华为开源自研AI框架昇思MindSpore应用案例：SSD目标检测_华为目标识别模型

SSD，全称Single Shot MultiBox Detector，是Wei Liu在ECCV 2016上提出的一种目标检测算法。使用Nvidia Titan X在VOC 2007测试集上，SSD对于输入尺寸300x300的网络，达到74.3%mAP以及59FPS；对于512x512的网络，达到了76.9%mAP ，超越当时最强的Faster RCNN(73.2%mAP)。具体可参考论文。 SSD目标检测主流算法分成可以两个类型：

1. two-stage方法：RCNN系列
   通过算法产生候选框，然后再对这些候选框进行分类和回归。
2. one-stage方法：yolo和SSD
   直接通过主干网络给出类别位置信息，不需要区域生成。

模型结构
SSD采用VGG16作为基础模型，然后在VGG16的基础上新增了卷积层来获得更多的特征图以用于检测。SSD的网络结构如图所示。上面是SSD模型，下面是Yolo模型，可以明显看到SSD利用了多尺度的特征图做检测。

如果你对MindSpore感兴趣，可以关注昇思MindSpore社区

一、环境准备

1.进入ModelArts官网

云平台帮助用户快速创建和部署模型，管理全周期AI工作流，选择下面的云平台以开始使用昇思MindSpore，获取安装命令，安装MindSpore2.0.0-alpha版本，可以在昇思教程中进入ModelArts官网

选择下方CodeLab立即体验

等待环境搭建完成

2.使用CodeLab体验Notebook实例

下载NoteBook样例代码，SSD目标检测 ，.ipynb为样例代码

选择ModelArts Upload Files上传.ipynb文件

选择Kernel环境

切换至GPU环境，切换成第一个限时免费

进入昇思MindSpore官网，点击上方的安装

获取安装命令

回到Notebook中，在第一块代码前加入命令

conda update -n base -c defaults conda
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安装MindSpore 2.0 GPU版本

conda install mindspore=2.0.0a0 -c mindspore -c conda-forge
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安装mindvision

pip install mindvision
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安装下载download

pip install download
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二、环境准备

本案例基于MindSpore实现，开始实验前，请确保本地已经安装了mindspore、download、pycocotools、opencv-python。

三、数据准备与处理

本案例所使用的数据集为coco2017。为了更加方便地保存和加载数据，本案例中在数据读取前首先将coco数据集转换成MindRecord格式。使用MindSpore Record数据格式可以减少磁盘IO、网络IO开销，从而获得更好的使用体验和性能提升。 首先我们需要下载处理好的MindRecord格式的coco数据集。 运行以下代码将数据集下载并解压到指定路径。

from download import download

dataset_url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/ssd_datasets.zip"
path = "./"
path = download(dataset_url, path, kind="zip", replace=True)

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首先我们为数据处理定义一些输入：

coco_root = "./datasets/"
anno_json = "./datasets/annotations/instances_val2017.json"

train_cls = ['background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
             'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
             'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
             'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
             'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
             'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
             'kite', 'baseball bat', 'baseball glove', 'skateboard',
             'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
             'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
             'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
             'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
             'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
             'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
             'refrigerator', 'book', 'clock', 'vase', 'scissors',
             'teddy bear', 'hair drier', 'toothbrush']

train_cls_dict = {}
for i, cls in enumerate(train_cls):
    train_cls_dict[cls] = i

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数据采样

为了使模型对于各种输入对象大小和形状更加鲁棒，SSD算法每个训练图像通过以下选项之一随机采样:

• 使用整个原始输入图像
• 采样一个区域，使采样区域和原始图片最小的交并比重叠为0.1,0.3,0.5,0.7或0.9
• 随机采样一个区域

每个采样区域的大小为原始图像大小的[0.3,1]，长宽比在1/2和2之间。如果真实标签框中心在采样区域内，则保留两者重叠部分作为新图片的真实标注框。在上述采样步骤之后，将每个采样区域大小调整为固定大小，并以0.5的概率水平翻转。

import cv2
import numpy as np

def _rand(a=0., b=1.):
    return np.random.rand() * (b - a) + a

def intersect(box_a, box_b):
    """Compute the intersect of two sets of boxes."""
    max_yx = np.minimum(box_a[:, 2:4], box_b[2:4])
    min_yx = np.maximum(box_a[:, :2], box_b[:2])
    inter = np.clip((max_yx - min_yx), a_min=0, a_max=np.inf)
    return inter[:, 0] * inter[:, 1]

def jaccard_numpy(box_a, box_b):
    """Compute the jaccard overlap of two sets of boxes."""
    inter = intersect(box_a, box_b)
    area_a = ((box_a[:, 2] - box_a[:, 0]) *
              (box_a[:, 3] - box_a[:, 1]))
    area_b = ((box_b[2] - box_b[0]) *
              (box_b[3] - box_b[1]))
    union = area_a + area_b - inter
    return inter / union

