######################################################################
log_file = open(args.log_file, 'w')
+pred_log_t = None
print(Fore.RED + 'Logging into ' + args.log_file + Style.RESET_ALL)
def log_string(s):
- s = Fore.GREEN + time.ctime() + Style.RESET_ALL + ' ' + s
+ global pred_log_t
+ t = time.time()
+
+ if pred_log_t is None:
+ elapsed = 'start'
+ else:
+ elapsed = '+{:.02f}s'.format(t - pred_log_t)
+ pred_log_t = t
+ s = Fore.BLUE + time.ctime() + ' ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s
log_file.write(s + '\n')
log_file.flush()
print(s)
for problem_number in range(1, 24):
- model_filename = model.name + '_' + \
- str(problem_number) + '_' + \
- str(args.nb_train_batches) + '.param'
-
model = AfrozeShallowNet()
if torch.cuda.is_available():
model.cuda()
+ model_filename = model.name + '_' + \
+ str(problem_number) + '_' + \
+ str(args.nb_train_batches) + '.param'
+
nb_parameters = 0
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)