add standalone train to ssd

model_zoo/official/cv/ssd/README.md

@@ -312,6 +312,12 @@ To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will ge
     bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [CONFIG_PATH] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
 ```
 
+- Standalone training
+
+```shell
+    bash run_standalone_train.sh [DEVICE_ID] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
+```
+
 We need five or seven parameters for this scripts.
 
 - `DEVICE_NUM`: the device number for distributed train.

model_zoo/official/cv/ssd/README_CN.md

@@ -110,6 +110,11 @@ SSD方法基于前向卷积网络，该网络产生固定大小的边界框集
 sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [CONFIG_PATH]
 ```
 
+```shell script
+# Ascend单卡训练
+bash run_standalone_train.sh [DEVICE_ID] [EPOCH_SIZE] [LR] [DATASET]
+```
+
 ```shell script
 # Ascend处理器环境运行eval
 sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]

model_zoo/official/cv/ssd/scripts/run_standalone_train.sh

@@ -0,0 +1,73 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "sh run_distribute_train.sh DEVICE_ID EPOCH_SIZE LR DATASET PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
+echo "for example: sh run_distribute_train.sh 0 500 0.2 coco /opt/ssd-300.ckpt(optional) 200(optional)"
+echo "It is better to use absolute path."
+echo "================================================================================================================="
+
+if [ $# != 4 ] && [ $# != 6 ]
+then
+    echo "Usage: sh run_distribute_train.sh [DEVICE_ID] [EPOCH_SIZE] [LR] [DATASET] \
+    [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)"
+    exit 1
+fi
+
+# Before start distribute train, first create mindrecord files.
+BASE_PATH=$(cd "`dirname $0`" || exit; pwd)
+cd $BASE_PATH/../ || exit
+python train.py --only_create_dataset=True --dataset=$4
+
+echo "After running the script, the network runs in the background. The log will be generated in LOGx/log.txt"
+DEVICE_ID=$1
+EPOCH_SIZE=$2
+LR=$3
+DATASET=$4
+PRE_TRAINED=$5
+PRE_TRAINED_EPOCH_SIZE=$6
+
+export DEVICE_ID=$DEVICE_ID
+rm -rf LOG$DEVICE_ID
+mkdir ./LOG$DEVICE_ID
+cp ./*.py ./LOG$DEVICE_ID
+cp -r ./src ./LOG$DEVICE_ID
+cd ./LOG$DEVICE_ID || exit
+
+echo "start training with device $DEVICE_ID"
+env > env.log
+if [ $# == 4 ]
+then
+    python train.py  \
+    --lr=$LR \
+    --dataset=$DATASET \
+    --device_id=$DEVICE_ID  \
+    --epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
+fi
+
+if [ $# == 6 ]
+then
+    python train.py  \
+    --lr=$LR \
+    --dataset=$DATASET \
+    --device_id=$DEVICE_ID  \
+    --pre_trained=$PRE_TRAINED \
+    --pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \
+    --epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
+fi
+
+cd ../

model_zoo/official/cv/yolov4/postprocess.py

@@ -34,7 +34,7 @@ parser.add_argument('--log_path', type=str, default='outputs/', help='checkpoint
 
 # detect_related
 parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS')
-parser.add_argument('--ann_file', type=str, default='', help='path to annotation')
+parser.add_argument('--ann_val_file', type=str, default='', help='path to annotation')
 parser.add_argument('--eval_ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
 
 parser.add_argument('--img_id_file_path', type=str, default='', help='path of image dataset')
@@ -64,7 +64,7 @@ if __name__ == "__main__":
     # init detection engine
     detection = DetectionEngine(args)
 
-    coco = COCO(args.ann_file)
+    coco = COCO(args.ann_val_file)
     result_path = args.result_files
 
     files = os.listdir(args.img_id_file_path)
@@ -91,8 +91,9 @@ if __name__ == "__main__":
     detection.do_nms_for_results()
     result_file_path = detection.write_result()
     args.logger.info('result file path: {}'.format(result_file_path))
+    args.logger.info('\n=============coco eval reulst=========\n')
     eval_result = detection.get_eval_result()
-
+    for item in eval_result:
+        print(item)
     cost_time = time.time() - start_time
-    args.logger.info('\n=============coco eval reulst=========\n' + eval_result)
     args.logger.info('testing cost time {:.2f}h'.format(cost_time / 3600.))

model_zoo/official/cv/yolov4/scripts/run_infer_310.sh

@@ -83,7 +83,7 @@ function infer()
 
 function cal_acc()
 {
-    python3.7 ../postprocess.py --ann_file=$annotation_file --img_id_file_path=$data_path --result_files=./result_Files &> acc.log &
+    python3.7 ../postprocess.py --ann_avl_file=$annotation_file --img_id_file_path=$data_path --result_files=./result_Files &> acc.log &
 }
 
 compile_app