def random_sample_crop(image, boxes):
    """Crop images and boxes randomly."""
    height, width, _ = image.shape
    min_iou = np.random.choice([None, 0.1, 0.3, 0.5, 0.7, 0.9])

    if min_iou is None:
        return image, boxes

    for _ in range(50):
        image_t = image
        w = _rand(0.3, 1.0) * width
        h = _rand(0.3, 1.0) * height
        # aspect ratio constraint b/t .5 & 2
        if h / w < 0.5 or h / w > 2:
            continue

        left = _rand() * (width - w)
        top = _rand() * (height - h)
        rect = np.array([int(top), int(left), int(top + h), int(left + w)])
        overlap = jaccard_numpy(boxes, rect)

        # dropout some boxes
        drop_mask = overlap > 0
        if not drop_mask.any():
            continue

        if overlap[drop_mask].min() < min_iou and overlap[drop_mask].max() > (min_iou + 0.2):
            continue

        image_t = image_t[rect[0]:rect[2], rect[1]:rect[3], :]
        centers = (boxes[:, :2] + boxes[:, 2:4]) / 2.0
        m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])
        m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])

        # mask in that both m1 and m2 are true
        mask = m1 * m2 * drop_mask

        # have any valid boxes? try again if not
        if not mask.any():
            continue

        # take only matching gt boxes
        boxes_t = boxes[mask, :].copy()
        boxes_t[:, :2] = np.maximum(boxes_t[:, :2], rect[:2])
        boxes_t[:, :2] -= rect[:2]
        boxes_t[:, 2:4] = np.minimum(boxes_t[:, 2:4], rect[2:4])
        boxes_t[:, 2:4] -= rect[:2]

        return image_t, boxes_t
    return image, boxes

def ssd_bboxes_encode(boxes):
    """Labels anchors with ground truth inputs."""

    def jaccard_with_anchors(bbox):
        """Compute jaccard score a box and the anchors."""
        # Intersection bbox and volume.
        ymin = np.maximum(y1, bbox[0])
        xmin = np.maximum(x1, bbox[1])
        ymax = np.minimum(y2, bbox[2])
        xmax = np.minimum(x2, bbox[3])
        w = np.maximum(xmax - xmin, 0.)
        h = np.maximum(ymax - ymin, 0.)

        # Volumes.
        inter_vol = h * w
        union_vol = vol_anchors + (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) - inter_vol
        jaccard = inter_vol / union_vol
        return np.squeeze(jaccard)

    pre_scores = np.zeros((8732), dtype=np.float32)
    t_boxes = np.zeros((8732, 4), dtype=np.float32)
    t_label = np.zeros((8732), dtype=np.int64)
    for bbox in boxes:
        label = int(bbox[4])
        scores = jaccard_with_anchors(bbox)
        idx = np.argmax(scores)
        scores[idx] = 2.0
        mask = (scores > matching_threshold)
        mask = mask & (scores > pre_scores)
        pre_scores = np.maximum(pre_scores, scores * mask)
        t_label = mask * label + (1 - mask) * t_label
        for i in range(4):
            t_boxes[:, i] = mask * bbox[i] + (1 - mask) * t_boxes[:, i]

    index = np.nonzero(t_label)

    # Transform to tlbr.
    bboxes = np.zeros((8732, 4), dtype=np.float32)
    bboxes[:, [0, 1]] = (t_boxes[:, [0, 1]] + t_boxes[:, [2, 3]]) / 2
    bboxes[:, [2, 3]] = t_boxes[:, [2, 3]] - t_boxes[:, [0, 1]]

    # Encode features.
    bboxes_t = bboxes[index]
    default_boxes_t = default_boxes[index]
    bboxes_t[:, :2] = (bboxes_t[:, :2] - default_boxes_t[:, :2]) / (default_boxes_t[:, 2:] * 0.1)
    tmp = np.maximum(bboxes_t[:, 2:4] / default_boxes_t[:, 2:4], 0.000001)
    bboxes_t[:, 2:4] = np.log(tmp) / 0.2
    bboxes[index] = bboxes_t

    num_match = np.array([len(np.nonzero(t_label)[0])], dtype=np.int32)
    return bboxes, t_label.astype(np.int32), num_match

def preprocess_fn(img_id, image, box, is_training):
    """Preprocess function for dataset."""
    cv2.setNumThreads(2)

    def _infer_data(image, input_shape):
        img_h, img_w, _ = image.shape
        input_h, input_w = input_shape

        image = cv2.resize(image, (input_w, input_h))

