DeepLabv3 is a semantic segmentation architecture that improves upon DeepLabv2 with several modifications.To handle the problem of segmenting objects at multiple scales, modules are designed which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates.
> Notes:
If you are running a fine-tuning or evaluation task, prepare the corresponding checkpoint file.
[Paper](https://arxiv.org/pdf/1706.05587.pdf) Chen L C , Papandreou G , Schroff F , et al. Rethinking Atrous Convolution for Semantic Image Segmentation[J]. 2017.
# [Model architecture](#contents)
The overall network architecture of DeepLabv3 is show below:
20 classes. The train/val data has 11,530 images containing 27,450 ROI annotated objects and 6,929 segmentations. And we need to remove color map from annotation.
# [Features](#contents)
## [Mixed Precision(Ascend)](#contents)
The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, 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](#contents)
- Hardware（Ascend/GPU）
- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
You can start training using python or shell scripts. The usage of shell scripts as follows:
sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH)
> Notes:
RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got in /etc/hccn.conf in ascend server.
### Launch
```
# training example
python:
python train.py --dataset_url DATA_PATH
### Parameters:
shell:
sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH)
```
Parameters for dataset and network:
distribute Run distribute, default is false.
data_url Train/Evaluation data url, required.
checkpoint_url Checkpoint path, default is None.
```
\ No newline at end of file
> Notes:
If you are running a fine-tuning or evaluation task, prepare the corresponding checkpoint file.
### Result
Training result will be stored in the example path. Checkpoints will be stored at `. /LOG0/chec_deeplabv3-*` by default, and training log will be redirected to `./log.txt` like followings.
```
epoch: 1 step: 732, loss is 0.11594
Epoch time: 78748.379, per step time: 107.378
epoch: 2 step: 732, loss is 0.092868
Epoch time: 160917.911, per step time: 36.631
```
## [Eval process](#contents)
### Usage
You can start training using python or shell scripts. The usage of shell scripts as follows:
sh scripts/run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH
