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main.py
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main.py
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import os
import cv2
from tensorboardX import SummaryWriter
from torch.backends import cudnn
from HRNetV2 import *
from HRNetWM import *
from MyDataset import *
def check_dir(root_dir=''):
if os.path.exists("{}".format(root_dir)) is False and root_dir != "":
os.mkdir("{}".format(root_dir))
if os.path.exists('{}result_tensorboard'.format(root_dir)) is False:
os.mkdir('{}result_tensorboard'.format(root_dir))
if os.path.exists("{}heatmap".format(root_dir)) is False:
os.mkdir("{}heatmap".format(root_dir))
if os.path.exists("{}heatmap2".format(root_dir)) is False:
os.mkdir("{}heatmap2".format(root_dir))
if os.path.exists("{}heatmap/train".format(root_dir)) is False:
os.mkdir("{}heatmap/train".format(root_dir))
if os.path.exists("{}heatmap/valid".format(root_dir)) is False:
os.mkdir("{}heatmap/valid".format(root_dir))
if os.path.exists("{}heatmap2/train".format(root_dir)) is False:
os.mkdir("{}heatmap2/train".format(root_dir))
if os.path.exists("{}heatmap2/valid".format(root_dir)) is False:
os.mkdir("{}heatmap2/valid".format(root_dir))
def train(end_epoch=150):
gpuid = "0"
root_dir = "HRNet8_SVD/"
check_dir(root_dir)
cudnn.benchmark = True
cudnn.deterministic = True
cudnn.enabled = True
device = torch.device("cuda:{}".format(gpuid) if torch.cuda.is_available() else "cpu")
torch.set_printoptions(precision=10)
dataset_train = MyDataset(root="DataSet", itemtype="Train")
dataset_valid = MyDataset(root="DataSet", itemtype="Valid")
data_loader_train = DataLoader(dataset=dataset_train, batch_size=8, shuffle=True)
data_loader_valid = DataLoader(dataset=dataset_valid, batch_size=64, shuffle=False)
model = HRNetMW(hrnet8(pretrained=False)) # HRNetMW is the HRNet8_SVD
model.to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=0.0001, nesterov=False)
RESUME = True
try:
assert (RESUME is True)
checkpoint = torch.load("{}checkpoint.pth".format(root_dir))
model.load_state_dict(checkpoint['model']) # load checkpoint
optimizer.load_state_dict(checkpoint['optimizer']) # load optimizer
start_epoch = checkpoint['epoch'] + 1 # set epoch num
train_loss = checkpoint["train_loss"]
train_wwloss = checkpoint["train_wwloss"]
train_mmloss = checkpoint["train_mmloss"]
train_wmloss = checkpoint["train_wmloss"]
train_stdloss = checkpoint["train_stdloss"]
valid_loss = checkpoint["valid_loss"]
valid_wwloss = checkpoint["valid_wwloss"]
valid_mmloss = checkpoint["valid_mmloss"]
valid_wmloss = checkpoint["valid_wmloss"]
valid_stdloss = checkpoint["valid_stdloss"]
best_epoch = checkpoint["best_epoch"]
best_model = checkpoint["best_model"]
# optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=0.0001, nesterov=False)
optimizer = torch.optim.SGD(model.parameters(), lr=0.003162277659685048, momentum=0.9, weight_decay=0.0001,
nesterov=False)
lrScheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
[1, 2, 3, 4, 5],
gamma=0.7943282347,
last_epoch=-1)
lrScheduler.step(5)
print(
"continue training\noptimizer param：{}\nstart epoch：{}\nbest epoch：{}\n".format
(optimizer, start_epoch, best_epoch))
print('################'.center(15), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("##########".center(10), end="")
print()
print('epoch'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print(str(epoch_num).center(10), end="")
print()
print('################'.center(15), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("##########".center(10), end="")
print()
