added component with tf slim model generator and pipeline adjustments

contrib/components/openvino/model_convert/README.md

@@ -236,17 +236,22 @@ Kaldi-specific parameters:
 The output folder specify then should be uploaded the generated model file in IR format with .bin and .xml
 extensions.
 
-The component also saved the generated model files in location: `/tmp/model.bin` and `/tmp/model.xml`
-so this path could be used in the argo pipeline for storing the workflow artifacts.
+The component also creates 3 files including the paths to generated model:
+- `/tmp/output.txt` - GSC path to the folder including the generated model files.
+- `/tmp/bin_path.txt` -  GSC path to weights model file 
+- `/tmp/xml_path.txt` - GSC path to graph model file 
+They can be used as parameters to be passed to other jobs in ML pipelines.
 
 ## Examples
 
-Input path - gs://tensorflow_model_path/resnet/1/saved_model.pb
-
-MO options - --saved_model_dir .
-
+Input path - gs://tensorflow_model_path/resnet/1/saved_model.pb<br />
+MO options - --saved_model_dir .<br />
 Output path - gs://tensorflow_model_path/resnet/1
 
+Input path - gs://tensorflow_model_path/resnet/1<br />
+MO options - --saved_model_dir 1<br />
+Output path - gs://tensorflow_model_path/resnet/dldt/1<br />
+
 
