!14788 add supplementary notes of mobilenetv2 and ssd in README

From: @zhao_ting_v
Reviewed-by: @oacjiewen,@c_34
Signed-off-by: @c_34

mindspore/ccsrc/backend/kernel_compiler/cpu/cast_cpu_kernel.cc

@@ -39,8 +39,7 @@ void CastCPUKernel<S, T>::InitKernel(const CNodePtr &kernel_node) {
 }
 
 template <typename S, typename T>
-bool CastCPUKernel<S, T>::Launch(const std::vector<kernel::AddressPtr> &inputs,
-                                 const std::vector<kernel::AddressPtr> & /*workspace*/,
+bool CastCPUKernel<S, T>::Launch(const std::vector<kernel::AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &,
                                  const std::vector<kernel::AddressPtr> &outputs) {
   S *input = reinterpret_cast<S *>(inputs[0]->addr);
   T *output = reinterpret_cast<T *>(outputs[0]->addr);

model_zoo/official/cv/mobilenetv2/README.md

@@ -91,6 +91,32 @@ You can start training using python or shell scripts. The usage of shell scripts
 - GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH] [FREEZE_LAYER] [FILTER_HEAD]
 - CPU: sh run_trian.sh CPU [DATASET_PATH] [CKPT_PATH] [FREEZE_LAYER] [FILTER_HEAD]
 
+`DATASET_PATH` is the train dataset path. We use `ImageFolderDataset` as default dataset, which is a source dataset that reads images from a tree of directories. The directory structure is as follows, and you should use `DATASET_PATH=dataset/train` for training and `DATASET_PATH=dataset/val` for evaluation:
+
+```path
+        └─dataset
+            └─train
+              ├─class1
+                ├─0001.jpg
+                ......
+                └─xxxx.jpg
+              ......
+              ├─classx
+                ├─0001.jpg
+                ......
+                └─xxxx.jpg
+            └─val
+              ├─class1
+                ├─0001.jpg
+                ......
+                └─xxxx.jpg
+              ......
+              ├─classx
+                ├─0001.jpg
+                ......
+                └─xxxx.jpg
+```
+
 `CKPT_PATH` `FREEZE_LAYER` and `FILTER_HEAD` are optional, when set `CKPT_PATH`, `FREEZE_LAYER` must be set. `FREEZE_LAYER` should be in ["none", "backbone"], and if you set `FREEZE_LAYER`="backbone", the parameter in backbone will be freezed when training and the parameter in head will not be load from checkpoint. if `FILTER_HEAD`=True, the parameter in head will not be load from checkpoint.
 
 > RANK_TABLE_FILE is HCCL configuration file when running on Ascend.

model_zoo/official/cv/mobilenetv2/README_CN.md

@@ -97,6 +97,32 @@ MobileNetV2总体网络架构如下：
 - GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH] [FREEZE_LAYER] [FILTER_HEAD]
 - CPU: sh run_trian.sh CPU [DATASET_PATH] [CKPT_PATH] [FREEZE_LAYER] [FILTER_HEAD]
 
+`DATASET_PATH`是训练的路径. 我们使用`ImageFolderDataset` 作为默认数据处理方式, 这种数据处理方式是从原始目录中读取图片，目录结构如下, 训练时设置`DATASET_PATH=dataset/train`，验证时设置`DATASET_PATH=dataset/val`:
+
+```path
+        └─dataset
+            └─train
+              ├─class1
+                ├─0001.jpg
+                ......
+                └─xxxx.jpg
+              ......
+              ├─classx
+                ├─0001.jpg
+                ......
+                └─xxxx.jpg
+            └─val
+              ├─class1
+                ├─0001.jpg
+                ......
+                └─xxxx.jpg
+              ......
+              ├─classx
+                ├─0001.jpg
+                ......
+                └─xxxx.jpg
+```
+
 `CKPT_PATH` `FREEZE_LAYER` 和 `FILTER_HEAD` 是可选择的选项, 如果设置`CKPT_PATH`, `FREEZE_LAYER` 也必须同时设置. `FREEZE_LAYER` 可以是 ["none", "backbone"], 如果设置 `FREEZE_LAYER`="backbone", 训练过程中backbone中的参数会被冻结，同时不会从checkpoint中加载head部分的参数. 如果`FILTER_HEAD`=True, 不会从checkpoint中加载head部分的参数.
 
