Adds container packages for PyTorch Mask R-CNN Training and Inference (…

…#89)

* Add PyTorch SPR Mask R-CNN package specs, docs, and quickstart filesg

* Update build arg

* Doc updates for training

* Print status

* update error handling

* try to run without pretrained model for training

* Fix else

* Doc update

* Doc updates

* Regenerated dockerfiles

* Add new line at EOF

dockerfiles/pytorch/pytorch-spr-maskrcnn-inference.Dockerfile

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+# Copyright (c) 2020-2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+#
+# THIS IS A GENERATED DOCKERFILE.
+#
+# This file was assembled from multiple pieces, whose use is documented
+# throughout. Please refer to the TensorFlow dockerfiles documentation
+# for more information.
+
+ARG PYTORCH_IMAGE="model-zoo"
+ARG PYTORCH_TAG="pytorch-ipex-spr"
+
+FROM ${PYTORCH_IMAGE}:${PYTORCH_TAG} AS intel-optimized-pytorch
+
+# Build Torch Vision
+ARG TORCHVISION_VERSION=v0.8.0
+
+RUN source ~/anaconda3/bin/activate pytorch && \
+    git clone https://github.com/pytorch/vision && \
+    cd vision && \
+    git checkout ${TORCHVISION_VERSION} && \
+    python setup.py install
+
+RUN source ~/anaconda3/bin/activate pytorch && \
+    pip install matplotlib Pillow pycocotools && \
+    pip install yacs opencv-python cityscapesscripts transformers && \
+    conda install -y libopenblas psutil && \
+    cd /workspace/installs && \
+    wget https://github.com/gperftools/gperftools/releases/download/gperftools-2.7.90/gperftools-2.7.90.tar.gz && \
+    tar -xzf gperftools-2.7.90.tar.gz && \
+    cd gperftools-2.7.90 && \
+    ./configure --prefix=$HOME/.local && \
+    make && \
+    make install && \
+    rm -rf /workspace/installs/
+
+ARG PACKAGE_DIR=model_packages
+
+ARG PACKAGE_NAME="pytorch-spr-maskrcnn-inference"
+
+ARG MODEL_WORKSPACE
+
+# ${MODEL_WORKSPACE} and below needs to be owned by root:root rather than the current UID:GID
+# this allows the default user (root) to work in k8s single-node, multi-node
+RUN umask 002 && mkdir -p ${MODEL_WORKSPACE} && chgrp root ${MODEL_WORKSPACE} && chmod g+s+w,o+s+r ${MODEL_WORKSPACE}
+
+ADD --chown=0:0 ${PACKAGE_DIR}/${PACKAGE_NAME}.tar.gz ${MODEL_WORKSPACE}
+
+RUN chown -R root ${MODEL_WORKSPACE}/${PACKAGE_NAME} && chgrp -R root ${MODEL_WORKSPACE}/${PACKAGE_NAME} && chmod -R g+s+w ${MODEL_WORKSPACE}/${PACKAGE_NAME} && find ${MODEL_WORKSPACE}/${PACKAGE_NAME} -type d | xargs chmod o+r+x 
+
+WORKDIR ${MODEL_WORKSPACE}/${PACKAGE_NAME}
+
+ARG MASKRCNN_DIR="/workspace/pytorch-spr-maskrcnn-inference/models/maskrcnn"
+
+RUN source ~/anaconda3/bin/activate pytorch && \
+    cd ${MASKRCNN_DIR} && \
+    cd maskrcnn-benchmark && \
+    python setup.py install && \
+    pip install onnx
+
+FROM intel-optimized-pytorch AS release
+COPY --from=intel-optimized-pytorch /root/anaconda3 /root/anaconda3
+COPY --from=intel-optimized-pytorch /workspace/lib/ /workspace/lib/
+COPY --from=intel-optimized-pytorch /root/.local/ /root/.local/
+
+ENV DNNL_MAX_CPU_ISA="AVX512_CORE_AMX"
+
+ENV PATH="~/anaconda3/bin:${PATH}"
+ENV LD_PRELOAD="/workspace/lib/jemalloc/lib/libjemalloc.so:$LD_PRELOAD"
+ENV MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000"
+ENV BASH_ENV=/root/.bash_profile
+WORKDIR /workspace/
+RUN yum install -y numactl mesa-libGL && \
+    yum clean all && \
+    echo "source activate pytorch" >> /root/.bash_profile

dockerfiles/pytorch/pytorch-spr-maskrcnn-training.Dockerfile

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+# Copyright (c) 2020-2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+#
+# THIS IS A GENERATED DOCKERFILE.
+#
+# This file was assembled from multiple pieces, whose use is documented
+# throughout. Please refer to the TensorFlow dockerfiles documentation
+# for more information.
+
+ARG PYTORCH_IMAGE="model-zoo"
+ARG PYTORCH_TAG="pytorch-ipex-spr"
+
+FROM ${PYTORCH_IMAGE}:${PYTORCH_TAG} AS intel-optimized-pytorch
+
+# Build Torch Vision
+ARG TORCHVISION_VERSION=v0.8.0
+
+RUN source ~/anaconda3/bin/activate pytorch && \
+    git clone https://github.com/pytorch/vision && \
+    cd vision && \
+    git checkout ${TORCHVISION_VERSION} && \
+    python setup.py install
+
+RUN source ~/anaconda3/bin/activate pytorch && \
+    pip install matplotlib Pillow pycocotools && \
+    pip install yacs opencv-python cityscapesscripts transformers && \
+    conda install -y libopenblas psutil && \
+    cd /workspace/installs && \
+    wget https://github.com/gperftools/gperftools/releases/download/gperftools-2.7.90/gperftools-2.7.90.tar.gz && \
+    tar -xzf gperftools-2.7.90.tar.gz && \
+    cd gperftools-2.7.90 && \
+    ./configure --prefix=$HOME/.local && \
+    make && \
+    make install && \
+    rm -rf /workspace/installs/
+
+ARG PACKAGE_DIR=model_packages
+
+ARG PACKAGE_NAME="pytorch-spr-maskrcnn-training"
+
+ARG MODEL_WORKSPACE
+
+# ${MODEL_WORKSPACE} and below needs to be owned by root:root rather than the current UID:GID
+# this allows the default user (root) to work in k8s single-node, multi-node
+RUN umask 002 && mkdir -p ${MODEL_WORKSPACE} && chgrp root ${MODEL_WORKSPACE} && chmod g+s+w,o+s+r ${MODEL_WORKSPACE}
+
+ADD --chown=0:0 ${PACKAGE_DIR}/${PACKAGE_NAME}.tar.gz ${MODEL_WORKSPACE}
+
+RUN chown -R root ${MODEL_WORKSPACE}/${PACKAGE_NAME} && chgrp -R root ${MODEL_WORKSPACE}/${PACKAGE_NAME} && chmod -R g+s+w ${MODEL_WORKSPACE}/${PACKAGE_NAME} && find ${MODEL_WORKSPACE}/${PACKAGE_NAME} -type d | xargs chmod o+r+x 
+
+WORKDIR ${MODEL_WORKSPACE}/${PACKAGE_NAME}
+
+ARG MASKRCNN_DIR="/workspace/pytorch-spr-maskrcnn-training/models/maskrcnn"
+
+RUN source ~/anaconda3/bin/activate pytorch && \
+    cd ${MASKRCNN_DIR} && \
+    cd maskrcnn-benchmark && \
+    python setup.py install && \
+    pip install onnx
+
+FROM intel-optimized-pytorch AS release
+COPY --from=intel-optimized-pytorch /root/anaconda3 /root/anaconda3
+COPY --from=intel-optimized-pytorch /workspace/lib/ /workspace/lib/
+COPY --from=intel-optimized-pytorch /root/.local/ /root/.local/
+
+ENV DNNL_MAX_CPU_ISA="AVX512_CORE_AMX"
+
+ENV PATH="~/anaconda3/bin:${PATH}"
+ENV LD_PRELOAD="/workspace/lib/jemalloc/lib/libjemalloc.so:$LD_PRELOAD"
+ENV MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000"
+ENV BASH_ENV=/root/.bash_profile
+WORKDIR /workspace/
+RUN yum install -y numactl mesa-libGL && \
+    yum clean all && \
+    echo "source activate pytorch" >> /root/.bash_profile

quickstart/image_segmentation/pytorch/maskrcnn/inference/cpu/.docs/container_build.md

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+## Build the container
+
+The <model name> <mode> package has scripts and a Dockerfile that are
+used to build a workload container that runs the model. This container
+uses the PyTorch/IPEX container as it's base, so ensure that you have built
+the `pytorch-ipex-spr.tar.gz` container prior to building this model container.
+
+Use `docker images` to verify that you have the base container built. For example:
+```
+$ docker images | grep pytorch-ipex-spr
+model-zoo         pytorch-ipex-spr         fecc7096a11e        40 minutes ago      8.31GB
+```
+
+To build the <model name> <mode> container, extract the package and
+run the `build.sh` script.
+```
+# Extract the package
+tar -xzf <package name>
+cd <package dir>
+
+# Build the container
+./build
+```
+
+After the build completes, you should have a container called
+`<docker image>` that will be used to run the model.

