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main_cswin_resnet50_crop_loss3.py
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#!/usr/bin/env python
# coding: utf-8
# %%
import torch
from torch.autograd import Variable
from datetime import datetime
from lib.TransFuse_resnet50_CSwin import TransFuse_B
from utils.dataloader1 import get_loader, test_dataset
from utils.utils import AvgMeter
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import os
import matplotlib.pyplot as plt
from tqdm import tqdm
import time
import shutil
# %%
def structure_loss(pred, mask):
weit = 1 + 5*torch.abs(F.avg_pool2d(mask, kernel_size=7, stride=1, padding=3) - mask)
wbce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none')
pred = torch.sigmoid(pred)
diff = torch.abs(pred-mask)
wfoc = torch.pow(diff, 2.5)
wbce = (wfoc*weit*wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3))
inter = ((pred * mask)*weit).sum(dim=(2, 3))
err = (diff*weit).sum(dim=(2, 3))
wiou = 1 - (inter + 1)/(err + (weit*mask).sum(dim=(2,3)) +1)
return (wbce + wiou).mean()
# %%
def structure_loss1(pred, mask):
weit = 1 + 5*torch.abs(F.avg_pool2d(mask, kernel_size=7, stride=1, padding=3) - mask)
wbce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none')
pred = torch.sigmoid(pred)
wfoc = torch.pow(torch.abs(mask-pred), 2)
wbce = (wfoc*weit*wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3))
inter = ((pred * mask)*weit).sum(dim=(2, 3))
err = (torch.abs(pred-mask)*weit).sum(dim=(2, 3))
wiou = 1 - (inter + 1)/(err + (weit*mask).sum(dim=(2,3)) +1)
return (wbce + wiou).mean()
# %%
def test(model, path):
model.eval()
image_root = '{}/images/'.format(path)
gt_root = '{}/masks/'.format(path)
test_loader = test_dataset(image_root, gt_root, testsize=384)
dice_bank = []
iou_bank = []
loss_bank = []
acc_bank = []
time_bank = []
for i in range(test_loader.size):
image_list, gt_list, h, w, name= test_loader.load_data()
gt = 1.*(gt_list[0] > 0.)
image = torch.cat(image_list, dim=0)
image = image.cuda()
start_time = time.perf_counter()
with torch.no_grad():
_, _, res = model(image)
elapsed_time = time.perf_counter() - start_time
res0 = F.interpolate(res[0:1], size=(h,w))
loss = structure_loss(res0, gt.cuda())
res0 = res0.sigmoid().data.cpu().numpy().squeeze()
w = np.ones((h,w))
ww = np.ones((384,384))
w[:384,:384] = w[:384,:384] + ww
w[-384:,:384] = w[-384:,:384] + ww
w[:384,-384:] = w[:384,-384:] + ww
w[-384:,-384:] = w[-384:,-384:] + ww
res = res.sigmoid().data.cpu().numpy().squeeze()
res0[:384,:384] = res0[:384,:384] + res[1:2]
res0[-384:,:384] = res0[-384:,:384] + res[2:3]
res0[:384,-384:] = res0[:384,-384:] + res[3:4]
res0[-384:,-384:] = res0[-384:,-384:] + res[4:5]
res0 = res0/w
res0 = (res0>0.5)
gt = gt.data.cpu().numpy().squeeze()
smooth = 0.001
input_flat = np.reshape(res0,(-1))
target_flat = np.reshape(gt,(-1))
intersection = (input_flat*target_flat)
iou = ( intersection.sum() + smooth) / (res0.sum() + gt.sum() - intersection.sum() + smooth)
dice = ( 2*intersection.sum() + smooth) / (res0.sum() + gt.sum() + smooth)
acc = np.sum(res0 == gt) / (res0.shape[0]*res0.shape[1])
loss_bank.append(loss.item())
dice_bank.append(dice)
iou_bank.append(iou)
acc_bank.append(acc)
time_bank.append(elapsed_time)
print('Loss: {:.4f}, Dice: {:.4f}, IoU: {:.4f}, fps: {:.4f}'.
