pytorch COCO2017 目标检测（一）DataLoader

时间 2019-11-18

标签 pytorch coco2017 coco 目标检测 dataloader 繁體版

原文原文链接

pytorch coco 目标检测 DataLoader实现

pytorch实现目标检测目标检测算法首先要实现数据的读入，即实现Dataset和DataLoader两个类。
借助pycocotools实现了CoCo2017用于目标检测数据的读取，并使用cv2显示。html

分析

使用cv2显示读入数据，或者要送入到网络的数据应该有三个部分python

图像，Nx3xHeight x Width
BBs，NxMx4
类型，NxMx1
所以，能够将BBs和类型组成一个。Pytorch默认的数据类型是batchsize x nChanns x H x W。

在目标检测中，通常将图像进行缩放，使其尺寸知足必定要求，具体能够参考以前的博客。也就是要实现一个Resizer()的类进行变换。此外，一般要对图像进行标准化处理，以及水平翻转等变换。所以，在实现Dataset时要实现的变换有三个: Resizer()、Normilizer()和Augmenter()。算法

Python中图像数据读入通常都是 nChanns x H x W的numpy数组。常规的作法是使用Dataset中的transform对数据进行转换，输出torch类型的数组。json

因为CoCo数据集中图像的尺寸不一致，不能直接得到Nx3xHeight x Width类型的数组，所以要重写DataLoader中的collate_fn，将一个minibatch中的图像尺寸调整一致。若是想要按照图像被缩放比例进行采样，就要重写DataLoader中的batch_sampler，
batch_sampler与DataLoader中的batch_size, shuffle, sampler, and drop_last参数是不兼容的，即在DataLoader中使用了batch_sampler，参数就不能再设置batch_size, shuffle, sampler, and drop_last参数。数组

从coco数据中读入图像、BBs以及类型

coco.getImgIds()返回了图像索引数组，能够分别结合coco.loadImgs()和coco.getAnnIds()分别得到图像、BBs和类型的具体信息。
要注意的事情有：网络

python中图像的读入的一般是numpy的uint8数组，须要转换成float类型，并除以255以使最大值为1.0；
coco数据中有80个类型，可是给的标签值最大为90，说明并不连续，须要设置新的标签，新的标签要从0到79，必定从0开始。
coco数据集中有些图片的BBs标签高宽小于1，标注的问题，要注意舍去。

下面就是一个简单的SimpleCoCoDataset类app

class SimpleCoCoDataset(Dataset):
    def __init__(self, rootdir, set_name='val2017', transform=None):
        self.rootdir, self.set_name = rootdir, set_name
        self.transform = transform
        self.coco = COCO(os.path.join(self.rootdir, 'annotations', 'instances_'
                                      + self.set_name + '.json'))
        self.image_ids = self.coco.getImgIds()
        self.load_classes()
    
    def load_classes(self):
        categories = self.coco.loadCats(self.coco.getCatIds())
        categories.sort(key=lambda x: x['id'])
        
        # coco ids is not from 1, and not continue
        # make a new index from 0 to 79, continuely
        
        # classes:             {names:      new_index}
        # coco_labels:         {new_index:  coco_index}
        # coco_labels_inverse: {coco_index: new_index}
        self.classes, self.coco_labels, self.coco_labels_inverse = {}, {}, {}
        for c in categories:
            self.coco_labels[len(self.classes)] = c['id']
            self.coco_labels_inverse[c['id']]   = len(self.classes)
            self.classes[c['name']] = len(self.classes)
        
        # labels:              {new_index:  names}
        self.labels = {}
        for k, v in self.classes.items():
            self.labels[v] = k

    def __len__(self):
        return len(self.image_ids)            
    
    def __getitem__(self, index):
        img = self.load_image(index)
        ann = self.load_anns(index)
        sample = {'img':img, 'ann': ann}
        
        if self.transform:
            sample = self.transform(sample)
        return sample
    
    def load_image(self, index):
        image_info = self.coco.loadImgs(self.image_ids[index])[0]
        imgpath       =  os.path.join(self.rootdir, 'images', self.set_name, 
                                   image_info['file_name'])
        
        img = skimage.io.imread(imgpath)
        return img.astype(np.float32) / 255.0
    
    def load_anns(self, index):
        annotation_ids = self.coco.getAnnIds(self.image_ids[index], iscrowd=False)
        # anns is num_anns x 5, (x1, x2, y1, y2, new_idx)
        anns = np.zeros((0, 5))
        
