其實現在用的最多的是faster rcnn，等下再弄項目~~~

• 圖像經過基礎網絡塊，三個減半模塊，每個減半模塊由兩個二維卷積層，加一個maxPool減半（通道數依次增加【16,32,64】）
• 然後是多個（3個）多尺度特征塊。每個特征塊依次都是一個減半模塊，通道數固定128
• 最後一個全局最大池化層模塊，高寬降到1
• 註意，每次添加一個模塊，後面都有兩個預測層，一個類比預測層，一個邊框預測層。類別預測層是一個二維卷積層，卷積層通道數是 錨框*(類別+1) ，然後用不改變圖像大小的卷積核3*3 ,padding = 1；邊框預測層類似，通道數改為 錨框 * 4

%matplotlib inline
 
import gluonbook as gb
from mxnet import autograd,gluon,image,init,nd,contrib
from mxnet.gluon import loss as gloss,nn
import time

# 類別預測層
def cls_predictor(num_anchors,num_classes):
    return nn.Conv2D(num_anchors*(num_classes+1),kernel_size=3,padding=1)

# 邊框預測層
def bbox_predictor(num_anchors):
    
 
return nn.Conv2D(num_anchors*4,kernel_size=3,padding=1)

# 連結多尺度
def forward(x,block):
    block.initialize()
    return block(x)
Y1 = forward(nd.zeros((2,8,20,20)),cls_predictor(5,10))
Y2 = forward(nd.zeros((2,16,10,10)),cls_predictor(3,10))

Y1.shape,Y2.shape

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)

concat_preds([Y1,Y2]).shape

# 減半模塊
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

blk = down_sample_blk(10)
blk.initialize()
x = nd.zeros((2,3,20,20))
y = blk(x)
y.shape

# 主體網絡塊
def base_net():
    blk = nn.Sequential()
    for num_filters in [16,32,64]:
        blk.add(down_sample_blk(num_filters))
    return blk
bnet = base_net()
bnet.initialize()
x = nd.random.uniform(shape=(2,3,256,256))
y = bnet(x)
y.shape

# 完整的模型
def get_blk(i):
    if i==0:              # 0 基礎網絡模塊
        blk = base_net()
    elif i==4:            #  4 全局最大池化層模塊，將高寬降到1
        blk = nn.GlobalMaxPool2D()
    else:                 # 1 ,2 ,3 高寬減半模塊
        blk = down_sample_blk(128)
    return blk

def blk_forward(X,blk,size,ratio,cls_predictor,bbox_predictor):
    Y = blk(X)
    anchors = contrib.nd.MultiBoxPrior(Y,sizes=size,ratios=ratio)
    cls_preds = cls_predictor(Y)
    bbox_preds = bbox_predictor(Y)
    return (Y, anchors, cls_preds,bbox_preds)

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


# 完整的TinySSD
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):
            # 賦值語句 self.blk_i = get_blk(i)
            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):
            # getattr(self, ‘blk_%d‘ % i ) 即訪問 self.blk_i
            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))
            
        return (nd.concat(*anchors, dim=1),
                concat_preds(cls_preds).reshape(
                    (0, -1, self.num_classes + 1)), concat_preds(bbox_preds))

# 測試形狀
net = TinySSD(num_classes=1)
net.initialize()
X = nd.zeros((32,3,256,256))
anchors, cls_preds, bbox_preds = net(X)
print(‘output anchors:‘,anchors.shape)
print(‘output class preds:‘,cls_preds.shape)
print(‘output bbox preds:‘,bbox_preds.shape)

SSD 單發多框檢測