bai-yangfan
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| README.md | ||
| eval.py | ||
| export.py | ||
| mindspore_hub_conf.py | ||
| train.py | ||
README.md
Contents
- Unet Description
- Model Architecture
- Dataset
- Environment Requirements
- Quick Start
- Script Description
- Model Description
- 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 divde 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
- For more information, please check the resources below：
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 56.99692546024671The 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)
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.