mindspore/model_zoo/official/cv/googlenet

Contents

• GoogleNet Description
• Model Architecture
• Dataset
• Features
  • Mixed Precision
• Environment Requirements
• Quick Start
• Script Description
  • Script and Sample Code
  • Script Parameters
  • Training Process
    • Training
    • Distributed Training
  • Evaluation Process
    • Evaluation
• Model Description
  • Performance
    • Evaluation Performance
    • Inference Performance
  • How to use
    • Inference
    • Continue Training on the Pretrained Model
    • Transfer Learning
• Description of Random Situation
• ModelZoo Homepage

GoogleNet Description

GoogleNet, a 22 layers deep network, was proposed in 2014 and won the first place in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). GoogleNet, also called Inception v1, has significant improvement over ZFNet (The winner in 2013) and AlexNet (The winner in 2012), and has relatively lower error rate compared to VGGNet. Typically deeper deep learning network means larger number of parameters, which makes it more prone to overfitting. Furthermore, the increased network size leads to increased use of computational resources. To tackle these issues, GoogleNet adopts 1*1 convolution middle of the network to reduce dimension, and thus further reduce the computation. Global average pooling is used at the end of the network, instead of using fully connected layers. Another technique, called inception module, is to have different sizes of convolutions for the same input and stacking all the outputs.

Paper: Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. "Going deeper with convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.

Model Architecture

Specifically, the GoogleNet contains numerous inception modules, which are connected together to go deeper. In general, an inception module with dimensionality reduction consists of 1×1 conv, 3×3 conv, 5×5 conv, and 3×3 max pooling, which are done altogether for the previous input, and stack together again at output.

Dataset

Dataset used: CIFAR-10

• Dataset size：175M，60,000 32*32 colorful images in 10 classes
  • Train：146M，50,000 images
  • Test：29.3M，10,000 images
• Data format：binary files
  • Note：Data will be processed in src/dataset.py

Features

Mixed Precision

The mixed precision training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.

Environment Requirements

• Hardware（Ascend/GPU）
  • Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the application form to ascend@huawei.com. Once approved, you can get the resources.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore tutorials
  • MindSpore API

Quick Start

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

• runing on Ascend

  # 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

  # 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]
  

Script Description

Script and Sample Code

├── model_zoo
    ├── README.md                          // descriptions about all the models
    ├── googlenet        
        ├── README.md                    // descriptions about googlenet
        ├── scripts 
        │   ├──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
        │   ├──config.py            // parameter configuration 
        ├── train.py               // training script 
        ├── eval.py               //  evaluation script 
        ├── export.py            // export checkpoint files into air/onnx 

Script Parameters

Parameters for both training and evaluation can be set in config.py

• config for GoogleNet, CIFAR-10 dataset

  '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

Training

• 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.

Distributed Training

• 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

Evaluation

• 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

Performance

Evaluation Performance

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	googlenet script

Inference Performance

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

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. Following the steps below, this is a simple example:

• 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

• 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.

Description of Random Situation

In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.

ModelZoo Homepage

Please check the official homepage.