pytorch多分类的语义分割

Posted 2022-11-30 lonelyrains

基于 segmentation_models.pytorch 实现的多分类的语义分割。

github中的示例没有包含多分类的内容，网上资料比较少，就动手调了一下。

segmentation_models.pytorch\\examples\\cars segmentation (camvid).ipynb 里写了怎么做单分类的调用，用来检测Camvid数据集中的Car类型。

实现多分类目标，要做的就是训练、测试时支持多分类。主要是把原来用到二维矩阵（图像）的地方，改为三维矩阵。

笔者只有一个GPU，只实践了单GPU的场景。

 

代码结构很清晰：数据加载/增强/训练/验证/测试

具体代码如下：

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

import numpy as np
import cv2
import matplotlib.pyplot as plt
DATA_DIR = r'D:\\cv_ai\\2d\\segmentation\\segmentation_models.pytorch\\examples\\data\\CamVid'

# load repo with data if it is not exists
if not os.path.exists(DATA_DIR):
    print('Loading data...')
    os.system('git clone https://github.com/alexgkendall/SegNet-Tutorial ./data')
    print('Done!')

x_train_dir = os.path.join(DATA_DIR, 'train')
y_train_dir = os.path.join(DATA_DIR, 'trainannot')

x_valid_dir = os.path.join(DATA_DIR, 'val')
y_valid_dir = os.path.join(DATA_DIR, 'valannot')

x_test_dir = os.path.join(DATA_DIR, 'test')
y_test_dir = os.path.join(DATA_DIR, 'testannot')

# helper function for data visualization
def visualize(**images):
    """PLot images in one row."""
    n = len(images)
    plt.figure(figsize=(16, 5))
    for i, (name, image) in enumerate(images.items()):
        plt.subplot(1, n, i + 1)
        plt.xticks([])
        plt.yticks([])
        plt.title(' '.join(name.split('_')).title())
        plt.imshow(image)
    plt.show()

from torch.utils.data import DataLoader
from torch.utils.data import Dataset as BaseDataset

class Dataset(BaseDataset):
    """CamVid Dataset. Read images, apply augmentation and preprocessing transformations.
    
    Args:
        images_dir (str): path to images folder
        masks_dir (str): path to segmentation masks folder
        class_values (list): values of classes to extract from segmentation mask
        augmentation (albumentations.Compose): data transfromation pipeline 
            (e.g. flip, scale, etc.)
        preprocessing (albumentations.Compose): data preprocessing 
            (e.g. noralization, shape manipulation, etc.)
    
    """
    
    CLASSES = ['sky', 'building', 'pole', 'road', 'pavement', 
               'tree', 'signsymbol', 'fence', 'car', 
               'pedestrian', 'bicyclist', 'unlabelled']
    
    def __init__(
            self, 
            images_dir, 
            masks_dir, 
            classes=None, 
            augmentation=None, 
            preprocessing=None,
    ):
        self.ids = os.listdir(images_dir)
        self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]
        self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]
        
        # convert str names to class values on masks
        self.class_values = [self.CLASSES.index(cls.lower()) for cls in classes]
        
        self.augmentation = augmentation
        self.preprocessing = preprocessing
    
    def __getitem__(self, i):
        
        # read data
        image = cv2.imread(self.images_fps[i])
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        mask = cv2.imread(self.masks_fps[i], 0)
        
        # extract certain classes from mask (e.g. cars)
        masks = [(mask == v) for v in self.class_values]
        mask = np.stack(masks, axis=-1).astype('float')
        
        # apply augmentations
        if self.augmentation:
            sample = self.augmentation(image=image, mask=mask)
            image, mask = sample['image'], sample['mask']
        
        # apply preprocessing
        if self.preprocessing:
            sample = self.preprocessing(image=image, mask=mask)
            image, mask = sample['image'], sample['mask']
            
        return image, mask
        
    def __len__(self):
        return len(self.ids)



