def train(epoch, iter_start=0):
global global_step, kt
netHg.train()
pbar = tqdm.tqdm(train_loader,
desc='Epoch %02d' % epoch,
dynamic_ncols=True)
pbar_info = tqdm.tqdm(bar_format='{bar}{postfix}')
for it, sample in enumerate(pbar, start=iter_start):
global_step += 1
if FLAGS.debug:
image, masks, keypoints, heatmaps, img_ids = sample
else:
image, masks, keypoints, heatmaps = sample
image = Variable(image)
masks = Variable(masks)
keypoints = Variable(keypoints)
heatmaps = Variable(heatmaps)
if FLAGS.cuda:
image = image.cuda(async=FLAGS.pinMem)
masks = masks.cuda(async=FLAGS.pinMem)
keypoints = keypoints.cuda(async=FLAGS.pinMem)
heatmaps = heatmaps.cuda(async=FLAGS.pinMem)
outputs = netHg(image)
push_loss, pull_loss, detection_loss = calc_loss(
outputs, keypoints, heatmaps, masks)
image_s = nn.functional.avg_pool2d(image, 4)
inp_real = torch.cat([image_s, heatmaps], dim=1)
d_real = netD(inp_real)
loss_d_real = criterion_D(d_real, heatmaps)
pred_heatmaps = outputs[:, -1, :17].squeeze(
dim=1
) # Notice: manually assign the dimension to avoid unexcepted freeze
inp_fake = torch.cat([image_s, pred_heatmaps], dim=1)
d_fake = netD(inp_fake)
loss_d_fake = criterion_D(d_fake, pred_heatmaps)
loss_hg = 0
toprint = ''
sum_dict = {}
for loss, weight, name in zip(
[push_loss, pull_loss, detection_loss], [1e-3, 1e-3, 1],
['push_loss', 'pull_loss', 'detection_loss']):
loss_temp = torch.mean(loss)
sum_dict[name] = getValue(loss_temp)
loss_temp *= weight
loss_hg += loss_temp
toprint += '{:.8f} '.format(getValue(loss_temp))
loss_d = loss_d_real - kt * loss_d_fake
loss_hg = loss_hg + FLAGS.lambda_G * loss_d_fake
optimD.zero_grad()
loss_d.backward(retain_graph=True)
optimD.step()
optimHg.zero_grad()
loss_hg.backward()
optimHg.step()
# update kt
loss_d_real_ = getValue(loss_d_real)
loss_d_fake_ = getValue(loss_d_fake)
balance = FLAGS.gamma * loss_d_real_ - loss_d_fake_
kt = kt + FLAGS.kt_lr * balance
kt = min(1, max(0, kt))
measure = loss_d_real_ + abs(balance)
# Summary
sumWriter.add_scalar('loss_hg', loss_hg, global_step)
for key, value in sum_dict.items():
sumWriter.add_scalar(key, loss_temp, global_step)
sumWriter.add_scalar('loss_d', loss_d, global_step)
toprint += ', loss_d: {:.8f}'.format(getValue(loss_d))
sumWriter.add_scalar('loss_d_real', loss_d_real, global_step)
sumWriter.add_scalar('loss_d_fake', loss_d_fake, global_step)
sumWriter.add_scalar('measure', measure, global_step)
sumWriter.add_scalar('kt', kt, global_step)
pbar_info.set_postfix_str(toprint)
pbar_info.update()
del outputs, push_loss, pull_loss, detection_loss, loss_hg, \
d_real, d_fake, loss_d_real, loss_d_fake, loss_d
pbar.close()
pbar_info.close()
for x, y in train_loader:
x, y = x.to(device), y.to(device)
#If want to get the input images with their Augmentation - To check the data flowing in net
#input_images(x, y, i, n_iter, k)
# grid_img = torchvision.utils.make_grid(x)
#writer1.add_image('images', grid_img, 0)
# grid_lab = torchvision.utils.make_grid(y)
opt.zero_grad()
y_pred = model_test(x)
lossT = calc_loss(y_pred, y) # Dice_loss Used
train_loss += lossT.item() * x.size(0)
lossT.backward()
# plot_grad_flow(model_test.named_parameters(), n_iter)
opt.step()
x_size = lossT.item() * x.size(0)
scheduler(opt, c, i)
c = c + 1
k = 2
# for name, param in model_test.named_parameters():
# name = name.replace('.', '/')
# writer1.add_histogram(name, param.data.cpu().numpy(), i + 1)
# writer1.add_histogram(name + '/grad', param.grad.data.cpu().numpy(), i + 1)
import torchvision.utils as vutils
from losses import calc_loss, dice_loss, threshold_predictions_v,threshold_predictions_p
model=U_Net(3,1)
model.load_state_dict(torch.load("unet.pt"))
model=model.cuda()
TrainData=CaptchaData("D:\\UnetData\\img\\")
