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4 年前 | |
|---|---|---|
| .. | ||
| ascend310_infer | 4 年前 | |
| scripts | 4 年前 | |
| src | 4 年前 | |
| README.md | 4 年前 | |
| README_CN.md | 4 年前 | |
| eval.py | 5 年前 | |
| export.py | 4 年前 | |
| mindspore_hub_conf.py | 5 年前 | |
| postprocess.py | 4 年前 | |
| requirements.txt | 5 年前 | |
| train.py | 4 年前 | |
README.md
Contents
- DeepLabV3 Description
- Model Architecture
- Dataset
- Features
- Environment Requirements
- Quick Start
- Script Description
- Model Description
- Description of Random Situation
- ModelZoo Homepage
DeepLabV3 Description
Description
DeepLab is a series of image semantic segmentation models, DeepLabV3 improves significantly over previous versions. Two keypoints of DeepLabV3: Its multi-grid atrous convolution makes it better to deal with segmenting objects at multiple scales, and augmented ASPP makes image-level features available to capture long range information.
This repository provides a script and recipe to DeepLabV3 model and achieve state-of-the-art performance.
Refer to this paper for network details.
Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation[J]. arXiv preprint arXiv:1706.05587, 2017.
Model Architecture
Resnet101 as backbone, atrous convolution for dense feature extraction.
Dataset
Pascal VOC datasets and Semantic Boundaries Dataset
-
Download segmentation dataset.
-
Prepare the training data list file. The list file saves the relative path to image and annotation pairs. Lines are like:
JPEGImages/00001.jpg SegmentationClassGray/00001.png
JPEGImages/00002.jpg SegmentationClassGray/00002.png
JPEGImages/00003.jpg SegmentationClassGray/00003.png
JPEGImages/00004.jpg SegmentationClassGray/00004.png
......
You can also generate the list file automatically by run script: python get_dataset_lst.py --data_root=/PATH/TO/DATA
- Configure and run build_data.sh to convert dataset to mindrecords. Arguments in scripts/build_data.sh:
--data_root root path of training data
--data_lst list of training data(prepared above)
--dst_path where mindrecords are saved
--num_shards number of shards of the mindrecords
--shuffle shuffle or not
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 types, 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）
-
Prepare hardware environment with Ascend. 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：
-
Install python packages in requirements.txt
-
Generate config json file for 8pcs training
# From the root of this project cd src/tools/ python3 get_multicards_json.py 10.111.*.* # 10.111.*.* is the computer's ip address.
Quick Start
After installing MindSpore via the official website, you can start training and evaluation as follows:
- Running on Ascend
Based on original DeepLabV3 paper, we reproduce two training experiments on vocaug (also as trainaug) dataset and evaluate on voc val dataset.
For single device training, please config parameters, training script is:
run_standalone_train.sh
For 8 devices training, training steps are as follows:
- Train s16 with vocaug dataset, finetuning from resnet101 pretrained model, script is:
run_distribute_train_s16_r1.sh
- Train s8 with vocaug dataset, finetuning from model in previous step, training script is:
run_distribute_train_s8_r1.sh
- Train s8 with voctrain dataset, finetuning from model in previous step, training script is:
run_distribute_train_s8_r2.sh
For evaluation, evaluating steps are as follows:
- Eval s16 with voc val dataset, eval script is:
run_eval_s16.sh
- Eval s8 with voc val dataset, eval script is:
run_eval_s8.sh
- Eval s8 multiscale with voc val dataset, eval script is:
run_eval_s8_multiscale.sh
- Eval s8 multiscale and flip with voc val dataset, eval script is:
run_eval_s8_multiscale_flip.sh
Script Description
Script and Sample Code
.