        # skip the image without annoations
        if len(annotation_ids) == 0:
            return anns
        
        coco_anns = self.coco.loadAnns(annotation_ids)
        for a in coco_anns:
            # skip the annotations with width or height < 1
            if a['bbox'][2] < 1 or a['bbox'][3] < 1:
                continue
            
            ann = np.zeros((1, 5))
            ann[0, :4] = a['bbox']
            ann[0, 4]  = self.coco_labels_inverse[a['category_id']]
            anns = np.append(anns, ann, axis=0)
        
        # (x1, y1, width, height) --> (x1, y1, x2, y2)
        anns[:, 2] += anns[:, 0]
        anns[:, 3] += anns[:, 1]
        
        return anns
    
    def image_aspect_ratio(self, index):
        image = self.coco.loadImgs(self.image_ids[index])[0]
        return float(image['width']) / float(image['height'])

Dateset中的transform类的实现

实现了两种transform类型， Resizer()和Normilizer()。数据的均值为[0.485, 0.456, 0.406]，方差为：[0.229, 0.224, 0.225]。利用数组广播机制能够很容易写出Normilizer():dom

class Normilizer(object):
    def __init__(self):
        self.mean = np.array([[[0.485, 0.456, 0.406]]], dtype=np.float32)
        self.std  = np.array([[[0.229, 0.224, 0.225]]], dtype=np.float32)
    
    def __call__(self, sample):
        image, anns = sample['img'], sample['ann']
        return {'img':(image.astype(np.float32)-self.mean)/ self.std,
                'ann':anns}

Resizer()类要返回原图片被放缩的倍数。字体

class Resizer():
    def __call__(self, sample, targetSize=608, maxSize=1024, pad_N=32):
        image, anns = sample['img'], sample['ann']
        rows, cols = image.shape[:2]
        
        smaller_size, larger_size = min(rows, cols), max(rows, cols)
        scale = targetSize / smaller_size
        if larger_size * scale > maxSize:
            scale = maxSize / larger_size
        image = skimage.transform.resize(image.astype(np.float64), 
                                         (int(round(rows*scale)), 
                                          int(round(cols*scale))), 
                                         mode='constant')
        rows, cols, cns = image.shape[:3]
        
        # 填补放缩后的图片，并使其尺寸为32的整倍数
        pad_w, pad_h = (pad_N - cols % pad_N), (pad_N - rows % pad_N)
        new_image = np.zeros((rows + pad_h, cols + pad_w, cns)).astype(np.float32)
        new_image[:rows, :cols, :] = image.astype(np.float32)
        
        anns[:, :4] *= scale
        return {'img': torch.from_numpy(new_image), 
                'ann': torch.from_numpy(anns),
                'scale':scale}

DataLoader中的collate_fn和batch_sampler实现

batch_sampler 提供了从Dataset中进行采样的方法，咱们按照原始图像尺寸比例进行排序进行采样。这个类要集成torch.utils.data.Sampler类，并实现__len__()和__iter__()两个方法。ui

drop_last参数是指当数据集中样本个数不能被batch_size整除时，不能组成完整minibatch样本的处理方式，具体能够经过处理__len__()方法控制长度实现。

class AspectRatioBasedSampler(Sampler):
    def __init__(self, dataset, batch_size, drop_last):
        self.dataset    = dataset
        self.batch_size = batch_size
        self.drop_last  = drop_last
        self.groups     = self.group_images()
    
    def group_images(self):
        order = list(range(len(self.dataset)))
        order.sort(key=lambda x: self.dataset.image_aspect_ratio(x))
        
        return [[order[x % len(order)] for x in range(i, i+self.batch_size)]
                       for i in range(0, len(order), self.batch_size)]
        
    def __iter__(self):
        random.shuffle(self.groups)
        for group in self.groups:
            yield group
    
    def __len__(self):
        if self.drop_last:
            return len(self.dataset) // self.batch_size
        else:
            return (len(self.dataset) + self.batch_size - 1) // self.batch_size

