def main():
cnn = CNN()
cnn.load_state_dict(torch.load('model.pkl'))
print("load cnn net.")
test_dataloader = my_dataset.get_test_data_loader()
correct = 0
total = 0
for i, (images, labels) in enumerate(test_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[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_label[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_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
true_label = one_hot_encoding.decode(labels.numpy()[0])
total += labels.size(0)
if (predict_label == true_label):
correct += 1
if (total % 200 == 0):
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
def calulate_rate():
cnn = CNN()
cnn.load_state_dict(torch.load('model.pkl'))
print("load cnn net.")
correct = 0
total = 0
for i, (images, labels) in enumerate(td):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[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_label[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_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
true_label = one_hot_encoding.decode(labels.numpy()[0])
total += labels.size(0)
if predict_label == true_label:
correct += 1
correct_total = 100 * correct / total
print(correct_total)
return correct_total
def main():
cnn = CNN()
cnn.load_state_dict(torch.load('model.pkl'))
print("load cnn net.")
predict_dataloader = my_dataset.get_predict_data_loader()
vis = Visdom()
for i, (images, labels) in enumerate(predict_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[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_label[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_label[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)
vis.images(image, opts=dict(caption=c))
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")
device = torch.device("cpu")
cnn = CNN()
cnn.eval()
cnn.load_state_dict(
torch.load('synthesizer_4.pkl', map_location={'cuda:0': 'cpu'}))
cnn.to(device)
generator = Generator()
generator.load_state_dict(
torch.load('7800.pkl', map_location={'cuda:0': 'cpu'}))
generator.to(device)
generator.eval()
print("load cnn net.")
#test_dataloader = my_dataset.get_test_train_data_loader()
test_dataloader = my_dataset.get_test_data_loader()
correct = 0
total = 0
for i, (images1, labels, _) in enumerate(test_dataloader):
try:
image = images1
image = Variable(image)
image, labels = image.to(device), labels.to(device)
# vimage = generator(image)
predict_label = cnn(image)
labels = labels.cpu()
predict_label = predict_label.cpu()
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[
0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[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_label[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_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
# c4 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[0, 4 * captcha_setting.ALL_CHAR_SET_LEN:5 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
# c5 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[0, 5 * captcha_setting.ALL_CHAR_SET_LEN:6 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
#c3 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
#predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
true_label = one_hot_encoding.decode(labels.numpy()[0])
total += labels.size(0)
#save_image(vimage,'temp_result/'+str(i)+'.png')
# print(predict_label.upper(),'>>>>>',true_label)
if (predict_label.upper() == true_label.upper()):
correct += 1
if (total % 2000 == 0):
print(
'Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
except:
pass
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
def main():
if os.path.exists(directory):
shutil.rmtree(directory)
if not os.path.exists(directory):
os.makedirs(directory)
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cnn = CNN()
# cnn.to(device)
cnn.eval()
cnn.load_state_dict(torch.load('model.pkl'))
print("Model Loaded")
criterion = nn.MultiLabelSoftMarginLoss()
data_loader = setting.get_test_data_loader()
correct = 0
total = 0
count = 1
for i, (images, labels) in enumerate(data_loader):
image = images
variable = Variable(image)
# variable = Variable(image).to(device)
variable.requires_grad = True
predict_label = cnn(variable)
# predict_label = cnn(variable).to(device)
loss = criterion(predict_label, labels.float())
cnn.zero_grad()
loss.backward()
data_grad = variable.grad.data
perturbed_image = fgsm_attack(variable, epsilon, data_grad)
predict_label = cnn(perturbed_image)
numpy_image = perturbed_image.squeeze().detach().cpu().numpy()
plt.imshow(numpy_image)
fig = plt.gcf()
fig.savefig(directory + '/' + str(count) + '.png')
count += 1
c0 = setting.character_set[np.argmax(predict_label[0, 0:setting.character_set_length].data.numpy())]
c1 = setting.character_set[np.argmax(predict_label[0, setting.character_set_length:2 * setting.character_set_length].data.numpy())]
c2 = setting.character_set[np.argmax(predict_label[0, 2 * setting.character_set_length:3 * setting.character_set_length].data.numpy())]
c3 = setting.character_set[np.argmax(predict_label[0, 3 * setting.character_set_length:4 * setting.character_set_length].data.numpy())]
predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
true_label = setting.decode(labels.numpy()[0])
total += labels.size(0)
if predict_label == true_label:
correct += 1
if total % 20 == 0:
print('Test Accuracy of the model on the %d test images: %f %%' % (total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' % (total, 100 * correct / total))
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():
cnn = CNN()
cnn.eval()
parser = argparse.ArgumentParser(description="model path")
parser.add_argument("--model-path", type=str)
args = parser.parse_args()
cnn.load_state_dict(torch.load(args.model_path))
predict_dataloader = datasets.get_predict_data_loader()
#vis = Visdom()
for i, (images, labels) in enumerate(predict_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[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_label[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_label[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)
def main():
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('model1.pkl'))
print("load cnn net.")
