Will accept Pull Requests on my fork / recent builds available on conda-forge #57
Comments
|
Is there any document/description on the supported Pytorch/CUDA versions? With |
|
The conda packages are built from this repository: https://github.com/conda-forge/carterbox-torch-radon-feedstock
As the solver will tell you, if you try to create that environment unconstrained, torchaudio is not available on the channel. You can submit a new conda recipe to build on the channel or assist with an existing PR in the conda-forge/staged-recipes repo. It seems like a few attempts have been made to build torchaudio using conda, but I'm not sure why none have been merged. |
|
Thank you for the hint! And sorry for my late reply. I feel like I didn't make myself clear. I had trouble installing your releases of torch-radon, probably because of the incompatibility issue. I was trying to install your release based on my RTX-3090 machine, which has an older CUDA version of 11.5 and unfortunately cannot be updated for some time (so not supporting pytorch >2.0 that requires CUDA > 11.7). And with So, I am wondering is there any way to install |
|
Update: I found it ok to run However, I found another problem related to the computing of gradient. Concretely, I wrote a test script that trains of a dual-domain dummy network, but found the The error messages are like: (carter) clma@my_server:~/projects/mar/RIL$ python radon_v2_example3.py
Batch: 0
>>> Forwarding sino net.
>>> Forwarding img net.
Traceback (most recent call last):
File "/home/clma/projects/mar/RIL/radon_v2_example3.py", line 119, in <module>
loss.backward()
File "/home/clma/miniconda3/envs/carter/lib/python3.9/site-packages/torch/_tensor.py", line 363, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File "/home/clma/miniconda3/envs/carter/lib/python3.9/site-packages/torch/autograd/__init__.py", line 173, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
File "/home/clma/miniconda3/envs/carter/lib/python3.9/site-packages/torch/autograd/function.py", line 253, in apply
return user_fn(self, *args)
File "/home/clma/miniconda3/envs/carter/lib/python3.9/site-packages/torch_radon-0.0.0-py3.9-linux-x86_64.egg/torch_radon/differentiable_functions.py", line 46, in backward
grad = cuda_backend.forward(grad_x, angles, ctx.tex_cache, ctx.vol_cfg, ctx.proj_cfg, exec_cfg)
RuntimeError: x must be contiguousAnd the test script import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
from torch_radon import FanBeam, Volume2D
class TestDataset(Dataset):
def __init__(self):
super().__init__()
img = np.zeros((512, 512), dtype=np.float32)
img[:, 255] = 1.
img[255, :] = 1.
imgs = [
img,
np.fliplr(img).copy(),
np.rot90(img, k=1).copy(),
np.rot90(img, k=2).copy(),
np.rot90(img, k=3).copy(),
]
self.imgs = [_ for _ in imgs] + [(1.0 - _.copy()) for _ in imgs]
def __len__(self):
return len(self.imgs)
def __getitem__(self, index):
img = self.imgs[index]
x = torch.from_numpy(img).unsqueeze(0).float().contiguous()
return x
class TestNet(nn.Module):
def __init__(self, channels=3) -> None:
super().__init__()
self.sino_model = nn.Sequential(
nn.Conv2d(1, channels, 5, padding=2),
nn.Conv2d(channels, 1, 5, padding=2)
)
self.img_model = nn.Conv2d(2, 1, 3, padding=1).to(device)
self.det_count = 672
def forward_sino(self, sino):
print('>>> Forwarding sino net.')
return self.sino_model(sino)
def forward_img(self, img1, img2):
print('>>> Forwarding img net.')
img = torch.cat([img1, img2], dim=1)
return self.img_model(img)
def projection(self, img, angles=None, filter=True):
if angles is None:
angles = np.linspace(0, np.pi * 2, 360, endpoint=False)
volume = Volume2D()
volume.set_size(img.shape[-2], img.shape[-1]) # [B, C, H, W]
radon = FanBeam(self.det_count, angles, volume=volume)
sino = radon.forward(img)
if filter:
sino = radon.filter_sinogram(sino)
return sino
def backprojection(self, sino, img_shape, angles=None):
if angles is None:
angles = np.linspace(0, np.pi * 2, 360, endpoint=False)
volume = Volume2D()
volume.set_size(img_shape[-2], img_shape[-1]) # [H, W]
radon = FanBeam(self.det_count, angles, volume=volume)
img = radon.backward(sino)
return img
def forward(self, sino, img_shape, angles=None):
img = self.backprojection(sino, img_shape, angles=angles)
sino_pred = self.forward_sino(sino)
img_sino = self.backprojection(sino_pred, img_shape, angles=angles)
img_pred = self.forward_img(img, img_sino)
return sino_pred, img_sino, img_pred
# ==== The following forward (adding contiguous()) does not work ====
# def forward(self, sino, img_shape, angles=None):
# img = self.backprojection(sino, img_shape, angles=angles)
# img = img.contiguous()
# sino_pred = self.forward_sino(sino)
# img_sino = self.backprojection(sino_pred, img_shape, angles=angles)
# img_sino = img_sino.contiguous()
# img_pred = self.forward_img(img, img_sino)
# return sino_pred, img_sino, img_pred
if __name__ == '__main__':
import torch.optim as optim
from torch.utils.data import DataLoader
device = torch.device('cuda')
dataset = TestDataset()
loader = DataLoader(dataset, batch_size=2, num_workers=4, pin_memory=True, shuffle=True)
net = TestNet().to(device)
optimizer = optim.Adam(net.parameters(), lr=1e-4)
angles = torch.linspace(0, np.pi * 2, 360, requires_grad=False).float()
for i, data in enumerate(loader):
print(f'Batch: {i}')
img_shape = data.shape[2:]
with torch.no_grad():
sino = net.projection(data.to(device).detach(), angles=angles).detach().contiguous()
img = net.backprojection(sino, img_shape=img_shape, angles=angles).detach().contiguous()
sino_pred, _, img_pred = net(sino, img_shape=img_shape, angles=angles)
loss1 = F.l1_loss(sino, sino_pred)
loss2 = F.l1_loss(img, img_pred)
loss = loss1 + loss2
optimizer.zero_grad()
loss.backward()
optimizer.step() |
Update 2:The above problems are solved by editing the |
|
The minimum driver version for all CUDA 11.x versions is the same., so if you have a driver from the CUDA 11.5 era, you should be able to run a package build with CUDA 11.8 (CUDA 11.2?).
The pytorch version that I build against is chosen by the conda-forge channel, which is already transitioning from 2.0 to 2.1. Please keep this issue on topic by only discussing the pre-compiled releases or issues related to forking. |
If you're reading this, it's because you're wondering if this package is still active. No. The last updates were merge in November 2020. However, I really like this package, so I will accept pull requests over on my fork. If Matteo comes back, I will happily help to merge my changes upstream.
https://github.com/carterbox/torch-radon
I also publish pre-compiled releases of my fork for the conda package manager on the conda-forge channel.
https://anaconda.org/conda-forge/carterbox-torch-radon
The text was updated successfully, but these errors were encountered: