Datasets:
jblitzar
/
github-python-corpus

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image, mask = data
if random.random() < self.p: return TF.vflip(image), TF.vflip(mask)
else: return image, mask
class myRandomRotation:
def __init__(self, p=0.5, degree=[0,360]):
self.angle = random.uniform(degree[0], degree[1])
self.p = p
def __call__(self, data):
image, mask = data
if random.random() < self.p: return TF.rotate(image,self.angle), TF.rotate(mask,self.angle)
else: return image, mask
class myNormalize:
def __init__(self, data_name, train=True):
if data_name == 'isic18':
if train:
self.mean = 157.561
self.std = 26.706
else:
self.mean = 149.034
self.std = 32.022
elif data_name == 'isic17':
if train:
self.mean = 159.922
self.std = 28.871
else:
self.mean = 148.429
self.std = 25.748
elif data_name == 'isic18_82':
if train:
self.mean = 156.2899
self.std = 26.5457
else:
self.mean = 149.8485
self.std = 35.3346
def __call__(self, data):
img, msk = data
img_normalized = (img-self.mean)/self.std
img_normalized = ((img_normalized - np.min(img_normalized))
/ (np.max(img_normalized)-np.min(img_normalized))) * 255.
return img_normalized, msk
from thop import profile ## 导入thop模块
def cal_params_flops(model, size, logger):
input = torch.randn(1, 3, size, size).cuda()
flops, params = profile(model, inputs=(input,))
print('flops',flops/1e9) ## 打印计算量
print('params',params/1e6) ## 打印参数量
total = sum(p.numel() for p in model.parameters())
print("Total params: %.2fM" % (total/1e6))
logger.info(f'flops: {flops/1e9}, params: {params/1e6}, Total params: : {total/1e6:.4f}')
def calculate_metric_percase(pred, gt):
pred[pred > 0] = 1
gt[gt > 0] = 1
if pred.sum() > 0 and gt.sum()>0:
dice = metric.binary.dc(pred, gt)
hd95 = metric.binary.hd95(pred, gt)
return dice, hd95
elif pred.sum() > 0 and gt.sum()==0:
return 1, 0
else:
return 0, 0
def test_single_volume(image, label, net, classes, patch_size=[256, 256],
test_save_path=None, case=None, z_spacing=1, val_or_test=False):
image, label = image.squeeze(0).cpu().detach().numpy(), label.squeeze(0).cpu().detach().numpy()
if len(image.shape) == 3:
prediction = np.zeros_like(label)
for ind in range(image.shape[0]):
slice = image[ind, :, :]
x, y = slice.shape[0], slice.shape[1]
if x != patch_size[0] or y != patch_size[1]:
slice = zoom(slice, (patch_size[0] / x, patch_size[1] / y), order=3) # previous using 0
input = torch.from_numpy(slice).unsqueeze(0).unsqueeze(0).float().cuda()
net.eval()
with torch.no_grad():
outputs = net(input)
out = torch.argmax(torch.softmax(outputs, dim=1), dim=1).squeeze(0)
out = out.cpu().detach().numpy()
if x != patch_size[0] or y != patch_size[1]:
pred = zoom(out, (x / patch_size[0], y / patch_size[1]), order=0)
else:
pred = out
prediction[ind] = pred
else: