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mindspore/model_zoo/official/cv/FCN8s
zhaojichen 9952da5a90 fix docs 4 years ago
..
scripts FCN8s 4 years ago
src FCN8s 4 years ago
README.md fix docs 4 years ago
eval.py FCN8s 4 years ago
train.py FCN8s 4 years ago

README.md

Contents

FCN 介绍

FCN主要用用于图像分割领域，是一种端到端的分割方法。FCN丢弃了全连接层，使得其能够处理任意大小的图像，且减少了模型的参数量，提高了模型的分割速度。FCN在编码部分使用了VGG的结构，在解码部分中使用反卷积/上采样操作恢复图像的分辨率。FCN-8s最后使用8倍的反卷积/上采样操作将输出分割图恢复到与输入图像相同大小。

[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

FCN-8s使用丢弃全连接操作的VGG16作为编码部分，并分别融合VGG16中第3,4,5个池化层特征，最后使用stride=8的反卷积获得分割图像。

数据集

Dataset used:

PASCAL VOC 2012

SBD

环境要求

快速开始

在通过官方网站安装MindSpore之后，你可以通过如下步骤开始训练以及评估：

  • running on Ascend with default parameters

    # 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

脚本以及简单代码

├── 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

训练以及评估的参数可以在config.py中设置

  • config for FCN8s

       # 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

训练数据

  • build mindrecord training data

    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，img目录下存放训练图像，cls_png目录下存放标签mask图像，
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

训练

  • running on Ascend with default parameters

    python train.py --device_id device_id

    训练时，训练过程中的epch和step以及此时的loss和精确度会呈现在终端上：

    epoch: * step: **, loss is ****
...

    此模型的checkpoint会在默认路径下存储

评估步骤

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，请检查用于评估的checkpoint的路径。请设置路径为到checkpoint的绝对路径，如 "/data/workspace/mindspore_dataset/FCN/FCN/model_new/FCN8s-500_82.ckpt"。

    python eval.py

    以上的python命令会在终端上运行，你可以在终端上查看此次评估的结果。测试集的精确度会以如下方式呈现：

    mean IoU  0.6467

模型介绍

性能

评估性能

FCN8s on PASCAL VOC 2012

Parameters Ascend
Model Version FCN-8s
Resource Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G
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

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

教程

如果你需要在不同硬件平台（如GPU，Ascend 910 或者 Ascend 310）使用训练好的模型，你可以参考这个 Link。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

我们在train.py中设置了随机种子

ModelZoo 主页

请查看官方网站 homepage.

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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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