!12798 Update readme of CRNN and SSD

From: @c_34
Reviewed-by: 
Signed-off-by:

model_zoo/official/cv/crnn/README.md

@@ -18,6 +18,10 @@
             - [Distributed Training](#distributed-training)
     - [Evaluation Process](#evaluation-process)
         - [Evaluation](#evaluation)
+    - [Inference Process](#inference-process)
+        - [Export MindIR](#export-mindir)
+        - [Infer on Ascend310](#infer-on-ascend310)
+        - [result](#result)
     - [Model Description](#model-description)
         - [Performance](#performance)
             - [Training Performance](#training-performance)
@@ -76,13 +80,37 @@ We provide `convert_ic03.py`, `convert_iiit5k.py`, `convert_svt.py` as exmples f
 
     # standalone training example in Ascend
     $ bash run_standalone_train.sh [DATASET_NAME] [DATASET_PATH] [PLATFORM]
+
+    # offline inference on Ascend310
+    $ bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE_PATH] [DATASET] [DEVICE_ID]
+
     ```
 
+    DATASET_NAME is one of `ic03`, `ic13`, `svt`, `iiit5k`, `synth`.
+
     For distributed training, a hccl configuration file with JSON format needs to be created in advance.
 
     Please follow the instructions in the link below:
     [hccl_tools](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
 
+- Run on docker
+
+Build docker images(Change version to the one you actually used)
+
+```shell
+# build docker
+docker build -t ssd:20.1.0 . --build-arg FROM_IMAGE_NAME=ascend-mindspore-arm:20.1.0
+```
+
+Create a container layer over the created image and start it
+
+```shell
+# start docker
+bash scripts/docker_start.sh ssd:20.1.0 [DATA_DIR] [MODEL_DIR]
+```
+
+Then you can run everything just like on Ascend.
+
 ## [Script Description](#contents)
 
 ### [Script and Sample Code](#contents)
@@ -197,6 +225,42 @@ Check the `eval/log.txt` and you will get outputs as following:
 result: {'CRNNAccuracy': (0.806)}
 ```
 
+## [Inference Process](#contents)
+
+### [Export MindIR](#contents)
+
+```shell
+python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
+```
+
+The ckpt_file parameter is required,
+`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
+
+### Infer on Ascend310
+
+Before performing inference, the mindir file must bu exported by export script on the 910 environment. We only provide an example of inference using MINDIR model.
+Current batch_Size can only be set to 1. The inference result will be just the network outputs, which will be save in binary file. The accuracy is calculated by `src/metric.`.
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE_PATH] [DATASET] [DEVICE_ID]
+```
+
+`MINDIR_PATH` is the MINDIR model exported by export.py
+`DATA_PATH` is the path of dataset. If the data has to be converted, passing the path to the converted data.
+`ANN_FILE_PATH` is the path of annotation file. For converted data, the annotation file is exported by convert scripts.
+`DATASET` is the name of dataset, which should be in ["synth", "svt", "iiit5k", "ic03", "ic13"]
+`DEVICE_ID` is optional, default value is 0.
+
+### result
+
+Inference result is saved in current path, you can find result like this in acc.log file.
+
+```shell
+correct num: 2042 , total num: 3000
+result CRNNAccuracy is: 0.806666666666
+```
+
 ## [Model Description](#contents)
 