test_dataloader = my_dataset.get_test_data_loader()
correct = 0
total = 0
#输入deepfool的最大迭代次数
max_iter = int(input("please input max_iter:"))
for i, (images, labels) in enumerate(test_dataloader):
images = torch.squeeze(images, dim=1)
perturbed_data = Variable(deepfool(images, cnn, 248, 0, max_iter))
perturbed_label = cnn(perturbed_data)
perturbed_label = perturbed_label.cpu()
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
perturbed_label[0,
0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
perturbed_label[0, captcha_setting.ALL_CHAR_SET_LEN:2 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c2 = captcha_setting.ALL_CHAR_SET[np.argmax(
perturbed_label[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(
perturbed_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
perturbed_label = '%s%s%s%s' % (c0, c1, c2, c3)
true_label = one_hot_encoding.decode(labels.numpy()[0])
total += labels.size(0)
if (perturbed_label == true_label):
correct += 1
if (total % 10 == 0):
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
def main():
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('model.pkl'))
print("load cnn net.")
predict_dataloader = my_dataset.get_predict_data_loader()
# vis = Visdom()
results = []
for i, (images, labels) in enumerate(predict_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[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_label[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_label[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)
results.append(c)
# vis.images(image, opts=dict(caption=c))
codes = get_file_codes()
hits = get_result(codes, results)
print float(len(hits)) / float(len(codes)) * 100
def predict(tensor_data): # 预测张量图像
try:
cnn = CNN()
cnn.eval() # 为什么会提升精度
cnn.load_state_dict(torch.load('model v2.5.pkl'))
v_image = Variable(tensor_data)
predict_label = cnn(v_image)
c0 = ALL_CHAR_SET[np.argmax(
predict_label[0, 0:ALL_CHAR_SET_LEN].data.numpy())]
c1 = ALL_CHAR_SET[np.argmax(
predict_label[0,
ALL_CHAR_SET_LEN:2 * ALL_CHAR_SET_LEN].data.numpy())]
c2 = ALL_CHAR_SET[np.argmax(
predict_label[0, 2 * ALL_CHAR_SET_LEN:3 *
ALL_CHAR_SET_LEN].data.numpy())]
c3 = ALL_CHAR_SET[np.argmax(
predict_label[0, 3 * ALL_CHAR_SET_LEN:4 *
ALL_CHAR_SET_LEN].data.numpy())]
# print(predict_label)
c = '%s%s%s%s' % (c0, c1, c2, c3)
# print(c)
return c
except:
return "ERR"
def main():
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('model.pkl'))
predict_dataloader = my_dataset.get_predict_data_loader()
for i, images in enumerate(predict_dataloader):
v_image = Variable(images)
print(type(v_image))
print(v_image.size())
predict_label = cnn(v_image)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[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_label[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_label[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)
def main():
cnn = CNN()
if use_cuda:
cnn = CNN().cuda()
cnn.eval()
cnn.load_state_dict(torch.load('model_splic_cos_300_512_0.001.pkl'))
print("load cnn net.")
predict_dataloader = my_dataset.get_predict_data_loader()
#vis = Visdom()
correct = 0
total = 0
for i, (images, labels) in enumerate(predict_dataloader):
image = images
vimage = Variable(image).cuda()
predict_label = cnn(vimage).cpu()
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[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_label[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_label[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_label = '%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_label, true_label)
if (predict_label == true_label):
correct += 1
if (total % 200 == 0):
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
def main():
cnn = CNN()
if use_cuda:
cnn = CNN().cuda()
cnn.eval()
cnn.load_state_dict(torch.load('../model_91.pkl'))
print("load cnn net.")
predict_dataloader = my_dataset.get_label_data_loader()
#vis = Visdom()
correct = 0
total = 0
currentpath = os.getcwd()
for i, (images, name) in enumerate(predict_dataloader):
image = images
vimage = Variable(image).cuda()
predict_label = cnn(vimage).cpu()
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[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_label[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_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
#true_label = one_hot_encoding.decode(labels.numpy()[0])
total += images.size(0)
# print(predict_label, true_label)
# if (predict_label == true_label):
# correct += 1
os.chdir("./dataset/label/")
#now = str(int(time.time()))
print(name[0], predict_label)
os.rename(str(name[0]), (str(predict_label) + '.png')) # 文件重新命名
os.chdir(currentpath) # 改回程序运行前的工作目录
sys.stdin.flush() # 刷新
os.chdir(currentpath) # 改回程序运行前的工作目录
sys.stdin.flush() # 刷新
def main():
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('model1.pkl'))
print("load cnn net.")