经过batch_sampler采样获得的样本数据，其图像尺寸可能不彻底一致，这时就须要用到collate_fn参数指定被采样样本图片尺寸的调整方式。一般的作法是，得到这组样本中图片尺寸的最大值 $Width_{max}$和$Height_{max} $，而后将改组样本中全部图像的尺寸调整 $ Height_{max}\times Width_{max} $ 最终返回图像数据为： $ BatchSize\times Height_{max}\times Width_{max}\times 3 $

此外，每一个样本中的BBs的数量也可能不一样，设BBs数量最大值为 $Ann_{max}$ ，也要将标签和类型尺寸调整相同，对于BBs小于 $Ann_{max}$ 的样本，补充-1。最终返回标签数据为：$BatchSize\times Ann_{max}\times 5$

def collater(data):

    imgs = [s['img'] for s in data]
    annots = [s['annot'] for s in data]
    scales = [s['scale'] for s in data]
        
    widths = [int(s.shape[0]) for s in imgs]
    heights = [int(s.shape[1]) for s in imgs]
    batch_size = len(imgs)

    max_width = np.array(widths).max()
    max_height = np.array(heights).max()

    padded_imgs = torch.zeros(batch_size, max_width, max_height, 3)

    for i in range(batch_size):
        img = imgs[i]
        padded_imgs[i, :int(img.shape[0]), :int(img.shape[1]), :] = img

    max_num_annots = max(annot.shape[0] for annot in annots)
    
    if max_num_annots > 0:

        annot_padded = torch.ones((len(annots), max_num_annots, 5)) * -1

        if max_num_annots > 0:
            for idx, annot in enumerate(annots):
                #print(annot.shape)
                if annot.shape[0] > 0:
                    annot_padded[idx, :annot.shape[0], :] = annot
    else:
        annot_padded = torch.ones((len(annots), 1, 5)) * -1


    padded_imgs = padded_imgs.permute(0, 3, 1, 2)

    return {'img': padded_imgs, 'annot': annot_padded, 'scale': scales}

数据显示模块

使用cv2实现了数据的显示。要注意从DataLoader中获得的数据是三部分的：

{'img': torch.tensor((batch_size, height, width, 3)), 'ann': torch.tensor((batch_size, num_ann, 5), 'scale': scalar }

其中‘ann'的第五列是类型索引，须要结合SimpleCoCoDataset类中的self.labels得到对应的类型。

def my_coco_show(samples, labels):
    image, anns, scales = samples['img'].numpy(), samples['ann'].numpy(), samples['scale']
    imgIdx = 1
    
    for img, ann, scale in zip(image, anns, scales):
        # 去掉补充的-1
        ann = ann[ann[:, 4] != -1]
        if ann.shape[0] == 0:
            continue

        # 经过类型索引得到类型
        classes = []
        for idx in ann[:, 4]:
             classes.append(labels[int(idx)])
        
        # 反标准化        
        img = np.transpose(img, (1, 2, 0))
        img = img * np.array([[[0.229, 0.224, 0.225]]]) + np.array([[[0.485, 0.456, 0.406]]])

        for idx in range(ann.shape[0]):
            p1 = (int(round(ann[idx, 0])), int(round(ann[idx, 1])))
            p2 = (int(round(ann[idx, 2])), int(round(ann[idx, 3])))
            cv2.rectangle(img, p1,p2, (255, 0, 0), 2)
             # 图像，文字内容， 坐标 ，字体，大小，颜色，字体厚度
            cv2.putText(img, classes[idx], (p2[0] - 40, p2[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, 8)
        
        winName = str(imgIdx)
        cv2.namedWindow(winName, cv2.WINDOW_AUTOSIZE)
        cv2.moveWindow(winName, 10, 10)
        cv2.imshow(winName, img[:,:,::-1])
        cv2.waitKey(0)
        cv2.destroyWindow(winName)       
        imgIdx += 1