手把手教你训练自己的Mask R-CNN图像实例分割模型（PyTorch官方教程）_mask rcnn标注训练自己的模型pytorch

近来在学习图像分割的相关算法，准备试试看Mask R-CNN的效果。

关于Mask R-CNN的详细理论说明，可以参见原作论文https://arxiv.org/abs/1703.06870，网上也有大量解读的文章。本篇博客主要是参考了PyTorch官方给出的训练教程，将如何在自己的数据集上训练Mask R-CNN模型的过程记录下来，希望能为感兴趣的读者提供一些帮助。

PyTorch官方教程（Object Detection finetuning tutorial）：

https://github.com/pytorch/tutorials/blob/master/_static/torchvision_finetuning_instance_segmentation.ipynb

或：

https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html

需要注意的是，TorchVision需要0.3之后的版本才可以使用。

目录

准备工作

数据集

定义模型

训练模型

1. 准备工作

2. 数据增强/转换

3. 训练

测试模型

---

 

准备工作

安装coco的api，主要用到其中的IOU计算的库来评价模型的性能。

git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install

API的安装也可以参考另一篇：

https://blog.csdn.net/u013685264/article/details/100331064

数据集

本教程使用Penn-Fudan的行人检测和分割数据集来训练Mask R-CNN实例分割模型。Penn-Fudan数据集中有170张图像，包含345个行人的实例。图像中场景主要是校园和城市街景，每张图中至少有一个行人，具体的介绍和下载地址如下：

https://www.cis.upenn.edu/~jshi/ped_html/

# 下载Penn-Fudan dataset
wget https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip
# 解压到当前目录
unzip PennFudanPed.zip

解压后的目录结构如下：

先看看Penn-Fudan数据集中的图像和mask：

from PIL import Image
 
Image.open('PennFudanPed/PNGImages/FudanPed00001.png')
 
mask = Image.open('PennFudanPed/PedMasks/FudanPed00001_mask.png')
 
mask.putpalette([
    0, 0, 0, # black background
    255, 0, 0, # index 1 is red
    255, 255, 0, # index 2 is yellow
    255, 153, 0, # index 3 is orange
])
 
mask

    

每一张图像都有对应的mask标注，不同的颜色表示不同的实例。在训练模型之前，需要写好数据集的载入接口。

import os
import torch
import numpy as np
import torch.utils.data
from PIL import Image
 
 
class PennFudanDataset(torch.utils.data.Dataset):
    def __init__(self, root, transforms=None):
        self.root = root
        self.transforms = transforms
        # load all image files, sorting them to ensure that they are aligned
        self.imgs = list(sorted(os.listdir(os.path.join(root, "PNGImages"))))
        self.masks = list(sorted(os.listdir(os.path.join(root, "PedMasks"))))
 
    def __getitem__(self, idx):
        # load images ad masks
        img_path = os.path.join(self.root, "PNGImages", self.imgs[idx])
        mask_path = os.path.join(self.root, "PedMasks", self.masks[idx])
        img = Image.open(img_path).convert("RGB")
        # note that we haven't converted the mask to RGB,
        # because each color corresponds to a different instance with 0 being background
        mask = Image.open(mask_path)
 
        mask = np.array(mask)
        # instances are encoded as different colors
        obj_ids = np.unique(mask)
        # first id is the background, so remove it
        obj_ids = obj_ids[1:]
 
        # split the color-encoded mask into a set of binary masks
        masks = mask == obj_ids[:, None, None]
 
        # get bounding box coordinates for each mask
        num_objs = len(obj_ids)
        boxes = []
        for i in range(num_objs):
            pos = np.where(masks[i])
            xmin = np.min(pos[1])
            xmax = np.max(pos[1])
            ymin = np.min(pos[0])
            ymax = np.max(pos[0])
            boxes.append([xmin, ymin, xmax, ymax])
 
        boxes = torch.as_tensor(boxes, dtype=torch.float32)
        # there is only one class
        labels = torch.ones((num_objs,), dtype=torch.int64)
        masks = torch.as_tensor(masks, dtype=torch.uint8)
 
        image_id = torch.tensor([idx])
        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
        # suppose all instances are not crowd
        iscrowd = torch.zeros((num_objs,), dtype=torch.int64)
 
        target = {}
        target["boxes"] = boxes
        target["labels"] = labels
        target["masks"] = masks
        target["image_id"] = image_id
        target["area"] = area
        target["iscrowd"] = iscrowd
 
        if self.transforms is not None:
            img, target = self.transforms(img, target)
 
        return img, target
 
    def __len__(self):
        return len(self.imgs)