 ## Building docker image
 
@@ -260,7 +265,7 @@ This component requires GCP authentication token in json format generated for th
 which has access to GCS location. In the example below it is in key.json in the current path.
 
 ```bash
-COMMAND="python3 ../convert_model.py --mo_options  \"--saved_model_dir .\" --input_path gs://tensorflow_model_path/resnet/1/saved_model.pb --output_path gs://tensorflow_model_path/resnet/1"
+COMMAND="convert_model.py --mo_options  \"--saved_model_dir .\" --input_path gs://tensorflow_model_path/resnet/1/saved_model.pb --output_path gs://tensorflow_model_path/resnet/1"
 docker run --rm -it -v $(pwd)/key.json:/etc/credentials/gcp-key.json \
 -e GOOGLE_APPLICATION_CREDENTIALS=/etc/credentials/gcp-key.json <image_name> $COMMAND
 

contrib/components/openvino/model_convert/containers/Dockerfile

@@ -1,10 +1,21 @@
 FROM ubuntu:16.04
-RUN apt-get update && apt-get install -y curl ca-certificates python3-pip python-dev libgfortran3 unzip vim
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        curl ca-certificates \
+        python3-pip \
+        python-dev \
+        gcc \
+        python-setuptools \
+        python3-setuptools \
+        libgfortran3 \
+        unzip \
+        vim && \
+        apt-get clean
 RUN curl -L -o 2018_R3.tar.gz https://github.com/opencv/dldt/archive/2018_R3.tar.gz && \
     tar -zxf 2018_R3.tar.gz && \
     rm 2018_R3.tar.gz && \
     rm -Rf dldt-2018_R3/inference-engine
 WORKDIR dldt-2018_R3/model-optimizer
+RUN pip3 install --upgrade pip setuptools
 RUN pip3 install -r requirements.txt
 RUN curl -L -o google-cloud-sdk.zip https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.zip && \
     unzip -qq google-cloud-sdk.zip -d tools && \
@@ -15,8 +26,11 @@ RUN curl -L -o google-cloud-sdk.zip https://dl.google.com/dl/cloudsdk/release/go
     tools/google-cloud-sdk/bin/gcloud -q components update \
         gcloud core gsutil && \
     tools/google-cloud-sdk/bin/gcloud config set component_manager/disable_update_check true && \
-    touch tools/google-cloud-sdk/lib/third_party/google.py
+    touch tools/google-cloud-sdk/lib/third_party/google.py && \
+    pip install -U crcmod
+ENV PATH ${PATH}:/dldt-2018_R3/model-optimizer:/dldt-2018_R3/model-optimizer/tools/google-cloud-sdk/bin
 COPY convert_model.py .
+RUN chmod 755 *.py
 WORKDIR input
 
 

contrib/components/openvino/model_convert/containers/convert_model.py

@@ -1,7 +1,9 @@
+#!/usr/bin/python3
+
 import argparse
 import subprocess
 import re
-from shutil import copyfile
+import os
 
 
 def is_insecure_path(path):
@@ -33,7 +35,6 @@ def main():
       '--output_path', type=str, help='GCS path of output folder')
     args = parser.parse_args()
 
-    bin_path = "../tools/google-cloud-sdk/bin/"
     # Validate parameters
 
     if is_insecure_path(args.input_path):
@@ -48,24 +49,28 @@ def main():
         print("Invalid model optimizer options")
         exit(1)
 
-    # Initialize gsutil creds
-    command = bin_path + "gcloud auth activate-service-account " \
+    # Initialize gsutil creds if needed
+    if "GOOGLE_APPLICATION_CREDENTIALS" in os.environ:
+        command = "gcloud auth activate-service-account " \
                          "--key-file=${GOOGLE_APPLICATION_CREDENTIALS}"
-    print("auth command", command)
-    return_code = subprocess.call(command, shell=True)
-    print("return code", return_code)
+        print("auth command", command)
+        return_code = subprocess.call(command, shell=True)
+        print("return code", return_code)
 
-    # Downloading input model
-    command = bin_path + "gsutil cp -r " + args.input_path + " ."
+    # Downloading input model or GCS folder with a model to current folder
+    command = "gsutil cp -r " + args.input_path + " ."
     print("gsutil download command", command)
     return_code = subprocess.call(command, shell=True)
     print("return code", return_code)
+    if return_code:
+        exit(1)
 
     # Executing model optimization
-    command = "python3 ../mo.py " + args.mo_options
-    print("mo command", command)
+    command = "mo.py " + args.mo_options
+    print("Starting model optimization:", command)
     output = subprocess.run(command, shell=True, stdout=subprocess.PIPE,
                             universal_newlines=True)
+    print("Model optimization output",output.stdout)
     XML = ""
     BIN = ""
     for line in output.stdout.splitlines():
@@ -77,19 +82,25 @@ def main():
         print("Error, model optimization failed")
         exit(1)
 
-    # copy generated model file to use them as workflow artifacts
-    copyfile(BIN, "/tmp/model.bin")
-    copyfile(XML, "/tmp/model.xml")
-
-    command = bin_path + "gsutil cp " + XML + " " + args.output_path
+    command = "gsutil cp " + XML + " " + os.path.join(args.output_path, os.path.split(XML)[1])
     print("gsutil upload command", command)
     return_code = subprocess.call(command, shell=True)
     print("return code", return_code)
-    command = bin_path + "gsutil cp " + BIN + " " + args.output_path
+    command = "gsutil cp " + BIN + " " + os.path.join(args.output_path, os.path.split(BIN)[1])
     print("gsutil upload command", command)
     return_code = subprocess.call(command, shell=True)
     print("return code", return_code)
+    if return_code:
+        exit(1)
+
+    with open('/tmp/output_path.txt', 'w') as f:
+        f.write(args.output_path)
+    with open('/tmp/bin_path.txt', 'w') as f:
+        f.write(os.path.join(args.output_path, os.path.split(BIN)[1]))
+    with open('/tmp/xml_path.txt', 'w') as f:
+        f.write(os.path.join(args.output_path, os.path.split(XML)[1]))
 
+    print("Model successfully generated and uploaded to ", args.output_path)
 
 if __name__ == "__main__":
     main()

contrib/components/openvino/tf-slim/README.md

@@ -0,0 +1,79 @@
+# Slim models generator
+
+This component is automating implementation of [slim models](https://github.com/tensorflow/models/blob/master/research/slim).
