def main(args):
mnasnet = models.mnasnet1_0(pretrained=True).to(device).eval()
cvae = CVAE(1000, 128, args.n_class * 2, args.n_class).to(device)
cvae.encoder.eval()
regressor = Regressor().to(device)
if Path(args.cvae_resume_model).exists():
print("load cvae model:", args.cvae_resume_model)
cvae.load_state_dict(torch.load(args.cvae_resume_model))
if Path(args.regressor_resume_model).exists():
print("load regressor model:", args.regressor_resume_model)
regressor.load_state_dict(torch.load(args.regressor_resume_model))
image_label = pandas.read_csv(
Path(args.data_root, args.metadata_file_name.format(
args.subset))).sample(frac=1, random_state=551)[:250]
image_label["class"] = image_label["class"] - 1
dataset = WBCDataset(args.n_class,
image_label[:250].values,
args.data_root,
subset=args.subset,
train=True)
data_loader = loader(dataset, args.batch_size, True)
cvae_optimizer = RAdam(cvae.parameters(), weight_decay=1e-3)
regressor_optimizer = RAdam(regressor.parameters(), weight_decay=1e-3)
train(args, mnasnet, cvae, regressor, cvae_optimizer, regressor_optimizer,
data_loader)
def main(args):
mnasnet = models.mnasnet1_0(pretrained=True).to(device).eval()
model = CVAE(1000, 128, 128, args.n_class, 128).to(device).eval()
if Path(args.resume_model).exists():
print("load regressor model:", args.resume_model)
model.load_state_dict(torch.load(args.resume_model))
image_label = pandas.read_csv(
Path(args.data_root,
args.metadata_file_name.format(args.subset))
).sample(frac=1, random_state=551) #[250:]
image_label["class"] = image_label["class"] - 1
dataset = WBCDataset(image_label.values, args.data_root, subset=args.subset)
data_loader = loader(dataset, 1, False)
test(args, mnasnet, model, data_loader)