def test_gcam_overlay(self):
layer = 'full'
metric = 'wioa'
model = gcam.inject(self.model,
output_dir=os.path.join(
self.current_path,
'results/unet_seg/gcam_overlay'),
backend='gcam',
layer=layer,
evaluate=True,
save_scores=False,
save_maps=True,
save_pickle=False,
metric=metric)
model.eval()
data_loader = DataLoader(self.dataset, batch_size=1, shuffle=False)
model.test_run(next(iter(data_loader))["img"])
for i, batch in enumerate(data_loader):
_ = model(batch["img"], mask=batch["gt"], raw_input=batch["img"])
del model
gc.collect()
torch.cuda.empty_cache()
if CLEAR and os.path.isdir(
os.path.join(self.current_path, 'results/unet_seg')):
shutil.rmtree(os.path.join(self.current_path, 'results/unet_seg'))
def test_gcam_overlay(self):
layer = 'layer4'
model = gcam.inject(self.model,
output_dir=os.path.join(
self.current_path,
'results/resnet152/test_gcam_overlay'),
backend='gcam',
layer=layer,
evaluate=False,
save_scores=False,
save_maps=True,
save_pickle=False)
model.eval()
data_loader = DataLoader(self.dataset, batch_size=1, shuffle=False)
for i, batch in enumerate(data_loader):
_ = model(batch[0][0], raw_input=batch[0][1])
del model
gc.collect()
torch.cuda.empty_cache()
if CLEAR and os.path.isdir(
os.path.join(self.current_path, 'results/resnet152')):
shutil.rmtree(os.path.join(self.current_path, 'results/resnet152'))
def test_gbp(self):
model = gcam.inject(self.model,
output_dir=os.path.join(self.current_path,
'results/unet_seg/gbp'),
backend='gbp',
evaluate=True,
save_scores=False,
save_maps=True,
save_pickle=False,
metric="wioa")
model.eval()
data_loader = DataLoader(self.dataset, batch_size=1, shuffle=False)
for i, batch in enumerate(data_loader):
_ = model(batch["img"], mask=batch["gt"])
del model
gc.collect()
torch.cuda.empty_cache()
if CLEAR and os.path.isdir(
os.path.join(self.current_path, 'results/unet_seg')):
shutil.rmtree(os.path.join(self.current_path, 'results/unet_seg'))
