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Heavy fix.
[pysvrt.git]
/
cnn-svrt.py
diff --git
a/cnn-svrt.py
b/cnn-svrt.py
index
7bef242
..
694f035
100755
(executable)
--- a/
cnn-svrt.py
+++ b/
cnn-svrt.py
@@
-184,15
+184,19
@@
for problem_number in range(1, 24):
for p in model.parameters(): nb_parameters += p.numel()
log_string('nb_parameters {:d}'.format(nb_parameters))
for p in model.parameters(): nb_parameters += p.numel()
log_string('nb_parameters {:d}'.format(nb_parameters))
+ need_to_train = False
try:
try:
-
model.load_state_dict(torch.load(model_filename))
log_string('loaded_model ' + model_filename)
model.load_state_dict(torch.load(model_filename))
log_string('loaded_model ' + model_filename)
-
except:
except:
+ need_to_train = True
+
+ if need_to_train:
log_string('training_model ' + model_filename)
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,
if args.compress_vignettes:
train_set = CompressedVignetteSet(problem_number,
args.nb_train_batches, args.batch_size,
@@
-208,6
+212,10
@@
for problem_number in range(1, 24):
args.nb_test_batches, args.batch_size,
cuda=torch.cuda.is_available())
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)
train_model(model, train_set)
torch.save(model.state_dict(), model_filename)
log_string('saved_model ' + model_filename)