OpenVINO model optimizer component and example pipeline (#178)

* OpenVINO model optimizer component and example pipeline

* component moved to contrib folder

* moved samples to contrib

contrib/components/openvino/model_convert/README.md

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+# Model optimization component
+
+This component is executing model optimization process using OpenVINO Toolkit and generate as output the model in 
+Intermediate Representation format.
+
+Component takes the following arguments:
+* model input GCS path
+* model optimizer parameters
+* model output GCS path
+
+```bash
+usage: convert_model.py [-h] [--input_path INPUT_PATH]
+                        [--mo_options MO_OPTIONS] [--output_path OUTPUT_PATH]
+
+Model converter to OpenVINO Intermediate Representation format
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --input_path INPUT_PATH
+                        GCS path of input model file or folder
+  --mo_options MO_OPTIONS
+                        OpenVINO Model Optimizer options
+  --output_path OUTPUT_PATH
+                        GCS path of output folder
+
+```
+
+## Component parameters
+
+It takes as input GCS path to the input model in any of the OpenVINO supported frameworks:
+* Tensorflow
+* Caffe
+* MXNET
+* Kaldi
+* ONNX
+
+Input model path can be a folder or an individual file which will be copied to a component working directory
+
+Model optimizer options can include any of the parameters supported by OpenVINO toolkit model optimizer.
+
+Refer to OpenVINO [documentation](https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer) for details. 
+```bash
+  --framework {tf,caffe,mxnet,kaldi,onnx}
+                        Name of the framework used to train the input model.
+
+Framework-agnostic parameters:
+  --input_model INPUT_MODEL, -w INPUT_MODEL, -m INPUT_MODEL
+                        Tensorflow*: a file with a pre-trained model (binary
+                        or text .pb file after freezing). Caffe*: a model
+                        proto file with model weights
+  --model_name MODEL_NAME, -n MODEL_NAME
+                        Model_name parameter passed to the final create_ir
+                        transform. This parameter is used to name a network in
+                        a generated IR and output .xml/.bin files.
+  --output_dir OUTPUT_DIR, -o OUTPUT_DIR
+                        Directory that stores the generated IR. By default, it
+                        is the directory from where the Model Optimizer is
+                        launched.
+  --input_shape INPUT_SHAPE
+                        Input shape(s) that should be fed to an input node(s)
+                        of the model. Shape is defined as a comma-separated
+                        list of integer numbers enclosed in parentheses, for
+                        example [1,3,227,227] or [1,227,227,3], where the
+                        order of dimensions depends on the framework input
+                        layout of the model. For example, [N,C,H,W] is used
+                        for Caffe* models and [N,H,W,C] for TensorFlow*
+                        models. Model Optimizer performs necessary
+                        transformations to convert the shape to the layout
+                        required by Inference Engine (N,C,H,W). Two types of
+                        brackets are allowed to enclose the dimensions: [...]
+                        or (...). The shape should not contain undefined
+                        dimensions (? or -1) and should fit the dimensions
+                        defined in the input operation of the graph. If there
+                        are multiple inputs in the model, --input_shape should
+                        contain definition of shape for each input separated
+                        by a comma, for example: [1,3,227,227],[2,4] for a
+                        model with two inputs with 4D and 2D shapes.
+  --scale SCALE, -s SCALE
+                        All input values coming from original network inputs
+                        will be divided by this value. When a list of inputs
+                        is overridden by the --input parameter, this scale is
+                        not applied for any input that does not match with the
+                        original input of the model.
+  --reverse_input_channels
+                        Switches the input channels order from RGB to BGR (or
+                        vice versa). Applied to original inputs of the model
+                        when and only when a number of channels equals 3.
+                        Applied after application of --mean_values and
+                        --scale_values options, so numbers in --mean_values
+                        and --scale_values go in the order of channels used in
+                        the original model.
+  --log_level {CRITICAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}
+                        Logger level
+  --input INPUT         The name of the input operation of the given model.
+                        Usually this is a name of the input placeholder of the
+                        model.
+  --output OUTPUT       The name of the output operation of the model. For
+                        TensorFlow*, do not add :0 to this name.
+  --mean_values MEAN_VALUES, -ms MEAN_VALUES
+                        Mean values to be used for the input image per
+                        channel. Values to be provided in the (R,G,B) or
+                        [R,G,B] format. Can be defined for desired input of
+                        the model, e.g.: "--mean_values
+                        data[255,255,255],info[255,255,255]" The exact meaning
+                        and order of channels depend on how the original model
+                        was trained.
+  --scale_values SCALE_VALUES
+                        Scale values to be used for the input image per
+                        channel. Values are provided in the (R,G,B) or [R,G,B]
+                        format.Can be defined for desired input of the model,
+                        e.g.: "--scale_values
+                        data[255,255,255],info[255,255,255]"The exact meaning
+                        and order of channels depend on how the original model
+                        was trained.
+  --data_type {FP16,FP32,half,float}
+                        Data type for all intermediate tensors and weights. If
+                        original model is in FP32 and --data_type=FP16 is
+                        specified, all model weights and biases are quantized
+                        to FP16.
+  --disable_fusing      Turns off fusing of linear operations to Convolution
+  --disable_resnet_optimization
+                        Turns off resnet optimization
+  --finegrain_fusing FINEGRAIN_FUSING
+                        Regex for layers/operations that won't be fused.
+                        Example: --finegrain_fusing Convolution1,.*Scale.*
+  --disable_gfusing     Turns off fusing of grouped convolutions
