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AVA_hpa is a two-stage model which contains self-supervised pre-training stage and supervised learning stage. The aim of AVA_hpa is to promote the performance on recognition of protein subcellular localization in immunofluorescence microscopic images.
The overall network architecture of AVA_hpa has been 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.
.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
├── ......
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.
. AVA_hpa
├── Readme.md # descriptions about AVA_hpa
├── 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 script
Parameters 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": 10, # 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
"breakpoint_training_path": "", # if not "": train from breakpoint ckpt
"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": 10, # 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
"breakpoint_training_path": "", # if not "": train from breakpoint ckpt
"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
running on Ascend
bash scripts/run_pretrain.sh
running on GPU
bash scripts/run_pretrain_gpu.sh
The loss value will be achieved as follows:
# grep YOUR_PREFIX/log-YOUR_TIMESTAMP.log
2021-01-15 00:11:06,861 - INFO: the 1 epoch's resnet result: training loss 5.881546497344971,training per step cost 0.23 s, total_cost 763.72 s
2021-01-15 00:24:20,573 - INFO: the 2 epoch's resnet result: training loss -4.4250054359436035,training per step cost 0.24 s, total_cost 793.71 s
2021-01-15 00:37:51,632 - INFO: the 3 epoch's resnet result: training loss -5.982544422149658,training per step cost 0.25 s, total_cost 811.06 s
...
running on Ascend
bash scripts/run_train.sh
running on GPU
bash scripts/run_train_gpu.sh
The loss value will be achieved as follows:
# grep YOUR_PREFIX/log-YOUR_TIMESTAMP.log
2020-12-18 23:19:33,929 - INFO: the 1 epoch's resnet result: training loss 0.2797913578611862,training per step cost 0.14 s, total_cost 1245.61 s
2020-12-18 23:25:39,857 - INFO: the 1 epoch's Eval result: f1_macro 0.29109520657475296, f1_micro 0.5977175463623395, auc 0.9131143166119532,eval cost 365.93 s
2020-12-18 23:44:25,443 - INFO: the 2 epoch's resnet result: training loss 0.23317653393904286,training per step cost 0.13 s, total_cost 1125.58 s
2020-12-18 23:48:54,067 - INFO: the 2 epoch's Eval result: f1_macro 0.4803126504695009, f1_micro 0.6413352272727273, auc 0.9364969330561459,eval cost 268.62 s
...
running on Ascend
bash scripts/run_eval.sh
running on GPU
bash scripts/run_eval_gpu.sh
The value of performance will be achieved as follows:
{'results_return': ( 0.6975821515082009, 0.7734114826162249, 0.9415286419176973)} #macroF1,microF1,auc
Before inference, please refer to MindSpore Inference with C++ Deployment Guide to set environment variables.
python export.py --ckpt_path [CKPT_PATH] --model_arch [MODEL_ARCH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
CKPT_PATH parameter is required.MODEL_ARCH is model architecture, should be in ['resnet18', 'resnet50', 'resnet101'].file_format should be in ["AIR", "MINDIR"]Before performing inference, the mindir file must be exported by export.py script. We only provide an example of inference using MINDIR model.
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_ID]
DATASET_PATH is the hpa dataset path.NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.DEVICE_ID is optional, default value is 0.Inference result is saved in current path, you can find result like this in acc.log file.
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