 ### [Performance](#contents)

model_zoo/official/cv/crnn/export.py

@@ -0,0 +1,47 @@
+# 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.
+# ============================================================================
+
+""" export model for CRNN """
+
+import argparse
+import numpy as np
+import mindspore as ms
+from mindspore import Tensor, context, load_checkpoint, export
+
+from src.crnn import CRNN
+from src.config import config1 as config
+
+parser = argparse.ArgumentParser(description="CRNN_export")
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="crnn", help="output file name.")
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
+parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
+                    help="device target")
+args = parser.parse_args()
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+if args.device_target == "Ascend":
+    context.set_context(device_id=args.device_id)
+
+if __name__ == "__main__":
+    config.batch_size = 1
+    net = CRNN(config)
+
+    load_checkpoint(args.ckpt_file, net=net)
+    net.set_train(False)
+
+    input_data = Tensor(np.zeros([1, 3, config.image_height, config.image_width]), ms.float32)
+
+    export(net, input_data, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/cv/crnn/scripts/docker_start.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+
+docker_image=$1
+data_dir=$2
+model_dir=$3
+
+docker run -it --ipc=host \
+               --device=/dev/davinci0 \
+               --device=/dev/davinci1 \
+               --device=/dev/davinci2 \
+               --device=/dev/davinci3 \
+               --device=/dev/davinci4 \
+               --device=/dev/davinci5 \
+               --device=/dev/davinci6 \
+               --device=/dev/davinci7 \
+               --device=/dev/davinci_manager \
+               --device=/dev/devmm_svm \
+               --device=/dev/hisi_hdc \
+               --privileged \
+               -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+               -v /usr/local/Ascend/add-ons/:/usr/local/Ascend/add-ons \
+               -v ${data_dir}:${data_dir} \
+               -v ${model_dir}:${model_dir} \
+               -v /var/log/npu/conf/slog/slog.conf:/var/log/npu/conf/slog/slog.conf \
+               -v /var/log/npu/slog/:/var/log/npu/slog/ \
+               -v /var/log/npu/profiling/:/var/log/npu/profiling \
+               -v /var/log/npu/dump/:/var/log/npu/dump \
+               -v /var/log/npu/:/usr/slog ${docker_image} \
+               /bin/bash

model_zoo/official/cv/ssd/README.md

@@ -14,10 +14,14 @@
         - [Training Process](#training-process)
             - [Training on Ascend](#training-on-ascend)
             - [Training on GPU](#training-on-gpu)
+            - [Transfer Training](#transfer-training)
         - [Evaluation Process](#evaluation-process)
             - [Evaluation on Ascend](#evaluation-on-ascend)
             - [Evaluation on GPU](#evaluation-on-gpu)
+    - [Inference Process](#inference-process)
         - [Export MindIR](#export-mindir)
+        - [Infer on Ascend310](#infer-on-ascend310)
+        - [result](#result)
     - [Model Description](#model-description)
         - [Performance](#performance)
             - [Evaluation Performance](#evaluation-performance)
@@ -130,20 +134,23 @@ After installing MindSpore via the official website, you can start training and
 
 ```shell
 # distributed training on Ascend
-sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE]
+bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE]
 
 # run eval on Ascend
-sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
+bash run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
+
+# run inference on Ascend310, MINDIR_PATH is the mindir model which you can export from checkpoint using export.py
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
 ```
 
 - running on GPU
 
 ```shell
 # distributed training on GPU
-sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET]
+bash run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET]
 
 # run eval on GPU
-sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
+bash run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
 ```
 
 - running on CPU(support Windows and Ubuntu)
@@ -158,6 +165,24 @@ python train.py --run_platform=CPU --lr=[LR] --dataset=[DATASET] --epoch_size=[E
 python eval.py --run_platform=CPU --dataset=[DATASET] --checkpoint_path=[PRETRAINED_CKPT]
 ```
 
+- Run on docker
+
+Build docker images(Change version to the one you actually used)
+
+```shell
+# build docker
+docker build -t ssd:20.1.0 . --build-arg FROM_IMAGE_NAME=ascend-mindspore-arm:20.1.0
+```
+
+Create a container layer over the created image and start it
+
+```shell
+# start docker
+bash scripts/docker_start.sh ssd:20.1.0 [DATA_DIR] [MODEL_DIR]
+```
+
+Then you can run everything just like on ascend.
+
 ## [Script Description](#contents)
 
 ### [Script and Sample Code](#contents)
@@ -224,7 +249,7 @@ To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will ge
 - Distribute mode
 
 ```shell
-    sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
+    bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
 ```
 
 We need five or seven parameters for this scripts.
@@ -261,7 +286,7 @@ epoch time: 39064.8467540741, per step time: 85.29442522723602
 - Distribute mode
 
