action='store_true', default = False,
help = 'Use lossless compression to reduce the memory footprint')
+parser.add_argument('--test_loaded_models',
+ action='store_true', default = False,
+ help = 'Should we compute the test error of models we load')
+
args = parser.parse_args()
######################################################################
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)
optimizer = optim.SGD(model.parameters(), lr = 1e-2)
+ start_t = time.time()
+
for e in range(0, args.nb_epochs):
acc_loss = 0.0
for b in range(0, train_set.nb_batches):
loss.backward()
optimizer.step()
log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss))
+ dt = (time.time() - start_t) / (e + 1)
+ print(Fore.CYAN + 'ETA ' + time.ctime(time.time() + dt * (args.nb_epochs - e)) + Style.RESET_ALL)
return model
for problem_number in range(1, 24):
+ log_string('**** problem ' + str(problem_number) + ' ****')
+
model = AfrozeShallowNet()
if torch.cuda.is_available():
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,
cuda=torch.cuda.is_available())
- test_set = CompressedVignetteSet(problem_number,
- args.nb_test_batches, args.batch_size,
- cuda=torch.cuda.is_available())
else:
train_set = VignetteSet(problem_number,
args.nb_train_batches, args.batch_size,
cuda=torch.cuda.is_available())
- test_set = VignetteSet(problem_number,
- 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 / (time.time() - t)))
train_model(model, train_set)
torch.save(model.state_dict(), model_filename)
train_set.nb_samples)
)
+ if need_to_train or args.test_loaded_models:
+
+ t = time.time()
+
+ if args.compress_vignettes:
+ test_set = CompressedVignetteSet(problem_number,
+ args.nb_test_batches, args.batch_size,
+ cuda=torch.cuda.is_available())
+ else:
+ test_set = VignetteSet(problem_number,
+ args.nb_test_batches, args.batch_size,
+ cuda=torch.cuda.is_available())
+
+ log_string('data_generation {:0.2f} samples / s'.format(test_set.nb_samples / (time.time() - t)))
+
nb_test_errors = nb_errors(model, test_set)
log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(