        # When the channels of image is 1
        if len(image.shape) == 2:
            image = np.expand_dims(image, axis=-1)
            image = np.concatenate([image, image, image], axis=-1)

        return img_id, image, np.array((img_h, img_w), np.float32)

    def _data_aug(image, box, is_training, image_size=(300, 300)):
        ih, iw, _ = image.shape
        h, w = image_size
        if not is_training:
            return _infer_data(image, image_size)
        # Random crop
        box = box.astype(np.float32)
        image, box = random_sample_crop(image, box)
        ih, iw, _ = image.shape
        # Resize image
        image = cv2.resize(image, (w, h))
        # Flip image or not
        flip = _rand() < .5
        if flip:
            image = cv2.flip(image, 1, dst=None)
        # When the channels of image is 1
        if len(image.shape) == 2:
            image = np.expand_dims(image, axis=-1)
            image = np.concatenate([image, image, image], axis=-1)
        box[:, [0, 2]] = box[:, [0, 2]] / ih
        box[:, [1, 3]] = box[:, [1, 3]] / iw
        if flip:
            box[:, [1, 3]] = 1 - box[:, [3, 1]]
        box, label, num_match = ssd_bboxes_encode(box)
        return image, box, label, num_match

    return _data_aug(image, box, is_training, image_size=[300, 300])

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数据集创建

from mindspore import Tensor
from mindspore.dataset import MindDataset
from mindspore.dataset.vision import Decode, HWC2CHW, Normalize, RandomColorAdjust


def create_ssd_dataset(mindrecord_file, batch_size=32, device_num=1, rank=0,
                       is_training=True, num_parallel_workers=1, use_multiprocessing=True):
    """Create SSD dataset with MindDataset."""
    dataset = MindDataset(mindrecord_file, columns_list=["img_id", "image", "annotation"], num_shards=device_num,
                          shard_id=rank, num_parallel_workers=num_parallel_workers, shuffle=is_training)

    decode = Decode()
    dataset = dataset.map(operations=decode, input_columns=["image"])

    change_swap_op = HWC2CHW()
    # Computed from random subset of ImageNet training images
    normalize_op = Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
                             std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
    color_adjust_op = RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
    compose_map_func = (lambda img_id, image, annotation: preprocess_fn(img_id, image, annotation, is_training))

    if is_training:
        output_columns = ["image", "box", "label", "num_match"]
        trans = [color_adjust_op, normalize_op, change_swap_op]
    else:
        output_columns = ["img_id", "image", "image_shape"]
        trans = [normalize_op, change_swap_op]

    dataset = dataset.map(operations=compose_map_func, input_columns=["img_id", "image", "annotation"],
                          output_columns=output_columns, python_multiprocessing=use_multiprocessing,
                          num_parallel_workers=num_parallel_workers)

    dataset = dataset.map(operations=trans, input_columns=["image"], python_multiprocessing=use_multiprocessing,
                          num_parallel_workers=num_parallel_workers)

    dataset = dataset.batch(batch_size, drop_remainder=True)
    return dataset

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四、模型构建

from mindspore import nn

def _make_layer(channels):
    in_channels = channels[0]
    layers = []
    for out_channels in channels[1:]:
        layers.append(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3))
        layers.append(nn.ReLU())
        in_channels = out_channels
    return nn.SequentialCell(layers)

class Vgg16(nn.Cell):
    """VGG16 module."""

    def __init__(self):
        super(Vgg16, self).__init__()
        self.b1 = _make_layer([3, 64, 64])
        self.b2 = _make_layer([64, 128, 128])
        self.b3 = _make_layer([128, 256, 256, 256])
        self.b4 = _make_layer([256, 512, 512, 512])
        self.b5 = _make_layer([512, 512, 512, 512])

        self.m1 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
        self.m2 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
        self.m3 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
        self.m4 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
        self.m5 = nn.MaxPool2d(kernel_size=3, stride=1, pad_mode='SAME')

    def construct(self, x):
        # block1
        x = self.b1(x)
        x = self.m1(x)

        # block2
        x = self.b2(x)
        x = self.m2(x)

        # block3
        x = self.b3(x)
        x = self.m3(x)

        # block4
        x = self.b4(x)
        block4 = x
        x = self.m4(x)

        # block5
        x = self.b5(x)
        x = self.m5(x)

        return block4, x

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import mindspore as ms
import mindspore.nn as nn
import mindspore.ops as ops

def _last_conv2d(in_channel, out_channel, kernel_size=3, stride=1, pad_mod='same', pad=0):
    in_channels = in_channel
    out_channels = in_channel
    depthwise_conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='same',
                               padding=pad, group=in_channels)
    conv = nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=1, padding=0, pad_mode='same', has_bias=True)
    bn = nn.BatchNorm2d(in_channel, eps=1e-3, momentum=0.97,
                        gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)

    return nn.SequentialCell([depthwise_conv, bn, nn.ReLU6(), conv])

class FlattenConcat(nn.Cell):
    """FlattenConcat module."""

    def __init__(self):
        super(FlattenConcat, self).__init__()
        self.num_ssd_boxes = 8732

    def construct(self, inputs):
        output = ()
        batch_size = ops.shape(inputs[0])[0]
        for x in inputs:
            x = ops.transpose(x, (0, 2, 3, 1))
            output += (ops.reshape(x, (batch_size, -1)),)
        res = ops.concat(output, axis=1)
        return ops.reshape(res, (batch_size, self.num_ssd_boxes, -1))

class MultiBox(nn.Cell):
    """
    Multibox conv layers. Each multibox layer contains class conf scores and localization predictions.
    """

    def __init__(self):
        super(MultiBox, self).__init__()
        num_classes = 81
        out_channels = [512, 1024, 512, 256, 256, 256]
        num_default = [4, 6, 6, 6, 4, 4]

        loc_layers = []
        cls_layers = []
        for k, out_channel in enumerate(out_channels):
            loc_layers += [_last_conv2d(out_channel, 4 * num_default[k],
                                        kernel_size=3, stride=1, pad_mod='same', pad=0)]
            cls_layers += [_last_conv2d(out_channel, num_classes * num_default[k],
                                        kernel_size=3, stride=1, pad_mod='same', pad=0)]

        self.multi_loc_layers = nn.CellList(loc_layers)
        self.multi_cls_layers = nn.CellList(cls_layers)
        self.flatten_concat = FlattenConcat()

    def construct(self, inputs):
        loc_outputs = ()
        cls_outputs = ()
        for i in range(len(self.multi_loc_layers)):
            loc_outputs += (self.multi_loc_layers[i](inputs[i]),)
            cls_outputs += (self.multi_cls_layers[i](inputs[i]),)
        return self.flatten_concat(loc_outputs), self.flatten_concat(cls_outputs)

class SSD300Vgg16(nn.Cell):
    """SSD300Vgg16 module."""

    def __init__(self):
        super(SSD300Vgg16, self).__init__()

        # VGG16 backbone: block1~5
        self.backbone = Vgg16()

        # SSD blocks: block6~7
        self.b6_1 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=6, dilation=6, pad_mode='pad')
        self.b6_2 = nn.Dropout(p=0.5)

        self.b7_1 = nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=1)
        self.b7_2 = nn.Dropout(p=0.5)

        # Extra Feature Layers: block8~11
        self.b8_1 = nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=1, padding=1, pad_mode='pad')
        self.b8_2 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, pad_mode='valid')

        self.b9_1 = nn.Conv2d(in_channels=512, out_channels=128, kernel_size=1, padding=1, pad_mode='pad')
        self.b9_2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, pad_mode='valid')

        self.b10_1 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1)
        self.b10_2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, pad_mode='valid')

        self.b11_1 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1)
        self.b11_2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, pad_mode='valid')

        # boxes
        self.multi_box = MultiBox()

    def construct(self, x):
        # VGG16 backbone: block1~5
        block4, x = self.backbone(x)

        # SSD blocks: block6~7
        x = self.b6_1(x)  # 1024
        x = self.b6_2(x)

        x = self.b7_1(x)  # 1024
        x = self.b7_2(x)
        block7 = x

        # Extra Feature Layers: block8~11
        x = self.b8_1(x)  # 256
        x = self.b8_2(x)  # 512
        block8 = x

        x = self.b9_1(x)  # 128
        x = self.b9_2(x)  # 256
        block9 = x

        x = self.b10_1(x)  # 128
        x = self.b10_2(x)  # 256
        block10 = x

        x = self.b11_1(x)  # 128
        x = self.b11_2(x)  # 256
        block11 = x

        # boxes
        multi_feature = (block4, block7, block8, block9, block10, block11)
        pred_loc, pred_label = self.multi_box(multi_feature)
        if not self.training:
            pred_label = ops.sigmoid(pred_label)
        pred_loc = pred_loc.astype(ms.float32)
        pred_label = pred_label.astype(ms.float32)
        return pred_loc, pred_label

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五、损失函数

def class_loss(logits, label):
    """Calculate category losses."""
    label = ops.one_hot(label, ops.shape(logits)[-1], Tensor(1.0, ms.float32), Tensor(0.0, ms.float32))
    weight = ops.ones_like(logits)
    pos_weight = ops.ones_like(logits)
    sigmiod_cross_entropy = ops.binary_cross_entropy_with_logits(logits, label, weight.astype(ms.float32), pos_weight.astype(ms.float32))
    sigmoid = ops.sigmoid(logits)
    label = label.astype(ms.float32)
    p_t = label * sigmoid + (1 - label) * (1 - sigmoid)
    modulating_factor = ops.pow(1 - p_t, 2.0)
    alpha_weight_factor = label * 0.75 + (1 - label) * (1 - 0.75)
    focal_loss = modulating_factor * alpha_weight_factor * sigmiod_cross_entropy
    return focal_loss

