From 9f6d2441053d1d36c58795b65a9c08d2d532fb09 Mon Sep 17 00:00:00 2001 From: Wenqi Li Date: Thu, 8 Jul 2021 22:04:54 +0100 Subject: [PATCH 1/2] update weekly dev version Signed-off-by: Wenqi Li --- .github/workflows/weekly-preview.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/weekly-preview.yml b/.github/workflows/weekly-preview.yml index 26887f512d..981ca5cdaf 100644 --- a/.github/workflows/weekly-preview.yml +++ b/.github/workflows/weekly-preview.yml @@ -33,7 +33,7 @@ jobs: export YEAR_WEEK=$(date +'%y%U') echo "Year week for tag is ${YEAR_WEEK}" if ! [[ $YEAR_WEEK =~ ^[0-9]{4}$ ]] ; then echo "Wrong 'year week' format. Should be 4 digits."; exit 1 ; fi - git tag "0.6.dev${YEAR_WEEK}" + git tag "0.7.dev${YEAR_WEEK}" git log -1 git tag --list python setup.py sdist bdist_wheel From 9db84548ee19fe0c798b67605274ad5f552daedc Mon Sep 17 00:00:00 2001 From: Wenqi Li Date: Thu, 8 Jul 2021 23:42:53 +0100 Subject: [PATCH 2/2] update doc Signed-off-by: Wenqi Li --- docs/source/whatsnew_0_6.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/docs/source/whatsnew_0_6.md b/docs/source/whatsnew_0_6.md index 309373c702..213b130b2f 100644 --- a/docs/source/whatsnew_0_6.md +++ b/docs/source/whatsnew_0_6.md @@ -8,8 +8,8 @@ ## Decollating mini-batches as an essential post-processing step -`decollate batch` is introduced in MONAI v0.6, to simplify the post-processing transforms and enable flexible following operations on a batch of model outputs. -It can decollate batched data (e.g. model inference predictions) into a list of tensors -- as an 'inverse' operation of `collate_fn` of the PyTorch data loader -- for the benefits such as: +`decollate batch` is introduced in MONAI v0.6, to simplify the post-processing transforms and enable flexible operations on a batch of model outputs. +It can decollate batched data (e.g. model inference results) into a list of tensors -- as an 'inverse' operation of `collate_fn` of the PyTorch data loader. It has the benefits such as: - enabling postprocessing transforms for each item independently, for example, randomised transforms could be applied differently for each predicted item in a batch. - simplifying the transform APIs and reducing the input validation burdens, because both the preprocessing and postprocessing transforms now only support the "channel-first" input format. - enabling the transform inverse operation for data items in different original shapes, as the inverted items are in a list, instead of being stacked in a single tensor. @@ -28,12 +28,14 @@ NVIDIA Clara provides [various MMARs of medical domain-specific models](https:// These MMARs include all the information about the model including configurations and scripts to provide a workspace to perform model development tasks. To better leverage the trained MMARs released on Nvidia GPU cloud, MONAI provides pythonic APIs to access them. To demonstrate this new feature, a medical image segmentation tutorial is created within -[`project-monai/tutorials`](https://github.com/Project-MONAI/tutorials/blob/master/modules/transfer_mmar.ipynb)). +[`project-monai/tutorials`](https://github.com/Project-MONAI/tutorials/blob/master/modules/transfer_mmar.ipynb). It mainly produces the following figure to compare the loss curves and validation scores for - training from scratch (the green line), - applying pretrained MMAR weights without training (the magenta line), - training from the MMAR model weights (the blue -line), according to the number of training epochs: +line), + +according to the number of training epochs: ![transfer_mmar](../images/transfer_mmar.png) @@ -59,4 +61,4 @@ New utilities are introduced on top of the existing semantic versioning modules, At the same time, we actively analyze efficient, scalable, and secure CI/CD solutions to accommodate fast and collaborative codebase development. -Although a complete mechanism is still under development, These provide another essential step towards API-stable versions of MONAI, sustainable release cycles, and efficient open-source collaborations. +Although a complete mechanism is still under development, these provide another essential step towards API-stable versions of MONAI, sustainable release cycles, and efficient open-source collaborations.