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)

Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/ssd_datasets.zip (16.6 MB)

file_sizes: 100%|██████████████████████████| 17.4M/17.4M [00:00<00:00, 26.9MB/s]
Extracting zip file...
Successfully downloaded / unzipped to ./

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

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])

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

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

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

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

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

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

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

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 =====================")

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

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()