+  --move_to_preprocess  Move mean values to IR preprocess section
+  --extensions EXTENSIONS
+                        Directory or a comma separated list of directories
+                        with extensions. To disable all extensions including
+                        those that are placed at the default location, pass an
+                        empty string.
+  --batch BATCH, -b BATCH
+                        Input batch size
+  --version             Version of Model Optimizer
+  --silent              Prevents any output messages except those that
+                        correspond to log level equalsERROR, that can be set
+                        with the following option: --log_level. By default,
+                        log level is already ERROR.
+  --freeze_placeholder_with_value FREEZE_PLACEHOLDER_WITH_VALUE
+                        Replace input layer with constant node with provided
+                        value, e.g.: node_name->True
+
+TensorFlow*-specific parameters:
+  --input_model_is_text
+                        TensorFlow*: treat the input model file in a text
+                        protobuf format instead of binary, which is default.
+  --input_checkpoint INPUT_CHECKPOINT
+                        TensorFlow*: variables file to load.
+  --input_meta_graph INPUT_META_GRAPH
+                        Tensorflow*: a file with a non-trained model before
+                        freezing
+  --saved_model_dir SAVED_MODEL_DIR
+                        TensorFlow*: directory representing non frozen model
+  --saved_model_tags SAVED_MODEL_TAGS
+                        Group of tag(s) of the MetaGraphDef to load, in string
+                        format, separated by ','. For tag-set contains
+                        multiple tags, all tags must be passed in.
+  --offload_unsupported_operations_to_tf
+                        TensorFlow*: automatically offload unsupported
+                        operations to TensorFlow*
+  --tensorflow_subgraph_patterns TENSORFLOW_SUBGRAPH_PATTERNS
+                        TensorFlow*: a list of comma separated patterns that
+                        will be applied to TensorFlow* node names to infer a
+                        part of the graph using TensorFlow*.
+  --tensorflow_operation_patterns TENSORFLOW_OPERATION_PATTERNS
+                        TensorFlow*: a list of comma separated patterns that
+                        will be applied to TensorFlow* node type (ops) to
+                        infer these operations using TensorFlow*.
+  --tensorflow_custom_operations_config_update TENSORFLOW_CUSTOM_OPERATIONS_CONFIG_UPDATE
+                        TensorFlow*: update the configuration file with node
+                        name patterns with input/output nodes information.
+  --tensorflow_use_custom_operations_config TENSORFLOW_USE_CUSTOM_OPERATIONS_CONFIG
+                        TensorFlow*: use the configuration file with custom
+                        operation description.
+  --tensorflow_object_detection_api_pipeline_config TENSORFLOW_OBJECT_DETECTION_API_PIPELINE_CONFIG
+                        TensorFlow*: path to the pipeline configuration file
+                        used to generate model created with help of Object
+                        Detection API.
+  --tensorboard_logdir TENSORBOARD_LOGDIR
+                        TensorFlow*: dump the input graph to a given directory
+                        that should be used with TensorBoard.
+
+Caffe*-specific parameters:
+  --input_proto INPUT_PROTO, -d INPUT_PROTO
+                        Deploy-ready prototxt file that contains a topology
+                        structure and layer attributes
+  -k K                  Path to CustomLayersMapping.xml to register custom
+                        layers
+  --mean_file MEAN_FILE, -mf MEAN_FILE
+                        Mean image to be used for the input. Should be a
+                        binaryproto file
+  --mean_file_offsets MEAN_FILE_OFFSETS, -mo MEAN_FILE_OFFSETS
+                        Mean image offsets to be used for the input
+                        binaryproto file. When the mean image is bigger than
+                        the expected input, it is cropped. By default, centers
+                        of the input image and the mean image are the same and
+                        the mean image is cropped by dimensions of the input
+                        image. The format to pass this option is the
+                        following: "-mo (x,y)". In this case, the mean file is
+                        cropped by dimensions of the input image with offset
+                        (x,y) from the upper left corner of the mean image
+  --disable_omitting_optional
+                        Disable omitting optional attributes to be used for
+                        custom layers. Use this option if you want to transfer
+                        all attributes of a custom layer to IR. Default
+                        behavior is to transfer the attributes with default
+                        values and the attributes defined by the user to IR.
+  --enable_flattening_nested_params
+                        Enable flattening optional params to be used for
+                        custom layers. Use this option if you want to transfer
+                        attributes of a custom layer to IR with flattened
+                        nested parameters. Default behavior is to transfer the
+                        attributes without flattening nested parameters.
+
+Mxnet-specific parameters:
+  --input_symbol INPUT_SYMBOL
+                        Symbol file (for example, model-symbol.json) that
+                        contains a topology structure and layer attributes
+  --nd_prefix_name ND_PREFIX_NAME
+                        Prefix name for args.nd and argx.nd files.
+  --pretrained_model_name PRETRAINED_MODEL_NAME
+                        Pretrained model without extension and epoch number
+                        which will be merged with args.nd and argx.nd files.
+  --save_params_from_nd
+                        Enable save built params file from nd files.
+  --legacy_mxnet_model  Load the model trained with less version of MXNet than
+                        1.0.0
+
+Kaldi-specific parameters:
+  --counts COUNTS       Path to the counts file
+  --remove_output_softmax
+                        Removes the Softmax layer that is the output layer
+```
+
+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.
+
+## Examples
+
+Input path - gs://tensorflow_model_path/resnet/1/saved_model.pb
+
+MO options - --saved_model_dir .
+
+Output path - gs://tensorflow_model_path/resnet/1
+
+
+## Building docker image
+
+```bash
+docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy .
+```
+
+## Starting and testing the component locally 
+
+This component requires GCP authentication token in json format generated for the service account,
+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"
+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

