BIOP/v0.2.2

add a tf2onnx environment to convert stardist TF model to OpenCV that are compatible with QuPath

Author: romainGuiet

docker/Dockerfile-ms

@@ -18,6 +18,7 @@ ARG INKSCAPE_VERSION=0.1.4
 ARG OMNI_VERSION=1.0.7-dev2
 ARG BRAINRENDER_VERSION=0.0.7
 ARG YOLO_VERSION=8.3.119
+ARG TF2ONNX_VERSION=1.9.2
 
 FROM ${ALIAS}biop-qupath:${QUPATH_VERSION} AS qupath-image
 FROM ${ALIAS}biop-ilastik:${ILASTIK_VERSION} AS ilastik-image
@@ -33,6 +34,7 @@ FROM ${ALIAS}biop-inkscape:${INKSCAPE_VERSION} AS inkscape-image
 FROM ${ALIAS}biop-omni:${OMNI_VERSION} AS omnipose-image
 FROM ${ALIAS}biop-brainrender:${BRAINRENDER_VERSION} AS brainrender-image
 FROM ${ALIAS}biop-yolo:${YOLO_VERSION} AS yolo-image
+FROM ${ALIAS}biop-tf2onnx:${TF2ONNX_VERSION} AS tf2onnx-image
 
 FROM ${ALIAS}biop-vnc-base:${BASE_IMAGE} AS base
 
@@ -121,6 +123,10 @@ COPY --from=brainrender-image /home/biop/.brainglobe/ /home/biop/.brainglobe/
 # Install yolo
 COPY --from=yolo-image /opt/conda/envs/yolo /opt/conda/envs/yolo
 
+##################################################################
+# Install tf2onnx
+COPY --from=tf2onnx-image /opt/conda/envs/tf2onnx /opt/conda/envs/tf2onnx
+
 #################################################################
 # Jupyter kernels 
 # 

docker/tf2onnx/Dockerfile-tf2onnx

@@ -0,0 +1,20 @@
+ARG ALIAS=biop/
+ARG STARDIST_VERSION=0.8.5
+FROM ${ALIAS}biop-stardist:${STARDIST_VERSION}
+
+USER root
+
+COPY tf2onnx/tf2onnx.txt /tmp/tf2onnx.txt
+RUN conda create -y -n tf2onnx python=3.7 \
+     && source activate tf2onnx \
+     && pip install -r /tmp/tf2onnx.txt
+
+RUN chmod -R a+rwX /home/biop/ \
+     && chown -R biop:biop /home/biop/
+ 
+ #################################################################
+ # Container start
+ USER biop
+ WORKDIR /home/biop
+ ENTRYPOINT ["/usr/local/bin/jupyter"]
+ CMD ["lab", "--allow-root", "--ip=*", "--port=8888", "--no-browser", "--NotebookApp.token=''", "--NotebookApp.allow_origin='*'", "--notebook-dir=/home/biop"]

docker/tf2onnx/readme-tf2onnx.md

@@ -0,0 +1,36 @@
+# Latest Version
+
+## build
+```
+docker build -f tf2onnx/Dockerfile-tf2onnx  -t biop-tf2onnx:1.9.2 . --no-cache
+
+```
+## start to test (see below)
+```
+docker run -it --rm -p 8888:8888 --gpus device=0  --mount src=D:/,target=/home/biop/local,type=bind  biop-tf2onnx:1.9.2
+```
+
+## after testing pass, tag 
+```
+docker tag  biop-tf2onnx:1.9.2 biop/biop-tf2onnx:1.9.2
+```
+
+## push on dockerhub
+```
+docker push biop/biop-tf2onnx:1.9.2
+```
+
+# Test(s)
+
+## tf2onnx on blob
+- train a stardist model
+- export using model.export_TF("model_name")
+- unzip the model_name.zip to a folder model_name (this folder will contain a "tensorflow_model.pb" file)
+
+- convert the model using tf2onnx, in the terminal :
+```
+source activate tf2onnx
+python -m tf2onnx.convert --saved-model "/path/to/folder/model_name" --output_frozen_graph "/path/to/output/model_name_converted.pb"
+```
+
+model_name_converted.pb is the converted model, you can use it in your QuPath script!.

docker/tf2onnx/tf2onnx.txt

@@ -0,0 +1,31 @@
+absl-py==0.14.1
+astor==0.8.1
+cached-property==1.5.2
+certifi==2021.5.30
+charset-normalizer==2.0.6
+flatbuffers==1.12
+gast==0.2.2
+google-pasta==0.2.0
+grpcio==1.41.0
+h5py==3.4.0
+idna==3.2
+importlib-metadata==4.8.1
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.2
+Markdown==3.3.4
+numpy==1.21.2
+onnx==1.10.1
+opt-einsum==3.3.0
+protobuf==3.18.0
+requests==2.26.0
+six==1.16.0
+tensorboard==1.15.0
+tensorflow==1.15.0
+tensorflow-estimator==1.15.1
+termcolor==1.1.0
+tf2onnx==1.9.2
+typing-extensions==3.10.0.2
+urllib3==1.26.7
+Werkzeug==2.0.1
+wrapt==1.13.1
+zipp==3.6.0