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| src | 4 years ago | |
| README.md | 4 years ago | |
| eval.py | 4 years ago | |
| train.py | 4 years ago | |
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.
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。
Dataset used:
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
├── model_zoo
├── README.md // descriptions about all the models
├── FCN8s
├── README.md // descriptions about FCN
├── scripts
├── run_train.sh
├── run_standalone_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
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': '',
'ckpt_pre_trained': '',
# train
'save_steps': 330,
'keep_checkpoint_max': 5,
'ckpt_dir': './ckpt',
如需获取更多信息,请查看config.py.
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
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
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
| Parameters | Ascend |
|---|---|
| Model Version | FCN-8s |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 |
| 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 |
| Parameters | Ascend |
|---|---|
| Model Version | FCN-8s |
| Resource | Ascend 910; OS Euler2.8 |
| 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.ckpt_dir, config=config_ck)
cbs.append(ckpoint_cb)
model.train(cfg.train_epochs, dataset, callbacks=cbs)
我们在train.py中设置了随机种子
请查看官方网站 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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