model_zoo README.md format change for googlenet

5 years ago · 983d6f16a7
--- a/model_zoo/official/cv/googlenet/README.md
+++ b/model_zoo/official/cv/googlenet/README.md
@@ -48,8 +48,7 @@ Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
  - Train：146M，50,000 images  
  - Test：29.3M，10,000 images 
 - Data format：binary files
  - Note：Data will be processed in dataset.py

  - Note：Data will be processed in src/dataset.py


 # [Features](#contents)
@@ -66,7 +65,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 - Hardware（Ascend/GPU）
  - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
 - Framework
  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
  - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) 
  - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
@@ -77,16 +76,45 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil

 After installing MindSpore via the official website, you can start training and evaluation as follows: 

 ```python
 # run training example
 python train.py > train.log 2>&1 & 
 - runing on Ascend

 # run distributed training example
 Ascend: sh scripts/run_train.sh rank_table.json OR GPU: sh scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7
  ```python
  # run training example
  python train.py > train.log 2>&1 & 
  
  # run distributed training example
  sh scripts/run_train.sh rank_table.json
  
  # run evaluation example
  python eval.py > eval.log 2>&1 & 
  OR
  sh run_eval.sh
  ```

  For distributed training, a hccl configuration file with JSON format needs to be created in advance.

  Please follow the instructions in the link below:

  https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.

 - running on GPU

  For running on GPU, please change `device_target` from `Ascend` to `GPU` in configuration file src/config.py

  ```python
  # run training example
  export CUDA_VISIBLE_DEVICES=0
  python train.py > train.log 2>&1 & 
  
  # run distributed training example
  sh scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7
  
  # run evaluation example
  python eval.py --checkpoint_path=[CHECKPOINT_PATH] > eval.log 2>&1 &  
  OR
  sh run_eval_gpu.sh [CHECKPOINT_PATH]
  ```

 # run evaluation example
 python eval.py > eval.log 2>&1 &  OR  Ascend: sh run_eval.sh OR GPU: sh run_eval_gpu.sh
 ```



@@ -100,8 +128,10 @@ python eval.py > eval.log 2>&1 &  OR  Ascend: sh run_eval.sh OR GPU: sh run_eval
    ├── googlenet        
        ├── README.md                    // descriptions about googlenet
        ├── scripts 
        │   ├──run_train.sh             // shell script for distributed 
        │   ├──run_eval.sh             // shell script for evaluation 
        │   ├──run_train.sh             // shell script for distributed on Ascend
        │   ├──run_train_gpu.sh         // shell script for distributed on GPU
        │   ├──run_eval.sh              // shell script for evaluation on Ascend
        │   ├──run_eval_gpu.sh          // shell script for evaluation on GPU
        ├── src 
        │   ├──dataset.py             // creating dataset
        │   ├──googlenet.py          // googlenet architecture
@@ -113,98 +143,153 @@ python eval.py > eval.log 2>&1 &  OR  Ascend: sh run_eval.sh OR GPU: sh run_eval

 ## [Script Parameters](#contents)

 ```python
 Major parameters in train.py and config.py are:

 --data_path: The absolute full path to the train and evaluation datasets. 
 --epoch_size: Total training epochs. 
 --batch_size: Training batch size. 
 --lr_init: Initial learning rate. 
 --num_classes: The number of classes in the training set.
 --weight_decay: Weight decay value. 
 --image_height: Image height used as input to the model.
 --image_width: Image width used as input the model.
 --pre_trained: Whether training from scratch or training based on the
               pre-trained model.Optional values are True, False. 
 --device_target: Device where the code will be implemented. Optional values
                 are "Ascend", "GPU". 
 --device_id: Device ID used to train or evaluate the dataset. Ignore it
             when you use run_train.sh for distributed training.
 --checkpoint_path: The absolute full path to the checkpoint file saved
                   after training.
 --onnx_filename: File name of the onnx model used in export.py. 
 --air_filename: File name of the air model used in export.py.    
 ```
 Parameters for both training and evaluation can be set in config.py

