def main():
cnn = CNN()
print('init net')
criterion = nn.MultiLabelSoftMarginLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)
# Train the Model
train_dataloader = my_dataset.get_train_data_loader()
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_dataloader):
images = Variable(images)
labels = Variable(labels.float())
predict_labels = cnn(images)
# print(predict_labels.type)
# print(labels.type)
loss = criterion(predict_labels, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 10 == 0:
print("epoch:", epoch, "step:", i, "loss:", loss.item())
if (i + 1) % 100 == 0:
torch.save(cnn.state_dict(),
"./model.pkl") #current is model.pkl
print("save model")
print("epoch:", epoch, "step:", i, "loss:", loss.item())
torch.save(cnn.state_dict(), "./model.pkl") #current is model.pkl
print("save last model")
def main():
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cnn = CNN()
# cnn.to(device)
cnn.train()
print('Model Initialization')
criterion = nn.MultiLabelSoftMarginLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)
# Train the Model
train_data_loader = setting.get_train_data_loader()
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_data_loader):
images = Variable(images)
# images = Variable(images).to(device)
labels = Variable(labels.float())
# labels = Variable(labels.float()).to(device)
predict_labels = cnn(images)
loss = criterion(predict_labels, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 10 == 0:
print("epoch:", epoch, "step:", i, "loss:", loss.item())
if (i + 1) % 100 == 0:
torch.save(cnn.state_dict(), "./model.pkl")
print("Model Saved")
print("epoch:", epoch, "step:", i, "loss:", loss.item())
torch.save(cnn.state_dict(), "./model.pkl")
print("Last Model Saved")
def main():
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
accuracy_list = []
# Train the Model
print("Loading Data")
train_dataloader = my_dataset.get_train_data_loader()
test_dataloader = my_dataset.get_test_data_loader()
print("Data Ready")
accuracy = 0
for ttest_num in range(0, 5):
print('>>>>>>>>>>>>>>>>> ROUND: ', ttest_num)
cnn = CNN()
cnn.to(device)
cnn.train()
print('init net')
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)
accuracy = 0
for epoch in range(num_epochs):
print('....')
for i, (images1, labels) in enumerate(train_dataloader):
images1 = Variable(images1)
labels = Variable(labels) #.view(-1,1))
images, labels = images1.to(device), labels.to(device)
# images = generator(images)
#labels = torch.tensor(labels, dtype=torch.long, device=device)
predict_labels = cnn(images) #.view(1,-1)[0]
# print(predict_labels.type)
# print(labels.type)
# print(images.shape)
#print(labels)
#print(predict_labels)
loss = criterion(predict_labels, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# if (i+1) % 1500 == 0:
# print("epoch:", epoch, "step:", i, "loss:", loss.item())
# if (i+1) % 2500 == 0:
# print("save model")
correct = 0
total = 0
for i, (images1, labels) in enumerate(test_dataloader):
image = images1
#print(image)
image = Variable(image)
image, labels = image.to(device), labels.to(device)
# image = generator(image)
predict_label = cnn(image)
labels = labels.cpu()
predict_label = predict_label.cpu()
_, predicted = torch.max(predict_label, 1)
total += labels.size(0)
# print(predicted,'>>>>>>>>',labels)
if (predicted == labels):
correct += 1
print(
'Test Accuracy of the model on the %d test images (%d): %f %%'
% (total, correct, 100 * correct / total))
if (correct / total > accuracy):
accuracy = correct / total
torch.save(
cnn.state_dict(), "./model_lake/" +
model_name.replace('.', '_' + str(ttest_num) + '.'))
# torch.save(cnn.state_dict(), "./model_lake/"+model_name) #current is model.pickle
print('saved!!!!!!!!!!!!!!!!!!!!!!!')
# torch.save(cnn.state_dict(), "./"+model_name) #current is model.pkl
print("epoch:", epoch, "step:", i, "loss:", loss.item())
# print('final accuracy: ',accuracy)
accuracy_list.append(accuracy)
print(accuracy_list)
def main():
cnn = CNN()
#resume
resume = './model_cos_90_0.01.pkl'
checkpoint = torch.load(resume)
cnn.load_state_dict(checkpoint)
if use_cuda:
cnn = CNN().cuda()
cnn.train()
print('init net')
criterion = nn.MultiLabelSoftMarginLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)
best_acc = 0
# Train the Model
train_dataloader = my_dataset.get_finetune_data_loader()
for epoch in range(num_epochs):
correct = 0
total = 0
for i, (images, labels) in enumerate(train_dataloader):
#lr_schedule
lr = cosine_anneal_schedule(epoch)
for param_group in optimizer.param_groups:
#print(param_group['lr'])
param_group['lr'] = lr
images = Variable(images).cuda()
labels = Variable(labels.float()).cuda()
predict_labels = cnn(images)
# print(predict_labels.type)
# print(labels.type)
loss = criterion(predict_labels, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
predict_labels = predict_labels.cpu()
labels = labels.cpu()
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_labels[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_labels[0, captcha_setting.ALL_CHAR_SET_LEN:2 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c2 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_labels[0,
2 * captcha_setting.ALL_CHAR_SET_LEN:3 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c3 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_labels[0,
3 * captcha_setting.ALL_CHAR_SET_LEN:4 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
# c = '%s%s%s%s' % (c0, c1, c2, c3)
# print(c)
predict_labels = '%s%s%s%s' % (c0, c1, c2, c3)
# predict = '%s%s%s%s' % (c0, c1, c2, c3)
# predict_label = one_hot_encoding.decode(predict.numpy()[0])
true_label = one_hot_encoding.decode(labels.numpy()[0])
total += labels.size(0)
print(predict_labels, true_label)
if (predict_labels == true_label):
correct += 1
acc = 100 * correct / total
# if (total % 200 == 0):
# print('Test Accuracy of the model on the %d train images: %f %%' % (total, 100 * correct / total))
if (i+1) % 10 == 0:
print("epoch:", epoch, "step:", i, "loss:", loss.item(),"Accuracy:", acc)
if acc >= best_acc:
torch.save(cnn.state_dict(), "./model_cos_90_0.01_finetune_2.pkl") #current is model.pkl
print("save model")
#print("epoch:", epoch, "loss:", loss.item(),"Accuracy:", acc)
#torch.save(cnn.state_dict(), "./model_cos_90_0.01_finetune.pkl") #current is model.pkl
print("save last model")