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CADA.py
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CADA.py
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import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2,3"
import torch
from torch.utils import data
from torch.autograd import Variable
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
from albumentations import (
HorizontalFlip,
VerticalFlip,
Compose,
Transpose,
RandomRotate90,
OneOf,
CLAHE,
RandomGamma,
HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise,
RandomBrightnessContrast,
IAASharpen, IAAEmboss
)
from models.unet import UNet
from models.discriminator import FCDiscriminator
from dataset.refuge import REFUGE
from pytorch_utils import (adjust_learning_rate, adjust_learning_rate_D,
calc_mse_loss, Weighted_Jaccard_loss, dice_loss)
from models import optim_weight_ema
from arguments import get_arguments
import tensorboard_logger as tb_logger
import numpy as np
aug_student = Compose([
OneOf([
Transpose(p=0.5),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
RandomRotate90(p=0.5)], p=0.2),
OneOf([
IAAAdditiveGaussianNoise(p=0.5),
GaussNoise(p=0.5),
], p=0.2),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(p=0.5),
IAAEmboss(p=0.5),
RandomBrightnessContrast(p=0.5),
], p=0.2),
HueSaturationValue(p=0.2),
RandomGamma(p=0.2)])
aug_teacher = Compose([
OneOf([
IAAAdditiveGaussianNoise(p=0.5),
GaussNoise(p=0.5),
], p=0.2),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(p=0.5),
IAAEmboss(p=0.5),
RandomBrightnessContrast(p=0.5),
], p=0.2),
HueSaturationValue(p=0.2),
RandomGamma(p=0.2)])
def main():
"""Create the model and start the training."""
args = get_arguments()
cudnn.enabled = True
n_discriminators = 5
logger = tb_logger.Logger(logdir= args.tensorboard_dir, flush_secs=2)
# create teacher & student
student_net = UNet(3, n_classes=args.num_classes)
# saved_state_dict = torch.load(args.restore_from)
# print('The pretrained weights have been loaded', args.restore_from)
# student_net.load_state_dict(saved_state_dict)
teacher_net = UNet(3, n_classes=args.num_classes)
# saved_state_dict = torch.load(args.restore_from)
# teacher_net.load_state_dict(saved_state_dict)
student_params = list(student_net.parameters())
# teacher doesn't need gradient as it's just a EMA of the student
teacher_params = list(teacher_net.parameters())
for param in teacher_params:
param.requires_grad = False
student_net.train()
student_net.cuda(args.gpu)
teacher_net.train()
teacher_net.cuda(args.gpu)
cudnn.benchmark = True
unsup_weights = [args.unsup_weight6, args.unsup_weight7, args.unsup_weight8,
args.unsup_weight9, args.unsup_weight10]
lambda_adv_tgts = [args.lambda_adv_tgt6, args.lambda_adv_tgt7,
args.lambda_adv_tgt8, args.lambda_adv_tgt9,
args.lambda_adv_tgt10]
mse_weights = [args.mse_weight6, args.mse_weight7, args.mse_weight8, args.mse_weight9,
args.mse_weight10]
# create a list of discriminators
discriminators = []
for dis_idx in range(n_discriminators):
discriminators.append(FCDiscriminator(num_classes=args.num_classes))
discriminators[dis_idx].train()
discriminators[dis_idx].cuda(args.gpu)
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
max_iters = args.num_steps * args.iter_size * args.batch_size
src_set = REFUGE(True, domain='REFUGE_SRC', is_transform=True,
augmentations=aug_student, aug_for_target=aug_teacher, max_iters=max_iters)