 - config for GoogleNet, CIFAR-10 dataset

  ```python
  'pre_trained': 'False'    # whether training based on the pre-trained model
  'nump_classes': 10        # the number of classes in the dataset
  'lr_init': 0.1            # initial learning rate
  'batch_size': 128         # training batch size
  'epoch_size': 125         # total training epochs
  'momentum': 0.9           # momentum
  'weight_decay': 5e-4      # weight decay value
  'buffer_size': 10         # buffer size
  'image_height': 224       # image height used as input to the model
  'image_width': 224        # image width used as input to the model
  'data_path': './cifar10'  # absolute full path to the train and evaluation datasets
  'device_target': 'Ascend' # device running the program
  'device_id': 4            # device ID used to train or evaluate the dataset. Ignore it when you use run_train.sh for distributed training
  'keep_checkpoint_max': 10 # only keep the last keep_checkpoint_max checkpoint
  'checkpoint_path': './train_googlenet_cifar10-125_390.ckpt'  # the absolute full path to save the checkpoint file
  'onnx_filename': 'googlenet.onnx' # file name of the onnx model used in export.py
  'geir_filename': 'googlenet.geir' # file name of the geir model used in export.py
  ```


 ## [Training Process](#contents)

 ### Training 

 ```
 python train.py > train.log 2>&1 & 
 ```

 The python command above will run in the background, you can view the results through the file `train.log`.

 After training, you'll get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
 - running on Ascend

  ```
  python train.py > train.log 2>&1 & 
  ```
  
  The python command above will run in the background, you can view the results through the file `train.log`.
  
  After training, you'll get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
  
  ```
  # grep "loss is " train.log
  epoch: 1 step: 390, loss is 1.4842823
  epcoh: 2 step: 390, loss is 1.0897788
  ...
  ```
  
  The model checkpoint will be saved in the current directory. 

 - running on GPU

  ```
  export CUDA_VISIBLE_DEVICES=0
  python train.py > train.log 2>&1 & 
  ```

  The python command above will run in the background, you can view the results through the file `train.log`.
  
  After training, you'll get some checkpoint files under the folder `./ckpt_0/` by default.

 ```
 # grep "loss is " train.log
 epoch: 1 step: 390, loss is 1.4842823
 epcoh: 2 step: 390, loss is 1.0897788
 ...
 ```

 The model checkpoint will be saved in the current directory. 

 ### Distributed Training

 ```
 Ascend: sh scripts/run_train.sh rank_table.json OR GPU: sh scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7
 ```

 The above shell script will run distribute training in the background. You can view the results through the file `train_parallel[X]/log`. The loss value will be achieved as follows:

 ```
 # grep "result: " train_parallel*/log
 train_parallel0/log:epoch: 1 step: 48, loss is 1.4302931
 train_parallel0/log:epcoh: 2 step: 48, loss is 1.4023874
 ...
 train_parallel1/log:epoch: 1 step: 48, loss is 1.3458025
 train_parallel1/log:epcoh: 2 step: 48, loss is 1.3729336
 ...
 ...
 ```
 - running on Ascend

  ```
  sh scripts/run_train.sh rank_table.json
  ```
  
  The above shell script will run distribute training in the background. You can view the results through the file `train_parallel[X]/log`. The loss value will be achieved as follows:
  
  ```
  # grep "result: " train_parallel*/log
  train_parallel0/log:epoch: 1 step: 48, loss is 1.4302931
  train_parallel0/log:epcoh: 2 step: 48, loss is 1.4023874
  ...
  train_parallel1/log:epoch: 1 step: 48, loss is 1.3458025
  train_parallel1/log:epcoh: 2 step: 48, loss is 1.3729336
  ...
  ...
  ```

 - running on GPU

  ```
  sh scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7
  ```
  
  The above shell script will run distribute training in the background. You can view the results through the file `train/train.log`.


