Merge pull request idealo#13 from bmachin/add-tfs-support

Adding TF Serving support.

README.md

@@ -189,6 +189,28 @@ data/TID2013/tid2013_labels_test.json
 
 For the AVA dataset we randomly assigned 90% of samples to the train set, and 10% to the test set, and throughout training a 5% validation set will be split from the training set to evaluate the training performance after each epoch. For the TID2013 dataset we split the train/test sets by reference images, to ensure that no reference image, and any of its distortions, enters both the train and test set.
 
+## Serving NIMA with TensorFlow Serving
+TensorFlow versions of both the technical and aesthetic MobileNet models are provided,
+along with the script to generate them from the original Keras files, under the `contrib/tf_serving` directory.
+
+There is also an already configured TFS `Dockerfile` that you can use.
+
+To get predictions from the aesthetic or technical model:
+1. Build the NIMA TFS Docker image `docker build -t tfs_nima contrib/tf_serving`
+2. Run a NIMA TFS container with `docker run -d --name tfs_nima -p 8500:8500 tfs_nima`
+3. Install python dependencies to run TF serving sample client
+```
+virtualenv -p python3 contrib/tf_serving/venv_tfs_nima
+source contrib/tf_serving/venv_tfs_nima/bin/activate
+pip install -r contrib/tf_serving/requirements.txt
+```
+4. Get predictions from aesthetic or technical model by running the sample client
+```
+python -m contrib.tf_serving.tfs_sample_client --image-path src/tests/test_images/42039.jpg --model-name mobilenet_aesthetic
+python -m contrib.tf_serving.tfs_sample_client --image-path src/tests/test_images/42039.jpg --model-name mobilenet_technical
+```
+
+
 ## Maintainers
 * Christopher Lennan, github: [clennan](https://github.com/clennan)
 * Hao Nguyen, github: [MrBanhBao](https://github.com/MrBanhBao)

contrib/tf_serving/Dockerfile

@@ -0,0 +1,13 @@
+FROM tensorflow/serving:latest
+
+WORKDIR /tf_serving
+
+# copy project files
+COPY tfs_models/mobilenet_aesthetic /models/mobilenet_aesthetic/1
+COPY tfs_models/mobilenet_technical /models/mobilenet_technical/1
+COPY tf_serving_models.cfg /tf_serving/tf_serving_models.cfg
+
+EXPOSE 8500
+ENTRYPOINT []
+
+CMD ["tensorflow_model_server" ,"--port=8500", "--model_config_file=tf_serving_models.cfg"]

contrib/tf_serving/requirements.txt

@@ -0,0 +1,3 @@
+Keras==2.2.*
+Pillow==5.3.*
+tensorflow-serving-api==1.12.*

contrib/tf_serving/save_tfs_model.py

@@ -0,0 +1,48 @@
+import keras.backend as K
+import argparse
+from keras.applications.mobilenet import DepthwiseConv2D, relu6
+from keras.utils.generic_utils import CustomObjectScope
+from tensorflow.python.saved_model import builder as saved_model_builder
+from tensorflow.python.saved_model import tag_constants
+from tensorflow.python.saved_model.signature_def_utils_impl import \
+    predict_signature_def
+
+from src.handlers.model_builder import Nima
+
+
+def main(base_model_name, weights_file, export_path):
+    # Load model and weights
+    nima = Nima(base_model_name, weights=None)
+    nima.build()
+    nima.nima_model.load_weights(weights_file)
+
+    # Tell keras that this will be used for making predictions
+    K.set_learning_phase(0)
+
+    # CustomObject required by MobileNet
+    with CustomObjectScope({'relu6': relu6, 'DepthwiseConv2D': DepthwiseConv2D}):
+        builder = saved_model_builder.SavedModelBuilder(export_path)
+        signature = predict_signature_def(
+            inputs={'input_image': nima.nima_model.input},
+            outputs={'quality_prediction': nima.nima_model.output}
+        )
+
+        builder.add_meta_graph_and_variables(
+            sess=K.get_session(),
+            tags=[tag_constants.SERVING],
+            signature_def_map={'image_quality': signature}
+        )
+        builder.save()
+
+    print(f'TF model exported to: {export_path}')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-b', '--base-model-name', help='CNN base model name', required=True)
+    parser.add_argument('-w', '--weights-file', help='path of weights file', required=True)
+    parser.add_argument('-ep', '--export-path', help='path to save the tfs model', required=True)
+
+    args = parser.parse_args()
+
+    main(**args.__dict__)

contrib/tf_serving/tf_serving_models.cfg

@@ -0,0 +1,13 @@
+model_config_list: {
+
+  config: {
+        name: "mobilenet_technical",
+        base_path: "/models/mobilenet_technical",
+        model_platform: "tensorflow"
+      },
+  config: {
+        name: "mobilenet_aesthetic",
+        base_path: "/models/mobilenet_aesthetic",
+        model_platform: "tensorflow"
+      }
+}

