Intervertebral disc labeling using pose estimation #787
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jcohenadad
reviewed
May 14, 2021
jcohenadad
reviewed
May 14, 2021
jcohenadad
reviewed
May 14, 2021
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@rezazad68 can you please add a description in the body PR |
joshuacwnewton
requested changes
May 14, 2021
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Excited to see this! I'm looking forward to seeing how this compares with spinalcordtoolbox/spinalcordtoolbox#2679.
the condition on the prediction mask is slightly changed. Co-authored-by: Joshua Newton <joshuacwnewton@gmail.com>
opencv removed from the requirements
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…e the input image based on the desired resolution
Pull Request Test Coverage Report for Build 870259786
💛 - Coveralls |
thank you @mariehbourget ! i'll try again after removing the problematic JSON files |
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ok, now i am getting this with ivadomed 8d96972 and data-multi-subject 9d27a7010d8d528e369a7ed86b6278c64a7cdf77 and this config file: config_pose_julien.json.zip Terminal output(venv) julien-macbook:~/code/ivadomed $ ivadomed --train -c ivadomed/config/config_pose_julien.json --gif 2
Output path already exists: /Users/julien/temp/20210625_reza
Cuda is not available.
Working on cpu.
Selected architecture: HourglassNet, with the following parameters:
dropout_rate: 0.3
bn_momentum: 0.1
depth: 3
is_2d: True
folder_name: pose_model
in_channel: 1
out_channel: 1
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Traceback (most recent call last):
File "/Users/julien/code/ivadomed/venv/bin/ivadomed", line 33, in <module>
sys.exit(load_entry_point('ivadomed', 'console_scripts', 'ivadomed')())
File "/Users/julien/code/ivadomed/ivadomed/main.py", line 550, in run_main
run_command(context=context,
File "/Users/julien/code/ivadomed/ivadomed/main.py", line 336, in run_command
bids_df = imed_loader_utils.BidsDataframe(loader_params, path_output, derivatives=True)
File "/Users/julien/code/ivadomed/ivadomed/loader/utils.py", line 624, in __init__
self.create_bids_dataframe()
File "/Users/julien/code/ivadomed/ivadomed/loader/utils.py", line 725, in create_bids_dataframe
raise RuntimeError("Derivatives not found.")
RuntimeError: Derivatives not found.@rezazad68 can you pls show exactly your terminal output, and mention your code git commit + database commit. Update: the issue was caused by the misleading "test this PR demo" in the PR body. It says: while the file |
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OK, fixing the JSON config file by changing T1w for T2w, I am still getting "Skipping derivative", but this time because of a discrepancy between the image file name and the GT file name. Example: image file name: While the derivatives is: And the config file states: So it no surprise that the GT cannot be associated with the images. Full terminal output(venv) julien-macbook:~/code/ivadomed $ ivadomed --train -c ivadomed/config/config_pose_julien.json --gif 2
Output path already exists: /Users/julien/temp/20210625_reza
Cuda is not available.
Working on cpu.
Selected architecture: HourglassNet, with the following parameters:
dropout_rate: 0.3
bn_momentum: 0.1
depth: 3
is_2d: True
folder_name: pose_model
in_channel: 1
out_channel: 1
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Dataframe has been saved in /Users/julien/temp/20210625_reza/bids_dataframe.csv.
After splitting: train, validation and test fractions are respectively 0.6, 0.2 and 0.2 of participant_id.
Selected transformations for the ['training'] dataset:
Resample: {'wspace': 256, 'hspace': 256, 'dspace': 1, 'flag_pixel': True}
VertebralSplitting: {'max_joint': 11, 'applied_to': ['gt']}
NumpyToTensor: {}
NormalizeInstance: {'applied_to': ['im']}
Selected transformations for the ['validation'] dataset:
Resample: {'wspace': 256, 'hspace': 256, 'dspace': 1, 'flag_pixel': True}
VertebralSplitting: {'max_joint': 11, 'applied_to': ['gt']}
NumpyToTensor: {}
NormalizeInstance: {'applied_to': ['im']}
Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 52/52 [00:00<00:00, 200.47it/s]
Loaded 51 sagittal slices for the validation set.
Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 156/156 [00:00<00:00, 212.50it/s]
Loaded 151 sagittal slices for the training set.
Creating model directory: /Users/julien/temp/20210625_reza/pose_model
Initialising model's weights from scratch.
Scheduler parameters: {'name': 'CosineAnnealingLR', 'base_lr': 1e-06, 'max_lr': 0.0001}
Selected Loss: JointsMSELoss
with the parameters: []
Training: 10%|███████████████▊ | 1/10 [00:00<?, ?it/s]/Users/julien/code/ivadomed/venv/lib/python3.8/site-packages/torch/nn/functional.py:2970: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
warnings.warn("Default upsampling behavior when mode={} is changed "@rezazad68 I spent enough time trying to figure out things. What I need from you at this point is an EXACT syntax to reproduce your work, including the EXACT git commit of the dataset and ivadomed code. UPDATE: these "skipping" messages are normal: they appear because there is in fact no GT for these images (e.g. sub-vuiisAchieva06_T2w.nii.gz). The script |
|
This is maybe more a question for the core dev of ivadomed, but I launched this: Expecting to get some fancy GIF files, but nothing there after 4 epochs 🤷♂️ Terminal output(venv) julien-macbook:~/code/ivadomed $ ivadomed --train -c ivadomed/config/config_pose_julien.json --gif 2
Output path already exists: /Users/julien/temp/20210625_reza
Cuda is not available.
Working on cpu.
Selected architecture: HourglassNet, with the following parameters:
dropout_rate: 0.3
bn_momentum: 0.1
depth: 3
is_2d: True
folder_name: pose_model
in_channel: 1
out_channel: 1
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Dataframe has been saved in /Users/julien/temp/20210625_reza/bids_dataframe.csv.
After splitting: train, validation and test fractions are respectively 0.6, 0.2 and 0.2 of participant_id.
Selected transformations for the ['training'] dataset:
Resample: {'wspace': 256, 'hspace': 256, 'dspace': 1, 'flag_pixel': True}
VertebralSplitting: {'max_joint': 11, 'applied_to': ['gt']}
NumpyToTensor: {}
NormalizeInstance: {'applied_to': ['im']}
Selected transformations for the ['validation'] dataset:
Resample: {'wspace': 256, 'hspace': 256, 'dspace': 1, 'flag_pixel': True}
VertebralSplitting: {'max_joint': 11, 'applied_to': ['gt']}
NumpyToTensor: {}
NormalizeInstance: {'applied_to': ['im']}
Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 52/52 [00:00<00:00, 200.47it/s]
Loaded 51 sagittal slices for the validation set.
Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 156/156 [00:00<00:00, 212.50it/s]
Loaded 151 sagittal slices for the training set.
Creating model directory: /Users/julien/temp/20210625_reza/pose_model
Initialising model's weights from scratch.
Scheduler parameters: {'name': 'CosineAnnealingLR', 'base_lr': 1e-06, 'max_lr': 0.0001}
Selected Loss: JointsMSELoss
with the parameters: []
Training: 10%|███████████████▊ | 1/10 [00:00<?, ?it/s]/Users/julien/code/ivadomed/venv/lib/python3.8/site-packages/torch/nn/functional.py:2970: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
warnings.warn("Default upsampling behavior when mode={} is changed "
Epoch 1 training loss: 0.0026. Dice training loss: -0.0010.
Epoch 1 validation loss: 0.0003. Dice validation loss: -0.0015.
Epoch 1 took 592.04 seconds.
Epoch 2 training loss: 0.0002. Dice training loss: -0.0235.
Epoch 2 validation loss: 0.0002. Dice validation loss: 0.0016.
