Comments (3)
train_tiramisu.py no module 'joint_transforms', how to do? thank u! @zuiyichangdi
from crack_segmentation.
train_tiramisu.py no module 'joint_transforms', how to do? thank u! @zuiyichangdi
Oh, sorry, I didn't find a solution. And it's been two years, I almost forgot about the project. I regret not being able to advise you.
from crack_segmentation.
I think this function should be useful.
def joint_transform(img, mask):
img = transforms.ToTensor()(img)
mask = transforms.ToTensor()(mask)
both_images = torch.cat((img, mask), 0)
# Apply the transformations to both images simultaneously:
transformed_images = transforms.RandomResizedCrop(224, antialias=True)(both_images)
# Get the transformed images:
img = transforms.ToPILImage()(transformed_images[0:3])
mask = transforms.ToPILImage()(transformed_images[3:6])
return img, mask
from crack_segmentation.
Related Issues (18)
- the link of pretraind weight of Resnet is invaild, pls resent agina. HOT 2
- Hi HOT 1
- Hello, where can I see the paper of this model? HOT 3
- Have you encountered this problem?Thanks to answer
- Code license HOT 1
- What is this project license?
- Where does the dataset Rissbilder_for_Florian come from? HOT 1
- Bro in the code their is problem HOT 1
- in the utils.py HOT 1
- where is unet_resnet_101 HOT 1
- Which paper can I study the model structure?
- the pretrained weight of resnet is invalid HOT 6
- Mask creation tool HOT 5
- Model not training with resnet101 and resnet34
- Crack mask is much more wider than the real crack
- Patchwise inference in unet_inference.py HOT 2
- 我跑通了您的代码,但是我没有搜到您论文的链接,可以分享您论文的题目和链接吗? HOT 4
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