dataload=DataLoader(TrainData,38,4,drop_last=False)
optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)#定义优化器
criterion = nn.BCEWithLogitsLoss()
for epoch in range(3000):
for img,label in dataload:
img=img.cuda()
label=label.cuda()
pred=model(img)
optimizer.zero_grad()
loss=calc_loss(pred,label.view(-1,1,256,256))
loss.backward()
optimizer.step()
if(epoch%10==0):
print(loss)
pred=pred*255
vutils.save_image(img.data,"img.jpg",)
vutils.save_image(pred.data,"Result.jpg",)
vutils.save_image(label.view(-1,1,256,256).data,"Label.jpg",)
torch.save(model.state_dict(),"unet.pt")
x=torch.rand(1,3,256,256,device="cuda")
torch.onnx._export(model, x,"unet.onnx",export_params=True)
def loss_function(self, y_pre, y, **kwargs) -> dict:
lossT = calc_loss(y_pre, y)
return {'loss': lossT}
valid_loss = 0.0
sum_precies = 0.0
sum_recall = 0.0
since = time.time()
scheduler.step(i)
lr = scheduler.get_lr()
#######################################################
# Training Data
#######################################################
model_test.train()
k = 1
for x, y in train_loader_1:
x, y = x.to(device), y.to(device)
opt.zero_grad()
y_pred = model_test(x)
lossT = calc_loss(y_pred, y) # Dice_loss Used
train_loss += lossT.item()
CE_loss = F.binary_cross_entropy_with_logits(y_pred, y)
crossen_loss += CE_loss.item()
y_0_1 = F.sigmoid(y_pred)
precies, recall = precies_recall(y_0_1, y)
# print(lossT.item(),CE_loss.item(),precies,recall)
sum_precies += precies
sum_recall += recall
# print(lossT.item())
lossT.backward()
# plot_grad_flow(model_test.named_parameters(), n_iter)
opt.step()
# y_pred=torch.tensor(y_pred,dtype=torch.long)
# y=torch.tensor(y,dtype=torch.long)
# y=y.to(device)
### single loss implementation
#y_pred = model_test(x)
#lossT = calc_loss(y_pred, y) # Dice_loss Used
### supervision training implementation
pred5, pred4, pred3, pred = model_test(x)
y3 = F.interpolate(y, scale_factor=0.5)
#print("inside train")
y4 = F.interpolate(y, scale_factor=0.25)
y5 = F.interpolate(y, scale_factor=0.125)
#print(y3.shape)
#print(y4.shape)
#print(y5.shape)
lossMain = calc_loss(pred, y)
loss3 = calc_loss(pred3, y3)
loss4 = calc_loss(pred4, y4)
loss5 = calc_loss(pred5, y5)
### weight parameters need to be finetune
### apply a weight decay strategy !!! ??
lossT = 0.35 * lossMain + 0.25 * loss3 + 0.25 * loss4 + 0.15 * loss5
train_loss += lossT.item() * x.size(0)
lossT.backward()
# plot_grad_flow(model_test.named_parameters(), n_iter)
opt.step()
x_size = lossT.item() * x.size(0)
scheduler(opt, c, i)
c = c + 1
#k = 2
def Net_Train(train_i=0):
# NAME = 'fold'+str(train_i+1)+'_25ATT-UNet'
NAME = 'fold'+str(train_i+1)+'_25NEST-UNet'
mylog = open('logs/' + NAME + '.log', 'w')
print(NAME)
print(NAME, file=mylog, flush=True)
# model = AttU_Net(img_ch=1, output_ch=1).cuda()
model = NestedUNet(in_ch=1, out_ch=1).cuda()
# print(model)
model = torch.nn.DataParallel(model, device_ids=range(torch.cuda.device_count()))
# model = FPN_Net(1, 1)
# print(model)
folds = data2()
test_data=folds[train_i]
batch_size1 = 4
batch_size = 2
(x_train, y_train), (x_test, y_test) = load_numpy(folds, train_i)
dataset = torch.utils.data.TensorDataset(x_train, y_train)
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size1,
shuffle=True,
num_workers=4)
# data_test = torch.utils.data.TensorDataset(x_test, y_test)
# loader_test = torch.utils.data.DataLoader(
# data_test,
# batch_size=batch_size,
# shuffle=True,
# num_workers=4)
no_optim = 0
lr = 2e-4
total_epoch=250
train_epoch_best_loss=10000
best_test_score = 0
decay_factor = 1.5
optimizer = torch.optim.Adam(params=model.parameters(), lr=lr)
for epoch in range(1, total_epoch + 1):
tic = time()
data_loader_iter = iter(data_loader)
