# Contents
- [FCN 介绍 ](#FCN-介绍 )
- [模型架构 ](#模型架构 )
- [数据集 ](#数据集 )
- [环境要求 ](#环境要求 )
- [快速开始 ](#快速开始 )
- [脚本介绍 ](#脚本介绍 )
- [脚本以及简单代码 ](#脚本以及简单代码 )
- [脚本参数 ](#脚本参数 )
- [训练步骤 ](#训练步骤 )
- [训练 ](#训练 )
- [评估步骤 ](#评估步骤 )
- [评估 ](#评估 )
- [模型介绍 ](#模型介绍 )
- [性能 ](#性能 )
- [评估性能 ](#评估性能 )
- [如何使用 ](#如何使用 )
- [教程 ](#教程 )
- [随机事件介绍 ](#随机事件介绍 )
- [ModelZoo 主页 ](#ModelZoo-主页 )
# [FCN 介绍](#contents)
FCN主要用用于图像分割领域， ， ， ， ，
[Paper]: Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
# [模型架构](#contents)
FCN-8s使用丢弃全连接操作的VGG16作为编码部分， ，
# [数据集](#contents)
Dataset used:
[PASCAL VOC 2012 ](<http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html> )
[SBD ](<http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz> )
# [环境要求](#contents)
- 硬件（ ）
- 需要准备具有Ascend或GPU处理能力的硬件环境.
- 框架
- [MindSpore ](https://www.mindspore.cn/install/en )
- 如需获取更多信息，请查看如下链接：
- [MindSpore Tutorials ](https://www.mindspore.cn/tutorial/training/en/master/index.html )
- [MindSpore Python API ](https://www.mindspore.cn/doc/api_python/en/master/index.html )
# [快速开始](#contents)
在通过官方网站安装MindSpore之后， ：
- running on Ascend with default parameters
```python
# run training example
python train.py --device_id device_id
# run evaluation example with default parameters
python eval.py --device_id device_id
```
# [脚本介绍](#contents)
## [脚本以及简单代码](#contents)
```python
├── model_zoo
├── README.md // descriptions about all the models
├── FCN8s
├── README.md // descriptions about FCN
├── scripts
├── run_train.sh
├── run_eval.sh
├── build_data.sh
├── src
│ ├──data
│ ├──build_seg_data.py // creating dataset
│ ├──dataset.py // loading dataset
│ ├──nets
│ ├──FCN8s.py // FCN-8s architecture
│ ├──loss
│ ├──loss.py // loss function
│ ├──utils
│ ├──lr_scheduler.py // getting learning_rateFCN-8s
├── train.py // training script
├── eval.py // evaluation script
```
## [脚本参数](#contents)
训练以及评估的参数可以在config.py中设置
- config for FCN8s
```python
# dataset
'data_file': '/data/workspace/mindspore_dataset/FCN/FCN/dataset/MINDRECORED_NAME.mindrecord', # path and name of one mindrecord file
'batch_size': 32,
'crop_size': 512,
'image_mean': [103.53, 116.28, 123.675],
'image_std': [57.375, 57.120, 58.395],
'min_scale': 0.5,
'max_scale': 2.0,
'ignore_label': 255,
'num_classes': 21,
# optimizer
'train_epochs': 500,
'base_lr': 0.015,
'loss_scale': 1024.0,
# model
'model': 'FCN8s',
'ckpt_vgg16': '/data/workspace/mindspore_dataset/FCN/FCN/model/0-150_5004.ckpt',
'ckpt_pre_trained': '/data/workspace/mindspore_dataset/FCN/FCN/model_new/FCN8s-500_82.ckpt',
# train
'save_steps': 330,
'keep_checkpoint_max': 500,
'train_dir': '/data/workspace/mindspore_dataset/FCN/FCN/model_new/',
```
如需获取更多信息，请查看`config.py`.
## [生成数据步骤](#contents)
### 训练数据
- build mindrecord training data
```python
sh build_data.sh
or
python src/data/build_seg_data.py --data_root=/home/sun/data/Mindspore/benchmark_RELEASE/dataset \
--data_lst=/home/sun/data/Mindspore/benchmark_RELEASE/dataset/trainaug.txt \
--dst_path=dataset/MINDRECORED_NAME.mindrecord \
--num_shards=1 \
--shuffle=True
data_root: 训练数据集的总目录包含两个子目录img和cls_png， ， ，
data_lst: 存放训练样本的名称列表文档，每行一个样本。
dst_path: 生成mindrecord数据的目标位置
```
## [训练步骤](#contents)
### 训练
- running on Ascend with default parameters
```python
python train.py --device_id device_id
```
训练时， ：
```python
epoch: * step: * *, loss is ** **
...