quickstart/image_segmentation/pytorch/maskrcnn/inference/cpu/.docs/datasets.md

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+## Datasets
+
+The [COCO dataset](https://cocodataset.org) is used to run <model name> <mode>.
+Download and extract the 2017 validation images and annotations from the
+[COCO dataset website](https://cocodataset.org/#download) to a `coco` folder
+and unzip the files. After extracting the zip files, your dataset directory
+structure should look something like this:
+```
+coco
+├── annotations
+│   ├── captions_train2017.json
+│   ├── captions_val2017.json
+│   ├── instances_train2017.json
+│   ├── instances_val2017.json
+│   ├── person_keypoints_train2017.json
+│   └── person_keypoints_val2017.json
+└── val2017
+    ├── 000000000139.jpg
+    ├── 000000000285.jpg
+    ├── 000000000632.jpg
+    └── ...
+```
+The parent of the `annotations` and `val2017` directory (in this example `coco`)
+is the directory that should be used when setting the `DATASET_DIR` environment
+variable for <model name> (for example: `export DATASET_DIR=/home/<user>/coco`).

quickstart/image_segmentation/pytorch/maskrcnn/inference/cpu/.docs/description.md

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+<!-- 10. Description -->
+## Description
+
+This document has instructions for running <model name> <mode> using
+Intel-optimized PyTorch.

quickstart/image_segmentation/pytorch/maskrcnn/inference/cpu/.docs/docker_spr.md

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+## Run the model
+
+Download the pretrained model and set the `PRETRAINED_MODEL` environment variable
+to point to the file.
+```
+curl -O https://download.pytorch.org/models/maskrcnn/e2e_mask_rcnn_R_50_FPN_1x.pth
+export PRETRAINED_MODEL=$(pwd)/e2e_mask_rcnn_R_50_FPN_1x.pth
+```
+
+After you've downloaded the pretrained model and followed the instructions to
+[build the container](#build-the-container) and [prepare the dataset](#datasets),
+use the `run.sh` script from the container package to run <model name> <mode>.
+Set environment variables to specify the dataset directory, precision to run, and
+an output directory. By default, the `run.sh` script will run the
+`inference_realtime.sh` quickstart script. To run a different script, specify
+the name of the script using the `SCRIPT` environment variable.
+```
+# Navigate to the container package directory
+cd <package dir>
+
+# Set the required environment vars
+export PRECISION=<specify the precision to run>
+export PRETRAINED_MODEL=<path to the downloaded .pth file>
+export DATASET_DIR=<path to the dataset>
+export OUTPUT_DIR=<directory where log files will be written>
+
+# Run the container with inference_realtime.sh quickstart script
+./run.sh
+
+# Specify a different quickstart script to run, for example, accuracy.sh
+SCRIPT=accuracy.sh ./run.sh
+```

quickstart/image_segmentation/pytorch/maskrcnn/inference/cpu/.docs/license.md

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+<!--- 80. License -->
+## License
+
+Licenses can be found in the model package, in the `licenses` directory.

quickstart/image_segmentation/pytorch/maskrcnn/inference/cpu/.docs/quickstart.md

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+<!--- 40. Quick Start Scripts -->
+## Quick Start Scripts
+
+| Script name | Description |
+|-------------|-------------|
+| `inference_realtime.sh` | Runs multi instance realtime inference using 4 cores per instance for the specified precision (fp32, int8, or bf16). |
+| `inference_throughput.sh` | Runs multi instance batch inference using 24 cores per instance for the specified precision (fp32, int8, or bf16). |
+| `accuracy.sh` | Measures the inference accuracy for the specified precision (fp32, int8, or bf16). |

quickstart/image_segmentation/pytorch/maskrcnn/inference/cpu/.docs/title.md

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+<!--- 0. Title -->
+# PyTorch <model name> <mode>

quickstart/image_segmentation/pytorch/maskrcnn/inference/cpu/.docs/wrapper_package.md

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+## Model Package
+
+The model package includes the Dockerfile and scripts needed to build and
+run <model name> <mode> in a container.
+```
+<package dir>
+├── README.md
+├── build.sh
+├── licenses
+│   ├── LICENSE
+│   └── third_party
+├── model_packages
+│   └── <package name>
+├── <package dir>.Dockerfile
+└── run.sh
+```