def test():
device = torch.device(args.devices if torch.cuda.is_available() else "cpu")
#test_dataset = Training_Dataset(args.test_dir, (args.image_size,args.image_size),(args.noise, args.noise_param))
# test_dataset = HongZhang_Dataset("/data_1/data/Noise2Noise/shenqingbiao/0202", "/data_1/data/Noise2Noise/hongzhang")
test_dataset = HongZhang_TestDataset("/data_1/data/红章图片/test/hongzhang", (256, 256))
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)
# choose the model
if args.model == "unet":
model = UNet(in_channels=args.image_channels, out_channels=args.image_channels)
elif args.model == "srresnet":
model = SRResnet(args.image_channels, args.image_channels)
elif args.model == "eesp":
model = EESPNet_Seg(args.image_channels, 2)
else:
model = UNet(in_channels=args.image_channels, out_channels=args.image_channels)
print('loading model')
# model.load_state_dict(torch.load(model_path))
# model.eval()
# model.to(device)
if args.resume_model:
resume_model(model, args.resume_model)
model.eval()
model.to(device)
# result_dir = args.denoised_dir
# if not os.path.exists(result_dir):
# os.mkdir(result_dir)
for batch_idx, image in enumerate(test_loader):
#PIL_ShowTensor(torch.squeeze(source))
#PIL_ShowTensor2(torch.squeeze(source),torch.squeeze(noise))
image = image.to(device)
denoised_img = model(image).detach().cpu()
CV2_showTensors(image.cpu(),denoised_img,timeout=5000)
def test(args):
model = UNet(3, 1)
model.load_state_dict(torch.load(args.weight, map_location='cpu'))
verse_data = DatasetVerse(dir_img,
dir_mask,
transform=x_transform,
target_transform=y_transform)
dataloaders = DataLoader(verse_data, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
for x, _ in dataloaders:
y = model(x).sigmoid()
img_y = torch.squeeze(y).numpy()
plt.imshow(img_y)
plt.pause(0.01)
plt.show()
def predict(args):
batch_size = 16
num_workers = 4
postprocess = True if args.postprocess == "True" else False
model = UNet(1, 1, first_out_channels=16)
model.eval()
if args.model_path is not None:
model_weights = torch.load(args.model_path)
model.load_state_dict(model_weights)
model = nn.DataParallel(model).cuda()
transforms = [tsfm.Window(-200, 1000), tsfm.MinMaxNorm(-200, 1000)]
image_path_list = sorted([
os.path.join(args.image_dir, file)
for file in os.listdir(args.image_dir) if "nii" in file
])
image_id_list = [
os.path.basename(path).split("-")[0] for path in image_path_list
]
progress = tqdm(total=len(image_id_list))
pred_info_list = []
for image_id, image_path in zip(image_id_list, image_path_list):
dataset = FracNetInferenceDataset(image_path, transforms=transforms)
dataloader = FracNetInferenceDataset.get_dataloader(
dataset, batch_size, num_workers)
pred_arr = _predict_single_image(model, dataloader, postprocess,
args.prob_thresh, args.bone_thresh,
args.size_thresh)
pred_image, pred_info = _make_submission_files(pred_arr, image_id,
dataset.image_affine)
pred_info_list.append(pred_info)
pred_path = os.path.join(args.pred_dir, f"{image_id}_pred.nii.gz")
nib.save(pred_image, pred_path)
progress.update()
pred_info = pd.concat(pred_info_list, ignore_index=True)
pred_info.to_csv(os.path.join(args.pred_dir, "pred_info.csv"), index=False)
# resume
if osp.isfile(resume_path):
checkpoint = torch.load(resume_path)
model.load_state_dict(checkpoint["model_state"])
best_iou = checkpoint['best_iou']
print(
"=====>",
"Loaded checkpoint '{}' (iter {})".format(resume_path,
checkpoint["epoch"]))
print("=====> best mIoU: %.4f best mean dice: %.4f" %
(best_iou, (best_iou * 2) / (best_iou + 1)))
else:
raise ValueError("can't find model")
print(">>>Test After Dense CRF: ")
model.eval()
running_metrics.reset()
with torch.no_grad():
for i, (img, mask) in tqdm(enumerate(val_loader)):
img = img.to(device)
output = model(img) #[-1, 9, 256, 256]
probs = F.softmax(output, dim=1)
pred = probs.cpu().data[0].numpy()
label = mask.cpu().data[0].numpy()
# crf
img = img.cpu().data[0].numpy()
pred = dense_crf(img * 255, pred)
# print(pred.shape)
# _, pred = torch.max(torch.tensor(pred), dim=-1)
pred = np.asarray(pred, dtype=np.int)
label = np.asarray(label, dtype=np.int)
class BaseModel:
losses = {'train': [], 'val': []}
acces = {'train': [], 'val': []}
scores = {'train': [], 'val': []}
pred = {'train': [], 'val': []}
true = {'train': [], 'val': []}
def __init__(self, args):
self.args = args
self.net = None
print(args.model_name)
if args.model_name == 'UNet':
self.net = UNet(args.in_channels, args.num_classes)
self.net.apply(weights_init)
elif args.model_name == 'UNetResNet34':
self.net = UNetResNet34(args.num_classes, dropout_2d=0.2)
elif args.model_name == 'UNetResNet152':
self.net = UNetResNet152(args.num_classes, dropout_2d=0.2)
elif args.model_name == 'UNet11':
self.net = UNet11(args.num_classes, pretrained=True)
elif args.model_name == 'UNetVGG16':
self.net = UNetVGG16(args.num_classes,
pretrained=True,
dropout_2d=0.0,
is_deconv=True)
elif args.model_name == 'deeplab50_v2':
if args.ms:
raise NotImplemented
else:
self.net = deeplab50_v2(args.num_classes,
pretrained=args.pretrained)
elif args.model_name == 'deeplab_v2':
if args.ms:
self.net = ms_deeplab_v2(args.num_classes,
pretrained=args.pretrained,
scales=args.ms_scales)
else:
self.net = deeplab_v2(args.num_classes,
pretrained=args.pretrained)
elif args.model_name == 'deeplab_v3':
if args.ms:
self.net = ms_deeplab_v3(args.num_classes,
out_stride=args.out_stride,
pretrained=args.pretrained,
scales=args.ms_scales)
else:
self.net = deeplab_v3(args.num_classes,
out_stride=args.out_stride,
pretrained=args.pretrained)
elif args.model_name == 'deeplab_v3_plus':
if args.ms:
self.net = ms_deeplab_v3_plus(args.num_classes,
out_stride=args.out_stride,
pretrained=args.pretrained,
scales=args.ms_scales)
else:
self.net = deeplab_v3_plus(args.num_classes,
out_stride=args.out_stride,
pretrained=args.pretrained)
self.interp = nn.Upsample(size=args.size, mode='bilinear')
self.iterations = args.epochs
self.lr_current = args.lr
self.cuda = args.cuda
self.phase = args.phase
self.lr_policy = args.lr_policy
self.cyclic_m = args.cyclic_m
if self.lr_policy == 'cyclic':
print('using cyclic')
assert self.iterations % self.cyclic_m == 0
if args.loss == 'CELoss':
self.criterion = nn.CrossEntropyLoss(size_average=True)
elif args.loss == 'DiceLoss':
self.criterion = DiceLoss(num_classes=args.num_classes)
elif args.loss == 'MixLoss':
self.criterion = MixLoss(args.num_classes,
weights=args.loss_weights)
elif args.loss == 'LovaszLoss':
self.criterion = LovaszSoftmax(per_image=args.loss_per_img)
elif args.loss == 'FocalLoss':
self.criterion = FocalLoss(args.num_classes, alpha=None, gamma=2)
else:
raise RuntimeError('must define loss')
if 'deeplab' in args.model_name:
self.optimizer = optim.SGD(
[{
'params': get_1x_lr_params_NOscale(self.net),
'lr': args.lr
}, {
'params': get_10x_lr_params(self.net),
'lr': 10 * args.lr
}],
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
self.optimizer = optim.SGD(filter(lambda p: p.requires_grad,
self.net.parameters()),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
self.iters = 0
self.best_val = 0.0
self.count = 0
def init_model(self):
if self.args.resume_model:
saved_state_dict = torch.load(
self.args.resume_model,
map_location=lambda storage, loc: storage)
if self.args.ms:
new_params = self.net.Scale.state_dict().copy()
for i in saved_state_dict:
# Scale.layer5.conv2d_list.3.weight
i_parts = i.split('.')
# print i_parts
if not (not i_parts[0] == 'layer5') and (not i_parts[0]
== 'decoder'):
new_params[i] = saved_state_dict[i]
self.net.Scale.load_state_dict(new_params)
else:
new_params = self.net.state_dict().copy()
for i in saved_state_dict:
# Scale.layer5.conv2d_list.3.weight
i_parts = i.split('.')
# print i_parts
if (not i_parts[0] == 'layer5') and (not i_parts[0]
== 'decoder'):
# if not i_parts[0] == 'layer5':
new_params[i] = saved_state_dict[i]
self.net.load_state_dict(new_params)
print('Resuming training, image net loading {}...'.format(
self.args.resume_model))
# self.load_weights(self.net, self.args.resume_model)
if self.args.mGPUs:
self.net = nn.DataParallel(self.net)
if self.args.cuda:
self.net = self.net.cuda()
cudnn.benchmark = True
def _adjust_learning_rate(self, epoch):
"""Sets the learning rate to the initial LR decayed by 10 at every specified step
# Adapted from PyTorch Imagenet example:
# https://github.com/pytorch/examples/blob/master/imagenet/main.py
"""
if epoch < int(self.iterations * 0.5):
self.lr_current = max(self.lr_current * self.args.gamma, 1e-4)
elif epoch < int(self.iterations * 0.85):
self.lr_current = max(self.lr_current * self.args.gamma, 1e-5)
else:
self.lr_current = max(self.lr_current * self.args.gamma, 1e-6)
self.optimizer.param_groups[0]['lr'] = self.lr_current
self.optimizer.param_groups[1]['lr'] = self.lr_current * 10
def save_network(self, net, net_name, epoch, label=''):
save_fname = '%s_%s_%s.pth' % (epoch, net_name, label)
save_path = os.path.join(self.args.save_folder, self.args.exp_name,
save_fname)
torch.save(net.state_dict(), save_path)
def load_weights(self, net, base_file):
other, ext = os.path.splitext(base_file)
if ext == '.pkl' or '.pth':
print('Loading weights into state dict...')
net.load_state_dict(
torch.load(base_file,
map_location=lambda storage, loc: storage))
print('Finished!')
else:
print('Sorry only .pth and .pkl files supported.')
def load_trained_model(self):
path = os.path.join(self.args.save_folder, self.args.exp_name,
self.args.trained_model)
print('eval cls, image net loading {}...'.format(path))
if self.args.ms:
self.load_weights(self.net.Scale, path)
else:
self.load_weights(self.net, path)
def eval(self, dataloader):
assert self.phase == 'test', "Command arg phase should be 'test'. "
from tqdm import tqdm
self.net.eval()
output = []
for i, image in tqdm(enumerate(dataloader)):
if self.cuda:
image = Variable(image.cuda(), volatile=True)
else:
image = Variable(image, volatile=True)
# cls forward
out = self.net(image)
if isinstance(out, list):
out_max = out[-1]
if out_max.size(2) != image.size(2):
out = self.interp(out_max)
else:
if out.size(2) != image.size(2):
out = self.interp(out)
# out [bs * num_tta, c, h, w]
if self.args.use_tta:
num_tta = len(tta_config)
# out = F.softmax(out, dim=1)
out = detta_score(
out.view(num_tta, -1, self.args.num_classes, out.size(2),
out.size(3))) # [num_tta, bs, nclass, H, W]
out = out.mean(dim=0) # [bs, nclass, H, W]
out = F.softmax(out)
output.extend([
resize(pred[1].data.cpu().numpy(), (101, 101)) for pred in out
])
return np.array(output)
def tta(self, dataloaders):
results = np.zeros(shape=(len(dataloaders[0].dataset),
self.args.num_classes))
for dataloader in dataloaders:
output = self.eval(dataloader)
results += output
return np.argmax(results, 1)
def tta_output(self, dataloaders):
results = np.zeros(shape=(len(dataloaders[0].dataset),
self.args.num_classes))
for dataloader in dataloaders:
output = self.eval(dataloader)
results += output
return results
def test_val(self, dataloader):
assert self.phase == 'test', "Command arg phase should be 'test'. "
from tqdm import tqdm
self.net.eval()
predict = []
true = []
t1 = time.time()
for i, (image, mask) in tqdm(enumerate(dataloader)):
if self.cuda:
image = Variable(image.cuda(), volatile=True)
label_image = Variable(mask.cuda(), volatile=True)
else:
image = Variable(image, volatile=True)
label_image = Variable(mask, volatile=True)
# cls forward
out = self.net(image)
if isinstance(out, list):
out_max = out[-1]
if out_max.size(2) != label_image.size(2):
out = self.interp(out_max)
else:
if out.size(2) != image.size(2):
out = self.interp(out)
# out [bs * num_tta, c, h, w]
if self.args.use_tta:
num_tta = len(tta_config)
# out = F.softmax(out, dim=1)
out = detta_score(
out.view(num_tta, -1, self.args.num_classes, out.size(2),
out.size(3))) # [num_tta, bs, nclass, H, W]
out = out.mean(dim=0) # [bs, nclass, H, W]
out = F.softmax(out)
if self.args.aug == 'heng':
out = out[:, :, 11:11 + 202, 11:11 + 202]
predict.extend([
resize(pred[1].data.cpu().numpy(), (101, 101)) for pred in out
])
# predict.extend([pred[1, :101, :101].data.cpu().numpy() for pred in out])
# pred.extend(out.data.cpu().numpy())
true.extend(label_image.data.cpu().numpy())
# pred_all = np.argmax(np.array(pred), 1)
for t in np.arange(0.05, 0.51, 0.01):
pred_all = np.array(predict) > t
true_all = np.array(true).astype(np.int)
# new_iou = intersection_over_union(true_all, pred_all)
# new_iou_t = intersection_over_union_thresholds(true_all, pred_all)
mean_iou, iou_t = mIoU(true_all, pred_all)
print('threshold : {:.4f}'.format(t))
print('mean IoU : {:.4f}, IoU threshold : {:.4f}'.format(
mean_iou, iou_t))
return predict, true
def run_epoch(self, dataloader, writer, epoch, train=True, metrics=True):
if train:
self.net.train()
flag = 'train'
else:
self.net.eval()
flag = 'val'
t2 = time.time()
for image, mask in dataloader:
if train and self.lr_policy != 'step':
adjust_learning_rate(self.args.lr, self.optimizer, self.iters,
self.iterations * len(dataloader), 0.9,
self.cyclic_m, self.lr_policy)
self.iters += 1
if self.cuda:
image = Variable(image.cuda(), volatile=(not train))
label_image = Variable(mask.cuda(), volatile=(not train))
else:
image = Variable(image, volatile=(not train))
label_image = Variable(mask, volatile=(not train))
# cls forward
out = self.net(image)
if isinstance(out, list):
out_max = None
loss = 0.0
for i, out_scale in enumerate(out):
if out_scale.size(2) != label_image.size(2):
out_scale = self.interp(out_scale)
if i == (len(out) - 1):
out_max = out_scale
loss += self.criterion(out_scale, label_image)
label_image_np = label_image.data.cpu().numpy()
sig_out_np = out_max.data.cpu().numpy()
acc = accuracy(label_image_np, np.argmax(sig_out_np, 1))
self.pred[flag].extend(sig_out_np)
self.true[flag].extend(label_image_np)
self.losses[flag].append(loss.data[0])
self.acces[flag].append(acc)
else:
if out.size(-1) != label_image.size(-1):
out = self.interp(out)
loss = self.criterion(out, label_image)
label_image_np = label_image.data.cpu().numpy()
sig_out_np = out.data.cpu().numpy()
acc = accuracy(label_image_np, np.argmax(sig_out_np, 1))
self.pred[flag].extend(sig_out_np)
