####################################################################################################################
# build results for od-centered with OD model
print('* Instantiating model {}, pretrained={}'.format(
model_name, pretrained))
model, mean, std = get_arch(model_name,
pretrained=pretrained,
n_classes=n_classes)
model, stats = load_model(model, load_path_od, device='cpu')
model = model.to(device)
print("Total params: {0:,}".format(
sum(p.numel() for p in model.parameters() if p.requires_grad)))
print('* Creating Dataloaders, batch size = {:d}'.format(bs))
test_loader = get_test_loader(csv_path_test=csv_test_od,
batch_size=bs,
mean=mean,
std=std)
if tta:
probs_od, preds_od, labels = test_cls_tta_dihedral(model,
test_loader,
n=3)
else:
probs_od, preds_od, labels = test_cls(model, test_loader)
df_od = pd.DataFrame(zip(list(test_loader.dataset.im_list), preds_od),
columns=['image_id', 'preds'])
####################################################################################################################
# build results for macula-centered with MAC model
print('* Instantiating model {}, pretrained={}'.format(
model_name, pretrained))
model, mean, std = get_arch(model_name,
# build results for MT model
n_classes = 18
print('* Instantiating MT model {}, pretrained={}'.format(
model_name_MT, pretrained))
model, mean, std = get_arch(model_name_MT,
pretrained=pretrained,
n_classes=n_classes)
model, stats = load_model(model, load_path_MT, device='cpu')
model = model.to(device)
print("Total params: {0:,}".format(
sum(p.numel() for p in model.parameters() if p.requires_grad)))
print('* Creating Dataloaders, batch size = {:d}'.format(bs))
test_loader = get_test_loader(csv_path_test=csv_test_q_MT,
batch_size=bs,
mean=mean,
std=std,
qualities=True)
probs_tta_q, preds_tta_q, probs_tta_a, preds_tta_a, probs_tta_c, preds_tta_c, probs_tta_f, preds_tta_f \
= test_cls_tta_dihedral_MT(model, test_loader, n=3)
####################################################################################################################
# build results for QUALITY model
n_classes = 2
print('* Instantiating QUALITY model {}, pretrained={}'.format(
model_name_quality, pretrained))
model, mean, std = get_arch(model_name_quality,
pretrained=pretrained,
n_classes=n_classes)