contrib/tf_serving/tfs_sample_client.py

@@ -0,0 +1,53 @@
+import json
+import argparse
+import keras
+import numpy as np
+import tensorflow as tf
+from src.utils import utils
+from grpc.beta import implementations
+from tensorflow_serving.apis import predict_pb2, prediction_service_pb2
+
+TFS_HOST = 'localhost'
+TFS_PORT = 8500
+
+
+def normalize_labels(labels):
+    labels_np = np.array(labels)
+    return labels_np / labels_np.sum()
+
+
+def calc_mean_score(score_dist):
+    score_dist = normalize_labels(score_dist)
+    return (score_dist * np.arange(1, 11)).sum()
+
+
+def get_image_quality_predictions(image_path, model_name):
+    # Load and preprocess image
+    image = utils.load_image(image_path, target_size=(224, 224))
+    image = keras.applications.mobilenet.preprocess_input(image)
+
+    # Run through model
+    channel = implementations.insecure_channel(TFS_HOST, TFS_PORT)
+    stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
+    request = predict_pb2.PredictRequest()
+    request.model_spec.name = model_name
+    request.model_spec.signature_name = 'image_quality'
+
+    request.inputs['input_image'].CopyFrom(
+        tf.contrib.util.make_tensor_proto(np.expand_dims(image, 0))
+    )
+
+    response = stub.Predict(request, 10.0)
+    result = round(calc_mean_score(response.outputs['quality_prediction'].float_val), 2)
+
+    print(json.dumps({'mean_score_prediction': np.round(result, 3)}, indent=2))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-ip', '--image-path', help='Path to image file.', required=True)
+    parser.add_argument(
+        '-mn', '--model-name', help='mobilenet_aesthetic or mobilenet_technical', required=True
+    )
+    args = parser.parse_args()
+    get_image_quality_predictions(**args.__dict__)

contrib/tf_serving/venv_tfs_nima/bin/activate

@@ -0,0 +1,78 @@
+# This file must be used with "source bin/activate" *from bash*
+# you cannot run it directly
+
+deactivate () {
+    unset -f pydoc >/dev/null 2>&1
+
+    # reset old environment variables
+    # ! [ -z ${VAR+_} ] returns true if VAR is declared at all
+    if ! [ -z "${_OLD_VIRTUAL_PATH+_}" ] ; then
+        PATH="$_OLD_VIRTUAL_PATH"
+        export PATH
+        unset _OLD_VIRTUAL_PATH
+    fi
+    if ! [ -z "${_OLD_VIRTUAL_PYTHONHOME+_}" ] ; then
+        PYTHONHOME="$_OLD_VIRTUAL_PYTHONHOME"
+        export PYTHONHOME
+        unset _OLD_VIRTUAL_PYTHONHOME
+    fi
+
+    # This should detect bash and zsh, which have a hash command that must
+    # be called to get it to forget past commands.  Without forgetting
+    # past commands the $PATH changes we made may not be respected
+    if [ -n "${BASH-}" ] || [ -n "${ZSH_VERSION-}" ] ; then
+        hash -r 2>/dev/null
+    fi
+
+    if ! [ -z "${_OLD_VIRTUAL_PS1+_}" ] ; then
+        PS1="$_OLD_VIRTUAL_PS1"
+        export PS1
+        unset _OLD_VIRTUAL_PS1
+    fi
+
+    unset VIRTUAL_ENV
+    if [ ! "${1-}" = "nondestructive" ] ; then
+    # Self destruct!
+        unset -f deactivate
+    fi
+}
+
+# unset irrelevant variables
+deactivate nondestructive
+
+VIRTUAL_ENV="/Users/christopher.lennan/github_repos/image-quality-assessment/contrib/tf_serving/venv_tfs_nima"
+export VIRTUAL_ENV
+
+_OLD_VIRTUAL_PATH="$PATH"
+PATH="$VIRTUAL_ENV/bin:$PATH"
+export PATH
+
+# unset PYTHONHOME if set
+if ! [ -z "${PYTHONHOME+_}" ] ; then
+    _OLD_VIRTUAL_PYTHONHOME="$PYTHONHOME"
+    unset PYTHONHOME
+fi
+
+if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT-}" ] ; then
+    _OLD_VIRTUAL_PS1="$PS1"
+    if [ "x" != x ] ; then
+        PS1="$PS1"
+    else
+        PS1="(`basename \"$VIRTUAL_ENV\"`) $PS1"
+    fi
+    export PS1
+fi
+
+# Make sure to unalias pydoc if it's already there
+alias pydoc 2>/dev/null >/dev/null && unalias pydoc
+
+pydoc () {
+    python -m pydoc "$@"
+}
+
+# This should detect bash and zsh, which have a hash command that must
+# be called to get it to forget past commands.  Without forgetting
+# past commands the $PATH changes we made may not be respected
+if [ -n "${BASH-}" ] || [ -n "${ZSH_VERSION-}" ] ; then
+    hash -r 2>/dev/null
+fi

