Comments (9)
Can you set the num_sanity_val_steps
arg of Trainer class to -1
and run it again? It will run the whole validation before starting the training.
I have never seen this error Trying to infer the batch_size from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use self.log(..., batch_size=batch_size).
then maybe there is a problem with the logging step and your data. I wrote it in validation_step
and validation_epoch_end
of LightningModule, you can try to comment out it first.
from 3d-ucaps.
@cndu234
I had this error in the beginning and I solved it.
Trying to recall what did I set in the trainer to get things to work.
I will check and write back to you.
from 3d-ucaps.
Do you remember how to solve this problem? I have the same problem as you. Thank you very much
from 3d-ucaps.
@kingjames1155 Yes, as @hoangtan96dl also mentioned you should either set num_sanity_val_steps
or turn it off. you can find more about it in this page.
from 3d-ucaps.
num_sanity_val_steps
Thank you very sincerely for your help, which has played a great role
According to your method, I'm not stuck Sanity Checking DataLoader,but always getting stuck at alidation DataLoader,As follows, how do you solve this problem,Thank you again for your help
Epoch 0: 0%| | 0/31 [00:00<?, ?it/s]The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
Trying to infer the batch_size
from an ambiguous collection. The batch size we found is 8. To avoid any miscalculations, use self.log(..., batch_size=batch_size)
.
Epoch 0: 26%|██████████████████████████████████▎ | 8/31 [01:43<04:57, 12.93s/it, loss=0.645, v_num=6]Trying to infer the batch_size
from an ambiguous collection. The batch size we found is 3. To avoid any miscalculations, use self.log(..., batch_size=batch_size)
.
Epoch 0: 29%|██████████████████████████████████████▌ | 9/31 [01:49<04:28, 12.20s/it, loss=0.647, v_num=6]
Validation DataLoader 0: 0%| | 0/22 [00:00<?, ?it/s]
from 3d-ucaps.
@kingjames1155 Yes, as @hoangtan96dl also mentioned you should either set
num_sanity_val_steps
or turn it off. you can find more about it in this page.
I found that the problem was Sliding Window Inference,, which caused me to get stuck in validation_step and predict_step,I haven't changed any parameters about Sliding Window Inference,Have you ever encountered such a situation?
from 3d-ucaps.
No, I didn't have such a problem. I had a problem with the number of outputs. I had 4 labels while the number of outputs was set to 2. However, it wasn't throwing a validation step error. What patch and batch size do you use?
from 3d-ucaps.
No, I didn't have such a problem. I had a problem with the number of outputs. I had 4 labels while the number of outputs was set to 2. However, it wasn't throwing a validation step error. What patch and batch size do you use?
I used luna16 with V100,batch size is 8, train and batch is [64,64,64],I tried to lower these parameters, but it was still stuck
from 3d-ucaps.
I had the same problem,How to avoid this problem?
from 3d-ucaps.
Related Issues (13)
- RuntimeError for training on hippocampus dataset HOT 4
- RuntimeError: Given groups=1, weight of size [16, 2, 5, 5, 5], expected input[1, 1, 32, 32, 32] to have 2 channels, but got 1 channels instead HOT 3
- a dimension parameter in Convolution HOT 2
- How to predict on LUNA16 using trained model? HOT 1
- Error building Environment HOT 1
- dimensionality problem when changing number of layers HOT 2
- How to compute the metrics between testset predictions and true labels? HOT 6
- How to enable all gpus? HOT 2
- RuntimeError: CUDA error: an illegal memory access was encountered HOT 4
- Qualitative results
- RuntimeError: CUDA error: device-side assert triggered HOT 1
- Complexity of the model
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from 3d-ucaps.