└──deeplabv3
├── README.md
├── scripts
├── build_data.sh # convert raw data to mindrecord dataset
├── run_distribute_train_s16_r1.sh # launch ascend distributed training(8 pcs) with vocaug dataset in s16 structure
├── run_distribute_train_s8_r1.sh # launch ascend distributed training(8 pcs) with vocaug dataset in s8 structure
├── run_distribute_train_s8_r2.sh # launch ascend distributed training(8 pcs) with voctrain dataset in s8 structure
├── run_eval_s16.sh # launch ascend evaluation in s16 structure
├── run_eval_s8.sh # launch ascend evaluation in s8 structure
├── run_eval_s8_multiscale.sh # launch ascend evaluation with multiscale in s8 structure
├── run_eval_s8_multiscale_filp.sh # launch ascend evaluation with multiscale and filp in s8 structure
├── run_standalone_train.sh # launch ascend standalone training(1 pc)
├── run_standalone_train_cpu.sh # launch CPU standalone training
├── src
├── data
├── dataset.py # mindrecord data generator
├── build_seg_data.py # data preprocessing
├── get_dataset_lst.py # dataset list file generator
├── loss
├── loss.py # loss definition for deeplabv3
├── nets
├── deeplab_v3
├── deeplab_v3.py # DeepLabV3 network structure
├── net_factory.py # set S16 and S8 structures
├── tools
├── get_multicards_json.py # get rank table file
└── utils
└── learning_rates.py # generate learning rate
├── eval.py # eval net
├── train.py # train net
└── requirements.txt # requirements file
Script Parameters
Default configuration
"data_file":"/PATH/TO/MINDRECORD_NAME" # dataset path
"device_target":Ascend # device target
"train_epochs":300 # total epochs
"batch_size":32 # batch size of input tensor
"crop_size":513 # crop size
"base_lr":0.08 # initial learning rate
"lr_type":cos # decay mode for generating learning rate
"min_scale":0.5 # minimum scale of data argumentation
"max_scale":2.0 # maximum scale of data argumentation
"ignore_label":255 # ignore label
"num_classes":21 # number of classes
"model":deeplab_v3_s16 # select model
"ckpt_pre_trained":"/PATH/TO/PRETRAIN_MODEL" # path to load pretrain checkpoint
"is_distributed": # distributed training, it will be True if the parameter is set
"save_steps":410 # steps interval for saving
"keep_checkpoint_max":200 # max checkpoint for saving
Training Process
Usage
Running on Ascend
Based on original DeepLabV3 paper, we reproduce two training experiments on vocaug (also as trainaug) dataset and evaluate on voc val dataset.
For single device training, please config parameters, training script is as follows:
# run_standalone_train.sh
python ${train_code_path}/train.py --data_file=/PATH/TO/MINDRECORD_NAME \
--train_dir=${train_path}/ckpt \
--train_epochs=200 \
--batch_size=32 \
--crop_size=513 \
--base_lr=0.015 \
--lr_type=cos \
--min_scale=0.5 \
--max_scale=2.0 \
--ignore_label=255 \
--num_classes=21 \
--model=deeplab_v3_s16 \
--ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL \
--save_steps=1500 \
--keep_checkpoint_max=200 >log 2>&1 &
For 8 devices training, training steps are as follows:
- Train s16 with vocaug dataset, finetuning from resnet101 pretrained model, script is as follows:
# run_distribute_train_s16_r1.sh
for((i=0;i<=$RANK_SIZE-1;i++));
do
export RANK_ID=${i}
export DEVICE_ID=$((i + RANK_START_ID))
echo 'start rank='${i}', device id='${DEVICE_ID}'...'
mkdir ${train_path}/device${DEVICE_ID}
cd ${train_path}/device${DEVICE_ID} || exit
python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \
--data_file=/PATH/TO/MINDRECORD_NAME \
--train_epochs=300 \
--batch_size=32 \
--crop_size=513 \
--base_lr=0.08 \
--lr_type=cos \
--min_scale=0.5 \
--max_scale=2.0 \
--ignore_label=255 \
--num_classes=21 \
--model=deeplab_v3_s16 \
--ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL \
--is_distributed \
--save_steps=410 \
--keep_checkpoint_max=200 >log 2>&1 &
done
- Train s8 with vocaug dataset, finetuning from model in previous step, training script is as follows:
# run_distribute_train_s8_r1.sh
for((i=0;i<=$RANK_SIZE-1;i++));
do
export RANK_ID=${i}
export DEVICE_ID=$((i + RANK_START_ID))
echo 'start rank='${i}', device id='${DEVICE_ID}'...'
