!12028 Add MobileNetV3 CPU scripts

From: @wuxuejian
Reviewed-by: @c_34,@liangchenghui,@oacjiewen,@liangchenghui
Signed-off-by: @liangchenghui

model_zoo/official/cv/mobilenetv3/README_CN.md

@@ -50,8 +50,8 @@ MobileNetV3总体网络架构如下：
 
 # 环境要求
 
-- 硬件：GPU
-    - 准备GPU处理器搭建硬件环境。
+- 硬件：GPU/CPU
+    - 准备GPU/CPU处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：
@@ -86,6 +86,7 @@ MobileNetV3总体网络架构如下：
 使用python或shell脚本开始训练。shell脚本的使用方法如下：
 
 - GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
+- CPU: sh run_trian.sh CPU [DATASET_PATH]
 
 ### 启动
 
@@ -93,8 +94,10 @@ MobileNetV3总体网络架构如下：
 # 训练示例
   python:
       GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
+      CPU: python train.py --dataset_path ~/cifar10/train/ --device_targe CPU
   shell:
       GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
+      CPU: sh run_train.sh CPU ~/cifar10/train/
 ```
 
 ### 结果
@@ -115,6 +118,7 @@ epoch time:138331.250, per step time:221.330, avg loss:3.917
 使用python或shell脚本开始训练。shell脚本的使用方法如下：
 
 - GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
+- CPU: sh run_infer.sh CPU [DATASET_PATH] [CHECKPOINT_PATH]
 
 ### 启动
 
@@ -122,9 +126,11 @@ epoch time:138331.250, per step time:221.330, avg loss:3.917
 # 推理示例
   python:
     GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU
+    CPU: python eval.py --dataset_path ~/cifar10/val/ --checkpoint_path mobilenet_199.ckpt --device_targe CPU
 
   shell:
     GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
+    CPU: sh run_infer.sh CPU ~/cifar10/val/ ~/train/mobilenet-200_625.ckpt
 ```
 
 > 训练过程中可以生成检查点。

model_zoo/official/cv/mobilenetv3/Readme.md

@@ -19,7 +19,6 @@
 
 # [MobileNetV3 Description](#contents)
 
-
 MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
 
 [Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for mobilenetv3." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
@@ -35,37 +34,35 @@ The overall network architecture of MobileNetV3 is show below:
 Dataset used: [imagenet](http://www.image-net.org/)
 
 - Dataset size: ~125G, 1.2W colorful images in 1000 classes
-	- Train: 120G, 1.2W images
-	- Test: 5G, 50000 images
+    - Train: 120G, 1.2W images
+    - Test: 5G, 50000 images
 - Data format: RGB images.
-	- Note: Data will be processed in src/dataset.py 
-
+    - Note: Data will be processed in src/dataset.py
 
 # [Environment Requirements](#contents)
 
-- Hardware（GPU）
-  - Prepare hardware environment with GPU processor.
+- Hardware（GPU/CPU）
+    - Prepare hardware environment with GPU/CPU processor.
 - Framework
-  - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-  - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
-  - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
-
+    - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
 # [Script description](#contents)
 
 ## [Script and sample code](#contents)
 
 ```python
-├── MobileNetV3        
-  ├── Readme.md              # descriptions about MobileNetV3                 
-  ├── scripts 
-  │   ├──run_train.sh        # shell script for train               
-  │   ├──run_eval.sh         # shell script for evaluation                
-  ├── src                              
-  │   ├──config.py           # parameter configuration               
+├── MobileNetV3
+  ├── Readme.md              # descriptions about MobileNetV3
+  ├── scripts
+  │   ├──run_train.sh        # shell script for train
+  │   ├──run_eval.sh         # shell script for evaluation
+  ├── src
+  │   ├──config.py           # parameter configuration
   │   ├──dataset.py          # creating dataset
-  │   ├──lr_generator.py     # learning rate config                            
+  │   ├──lr_generator.py     # learning rate config
   │   ├──mobilenetV3.py      # MobileNetV3 architecture
   ├── train.py               # training script
   ├── eval.py                #  evaluation script
@@ -80,22 +77,25 @@ Dataset used: [imagenet](http://www.image-net.org/)
 You can start training using python or shell scripts. The usage of shell scripts as follows:
 
 - GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
+- CPU: sh run_trian.sh CPU [DATASET_PATH]
 
 ### Launch
 
-``` 
+```shell
 # training example
   python:
       GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
+      CPU: python train.py --dataset_path ~/cifar10/train/ --device_targe CPU
   shell:
       GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
+      CPU: sh run_train.sh CPU ~/cifar10/train/
 ```
 
 ### Result
 
-Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log  will be redirected to `./train/train.log` like followings. 
+Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log  will be redirected to `./train/train.log` like followings.
 
-``` 
+```bash
 epoch: [  0/200], step:[  624/  625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
 epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
 epoch: [  1/200], step:[  624/  625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
@@ -109,25 +109,28 @@ epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
 You can start training using python or shell scripts. The usage of shell scripts as follows:
 
 - GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
+- CPU: sh run_infer.sh CPU [DATASET_PATH] [CHECKPOINT_PATH]
 
 ### Launch
 
-``` 
+```shell
 # infer example
   python:
     GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU
+    CPU: python eval.py --dataset_path ~/cifar10/val/ --checkpoint_path mobilenet_199.ckpt --device_targe CPU
 
   shell:
     GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
+    CPU: sh run_infer.sh CPU ~/cifar10/val/ ~/train/mobilenet-200_625.ckpt
 ```
 
-> checkpoint can be produced in training process. 
+> checkpoint can be produced in training process.
 
 ### Result
 
-Inference result will be stored in the example path, you can find result like the followings in `val.log`. 
+Inference result will be stored in the example path, you can find result like the followings in `val.log`.
 
-``` 
+```bash
 result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
 ```
 
@@ -135,7 +138,7 @@ result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.
 
 Change the export mode and export file in `src/config.py`, and run `export.py`.
 
-```
+```python
 python export.py --device_target [PLATFORM] --checkpoint_path [CKPT_PATH]
 ```
 
@@ -168,5 +171,5 @@ python export.py --device_target [PLATFORM] --checkpoint_path [CKPT_PATH]
 In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
 
 # [ModelZoo Homepage](#contents)
- 
-Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). 
+
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

model_zoo/official/cv/mobilenetv3/eval.py

@@ -21,7 +21,9 @@ from mindspore import nn
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from src.dataset import create_dataset
+from src.dataset import create_dataset_cifar
 from src.config import config_gpu
+from src.config import config_cpu
 from src.mobilenetV3 import mobilenet_v3_large
 
 
@@ -38,17 +40,24 @@ if __name__ == '__main__':
         config = config_gpu
         context.set_context(mode=context.GRAPH_MODE,
                             device_target="GPU", save_graphs=False)
+        dataset = create_dataset(dataset_path=args_opt.dataset_path,
+                                 do_train=False,
+                                 config=config,
+                                 device_target=args_opt.device_target,
+                                 batch_size=config.batch_size)
+    elif args_opt.device_target == "CPU":
+        config = config_cpu
+        context.set_context(mode=context.GRAPH_MODE,
+                            device_target="CPU", save_graphs=False)
+        dataset = create_dataset_cifar(dataset_path=args_opt.dataset_path,
+                                       do_train=False,
+                                       batch_size=config.batch_size)
     else:
         raise ValueError("Unsupported device_target.")
 
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
     net = mobilenet_v3_large(num_classes=config.num_classes, activation="Softmax")
 
-    dataset = create_dataset(dataset_path=args_opt.dataset_path,
-                             do_train=False,
-                             config=config,
-                             device_target=args_opt.device_target,
-                             batch_size=config.batch_size)
     step_size = dataset.get_dataset_size()
 
     if args_opt.checkpoint_path:

model_zoo/official/cv/mobilenetv3/export.py

@@ -19,6 +19,7 @@ import argparse
 import numpy as np
 from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
 from src.config import config_gpu
+from src.config import config_cpu
 from src.mobilenetV3 import mobilenet_v3_large
 
 
@@ -32,6 +33,9 @@ if __name__ == '__main__':
     if args_opt.device_target == "GPU":
         cfg = config_gpu
         context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    elif args_opt.device_target == "CPU":
+        cfg = config_cpu
+        context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     else:
         raise ValueError("Unsupported device_target.")
 

model_zoo/official/cv/mobilenetv3/scripts/run_infer.sh

@@ -16,6 +16,7 @@
 if [ $# != 3 ]
 then
     echo "GPU: sh run_infer.sh [DEVICE_TARGET] [DATASET_PATH] [CHECKPOINT_PATH]"
+    echo "CPU: sh run_infer.sh [DEVICE_TARGET] [DATASET_PATH] [CHECKPOINT_PATH]"
 exit 1
 fi
 

model_zoo/official/cv/mobilenetv3/scripts/run_train.sh

@@ -16,6 +16,14 @@
 
 run_gpu()
 {
+    if [ $# -gt 5 ] || [ $# -lt 4 ]
+    then
+        echo "Usage:\n \
+              GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
+              CPU: sh run_train.sh CPU [DATASET_PATH]\n \
+             "
+    exit 1
+    fi
     if [ $2 -lt 1 ] && [ $2 -gt 8 ]
     then
         echo "error: DEVICE_NUM=$2 is not in (1-8)"
@@ -45,16 +53,42 @@ run_gpu()
         &> ../train.log &  # dataset train folder
 }
 
-if [ $# -gt 5 ] || [ $# -lt 4 ]
-then
-    echo "Usage:\n \
-          GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
-          "
-exit 1
-fi
+run_cpu()
+{
+    if [ $# -gt 3 ] || [ $# -lt 2 ]
+    then
+        echo "Usage:\n \
+              GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
+              CPU: sh run_train.sh CPU [DATASET_PATH]\n \
+             "
+    exit 1
+    fi
+
+    if [ ! -d $2 ]
+    then
+        echo "error: DATASET_PATH=$2 is not a directory"
+    exit 1
+    fi
+
+    BASEPATH=$(cd "`dirname $0`" || exit; pwd)
+    export PYTHONPATH=${BASEPATH}:$PYTHONPATH
+    if [ -d "../train" ];
+    then
+        rm -rf ../train
+    fi
+    mkdir ../train
+    cd ../train || exit
+
+    python ${BASEPATH}/../train.py \
+        --dataset_path=$2 \
+        --device_target=$1 \
+        &> ../train.log &  # dataset train folder
+}
 
 if [ $1 = "GPU" ] ; then
     run_gpu "$@"
+elif [ $1 = "CPU" ] ; then
+    run_cpu "$@"
 else
     echo "Unsupported device_target"
 fi;

model_zoo/official/cv/mobilenetv3/src/config.py

@@ -36,3 +36,23 @@ config_gpu = ed({
     "export_format": "MINDIR",
     "export_file": "mobilenetv3"
 })
+
+config_cpu = ed({
+    "num_classes": 10,
+    "image_height": 224,
+    "image_width": 224,
+    "batch_size": 32,
+    "epoch_size": 120,
+    "warmup_epochs": 5,
+    "lr": 0.1,
+    "momentum": 0.9,
+    "weight_decay": 1e-4,
+    "label_smooth": 0.1,
+    "loss_scale": 1024,
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 1,
+    "keep_checkpoint_max": 500,
+    "save_checkpoint_path": "./checkpoint",
+    "export_format": "MINDIR",
+    "export_file": "mobilenetv3"
+})

model_zoo/official/cv/mobilenetv3/src/dataset.py

@@ -83,3 +83,60 @@ def create_dataset(dataset_path, do_train, config, device_target, repeat_num=1,
     data_set = data_set.repeat(repeat_num)
 
     return data_set
+
+def create_dataset_cifar(dataset_path,
+                         do_train,
+                         repeat_num=1,
+                         batch_size=32,
+                         target="CPU"):
+    """
+    create a train or evaluate cifar10 dataset
+    Args:
+        dataset_path(string): the path of dataset.
+        do_train(bool): whether dataset is used for train or eval.
+        repeat_num(int): the repeat times of dataset. Default: 1
+        batch_size(int): the batch size of dataset. Default: 32
+        target(str): the device target. Default: Ascend
+
+    Returns:
+        dataset
+    """
+    data_set = ds.Cifar10Dataset(dataset_path,
+                                 num_parallel_workers=8,
+                                 shuffle=True)
+    # define map operations
+    if do_train:
+        trans = [
+            C.RandomCrop((32, 32), (4, 4, 4, 4)),
+            C.RandomHorizontalFlip(prob=0.5),
+            C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
+            C.Resize((224, 224)),
+            C.Rescale(1.0 / 255.0, 0.0),
+            C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
+            C.CutOut(112),
+            C.HWC2CHW()
+        ]
+    else:
+        trans = [
+            C.Resize((224, 224)),
+            C.Rescale(1.0 / 255.0, 0.0),
+            C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
+            C.HWC2CHW()
+        ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+
+    data_set = data_set.map(operations=type_cast_op,
+                            input_columns="label",
+                            num_parallel_workers=8)
+    data_set = data_set.map(operations=trans,
+                            input_columns="image",
+                            num_parallel_workers=8)
+
+    # apply batch operations
+    data_set = data_set.batch(batch_size, drop_remainder=True)
+
+    # apply dataset repeat operation
+    data_set = data_set.repeat(repeat_num)
+
+    return data_set

model_zoo/official/cv/mobilenetv3/train.py

@@ -37,8 +37,10 @@ from mindspore.common import set_seed
 from mindspore.communication.management import init, get_group_size, get_rank
 
 from src.dataset import create_dataset
+from src.dataset import create_dataset_cifar
 from src.lr_generator import get_lr
 from src.config import config_gpu
+from src.config import config_cpu
 from src.mobilenetV3 import mobilenet_v3_large
 
 set_seed(1)
@@ -59,6 +61,10 @@ if args_opt.device_target == "GPU":
         context.set_auto_parallel_context(device_num=get_group_size(),
                                           parallel_mode=ParallelMode.DATA_PARALLEL,
                                           gradients_mean=True)
+elif args_opt.device_target == "CPU":
+    context.set_context(mode=context.GRAPH_MODE,
+                        device_target="CPU",
+                        save_graphs=False)
 else:
     raise ValueError("Unsupported device_target.")
 
@@ -151,58 +157,71 @@ class Monitor(Callback):
 
 
 if __name__ == '__main__':
+    config_ = None
+    if args_opt.device_target == "GPU":
+        config_ = config_gpu
+    elif args_opt.device_target == "CPU":
+        config_ = config_cpu
+    else:
+        raise ValueError("Unsupported device_target.")
+    # train on device
+    print("train args: ", args_opt)
+    print("cfg: ", config_)
+
+    # define net
+    net = mobilenet_v3_large(num_classes=config_.num_classes)
+    # define loss
+    if config_.label_smooth > 0:
+        loss = CrossEntropyWithLabelSmooth(
+            smooth_factor=config_.label_smooth, num_classes=config_.num_classes)
+    else:
+        loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    # define dataset
+    epoch_size = config_.epoch_size
     if args_opt.device_target == "GPU":
-        # train on gpu
-        print("train args: ", args_opt)
-        print("cfg: ", config_gpu)
-
-        # define net
-        net = mobilenet_v3_large(num_classes=config_gpu.num_classes)
-        # define loss
-        if config_gpu.label_smooth > 0:
-            loss = CrossEntropyWithLabelSmooth(
-                smooth_factor=config_gpu.label_smooth, num_classes=config_gpu.num_classes)
-        else:
-            loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
-        # define dataset
-        epoch_size = config_gpu.epoch_size
         dataset = create_dataset(dataset_path=args_opt.dataset_path,
                                  do_train=True,
-                                 config=config_gpu,
+                                 config=config_,
                                  device_target=args_opt.device_target,
                                  repeat_num=1,
-                                 batch_size=config_gpu.batch_size,
-                                 run_distribute=args_opt.run_distribute)
-        step_size = dataset.get_dataset_size()
-        # resume
-        if args_opt.pre_trained:
-            param_dict = load_checkpoint(args_opt.pre_trained)
-            load_param_into_net(net, param_dict)
-        # define optimizer
-        loss_scale = FixedLossScaleManager(
-            config_gpu.loss_scale, drop_overflow_update=False)
-        lr = Tensor(get_lr(global_step=0,
-                           lr_init=0,
-                           lr_end=0,
-                           lr_max=config_gpu.lr,
-                           warmup_epochs=config_gpu.warmup_epochs,
-                           total_epochs=epoch_size,
-                           steps_per_epoch=step_size))
-        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_gpu.momentum,
-                       config_gpu.weight_decay, config_gpu.loss_scale)
-        # define model
-        model = Model(net, loss_fn=loss, optimizer=opt,
-                      loss_scale_manager=loss_scale)
-
-        cb = [Monitor(lr_init=lr.asnumpy())]
-        if args_opt.run_distribute:
-            ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
-        else:
-            ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + "/"
-        if config_gpu.save_checkpoint:
-            config_ck = CheckpointConfig(save_checkpoint_steps=config_gpu.save_checkpoint_epochs * step_size,
-                                         keep_checkpoint_max=config_gpu.keep_checkpoint_max)
-            ckpt_cb = ModelCheckpoint(prefix="mobilenetV3", directory=ckpt_save_dir, config=config_ck)
-            cb += [ckpt_cb]
-        # begine train
-        model.train(epoch_size, dataset, callbacks=cb)
+                                 batch_size=config_.batch_size,
+                                 run_distribute=False)
+    elif args_opt.device_target == "CPU":
+        dataset = create_dataset_cifar(args_opt.dataset_path,
+                                       do_train=True,
+                                       batch_size=config_.batch_size)
+    else:
+        raise ValueError("Unsupported device_target.")
+    step_size = dataset.get_dataset_size()
+    # resume
+    if args_opt.pre_trained:
+        param_dict = load_checkpoint(args_opt.pre_trained)
+        load_param_into_net(net, param_dict)
+    # define optimizer
+    loss_scale = FixedLossScaleManager(
+        config_.loss_scale, drop_overflow_update=False)
+    lr = Tensor(get_lr(global_step=0,
+                       lr_init=0,
+                       lr_end=0,
+                       lr_max=config_.lr,
+                       warmup_epochs=config_.warmup_epochs,
+                       total_epochs=epoch_size,
+                       steps_per_epoch=step_size))
+    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_.momentum,
+                   config_.weight_decay, config_.loss_scale)
+    # define model
+    model = Model(net, loss_fn=loss, optimizer=opt,
+                  loss_scale_manager=loss_scale)
+
+    cb = [Monitor(lr_init=lr.asnumpy())]
+    if args_opt.run_distribute:
+        ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
+    else:
+        ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + "/"
+    if config_.save_checkpoint:
+        config_ck = CheckpointConfig(save_checkpoint_steps=config_.save_checkpoint_epochs * step_size,
+                                     keep_checkpoint_max=config_.keep_checkpoint_max)
+        ckpt_cb = ModelCheckpoint(prefix="mobilenetV3", directory=ckpt_save_dir, config=config_ck)
+        cb += [ckpt_cb]
+    # begine train
+    model.train(epoch_size, dataset, callbacks=cb)