print('train loss'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("{:.6f}".format(train_loss[epoch_num - 1]).center(10), end="")
print()
print('train wwloss'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("{:.6f}".format(train_wwloss[epoch_num - 1]).center(10), end="")
print()
print('train mmloss'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("{:.6f}".format(train_mmloss[epoch_num - 1]).center(10), end="")
print()
print('train wmloss'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("{:.6f}".format(train_wmloss[epoch_num - 1]).center(10), end="")
print()
print('train stdloss'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("{:.6f}".format(train_stdloss[epoch_num - 1]).center(10), end="")
print()
print('---------------'.center(15), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("----------".center(10), end="")
print()
print('valid loss'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("{:.6f}".format(valid_loss[epoch_num - 1]).center(10), end="")
print()
print('valid wwloss'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("{:.6f}".format(valid_wwloss[epoch_num - 1]).center(10), end="")
print()
print('valid mmloss'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("{:.6f}".format(valid_mmloss[epoch_num - 1]).center(10), end="")
print()
print('valid wmloss'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("{:.6f}".format(valid_wmloss[epoch_num - 1]).center(10), end="")
print()
print('valid stdloss'.center(15), end="")
print('|'.center(1), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("{:.6f}".format(valid_stdloss[epoch_num - 1]).center(10), end="")
print()
print('################'.center(15), end="")
for epoch_num in range(1, checkpoint['epoch'] + 1):
print("##########".center(10), end="")
print()
except Exception:
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=0.0001, nesterov=False)
lrScheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
[1, 2, 3, 4, 5],
gamma=1.5848931925,
last_epoch=-1) # 0.7943282347
start_epoch = 1
train_loss = []
train_wwloss = []
train_mmloss = []
train_wmloss = []
train_stdloss = []
valid_loss = []
valid_wwloss = []
valid_mmloss = []
valid_wmloss = []
valid_stdloss = []
best_epoch = 0
best_model = model.state_dict()
print(
"traing a new model\noptimizer paraml：{}\nstart epoch：{}\ntrain loss：{}\nvalid loss:{}\nbest epoch：{}\n".format
(optimizer, start_epoch, train_loss, valid_loss, best_epoch))
assert (end_epoch >= start_epoch)
assert (len(train_loss) == len(valid_loss))
assert (len(train_loss) + 1 == start_epoch)
writer = SummaryWriter('{}result_tensorboard'.format(root_dir))
for epoch_index in range(start_epoch, end_epoch + 1):
# training ############################################################
model.train()
model.model.__setattr__("Istrain", True)
torch.set_grad_enabled(True)
this_train_loss = 0
this_train_wwloss = 0
this_train_mmloss = 0
this_train_wmloss = 0
this_train_stdloss = 0
print("epoch:{}".format(epoch_index))
for i, data in enumerate(data_loader_train):
images, indexX, indexY, labels, labels_w = data
images = images.to(device)
indexX = indexX.to(device)
indexY = indexY.to(device)
labels = labels.to(device)
labels_w = labels_w.to(device)
deg_pre_ww, deg_pre_mm, deg_pre_wm, std_dev = model(images, indexX, indexY)
loss_ww = torch.mean(torch.abs(deg_pre_ww - labels_w))
loss_mm = torch.mean(torch.abs(deg_pre_mm - (labels_w - labels)))
loss_wm = torch.mean(torch.abs(deg_pre_wm - labels))
loss = loss_mm + loss_ww + loss_wm + std_dev
print("loss:{:.6f}, wwloss:{:.6f}, mmloss:{:.6f}, wmloss:{:.6f}, stdloss:{:.6f}".