 ```shell
-    sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
+    bash run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
 ```
 
 We need five or seven parameters for this scripts.
@@ -283,15 +308,23 @@ epoch: 1 step: 3, loss is 476.802
 epoch: 1 step: 458, loss is 3.1283689
 epoch time: 150753.701, per step time: 329.157
 ...
-
 ```
 
+#### Transfer Training
+
+You can train your own model based on either pretrained classification model or pretrained detection model. You can perform transfer training by following steps.
+
+1. Convert your own dataset to COCO or VOC style. Otherwise you havet to add your own data preprocess code.
+2. Change config.py according to your own dataset, especially the `num_classes`.
+3. Set argument `filter_weight` to `True` while calling `train.py`, this will filter the final detection box weight from the pretrained model.
+4. Build your own bash scripts using new config and arguments for further convenient.
+
 ### [Evaluation Process](#contents)
 
 #### Evaluation on Ascend
 
 ```shell
-sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
+bash run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
 ```
 
 We need two parameters for this scripts.
@@ -326,7 +359,7 @@ mAP: 0.23808886505483504
 #### Evaluation on GPU
 
 ```shell
-sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
+bash run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
 ```
 
 We need two parameters for this scripts.
@@ -358,6 +391,8 @@ Average Recall    (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.686
 mAP: 0.2244936111705981
 ```
 
+## Inference Process
+
 ### [Export MindIR](#contents)
 
 ```shell
@@ -365,18 +400,16 @@ python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [
 ```
 
 The ckpt_file parameter is required,
-`EXPORT_FORMAT` should be in ["AIR", "ONNX", "MINDIR"]
-
-## Inference Process
+`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
 
-### Usage
+### Infer on Ascend310
 
-Before performing inference, the air file must bu exported by export script on the 910 environment.
-Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space.
+Before performing inference, the mindir file must bu exported by export script on the 910 environment. We only provide an example of inference using MINDIR model.
+Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space, otherwise the process will be killed for execeeding memory limits.
 
 ```shell
 # Ascend310 inference
-sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
 ```
 
 `DEVICE_ID` is optional, default value is 0.
@@ -386,19 +419,18 @@ sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
 Inference result is saved in current path, you can find result like this in acc.log file.
 
 ```bash
-Average Precision (AP) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.354
-Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.459
-Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.432
-Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.228
-Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.455
-Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.255
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.409
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.507
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.325
-Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.670
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.733
-mAP: 0.35406563212712244
+Average Precision (AP) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.339
+Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.521
+Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.370
+Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168
+Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.386
+Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.310
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.481
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.515
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.293
+Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.659
+mAP: 0.33880018942412393
 ```
 
 ## [Model Description](#contents)

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

@@ -0,0 +1,29 @@
+#!/bin/bash
+
+docker_image=$1
+data_dir=$2
+model_dir=$3
+
+docker run -it --ipc=host \
+               --device=/dev/davinci0 \
+               --device=/dev/davinci1 \
+               --device=/dev/davinci2 \
+               --device=/dev/davinci3 \
+               --device=/dev/davinci4 \
+               --device=/dev/davinci5 \
+               --device=/dev/davinci6 \
+               --device=/dev/davinci7 \
+               --device=/dev/davinci_manager \
+               --device=/dev/devmm_svm \
+               --device=/dev/hisi_hdc \
+               --privileged \
+               -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+               -v /usr/local/Ascend/add-ons/:/usr/local/Ascend/add-ons \
+               -v ${data_dir}:${data_dir} \
+               -v ${model_dir}:${model_dir} \
+               -v /var/log/npu/conf/slog/slog.conf:/var/log/npu/conf/slog/slog.conf \
+               -v /var/log/npu/slog/:/var/log/npu/slog/ \
+               -v /var/log/npu/profiling/:/var/log/npu/profiling \
+               -v /var/log/npu/dump/:/var/log/npu/dump \
+               -v /var/log/npu/:/usr/slog ${docker_image} \
+               /bin/bash