initial unit tests for 2d/3d unet #26
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ericspod
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Jan 16, 2020
ericspod
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Jan 16, 2020
- triggering unit tests via github workflow - renamed testconvolutions.py to test_convolutions.py - test unet test cases as variables for readability
ericspod
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Jan 17, 2020
Nic-Ma
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Jan 21, 2020
* Adding script to run unit tests and example test cases (#29) Adding script to run unit tests and example test cases * initial unit tests for dice loss (#27) * initial unit tests for 2d/3d unet * unit tests update - triggering unit tests via github workflow - renamed testconvolutions.py to test_convolutions.py - test unet test cases as variables for readability * initial unit tests for 2d/3d unet (#26) * initial unit tests for 2d/3d unet * unit tests update - triggering unit tests via github workflow - renamed testconvolutions.py to test_convolutions.py - test unet test cases as variables for readability * 14 code examples of monai input data pipeline (#24) * fixes cardiac example * update example cardiac segmentation * Create .gitlab-ci.yml (#30) an initial step towards #19 * tests intensity normalizer - revised to support both `[key]` and `key` as an input for apply_keys - added `NumpyImageTestCase2D` and `TorchImageTestCase2D` * style updates and new test cases: - adding copyright notice - validate user input before setting class member - one line space after copyright - testing multiple keys input data Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> Co-authored-by: Isaac Yang <isaacy@nvidia.com>
ericspod
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Jan 22, 2020
* [DLMED] implement intensity normalization transform design according to our latest discussion: 1. input data is dict format with keys for fields. 2. only based on PyTorch and data shape is channel_last. * 9 part a adding test intensity normalisation transform (#33) * Adding script to run unit tests and example test cases (#29) Adding script to run unit tests and example test cases * initial unit tests for dice loss (#27) * initial unit tests for 2d/3d unet * unit tests update - triggering unit tests via github workflow - renamed testconvolutions.py to test_convolutions.py - test unet test cases as variables for readability * initial unit tests for 2d/3d unet (#26) * initial unit tests for 2d/3d unet * unit tests update - triggering unit tests via github workflow - renamed testconvolutions.py to test_convolutions.py - test unet test cases as variables for readability * 14 code examples of monai input data pipeline (#24) * fixes cardiac example * update example cardiac segmentation * Create .gitlab-ci.yml (#30) an initial step towards #19 * tests intensity normalizer - revised to support both `[key]` and `key` as an input for apply_keys - added `NumpyImageTestCase2D` and `TorchImageTestCase2D` * style updates and new test cases: - adding copyright notice - validate user input before setting class member - one line space after copyright - testing multiple keys input data Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> Co-authored-by: Isaac Yang <isaacy@nvidia.com> * [DLMED] simplify intensity normalization transform for MVP Co-authored-by: Wenqi Li <wenqi.li@ucl.ac.uk> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> Co-authored-by: Isaac Yang <isaacy@nvidia.com>
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2D/3D unet is already implemented in the repo and should be enough for the MVP. This PR provides a gentle unit test for the module.
closes #13.
next steps:
the network should support multichannel inputs (which is not the case currently)