test_dataloader = my_dataset.get_test_data_loader()
correct = 0
total = 0
epsilon = float(input("please input epsilon = "))
iteration = int(input("please input iteration = "))
criterion = nn.MultiLabelSoftMarginLoss()
alpha = 1
for i, (image, label) in enumerate(test_dataloader):
vimage = Variable(image)
labels = Variable(label.float())
perturbed_data = i_fgsm(vimage, image, labels, criterion, cnn, alpha,
iteration, epsilon)
perturbed_label = cnn(perturbed_data)
perturbed_label = decode_to(perturbed_label)
true_label = one_hot_encoding.decode(label.numpy()[0])
total += label.size(0)
if (perturbed_label == true_label):
correct += 1
if (total % 10 == 0):
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
def main():
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('model.pkl'))
print("load cnn net.")
test_dataloader = my_dataset.get_test_data_loader()
correct = 0
total = 0
wrong_letter = []
total_letter = {}
for i in captcha_setting.ALPHABET:
total_letter[i] = {
'times': 0,
'success': 0,
'fail': 0
}
for i in captcha_setting.NUMBER:
total_letter[i] = {
'times': 0,
'success': 0,
'fail': 0
}
for i, (images, labels) in enumerate(test_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
predict_label = '%s' % (c)
print('predict_label: ' + predict_label)
true_label = one_hot_encoding.decode(labels.numpy()[0])
total += labels.size(0)
print('true_label: ' + true_label)
total_letter[true_label]['times'] += 1
if(predict_label == true_label):
total_letter[true_label]['success'] += 1
correct += 1
else:
total_letter[true_label]['fail'] += 1
wrong_letter.append(true_label)
if(total%200==0):
print('Test Accuracy of the model on the %d test images: %f %%' % (total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' % (total, 100 * correct / total))
print(wrong_letter)
for i in total_letter:
print('%s, success: %d fail: %d' % (i, total_letter[i]['success'], total_letter[i]['fail']))
def cnn_recog(image_path):
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('./model.pkl'))
print("load cnn net.")
recog_dataloader = get_recog_data_loader(image_path)
print(image_path)
#vis = Visdom()
for i, (images, labels) in enumerate(recog_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c = '%s' % (c)
return c
def main():
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cnn = CNN()
# cnn.to(device)
cnn.eval()
cnn.load_state_dict(torch.load('model.pkl'))
print("Model Loaded")
data_loader = setting.get_test_data_loader()
correct = 0
total = 0
for i, (images, labels) in enumerate(data_loader):
image = images
variable = Variable(image)
# variable = Variable(image).to(device)
predict_label = cnn(variable)
# predict_label = cnn(variable).to(device)
c0 = setting.character_set[np.argmax(
predict_label[0, 0:setting.character_set_length].data.numpy())]
c1 = setting.character_set[np.argmax(
predict_label[0, setting.character_set_length:2 *
setting.character_set_length].data.numpy())]
c2 = setting.character_set[np.argmax(
predict_label[0, 2 * setting.character_set_length:3 *
setting.character_set_length].data.numpy())]
c3 = setting.character_set[np.argmax(
predict_label[0, 3 * setting.character_set_length:4 *
setting.character_set_length].data.numpy())]
predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
true_label = setting.decode(labels.numpy()[0])
total += labels.size(0)
if predict_label == true_label:
correct += 1
if total % 200 == 0:
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
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()
cnn.eval()
cnn.load_state_dict(torch.load('model1.pkl'))
print("load cnn net.")
