Open andife opened 3 years ago
I have a question about running the code. Have you actually ran the code, I mean trained the model and tested the model on their images ? And did you get the same or similar results as in the their paper ?
Thanks
Hello, it seems that the code currently only works on grayscale images. II am interested in processing images with 3 channels (RGB). Has anyone already modified the code accordingly? What do I have to pay attention to?
@andife Hello, this repo also supports RGB image with 3 channels.
The network is original support 3 channels input (See line 386-387 in vit_seg_modeling.py): if x.size()[1] == 1: x = x.repeat(1,3,1,1)
Hello, it seems that I still have problems to prepare the dataset
So at least the class RandomGenerator cannot be used directly, because of x, y = image.shape ValueError: too many values to unpack (expected 2)
I tried the pipeline using it with RGB data, and get the following:
Traceback (most recent call last):
File "train.py", line 103, in <module>
trainer[dataset_name](args, net, snapshot_path)
File "project_TransUNet/TransUNet/trainer.py", line 133, in trainer_owndataset
for i_batch, sampled_batch in enumerate(trainloader):
File "anaconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
data = self._next_data()
File anaconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 856, in _next_data
return self._process_data(data)
File anaconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 881, in _process_data
data.reraise()
File "anaconda3/lib/python3.8/site-packages/torch/_utils.py", line 394, in reraise
raise self.exc_type(msg)
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "anaconda3/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
data = fetcher.fetch(index)
File anaconda3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File anaconda3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "project_TransUNet/TransUNet/datasets/dataset_owndataset.py", line 74, in __getitem__
sample = self.transform(sample)
File anaconda3/lib/python3.8/site-packages/torchvision/transforms/transforms.py", line 70, in __call__
img = t(img)
File project_TransUNet/TransUNet/datasets/dataset_owndataset.py", line 39, in __call__
x, y = image.shape
ValueError: too many values to unpack (expected 2)
Hello, it seems that the code currently only works on grayscale images. II am interested in processing images with 3 channels (RGB). Has anyone already modified the code accordingly? What do I have to pay attention to?
@andife Hello, this repo also supports RGB image with 3 channels.
The network is original support 3 channels input (See line 386-387 in vit_seg_modeling.py): if x.size()[1] == 1: x = x.repeat(1,3,1,1)
@Beckschen I'm trying to use this model for RGB images. I removed the random rotations (they seemed buggy for RGB images), and instead now get an error on the lines you have mentioned (386-387 in vit_seg_modeling.py
). The error is as follows:
RuntimeError: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor
Hello,
Can someone point me to the solution? Does one have working code?
Currently TransUnet expects/uses shape x: torch.Size([12, 1, 224, 224]) for the synapse dataset.
When I tried to use my files with RGB-Channel. I got shape x: torch.Size([12, 1, 3, 736, 736])
Obviously, the dimensions did not fit . Think I have to get rid of the '1' I squeezed the dataset, but then I got the following error:
Traceback (most recent call last):
File "train.py", line 114, in <module>
trainer[dataset_name](args, net, snapshot_path)
File "/home/andife/project_TransUNet/TransUNet/trainer.py", line 223, in trainer_ulm3D
outputs = model(image_batch)
File "/home/andife/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/home/andife/project_TransUNet/TransUNet/networks/vit_seg_modeling.py", line 393, in forward
x, attn_weights, features = self.transformer(x) # (B, n_patch, hidden)
File "/home/andife/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/home/andife/project_TransUNet/TransUNet/networks/vit_seg_modeling.py", line 254, in forward
embedding_output, features = self.embeddings(input_ids)
File "/home/andife/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/home/andife/project_TransUNet/TransUNet/networks/vit_seg_modeling.py", line 163, in forward
embeddings = x + self.position_embeddings
RuntimeError: The size of tensor a (2116) must match the size of tensor b (196) at non-singleton dimension 1
Hello, it seems that the code currently only works on grayscale images. II am interested in processing images with 3 channels (RGB). Has anyone already modified the code accordingly? What do I have to pay attention to?
@andife Hello, this repo also supports RGB image with 3 channels. The network is original support 3 channels input (See line 386-387 in vit_seg_modeling.py): if x.size()[1] == 1: x = x.repeat(1,3,1,1)
@Beckschen I'm trying to use this model for RGB images. I removed the random rotations (they seemed buggy for RGB images), and instead now get an error on the lines you have mentioned (386-387 in
vit_seg_modeling.py
). The error is as follows:RuntimeError: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor
Did you fix this? I am also trying to repeat this for RGB images.
Hello, I have the same problem. Have you solved it?@Some1OutThere
Hello, it seems that I still have problems to prepare the dataset
So at least the class RandomGenerator cannot be used directly, because of x, y = image.shape ValueError: too many values to unpack (expected 2)
I tried the pipeline using it with RGB data, and get the following:
Traceback (most recent call last): File "train.py", line 103, in <module> trainer[dataset_name](args, net, snapshot_path) File "project_TransUNet/TransUNet/trainer.py", line 133, in trainer_owndataset for i_batch, sampled_batch in enumerate(trainloader): File "anaconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 345, in __next__ data = self._next_data() File anaconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 856, in _next_data return self._process_data(data) File anaconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 881, in _process_data data.reraise() File "anaconda3/lib/python3.8/site-packages/torch/_utils.py", line 394, in reraise raise self.exc_type(msg) ValueError: Caught ValueError in DataLoader worker process 0. Original Traceback (most recent call last): File "anaconda3/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop data = fetcher.fetch(index) File anaconda3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File anaconda3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "project_TransUNet/TransUNet/datasets/dataset_owndataset.py", line 74, in __getitem__ sample = self.transform(sample) File anaconda3/lib/python3.8/site-packages/torchvision/transforms/transforms.py", line 70, in __call__ img = t(img) File project_TransUNet/TransUNet/datasets/dataset_owndataset.py", line 39, in __call__ x, y = image.shape ValueError: too many values to unpack (expected 2)
I have solved this issue. If the image is a RGB image, the image.shape would be a tuple like (h, w, 3), the original code
x, y = image.shape
is unpacking two elements, but image.shape has three elements. So you can fix it by changing the code like x, y, z = image.shape
.
Hello, it seems that the code currently only works on grayscale images. II am interested in processing images with 3 channels (RGB). Has anyone already modified the code accordingly? What do I have to pay attention to?
@andife Hello, this repo also supports RGB image with 3 channels. The network is original support 3 channels input (See line 386-387 in vit_seg_modeling.py): if x.size()[1] == 1: x = x.repeat(1,3,1,1)
@Beckschen I'm trying to use this model for RGB images. I removed the random rotations (they seemed buggy for RGB images), and instead now get an error on the lines you have mentioned (386-387 in
vit_seg_modeling.py
). The error is as follows:RuntimeError: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor
Did you fix this? I am also trying to repeat this for RGB images.
Hello, I had the same problem when running test.py, did you solve it?
Hello, it seems that the code currently only works on grayscale images. II am interested in processing images with 3 channels (RGB). Has anyone already modified the code accordingly? What do I have to pay attention to?