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六、Metrics

在SSD中，训练过程是不需要用到非极大值抑制(NMS)，但当进行检测时，例如输入一张图片要求输出框的时候，需要用到NMS过滤掉那些重叠度较大的预测框。 非极大值抑制的流程如下：

1. 根据置信度得分进行排序
2. 选择置信度最高的比边界框添加到最终输出列表中，将其从边界框列表中删除
3. 计算所有边界框的面积
4. 计算置信度最高的边界框与其它候选框的IoU
5. 删除IoU大于阈值的边界框
6. 重复上述过程，直至边界框列表为空

import json
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval


def apply_eval(eval_param_dict):
    net = eval_param_dict["net"]
    net.set_train(False)
    ds = eval_param_dict["dataset"]
    anno_json = eval_param_dict["anno_json"]
    coco_metrics = COCOMetrics(anno_json=anno_json,
                               classes=train_cls,
                               num_classes=81,
                               max_boxes=100,
                               nms_threshold=0.6,
                               min_score=0.1)
    for data in ds.create_dict_iterator(output_numpy=True, num_epochs=1):
        img_id = data['img_id']
        img_np = data['image']
        image_shape = data['image_shape']

        output = net(Tensor(img_np))

        for batch_idx in range(img_np.shape[0]):
            pred_batch = {
                "boxes": output[0].asnumpy()[batch_idx],
                "box_scores": output[1].asnumpy()[batch_idx],
                "img_id": int(np.squeeze(img_id[batch_idx])),
                "image_shape": image_shape[batch_idx]
            }
            coco_metrics.update(pred_batch)
    eval_metrics = coco_metrics.get_metrics()
    return eval_metrics


def apply_nms(all_boxes, all_scores, thres, max_boxes):
    """Apply NMS to bboxes."""
    y1 = all_boxes[:, 0]
    x1 = all_boxes[:, 1]
    y2 = all_boxes[:, 2]
    x2 = all_boxes[:, 3]
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)

    order = all_scores.argsort()[::-1]
    keep = []

    while order.size > 0:
        i = order[0]
        keep.append(i)

        if len(keep) >= max_boxes:
            break

        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])

        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h

        ovr = inter / (areas[i] + areas[order[1:]] - inter)

        inds = np.where(ovr <= thres)[0]

        order = order[inds + 1]
    return keep


class COCOMetrics:
    """Calculate mAP of predicted bboxes."""

    def __init__(self, anno_json, classes, num_classes, min_score, nms_threshold, max_boxes):
        self.num_classes = num_classes
        self.classes = classes
        self.min_score = min_score
        self.nms_threshold = nms_threshold
        self.max_boxes = max_boxes

        self.val_cls_dict = {i: cls for i, cls in enumerate(classes)}
        self.coco_gt = COCO(anno_json)
        cat_ids = self.coco_gt.loadCats(self.coco_gt.getCatIds())
        self.class_dict = {cat['name']: cat['id'] for cat in cat_ids}

        self.predictions = []
        self.img_ids = []

    def update(self, batch):
        pred_boxes = batch['boxes']
        box_scores = batch['box_scores']
        img_id = batch['img_id']
        h, w = batch['image_shape']

        final_boxes = []
        final_label = []
        final_score = []
        self.img_ids.append(img_id)

        for c in range(1, self.num_classes):
            class_box_scores = box_scores[:, c]
            score_mask = class_box_scores > self.min_score
            class_box_scores = class_box_scores[score_mask]
            class_boxes = pred_boxes[score_mask] * [h, w, h, w]

            if score_mask.any():
                nms_index = apply_nms(class_boxes, class_box_scores, self.nms_threshold, self.max_boxes)
                class_boxes = class_boxes[nms_index]
                class_box_scores = class_box_scores[nms_index]

                final_boxes += class_boxes.tolist()
                final_score += class_box_scores.tolist()
                final_label += [self.class_dict[self.val_cls_dict[c]]] * len(class_box_scores)

        for loc, label, score in zip(final_boxes, final_label, final_score):
            res = {}
            res['image_id'] = img_id
            res['bbox'] = [loc[1], loc[0], loc[3] - loc[1], loc[2] - loc[0]]
            res['score'] = score
            res['category_id'] = label
            self.predictions.append(res)

    def get_metrics(self):
        with open('predictions.json', 'w') as f:
            json.dump(self.predictions, f)

        coco_dt = self.coco_gt.loadRes('predictions.json')
        E = COCOeval(self.coco_gt, coco_dt, iouType='bbox')
        E.params.imgIds = self.img_ids
        E.evaluate()
        E.accumulate()
        E.summarize()
        return E.stats[0]


class SsdInferWithDecoder(nn.Cell):
    """
SSD Infer wrapper to decode the bbox locations."""