检查一下上面接口返回的dataset的内部结构

dataset = PennFudanDataset('PennFudanPed/')
 
dataset[0]

可以看到，dataset返回了一个PIL.Image以及一个dictionary，包含boxes、labels和masks等域，这都是训练的时候网络需要用到的。

定义模型

Mask R-CNN是基于Faster R-CNN改造而来的。Faster R-CNN用于预测图像中潜在的目标框和分类得分，而Mask R-CNN在此基础上加了一个额外的分支，用于预测每个实例的分割mask。

有两种方式来修改torchvision modelzoo中的模型，以达到预期的目的。第一种，采用预训练的模型，在修改网络最后一层后finetune。第二种，根据需要替换掉模型中的骨干网络，如将ResNet替换成MobileNet等。

1. Finetune预训练的模型

场景：利用COCO上预训练的模型，为指定类别的任务进行finetune。

import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
 
# load a model pre-trained on COCO
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
 
# replace the classifier with a new one, that has num_classes which is user-defined
num_classes = 2  # 1 class (person) + background
 
# get number of input features for the classifier
in_features = model.roi_heads.box_predictor.cls_score.in_features
 
# replace the pre-trained head with a new one
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

2. 替换模型的骨干网络

场景：替换掉模型的骨干网络。举例来说，默认的骨干网络（ResNet-50）对于某些应用来说可能参数过多不易部署，可以考虑将其替换成更轻量的网络（如MobileNet）。

import torchvision
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator
 
# load a pre-trained model for classification and return only the features
backbone = torchvision.models.mobilenet_v2(pretrained=True).features
 
# FasterRCNN needs to know the number of output channels in a backbone. 
# For mobilenet_v2, it's 1280. So we need to add it here
backbone.out_channels = 1280
 
# let's make the RPN generate 5 x 3 anchors per spatial
# location, with 5 different sizes and 3 different aspect
# ratios. We have a Tuple[Tuple[int]] because each feature
# map could potentially have different sizes and aspect ratios 
anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
                                   aspect_ratios=((0.5, 1.0, 2.0),))
 
# let's define what are the feature maps that we will use to perform the region of 
# interest cropping, as well as the size of the crop after rescaling.
# if your backbone returns a Tensor, featmap_names is expected to
# be [0]. More generally, the backbone should return an OrderedDict[Tensor], 
# and in featmap_names you can choose which feature maps to use.
roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],
                                                output_size=7,
                                                sampling_ratio=2)
 
# put the pieces together inside a FasterRCNN model
model = FasterRCNN(backbone,
                   num_classes=2,
                   rpn_anchor_generator=anchor_generator,
                   box_roi_pool=roi_pooler)

3. 定义Mask R-CNN模型

言归正传，本文的目的是在PennFudan数据集上训练Mask R-CNN实例分割模型，即上述第一种情况。在torchvision.models.detection中有官方的网络定义和接口的文件，可以直接使用。

import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor
 
      
def get_instance_segmentation_model(num_classes):
    # load an instance segmentation model pre-trained on COCO
    model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
 
    # get the number of input features for the classifier
    in_features = model.roi_heads.box_predictor.cls_score.in_features
 
    # replace the pre-trained head with a new one
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
 
    # now get the number of input features for the mask classifier
    in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
    hidden_layer = 256
 
    # and replace the mask predictor with a new one
    model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,
                                                       hidden_layer,
                                                       num_classes)
 
    return model

至此，模型就定义好了，接下来可以在PennFudan数据集进行训练和测试了。

训练模型

1. 准备工作

在PyTorch官方的references/detection/中，有一些封装好的用于模型训练和测试的函数，其中references/detection/engine.py、references/detection/utils.py、references/detection/transforms.py是我们需要用到的。首先，将这些文件拷贝过来