mkdir ${train_path}/device${DEVICE_ID}
cd ${train_path}/device${DEVICE_ID} || exit
python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \
--data_file=/PATH/TO/MINDRECORD_NAME \
--train_epochs=800 \
--batch_size=16 \
--crop_size=513 \
--base_lr=0.02 \
--lr_type=cos \
--min_scale=0.5 \
--max_scale=2.0 \
--ignore_label=255 \
--num_classes=21 \
--model=deeplab_v3_s8 \
--loss_scale=2048 \
--ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL \
--is_distributed \
--save_steps=820 \
--keep_checkpoint_max=200 >log 2>&1 &
done
- Train s8 with voctrain dataset, finetuning from model in previous step, training script is as follows:
# run_distribute_train_s8_r2.sh
for((i=0;i<=$RANK_SIZE-1;i++));
do
export RANK_ID=${i}
export DEVICE_ID=$((i + RANK_START_ID))
echo 'start rank='${i}', device id='${DEVICE_ID}'...'
mkdir ${train_path}/device${DEVICE_ID}
cd ${train_path}/device${DEVICE_ID} || exit
python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \
--data_file=/PATH/TO/MINDRECORD_NAME \
--train_epochs=300 \
--batch_size=16 \
--crop_size=513 \
--base_lr=0.008 \
--lr_type=cos \
--min_scale=0.5 \
--max_scale=2.0 \
--ignore_label=255 \
--num_classes=21 \
--model=deeplab_v3_s8 \
--loss_scale=2048 \
--ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL \
--is_distributed \
--save_steps=110 \
--keep_checkpoint_max=200 >log 2>&1 &
done
Running on CPU
For CPU training, please config parameters, training script is as follows:
# run_standalone_train_cpu.sh
python ${train_code_path}/train.py --data_file=/PATH/TO/MINDRECORD_NAME \
--device_target=CPU \
--train_dir=${train_path}/ckpt \
--train_epochs=200 \
--batch_size=32 \
--crop_size=513 \
--base_lr=0.015 \
--lr_type=cos \
--min_scale=0.5 \
--max_scale=2.0 \
--ignore_label=255 \
--num_classes=21 \
--model=deeplab_v3_s16 \
--ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL \
--save_steps=1500 \
--keep_checkpoint_max=200 >log 2>&1 &
Result
Running on Ascend
- Training vocaug in s16 structure
# distribute training result(8p)
epoch: 1 step: 41, loss is 0.8319108
epoch time: 213856.477 ms, per step time: 5216.012 ms
epoch: 2 step: 41, loss is 0.46052963
epoch time: 21233.183 ms, per step time: 517.883 ms
epoch: 3 step: 41, loss is 0.45012417
epoch time: 21231.951 ms, per step time: 517.852 ms
epoch: 4 step: 41, loss is 0.30687785
epoch time: 21199.911 ms, per step time: 517.071 ms
epoch: 5 step: 41, loss is 0.22769661
epoch time: 21240.281 ms, per step time: 518.056 ms
epoch: 6 step: 41, loss is 0.25470978
...
- Training vocaug in s8 structure
# distribute training result(8p)
epoch: 1 step: 82, loss is 0.024167
epoch time: 322663.456 ms, per step time: 3934.920 ms
epoch: 2 step: 82, loss is 0.019832281
epoch time: 43107.238 ms, per step time: 525.698 ms
epoch: 3 step: 82, loss is 0.021008959
epoch time: 43109.519 ms, per step time: 525.726 ms
epoch: 4 step: 82, loss is 0.01912349
epoch time: 43177.287 ms, per step time: 526.552 ms
epoch: 5 step: 82, loss is 0.022886964
epoch time: 43095.915 ms, per step time: 525.560 ms
epoch: 6 step: 82, loss is 0.018708453
epoch time: 43107.458 ms per step time: 525.701 ms
...
- Training voctrain in s8 structure
# distribute training result(8p)
epoch: 1 step: 11, loss is 0.00554624
epoch time: 199412.913 ms, per step time: 18128.447 ms
epoch: 2 step: 11, loss is 0.007181881
epoch time: 6119.375 ms, per step time: 556.307 ms
epoch: 3 step: 11, loss is 0.004980865
epoch time: 5996.978 ms, per step time: 545.180 ms
epoch: 4 step: 11, loss is 0.0047651967
epoch time: 5987.412 ms, per step time: 544.310 ms
epoch: 5 step: 11, loss is 0.006262637
epoch time: 5956.682 ms, per step time: 541.517 ms
epoch: 6 step: 11, loss is 0.0060750707
epoch time: 5962.164 ms, per step time: 542.015 ms
...