 ## [Evaluation Process](#contents)

 ### Evaluation

 Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/googlenet/train_googlenet_cifar10-125_390.ckpt".

 ```
 python eval.py > eval.log 2>&1 &  
 OR
 Ascned: sh scripts/run_eval.sh
 OR
 GPU: sh scripts/run_eval_gpu.sh
 ```

 The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:

 ```
 # grep "accuracy: " eval.log
 accuracy: {'acc': 0.934}
 ```

 Note that for evaluation after distributed training, please set the checkpoint_path to be the last saved checkpoint file such as "username/googlenet/train_parallel0/train_googlenet_cifar10-125_48.ckpt". The accuracy of the test dataset will be as follows:

 ```
 # grep "accuracy: " dist.eval.log
 accuracy: {'acc': 0.9217}
 ```
 - evaluation on CIFAR-10 dataset when running on Ascend

  Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/googlenet/train_googlenet_cifar10-125_390.ckpt".
  
  ```
  python eval.py > eval.log 2>&1 &  
  OR
  sh scripts/run_eval.sh
  ```
  
  The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
  
  ```
  # grep "accuracy: " eval.log
  accuracy: {'acc': 0.934}
  ```
  
  Note that for evaluation after distributed training, please set the checkpoint_path to be the last saved checkpoint file such as "username/googlenet/train_parallel0/train_googlenet_cifar10-125_48.ckpt". The accuracy of the test dataset will be as follows:
  
  ```
  # grep "accuracy: " dist.eval.log
  accuracy: {'acc': 0.9217}
  ```

 - evaluation on CIFAR-10 dataset when running on GPU

  Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/googlenet/train/ckpt_0/train_googlenet_cifar10-125_390.ckpt".
  
  ```
  python eval.py --checkpoint_path=[CHECKPOINT_PATH] > eval.log 2>&1 &  
  ```
  
  The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
  
  ```
  # grep "accuracy: " eval.log
  accuracy: {'acc': 0.930}
  ```

  OR,

  ```
  sh scripts/run_eval_gpu.sh [CHECKPOINT_PATH]
  ```
  
  The above python command will run in the background. You can view the results through the file "eval/eval.log". The accuracy of the test dataset will be as follows:
  
  ```
  # grep "accuracy: " eval/eval.log
  accuracy: {'acc': 0.930}
  ```

 


 # [Model Description](#contents)
@@ -212,100 +297,170 @@ accuracy: {'acc': 0.9217}

 ### Evaluation Performance 

 | Parameters                 | GoogleNet                                                   |
 | -------------------------- | ----------------------------------------------------------- |
 | Model Version              | Inception V1                                                |
 | Resource                   | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G             |
 | uploaded Date              | 06/09/2020 (month/day/year)                                 |
 | MindSpore Version          | 0.3.0-alpha                                                       |
 | Dataset                    | CIFAR-10                                                    |
 | Training Parameters        | epoch=125, steps=390, batch_size = 128, lr=0.1              |
 | Optimizer                  | SGD                                                         |
 | Loss Function              | Softmax Cross Entropy                                       |
 | outputs                    | probability                                                 |
 | Loss                       | 0.0016                                                      |
 | Speed                      | 1pc: 79 ms/step;  8pcs: 82 ms/step                          |
 | Total time                 | 1pc: 63.85 mins;  8pcs: 11.28 mins                          |
 | Parameters (M)             | 13.0                                                         |
 | Checkpoint for Fine tuning | 43.07M (.ckpt file)                                         |
 | Model for inference        | 21.50M (.onnx file),  21.60M(.air file)                    |
 | Scripts                    | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet |
 | Parameters                 | Ascend                                                      | GPU                    |
 | -------------------------- | ----------------------------------------------------------- | ---------------------- |
 | Model Version              | Inception V1                                                | Inception V1           |
 | Resource                   | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G             | NV SMX2 V100-32G       |
 | uploaded Date              | 06/09/2020 (month/day/year)                                 | 08/20/2020             |
 | MindSpore Version          | 0.2.0-alpha                                                 | 0.6.0-alpha            |
 | Dataset                    | CIFAR-10                                                    | CIFAR-10               |
 | Training Parameters        | epoch=125, steps=390, batch_size = 128, lr=0.1              | epoch=125, steps=390, batch_size=128, lr=0.1    |
 | Optimizer                  | SGD                                                         | SGD                    |
 | Loss Function              | Softmax Cross Entropy                                       | Softmax Cross Entropy  |
 | outputs                    | probability                                                 | probobility            |
 | Loss                       | 0.0016                                                      | 0.0016                 |
 | Speed                      | 1pc: 79 ms/step;  8pcs: 82 ms/step                          | 1pc: 150 ms/step;  8pcs: 164 ms/step      |
 | Total time                 | 1pc: 63.85 mins;  8pcs: 11.28 mins                          | 1pc: 126.87 mins;  8pcs: 21.65 mins      | 
 | Parameters (M)             | 13.0                                                        | 13.0                   |
 | Checkpoint for Fine tuning | 43.07M (.ckpt file)                                         | 43.07M (.ckpt file)    |
 | Model for inference        | 21.50M (.onnx file),  21.60M(.air file)                     |      | 
 | Scripts                    | [googlenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet) | [googlenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet) |


