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dscsi_train.py
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dscsi_train.py
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import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torch import optim
import os.path
from wsi.bin.image_producer import ImageDataset
import pytorch_ssim
import pytorch_dscsi
from unet_generator import UNet
from dcgan_discriminator import Discriminator
from resnet_classifier import TranResnet34
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
def freeze_model(model):
model.eval()
for params in model.parameters():
params.requires_grad = False
def train(epochs=8, batch_size=2, learning_rate=0.001, sample_interval=1):
time_now = time.time()
G = UNet(3, 3).cuda()
D = Discriminator(3, [32, 64, 128, 256, 512, 1024], 1).cuda()
R = TranResnet34().cuda()
R.load_state_dict(torch.load('./TranResnet34/save_models/TranResnet34_params.pkl'))
freeze_model(R)
criterion_fp = nn.KLDivLoss().cuda()
criterion_gan = nn.BCELoss().cuda()
ssim_loss = pytorch_ssim.SSIM()
dscsi_loss = pytorch_dscsi.COLOR_DSCSI(7,1)
criterion_reco = nn.MSELoss().cuda()
betas = (0.5, 0.999)
# Optimizers
G_optimizer = optim.Adam(G.parameters(), lr=learning_rate, betas=betas)
D_optimizer = optim.Adam(D.parameters(), lr=learning_rate, betas=betas)
valid = Variable(torch.ones(batch_size * 2).cuda())
fake = Variable(torch.zeros(batch_size * 2).cuda())
# Change paths below to where you store your data
LOSS_DIR = "./losses"
LOSS_SSIM_FILE = os.path.join(LOSS_DIR, "dscsi_loss.txt")
with open(LOSS_SSIM_FILE, "w") as lossSSIM:
for epoch in range(epochs):
dataset_tumor_train = ImageDataset('./wsi/patches/tumor_train', './wsi/jsons/train', normalize=True)
dataloader_tumor = DataLoader(dataset_tumor_train, batch_size=batch_size, num_workers=3)
dataset_normal_train = ImageDataset('./wsi/patches/normal_train', './wsi/jsons/train', normalize=True)
dataloader_normal = DataLoader(dataset_normal_train, batch_size=batch_size, num_workers=3)
steps1 = len(dataloader_tumor) - 1 # considering list.txt
steps2 = len(dataloader_normal) - 1
steps = min(steps1, steps2)
batch_size = dataloader_tumor.batch_size
dataiter_tumor = iter(dataloader_tumor)
dataiter_normal = iter(dataloader_normal)
D_losses = []
G_losses = []
correct = 0
total = 0
for step in range(steps):
# image data and labels
data_tumor, target_tumor, _ = next(dataiter_tumor)
data_tumor = Variable(data_tumor.cuda(), requires_grad=False)
target_tumor = Variable(target_tumor.cuda(), requires_grad=False)
# data_tumor_gray = Variable(data_tumor_gray.cuda(),requires_grad=False)
data_normal, target_normal, _ = next(dataiter_normal)
data_normal = Variable(data_normal.cuda(), requires_grad=False)
target_normal = Variable(target_normal.cuda(), requires_grad=False)
# data_normal_gray = Variable(data_normal_gray.cuda(),requires_grad=False)
idx_rand = Variable(torch.randperm(batch_size * 2).cuda())
data = torch.cat([data_tumor, data_normal])[idx_rand]
# data_gray = torch.cat([data_tumor_gray, data_normal_gray])[idx_rand]
target = torch.cat([target_tumor, target_normal])[idx_rand]
# Train discriminator with real data
D_valid_decision = D(data).squeeze()
D_valid_loss = criterion_gan(D_valid_decision, valid)
# Train discriminator with fake data
data_gene = G(data)
D_fake_decision = D(data_gene).squeeze()
D_fake_loss = criterion_gan(D_fake_decision, fake)
# Back propagation
D_loss = D_valid_loss + D_fake_loss
lossSSIM.write("%5d,%5d,%10lf," % (epoch + 1, (epoch + 1) * steps + step, D_loss.data))
D_optimizer.zero_grad()
D_loss.backward()
D_optimizer.step()
# Train generator
data_gene = G(data)
D_fake_decision = D(data_gene).squeeze()
# Computer feature preservation loss
valid_features, _ = R(data)
valid_features = Variable(valid_features).cuda() # , requires_grad=False)
fake_features, result = R(data_gene)
fake_features_lsm = F.log_softmax(fake_features, 1)
valid_features_sm = F.softmax(valid_features, 1)
fp_loss = criterion_fp(fake_features_lsm, valid_features_sm)
# Compute image reconstruction loss
mse_loss = criterion_reco(data_gene, data)
ssim = ssim_loss(data,data_gene)
dscsi = dscsi_loss(data, data_gene)
reco_loss = torch.ones(1).cuda().float() - dscsi
# Back propagation # you can also give weight to three kinds of loss below
G_loss = 0.2 * criterion_gan(D_fake_decision, valid) + 0.4 * fp_loss + 0.4 * reco_loss
lossSSIM.write("%10lf,%10lf,%10lf,%10lf," % (
G_loss.data, reco_loss.data, 1-ssim.data, mse_loss.data))
D_optimizer.zero_grad()
G_optimizer.zero_grad()
G_loss.backward()
G_optimizer.step()
D_losses.append(D_loss.item())
G_losses.append(G_loss.item())
# Test the result
_, predicted = result.max(1)
total += target.size(0)
correct += predicted.eq(target).sum().item()
lossSSIM.write("%10lf\n" % (correct / total))
time_spent = time.time() - time_now
if (step + 1) % 20 == 0:
print("[Epoch %d/%d], [Step %d/%d], [D_loss: %.4f], [G_loss: %.4f],[FP_loss: %.4f], [Reco_loss: %.4f], [Accu:%3d%%], [RunTime:%.4f]"
% (epoch + 1, epochs, step + 1, steps, D_loss.item(), G_loss.item(),fp_loss.item(), reco_loss.item(), 100. * correct / total,
time_spent))
D_avg_loss = torch.mean(torch.FloatTensor(D_losses))
G_avg_loss = torch.mean(torch.FloatTensor(G_losses))
print("[Epoch %d/%d], [Step %d/%d], [D_avg_loss: %.4f], [G_avg_loss: %.4f], [Accu:%3d%%], [RunTime:%.4f]"
% (epoch + 1, epochs, step + 1, steps, D_avg_loss, G_avg_loss, 100. * correct / total, time_spent))
if (epoch + 1) % sample_interval == 0:
torch.save(D.state_dict(), './save_models/D_params_dscsi_%d.pkl' % (epoch + 1))
torch.save(G.state_dict(), './save_models/G_params_dscsi_%d.pkl' % (epoch + 1))
print("FINAL:[Epoch %d/%d], [Step %d/%d], [D_avg_loss: %.4f], [G_avg_loss: %.4f], [Accu:%3d%%], [RunTime:%.4f]"
% (epoch + 1, epochs, step + 1, steps, D_avg_loss, G_avg_loss, 100. * correct / total, time_spent))
print("Model saved.")
if __name__ == '__main__':
train()