mindspore/model_zoo/official/cv/unet

• Contents
• • Unet Description
• Model Architecture
• Dataset
• 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
  • How to use
  • • Inference
    • Continue Training on the Pretrained Model
• Description of Random Situation
• ModelZoo Homepage

Contents

• Unet Description
• Model Architecture
• Dataset
• 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
  • How to use
    • Inference
    • Continue Training on the Pretrained Model
• Description of Random Situation
• ModelZoo Homepage

Unet Description

Unet Medical model for 2D image segmentation. This implementation is as described in the original paper UNet: Convolutional Networks for Biomedical Image Segmentation. Unet, in the 2015 ISBI cell tracking competition, many of the best are obtained. In this paper, a network model for medical image segmentation is proposed, and a data enhancement method is proposed to effectively use the annotation data to solve the problem of insufficient annotation data in the medical field. A U-shaped network structure is also used to extract the context and location information.

Paper: Olaf Ronneberger, Philipp Fischer, Thomas Brox. "U-Net: Convolutional Networks for Biomedical Image Segmentation." conditionally accepted at MICCAI 2015. 2015.

Model Architecture

Specifically, the U network structure is proposed in UNET, which can better extract and fuse high-level features and obtain context information and spatial location information. The U network structure is composed of encoder and decoder. The encoder is composed of two 3x3 conv and a 2x2 max pooling iteration. The number of channels is doubled after each down sampling. The decoder is composed of a 2x2 deconv, concat layer and two 3x3 convolutions, and then outputs after a 1x1 convolution.

Dataset

Dataset used: ISBI Challenge

• Description: The training and test datasets are two stacks of 30 sections from a serial section Transmission Electron Microscopy (ssTEM) data set of the Drosophila first instar larva ventral nerve cord (VNC). The microcube measures 2 x 2 x 1.5 microns approx., with a resolution of 4x4x50 nm/pixel.
• License: You are free to use this data set for the purpose of generating or testing non-commercial image segmentation software. If any scientific publications derive from the usage of this data set, you must cite TrakEM2 and the following publication: Cardona A, Saalfeld S, Preibisch S, Schmid B, Cheng A, Pulokas J, Tomancak P, Hartenstein V. 2010. An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy. PLoS Biol 8(10): e1000502. doi:10.1371/journal.pbio.1000502.
• Dataset size：22.5M,
  • Train：15M, 30 images (Training data contains 2 multi-page TIF files, each containing 30 2D-images. train-volume.tif and train-labels.tif respectly contain data and label.)
  • Val：(We randomly divide the training data into 5-fold and evaluate the model by across 5-fold cross-validation.)
  • Test：7.5M, 30 images (Testing data contains 1 multi-page TIF files, each containing 30 2D-images. test-volume.tif respectly contain data.)
• Data format：binary files(TIF file)
  • Note：Data will be processed in src/data_loader.py

Environment Requirements

• Hardware（Ascend）
  • Prepare hardware environment with Ascend 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 Python API

Quick Start

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

• running on Ascend

  # run training example
  python train.py --data_url=/path/to/data/ > train.log 2>&1 &
  OR
  bash scripts/run_standalone_train.sh [DATASET]
  
  # run distributed training example
  bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]
  
  # run evaluation example
  python eval.py --data_url=/path/to/data/ --ckpt_path=/path/to/checkpoint/ > eval.log 2>&1 &
  OR
  bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]
  

Script Description

Script and Sample Code

├── model_zoo
    ├── README.md                           // descriptions about all the models
    ├── unet
        ├── README.md                       // descriptions about Unet
        ├── scripts
        │   ├──run_standalone_train.sh      // shell script for distributed on Ascend
        │   ├──run_standalone_eval.sh       // shell script for evaluation on Ascend
        ├── src
        │   ├──config.py                    // parameter configuration
        │   ├──data_loader.py               // creating dataset
        │   ├──loss.py                      // loss
        │   ├──utils.py                     // General components (callback function)
        │   ├──unet.py                      // Unet architecture
                ├──__init__.py              // init file
                ├──unet_model.py            // unet model
                ├──unet_parts.py            // unet part
        ├── train.py                        // training script
        ├──launch_8p.py                     // training 8P script
        ├── eval.py                         //  evaluation script

Script Parameters

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

• config for Unet, ISBI dataset

  'name': 'Unet',                     # model name
  'lr': 0.0001,                       # learning rate
  'epochs': 400,                      # total training epochs when run 1p
  'distribute_epochs': 1600,          # total training epochs when run 8p
  'batchsize': 16,                    # training batch size
  'cross_valid_ind': 1,               # cross valid ind
  'num_classes': 2,                   # the number of classes in the dataset
  'num_channels': 1,                  # the number of channels
  'keep_checkpoint_max': 10,          # only keep the last keep_checkpoint_max checkpoint
  'weight_decay': 0.0005,             # weight decay value
  'loss_scale': 1024.0,               # loss scale
  'FixedLossScaleManager': 1024.0,    # fix loss scale
  'resume': False,                    # whether training with pretrain model
  'resume_ckpt': './',                # pretrain model path
  

Training Process

Training

• running on Ascend

  python train.py --data_url=/path/to/data/ > train.log 2>&1 &
  OR
  bash scripts/run_standalone_train.sh [DATASET]
  