src_loader = data.DataLoader(src_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
src_loader_iter = enumerate(src_loader)
tgt_set = REFUGE(True, domain='REFUGE_DST', is_transform=True,
augmentations=aug_student, aug_for_target=aug_teacher,
max_iters=max_iters)
tgt_loader = data.DataLoader(tgt_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
tgt_loader_iter = enumerate(tgt_loader)
student_optimizer = optim.SGD(student_params,
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
teacher_optimizer = optim_weight_ema.WeightEMA(
teacher_params, student_params, alpha=args.teacher_alpha)
d_optimizers = []
for idx in range(n_discriminators):
optimizer = optim.Adam(discriminators[idx].parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
d_optimizers.append(optimizer)
calc_bce_loss = torch.nn.BCEWithLogitsLoss()
# labels for adversarial training
source_label, tgt_label = 0, 1
for i_iter in range(args.num_steps):
total_seg_loss = 0
seg_loss_vals = [0] * n_discriminators
adv_tgt_loss_vals = [0] * n_discriminators
d_loss_vals = [0] * n_discriminators
unsup_loss_vals = [0] * n_discriminators
for d_optimizer in d_optimizers:
d_optimizer.zero_grad()
adjust_learning_rate_D(d_optimizer, i_iter, args)
student_optimizer.zero_grad()
adjust_learning_rate(student_optimizer, i_iter, args)
for sub_i in range(args.iter_size):
# ******** Optimize source network with segmentation loss ********
# As we don't change the discriminators, their parameters are fixed
for discriminator in discriminators:
for param in discriminator.parameters():
param.requires_grad = False
_, src_batch = src_loader_iter.__next__()
_, _, src_images, src_labels, _ = src_batch
src_images = Variable(src_images).cuda(args.gpu)
# calculate the segmentation losses
sup_preds = list(student_net(src_images))
seg_losses, total_seg_loss = [], 0
for idx, sup_pred in enumerate(sup_preds):
sup_interp_pred = (sup_pred)
# you also can use dice loss like: dice_loss(src_labels, sup_interp_pred)
seg_loss = Weighted_Jaccard_loss(src_labels, sup_interp_pred, args.class_weights, args.gpu)
seg_losses.append(seg_loss)
total_seg_loss += seg_loss * unsup_weights[idx]
seg_loss_vals[idx] += seg_loss.item() / args.iter_size
_, tgt_batch = tgt_loader_iter.__next__()
tgt_images0, tgt_lbl0, tgt_images1, tgt_lbl1, _ = tgt_batch
tgt_images0 = Variable(tgt_images0).cuda(args.gpu)
tgt_images1 = Variable(tgt_images1).cuda(args.gpu)
# calculate ensemble losses
stu_unsup_preds = list(student_net(tgt_images1))
tea_unsup_preds = teacher_net(tgt_images0)
total_mse_loss = 0
for idx in range(n_discriminators):
stu_unsup_probs = F.softmax(stu_unsup_preds[idx], dim=-1)
tea_unsup_probs = F.softmax(tea_unsup_preds[idx], dim=-1)
unsup_loss = calc_mse_loss(stu_unsup_probs, tea_unsup_probs, args.batch_size)
unsup_loss_vals[idx] += unsup_loss.item() / args.iter_size
total_mse_loss += unsup_loss * mse_weights[idx]
total_mse_loss = total_mse_loss / args.iter_size
# As the requires_grad is set to False in the discriminator, the
# gradients are only accumulated in the generator, the target
# student network is optimized to make the outputs of target domain
# images close to the outputs of source domain images
stu_unsup_preds = list(student_net(tgt_images0))
d_outs, total_adv_loss = [], 0
for idx in range(n_discriminators):
stu_unsup_interp_pred = (stu_unsup_preds[idx])
d_outs.append(discriminators[idx](stu_unsup_interp_pred))
label_size = d_outs[idx].data.size()