# data_test_iter = iter(loader_test)
train_epoch_loss = 0
train_score = 0
test_epoch_loss = 0
test_score = 0
test_sen = 0
test_ppv = 0
for img, mask in data_loader_iter:
img = Variable(img.cuda(), volatile=False)
mask = Variable(mask.cuda(), volatile=False)
optimizer.zero_grad()
pre = model.forward(img)
loss = calc_loss(pre, mask)
loss.backward()
optimizer.step()
train_epoch_loss += loss.data
train_score_b = dice_coeff(mask, pre, False)
train_score += train_score_b.data*batch_size1
train_score /= x_train.size(0)
train_score = train_score.cpu().data.numpy()
train_epoch_loss /= len(data_loader_iter)
train_epoch_loss = train_epoch_loss.cpu().data.numpy()
# print('epoch:', epoch, ' time:', int(time() - tic), 'train_loss:', train_epoch_loss, 'train_score:', train_score)
with torch.no_grad():
# for img, mask in data_test_iter:
img = Variable(x_test.cuda(), volatile=True)
mask = Variable(y_test.cuda(), volatile=True)
pre = model.forward(img)
test_epoch_loss = calc_loss(pre, mask)
# test_epoch_loss += loss.data
test_score = dice_coeff(mask, pre, False)
# test_score += test_score_b.data*batch_size
pre[pre>0.5]=1
pre[pre<=0.5]=0
test_sen = sensitive(mask, pre)
# test_sen += test_sen_b.data*batch_size
test_ppv = positivepv(mask, pre)
# test_ppv += test_ppv_b.data*batch_size
# test_score /= x_test.size(0)
test_score = test_score.cpu().data.numpy()
# test_sen /= x_test.size(0)
test_sen = test_sen.cpu().data.numpy()
# test_ppv /= x_test.size(0)
test_ppv = test_ppv.cpu().data.numpy()
# test_epoch_loss /= len(data_test_iter)
test_epoch_loss = test_epoch_loss.cpu().data.numpy()
# x_test = Variable(x_test.cuda(), volatile=True)
# pre_test = model.forward(x_test).cpu().data
# loss_test = calc_loss(y_test, pre_test)
# # loss_test = loss_test.cpu().data.numpy()
# test_score = dice_coeff(y_test, pre_test, False)
# # test_score = test_score.cpu().data.numpy()
print('********', file=mylog, flush=True)
print('epoch:', epoch, train_i, ' time:', int(time() - tic), 'train_loss:', train_epoch_loss, 'train_score:', train_score, end=' ', file=mylog, flush=True)
print('test_loss:', test_epoch_loss, 'test_dice_score: ', test_score, 'test_sen: ', test_sen, 'test_ppv: ', test_ppv, 'best_score is ', best_test_score, file=mylog, flush=True)
print('********')
print('epoch:', epoch, train_i, ' time:', int(time() - tic), 'train_loss:', train_epoch_loss, 'train_score:', train_score, end=' ')
print('test_loss:', test_epoch_loss, 'test_dice_score: ', test_score, 'test_sen: ', test_sen, 'test_ppv: ', test_ppv, 'best_score is ', best_test_score)
if test_score > best_test_score:
print('1. the dice score up to ', test_score, 'from ', best_test_score, 'saving the model', file=mylog, flush=True)
print('1. the dice score up to ', test_score, 'from ', best_test_score, 'saving the model')
best_test_score = test_score
torch.save(model, './weights/' + NAME + '.pkl')
if best_test_score>0.75:
with torch.no_grad():
for test in test_data:
img = test[0].reshape(1, 1, 256, 256).astype('float32')
img = torch.from_numpy(img)
img = Variable(img.cuda())
pre = model.forward(img).cpu().data.numpy()
plt.imsave('../../model/fold/png_constract/'+test[-1]+'_fold_z5_nest.png', pre[0,0,:,:], cmap='gray')
if train_epoch_loss >= train_epoch_best_loss:
no_optim += 1
else:
no_optim = 0
train_epoch_best_loss = train_epoch_loss
if no_optim > 10:
if lr < 1e-7:
break
if lr > 1e-5:
# model.load_state_dict(torch.load('./weights/' + NAME + '.th'))
lr /= decay_factor
print ('update learning rate: %f -> %f' % (lr*decay_factor, lr), file=mylog, flush=True)
print ('update learning rate: %f -> %f' % (lr*decay_factor, lr))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print('Finish!', file=mylog, flush=True)
print('Finish!')
mylog.close()