  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
  step: 1, loss is 0.7011719, fps is 0.25025035060906264
  step: 2, loss is 0.69433594, fps is 56.77693756377044
  step: 3, loss is 0.69189453, fps is 57.3293877244179
  step: 4, loss is 0.6894531, fps is 57.840651522059716
  step: 5, loss is 0.6850586, fps is 57.89903776054361
  step: 6, loss is 0.6777344, fps is 58.08073627299014
  ...  
  step: 597, loss is 0.19030762, fps is 58.28088370287449
  step: 598, loss is 0.19958496, fps is 57.95493929352674
  step: 599, loss is 0.18371582, fps is 58.04039977720966
  step: 600, loss is 0.22070312, fps is 56.99692546024671
  
  

  The model checkpoint will be saved in the current directory.

Distributed Training

bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]

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

# grep "loss is" logs/device0/log.log
step: 1, loss is 0.70524895, fps is 0.15914689861221412
step: 2, loss is 0.6925452, fps is 56.43668656967454
...
step: 299, loss is 0.20551169, fps is 58.4039329983891
step: 300, loss is 0.18949677, fps is 57.63118508760329

Evaluation Process

Evaluation

• evaluation on ISBI 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/unet/ckpt_unet_medical_adam-48_600.ckpt".

  python eval.py --data_url=/path/to/data/ --ckpt_path=/path/to/checkpoint/ > eval.log 2>&1 &
  OR
  bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]
  

  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 "Cross valid dice coeff is:" eval.log
  ============== Cross valid dice coeff is: {'dice_coeff': 0.9085704886070473}
  
  

Model Description

Performance

Evaluation Performance

Parameters	Ascend
Model Version	Unet
Resource	Ascend 910 ;CPU 2.60GHz,192cores; Memory,755G
uploaded Date	09/15/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	ISBI
Training Parameters	1pc: epoch=400, total steps=600, batch_size = 16, lr=0.0001
	8pc: epoch=1600, total steps=300, batch_size = 16, lr=0.0001
Optimizer	ADAM
Loss Function	Softmax Cross Entropy
outputs	probability
Loss	0.22070312
Speed	1pc: 267 ms/step; 8pc: 280 ms/step;
Total time	1pc: 2.67 mins; 8pc: 1.40 mins
Parameters (M)	93M
Checkpoint for Fine tuning	355.11M (.ckpt file)
Scripts	unet script

How to use

Inference

If you need to use the trained model to perform inference on multiple hardware platforms, such as 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
  device_id = int(os.getenv('DEVICE_ID'))
  context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",save_graphs=True,device_id=device_id)
  
  # Load unseen dataset for inference
  _, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False)
  
  # Define model and Load pre-trained model
  net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
  param_dict= load_checkpoint(ckpt_path)
  load_param_into_net(net , param_dict)
  criterion = CrossEntropyWithLogits()
  model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
  
  # Make predictions on the unseen dataset
  print("============== Starting Evaluating ============")
  dice_score = model.eval(valid_dataset, dataset_sink_mode=False)
  print("============== Cross valid dice coeff is:", dice_score)
  
  

• Running on Ascend 310

  Export MindIR

  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"]

  Before performing inference, the MINDIR file must be exported by export script on the 910 environment.
  Current batch_size can only be set to 1.

  # Ascend310 inference
  bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
  

  DEVICE_ID is optional, default value is 0.

  Inference result is saved in current path, you can find result in acc.log file.

  Cross valid dice coeff is: 0.9054352151297033
  

Continue Training on the Pretrained Model

• running on Ascend

  # Define model
  net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
  # Continue training if set 'resume' to be True
  if cfg['resume']:
      param_dict = load_checkpoint(cfg['resume_ckpt'])
      load_param_into_net(net, param_dict)
  
  # Load dataset
  train_dataset, _ = create_dataset(data_dir, epochs, batch_size, True, cross_valid_ind, run_distribute)
  train_data_size = train_dataset.get_dataset_size()
  
  optimizer = nn.Adam(params=net.trainable_params(), learning_rate=lr, weight_decay=cfg['weight_decay'],
                        loss_scale=cfg['loss_scale'])
  criterion = CrossEntropyWithLogits()
  loss_scale_manager = mindspore.train.loss_scale_manager.FixedLossScaleManager(cfg['FixedLossScaleManager'], False)
  
  model = Model(net, loss_fn=criterion, loss_scale_manager=loss_scale_manager, optimizer=optimizer, amp_level="O3")
  
  
  # Set callbacks
  ckpt_config = CheckpointConfig(save_checkpoint_steps=train_data_size,
                                 keep_checkpoint_max=cfg['keep_checkpoint_max'])
  ckpoint_cb = ModelCheckpoint(prefix='ckpt_unet_medical_adam',
                               directory='./ckpt_{}/'.format(device_id),
                               config=ckpt_config)
  
  print("============== Starting Training ==============")
  model.train(1, train_dataset, callbacks=[StepLossTimeMonitor(batch_size=batch_size), ckpoint_cb],
              dataset_sink_mode=False)
  print("============== End Training ==============")
  

Description of Random Situation

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

ModelZoo Homepage

Please check the official homepage.