    def __init__(self, network, default_boxes, ckpt_path):
        super(SsdInferWithDecoder, self).__init__()
        param_dict = ms.load_checkpoint(ckpt_path)
        ms.load_param_into_net(network, param_dict)
        self.network = network
        self.default_boxes = default_boxes
        self.prior_scaling_xy = 0.1
        self.prior_scaling_wh = 0.2

    def construct(self, x):
        pred_loc, pred_label = self.network(x)

        default_bbox_xy = self.default_boxes[..., :2]
        default_bbox_wh = self.default_boxes[..., 2:]
        pred_xy = pred_loc[..., :2] * self.prior_scaling_xy * default_bbox_wh + default_bbox_xy
        pred_wh = ops.exp(pred_loc[..., 2:] * self.prior_scaling_wh) * default_bbox_wh

        pred_xy_0 = pred_xy - pred_wh / 2.0
        pred_xy_1 = pred_xy + pred_wh / 2.0
        pred_xy = ops.concat((pred_xy_0, pred_xy_1), -1)
        pred_xy = ops.maximum(pred_xy, 0)
        pred_xy = ops.minimum(pred_xy, 1)
        return pred_xy, pred_label

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七、训练过程

import math
import itertools as it

from mindspore.common import set_seed

class GeneratDefaultBoxes():
    """
    Generate Default boxes for SSD, follows the order of (W, H, archor_sizes).
    `self.default_boxes` has a shape of [archor_sizes, H, W, 4], the last dimension is [y, x, h, w].
    `self.default_boxes_tlbr` has a shape as `self.default_boxes`, the last dimension is [y1, x1, y2, x2].
    """

    def __init__(self):
        fk = 300 / np.array([8, 16, 32, 64, 100, 300])
        scale_rate = (0.95 - 0.1) / (len([4, 6, 6, 6, 4, 4]) - 1)
        scales = [0.1 + scale_rate * i for i in range(len([4, 6, 6, 6, 4, 4]))] + [1.0]
        self.default_boxes = []
        for idex, feature_size in enumerate([38, 19, 10, 5, 3, 1]):
            sk1 = scales[idex]
            sk2 = scales[idex + 1]
            sk3 = math.sqrt(sk1 * sk2)
            if idex == 0 and not [[2], [2, 3], [2, 3], [2, 3], [2], [2]][idex]:
                w, h = sk1 * math.sqrt(2), sk1 / math.sqrt(2)
                all_sizes = [(0.1, 0.1), (w, h), (h, w)]
            else:
                all_sizes = [(sk1, sk1)]
                for aspect_ratio in [[2], [2, 3], [2, 3], [2, 3], [2], [2]][idex]:
                    w, h = sk1 * math.sqrt(aspect_ratio), sk1 / math.sqrt(aspect_ratio)
                    all_sizes.append((w, h))
                    all_sizes.append((h, w))
                all_sizes.append((sk3, sk3))

            assert len(all_sizes) == [4, 6, 6, 6, 4, 4][idex]

            for i, j in it.product(range(feature_size), repeat=2):
                for w, h in all_sizes:
                    cx, cy = (j + 0.5) / fk[idex], (i + 0.5) / fk[idex]
                    self.default_boxes.append([cy, cx, h, w])

        def to_tlbr(cy, cx, h, w):
            return cy - h / 2, cx - w / 2, cy + h / 2, cx + w / 2

        # For IoU calculation
        self.default_boxes_tlbr = np.array(tuple(to_tlbr(*i) for i in self.default_boxes), dtype='float32')
        self.default_boxes = np.array(self.default_boxes, dtype='float32')

default_boxes_tlbr = GeneratDefaultBoxes().default_boxes_tlbr
default_boxes = GeneratDefaultBoxes().default_boxes

y1, x1, y2, x2 = np.split(default_boxes_tlbr[:, :4], 4, axis=-1)
vol_anchors = (x2 - x1) * (y2 - y1)
matching_threshold = 0.5

