- height, width, many_colors = False,
+ height, width, nb_colors = 5,
device = torch.device('cpu')):
def generate_descr(nb):
descr = picoclvr.generate(
nb,
height = self.height, width = self.width,
- many_colors = many_colors
+ nb_colors = nb_colors
)
descr = [ s.strip().split(' ') for s in descr ]
t_generated = [ ]
for j in range(nb_tokens):
- t = [ [ self.token2id[u] for u in t_primer + t_generated ] + [ 0 ] ]
+ t = [ [ self.token2id[u] for u in t_primer + t_generated ] ]
input = torch.tensor(t, device = self.device)
+ input = F.pad(input, (0, 1)) # Add the next token, the one to predict
output = model(input)
logits = output[0, -1]
if args.synthesis_sampling:
dist = torch.distributions.categorical.Categorical(logits = logits)
- t = dist.sample()
+ t_next = dist.sample()
else:
- t = logits.argmax()
- t_generated.append(self.id2token[t.item()])
+ t_next = logits.argmax()
+ t_generated.append(self.id2token[t_next.item()])
return ' '.join(t_primer + t_generated)
for j in range(nb_tokens):
input = self.tensorize([ t_primer + t_generated ]).to(self.device)
+ input = F.pad(input, (0, 1)) # Add the next token, the one to predict
output = model(input)
logits = output[0, -1]
if args.synthesis_sampling:
dist = torch.distributions.categorical.Categorical(logits = logits)
- t = dist.sample()
+ t_next = dist.sample()
else:
- t = logits.argmax()
- t_generated.append(self.vocab.lookup_token(t))
+ t_next = logits.argmax()
+ t_generated.append(self.vocab.lookup_token(t_next))
if t_generated[-1] == '<non>': break
s = ' '.join(t_generated)
return 256
def produce_results(self, n_epoch, model, nb_samples = 64):
- results = torch.zeros(nb_samples, 28 * 28, dtype = torch.int64, device = self.device)
- for input in results.split(self.batch_size):
- for s in tqdm.tqdm(range(input.size(1)), desc = 'synth'):
- output = model(input)
- logits = output[:, s]
- if args.synthesis_sampling:
- dist = torch.distributions.categorical.Categorical(logits = logits)
- t = dist.sample()
- else:
- t = logits.argmax(1)
- input[:, s] = t
-
+ results = autoregression(model, nb_samples, 28 * 28, device = self.device)
image_name = f'result_mnist_{n_epoch:04d}.png'
torchvision.utils.save_image(1 - results.reshape(-1, 1, 28, 28) / 255.,
image_name, nrow = 16, pad_value = 0.8)
task = TaskPicoCLVR(batch_size = args.batch_size,
height = args.picoclvr_height,
width = args.picoclvr_width,
- many_colors = args.picoclvr_many_colors,
+ nb_colors = args.picoclvr_nb_colors,
device = device)
else:
raise ValueError(f'Unknown dataset {args.data}.')
nb_epochs_finished = 0
if args.no_checkpoint:
- log_string(f'Not trying to load checkpoint.')
+ log_string(f'not trying to load checkpoint.')
else:
try:
nb_epochs_finished = checkpoint['nb_epochs_finished']
model.load_state_dict(checkpoint['model_state'])
optimizer.load_state_dict(checkpoint['optimizer_state'])
- log_string(f'Checkpoint loaded with {nb_epochs_finished} epochs finished.')
+ log_string(f'checkpoint loaded with {nb_epochs_finished} epochs finished.')
except FileNotFoundError:
- log_string('Starting from scratch.')
+ log_string('starting from scratch.')
except:
- log_string('Error when loading the checkpoint.')
+ log_string('error when loading the checkpoint.')
exit(1)
######################################################################
token_probas = token_count / token_count.sum()
h = -torch.xlogy(token_probas, token_probas).sum()
train_set_perplexity = math.exp(h)
-log_string(f'Train set perplexity {train_set_perplexity}')
+log_string(f'train set perplexity {train_set_perplexity}')
for k in range(nb_epochs_finished, nb_epochs):
projects / mygpt.git / blobdiff