labels = torch.FloatTensor(label_size).fill_(source_label)
labels = Variable(labels).cuda(args.gpu)
adv_tgt_loss = calc_bce_loss(d_outs[idx], labels)
total_adv_loss += lambda_adv_tgts[idx] * adv_tgt_loss
adv_tgt_loss_vals[idx] += adv_tgt_loss.item() / args.iter_size
total_adv_loss = total_adv_loss / args.iter_size
# requires_grad is set to True in the discriminator, we only
# accumulate gradients in the discriminators, the discriminators are
# optimized to make true predictions
d_losses = []
for idx in range(n_discriminators):
discriminator = discriminators[idx]
for param in discriminator.parameters():
param.requires_grad = True
sup_preds[idx] = sup_preds[idx].detach()
d_outs[idx] = discriminators[idx](sup_preds[idx])
label_size = d_outs[idx].data.size()
labels = torch.FloatTensor(label_size).fill_(source_label)
labels = Variable(labels).cuda(args.gpu)
d_losses.append(calc_bce_loss(d_outs[idx], labels))
d_losses[idx] = d_losses[idx] / args.iter_size / 2
d_losses[idx].backward()
d_loss_vals[idx] += d_losses[idx].item()
for idx in range(n_discriminators):
stu_unsup_preds[idx] = stu_unsup_preds[idx].detach()
d_outs[idx] = discriminators[idx](stu_unsup_preds[idx])
label_size = d_outs[idx].data.size()
labels = torch.FloatTensor(label_size).fill_(tgt_label)
labels = Variable(labels).cuda(args.gpu)
d_losses[idx] = calc_bce_loss(d_outs[idx], labels)
d_losses[idx] = d_losses[idx] / args.iter_size / 2
d_losses[idx].backward()
d_loss_vals[idx] += d_losses[idx].item()
for d_optimizer in d_optimizers:
d_optimizer.step()
total_loss = total_seg_loss + total_adv_loss + total_mse_loss
logger.log_value('total_seg_loss', total_seg_loss, i_iter)
logger.log_value('adv_loss', total_adv_loss, i_iter)
logger.log_value('mse_loss', total_mse_loss, i_iter)
logger.log_value('target_loss', total_adv_loss + total_mse_loss, i_iter)
logger.log_value('d_loss_0,', d_loss_vals[0], i_iter)
logger.log_value('d_loss_1,', d_loss_vals[1], i_iter)
logger.log_value('d_loss_2,', d_loss_vals[2], i_iter)
logger.log_value('d_loss_3,', d_loss_vals[3], i_iter)
logger.log_value('d_loss_4,', d_loss_vals[4], i_iter)
logger.log_value('d_loss', np.mean(d_loss_vals[0] + d_loss_vals[1] + d_loss_vals[2] + d_loss_vals[3] + d_loss_vals[4]), i_iter)
total_loss.backward()
student_optimizer.step()
teacher_optimizer.step()
log_str = 'iter = {0:7d}/{1:7d}'.format(i_iter, args.num_steps)
log_str += ', total_seg_loss = {0:.3f} '.format(total_seg_loss)
templ = 'seg_losses = [' + ', '.join(['%.2f'] * len(seg_loss_vals))
log_str += templ % tuple(seg_loss_vals) + '] '
templ = 'ens_losses = [' + ', '.join(['%.5f'] * len(unsup_loss_vals))
log_str += templ % tuple(unsup_loss_vals) + '] '
templ = 'adv_losses = [' + ', '.join(['%.2f'] * len(adv_tgt_loss_vals))
log_str += templ % tuple(adv_tgt_loss_vals) + '] '
templ = 'd_losses = [' + ', '.join(['%.2f'] * len(d_loss_vals))
log_str += templ % tuple(d_loss_vals) + '] '
print(log_str)
if i_iter >= args.num_steps_stop - 1:
print('save model ...')
filename = 'UNet' + str(args.num_steps_stop) + '_v18_weightedclass.pth'
torch.save(teacher_net.cpu().state_dict(),
os.path.join(args.snapshot_dir, filename))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('taking snapshot ...')
filename = 'UNet' + str(i_iter) + '_v18_weightedclass.pth'
torch.save(teacher_net.cpu().state_dict(),
os.path.join(args.snapshot_dir, filename))
teacher_net.cuda(args.gpu)
if __name__ == '__main__':
main()