+It can create a graph from slim models zoo, load the variables pre-trained checkpoint and export the model in the form 
+of Tensorflow `frozen graph` and `saved model`.
+
+The results of the component can be saved in a local path or in GCS cloud storage. The can be used to other ML pipeline
+components like OpenVINO model optimizer, OpenVINO predict or OpenVINO Model Server.
+
+## Building
+
+```bash
+docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy .
+```
+
+
+## Using the component
+
+```bash
+python slim_model.py --help
+usage: slim_model.py [-h] [--model_name MODEL_NAME] [--export_dir EXPORT_DIR]
+                     [--batch_size BATCH_SIZE]
+                     [--checkpoint_url CHECKPOINT_URL]
+                     [--num_classes NUM_CLASSES]
+
+Slim model generator
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --model_name MODEL_NAME
+  --export_dir EXPORT_DIR
+                        GCS or local path to save the generated model
+  --batch_size BATCH_SIZE
+                        batch size to be used in the exported model
+  --checkpoint_url CHECKPOINT_URL
+                        URL to the pretrained compressed checkpoint
+  --num_classes NUM_CLASSES
+                        number of model classes
+```
+
+*Model name* can be any model defined in the slim repository. The naming convention needs to match the key name from 
+[net_factory.py]()https://github.com/tensorflow/models/blob/master/research/slim/nets/nets_factory.py#L39)
+
+*export dir* can be a local path in the container or it might be GCS path to store generated files:
+- model graph file in pb format
+- frozen graph including weights from the provided checkpoint
+- event file which can be imported in tensorboard
+- saved model which will be stored in subfolder called `1`.
+
+*batch size* represent the batch used in the exported models. It can be natural number to represent fixed batch size
+or `-1` value can be set for dynamic batch size.
+
+*checkpoint_url* is the URL to a pre-trained checkpoint https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models
+It must match the model specified in model_name parameter.
+
+*num classes* should include model specific number of classes in the outputs. For slim models it should be a value 
+of `1000` or `1001`. It must match the number of classes used in the requested model name.
+
+
+## Examples
+
+```
+python slim_model.py --model_name mobilenet_v1_050 --export_dir /tmp/mobilnet 
+--batch_size 1 --num_classes=1001 \
+--checkpoint_url http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160.tgz
+
+python slim_model.py --model_name resnet_v1_50 --export_dir gs://<bucket_name>/resnet \
+--batch_size -1 --num_classes=1000 \
+--checkpoint_url http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
+
+python slim_model.py --model_name inception_v4 --export_dir gs://<bucket_name>/inception \
+--batch_size -1 --num_classes=1001 \
+--checkpoint_url http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz
+
+python slim_model.py --model_name vgg_19 --export_dir /tmp/vgg \
+--batch_size 1 --num_classes=1000 \
+--checkpoint_url http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz
+```
+

contrib/components/openvino/tf-slim/containers/Dockerfile

@@ -0,0 +1,74 @@
+FROM intelpython/intelpython3_core as BUILD
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        openjdk-8-jdk \
+        openjdk-8-jre-headless \
+        build-essential \
+        curl \
+        git \
+        libcurl3-dev \
+        libfreetype6-dev \
+        libhdf5-serial-dev \
+        libpng-dev \
+        libzmq3-dev \
+        pkg-config \
+        rsync \
+        software-properties-common \
+        unzip \
+        zip \
+        zlib1g-dev && \
+        apt-get clean
+
+RUN git clone --depth 1 https://github.com/tensorflow/tensorflow
+
+
+RUN conda create --name myenv -y
+ENV PATH /opt/conda/envs/myenv/bin:$PATH
+
+# Set up Bazel.
+
+
+# Running bazel inside a `docker build` command causes trouble, cf:
+#   https://github.com/bazelbuild/bazel/issues/134
+# The easiest solution is to set up a bazelrc file forcing --batch.
+RUN echo "startup --batch" >>/etc/bazel.bazelrc
+# Similarly, we need to workaround sandboxing issues:
+#   https://github.com/bazelbuild/bazel/issues/418
+RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
+    >>/etc/bazel.bazelrc
+# Install the most recent bazel release.
+ENV BAZEL_VERSION 0.15.0
+WORKDIR /
+RUN mkdir /bazel && \
+    cd /bazel && \
+    curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
+    curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -o /bazel/LICENSE.txt https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE && \
+    chmod +x bazel-*.sh && \
+    ./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
+    cd / && \
+    rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh
+
+RUN cd tensorflow && bazel build tensorflow/tools/graph_transforms:summarize_graph
+
+FROM intelpython/intelpython3_core as PROD
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        git && \
+        apt-get clean
+
+WORKDIR /slim
+
+RUN git clone --depth 1 https://github.com/tensorflow/models && rm -Rf models/.git && \
+    git clone --depth 1 https://github.com/tensorflow/tensorflow && rm -Rf tensorflow/.git
+
+RUN conda create --name myenv -y
+ENV PATH /opt/conda/envs/myenv/bin:$PATH
+
+RUN pip install --no-cache-dir tensorflow validators google-cloud-storage
+ENV PYTHONPATH=models/research/slim:tensorflow/python/tools
+
+COPY --from=BUILD /tensorflow/bazel-bin/tensorflow/tools/graph_transforms/summarize_graph summarize_graph
+COPY --from=BUILD /root/.cache/bazel/_bazel_root/*/execroot/org_tensorflow/bazel-out/k8-opt/bin/_solib_k8/_U_S_Stensorflow_Stools_Sgraph_Utransforms_Csummarize_Ugraph___Utensorflow/libtensorflow_framework.so libtensorflow_framework.so
+COPY slim_model.py .
+
+
+