Epoch 2 took 592.96 seconds.
Epoch 3 training loss: 0.0002. Dice training loss: -0.0004.
Epoch 3 validation loss: 0.0004. Dice validation loss: -0.0011.
Epoch 3 took 597.11 seconds.
Epoch 4 training loss: 0.0003. Dice training loss: -0.0010.
Epoch 4 validation loss: 0.0002. Dice validation loss: 0.0015.
Epoch 4 took 591.33 seconds.
Training: 50%|██████████████████████████████████████████████████████████████████████████▌ | 5/10 [39:35<49:27, 593.59s/it] |
Ivadomed saves the gif file at the end of the training. It saves the result at |
|
From your terminal, it seems no mask did not find for the data. Please double-check you have the generated mid
|
joshuacwnewton
requested changes
Jul 2, 2021
There was a problem hiding this comment.
This review is to help tidy up the PR. The suggestions here will help make the PR easier to review, so that reviewers can focus on the more important parts.
In the future, I would recommend trying to prepare your PR for review by removing these "noise" changes in advance. The article "How to pull request" also provides some important tips. The most relevant point here is "3. Go back and remove noise":
Often while self reviewing, you come across a file with only white space changes, formatting changes, optimized imports or some text change that has no impact related to the intention of the PR. Make the effort, go back to your branch and revert these files to how they were before, it doesn’t matter that you have slightly improved them — functionally they are the same, and the extra file in the list of ‘changed files’ listed in the PR provokes groans from the reviewer and lowers their motivation to do a proper review — especially on larger PRs.
Once your PR has been cleaned up and prepared for review, please "Re-request review" and I will be happy to provide feedback on the more critical parts of the PR. 🙂
| @@ -12,6 +11,7 @@ | |||
| from scipy.ndimage.measurements import label, center_of_mass | |||
| from scipy.ndimage.morphology import binary_dilation, binary_fill_holes, binary_closing | |||
| from skimage.exposure import equalize_adapthist | |||
| from skimage import measure | |||
| from torchvision import transforms as torchvision_transforms | |||
|
|
|||
There was a problem hiding this comment.
Currently, your branch is conflicting with the master branch.
Could you please merge the changes from the branch ivadomed:master into your branch, and resolve the conflicts?
There was a problem hiding this comment.
Also, in the future, could you please check for these conflicts and fix them when you see them?
Ideally, the PR should never have conflicts, so when you see this message on your PR, it is helpful to try fix it immediately (so that the people reviewing your PR won't have to take the time to ask).
|
@rezazad68 can you please fix the conflicts: thank you |
|
I'm testing 2cdc40a, and the usage showed here does not appear: @rezazad68 are you sure you pushed your changes with the modified usage? |
joshuacwnewton
pushed a commit
that referenced
this pull request
Jul 6, 2021
This commit contains all commits between: - 2b0b5ba (Start) - 8d7d45b (End) This commit was also rebased onto ccd82d2 from the master branch, because that commit was the last point at which master was merged into Reza's branch. (See: a502b37) All former commits are preserved on the branch 'ra/intervertebral-disc-labeling'. Co-authored-by: Reza Azad <rezazad68@gmail.com>
I think you mentioned the wrong commit. Please use the last version 8de3c1e |
16 tasks
|
As discussed with @jcohenadad, because this PR was created from the So, I'm going to close this PR, as it is now a duplicate of #852. |
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Checklist
GitHub
PR contents
Description
In this pull, we aim to reformulate the semantic intervertebral disc labeling using the pose estimation technique. To this end, we utilize the stacked hourglass network. The proposed deep model takes into account the strength of semantic segmentation and pose estimation technique to handle the missing and falsely additional disc areas.
Linked issues
resolves #539
resolves #766
Run Demo:
After running the above command, it will generate the mid files. While running it will print the list of subjects that the mid file is generating for them. For example:
3- Run ivadomed --train -c
config_pose.json--gif 2