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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）：
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
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 installAPI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
]
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