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from mindspore.common.initializer import initializer, TruncatedNormal


def init_net_param(network, initialize_mode='TruncatedNormal'):
    """Init the parameters in net."""
    params = network.trainable_params()
    for p in params:
        if 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
            if initialize_mode == 'TruncatedNormal':
                p.set_data(initializer(TruncatedNormal(0.02), p.data.shape, p.data.dtype))
            else:
                p.set_data(initialize_mode, p.data.shape, p.data.dtype)


def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
    """ generate learning rate array"""
    lr_each_step = []
    total_steps = steps_per_epoch * total_epochs
    warmup_steps = steps_per_epoch * warmup_epochs
    for i in range(total_steps):
        if i < warmup_steps:
            lr = lr_init + (lr_max - lr_init) * i / warmup_steps
        else:
            lr = lr_end + (lr_max - lr_end) * (1. + math.cos(math.pi * (i - warmup_steps) / (total_steps - warmup_steps))) / 2.
        if lr < 0.0:
            lr = 0.0
        lr_each_step.append(lr)

    current_step = global_step
    lr_each_step = np.array(lr_each_step).astype(np.float32)
    learning_rate = lr_each_step[current_step:]

    return learning_rate

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import time

from mindspore.amp import DynamicLossScaler

set_seed(1)

# load data
mindrecord_dir = "./datasets/MindRecord_COCO"
mindrecord_file = "./datasets/MindRecord_COCO/ssd.mindrecord0"

dataset = create_ssd_dataset(mindrecord_file, batch_size=5, rank=0, use_multiprocessing=True)
dataset_size = dataset.get_dataset_size()

image, get_loc, gt_label, num_matched_boxes = next(dataset.create_tuple_iterator())

# Network definition and initialization
network = SSD300Vgg16()
init_net_param(network)

# Define the learning rate
lr = Tensor(get_lr(global_step=0 * dataset_size,
                   lr_init=0.001, lr_end=0.001 * 0.05, lr_max=0.05,
                   warmup_epochs=2, total_epochs=60, steps_per_epoch=dataset_size))

# Define the optimizer
opt = nn.Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), lr,
                  0.9, 0.00015, float(1024))

# Define the forward procedure
def forward_fn(x, gt_loc, gt_label, num_matched_boxes):
    pred_loc, pred_label = network(x)
    mask = ops.less(0, gt_label).astype(ms.float32)
    num_matched_boxes = ops.sum(num_matched_boxes.astype(ms.float32))

    # Positioning loss
    mask_loc = ops.tile(ops.expand_dims(mask, -1), (1, 1, 4))
    smooth_l1 = nn.SmoothL1Loss()(pred_loc, gt_loc) * mask_loc
    loss_loc = ops.sum(ops.sum(smooth_l1, -1), -1)

    # Category loss
    loss_cls = class_loss(pred_label, gt_label)
    loss_cls = ops.sum(loss_cls, (1, 2))

    return ops.sum((loss_cls + loss_loc) / num_matched_boxes)

grad_fn = ms.value_and_grad(forward_fn, None, opt.parameters, has_aux=False)
loss_scaler = DynamicLossScaler(1024, 2, 1000)

# Gradient updates
def train_step(x, gt_loc, gt_label, num_matched_boxes):
    loss, grads = grad_fn(x, gt_loc, gt_label, num_matched_boxes)
    opt(grads)
    return loss

print("=================== Starting Training =====================")
for epoch in range(60):
    network.set_train(True)
    begin_time = time.time()
    for step, (image, get_loc, gt_label, num_matched_boxes) in enumerate(dataset.create_tuple_iterator()):
        loss = train_step(image, get_loc, gt_label, num_matched_boxes)
    end_time = time.time()
    times = end_time - begin_time
    print(f"Epoch:[{int(epoch + 1)}/{int(60)}], "
          f"loss:{loss} , "
          f"time:{times}s ")
ms.save_checkpoint(network, "ssd-60_9.ckpt")
print("=================== Training Success =====================")