format(loss.data,
loss_ww.data,
loss_mm.data,
loss_wm.data,
std_dev.data
))
this_train_loss = this_train_loss + float(loss.data * labels.shape[0])
this_train_wwloss = this_train_wwloss + float(loss_ww.data * labels.shape[0])
this_train_mmloss = this_train_mmloss + float(loss_mm.data * labels.shape[0])
this_train_wmloss = this_train_wmloss + float(loss_wm.data * labels.shape[0])
this_train_stdloss = this_train_stdloss + float(std_dev.data * labels.shape[0])
optimizer.zero_grad()
loss.backward()
optimizer.step()
lrScheduler.step()
this_train_loss = this_train_loss / dataset_train.__len__()
this_train_wwloss = this_train_wwloss / dataset_train.__len__()
this_train_mmloss = this_train_mmloss / dataset_train.__len__()
this_train_wmloss = this_train_wmloss / dataset_train.__len__()
this_train_stdloss = this_train_stdloss / dataset_train.__len__()
train_loss.append(this_train_loss)
train_wwloss.append(this_train_wwloss)
train_mmloss.append(this_train_mmloss)
train_wmloss.append(this_train_wmloss)
train_stdloss.append(this_train_stdloss)
# validiation #########################################################
model.eval()
model.model.__setattr__("Istrain", False)
torch.set_grad_enabled(False)
this_valid_loss = 0
this_valid_wwloss = 0
this_valid_mmloss = 0
this_valid_wmloss = 0
this_valid_stdloss = 0
for i, data in enumerate(data_loader_valid):
images, indexX, indexY, labels, labels_w = data
images = images.to(device)
indexX = indexX.to(device)
indexY = indexY.to(device)
labels = labels.to(device)
labels_w = labels_w.to(device)
deg_pre_ww, deg_pre_mm, deg_pre_wm, std_dev = model(images, indexX, indexY)
loss_ww = torch.mean(torch.abs(deg_pre_ww - labels_w))
loss_mm = torch.mean(torch.abs(deg_pre_mm - (labels_w - labels)))
loss_wm = torch.mean(torch.abs(deg_pre_wm - labels))
loss = loss_mm + loss_ww + loss_wm + std_dev
this_valid_loss = this_valid_loss + float(loss.data * labels.shape[0])
this_valid_wwloss = this_valid_wwloss + float(loss_ww.data * labels.shape[0])
this_valid_mmloss = this_valid_mmloss + float(loss_mm.data * labels.shape[0])
this_valid_wmloss = this_valid_wmloss + float(loss_wm.data * labels.shape[0])
this_valid_stdloss = this_valid_stdloss + float(std_dev.data * labels.shape[0])
this_valid_loss = this_valid_loss / dataset_valid.__len__()
this_valid_wwloss = this_valid_wwloss / dataset_valid.__len__()
this_valid_mmloss = this_valid_mmloss / dataset_valid.__len__()
this_valid_wmloss = this_valid_wmloss / dataset_valid.__len__()
this_valid_stdloss = this_valid_stdloss / dataset_valid.__len__()
if len(valid_loss) > 0 and this_valid_loss < min(valid_loss):
best_epoch = epoch_index
best_model = model.state_dict()
valid_loss.append(this_valid_loss)
valid_wwloss.append(this_valid_wwloss)
valid_mmloss.append(this_valid_mmloss)
valid_wmloss.append(this_valid_wmloss)
valid_stdloss.append(this_valid_stdloss)
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"epoch": epoch_index,
"train_loss": train_loss,
"train_wwloss": train_wwloss,
"train_mmloss": train_mmloss,
"train_wmloss": train_wmloss,
"train_stdloss": train_stdloss,
"valid_loss": valid_loss,
"valid_wwloss": valid_wwloss,
"valid_mmloss": valid_mmloss,
"valid_wmloss": valid_wmloss,
"valid_stdloss": valid_stdloss,
"best_epoch": best_epoch,
"best_model": best_model
}
torch.save(checkpoint, "{}checkpoint.pth".format(root_dir))
