def train_val(config):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
train_loader = get_dataloader(img_dir=config.train_img_dir,
mask_dir=config.train_mask_dir,
mode="train",
batch_size=config.batch_size,
num_workers=config.num_workers,
smooth=config.smooth)
val_loader = get_dataloader(img_dir=config.val_img_dir,
mask_dir=config.val_mask_dir,
mode="val",
batch_size=config.batch_size,
num_workers=config.num_workers)
writer = SummaryWriter(
comment="LR_%f_BS_%d_MODEL_%s_DATA_%s" %
(config.lr, config.batch_size, config.model_type, config.data_type))
if config.model_type == "UNet":
model = UNet()
elif config.model_type == "UNet++":
model = UNetPP()
elif config.model_type == "SEDANet":
model = SEDANet()
elif config.model_type == "RefineNet":
model = rf101()
elif config.model_type == "DANet":
# src = "./pretrained/60_DANet_0.8086.pth"
# pretrained_dict = torch.load(src, map_location='cpu').module.state_dict()
# print("load pretrained params from stage 1: " + src)
# pretrained_dict.pop('seg1.1.weight')
# pretrained_dict.pop('seg1.1.bias')
model = DANet(backbone='resnext101',
nclass=config.output_ch,
pretrained=True,
norm_layer=nn.BatchNorm2d)
# model_dict = model.state_dict()
# model_dict.update(pretrained_dict)
# model.load_state_dict(model_dict)
elif config.model_type == "Deeplabv3+":
# src = "./pretrained/Deeplabv3+.pth"
# pretrained_dict = torch.load(src, map_location='cpu').module.state_dict()
# print("load pretrained params from stage 1: " + src)
# # print(pretrained_dict.keys())
# for key in list(pretrained_dict.keys()):
# if key.split('.')[0] == "cbr_last":
# pretrained_dict.pop(key)
model = deeplabv3_plus.DeepLabv3_plus(in_channels=3,
num_classes=config.output_ch,
backend='resnet101',
os=16,
pretrained=True,
norm_layer=nn.BatchNorm2d)
# model_dict = model.state_dict()
# model_dict.update(pretrained_dict)
# model.load_state_dict(model_dict)
elif config.model_type == "HRNet_OCR":
model = seg_hrnet_ocr.get_seg_model()
elif config.model_type == "scSEUNet":
model = scSEUNet(pretrained=True, norm_layer=nn.BatchNorm2d)
else:
model = UNet()
if config.iscontinue:
model = torch.load("./exp/13_Deeplabv3+_0.7619.pth",
map_location='cpu').module
for k, m in model.named_modules():
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatability
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model)
model = model.to(device)
labels = [1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
objects = [
'水体', '道路', '建筑物', '机场', '停车场', '操场', '普通耕地', '农业大棚', '自然草地', '绿地绿化',
'自然林', '人工林', '自然裸土', '人为裸土', '其它'
]
frequency = np.array([
0.0279, 0.0797, 0.1241, 0.00001, 0.0616, 0.0029, 0.2298, 0.0107,
0.1207, 0.0249, 0.1470, 0.0777, 0.0617, 0.0118, 0.0187
])
if config.optimizer == "sgd":
optimizer = SGD(model.parameters(),
lr=config.lr,
weight_decay=1e-4,
momentum=0.9)
elif config.optimizer == "adamw":
optimizer = adamw.AdamW(model.parameters(), lr=config.lr)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=config.lr)
# weight = torch.tensor([1, 1.5, 1, 2, 1.5, 2, 2, 1.2]).to(device)
# criterion = nn.CrossEntropyLoss(weight=weight)
if config.smooth == "all":
criterion = LabelSmoothSoftmaxCE()
elif config.smooth == "edge":
criterion = LabelSmoothCE()
else:
criterion = nn.CrossEntropyLoss()
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[25, 30, 35, 40], gamma=0.5)
# scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode="max", factor=0.1, patience=5, verbose=True)
scheduler = lr_scheduler.CosineAnnealingWarmRestarts(optimizer,
T_0=15,
eta_min=1e-4)
global_step = 0
max_fwiou = 0
for epoch in range(config.num_epochs):
epoch_loss = 0.0
seed = np.random.randint(0, 2, 1)
seed = 0
print("seed is ", seed)
if seed == 1:
train_loader = get_dataloader(img_dir=config.train_img_dir,
mask_dir=config.train_mask_dir,
mode="train",
batch_size=config.batch_size // 2,
num_workers=config.num_workers,
smooth=config.smooth)
val_loader = get_dataloader(img_dir=config.val_img_dir,
mask_dir=config.val_mask_dir,
mode="val",
batch_size=config.batch_size // 2,
num_workers=config.num_workers)
else:
train_loader = get_dataloader(img_dir=config.train_img_dir,
mask_dir=config.train_mask_dir,
mode="train",
batch_size=config.batch_size,
num_workers=config.num_workers,
smooth=config.smooth)
val_loader = get_dataloader(img_dir=config.val_img_dir,
mask_dir=config.val_mask_dir,
mode="val",
batch_size=config.batch_size,
num_workers=config.num_workers)
cm = np.zeros([15, 15])
print(optimizer.param_groups[0]['lr'])
with tqdm(total=config.num_train,
desc="Epoch %d / %d" % (epoch + 1, config.num_epochs),
unit='img',
ncols=100) as train_pbar:
model.train()
for image, mask in train_loader:
image = image.to(device, dtype=torch.float32)
if seed == 0:
pass
elif seed == 1:
image = F.interpolate(image,
size=(384, 384),
mode='bilinear',
align_corners=True)
mask = F.interpolate(mask.float(),
size=(384, 384),
mode='nearest')
if config.smooth == "edge":
mask = mask.to(device, dtype=torch.float32)
else:
mask = mask.to(device, dtype=torch.long).argmax(dim=1)
aux_out, out = model(image)
aux_loss = criterion(aux_out, mask)
seg_loss = criterion(out, mask)
loss = aux_loss + seg_loss
# pred = model(image)
# loss = criterion(pred, mask)
epoch_loss += loss.item()
writer.add_scalar('Loss/train', loss.item(), global_step)
train_pbar.set_postfix(**{'loss (batch)': loss.item()})
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_pbar.update(image.shape[0])
global_step += 1
# if global_step > 10:
# break
# scheduler.step()
print("\ntraining epoch loss: " +
str(epoch_loss / (float(config.num_train) /
(float(config.batch_size)))))
torch.cuda.empty_cache()
val_loss = 0
with torch.no_grad():
with tqdm(total=config.num_val,
desc="Epoch %d / %d validation round" %
(epoch + 1, config.num_epochs),
unit='img',
ncols=100) as val_pbar:
model.eval()
locker = 0
for image, mask in val_loader:
image = image.to(device, dtype=torch.float32)
target = mask.to(device, dtype=torch.long).argmax(dim=1)
mask = mask.cpu().numpy()
_, pred = model(image)
val_loss += F.cross_entropy(pred, target).item()
pred = pred.cpu().detach().numpy()
mask = semantic_to_mask(mask, labels)
pred = semantic_to_mask(pred, labels)
cm += get_confusion_matrix(mask, pred, labels)
val_pbar.update(image.shape[0])
if locker == 5:
writer.add_images('mask_a/true',
mask[2, :, :],
epoch + 1,
dataformats='HW')
writer.add_images('mask_a/pred',
pred[2, :, :],
epoch + 1,
dataformats='HW')
writer.add_images('mask_b/true',
mask[3, :, :],
epoch + 1,
dataformats='HW')
writer.add_images('mask_b/pred',
pred[3, :, :],
epoch + 1,
dataformats='HW')
locker += 1
# break
miou = get_miou(cm)
fw_miou = (miou * frequency).sum()
scheduler.step()
if True:
if torch.__version__ == "1.6.0":