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=================== Starting Training =====================
Epoch:[1/60], loss:1365.3849 , time:42.76231384277344s 
Epoch:[2/60], loss:1350.9009 , time:43.63900399208069s 
Epoch:[3/60], loss:1325.2102 , time:48.01434779167175s 
Epoch:[4/60], loss:1297.8125 , time:40.65014576911926s 
Epoch:[5/60], loss:1269.7281 , time:40.627623081207275s 
Epoch:[6/60], loss:1240.8068 , time:42.14572191238403s 
Epoch:[7/60], loss:1210.52 , time:41.091148853302s 
Epoch:[8/60], loss:1178.0127 , time:41.88719820976257s 
Epoch:[9/60], loss:1142.2338 , time:41.147764444351196s 
Epoch:[10/60], loss:1101.929 , time:42.21702218055725s 
Epoch:[11/60], loss:1055.7747 , time:40.66824555397034s 
Epoch:[12/60], loss:1002.66125 , time:40.70291781425476s 
Epoch:[13/60], loss:942.0149 , time:42.10250663757324s 
Epoch:[14/60], loss:874.245 , time:41.27074885368347s 
Epoch:[15/60], loss:801.06055 , time:40.62501621246338s 
Epoch:[16/60], loss:725.4527 , time:41.78050708770752s 
Epoch:[17/60], loss:651.15564 , time:40.619580030441284s 
Epoch:[18/60], loss:581.7435 , time:41.07759237289429s 
Epoch:[19/60], loss:519.85223 , time:41.74708104133606s 
Epoch:[20/60], loss:466.71866 , time:40.79696846008301s 
Epoch:[21/60], loss:422.35846 , time:40.40337634086609s 
Epoch:[22/60], loss:385.95758 , time:41.0706627368927s 
Epoch:[23/60], loss:356.3252 , time:41.02973508834839s 
Epoch:[24/60], loss:332.2302 , time:41.101938009262085s 
Epoch:[25/60], loss:312.56158 , time:40.12760329246521s 
Epoch:[26/60], loss:296.3943 , time:40.62085247039795s 
Epoch:[27/60], loss:282.99237 , time:42.20474720001221s 
Epoch:[28/60], loss:271.7844 , time:40.27843761444092s 
Epoch:[29/60], loss:262.32687 , time:40.6625394821167s 
Epoch:[30/60], loss:254.28302 , time:41.42288422584534s 
Epoch:[31/60], loss:247.38882 , time:40.49200940132141s 
Epoch:[32/60], loss:241.44067 , time:41.48827362060547s 
Epoch:[33/60], loss:236.28123 , time:41.1355299949646s 
Epoch:[34/60], loss:231.78201 , time:40.45781660079956s 
Epoch:[35/60], loss:227.84433 , time:40.92684364318848s 
Epoch:[36/60], loss:224.38614 , time:40.89856195449829s 
Epoch:[37/60], loss:221.34372 , time:41.585039138793945s 
Epoch:[38/60], loss:218.66156 , time:40.8972954750061s 
Epoch:[39/60], loss:216.29553 , time:42.22093486785889s 
Epoch:[40/60], loss:214.20854 , time:40.75188755989075s 
Epoch:[41/60], loss:212.36868 , time:41.51768183708191s 
Epoch:[42/60], loss:210.74985 , time:40.3460476398468s 
Epoch:[43/60], loss:209.32901 , time:40.65240502357483s 
Epoch:[44/60], loss:208.08626 , time:41.250218629837036s 
Epoch:[45/60], loss:207.00375 , time:40.334686040878296s 
Epoch:[46/60], loss:206.06656 , time:40.822086811065674s 
Epoch:[47/60], loss:205.2609 , time:40.492422103881836s 
Epoch:[48/60], loss:204.57387 , time:41.39555335044861s 
Epoch:[49/60], loss:203.9947 , time:40.29546666145325s 
Epoch:[50/60], loss:203.51189 , time:39.61115860939026s 
Epoch:[51/60], loss:203.11642 , time:41.232492446899414s 
Epoch:[52/60], loss:202.79791 , time:40.896180152893066s 
Epoch:[53/60], loss:202.54779 , time:40.62282419204712s 
Epoch:[54/60], loss:202.35779 , time:40.751235485076904s 
Epoch:[55/60], loss:202.2188 , time:41.790447473526s 
Epoch:[56/60], loss:202.12277 , time:41.371476888656616s 
Epoch:[57/60], loss:202.05978 , time:41.00389575958252s 
Epoch:[58/60], loss:202.02513 , time:40.384965658187866s 
Epoch:[59/60], loss:202.00772 , time:40.91265916824341s 
Epoch:[60/60], loss:201.9999 , time:41.31216502189636s 
=================== Training Success =====================
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八、评估

mindrecord_file = "./datasets/MindRecord_COCO/ssd_eval.mindrecord0"

def ssd_eval(dataset_path, ckpt_path, anno_json):
    """SSD evaluation."""
    batch_size = 1
    ds = create_ssd_dataset(dataset_path, batch_size=batch_size,
                            is_training=False, use_multiprocessing=False)

    network = SSD300Vgg16()
    print("Load Checkpoint!")
    net = SsdInferWithDecoder(network, Tensor(default_boxes), ckpt_path)

    net.set_train(False)
    total = ds.get_dataset_size() * batch_size
    print("\n========================================\n")
    print("total images num: ", total)
    eval_param_dict = {"net": net, "dataset": ds, "anno_json": anno_json}
    mAP = apply_eval(eval_param_dict)
    print("\n========================================\n")
    print(f"mAP: {mAP}")

def eval_net():
    print("Start Eval!")
    ssd_eval(mindrecord_file, "./ssd-60_9.ckpt", anno_json)

eval_net()

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Start Eval!
Load Checkpoint!

========================================

total images num:  9
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
Loading and preparing results...
DONE (t=0.47s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.97s).
Accumulating evaluation results...
DONE (t=0.20s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.003
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.006
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.052
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.016
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.037
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.071
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.057
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.328

========================================

mAP: 0.0025924737758294216
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