add image_classification benchmark

benchmarks/image_classification/docker/Dockerfile.cpu

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+FROM ubuntu:16.04
+
+ENV PYTHON_VERSION=3.7
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PATH /opt/anaconda3/bin:$PATH
+
+WORKDIR /root
+ENV HOME /root
+
+RUN apt-get update
+
+RUN apt-get install -y --no-install-recommends \
+      git \
+      build-essential \
+      software-properties-common \
+      ca-certificates \
+      wget \
+      curl \
+      htop \
+      zip \
+      unzip
+
+RUN cd /opt && \
+    wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh -O miniconda.sh && \
+    /bin/bash ./miniconda.sh -b -p /opt/anaconda3 && \
+    rm miniconda.sh && \
+    /opt/anaconda3/bin/conda clean -tipsy && \
+    ln -s /opt/anaconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
+    echo ". /opt/anaconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \
+    echo "conda activate base" >> ~/.bashrc && \
+    conda config --set always_yes yes --set changeps1 no
+
+RUN conda install pytorch-cpu torchvision-cpu -c pytorch
+RUN pip install --upgrade pip
+RUN pip install cmake
+RUN pip install future pillow onnx opencv-python-headless tensorflow onnxruntime
+RUN pip install Cython && pip install pycocotools
+RUN pip install protobuf==3.20.*
+RUN cd /tmp && \
+    git clone --recursive https://github.com/manyiw99/mlperf_inference && \
+    cd mlperf_inference/vision/classification_and_detection/data_imagenet && \
+    wget --quiet https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar && \
+    tar xf ILSVRC2012_img_val.tar && \
+    cd ../models && \
+    wget --quiet https://zenodo.org/record/2535873/files/resnet50_v1.pb && \
+    cd ../../../loadgen && \
+    pip install pybind11 && \
+    CFLAGS="-std=c++14" python setup.py install && \
+    rm -rf mlperf
+
+ENTRYPOINT ["/bin/bash", "/tmp/mlperf_inference/vision/classification_and_detection/run_helper.sh"]

benchmarks/image_classification/python/backend_tf.py

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+"""
+tensorflow backend (https://github.com/tensorflow/tensorflow)
+"""
+
+# pylint: disable=unused-argument,missing-docstring,useless-super-delegation
+
+import tensorflow as tf
+from tensorflow import dtypes
+from tensorflow.python.tools.optimize_for_inference_lib import optimize_for_inference
+
+import os
+
+
+class BackendTensorflow():
+    def __init__(self):
+        # super(BackendTensorflow, self).__init__()
+        self.inputs = []
+        self.outputs = []
+
+    def version(self):
+        return tf.__version__ + "/" + tf.__git_version__
+
+    def name(self):
+        return "tensorflow"
+
+    def image_format(self):
+        # By default tensorflow uses NHWC (and the cpu implementation only does NHWC)
+        return "NHWC"
+
+    def load(self, model_path, inputs=None, outputs=None):
+        # there is no input/output meta data i the graph so it need to come from config.
+
+        if not inputs:
+            raise ValueError("BackendTensorflow needs inputs")
+        if not outputs:
+            raise ValueError("BackendTensorflow needs outputs")
+        self.outputs = outputs
+        self.inputs = inputs
+
+        infer_config = tf.compat.v1.ConfigProto()
+        infer_config.intra_op_parallelism_threads = int(os.environ['TF_INTRA_OP_PARALLELISM_THREADS']) \
+                if 'TF_INTRA_OP_PARALLELISM_THREADS' in os.environ else os.cpu_count()
+        infer_config.inter_op_parallelism_threads = int(os.environ['TF_INTER_OP_PARALLELISM_THREADS']) \
+                if 'TF_INTER_OP_PARALLELISM_THREADS' in os.environ else os.cpu_count()
+        infer_config.use_per_session_threads = 1
+
+        # TODO: support checkpoint and saved_model formats?
+        graph_def = tf.compat.v1.GraphDef()
+        with tf.compat.v1.gfile.FastGFile(model_path, "rb") as f:
+            graph_def.ParseFromString(f.read())
+        try:
+            optimized_graph_def = optimize_for_inference(graph_def, [item.split(':')[0] for item in inputs],
+                    [item.split(':')[0] for item in outputs], dtypes.float32.as_datatype_enum, False)
+            g = tf.compat.v1.import_graph_def(optimized_graph_def, name='')
+        except ValueError:
+            try:
+                optimized_graph_def = optimize_for_inference(graph_def, [item.split(':')[0] for item in inputs],
+                        [item.split(':')[0] for item in outputs], dtypes.uint8.as_datatype_enum, False)
+                g = tf.compat.v1.import_graph_def(optimized_graph_def, name='')
+            except ValueError:
+                g = tf.compat.v1.import_graph_def(graph_def, name='')
+        self.sess = tf.compat.v1.Session(graph=g, config=infer_config)
+        return self
+
+    def predict(self, feed):
+        return self.sess.run(self.outputs, feed_dict=feed)