format(np.mean(loss_bank), np.mean(dice_bank), np.mean(iou_bank), 1./np.mean(time_bank)))
total_vali_loss_history.append(np.mean(loss_bank))
vali_accuracy_history.append(np.mean(acc_bank))
dice_history.append(np.mean(dice_bank))
iou_history.append(np.mean(iou_bank))
return np.mean(loss_bank), np.mean(acc_bank), np.mean(dice_bank), np.mean(iou_bank), 1./np.mean(time_bank)
# %%
def test1(model, path):
model.eval()
image_root = '{}/images/'.format(path)
gt_root = '{}/masks/'.format(path)
test_loader = test_dataset(image_root, gt_root, testsize=384)
dice_bank = []
iou_bank = []
loss_bank = []
acc_bank = []
time_bank = []
for i in range(test_loader.size):
image_list, gt_list, h, w, name= test_loader.load_data()
gt = 1.*(gt_list[0] > 0.)
image = torch.cat(image_list, dim=0)
image = image.cuda()
start_time = time.perf_counter()
with torch.no_grad():
_, _, res = model(image)
elapsed_time = time.perf_counter() - start_time
res0 = F.interpolate(res[0:1], size=(h,w))
w0 = 2*torch.abs(F.avg_pool2d(res0.sigmoid(), kernel_size=15, stride=1, padding=7)-0.5)
res0 = res0.squeeze()
w0 = w0.squeeze()
ww = torch.ones(384,384).to(device)
w1 = torch.zeros(h,w).to(device)
w1[:384,:384] = w1[:384,:384] + ww
w1[-384:,:384] = w1[-384:,:384] + ww
w1[:384,-384:] = w1[:384,-384:] + ww
w1[-384:,-384:] = w1[-384:,-384:] + ww
res1 = torch.zeros(h,w).to(device)
res1[:384,:384] = res1[:384,:384] + res[1,0]
res1[-384:,:384] = res1[-384:,:384] + res[2,0]
res1[:384,-384:] = res1[:384,-384:] + res[3,0]
res1[-384:,-384:] = res1[-384:,-384:] + res[4,0]
res_final = (res1+res0*w0)/(w0+w1)
loss = structure_loss(res_final.unsqueeze(0).unsqueeze(0), gt.cuda())
res_final = res_final.sigmoid().data.cpu().numpy().squeeze()
res_final = (res_final>0.5)
gt = gt.data.cpu().numpy().squeeze()
smooth = 0.001
input_flat = np.reshape(res_final,(-1))
target_flat = np.reshape(gt,(-1))
intersection = (input_flat*target_flat)
iou = ( intersection.sum() + smooth) / (res_final.sum() + gt.sum() - intersection.sum() + smooth)
dice = ( 2*intersection.sum() + smooth) / (res_final.sum() + gt.sum() + smooth)
acc = np.sum(res0 == gt) / (res0.shape[0]*res0.shape[1])
loss_bank.append(loss.item())
dice_bank.append(dice)
iou_bank.append(iou)
acc_bank.append(acc)
time_bank.append(elapsed_time)
print('Loss: {:.4f}, Dice: {:.4f}, IoU: {:.4f}, fps: {:.4f}'.
format(np.mean(loss_bank), np.mean(dice_bank), np.mean(iou_bank), 1./np.mean(time_bank)))
total_vali_loss_history.append(np.mean(loss_bank))
vali_accuracy_history.append(np.mean(acc_bank))
dice_history.append(np.mean(dice_bank))
iou_history.append(np.mean(iou_bank))
return np.mean(loss_bank), np.mean(acc_bank), np.mean(dice_bank), np.mean(iou_bank), 1./np.mean(time_bank)
# %%
best = 0.0
def train(train_loader, model, optimizer, optimizer1, epoch):
model.train()
loss_record2, loss_record3, loss_record4 = AvgMeter(), AvgMeter(), AvgMeter()
total_loss = 0.