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+FROM ubuntu:16.04
+RUN apt-get update && apt-get install -y curl ca-certificates python3-pip python-dev libgfortran3 unzip vim
+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 -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 && \
+    rm google-cloud-sdk.zip && \
+    tools/google-cloud-sdk/install.sh --usage-reporting=false \
+        --path-update=true --bash-completion=false \
+        --disable-installation-options && \
+    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
+COPY convert_model.py .
+WORKDIR input
+
+

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

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+import argparse
+import subprocess
+import re
+from shutil import copyfile
+
+
+def is_insecure_path(path):
+    # check if the path do not include insecure characters
+    if not re.match(r"^gs:\/\/[\.\w\/-]*$", path):
+        is_insecure = True
+    else:
+        is_insecure = False
+    return is_insecure
+
+
+def are_insecure_mo_options(all_options):
+    # check if all passed options do not include insecure characters
+    is_insecure = False
+    for option in all_options.split():
+        if not re.match(r"^[\.\w:\/-]*$", option):
+            is_insecure = True
+    return is_insecure
+
+
+def main():
+    parser = argparse.ArgumentParser(
+      description='Model converter to OpenVINO IR format')
+    parser.add_argument(
+      '--input_path', type=str, help='GCS path of input model file or folder')
+    parser.add_argument(
+      '--mo_options', type=str, help='OpenVINO Model Optimizer options')
+    parser.add_argument(
+      '--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):
+        print("Invalid input format")
+        exit(1)
+
+    if is_insecure_path(args.output_path):
+        print("Invalid output format")
+        exit(1)
+
+    if are_insecure_mo_options(args.mo_options):
+        print("Invalid model optimizer options")
+        exit(1)
+
+    # Initialize gsutil creds
+    command = bin_path + "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)
+
+    # Downloading input model
+    command = bin_path + "gsutil cp -r " + args.input_path + " ."
+    print("gsutil download command", command)
+    return_code = subprocess.call(command, shell=True)
+    print("return code", return_code)
+
+    # Executing model optimization
+    command = "python3 ../mo.py " + args.mo_options
+    print("mo command", command)
+    output = subprocess.run(command, shell=True, stdout=subprocess.PIPE,
+                            universal_newlines=True)
+    XML = ""
+    BIN = ""
+    for line in output.stdout.splitlines():
+        if "[ SUCCESS ] XML file" in line:
+            XML = line.split(":")[1].strip()
+        if "[ SUCCESS ] BIN file" in line:
+            BIN = line.split(":")[1].strip()
+    if XML == "" or BIN == "":
+        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
+    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
+    print("gsutil upload command", command)
+    return_code = subprocess.call(command, shell=True)
+    print("return code", return_code)
+
+
+if __name__ == "__main__":
+    main()