test_dataloader = my_dataset.get_test_data_loader()
correct = 0
total = 0
epsilon = float(input("please input epsilon = "))
criterion = nn.MultiLabelSoftMarginLoss()
for i, (images, labels) in enumerate(test_dataloader):
vimage = Variable(images)
labels = Variable(labels.float())
vimage.requires_grad = True
predict_labels = cnn(vimage)
loss = criterion(predict_labels, labels)
cnn.zero_grad()
loss.backward()
data_grad = vimage.grad.data
perturbed_data = fgsm_attack(vimage, epsilon, data_grad)
perturbed_label = cnn(perturbed_data)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
perturbed_label[0,
0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
perturbed_label[0, captcha_setting.ALL_CHAR_SET_LEN:2 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c2 = captcha_setting.ALL_CHAR_SET[np.argmax(
perturbed_label[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(
perturbed_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
perturbed_label = '%s%s%s%s' % (c0, c1, c2, c3)
true_label = one_hot_encoding.decode(labels.numpy()[0])
total += labels.size(0)
if (perturbed_label == true_label):
correct += 1
if (total % 100 == 0):
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
import PIL.ImageFile as ImageFile
import numpy as np
import torch
import torchvision.transforms as transforms
from PIL import Image
from torch.autograd import Variable
import captcha_setting
from captcha_cnn_model import CNN
transform = transforms.Compose([
transforms.Grayscale(),
transforms.ToTensor(),
])
cnn = CNN()
cnn.load_state_dict(torch.load('model.pkl'))
def predict_image(image):
"""
预测image
:param image:
:return:
"""
image = image.resize((captcha_setting.IMAGE_WIDTH, captcha_setting.IMAGE_HEIGHT))
image = transform(image)
vimage = Variable(image[np.newaxis, :])
predict_label = cnn(vimage)
char_set_len = captcha_setting.ALL_CHAR_SET_LEN
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[0, 0:char_set_len].data.numpy())]
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")
def main():
device = torch.device("cuda") #if torch.cuda.is_available() else "cpu")
# Train the Model
print('loading data')
train_dataloader = my_dataset.get_train_data_loader()
test_dataloader = my_dataset.get_test_data_loader()
print('start training')
loss_list = []
accuracy_list = []
for ttest_num in range(0, 5):
cnn = nn.DataParallel(CNN())
# cnn.load_state_dict(torch.load('./'+model_name))
# cnn.to(device)
cnn.cuda()
# generator = Generator()
# generator.load_state_dict(torch.load('7800.pkl'))
# generator.to(device)
# generator.eval()
cnn.train()
print('init net')
criterion = nn.MultiLabelSoftMarginLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)
print('>>>>>>>>>>>>Round:', ttest_num)
accuracy = 0
for epoch in range(num_epochs):
loss_total = 0
start = datetime.now()
for i, (images1, labels, _) in enumerate(train_dataloader):
#print(i)
images1 = Variable(images1)
labels = Variable(labels.float())
images1, labels = images1.to(device), labels.to(device)
# images1 = generator(images1)
predict_labels = cnn(images1)
# print(predict_labels.type)
# print(labels.type)
loss = criterion(predict_labels, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_total += loss.item()
# if (i+1) % 5000 == 0:
# print("epoch:", epoch, "step:", i, "loss:", loss.item())
# if (i+1) % 25000 == 0:
# print("save model")
correct = 0
total = 0
# print(len(test_dataloader))
for i, (images1, labels, _) in enumerate(test_dataloader):
# print('///////////////////////////////')
# try:
image = images1
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()
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[
0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[
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_label[
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_label[
0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c4 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[
0, 4 * captcha_setting.ALL_CHAR_SET_LEN:5 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c5 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[
0, 5 * captcha_setting.ALL_CHAR_SET_LEN:6 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c6 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[
0, 6 * captcha_setting.ALL_CHAR_SET_LEN:7 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c7 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[
0, 7 * captcha_setting.ALL_CHAR_SET_LEN:8 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
#c3 = captcha_setting.ALL_CHAR_SET[np.argmax(predict_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 * captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
predict_label = '%s%s%s%s%s%s%s%s' % (c0, c1, c2, c3, c4, c5,
c6, c7)
# predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
true_label = one_hot_encoding.decode(labels.numpy()[0])
total += 1 #labels.size(0)
#save_image(vimage,'temp_result/'+str(i)+'.png')
# print(predict_label.upper(),'>>>>>',true_label)
if (predict_label.upper() == true_label.upper()):
correct += 1
# try:
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
# except:
# pass
loss_list.append(loss_total)
with open("loss_record_6.txt", "wb") as fp: #Pickling
pickle.dump(loss_list, fp)
stop = datetime.now()
# try:
if (correct / total > accuracy):
accuracy = correct / total
# torch.save(cnn.state_dict(), "./model_lake/"+model_name.replace('.','_'+str(ttest_num)+'.')) #current is model.pickle
torch.save(cnn.state_dict(), "./model_lake/" +
model_name) #current is model.pickle
print('saved!!!!!!!!!!!!!!!!!!!!!!!')
# except:
# pass
print("epoch:", epoch, "step:", i, " time:<", stop - start,
"> loss:", loss_total)
accuracy_list.append(accuracy)
print(sum(accuracy_list) / len(accuracy_list))
# torch.save(cnn.state_dict(), "./"+model_name) #current is model.pkl
# print("save last model")
print(accuracy_list)