# Download TorchVision repo to use some files from references/detection
git clone https://github.com/pytorch/vision.git
cd vision
git checkout v0.4.0
 
cp references/detection/utils.py ../
cp references/detection/transforms.py ../
cp references/detection/coco_eval.py ../
cp references/detection/engine.py ../
cp references/detection/coco_utils.py ../

2. 数据增强/转换

在图像输入到网络前，需要对其进行旋转操作（数据增强）。这里需要注意的是，由于Mask R-CNN模型本身可以处理归一化及尺度变化的问题，因而无需在这里进行mean/std normalization或图像缩放的操作。

import utils
import transforms as T
from engine import train_one_epoch, evaluate
 
 
def get_transform(train):
    transforms = []
    # converts the image, a PIL image, into a PyTorch Tensor
    transforms.append(T.ToTensor())
    if train:
        # during training, randomly flip the training images
        # and ground-truth for data augmentation
        transforms.append(T.RandomHorizontalFlip(0.5))
 
    return T.Compose(transforms)

3. 训练

至此，数据集、模型、数据增强的部分都已经写好。在模型初始化、优化器及学习率调整策略选定后，就可以开始训练了。这里，设置模型训练10个epochs，并且在每个epoch完成后在测试集上对模型的性能进行评价。

# use the PennFudan dataset and defined transformations
dataset = PennFudanDataset('PennFudanPed', get_transform(train=True))
dataset_test = PennFudanDataset('PennFudanPed', get_transform(train=False))
 
# split the dataset in train and test set
torch.manual_seed(1)
indices = torch.randperm(len(dataset)).tolist()
dataset = torch.utils.data.Subset(dataset, indices[:-50])
dataset_test = torch.utils.data.Subset(dataset_test, indices[-50:])
 
# define training and validation data loaders
data_loader = torch.utils.data.DataLoader(
    dataset, batch_size=2, shuffle=True, num_workers=4,
    collate_fn=utils.collate_fn)
 
data_loader_test = torch.utils.data.DataLoader(
    dataset_test, batch_size=1, shuffle=False, num_workers=4,
    collate_fn=utils.collate_fn)
 
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
 
# the dataset has two classes only - background and person
num_classes = 2
 
# get the model using the helper function
model = get_instance_segmentation_model(num_classes)
# move model to the right device
model.to(device)
 
# construct an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005,
                            momentum=0.9, weight_decay=0.0005)
 
# the learning rate scheduler decreases the learning rate by 10x every 3 epochs
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
                                               step_size=3,
                                               gamma=0.1)
 
# training
num_epochs = 10
for epoch in range(num_epochs):
    # train for one epoch, printing every 10 iterations
    train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
 
    # update the learning rate
    lr_scheduler.step()
 
    # evaluate on the test dataset
    evaluate(model, data_loader_test, device=device)

测试模型

现在，模型已经训练好了，来检查一下模型在测试图像上预测的结果。

# pick one image from the test set
img, _ = dataset_test[0]
 
# put the model in evaluation mode
model.eval()
with torch.no_grad():
    prediction = model([img.to(device)])

这里输出的prediction中，包含了在图像中预测出的boxes、labels、masks和scores等信息。

接下来，将测试图像及对应的预测结果可视化出来，看看效果如何。

Image.fromarray(img.mul(255).permute(1, 2, 0).byte().numpy())
 
Image.fromarray(prediction[0]['masks'][0, 0].mul(255).byte().cpu().numpy())

   

可以看到，分割的结果还是不错的。到此，训练自己的Mask R-CNN模型就完成了。

 

Bug解决

在测试模型性能的时候，如果出现ValueError: Does not understand character buffer dtype format string ('?')：

File "build/bdist.linux-x86_64/egg/pycocotools/mask.py", line 82, in encode
  File "pycocotools/_mask.pyx", line 137, in pycocotools._mask.encode
ValueError: Does not understand character buffer dtype format string ('?')

通过修改coco_eval.py中mask_util.encode一行，添加dtype=np.uint8，即可搞定。

In coco_eval.py:
 
rles = [
mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0]
for mask in masks
]