实践目标检测--构造SSD网络与训练

缺乏、安全感 2022-04-18 06:14 458阅读 0赞

### 网络结构图 ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMDA0MDA3_size_16_color_FFFFFF_t_70][]

基础网络块可自定义，可使用VGG、ResNet、DenseNet,这里使用了简单三层卷积+SSD的网络配置。

多尺度特征块：使用两层3\*3的网络保持图片大小不变后，使用2\*2的平均池化，将图片大小减半，来提取不同尺度的特征。

类别、边界框预测：使用多通道的输出来预测结果。

文件名SSD\_model.py

import sys
    sys.path.insert(0, '..')
    
    import gluonbook as gb
    from mxnet import autograd, contrib, gluon, image, init, nd
    from mxnet.gluon import loss as gloss, nn
    import time
    
    def cls_predictor(num_anchors, num_classes):
        return nn.Conv2D(num_anchors * (num_classes + 1), kernel_size=3,
                         padding=1)
    
    def bbox_predictor(num_anchors):
        return nn.Conv2D(num_anchors * 4, kernel_size=3, padding=1)
    
    def forward(x, block):
        block.initialize()
        return block(x)
    
    def flatten_pred(pred):
        return pred.transpose((0, 2, 3, 1)).flatten()
    
    def concat_preds(preds):
        return nd.concat(*[flatten_pred(p) for p in preds], dim=1)
    
    def down_sample_blk(num_channels):
        blk = nn.Sequential()
        for _ in range(2):
            blk.add(nn.Conv2D(num_channels, kernel_size=3, padding=1),
                    nn.BatchNorm(in_channels=num_channels),
                    nn.Activation('relu'))
        blk.add(nn.MaxPool2D(2))
        return blk
    
    def body_blk():
        blk = nn.Sequential()
        for num_filters in [16, 32, 64]:
            blk.add(down_sample_blk(num_filters))
        return blk
    
    def get_blk(i):
        if i == 0:
            blk = body_blk()
        elif i == 4:
            blk = nn.GlobalMaxPool2D()
        else:
            blk = down_sample_blk(128)
        return blk
    
    def blk_forward(X, blk, size, ratio, cls_predictor, bbox_predictor):
        Y = blk(X)
        anchor = contrib.ndarray.MultiBoxPrior(Y, sizes=size, ratios=ratio)
        cls_pred = cls_predictor(Y)
        bbox_pred = bbox_predictor(Y)
        return (Y, anchor, cls_pred, bbox_pred)
    
    sizes = [[0.2, 0.272], [0.37, 0.447], [0.54, 0.619], [0.71, 0.79],
             [0.88, 0.961]]
    ratios = [[1, 2, 0.5]] * 5
    num_anchors = len(sizes[0]) + len(ratios[0]) - 1
    
    class TinySSD(nn.Block):
        def __init__(self, num_classes, **kwargs):
            super(TinySSD, self).__init__(**kwargs)
            self.num_classes = num_classes
            for i in range(5):
                setattr(self, 'blk_%d' % i, get_blk(i))
                setattr(self, 'cls_%d' % i, cls_predictor(num_anchors,
                                                          num_classes))
                setattr(self, 'bbox_%d' % i, bbox_predictor(num_anchors))
    
        def forward(self, X):
            anchors, cls_preds, bbox_preds = [None] * 5, [None] * 5, [None] * 5
            for i in range(5):
                X, anchors[i], cls_preds[i], bbox_preds[i] = blk_forward(
                    X, getattr(self, 'blk_%d' % i), sizes[i], ratios[i],
                    getattr(self, 'cls_%d' % i), getattr(self, 'bbox_%d' % i))
            # 每个模块的锚框需要连结。
            return (nd.concat(*anchors, dim=1),
                    concat_preds(cls_preds).reshape(
                        (0, -1, self.num_classes + 1)), concat_preds(bbox_preds))

训练

文件名：SSD\_run.py

from SSD_model import *
    from load_my_data import load_my_data
    import time
    
    
    # 本函数已保存在 gluonbook 包中方便以后使用。
    path = './VOCtemplate/VOC2012/Annotations/'
    batch_size,edge_size = 4,256
    train_data=load_my_data(batch_size,edge_size)
    #batch = train_iter.next()
    #print(batch.data[0])
    
    train_data.reshape(label_shape=(3, 5))
    
    net = TinySSD(num_classes=5)
    #net.initialize(init=init.Xavier())
    net.load_parameters('my_model.params')
    trainer = gluon.Trainer(net.collect_params(), 'sgd',
                            {'learning_rate': 0.2, 'wd': 5e-4})
    
    cls_loss = gloss.SoftmaxCrossEntropyLoss()
    bbox_loss = gloss.L1Loss()
    
    def calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks):
        cls = cls_loss(cls_preds, cls_labels)
        bbox = bbox_loss(bbox_preds * bbox_masks, bbox_labels * bbox_masks)
        return cls + bbox
    
    def cls_eval(cls_preds, cls_labels):
        # 由于类别预测结果放在最后一维，argmax 需要指定最后一维。
        return (cls_preds.argmax(axis=-1) == cls_labels).mean().asscalar()
    
    def bbox_eval(bbox_preds, bbox_labels, bbox_masks):
        return ((bbox_labels - bbox_preds) * bbox_masks).abs().mean().asscalar()
    
    
    acc, mae = 0, 0
    train_data.reset()  # 从头读取数据。
    start = time.time()
    for i, batch in enumerate(train_data):
        X = batch.data[0]
        Y = batch.label[0]
        with autograd.record():
            # 生成多尺度的锚框，为每个锚框预测类别和偏移量。
            anchors, cls_preds, bbox_preds = net(X)
            # 为每个锚框标注类别和偏移量。
            bbox_labels, bbox_masks, cls_labels = contrib.nd.MultiBoxTarget(
                anchors, Y, cls_preds.transpose((0, 2, 1)))
            # 根据类别和偏移量的预测和标注值计算损失函数。
            l = calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels,
                          bbox_masks)
        l.backward()
        trainer.step(batch_size)
        acc += cls_eval(cls_preds, cls_labels)
        mae += bbox_eval(bbox_preds, bbox_labels, bbox_masks)
        if (i + 1) % 10 == 0:
            print('step %2d, class err %.2e, bbox mae %.2e, time %.1f sec' % (
                i + 1, 1 - acc / (10), mae / (10), time.time() - start))
            acc, mae = 0, 0
            start = time.time()
        if (i+1) % 100==0:
            print('writing params......')
            net.save_parameters('my_model.params')

开始训练:

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMDA0MDA3_size_16_color_FFFFFF_t_70 1][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMDA0MDA3_size_16_color_FFFFFF_t_70]: /images/20220418/e3b9c5387dd5478f95919cb67de1f9a6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMDA0MDA3_size_16_color_FFFFFF_t_70 1]: /images/20220418/83ae2e2142f5453d94a7172aa8df9aa6.png