torch.save(model,
config.result_path + "/%d_%s_%.4f.pth" %
(epoch + 1, config.model_type, fw_miou),
_use_new_zipfile_serialization=False)
else:
torch.save(
model, config.result_path + "/%d_%s_%.4f.pth" %
(epoch + 1, config.model_type, fw_miou))
max_fwiou = fw_miou
print("\n")
print(miou)
print("testing epoch loss: " + str(val_loss),
"FWmIoU = %.4f" % fw_miou)
writer.add_scalar('FWIoU/val', fw_miou, epoch + 1)
writer.add_scalar('loss/val', val_loss, epoch + 1)
for idx, name in enumerate(objects):
writer.add_scalar('iou/val' + name, miou[idx], epoch + 1)
torch.cuda.empty_cache()
writer.close()
print("Training finished")
def train_val(config):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
train_loader = get_dataloader(img_dir=config.train_img_dir,
mask_dir=config.train_mask_dir,
mode="train",
batch_size=config.batch_size,
num_workers=config.num_workers,
smooth=config.smooth)
val_loader = get_dataloader(img_dir=config.val_img_dir,
mask_dir=config.val_mask_dir,
mode="val",
batch_size=4,
num_workers=config.num_workers)
writer = SummaryWriter(
comment="LR_%f_BS_%d_MODEL_%s_DATA_%s" %
(config.lr, config.batch_size, config.model_type, config.data_type))
if config.model_type == "UNet":
model = UNet()
elif config.model_type == "UNet++":
model = UNetPP()
elif config.model_type == "SEDANet":
model = SEDANet()
elif config.model_type == "RefineNet":
model = rf101()
elif config.model_type == "BASNet":
model = BASNet(n_classes=8)
elif config.model_type == "DANet":
model = DANet(backbone='resnet101',
nclass=config.output_ch,
pretrained=True,
norm_layer=nn.BatchNorm2d)
elif config.model_type == "Deeplabv3+":
model = deeplabv3_plus.DeepLabv3_plus(in_channels=3,
num_classes=8,
backend='resnet101',
os=16,
pretrained=True,
norm_layer=nn.BatchNorm2d)
elif config.model_type == "HRNet_OCR":
model = seg_hrnet_ocr.get_seg_model()
elif config.model_type == "scSEUNet":
model = scSEUNet(pretrained=True, norm_layer=nn.BatchNorm2d)
else:
model = UNet()
if config.iscontinue:
model = torch.load("./exp/24_Deeplabv3+_0.7825757691389714.pth").module
for k, m in model.named_modules():
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatability
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model)
model = model.to(device)
labels = [100, 200, 300, 400, 500, 600, 700, 800]
objects = ['水体', '交通建筑', '建筑', '耕地', '草地', '林地', '裸土', '其他']
if config.optimizer == "sgd":
optimizer = SGD(model.parameters(),
lr=config.lr,
weight_decay=1e-4,
momentum=0.9)
elif config.optimizer == "adamw":
optimizer = adamw.AdamW(model.parameters(), lr=config.lr)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=config.lr)
# weight = torch.tensor([1, 1.5, 1, 2, 1.5, 2, 2, 1.2]).to(device)
# criterion = nn.CrossEntropyLoss(weight=weight)
criterion = BasLoss()
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[25, 30, 35, 40], gamma=0.5)
# scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode="max", factor=0.1, patience=5, verbose=True)
scheduler = lr_scheduler.CosineAnnealingWarmRestarts(optimizer,
T_0=15,
eta_min=1e-4)
global_step = 0
max_fwiou = 0
frequency = np.array(
[0.1051, 0.0607, 0.1842, 0.1715, 0.0869, 0.1572, 0.0512, 0.1832])
for epoch in range(config.num_epochs):
epoch_loss = 0.0
cm = np.zeros([8, 8])
print(optimizer.param_groups[0]['lr'])
with tqdm(total=config.num_train,
desc="Epoch %d / %d" % (epoch + 1, config.num_epochs),
