for p in model.parameters(): nb_parameters += p.numel()
log_string('nb_parameters {:d}'.format(nb_parameters))
+ need_to_train = False
try:
-
model.load_state_dict(torch.load(model_filename))
log_string('loaded_model ' + model_filename)
-
except:
+ need_to_train = True
+
+ if need_to_train:
log_string('training_model ' + model_filename)
+ t = time.time()
+
if args.compress_vignettes:
train_set = CompressedVignetteSet(problem_number,
args.nb_train_batches, args.batch_size,
args.nb_test_batches, args.batch_size,
cuda=torch.cuda.is_available())
+ log_string('data_generation {:0.2f} samples / s'.format(
+ (train_set.nb_samples + test_set.nb_samples) / (time.time() - t))
+ )
+
train_model(model, train_set)
torch.save(model.state_dict(), model_filename)
log_string('saved_model ' + model_filename)