def _built_net(self):
self.end_points = {} # 用来记录用于检测的特征层
self._images = tf.placeholder(tf.float32,
shape=[None,self.ssd_params.img_shape[0],self.ssd_params.img_shape[1],3]) # 输入图片的占位节点
with tf.variable_scope('ssd_300_vgg'):
# “ssd_300_vgg”不能修改，否则导入模型会找不到
# 【1】原来经典的vgg layers
# -----------------------------block 1-------------------------------
net = conv2d(self._images, filters=64, kernel_size=3, scope='conv1_1')
net = conv2d(net, 64, 3, scope='conv1_2')
self.end_points['block1'] = net
net = max_pool2d(net, pool_size=2, scope='pool1')
# ----------------------------block 2--------------------------------
net = conv2d(net, 128, 3, scope='conv2_1')
net = conv2d(net, 128, 3, scope='conv2_2')
self.end_points['block2'] = net
net = max_pool2d(net, 2, scope='pool2')
# ----------------------------block 3--------------------------------
net = conv2d(net, 256, 3, scope="conv3_1")
net = conv2d(net, 256, 3, scope="conv3_2")
net = conv2d(net, 256, 3, scope="conv3_3")
self.end_points["block3"] = net
net = max_pool2d(net, 2, scope="pool3")
# ---------------------------block 4---------------------------------
net = conv2d(net, 512, 3, scope="conv4_1")
net = conv2d(net, 512, 3, scope="conv4_2")
net = conv2d(net, 512, 3, scope="conv4_3")
self.end_points["block4"] = net
net = max_pool2d(net, 2, scope="pool4")
# ---------------------------block 5---------------------------------
net = conv2d(net, 512, 3, scope="conv5_1")
net = conv2d(net, 512, 3, scope="conv5_2")
net = conv2d(net, 512, 3, scope="conv5_3")
self.end_points["block5"] = net
# print(net)
net = max_pool2d(net, pool_size=3, stride=1, scope="pool5") # 核大小为3*3，步长为1
# print(net)
# 【2】添加的SSD layers
# ---------------------------block 6---------------------------------
net = conv2d(net, filters=1024, kernel_size=3, dilation_rate=6, scope='conv6') # 使用空洞卷积(带膨胀系数的dilate conv)
self.end_points['block6'] = net
# net = dropout(net, is_training=self.is_training)
# ---------------------------block 7---------------------------------
net = conv2d(net, 1024, 1, scope='conv7')
self.end_points['block7'] = net
# ---------------------------block 8---------------------------------
net = conv2d(net, 256, 1, scope='conv8_1x1')
net = conv2d(pad2d(net,1), 512, 3, stride=2, scope='conv8_3x3', padding='valid')
self.end_points['block8'] = net
# ---------------------------block 9---------------------------------
net = conv2d(net, 128, 1, scope="conv9_1x1")
net = conv2d(pad2d(net, 1), 256, 3, stride=2, scope="conv9_3x3", padding="valid")
self.end_points["block9"] = net
# ---------------------------block 10--------------------------------
net = conv2d(net, 128, 1, scope="conv10_1x1")
net = conv2d(net, 256, 3, scope="conv10_3x3", padding="valid")
self.end_points["block10"] = net
# ---------------------------block 11--------------------------------
net = conv2d(net, 128, 1, scope="conv11_1x1")
net = conv2d(net, 256, 3, scope="conv11_3x3", padding="valid")
self.end_points["block11"] = net
predictions = []
locations = []
for i, layer in enumerate(self.ssd_params.feature_layers):
cls, loc = ssd_multibox_layer(self.end_points[layer], self.ssd_params.num_classes,
self.ssd_params.anchor_sizes[i],
self.ssd_params.anchor_ratios[i],
self.ssd_params.normalizations[i],
scope=layer + '_box') # 从相应的layer层预测出类别和位置
predictions.append(tf.nn.softmax(cls)) # 解码class得分：用softmax函数
locations.append(loc) # 解码边界框位置xywh
return predictions, locations
def _built_net(self):
"""Construct the SSD net"""
self.end_points = {} # record the detection layers output
self._images = tf.placeholder(tf.float32,
shape=[
None, self.ssd_params.img_shape[0],
self.ssd_params.img_shape[1], 3
])
with tf.variable_scope("ssd_300_vgg"):
# original vgg layers
# block 1
net = conv2d(self._images, 64, 3, scope="conv1_1")
net = conv2d(net, 64, 3, scope="conv1_2")
self.end_points["block1"] = net
net = max_pool2d(net, 2, scope="pool1")
# block 2
net = conv2d(net, 128, 3, scope="conv2_1")
net = conv2d(net, 128, 3, scope="conv2_2")
self.end_points["block2"] = net
net = max_pool2d(net, 2, scope="pool2")
# block 3
net = conv2d(net, 256, 3, scope="conv3_1")
net = conv2d(net, 256, 3, scope="conv3_2")
net = conv2d(net, 256, 3, scope="conv3_3")
self.end_points["block3"] = net
net = max_pool2d(net, 2, scope="pool3")
# block 4
net = conv2d(net, 512, 3, scope="conv4_1")
net = conv2d(net, 512, 3, scope="conv4_2")
net = conv2d(net, 512, 3, scope="conv4_3")