contrib/tf_serving/venv_tfs_nima/bin/activate.csh

@@ -0,0 +1,36 @@
+# This file must be used with "source bin/activate.csh" *from csh*.
+# You cannot run it directly.
+# Created by Davide Di Blasi <[email protected]>.
+
+alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; test "\!:*" != "nondestructive" && unalias deactivate && unalias pydoc'
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+setenv VIRTUAL_ENV "/Users/christopher.lennan/github_repos/image-quality-assessment/contrib/tf_serving/venv_tfs_nima"
+
+set _OLD_VIRTUAL_PATH="$PATH"
+setenv PATH "$VIRTUAL_ENV/bin:$PATH"
+
+
+
+if ("" != "") then
+    set env_name = ""
+else
+    set env_name = `basename "$VIRTUAL_ENV"`
+endif
+
+# Could be in a non-interactive environment,
+# in which case, $prompt is undefined and we wouldn't
+# care about the prompt anyway.
+if ( $?prompt ) then
+    set _OLD_VIRTUAL_PROMPT="$prompt"
+    set prompt = "[$env_name] $prompt"
+endif
+
+unset env_name
+
+alias pydoc python -m pydoc
+
+rehash
+

contrib/tf_serving/venv_tfs_nima/bin/activate.fish

@@ -0,0 +1,76 @@
+# This file must be used using `. bin/activate.fish` *within a running fish ( http://fishshell.com ) session*.
+# Do not run it directly.
+
+function deactivate -d 'Exit virtualenv mode and return to the normal environment.'
+    # reset old environment variables
+    if test -n "$_OLD_VIRTUAL_PATH"
+        set -gx PATH $_OLD_VIRTUAL_PATH
+        set -e _OLD_VIRTUAL_PATH
+    end
+
+    if test -n "$_OLD_VIRTUAL_PYTHONHOME"
+        set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME
+        set -e _OLD_VIRTUAL_PYTHONHOME
+    end
+
+    if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
+        # Set an empty local `$fish_function_path` to allow the removal of `fish_prompt` using `functions -e`.
+        set -l fish_function_path
+
+        # Erase virtualenv's `fish_prompt` and restore the original.
+        functions -e fish_prompt
+        functions -c _old_fish_prompt fish_prompt
+        functions -e _old_fish_prompt
+        set -e _OLD_FISH_PROMPT_OVERRIDE
+    end
+
+    set -e VIRTUAL_ENV
+
+    if test "$argv[1]" != 'nondestructive'
+        # Self-destruct!
+        functions -e pydoc
+        functions -e deactivate
+    end
+end
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+set -gx VIRTUAL_ENV "/Users/christopher.lennan/github_repos/image-quality-assessment/contrib/tf_serving/venv_tfs_nima"
+
+set -gx _OLD_VIRTUAL_PATH $PATH
+set -gx PATH "$VIRTUAL_ENV/bin" $PATH
+
+# Unset `$PYTHONHOME` if set.
+if set -q PYTHONHOME
+    set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
+    set -e PYTHONHOME
+end
+
+function pydoc
+    python -m pydoc $argv
+end
+
+if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
+    # Copy the current `fish_prompt` function as `_old_fish_prompt`.
+    functions -c fish_prompt _old_fish_prompt
+
+    function fish_prompt
+        # Save the current $status, for fish_prompts that display it.
+        set -l old_status $status
+
+        # Prompt override provided?
+        # If not, just prepend the environment name.
+        if test -n ""
+            printf '%s%s' "" (set_color normal)
+        else
+            printf '%s(%s) ' (set_color normal) (basename "$VIRTUAL_ENV")
+        end
+
+        # Restore the original $status
+        echo "exit $old_status" | source
+        _old_fish_prompt
+    end
+
+    set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
+end

contrib/tf_serving/venv_tfs_nima/bin/activate_this.py

@@ -0,0 +1,34 @@
+"""By using execfile(this_file, dict(__file__=this_file)) you will
+activate this virtualenv environment.
+
+This can be used when you must use an existing Python interpreter, not
+the virtualenv bin/python
+"""
+
+try:
+    __file__
+except NameError:
+    raise AssertionError(
+        "You must run this like execfile('path/to/activate_this.py', dict(__file__='path/to/activate_this.py'))")
+import sys
+import os
+
+old_os_path = os.environ.get('PATH', '')
+os.environ['PATH'] = os.path.dirname(os.path.abspath(__file__)) + os.pathsep + old_os_path
+base = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if sys.platform == 'win32':
+    site_packages = os.path.join(base, 'Lib', 'site-packages')
+else:
+    site_packages = os.path.join(base, 'lib', 'python%s' % sys.version[:3], 'site-packages')
+prev_sys_path = list(sys.path)
+import site
+site.addsitedir(site_packages)
+sys.real_prefix = sys.prefix
+sys.prefix = base
+# Move the added items to the front of the path:
+new_sys_path = []
+for item in list(sys.path):
+    if item not in prev_sys_path:
+        new_sys_path.append(item)
+        sys.path.remove(item)
+sys.path[:0] = new_sys_path