# Lets look at data we have
# 这里选择定义了两种类型：车和行人
dataset = Dataset(x_train_dir, y_train_dir, classes=['car','pedestrian'])

image, mask = dataset[4] # get some sample
visualize(
    image=image, 
    cars_mask=mask[:,:,0].squeeze(),
    ped_mask=mask[:,:,1].squeeze(),
)


# 数据增广
import albumentations as albu
def get_training_augmentation():
    train_transform = [

        albu.HorizontalFlip(p=0.5),

        albu.ShiftScaleRotate(scale_limit=0.5, rotate_limit=0, shift_limit=0.1, p=1, border_mode=0),

        albu.PadIfNeeded(min_height=320, min_width=320, always_apply=True, border_mode=0),
        albu.RandomCrop(height=320, width=320, always_apply=True),

        albu.IAAAdditiveGaussianNoise(p=0.2),
        albu.IAAPerspective(p=0.5),

        albu.OneOf(
            [
                albu.CLAHE(p=1),
                albu.RandomBrightness(p=1),
                albu.RandomGamma(p=1),
            ],
            p=0.9,
        ),

        albu.OneOf(
            [
                albu.IAASharpen(p=1),
                albu.Blur(blur_limit=3, p=1),
                albu.MotionBlur(blur_limit=3, p=1),
            ],
            p=0.9,
        ),

        albu.OneOf(
            [
                albu.RandomContrast(p=1),
                albu.HueSaturationValue(p=1),
            ],
            p=0.9,
        ),
    ]
    return albu.Compose(train_transform)


def get_validation_augmentation():
    """Add paddings to make image shape divisible by 32"""
    test_transform = [
        albu.PadIfNeeded(384, 480)
    ]
    return albu.Compose(test_transform)


def to_tensor(x, **kwargs):
    return x.transpose(2, 0, 1).astype('float32')


def get_preprocessing(preprocessing_fn):
    """Construct preprocessing transform
    
    Args:
        preprocessing_fn (callbale): data normalization function 
            (can be specific for each pretrained neural network)
    Return:
        transform: albumentations.Compose
    
    """
    
    _transform = [
        albu.Lambda(image=preprocessing_fn),
        albu.Lambda(image=to_tensor, mask=to_tensor),
    ]
    return albu.Compose(_transform)

    #### Visualize resulted augmented images and masks

augmented_dataset = Dataset(
    x_train_dir, 
    y_train_dir, 
    augmentation=get_training_augmentation(), 
    classes=['car', 'pedestrian'],
)

# same image with different random transforms
for i in range(1):
    image, mask = augmented_dataset[1]
    visualize(image=image, 
    cars_mask=mask[:,:,0].squeeze(),
    ped_mask=mask[:,:,1].squeeze(),)

import torch
import numpy as np
import segmentation_models_pytorch as smp

ENCODER = 'se_resnext50_32x4d'
ENCODER_WEIGHTS = 'imagenet'
DEVICE = 'cuda'

CLASSES = ['car', 'pedestrian']
ACTIVATION = 'sigmoid'

# create segmentation model with pretrained encoder
model = smp.Unet(
    encoder_name=ENCODER, 
    encoder_weights=ENCODER_WEIGHTS, 
    classes=len(CLASSES), 
    activation=ACTIVATION,
)

preprocessing_fn = smp.encoders.get_preprocessing_fn(ENCODER, ENCODER_WEIGHTS)

train_dataset = Dataset(
    x_train_dir, 
    y_train_dir, 
    augmentation=get_training_augmentation(), 
    preprocessing=get_preprocessing(preprocessing_fn),
    classes=CLASSES,
)

valid_dataset = Dataset(
    x_valid_dir, 
    y_valid_dir, 
    augmentation=get_validation_augmentation(), 
    preprocessing=get_preprocessing(preprocessing_fn),
    classes=CLASSES,
)

train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True, num_workers=0)
valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=False, num_workers=0)

# Dice/F1 score - https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
# IoU/Jaccard score - https://en.wikipedia.org/wiki/Jaccard_index

loss = smp.utils.losses.BCEDiceLoss(eps=1.)
metrics = [
    smp.utils.metrics.IoUMetric(eps=1.),
    smp.utils.metrics.FscoreMetric(eps=1.),
]

optimizer = torch.optim.Adam([
    'params': model.decoder.parameters(), 'lr': 1e-4, 
    