Running on CPU
- Training voctrain in s16 structure
epoch: 1 step: 1, loss is 3.655448
epoch: 2 step: 1, loss is 1.5531876
epoch: 3 step: 1, loss is 1.5099041
...
Transfer Training
You can train your own model based on pretrained model. You can perform transfer training by following steps.
- Convert your own dataset to Pascal VOC datasets. Otherwise you have to add your own data preprocess code.
- Set argument
filter_weighttoTrue,ckpt_pre_trainedto pretrained checkpoint andnum_classesto the classes of your dataset while callingtrain.py, this will filter the final conv weight from the pretrained model. - Build your own bash scripts using new config and arguments for further convenient.
Evaluation Process
Usage
Running on Ascend
Configure checkpoint with --ckpt_path and dataset path. Then run script, mIOU will be printed in eval_path/eval_log.
./run_eval_s16.sh # test s16
./run_eval_s8.sh # test s8
./run_eval_s8_multiscale.sh # test s8 + multiscale
./run_eval_s8_multiscale_flip.sh # test s8 + multiscale + flip
Example of test script is as follows:
python ${train_code_path}/eval.py --data_root=/PATH/TO/DATA \
--data_lst=/PATH/TO/DATA_lst.txt \
--batch_size=16 \
--crop_size=513 \
--ignore_label=255 \
--num_classes=21 \
--model=deeplab_v3_s8 \
--scales=0.5 \
--scales=0.75 \
--scales=1.0 \
--scales=1.25 \
--scales=1.75 \
--flip \
--freeze_bn \
--ckpt_path=/PATH/TO/PRETRAIN_MODEL >${eval_path}/eval_log 2>&1 &
Result
Our result were obtained by running the applicable training script. To achieve the same results, follow the steps in the Quick Start Guide.
Training accuracy
| Network | OS=16 | OS=8 | MS | Flip | mIOU | mIOU in paper |
|---|---|---|---|---|---|---|
| deeplab_v3 | √ | 77.37 | 77.21 | |||
| deeplab_v3 | √ | 78.84 | 78.51 | |||
| deeplab_v3 | √ | √ | 79.70 | 79.45 | ||
| deeplab_v3 | √ | √ | √ | 79.89 | 79.77 |
Note: There OS is output stride, and MS is multiscale.
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"]
Inference Process
Usage
Before performing inference, the air file must bu exported by export script on the 910 environment.
Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space.
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_ID]
DEVICE_ID is optional, default value is 0.
result
Inference result is saved in current path, you can find result in acc.log file.
| Network | OS=16 | OS=8 | MS | Flip | mIOU | mIOU in paper |
|---|---|---|---|---|---|---|
| deeplab_v3 | √ | 78.84 | 78.51 |
Model Description
Performance
Evaluation Performance
| Parameters | Ascend 910 |
|---|---|
| Model Version | DeepLabV3 |
| Resource | Ascend 910 |
| Uploaded Date | 09/04/2020 (month/day/year) |
| MindSpore Version | 0.7.0-alpha |
| Dataset | PASCAL VOC2012 + SBD |
| Training Parameters | epoch = 300, batch_size = 32 (s16_r1) epoch = 800, batch_size = 16 (s8_r1) epoch = 300, batch_size = 16 (s8_r2) |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| Outputs | probability |
| Loss | 0.0065883575 |
| Speed | 60 fps（1pc, s16） 480 fps（8pcs, s16） 244 fps (8pcs, s8) |
| Total time | 8pcs: 706 mins |
| Parameters (M) | 58.2 |
| Checkpoint for Fine tuning | 443M (.ckpt file) |
| Model for inference | 223M (.air file) |
| Scripts | Link |
Inference Performance
| Parameters | Ascend |
|---|---|
| Model Version | DeepLabV3 V1 |
| Resource | Ascend 910 |
| Uploaded Date | 09/04/2020 (month/day/year) |
| MindSpore Version | 0.7.0-alpha |
| Dataset | VOC datasets |
| batch_size | 32 (s16); 16 (s8) |
| outputs | probability |
| Accuracy | 8pcs: s16: 77.37 s8: 78.84% s8_multiscale: 79.70% s8_Flip: 79.89% |
| Model for inference | 443M (.ckpt file) |
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.
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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