Merge remote-tracking branch 'origin/main' into v0.2

Author: gigony

.github/workflows/pr.yml

@@ -14,10 +14,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
-      - name: Set up Python 3.6
+      - name: Set up Python 3.7
         uses: actions/setup-python@v2
         with:
-          python-version: 3.6
+          python-version: 3.7
       - name: Setup Dev Environment
         run: |
           pip install virtualenv

.readthedocs.yml

@@ -18,7 +18,7 @@ sphinx:
 
 # Optionally set the version of Python and requirements required to build your docs
 python:
-  version: 3.6
+  version: 3.7
   install:
     - requirements: docs/requirements.txt
 #  system_packages: true

docs/source/conf.py

@@ -16,7 +16,7 @@
 
     subprocess.call(
         "/bin/bash -c 'source /home/docs/checkouts/readthedocs.org/user_builds/"
-        f"{READTHEDOCS_PROJECT}/envs/{READTHEDOCS_VERSION}/bin/activate; ../../run setup'",
+        f"{READTHEDOCS_PROJECT}/envs/{READTHEDOCS_VERSION}/bin/activate; ../../run setup read_the_docs'",
         shell=True,
     )
     subprocess.call(

docs/source/getting_started/installing_app_sdk.md

@@ -2,7 +2,7 @@
 
 MONAI Deploy App SDK is available as a Python package through [Python Package Index (PyPI)](https://pypi.org/project/monai-deploy-app-sdk/).
 
-MONAI Deploy App SDK's core functionality is written in Python 3 (>= 3.6) for Linux.
+MONAI Deploy App SDK's core functionality is written in Python 3 (>= 3.7) for Linux.
 
 ```bash
 pip install monai-deploy-app-sdk

docs/source/getting_started/tutorials/01_simple_app.md

@@ -3,8 +3,11 @@
 ## Setup
 
 ```bash
-# Create a virtual environment. Skip if you are already in a virtual environment.
-conda create -n monai python=3.6 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge
+# Create a virtual environment with Python 3.7.
+# Skip if you are already in a virtual environment.
+# (JupyterLab dropped its support for Python 3.6 since 2021-12-23.
+#  See https://github.com/jupyterlab/jupyterlab/pull/11740)
+conda create -n monai python=3.7 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge
 conda activate monai
 
 # Launch JupyterLab if you want to work on Jupyter Notebook

docs/source/getting_started/tutorials/02_mednist_app.md

@@ -3,8 +3,11 @@
 ## Setup
 
 ```bash
-# Create a virtual environment. Skip if you are already in a virtual environment.
-conda create -n mednist python=3.6 pytorch jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge
+# Create a virtual environment with Python 3.7.
+# Skip if you are already in a virtual environment.
+# (JupyterLab dropped its support for Python 3.6 since 2021-12-23.
+#  See https://github.com/jupyterlab/jupyterlab/pull/11740)
+conda create -n mednist python=3.7 pytorch jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge
 conda activate mednist
 
 # Launch JupyterLab if you want to work on Jupyter Notebook

docs/source/getting_started/tutorials/03_segmentation_app.md

@@ -3,8 +3,11 @@
 ## Setup
 
 ```bash
-# Create a virtual environment. Skip if you are already in a virtual environment.
-conda create -n monai python=3.6 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge
+# Create a virtual environment with Python 3.7.
+# Skip if you are already in a virtual environment.
+# (JupyterLab dropped its support for Python 3.6 since 2021-12-23.
+#  See https://github.com/jupyterlab/jupyterlab/pull/11740)
+conda create -n monai python=3.7 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge
 conda activate monai
 
 # Launch JupyterLab if you want to work on Jupyter Notebook
@@ -50,25 +53,25 @@ cd monai-deploy-app-sdk
 # Install monai-deploy-app-sdk package
 pip install monai-deploy-app-sdk
 
-# Download/Extract ai_spleen_seg_data.zip from https://drive.google.com/file/d/1uTQsm8omwimBcp_kRXlduWBP2M6cspr1/view?usp=sharing
+# Download/Extract ai_spleen_seg_data zip file from https://drive.google.com/file/d/1GC_N8YQk_mOWN02oOzAU_2YDmNRWk--n/view?usp=sharing
 
 # Download ai_spleen_seg_data.zip
 pip install gdown
-gdown https://drive.google.com/uc?id=1uTQsm8omwimBcp_kRXlduWBP2M6cspr1
+gdown https://drive.google.com/uc?id=1GC_N8YQk_mOWN02oOzAU_2YDmNRWk--n
 