unit='img',
ncols=100) as train_pbar:
model.train()
for image, mask in train_loader:
image = image.to(device, dtype=torch.float32)
mask = mask.to(device, dtype=torch.float16)
pred = model(image)
loss = criterion(pred, mask)
epoch_loss += loss.item()
writer.add_scalar('Loss/train', loss.item(), global_step)
train_pbar.set_postfix(**{'loss (batch)': loss.item()})
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_pbar.update(image.shape[0])
global_step += 1
# if global_step > 10:
# break
# scheduler.step()
print("\ntraining epoch loss: " +
str(epoch_loss / (float(config.num_train) /
(float(config.batch_size)))))
torch.cuda.empty_cache()
val_loss = 0
with torch.no_grad():
with tqdm(total=config.num_val,
desc="Epoch %d / %d validation round" %
(epoch + 1, config.num_epochs),
unit='img',
ncols=100) as val_pbar:
model.eval()
locker = 0
for image, mask in val_loader:
image = image.to(device, dtype=torch.float32)
target = mask.to(device, dtype=torch.long).argmax(dim=1)
mask = mask.cpu().numpy()
pred, _, _, _, _, _, _, _ = model(image)
val_loss += F.cross_entropy(pred, target).item()
pred = pred.cpu().detach().numpy()
mask = semantic_to_mask(mask, labels)
pred = semantic_to_mask(pred, labels)
cm += get_confusion_matrix(mask, pred, labels)
val_pbar.update(image.shape[0])
if locker == 25:
writer.add_images('mask_a/true',
mask[2, :, :],
epoch + 1,
dataformats='HW')
writer.add_images('mask_a/pred',
pred[2, :, :],
epoch + 1,
dataformats='HW')
writer.add_images('mask_b/true',
mask[3, :, :],
epoch + 1,
dataformats='HW')
writer.add_images('mask_b/pred',
pred[3, :, :],
epoch + 1,
dataformats='HW')
locker += 1
# break
miou = get_miou(cm)
fw_miou = (miou * frequency).sum()
scheduler.step()
if fw_miou > max_fwiou:
if torch.__version__ == "1.6.0":
torch.save(model,
config.result_path + "/%d_%s_%.4f.pth" %
(epoch + 1, config.model_type, fw_miou),
_use_new_zipfile_serialization=False)
else:
torch.save(
model, config.result_path + "/%d_%s_%.4f.pth" %
(epoch + 1, config.model_type, fw_miou))
max_fwiou = fw_miou
print("\n")
print(miou)
print("testing epoch loss: " + str(val_loss),
"FWmIoU = %.4f" % fw_miou)
writer.add_scalar('mIoU/val', miou.mean(), epoch + 1)
writer.add_scalar('FWIoU/val', fw_miou, epoch + 1)
writer.add_scalar('loss/val', val_loss, epoch + 1)
for idx, name in enumerate(objects):
writer.add_scalar('iou/val' + name, miou[idx], epoch + 1)
torch.cuda.empty_cache()
writer.close()
print("Training finished")
def main():
##################################################################data
CAMVID_PATH = Path('./Data/dataprocess/RITE-RGB-40-True/')
transform_train = transforms.Compose([transforms.ToTensor()])
train_joint_transformer = transforms.Compose([joint_transforms.JointRandomHorizontalFlip()])
#train
train_dset = camvid.CamVid(CAMVID_PATH, Gray = Gray_Flag, index_start = 0, index_end = 20, joint_transform=train_joint_transformer, transform=transform_train)
train_loader = torch.utils.data.DataLoader(train_dset, batch_size=batch_size, shuffle=True)
#val
val_dset = camvid.CamVid(CAMVID_PATH, Gray = Gray_Flag, index_start = 20, index_end = 40, joint_transform=None, transform=transform_train)
val_loader = torch.utils.data.DataLoader(val_dset, batch_size=batch_size, shuffle=True)
makedir_Flag = True
if makedir_Flag:
dirs = ['./result/' +PATH +'/visulizeT0/','./result/' +PATH+'/visulizeT1/']