```
此模型的checkpoint会在默认路径下存储
## [评估步骤](#contents)
### 评估
- 在Ascend上使用PASCAL VOC 2012 验证集进行评估
在使用命令运行前， ，
```python
python eval.py
```
以上的python命令会在终端上运行， ：
```python
mean IoU 0.6467
```
# [模型介绍](#contents)
## [性能](#contents)
### 评估性能
#### FCN8s on PASCAL VOC 2012
| Parameters | Ascend
| -------------------------- | -----------------------------------------------------------
| Model Version | FCN-8s
| Resource | Ascend 910 ； ， ； ，
| uploaded Date | 12/30/2020 (month/day/year)
| MindSpore Version | 1.1.0-alpha
| Dataset | PASCAL VOC 2012 and SBD
| Training Parameters | epoch=500, steps=330, batch_size = 32, lr=0.015
| Optimizer | Momentum
| Loss Function | Softmax Cross Entropy
| outputs | probability
| Loss | 0.038
| Speed | 1pc: 564.652 ms/step;
| Scripts | [FCN script ](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/FCN8s )
### Inference Performance
#### FCN8s on PASCAL VOC
| Parameters | Ascend
| ------------------- | ---------------------------
| Model Version | FCN-8s
| Resource | Ascend 910
| Uploaded Date | 10/29/2020 (month/day/year)
| MindSpore Version | 1.1.0-alpha
| Dataset | PASCAL VOC 2012
| batch_size | 16
| outputs | probability
| mean IoU | 64.67
## [如何使用](#contents)
### 教程
如果你需要在不同硬件平台（ ， ） ， Link ](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/migrate_3rd_scripts.html )。以下是一个简单例子的步骤介绍：
- Running on Ascend
```
# Set context
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False)
context.set_auto_parallel_context(device_num=device_num,parallel_mode=ParallelMode.DATA_PARALLEL)
init()
# Load dataset
dataset = data_generator.SegDataset(image_mean=cfg.image_mean,
image_std=cfg.image_std,
data_file=cfg.data_file,
batch_size=cfg.batch_size,
crop_size=cfg.crop_size,
max_scale=cfg.max_scale,
min_scale=cfg.min_scale,
ignore_label=cfg.ignore_label,
num_classes=cfg.num_classes,
num_readers=2,
num_parallel_calls=4,
shard_id=args.rank,
shard_num=args.group_size)
dataset = dataset.get_dataset(repeat=1)
# Define model
net = FCN8s(n_class=cfg.num_classes)
loss_ = loss.SoftmaxCrossEntropyLoss(cfg.num_classes, cfg.ignore_label)
# optimizer
iters_per_epoch = dataset.get_dataset_size()
total_train_steps = iters_per_epoch * cfg.train_epochs
lr_scheduler = CosineAnnealingLR(cfg.base_lr,
cfg.train_epochs,
iters_per_epoch,
cfg.train_epochs,
warmup_epochs=0,
eta_min=0)
lr = Tensor(lr_scheduler.get_lr())
# loss scale
manager_loss_scale = FixedLossScaleManager(cfg.loss_scale, drop_overflow_update=False)
optimizer = nn.Momentum(params=net.trainable_params(), learning_rate=lr, momentum=0.9, weight_decay=0.0001,
loss_scale=cfg.loss_scale)
model = Model(net, loss_fn=loss_, loss_scale_manager=manager_loss_scale, optimizer=optimizer, amp_level="O3")
# callback for saving ckpts
time_cb = TimeMonitor(data_size=iters_per_epoch)
loss_cb = LossMonitor()
cbs = [time_cb, loss_cb]
if args.rank == 0:
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_steps,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix=cfg.model, directory=cfg.train_dir, config=config_ck)
cbs.append(ckpoint_cb)
model.train(cfg.train_epochs, dataset, callbacks=cbs)
# [随机事件介绍](#contents)
我们在train.py中设置了随机种子
# [ModelZoo 主页](#contents)
请查看官方网站 [homepage ](https://gitee.com/mindspore/mindspore/tree/master/model_zoo ).