 ### Inference Performance

 | Parameters          | GoogleNet                   |
 | ------------------- | --------------------------- |
 | Model Version       | Inception V1                |
 | Resource            | Ascend 910                  |
 | Uploaded Date       | 06/09/2020 (month/day/year) |
 | MindSpore Version   | 0.2.0-alpha                       |
 | Dataset             | CIFAR-10, 10,000 images     |
 | batch_size          | 128                         |
 | outputs             | probability                 |
 | Accuracy            | 1pc: 93.4%;  8pcs: 92.17%   |
 | Model for inference | 21.50M (.onnx file)         |
 | Parameters          | Ascend                      | GPU                         |
 | ------------------- | --------------------------- | --------------------------- |
 | Model Version       | Inception V1                | Inception V1                |
 | Resource            | Ascend 910                  | GPU                         |
 | Uploaded Date       | 06/09/2020 (month/day/year) | 08/20/2020 (month/day/year) |
 | MindSpore Version   | 0.2.0-alpha                 | 0.6.0-alpha                 |
 | Dataset             | CIFAR-10, 10,000 images     | CIFAR-10, 10,000 images     |
 | batch_size          | 128                         | 128                         |
 | outputs             | probability                 | probability                 |
 | Accuracy            | 1pc: 93.4%;  8pcs: 92.17%   | 1pc: 93%, 8pcs: 92.89%      |
 | Model for inference | 21.50M (.onnx file)         |  |

 ## [How to use](#contents)
 ### Inference

 If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/network_migration.html). Following the steps below, this is a simple example:

 ```
 # Load unseen dataset for inference
 dataset = dataset.create_dataset(cfg.data_path, 1, False)

 # Define model 
 net = GoogleNet(num_classes=cfg.num_classes)
 opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01,
               cfg.momentum, weight_decay=cfg.weight_decay)
 loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', 
                                        is_grad=False)
 model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})

 # Load pre-trained model
 param_dict = load_checkpoint(cfg.checkpoint_path)
 load_param_into_net(net, param_dict)
 net.set_train(False)

 # Make predictions on the unseen dataset
 acc = model.eval(dataset)
 print("accuracy: ", acc)
 ```
 - Running on Ascend

  ```
  # Set context
  context.set_context(mode=context.GRAPH_HOME, device_target=cfg.device_target)
  context.set_context(device_id=cfg.device_id)
  
  # Load unseen dataset for inference
  dataset = dataset.create_dataset(cfg.data_path, 1, False)
  
  # Define model 
  net = GoogleNet(num_classes=cfg.num_classes)
  opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01,
                 cfg.momentum, weight_decay=cfg.weight_decay)
  loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', 
                                          is_grad=False)
  model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
  
  # Load pre-trained model
  param_dict = load_checkpoint(cfg.checkpoint_path)
  load_param_into_net(net, param_dict)
  net.set_train(False)
  
  # Make predictions on the unseen dataset
  acc = model.eval(dataset)
  print("accuracy: ", acc)
  ```

