Skip to content

Fix CAS computation #177

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Fix CAS computation #177

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 4 additions & 2 deletions src/worker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1565,6 +1565,7 @@ def compute_GAN_train_or_test_classifier_accuracy_score(self, GAN_train=False, G

train_top1_acc, train_top5_acc, train_loss = misc.AverageMeter(), misc.AverageMeter(), misc.AverageMeter()
for i, (images, labels) in enumerate(self.train_dataloader):
optimizer.zero_grad()
if GAN_train:
images, labels, _, _, _, _, _ = sample.generate_images(z_prior=self.MODEL.z_prior,
truncation_factor=self.RUN.truncation_factor,
Expand Down Expand Up @@ -1616,19 +1617,20 @@ def compute_GAN_train_or_test_classifier_accuracy_score(self, GAN_train=False, G
model_ = misc.peel_model(model)
states = {"state_dict": model_.state_dict(), "optimizer": optimizer.state_dict(), "epoch": current_epoch+1,
"best_top1": best_top1, "best_top5": best_top5, "best_epoch": best_epoch}
if self.local_rank == 0:
self.logger.info("Save model to {}".format(self.RUN.ckpt_dir))
misc.save_model_c(states, mode, self.RUN)

if self.local_rank == 0:
self.logger.info("Current best accuracy: Top-1: {top1:.4f}% and Top-5 {top5:.4f}%".format(top1=best_top1, top5=best_top5))
self.logger.info("Save model to {}".format(self.RUN.ckpt_dir))

# -----------------------------------------------------------------------------
# validate GAN_train or GAN_test classifier using generated or training dataset
# -----------------------------------------------------------------------------
def validate_classifier(self,model, generator, generator_mapping, generator_synthesis, epoch, GAN_test, setting):
model.eval()
valid_top1_acc, valid_top5_acc, valid_loss = misc.AverageMeter(), misc.AverageMeter(), misc.AverageMeter()
for i, (images, labels) in enumerate(self.train_dataloader):
for i, (images, labels) in enumerate(self.eval_dataloader):
if GAN_test:
images, labels, _, _, _, _, _ = sample.generate_images(z_prior=self.MODEL.z_prior,
truncation_factor=self.RUN.truncation_factor,
Expand Down