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):
mnasnet1_0 = models.mnasnet1_0(pretrained=True).to(device).eval()
model = CVAE(1000, 128, 128, args.n_class, 128).to(device)
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, args.batch_size, True)
optimizer = RAdam(model.parameters(), weight_decay=1e-3)
train(args, mnasnet1_0, model, optimizer, data_loader)
# the parameter name suggest what to evaluate on
model_config[
"test_user_item_interaction_dict"] = val_user_item_interaction_dict
model_config[
"train_user_item_interaction_dict"] = train_user_item_interaction_dict
##### define the model #####
if args.model_type == "cvae":
model = CVAE(config=model_config).to(device)
print(model)
#criterion = torch.nn.MSELoss()
criterion = torch.nn.CrossEntropyLoss()
# size_average is set to False, the losses are instead summed for each minibatch
#criterion.size_average = False
learning_rate = 1e-4
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=1e-5)
# train the model
if args.exp_type == "train":
train.train_and_val(model, train_dataloader, val_dataloader, \
criterion, optimizer, args, model_config)
if args.exp_type == "evaluate":
eval_config = {
"num_items": num_items,
"train_user_item_interaction_dict":
train_user_item_interaction_dict,
"test_user_item_interaction_dict": test_user_item_interaction_dict,
"exp_save_models_dir":
'test':
torch.utils.data.DataLoader(test_dataset,
batch_size=opts.batch_size,
shuffle=False)
}
cvae = CVAE(opts.latent_size, device).to(device)
dis = Discriminator().to(device)
classifier = Classifier(opts.latent_size).to(device)
classer = CLASSIFIERS().to(device)
print(cvae)
print(dis)
print(classifier)
optimizer_cvae = torch.optim.Adam(cvae.parameters(),
lr=opts.lr,
betas=(opts.b1, opts.b2),
weight_decay=opts.weight_decay)
optimizer_dis = torch.optim.Adam(dis.parameters(),
lr=opts.lr,
betas=(opts.b1, opts.b2),
weight_decay=opts.weight_decay)
optimizer_classifier = torch.optim.Adam(classifier.parameters(),
lr=opts.lr,
betas=(opts.b1, opts.b2),
weight_decay=opts.weight_decay)
i = 1
while os.path.isdir('./ex/' + str(i)):
i += 1