for i, pack in tqdm(enumerate(train_loader, start=1), desc=str(epoch)):
# ---- data prepare ----
images, gts, images1, gts1 = pack
images = Variable(torch.cat([images,images1], dim=0)).cuda()
gts = Variable(torch.cat([gts,gts1], dim=0)).cuda()
# ---- forward ----
lateral_map_4, lateral_map_3, lateral_map_2 = model(images)
# # ---- loss function ----
loss4 = structure_loss(lateral_map_4, gts)
loss3 = structure_loss(lateral_map_3, gts)
loss2 = structure_loss(lateral_map_2, gts)
loss = 0.5 * loss2 + 0.3 * loss3 + 0.2 * loss4
total_loss += loss2
# ---- backward ----
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), grad_norm)
optimizer.step()
optimizer.zero_grad()
optimizer1.step()
optimizer1.zero_grad()
# # ---- recording loss ----
loss_record2.update(loss2.data, batch_size)
loss_record3.update(loss3.data, batch_size)
loss_record4.update(loss4.data, batch_size)
#
print("Epoch" + str(epoch))
save_path = '/work/u6859530/DFUC/snapshots/{}/'.format(train_save)
os.makedirs(save_path, exist_ok=True)
print('Saving checkpoint...')
torch.save({
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'epoch':epoch,
}, save_path + 'checkpoint.pt')
meanloss, mean_acc, mean_dice, mean_iou, mean_fps = test(model, vali_path)
global best
if mean_dice > best:
print('[Saving Snapshot:]', save_path + 'best.pt Dice: {:0.4f}'.format(mean_dice))
best = mean_dice
shutil.copyfile(save_path + 'checkpoint.pt', save_path + 'best.pt')
if epoch == 1 :
fp = open(save_path + 'record.txt','w')
else:
fp = open(save_path + 'record.txt','a')
fp.write('{} {:0.4f} {:0.4f} {:0.4f}\n'.format(epoch, mean_iou, mean_dice, total_loss/len(train_loader)))
# %%
# hyperparameters
epochs = 100
lr = 3e-5
batch_size = 4
grad_norm =2.0
beta1 = 0.5
beta2 = 0.99
train_path = "/home/u6859530/DFUC/DFUC2022_train_release/train"
vali_path = "/home/u6859530/DFUC/DFUC2022_train_release/val"
train_image_root = '{}/images/'.format(train_path)
train_gt_root = '{}/masks/'.format(train_path)
train_save = 'TransFuse_B_crop_iou_focal_filter_k7'
# %%
total_vali_loss_history = []
vali_accuracy_history = []
dice_history = []
iou_history = []
# %%
# Create device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = TransFuse_B(pretrained=True)
model = torch.nn.DataParallel(model)
weight = torch.load('/work/u6859530/DFUC/snapshots/TransFuse_B_lr_test2/best.pt')
model.load_state_dict(weight['model_state_dict'], strict=True)
# %%
my_list = model.named_parameters()
params = []
resnet_params = []
base_params = []
for n, p in my_list:
#if n.startswith('module.transformer.'):
# params.append(p)
if n.startswith('module.resnet.'):
resnet_params.append(p)
else:
base_params.append(p)
optimizer = torch.optim.AdamW([
{'params': base_params},
], 3e-5, betas=(beta1, beta2))
optimizer1 = torch.optim.SGD([
{'params': resnet_params},
], 1e-4, momentum=0.9)
#scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,epochs,eta_min=1e-5,last_epoch=-1)
# %%
train_loader = get_loader(train_image_root, train_gt_root, batchsize=batch_size, img_size=384)
total_step = len(train_loader)
print("#"*20, "Start Training", "#"*20)
for epoch in range(1, epochs + 1):
train(train_loader, model, optimizer, optimizer1, epoch)
#scheduler.step()
# %%