 # After downloading ai_spleen_seg_data.zip from the web browser or using gdown,
-unzip -o ai_spleen_seg_data.zip
+unzip -o ai_spleen_seg_data_updated_1203.zip
 
 # Install necessary packages from the app
 pip install monai pydicom SimpleITK Pillow nibabel
 
 # Local execution of the app
-python examples/apps/ai_spleen_seg_app/app.py -i dcm/ -o output -m model.pt
+python examples/apps/ai_spleen_seg_app/app.py -i dcm/ -o output -m model.ts
 
 # Package app (creating MAP docker image) using `-l DEBUG` option to see progress.
 # This assumes that nvidia docker is installed in the local machine.
 # Please see https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker to install nvidia-docker2.
-monai-deploy package examples/apps/ai_spleen_seg_app --tag seg_app:latest --model model.pt -l DEBUG
+monai-deploy package examples/apps/ai_spleen_seg_app --tag seg_app:latest --model model.ts -l DEBUG
 
 # Run the app with docker image and input file locally
 monai-deploy run seg_app:latest dcm/ output

docs/source/index.md

@@ -7,5 +7,11 @@
 introduction/index
 getting_started/index
 developing_with_sdk/index
+```
+
+```{toctree}
+:glob:
+:maxdepth: 2
+
 modules/index
 ```

docs/source/modules/index.md

@@ -1,58 +1,14 @@
 # MODULES APIS
 
-## Core
-
 ```{toctree}
-:maxdepth: 3
-:hidden:
+:glob:
+:maxdepth: 2
 
