def predict(opt):
# net=torch.load('Lenet.pth') # pth格式 只保留参数
# print('net', net)
'''
需要将basic_option中的is_train 修改为false
'''
# opt.is_train = False
acc = 0
total = 0
test_dataloader = create_dataloader(opt)
net = Classification()
net.load_state_dict(
torch.load(
f"./output/train/weights/exp_1/Basic_Epoch_20_Accuracy_0.99.pth"))
net = net.to(device)
with torch.no_grad():
for index, data in enumerate(test_dataloader, start=1):
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, dim=1)
print(f'number {index} picture maybe : {classes[predicted[0]]}')
total += labels.size(0)
acc += (predicted == labels).sum().item()
print('Accuracy on test set : {}%'.format(100 * acc / total))
classification = Classification().to(device)
encoder_optim = Adam(encoder.parameters(), lr=1e-4)
loss_optim = Adam(loss_fn.parameters(), lr=1e-4)
classification_optim = Adam(classification.parameters(), lr=1e-4)
epoch_restart = 0
root = Path(r'models')
if epoch_restart > 0 and root is not None:
enc_file = root / Path('encoder' + str(epoch_restart) + '.wgt')
loss_file = root / Path('loss' + str(epoch_restart) + '.wgt')
classification_loss_file = root / Path('classification_loss' +
str(epoch_restart) + '.wgt')
encoder.load_state_dict(torch.load(str(enc_file)))
loss_fn.load_state_dict(torch.load(str(loss_file)))
classification.load_state_dict(
torch.load(str(classification_loss_file)))
for epoch in range(epoch_restart + 1, 501):
batch = tqdm(cifar_10_train_l,
total=len(cifar_10_train_dt) // batch_size)
train_loss = []
for x, target, rot in batch:
x = x.to(device)
rot = rot.to(device)
encoder_optim.zero_grad()
loss_optim.zero_grad()
classification_optim.zero_grad()
y, M = encoder(x)
predicted_value = classification(y)
criterion = nn.CrossEntropyLoss()
loss_classification = criterion(predicted_value, rot)