self.true[flag].extend(label_image_np)
self.losses[flag].append(loss.data[0])
self.acces[flag].append(acc)
if train:
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if metrics:
n = len(self.losses[flag])
loss = sum(self.losses[flag]) / n
scalars = [
loss,
]
names = [
'loss',
]
write_scalars(writer, scalars, names, epoch, tag=flag + '_loss')
all_acc = sum(self.acces[flag]) / n
scalars = [
all_acc,
]
names = [
'all_acc',
]
write_scalars(writer, scalars, names, epoch, tag=flag + '_acc')
# all_score = sum(self.scores[flag]) / n
# scalars = [all_score, ]
# names = ['all_score', ]
# write_scalars(writer, scalars, names, epoch, tag=flag + '_score')
pred_all = np.argmax(np.array(self.pred[flag]), 1)
true_all = np.array(self.true[flag]).astype(np.int)
mean_iou, iou_t = mIoU(true_all, pred_all)
# new_iou = intersection_over_union(true_all, pred_all)
# new_iou_t = intersection_over_union_thresholds(true_all, pred_all)
scalars = [
mean_iou,
iou_t,
]
names = [
'mIoU',
'mIoU_threshold',
]
write_scalars(writer, scalars, names, epoch, tag=flag + '_IoU')
scalars = [
self.optimizer.param_groups[0]['lr'],
]
names = [
'learning_rate',
]
write_scalars(writer, scalars, names, epoch, tag=flag + '_lr')
print(
'{} loss: {:.4f} | acc: {:.4f} | mIoU: {:.4f} | mIoU_threshold: {:.4f} | n_iter: {} | learning_rate: {} | time: {:.2f}'
.format(flag, loss, all_acc, mean_iou, iou_t, epoch,
self.optimizer.param_groups[0]['lr'],
time.time() - t2))
self.losses[flag] = []
self.pred[flag] = []
self.true[flag] = []
self.acces[flag] = []
self.scores[flag] = []
if (not train) and (iou_t >= self.best_val):
if self.args.ms:
if self.args.mGPUs:
self.save_network(self.net.module.Scale,
self.args.model_name,
epoch=epoch,
label='best')
else:
self.save_network(self.net.Scale,
self.args.model_name,
epoch=epoch,
label='best')
else:
if self.args.mGPUs:
self.save_network(self.net.module,
self.args.model_name,
epoch=epoch,
label='best')
else:
self.save_network(self.net,
self.args.model_name,
epoch=epoch,
label='best')
print(
'val improve from {:.4f} to {:.4f} saving in best val_iteration {}'
.format(self.best_val, iou_t, epoch))
self.best_val = iou_t
self.count = 0
if (not train) and (self.best_val - iou_t > 0.003) and (
self.count < 10) and (self.lr_policy == 'step'):
self.count += 1
if (not train) and (self.count >= 10) and (self.lr_policy
== 'step'):
self._adjust_learning_rate(epoch)
self.count = 0
def train_val(self, dataloader_train, dataloader_val, writer):
val_epoch = 0
for epoch in range(self.iterations):
if (self.lr_policy == 'cyclic') and (
epoch % int(self.iterations / self.cyclic_m) == 0):
print('-------start cycle {}------------'.format(
epoch // int(self.iterations / self.cyclic_m)))
self.best_val = 0.0
self.run_epoch(dataloader_train,
writer,
epoch,
train=True,
metrics=True)
self.run_epoch(dataloader_val,
writer,
val_epoch,
train=False,
metrics=True)
val_epoch += 1
if (epoch + 1) % self.args.save_freq == 0:
if self.args.ms:
if self.args.mGPUs:
self.save_network(
self.net.module.Scale,
self.args.model_name,
epoch=val_epoch,
)
else:
self.save_network(
self.net.Scale,
self.args.model_name,
epoch=val_epoch,
)
else:
if self.args.mGPUs:
self.save_network(
self.net.module,
self.args.model_name,
epoch=val_epoch,
)
else:
self.save_network(
self.net,
self.args.model_name,
epoch=val_epoch,
)
print('saving in val_iteration {}'.format(val_epoch))
