/
train_swin_gz.py
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/
train_swin_gz.py
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import torch
import torch.nn as nn
import tqdm
from my_scheduler import LR_Scheduler
from swin_ynet import Encoder
from data.dataset_swin_GZ import MyData
import torch.optim as optim
from torch.utils.data import DataLoader
import warnings
warnings.filterwarnings("ignore")
model = Encoder().cuda()
import pytorch_iou
import pytorch_ssim
deal = nn.Softmax(dim=1)
def all_loss(pred, gt):
ce_loss = nn.CrossEntropyLoss()
ssim_loss = pytorch_ssim.SSIM(window_size=11, size_average=True).cuda()
iou_loss = pytorch_iou.IOU().cuda()
ce_out = ce_loss(pred, gt.long())
ssim_out = 1 - ssim_loss(deal(pred), gt)
iou_out = iou_loss(deal(pred), gt)
loss = ce_out + ssim_out + iou_out
return loss
model = model.train()
ce_loss = nn.CrossEntropyLoss()
ssim_loss = pytorch_ssim.SSIM(window_size=7, size_average=True).cuda()
iou_loss = pytorch_iou.IOU().cuda()
LR = 0.01
LR_VGG = 0.00001
EPOCH = 80
scheduler = LR_Scheduler('cos', LR, EPOCH, 3743 // 10 + 1)
optimizer = optim.SGD(model.parameters(), lr=LR, momentum=0.9, weight_decay=0.0005, nesterov=False)
def make_optimizer(LR, model):
params = []
for key, value in model.named_parameters():
if not value.requires_grad:
continue
if "encoder1" in key:
lr = LR * 0.1
else:
lr = LR
params += [{"params": [value], "lr": lr}]
optimizer = getattr(torch.optim, "SGD")(params, momentum=0.9, weight_decay=0.0005, nesterov=False)
return optimizer
train_loader = DataLoader(MyData(),
shuffle=True,
batch_size=10,
pin_memory=True,
num_workers=16,
)
losses0 = 0
losses1 = 0
losses2 = 0
losses3 = 0
losses4 = 0
losses5 = 0
losses6 = 0
losses7 = 0
losses8 = 0
losses9 = 0
losses10 = 0
losses11 = 0
print(len(train_loader))
def adjust_learning_rate(optimizer, epoch, start_lr):
if epoch % 20 == 0: # epoch != 0 and
for param_group in optimizer.param_groups:
param_group["lr"] = param_group["lr"] * 0.1
print(param_group["lr"])
loss_least = 100000
for epoch_num in range(EPOCH):
print(epoch_num)
adjust_learning_rate(optimizer, epoch_num, LR)
print('LR is:', optimizer.state_dict()['param_groups'][0]['lr'])
show_dict = {'epoch': epoch_num}
loss_all = 0
for i_batch, (im1, im2, label0, label1, label2, label3) in enumerate(
tqdm.tqdm(train_loader, ncols=60, postfix=show_dict)): # ,edge0,edge1,edge2,edge3
im1 = im1.cuda()
im2 = im2.cuda()
label0 = label0.cuda()
label1 = label1.cuda()
label2 = label2.cuda()
label3 = label3.cuda()
outputs = model(im1, im2)
loss0 = ce_loss(outputs[0], label0.long())
loss1 = ce_loss(outputs[1], label1.long())
loss2 = ce_loss(outputs[2], label2.long())
loss3 = ce_loss(outputs[3], label3.long())
loss4 = 1. - ssim_loss(deal(outputs[0]), label0)
loss5 = 1. - ssim_loss(deal(outputs[1]), label1)
loss6 = 1. - ssim_loss(deal(outputs[2]), label2)
loss7 = 1. - ssim_loss(deal(outputs[3]), label3)
loss8 = iou_loss(deal(outputs[0]), label0)
loss9 = iou_loss(deal(outputs[1]), label1)
loss10 = iou_loss(deal(outputs[2]), label2)
loss11 = iou_loss(deal(outputs[3]), label3)
loss = loss0 + loss1 + loss2 + loss3 + loss4 + loss5 + loss6 + loss7 + loss8 + loss9 + loss10 + loss11
loss_all += loss
losses0 += loss0
losses1 += loss1
losses2 += loss2
losses3 += loss3
losses4 += loss4
losses5 += loss5
losses6 += loss6
losses7 += loss7
losses8 += loss8
losses9 += loss9
losses10 += loss10
losses11 += loss11
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i_batch % 100 == 0:
print(i_batch, '|', 'losses0: {:.3f}'.format(losses0.data), '|', 'losses1: {:.3f}'.format(losses1.data),
'|', 'losses2: {:.3f}'.format(losses2.data), '|', 'losses3: {:.3f}'.format(losses3.data), '|',
'losses4: {:.3f}'.format(losses4.data), '|', 'losses5: {:.3f}'.format(losses5.data), '|',
'losses6: {:.3f}'.format(losses6.data), '|', 'losses7: {:.3f}'.format(losses7.data),
'losses8: {:.3f}'.format(losses8.data), '|', 'losses9: {:.3f}'.format(losses9.data), '|',
'losses10: {:.3f}'.format(losses10.data), '|', 'losses11: {:.3f}'.format(losses11.data))
losses0 = 0
losses1 = 0
losses2 = 0
losses3 = 0
losses4 = 0
losses5 = 0
losses6 = 0
losses7 = 0
losses8 = 0
losses9 = 0
losses10 = 0
losses11 = 0
if loss_all <= loss_least:
loss_least = loss_all
torch.save(model.state_dict(), 'new_try3.pth')
print('\n', 'epoch:', epoch_num, 'epoch loss:', loss_all)