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[MaskRCNN/TF&PyT] Adding Ampere support
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‎PyTorch/Segmentation/MaskRCNN/Dockerfile‎

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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:20.06-py3
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FROM ${FROM_IMAGE_NAME}
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# Install Python dependencies
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RUN pip install --upgrade --no-cache-dir pip \
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&& pip install --no-cache-dir \
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mlperf-compliance==0.0.10 \
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opencv-python==3.4.1.15 \
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git+https://github.com/NVIDIA/dllogger \
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yacs
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# Copy detectron code and build
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WORKDIR /workspace/object_detection
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RUN mkdir -p /datasets/data
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RUN mkdir /results
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COPY pytorch/. .
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RUN pip install -e .
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RUN /opt/conda/bin/conda install -y numpy==1.17.4
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ENV OMP_NUM_THREADS=1

‎PyTorch/Segmentation/MaskRCNN/README.md‎

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‎PyTorch/Segmentation/MaskRCNN/img/accuracy_curve.png‎

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‎PyTorch/Segmentation/MaskRCNN/img/loss_curve.png‎

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‎PyTorch/Segmentation/MaskRCNN/pytorch/Dockerfile‎

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# See the License for the specific language governing permissions and
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# limitations under the License.
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ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:19.05-py3
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ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:20.06-py3
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FROM ${FROM_IMAGE_NAME}
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# Install Python dependencies
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RUN pip install --upgrade --no-cache-dir pip \
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&& pip install --no-cache-dir \
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mlperf-compliance==0.0.10 \
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opencv-python==3.4.1.15 \
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git+https://github.com/NVIDIA/dllogger \
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yacs
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WORKDIR /opt
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# Copy detectron code and build
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WORKDIR /workspace/object_detection
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RUN mkdir -p /datasets/data
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RUN mkdir /results
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COPY . .
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RUN pip install -e .
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RUN /opt/conda/bin/conda install -y numpy==1.17.4
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ENV OMP_NUM_THREADS=1

‎PyTorch/Segmentation/MaskRCNN/pytorch/configs/ci/e2e_mask_rcnn_R_50_FPN_1x_1gpu.yaml‎

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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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WEIGHT: "/data3/joc_checkpoints/pytorch/maskrcnn/R-50.pkl"
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BACKBONE:
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CONV_BODY: "R-50-FPN"
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OUT_CHANNELS: 256
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RPN:
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USE_FPN: True
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ANCHOR_STRIDE: (4, 8, 16, 32, 64)
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PRE_NMS_TOP_N_TRAIN: 2000
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PRE_NMS_TOP_N_TEST: 1000
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POST_NMS_TOP_N_TEST: 1000
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FPN_POST_NMS_TOP_N_TEST: 1000
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FPN_POST_NMS_TOP_N_TRAIN: 4000
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ROI_HEADS:
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USE_FPN: True
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ROI_BOX_HEAD:
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POOLER_RESOLUTION: 7
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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POOLER_SAMPLING_RATIO: 2
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FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
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PREDICTOR: "FPNPredictor"
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ROI_MASK_HEAD:
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
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PREDICTOR: "MaskRCNNC4Predictor"
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POOLER_RESOLUTION: 14
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POOLER_SAMPLING_RATIO: 2
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RESOLUTION: 28
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SHARE_BOX_FEATURE_EXTRACTOR: False
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MASK_ON: True
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DATASETS:
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TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
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TEST: ("coco_2014_minival",)
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DATALOADER:
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SIZE_DIVISIBILITY: 32
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SOLVER:
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BASE_LR: 0.005
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WEIGHT_DECAY: 0.0001
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STEPS: (33000, 44000)
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MAX_ITER: 1000
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IMS_PER_BATCH: 4
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WARMUP_ITERS: 625
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WARMUP_FACTOR: 0.000064
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WARMUP_METHOD: "mlperf_linear"
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TEST:
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IMS_PER_BATCH: 1
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PATHS_CATALOG: "maskrcnn_benchmark/config/paths_catalog_ci.py"
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OUTPUT_DIR: "."

‎PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml‎

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SOLVER:
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BASE_LR: 0.04
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WEIGHT_DECAY: 0.0001
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STEPS: (36000, 48000)
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MAX_ITER: 50000
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STEPS: (33000, 44000)
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MAX_ITER: 45000
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IMS_PER_BATCH: 32
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TEST:
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IMS_PER_BATCH: 8

‎PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x_ampere_bs64.yaml‎

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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
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BACKBONE:
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CONV_BODY: "R-50-FPN"
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OUT_CHANNELS: 256
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RPN:
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USE_FPN: True
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ANCHOR_STRIDE: (4, 8, 16, 32, 64)
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PRE_NMS_TOP_N_TRAIN: 2000
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PRE_NMS_TOP_N_TEST: 1000
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POST_NMS_TOP_N_TEST: 1000
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FPN_POST_NMS_TOP_N_TEST: 1000
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FPN_POST_NMS_TOP_N_TRAIN: 8000
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ROI_HEADS:
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USE_FPN: True
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ROI_BOX_HEAD:
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POOLER_RESOLUTION: 7
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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POOLER_SAMPLING_RATIO: 2
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FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
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PREDICTOR: "FPNPredictor"
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ROI_MASK_HEAD:
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
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PREDICTOR: "MaskRCNNC4Predictor"
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POOLER_RESOLUTION: 14
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POOLER_SAMPLING_RATIO: 2
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RESOLUTION: 28
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SHARE_BOX_FEATURE_EXTRACTOR: False
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MASK_ON: True
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DATASETS:
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TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
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TEST: ("coco_2014_minival",)
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DATALOADER:
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SIZE_DIVISIBILITY: 32
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SOLVER:
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BASE_LR: 0.08
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WEIGHT_DECAY: 0.0001
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STEPS: (18000,24000)
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MAX_ITER: 27500
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IMS_PER_BATCH: 64
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WARMUP_FACTOR: 0.0001
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WARMUP_ITERS: 800
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WARMUP_METHOD: "mlperf_linear"
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TEST:
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IMS_PER_BATCH: 8
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DTYPE: "tf32"

‎PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/defaults.py‎

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_C.PER_EPOCH_EVAL = False
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_C.MIN_BBOX_MAP = 0.377
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_C.MIN_MASK_MAP = 0.342
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_C.SAVE_CHECKPOINT = True

‎PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/config/paths_catalog_ci.py‎

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class DatasetCatalog(object):
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DATA_DIR = "/data/coco/coco-2014"
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DATA_DIR = "/data2/coco/coco-2014"
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DATASETS = {
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"coco_2014_train": {
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"img_dir": "coco_train2014",

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