for dir_num in range(len(dirs)):
os.makedirs(dirs[dir_num], exist_ok = True)
########################
global best_dice
global best_acc
print(args.model)
if args.model =='unet':
model = Unet3.UNet(in_channels = inch, num_classes = class_num, filter_scale=1)
elif args.model =='deeplabv3':
model = deeplabv3.DeepLabV3(class_num = class_num)
elif args.model =='deeplabv3_plus':
model = deeplabv3_plus.DeepLabv3_plus(in_channels = inch, num_classes = class_num, backend = 'mobilenet_v2', os = 16, pretrained = 'imagenet')
elif args.model =='pspnet':
model = pspnet.PSPNet(in_channels = inch, num_classes = class_num, backend = 'resnet101',pool_scales = (1, 2, 3, 6), pretrained = 'imagenet')
elif args.model =='unet_ae':
model = unet_ae.UnetResnetAE(in_channels = inch, num_classes = class_num, backend = 'resnet101', pretrained = 'imagenet')
elif args.model =='U_Net':
model = Unet2.U_Net(img_ch= inch,output_ch = class_num)
elif args.model =='R2U_Net':
model = R2Unet.R2U_Net(img_ch = inch,output_ch = class_num,t = args.t)
elif args.model =='Attunet':
model = AttUnet.AttU_Net(img_ch = inch,output_ch = class_num)
elif args.model == 'R2Attunet':
model = R2AttUnet.R2AttU_Net(img_ch = inch,output_ch = class_num,t = args.t)
elif args.model == 'segnet':
model = segnet.SegNet(num_classes = class_num, in_channels= inch)
elif args.model == 'cenet':
model = cenet.CE_Net(num_classes = class_num, num_channels= inch)
elif args.model == 'unet_nested':
model = UNet_Nested.UNet_Nested(in_channels = inch, n_classes= class_num)
elif args.model == 'denseaspp':
model = denseaspp.DenseASPP(class_num = class_num)
elif args.model == 'refinenet':
model = RefineNet.get_refinenet(input_size = 256, num_classes = class_num)
# elif args.model == 'rdfnet':#out = net(left, right)
# model = rdfnet.RDF(input_size = 256, num_classes = class_num)
device = torch.device("cuda")
model = torch.nn.DataParallel(model).to(device).cuda()
print('model',model)
print('loss',args.model, args.loss)
criterion = LossSelector[args.loss](**args.loss_params[args.loss])
cudnn.benchmark = True
print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
optimizer = optim.Adam(model.parameters(), lr = args.lr)
test_dices = []
test_aucs = []
model_fold = "./PKL/"+PATH + '/'
os.makedirs(model_fold, exist_ok = True)
for epoch in range(start_epoch, args.epochs):
print('--------------------------------------')
###########################
#####################
print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, state['lr']))
print('val', va)
train_loss, train_dice_x1, train_dice_x2, train_dice_x3, train_acc = train(train_loader, model, optimizer, epoch, dirs[0], criterion)
val_loss, val_dice1, val_dice2, val_dice3, val_acc = validate(val_loader, model, epoch, dirs[1])
# test(train_test_loader, model, epoch, dirs[2])
# save model
monitor_dice = val_dice1 + val_dice2 + val_dice3
monitor_acc = val_acc
is_best_dice = monitor_dice > best_dice
is_best_acc = monitor_acc > best_acc
best_dice = max(monitor_dice, best_dice)
best_acc = max(monitor_acc, best_acc)
if is_best_dice or is_best_acc:
name = model_fold + str(epoch) + '_D1-' + str(round(val_dice1,4)) + '_D2-' + str(round(val_dice2,4))+ '_D3-' + str(round(val_dice3,4))+ '_A-' + str(round(val_acc,4)) + '.pkl'
print(name)
# torch.save(model, name)
print('Best dice:', best_dice)
print('Best acc:', best_acc)