 > RANK_TABLE_FILE 是在Ascned上运行分布式任务时HCCL的配置文件

model_zoo/official/cv/resnext50/mindspore_hub_conf.py

@@ -16,7 +16,7 @@
 from src.image_classification import get_network
 
 def create_network(name, *args, **kwargs):
-    if name == "renext50":
-        get_network("renext50", *args, **kwargs)
-        return net
-    raise NotImplementedError(f"{name} is not implemented in the repo")
+    if name != "renext50":
+        raise NotImplementedError(f"{name} is not implemented in the repo")
+    net = get_network(*args, **kwargs)
+    return net

model_zoo/official/cv/ssd/README.md

@@ -78,7 +78,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
 
     1. If coco dataset is used. **Select dataset to coco when run script.**
 
-        Change the `coco_root` and other settings you need in `src/config.py`. The directory structure is as follows:
+        Change the `coco_root` and other settings you need in `src/config_xxx.py`. The directory structure is as follows:
 
         ```shell
         .
@@ -91,7 +91,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
         ```
 
     2. If VOC dataset is used. **Select dataset to voc when run script.**
-        Change `classes`, `num_classes`, `voc_json` and `voc_root` in `src/config.py`. `voc_json` is the path of json file with coco format for evaluation, `voc_root` is the path of VOC dataset, the directory structure is as follows:
+        Change `classes`, `num_classes`, `voc_json` and `voc_root` in `src/config_xxx.py`. `voc_json` is the path of json file with coco format for evaluation, `voc_root` is the path of VOC dataset, the directory structure is as follows:
 
         ```shell
         .
@@ -117,15 +117,15 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
         train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
         ```
 
-        Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `image_dir`(dataset directory) and the relative path in `anno_path`(the TXT file path), `image_dir` and `anno_path` are setting in `src/config.py`.
+        Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `image_dir`(dataset directory) and the relative path in `anno_path`(the TXT file path), `image_dir` and `anno_path` are setting in `src/config_xxx.py`.
 
 ## [Quick Start](#contents)
 
 ### Prepare the model
 
-1. Chose the model by changing the `using_model` in `src/confgi.py`. The optional models are: `ssd300`, `ssd_mobilenet_v1_fpn`, `ssd_mobilenet_v1_fpn`, `ssd_resnet50_fpn`.
-2. Change the dataset config in the corresponding config. `src/config_ssd300.py`, `src/config_ssd_mobilenet_v1_fpn.py`, `src/config_ssd_resnet50_fpn.py`, `src/config_ssd_vgg16.py`.
-3. If you are running with `ssd_mobilenet_v1_fpn`, you need a pretrained model for `mobilenet_v1`. Set the checkpoint path to `feature_extractor_base_param` in `src/config_ssd_mobilenet_v1_fpn.py`. For more detail about training mobilnet_v1, please refer to the mobilenetv1 model.
+1. Chose the model by changing the `using_model` in `src/config.py`. The optional models are: `ssd300`, `ssd_mobilenet_v1_fpn`, `ssd_vgg16`, `ssd_resnet50_fpn`.
+2. Change the dataset config in the corresponding config. `src/config_xxx.py`, `xxx` is the corresponding backbone network name
+3. If you are running with `ssd_mobilenet_v1_fpn` or `ssd_resnet50_fpn`, you need a pretrained model for `mobilenet_v1` or `resnet50`. Set the checkpoint path to `feature_extractor_base_param` in `src/config_xxx.py`. For more detail about training pre-trained model, please refer to the corresponding backbone network.
 
 ### Run the scripts
 
@@ -325,7 +325,7 @@ You can add `run_eval` to start shell and set it True, if you want evaluation wh
 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 have to add your own data preprocess code.
-2. Change config.py according to your own dataset, especially the `num_classes`.
+2. Change config_xxx.py according to your own dataset, especially the `num_classes`.
 3. Prepare a pretrained checkpoint. You can load the pretrained checkpoint by `pre_trained` argument. Transfer training means a new training job, so just keep `pre_trained_epoch_size`  same as default value `0`.
 4. Set argument `filter_weight` to `True` while calling `train.py`, this will filter the final detection box weight from the pretrained model.
 5. Build your own bash scripts using new config and arguments for further convenient.