support multy node training and remove code

model_zoo/official/cv/deeplabv3/README.md

@@ -5,8 +5,7 @@ This is an example of training DeepLabV3 with PASCAL VOC 2012 dataset in MindSpo
 
 ## Requirements
 - Install [MindSpore](https://www.mindspore.cn/install/en).
-- Download the VOC 2012 dataset for training.
-- We need to run `./src/remove_gt_colormap.py` to remove the label colormap. 
+- Download the VOC 2012 dataset for training. 
   ``` bash
   python remove_gt_colormap.py --original_gt_folder GT_FOLDER --output_dir OUTPUT_DIR
 

model_zoo/official/cv/deeplabv3/scripts/run_distribute_train.sh

@@ -26,6 +26,7 @@ DATA_DIR=$2
 export MINDSPORE_HCCL_CONFIG_PATH=$1
 export RANK_TABLE_FILE=$1
 export RANK_SIZE=8
+export DEVICE_NUM=8
 PATH_CHECKPOINT=""
 if [ $# == 3 ]
 then
@@ -37,11 +38,13 @@ avg_core_per_rank=`expr $cores \/ $RANK_SIZE`
 core_gap=`expr $avg_core_per_rank \- 1`
 echo "avg_core_per_rank" $avg_core_per_rank
 echo "core_gap" $core_gap
-for((i=0;i<RANK_SIZE;i++))
+export SERVER_ID=0
+rank_start=$((DEVICE_NUM * SERVER_ID))
+for((i=0;i<DEVICE_NUM;i++))
 do
     start=`expr $i \* $avg_core_per_rank`
     export DEVICE_ID=$i
-    export RANK_ID=$i
+    export RANK_ID=$((rank_start + i))
     export DEPLOY_MODE=0
     export GE_USE_STATIC_MEMORY=1
     end=`expr $start \+ $core_gap`

model_zoo/official/cv/deeplabv3/src/remove_gt_colormap.py

@@ -1,76 +0,0 @@
-# Copyright 2020 The Huawei Authors All Rights Reserved.
-#
-# 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.
-# ==============================================================================
-
-"""Removes the color map from segmentation annotations.
-Removes the color map from the ground truth segmentation annotations and save
-the results to output_dir.
-"""
-import glob
-import argparse
-import os.path
-import numpy as np
-
-
-from PIL import Image
-
-
-def _remove_colormap(filename):
-    """Removes the color map from the annotation.
-    Args:
-      filename: Ground truth annotation filename.
-    Returns:
-      Annotation without color map.
-    """
-    return np.array(Image.open(filename))
-
-
-def _save_annotation(annotation, filename):
-    """Saves the annotation as png file.
-    Args:
-      annotation: Segmentation annotation.
-      filename: Output filename.
-    """
-    pil_image = Image.fromarray(annotation.astype(dtype=np.uint8))
-    pil_image.save(filename, 'PNG')
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Demo of argparse")
-    parser.add_argument('--original_gt_folder', type=str, default='./VOCdevkit/VOC2012/SegmentationClass',
-                        help='Original ground truth annotations.')
-    parser.add_argument('--segmentation_format', type=str, default='png',
-                        help='Segmentation format.')
-    parser.add_argument('--output_dir', type=str, default='./VOCdevkit/VOC2012/SegmentationClassRaw',
-                        help='folder to save modified ground truth annotations.')
-    args = parser.parse_args()
-
-    # Create the output directory if not exists.
-
-    if not os.path.isdir(args.output_dir):
-        os.mkdir(args.output_dir)
-
-    annotations = glob.glob(os.path.join(args.original_gt_folder,
-                                         '*.' + args.segmentation_format))
-
-    for annotation in annotations:
-        raw_annotation = _remove_colormap(annotation)
-        filename = os.path.basename(annotation)[:-4]
-        _save_annotation(raw_annotation,
-                         os.path.join(
-                             args.output_dir,
-                             filename + '.' + args.segmentation_format))
-
-if __name__ == '__main__':
-    main()