exampleImageindex = [12, 99]
dirtype = ['valid', 'train']
for imgtype in dirtype:
for index in exampleImageindex:
if imgtype == 'train':
images, indexX, indexY, _, _ = dataset_train.__getitem__(index)
img255 = dataset_train.getimg(index)
else:
images, indexX, indexY, _, _ = dataset_valid.__getitem__(index)
img255 = dataset_valid.getimg(index)
maskimg = img255[30:33].cpu().numpy().astype(np.uint8)
maskimg = np.transpose(maskimg, [1, 2, 0])[:, :, [2, 1, 0]]
waferimg = img255[3:6].cpu().numpy().astype(np.uint8)
waferimg = np.transpose(waferimg, [1, 2, 0])[:, :, [2, 1, 0]]
images = torch.unsqueeze(images, dim=0).to(device)
indexX = torch.unsqueeze(indexX, dim=0).to(device)
indexY = torch.unsqueeze(indexY, dim=0).to(device)
waferheatmap, wx, wy, maskheatmap, mx, my = model.forward(images, indexX, indexY, heatmap_flag=True)
wx = wx.cpu().numpy()[0][0]
wy = wy.cpu().numpy()[0][0]
mx = mx.cpu().numpy()[0][0]
my = my.cpu().numpy()[0][0]
waferheatmap = waferheatmap[:32]
maskheatmap = maskheatmap[:32]
maskheatmap = maskheatmap - torch.min(maskheatmap)
maskheatmap = (maskheatmap / torch.max(maskheatmap) * 255).cpu().numpy().astype(np.uint8)
maskheatmap = cv2.applyColorMap(maskheatmap, cv2.COLORMAP_JET)
maskheatmap = cv2.addWeighted(maskheatmap, 0.15, maskimg, 1, 0)
cv2.imwrite("{}heatmap2/{}/mask{}_epoch{}_heatmap.png".format(root_dir, imgtype, index, epoch_index),
maskheatmap)
cv2.circle(maskheatmap, (int(mx + 0.5), int(my + 0.5)), 1, (0, 255, 0))
cv2.imwrite("{}heatmap/{}/mask{}_epoch{}_heatmap.png".format(root_dir, imgtype, index, epoch_index),
maskheatmap)
cv2.imwrite("{}heatmap/{}/mask{}_epoch{}_heatmap.png".format(root_dir, imgtype, index, 0), maskimg)
waferheatmap = waferheatmap - torch.min(waferheatmap)
waferheatmap = (waferheatmap / torch.max(waferheatmap) * 255).cpu().numpy().astype(np.uint8)
waferheatmap = cv2.applyColorMap(waferheatmap, cv2.COLORMAP_JET)
waferheatmap = cv2.addWeighted(waferheatmap, 0.15, waferimg, 1, 0)
cv2.imwrite("{}heatmap2/{}/wafer{}_epoch{}_heatmap.png".format(root_dir, imgtype, index, epoch_index),
waferheatmap)
cv2.circle(waferheatmap, (int(wx + 0.5), int(wy + 0.5)), 1, (0, 255, 0))
cv2.imwrite("{}heatmap/{}/wafer{}_epoch{}_heatmap.png".format(root_dir, imgtype, index, epoch_index),
waferheatmap)
cv2.imwrite("{}heatmap/{}/wafer{}_epoch{}_heatmap.png".format(root_dir, imgtype, index, 0), waferimg)
print(
"train loss:{:.6f}, train loss_ww:{:.6f}, train loss_mm:{:.6f}, train loss_wm:{:.6f}, train dev_std:{:.6f}"
.format(this_train_loss, this_train_wwloss, this_train_mmloss, this_train_wmloss, this_train_stdloss))
print(
"valid loss:{:.6f}, valid loss_ww:{:.6f}, valid loss_mm:{:.6f}, valid loss_wm:{:.6f}, valid dev_std:{:.6f}"
.format(this_valid_loss, this_valid_wwloss, this_valid_mmloss, this_valid_wmloss, this_valid_stdloss))
writer.add_scalars("loss", {
"train": this_train_loss,
"valid": this_valid_loss
}, epoch_index)
writer.add_scalars("wwloss", {
"train": this_train_wwloss,
"valid": this_valid_wwloss
}, epoch_index)
writer.add_scalars("mmloss", {
"train": this_train_mmloss,
"valid": this_valid_mmloss
}, epoch_index)
writer.add_scalars("wmloss", {
"train": this_train_wmloss,
"valid": this_valid_wmloss
}, epoch_index)
writer.add_scalars("stdloss", {
"train": this_train_stdloss,
"valid": this_valid_stdloss
}, epoch_index)
writer.close()
if __name__ == "__main__":
pass
train()