self.end_points["block4"] = net
net = max_pool2d(net, 2, scope="pool4")
# block 5
net = conv2d(net, 512, 3, scope="conv5_1")
net = conv2d(net, 512, 3, scope="conv5_2")
net = conv2d(net, 512, 3, scope="conv5_3")
self.end_points["block5"] = net
print(net)
net = max_pool2d(net, 3, stride=1, scope="pool5")
print(net)
# additional SSD layers
# block 6: use dilate conv
net = conv2d(net, 1024, 3, dilation_rate=6, scope="conv6")
self.end_points["block6"] = net
#net = dropout(net, is_training=self.is_training)
# block 7
net = conv2d(net, 1024, 1, scope="conv7")
self.end_points["block7"] = net
# block 8
net = conv2d(net, 256, 1, scope="conv8_1x1")
net = conv2d(pad2d(net, 1),
512,
3,
stride=2,
scope="conv8_3x3",
padding="valid")
self.end_points["block8"] = net
# block 9
net = conv2d(net, 128, 1, scope="conv9_1x1")
net = conv2d(pad2d(net, 1),
256,
3,
stride=2,
scope="conv9_3x3",
padding="valid")
self.end_points["block9"] = net
# block 10
net = conv2d(net, 128, 1, scope="conv10_1x1")
net = conv2d(net, 256, 3, scope="conv10_3x3", padding="valid")
self.end_points["block10"] = net
# block 11
net = conv2d(net, 128, 1, scope="conv11_1x1")
net = conv2d(net, 256, 3, scope="conv11_3x3", padding="valid")
self.end_points["block11"] = net
# class and location predictions
predictions = []
logits = []
locations = []
for i, layer in enumerate(self.ssd_params.feat_layers):
cls, loc = ssd_multibox_layer(
self.end_points[layer],
self.ssd_params.num_classes,
self.ssd_params.anchor_sizes[i],
self.ssd_params.anchor_ratios[i],
self.ssd_params.normalizations[i],
scope=layer + "_box")
predictions.append(tf.nn.softmax(cls))
logits.append(cls)
locations.append(loc)
return predictions, logits, locations
def _built_net(self):
"""Construct the SSD net"""
self.end_points = {} # record the detection layers output
self._images = tf.placeholder(tf.float32, shape=[None, self.ssd_params.img_shape[0],
self.ssd_params.img_shape[1], 3])
with tf.variable_scope("ssd_300_vgg"):
# original vgg layers
# block 1
net = conv2d(self._images, 64, 3, scope="conv1_1")
net = conv2d(net, 64, 3, scope="conv1_2")
self.end_points["block1"] = net
net = max_pool2d(net, 2, scope="pool1")
# block 2
net = conv2d(net, 128, 3, scope="conv2_1")
net = conv2d(net, 128, 3, scope="conv2_2")
self.end_points["block2"] = net
net = max_pool2d(net, 2, scope="pool2")
# block 3
net = conv2d(net, 256, 3, scope="conv3_1")
net = conv2d(net, 256, 3, scope="conv3_2")
net = conv2d(net, 256, 3, scope="conv3_3")
self.end_points["block3"] = net
net = max_pool2d(net, 2, scope="pool3")
# block 4
net = conv2d(net, 512, 3, scope="conv4_1")
net = conv2d(net, 512, 3, scope="conv4_2")
net = conv2d(net, 512, 3, scope="conv4_3")
self.end_points["block4"] = net
net = max_pool2d(net, 2, scope="pool4")
# block 5
net = conv2d(net, 512, 3, scope="conv5_1")
net = conv2d(net, 512, 3, scope="conv5_2")
net = conv2d(net, 512, 3, scope="conv5_3")
self.end_points["block5"] = net
print(net)
net = max_pool2d(net, 3, stride=1, scope="pool5")
print(net)
# additional SSD layers
# block 6: use dilate conv
net = conv2d(net, 1024, 3, dilation_rate=6, scope="conv6")
self.end_points["block6"] = net
#net = dropout(net, is_training=self.is_training)
# block 7
net = conv2d(net, 1024, 1, scope="conv7")
self.end_points["block7"] = net
# block 8
net = conv2d(net, 256, 1, scope="conv8_1x1")
net = conv2d(pad2d(net, 1), 512, 3, stride=2, scope="conv8_3x3",
padding="valid")
self.end_points["block8"] = net
# block 9
net = conv2d(net, 128, 1, scope="conv9_1x1")
net = conv2d(pad2d(net, 1), 256, 3, stride=2, scope="conv9_3x3",
padding="valid")
self.end_points["block9"] = net
# block 10
net = conv2d(net, 128, 1, scope="conv10_1x1")
net = conv2d(net, 256, 3, scope="conv10_3x3", padding="valid")
self.end_points["block10"] = net
# block 11
net = conv2d(net, 128, 1, scope="conv11_1x1")
net = conv2d(net, 256, 3, scope="conv11_3x3", padding="valid")
self.end_points["block11"] = net
# class and location predictions
predictions = []
logits = []
locations = []
for i, layer in enumerate(self.ssd_params.feat_layers):
cls, loc = ssd_multibox_layer(self.end_points[layer], self.ssd_params.num_classes,
self.ssd_params.anchor_sizes[i],
self.ssd_params.anchor_ratios[i],
self.ssd_params.normalizations[i], scope=layer+"_box")
predictions.append(tf.nn.softmax(cls))
logits.append(cls)
locations.append(loc)
return predictions, logits, locations