- SIFLoc Description
- Model Architecture
- Dataset
- Environment Requirements
- Quick Start
- Script Description
SIFLoc is a two-stage model which contains self-supervised pre-training stage and supervised learning stage. The aim of SIFLoc is to promote the performance on recognition of protein subcellular localization in immunofluorescence microscopic images.
Paper: SIFLoc: A self-supervised pre-training method for enhancing the recognition of protein subcellular localization in immunofluorescence microscopic images
The overall network architecture of SIFLoc is shown in original paper.
Original dataset is from Human Protein Atlas (www.proteinatlas.org). After post-processing, we obtain a custom dataset including 4 parts ( link1, link2, link3, link4) which is about 6.5GB.
- Dataset size: 173,594 color images (512$\times$512), 13,261 bags in 27 classes
- Data format: RGB images.
- Note: Data will be processed in src/datasets.py
- Directory structure of the dataset:
.hpa
├── ENSG00000001167
│ ├──686_A3_2_blue_red_green.jpg_1.jpg
│ ├──686_A3_2_blue_red_green.jpg_2.jpg
│ ├──686_A3_2_blue_red_green.jpg_3.jpg
│ ├──......
├── ENSG00000001630
├── ENSG00000002330
├── ENSG00000003756
├── ......- Hardware (Ascend/GPU)
- Prepare hardware environment with Ascend or GPU. 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：
After installing MindSpore via the official website, you can start training and evaluation as follows:
- run on Ascend
# standalone pre-training bash scripts/run_pretrain.sh # standalone training bash scripts/run_train.sh # standalone evaluation bash scripts/run_eval.sh
- run on GPU
# standalone pre-training bash scripts/run_pretrain_gpu.sh # standalone training bash scripts/run_train_gpu.sh # standalone evaluation bash scripts/run_eval_gpu.sh
Inside scripts, there are some parameter settings that can be adjusted for pre-training/training/evaluation.
. SIFLoc
├── Readme.md # descriptions about SIFLoc
├── scripts
│ ├──run_pretrain.sh # script to pre-train
│ ├──run_train.sh # script to train
│ ├──run_eval.sh # script to eval
├── src
│ ├──RandAugment # data augmentation polices
│ ├──callbacks.py # loss callback
│ ├──config.py # parameter configuration
│ ├──datasets.py # creating dataset
│ ├──eval_metrics.py # evaluation metrics
│ ├──loss.py # contrastive loss and BCE loss
│ ├──lr_schedule.py # learning rate config
│ ├──network_define_eval.py # evaluation cell
│ ├──network_define_pretrain.py # pre-train cell
│ ├──network_define_train.py # train cell
│ └──resnet.py # backbone network
├── enhanced.csv # labels of hpa dataset
├── eval.py # evaluation script
├── pretrain.py # pre-training script
└── train.py # training scriptParameters for both pre-training and training can be set in src/config.py
-
config for pre-training
# base setting "description": "description.", # description for pre-training "prefix": prefix, # prefix for pre-training "time_prefix": time_prefix, # time prefix "network": "resnet18", # network architecture "low_dims": 128, # the dim of last layer's feature "use_MLP": True, # whether use MLP # save "save_checkpoint": True, # whether save ckpt "save_checkpoint_epochs": 1, # save per <num> epochs "keep_checkpoint_max": 2, # save at most <num> ckpt # dataset "dataset": "hpa", # dataset name "bag_size": 1, # bag size = 1 for pre-training "classes": 27, # class number "num_parallel_workers": 8, # num_parallel_workers # optimizer "base_lr": 0.003, # init learning rate "type": "SGD", # optimizer type "momentum": 0.9, # momentum "weight_decay": 5e-4, # weight decay "loss_scale": 1, # loss scale "sigma": 0.1, # $\tau$ # trainer "batch_size": 32, # batch size "epochs": 100, # epochs for pre-training "lr_schedule": "cosine_lr", # learning rate schedule "lr_mode": "epoch", # "epoch" or "step" "warmup_epoch": 0, # epochs for warming up
-
config for training
# base setting "description": "description.", # description for pre-training "prefix": prefix, # prefix for training "time_prefix": time_prefix, # time prefix "network": "resnet18", # network architecture "low_dims": 128, # ignoring this for training "use_MLP": False, # whether use MLP (False) # save "save_checkpoint": True, # whether save ckpt "save_checkpoint_epochs": 1, # save per <num> epochs "keep_checkpoint_max": 2, # save at most <num> ckpt # dataset "dataset": "hpa", # dataset name "bag_size_for_train": 1, # bag size = 1 for training "bag_size_for_eval": 20, # bag size = 20 for evaluation "classes": 27, # class number "num_parallel_workers": 8, # num_parallel_workers # optimizer "base_lr": 0.0001, # init learning rate "type": "Adam", # optimizer type "beta1": 0.5, # beta1 "beta2": 0.999, # beta2 "weight_decay": 0, # weight decay "loss_scale": 1, # loss scale # trainer "batch_size_for_train": 8, # batch size for training "batch_size_for_eval": 1, # batch size for evaluation "epochs": 20, # training epochs "eval_per_epoch": 1, # eval per <num> epochs "lr_schedule": "cosine_lr", # learning rate schedule "lr_mode": "epoch", # "epoch" or "step" "warmup_epoch": 0, # epochs for warming up