 - Running on GPU:

  ```
  # Set context
  context.set_context(mode=context.GRAPH_HOME, device_target="GPU")
  
  # Load unseen dataset for inference
  dataset = dataset.create_dataset(cfg.data_path, 1, False)
  
  # Define model 
  net = GoogleNet(num_classes=cfg.num_classes)
  opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01,
                 cfg.momentum, weight_decay=cfg.weight_decay)
  loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', 
                                          is_grad=False)
  model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
  
  # Load pre-trained model
  param_dict = load_checkpoint(args_opt.checkpoint_path)
  load_param_into_net(net, param_dict)
  net.set_train(False)
  
  # Make predictions on the unseen dataset
  acc = model.eval(dataset)
  print("accuracy: ", acc)

  ```

 ### Continue Training on the Pretrained Model 

 ```
 # Load dataset
 dataset = create_dataset(cfg.data_path, cfg.epoch_size)
 batch_num = dataset.get_dataset_size()

 # Define model
 net = GoogleNet(num_classes=cfg.num_classes)
 # Continue training if set pre_trained to be True
 if cfg.pre_trained:
    param_dict = load_checkpoint(cfg.checkpoint_path)
    load_param_into_net(net, param_dict)
 lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size,    
              steps_per_epoch=batch_num)
 opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 
               Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay)
 loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
 model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
              amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)

 # Set callbacks 
 config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, 
                             keep_checkpoint_max=cfg.keep_checkpoint_max)
 time_cb = TimeMonitor(data_size=batch_num)
 ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory="./", 
                             config=config_ck)
 loss_cb = LossMonitor()

 # Start training
 model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
 print("train success")
 ```
 - running on Ascend

  ```
  # Load dataset
  dataset = create_dataset(cfg.data_path, 1)
  batch_num = dataset.get_dataset_size()
  
  # Define model
  net = GoogleNet(num_classes=cfg.num_classes)
  # Continue training if set pre_trained to be True
  if cfg.pre_trained:
      param_dict = load_checkpoint(cfg.checkpoint_path)
      load_param_into_net(net, param_dict)
  lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size,    
                steps_per_epoch=batch_num)
  opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 
                 Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay)
  loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
  model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
                amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
  
  # Set callbacks 
  config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, 
                               keep_checkpoint_max=cfg.keep_checkpoint_max)
  time_cb = TimeMonitor(data_size=batch_num)
  ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory="./", 
                               config=config_ck)
  loss_cb = LossMonitor()
  
  # Start training
  model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
  print("train success")
  ```

 - running on GPU

  ```
  # Load dataset
  dataset = create_dataset(cfg.data_path, 1)
  batch_num = dataset.get_dataset_size()
  
  # Define model
  net = GoogleNet(num_classes=cfg.num_classes)
  # Continue training if set pre_trained to be True
  if cfg.pre_trained:
      param_dict = load_checkpoint(cfg.checkpoint_path)
      load_param_into_net(net, param_dict)
  lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size,    
                steps_per_epoch=batch_num)
  opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 
                 Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay)
  loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
  model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
                amp_level="O2", keep_batchnorm_fp32=True, loss_scale_manager=None)
  
  # Set callbacks 
  config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, 
                               keep_checkpoint_max=cfg.keep_checkpoint_max)
  time_cb = TimeMonitor(data_size=batch_num)
  ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory="./ckpt_" + str(get_rank()) + "/", 
                               config=config_ck)
  loss_cb = LossMonitor()
  
  # Start training
  model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
  print("train success")
  ```

 ### Transfer Learning
 To be added.