    # decrease lr for encoder in order not to permute 
    # pre-trained weights with large gradients on training start
    'params': model.encoder.parameters(), 'lr': 1e-6,  
])

# create epoch runners 
# it is a simple loop of iterating over dataloader`s samples
train_epoch = smp.utils.train.TrainEpoch(
    model, 
    loss=loss, 
    metrics=metrics, 
    optimizer=optimizer,
    device=DEVICE,
    verbose=True,
)

valid_epoch = smp.utils.train.ValidEpoch(
    model, 
    loss=loss, 
    metrics=metrics, 
    device=DEVICE,
    verbose=True,
)

# train model for 40 epochs

Train = False
TrainIters = 500 # 40
if Train == True:
    max_score = 0

    for i in range(0, TrainIters):
        
        print('\\nEpoch: '.format(i))
        train_logs = train_epoch.run(train_loader)
        valid_logs = valid_epoch.run(valid_loader)
        
        # do something (save model, change lr, etc.)
        if max_score < valid_logs['iou']:
            max_score = valid_logs['iou']
            torch.save(model, './best_model_Iter' + str(TrainIters) + '_.pth')
            print('Model saved!')
            
        if i == 25:
            optimizer.param_groups[0]['lr'] = 1e-5
            print('Decrease decoder learning rate to 1e-5!')

# load best saved checkpoint
best_model = torch.load(r'D:\\cv_ai\\2d\\segmentation\\segmentation_models.pytorch\\best_model_Iter500_.pth')

# 如果模型中有dropout或者batchnorm的话，一定要先将模型设置为eval模式，再保存，否则在用libtorch调用后会出现随机干扰；
best_model.eval()

# 生成一个样本供网络前向传播 forward()
example = torch.rand(1, 3, 384, 480)
# # 使用 torch.jit.trace 生成 torch.jit.ScriptModule 来跟踪
traced_script_module = torch.jit.trace(best_model, example.cuda())
traced_script_module.save("best_model.pt")

# create test dataset
test_dataset = Dataset(
    x_test_dir, 
    y_test_dir, 
    augmentation=get_validation_augmentation(), 
    preprocessing=get_preprocessing(preprocessing_fn),
    classes=CLASSES,
)

test_dataloader = DataLoader(test_dataset)

# evaluate model on test set
test_epoch = smp.utils.train.ValidEpoch(
    model=best_model,
    loss=loss,
    metrics=metrics,
    device=DEVICE,
)

logs = test_epoch.run(test_dataloader)

# test dataset without transformations for image visualization
test_dataset_vis = Dataset(
    x_test_dir, y_test_dir, 
    classes=CLASSES,
)

for i in range(50):
    n = np.random.choice(len(test_dataset))
    
    image_vis = test_dataset_vis[n][0].astype('uint8')
    image, gt_mask = test_dataset[n]
    
    gt_mask_car = gt_mask[0,:,:].squeeze()
    gt_mask_ped = gt_mask[1,:,:].squeeze()
    
    x_tensor = torch.from_numpy(image).to(DEVICE).unsqueeze(0)
    pr_mask = best_model.predict(x_tensor)
    pr_mask_car = pr_mask.squeeze()[0,:,:].cpu().numpy().round()
    pr_mask_ped = pr_mask.squeeze()[1,:,:].cpu().numpy().round()
        
    visualize(
        image=image_vis, 
        ground_truth_mask_car=gt_mask_car, 
        ground_truth_mask_ped=gt_mask_ped,
        predicted_mask_car=pr_mask_car,
        predicted_mask_ped=pr_mask_ped,
    )

camvid数据集下载：https://pan.baidu.com/s/15L0QAE6RMaCA4ZkZD8dbDg   提取码  57ge

验证用的编码器se_resnext50_32x4d下载：https://pan.baidu.com/s/1hu3dW7z0ENLC0P1tY1UYkA  提取码  7489

结果示例：