 core
-```
-
-## Domain objects
-
-```{toctree}
-:maxdepth: 2
-:hidden:
 domain_objects
-```
-
-## Operators
-
-```{toctree}
-:maxdepth: 2
-:hidden:
 operators
-```
-
-## Models
-
-```{toctree}
-:maxdepth: 2
-:hidden:
 models
-```
-
-## Graphs
-
-```{toctree}
-:maxdepth: 2
-:hidden:
 graphs
-```
-
-## Executors
-
-```{toctree}
-:maxdepth: 2
-:hidden:
 executors
-```
-
-## Datastores
-
-```{toctree}
-:maxdepth: 2
-:hidden:
 datastores
 ```

docs/srs.md

@@ -698,7 +698,7 @@ The SDK shall support the following system configurations for a given target rel
 | Docker CE                | 19.03.13+ ([2020-09-16](https://docs.docker.com/engine/release-notes/19.03/#190313)) |
 | NVIDIA Docker            | [v2.5.0+](https://github.com/NVIDIA/nvidia-docker/)                                  |
 | CUDA Compute Capability  | 6.0 or larger (Pascal or newer, including Pascal, Volta, Turing and Ampere families) |
-| Python                   | Python 3.6+                                                                          |
+| Python                   | Python 3.7+                                                                          |
 
 
 ### System Config #2 (Desktop ARM64)
@@ -714,7 +714,7 @@ The SDK shall support the following system configurations for a given target rel
 | Docker CE                | 19.03.13+ ([2020-09-16](https://docs.docker.com/engine/release-notes/19.03/#190313)) |
 | NVIDIA Docker            | [v2.5.0+](https://github.com/NVIDIA/nvidia-docker/)                                  |
 | CUDA Compute Capability  | 6.0 or larger (Pascal or newer, including Pascal, Volta, Turing and Ampere families) |
-| Python                   | Python 3.6+                                                                          |
+| Python                   | Python 3.7+                                                                          |
 
 
 ### System Config #3 (DGX-1)
@@ -730,4 +730,4 @@ The SDK shall support the following system configurations for a given target rel
 | Docker CE                | 19.03.13+ ([2020-09-16](https://docs.docker.com/engine/release-notes/19.03/#190313)) |
 | NVIDIA Docker            | [v2.5.0+](https://github.com/NVIDIA/nvidia-docker/)                                  |
 | CUDA Compute Capability  | 7.0 or larger (Volta or newer, including Volta, Turing and Ampere families)          |
-| Python                   | Python 3.6+                                                                          |
+| Python                   | Python 3.7+                                                                          |

monai/deploy/cli/exec_command.py

@@ -24,7 +24,9 @@
 def create_exec_parser(subparser: _SubParsersAction, command: str, parents: List[ArgumentParser]) -> ArgumentParser:
     # Intentionally use `argparse.HelpFormatter` instead of `argparse.ArgumentDefaultsHelpFormatter`.
     # Default values for those options are None and those would be filled by `RuntimeEnv` object later.
-    parser = subparser.add_parser(command, formatter_class=argparse.HelpFormatter, parents=parents, add_help=False)
+    parser: ArgumentParser = subparser.add_parser(
+        command, formatter_class=argparse.HelpFormatter, parents=parents, add_help=False
+    )
 
     parser.add_argument("--input", "-i", help="Path to input folder/file (default: input)")
     parser.add_argument("--output", "-o", help="Path to output folder (default: output)")

monai/deploy/core/application.py

@@ -95,7 +95,7 @@ def __init__(
             self.path = get_class_file_path(self.__class__)
 
         # Set the runtime environment
-        if str(self.path).startswith("<ipython-"):
+        if str(self.path) == "ipython":
             self.in_ipython = True
         else:
             self.in_ipython = False

monai/deploy/operators/dicom_series_to_volume_operator.py

@@ -84,8 +84,16 @@ def generate_voxel_data(self, series):
         # This is to have the same numpy ndarray as from Monai ImageReader (ITK, NiBabel etc).
         vol_data = np.stack([np.transpose(s.get_pixel_array()) for s in slices], axis=-1)
         vol_data = vol_data.astype(np.int16)
-        intercept = slices[0][0x0028, 0x1052].value
-        slope = slices[0][0x0028, 0x1053].value
+        # Rescale Intercept and Slope attributes might be missing, but safe to assume defaults.
+        try:
+            intercept = slices[0][0x0028, 0x1052].value
+        except KeyError:
+            intercept = 0
+
+        try:
+            slope = slices[0][0x0028, 0x1053].value
+        except KeyError:
+            slope = 1
 
         if slope != 1:
             vol_data = slope * vol_data.astype(np.float64)

monai/deploy/operators/monai_seg_inference_operator.py

@@ -20,9 +20,11 @@
 np_str_obj_array_pattern, _ = optional_import("torch.utils.data._utils.collate", name="np_str_obj_array_pattern")
 Dataset, _ = optional_import("monai.data", name="Dataset")
 DataLoader, _ = optional_import("monai.data", name="DataLoader")
-ImageReader_, _ = optional_import("monai.data", name="ImageReader")
+ImageReader_, image_reader_ok_ = optional_import("monai.data", name="ImageReader")
 # Dynamic class is not handled so make it Any for now: https://github.com/python/mypy/issues/2477
 ImageReader: Any = ImageReader_
+if not image_reader_ok_:
+    ImageReader = object  # for 'class InMemImageReader(ImageReader):' to work
 decollate_batch, _ = optional_import("monai.data", name="decollate_batch")
 sliding_window_inference, _ = optional_import("monai.inferers", name="sliding_window_inference")
 ensure_tuple, _ = optional_import("monai.utils", name="ensure_tuple")

monai/deploy/packager/package_command.py

@@ -17,7 +17,9 @@
 
 
 def create_package_parser(subparser: _SubParsersAction, command: str, parents: List[ArgumentParser]) -> ArgumentParser:
-    parser = subparser.add_parser(command, formatter_class=argparse.HelpFormatter, parents=parents, add_help=False)
+    parser: ArgumentParser = subparser.add_parser(
+        command, formatter_class=argparse.HelpFormatter, parents=parents, add_help=False
+    )
 
     parser.add_argument("application", type=str, help="MONAI application path")
     parser.add_argument(

monai/deploy/packager/templates.py

@@ -12,47 +12,36 @@
 COMMON_FOOTPRINT = """
     USER root
 
-    RUN pip install --no-cache-dir --upgrade setuptools==57.4.0 pip==21.2.4 wheel==0.37.0
+    RUN pip install --no-cache-dir --upgrade setuptools==57.4.0 pip==21.2.4 wheel==0.37.0 monai-deploy-app-sdk==0.2.0
 
-    RUN groupadd -g $MONAI_GID -o -r monai
-    RUN useradd -g $MONAI_GID -u $MONAI_UID -m -o -r monai
-
-    RUN mkdir -p /etc/monai/ && chown -R monai:monai /etc/monai \\
-     && mkdir -p /opt/monai/ && chown -R monai:monai /opt/monai \\
-     && mkdir -p {working_dir} && chown -R monai:monai {working_dir} \\
-     && mkdir -p {app_dir} && chown -R monai:monai {app_dir} \\
-     && mkdir -p {executor_dir} && chown -R monai:monai {executor_dir} \\
-     && mkdir -p {full_input_path} && chown -R monai:monai {full_input_path} \\
-     && mkdir -p {full_output_path} && chown -R monai:monai {full_output_path} \\
-     && mkdir -p {models_dir} && chown -R monai:monai {models_dir}
+    RUN mkdir -p /etc/monai/ \\
+     && mkdir -p /opt/monai/ \\
+     && mkdir -p {working_dir} \\
+     && mkdir -p {app_dir} \\
+     && mkdir -p {executor_dir} \\
+     && mkdir -p {full_input_path} \\
+     && mkdir -p {full_output_path} \\
+     && mkdir -p {models_dir}
 
     {models_string}
 
-    COPY --chown=monai:monai ./pip/requirements.txt {map_requirements_path}
+    COPY ./pip/requirements.txt {map_requirements_path}
 
     RUN curl {executor_url} -o {executor_dir}/executor.zip \\
      && unzip {executor_dir}/executor.zip -d {executor_dir}/executor_pkg \\
      && mv {executor_dir}/executor_pkg/lib/native/linux-x64/* {executor_dir} \\
      && rm -f {executor_dir}/executor.zip \\
      && rm -rf {executor_dir}/executor_pkg \\
-     && chown -R monai:monai {executor_dir} \\
      && chmod +x {executor_dir}/monai-exec
 
-    USER monai
-    ENV PATH=/home/monai/.local/bin:$PATH
-
-    RUN pip install --no-cache-dir --upgrade -r {map_requirements_path}
-
-    # Override monai-deploy-app-sdk module
-    COPY --chown=monai:monai ./monai-deploy-app-sdk /home/monai/.local/lib/python3.8/site-packages/monai/deploy/
+    ENV PATH=/home/root/.local/bin:$PATH
 
-    COPY --chown=monai:monai ./map/app.json /etc/monai/
-    COPY --chown=monai:monai ./map/pkg.json /etc/monai/
+    RUN pip install --no-cache-dir -r {map_requirements_path}
 
-    COPY --chown=monai:monai ./app {app_dir}
+    COPY ./map/app.json /etc/monai/
+    COPY ./map/pkg.json /etc/monai/
 
-    # Create bytecodes for monai and app's code. This would help speed up loading time a little bit.
-    RUN python -m compileall -q -j 0 /home/monai/.local/lib/python3.8/site-packages/monai /opt/monai/app
+    COPY ./app {app_dir}
 
     # Set the working directory
     WORKDIR {working_dir}
@@ -63,9 +52,6 @@
 UBUNTU_DOCKERFILE_TEMPLATE = (
     """FROM {base_image}
 
-    ARG MONAI_GID=1000
-    ARG MONAI_UID=1000
-
     LABEL base="{base_image}"
     LABEL tag="{tag}"
     LABEL version="{app_version}"
@@ -100,9 +86,6 @@
 PYTORCH_DOCKERFILE_TEMPLATE = (
     """FROM {base_image}
 
-    ARG MONAI_GID=1000
-    ARG MONAI_UID=1000
-
     LABEL base="{base_image}"
     LABEL tag="{tag}"
     LABEL version="{app_version}"

monai/deploy/packager/util.py

@@ -194,8 +194,8 @@ def build_image(args: dict, temp_dir: str):
         else:
             shutil.copytree(model_path, dest_model_path)
 
-    models_string = f"RUN mkdir -p {models_dir} && chown -R monai:monai {models_dir}\n"
-    models_string += f"COPY --chown=monai:monai ./models {models_dir}\n"
+    models_string = f"RUN mkdir -p {models_dir}\n"
+    models_string += f"COPY ./models {models_dir}\n"
 
     # Dockerfile template
     template_params = {

monai/deploy/runner/run_command.py

@@ -20,7 +20,9 @@
 
 
 def create_run_parser(subparser: _SubParsersAction, command: str, parents: List[ArgumentParser]) -> ArgumentParser:
-    parser = subparser.add_parser(command, formatter_class=HelpFormatter, parents=parents, add_help=False)
+    parser: ArgumentParser = subparser.add_parser(
+        command, formatter_class=HelpFormatter, parents=parents, add_help=False
+    )
 
     parser.add_argument("map", metavar="<map-image[:tag]>", help="MAP image name")