ssd

YHR98 · Nov 7, 2019 · 85af3f8 · 85af3f8
1 parent 101a696
commit 85af3f8
Showing 1 changed file with 31 additions and 5 deletions.
diff --git a/4-Object_Detection/SSD/ssd.py b/4-Object_Detection/SSD/ssd.py
@@ -45,12 +45,23 @@ def __init__(self, num_class=21):
         self.conv5_3 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same')
         self.pool5   = tf.keras.layers.MaxPooling2D(3, strides=1, padding='same')
 
-        # fc6
+        # fc6, => vgg backbone is finished. now they are all SSD blocks
         self.fc6 = tf.keras.layers.Conv2D(1024, 3, dilation_rate=6, activation='relu', padding='same')
         # fc7
         self.fc7 = tf.keras.layers.Conv2D(1024, 1, activation='relu', padding='same')
-
+        # Block 8/9/10/11: 1x1 and 3x3 convolutions strides 2 (except lasts)
         # conv8
+        self.conv8_1 = tf.keras.layers.Conv2D(256, 1, activation='relu', padding='same')
+        self.conv8_2 = tf.keras.layers.Conv2D(512, 3, strides=2, activation='relu', padding='same')
+        # conv9
+        self.conv9_1 = tf.keras.layers.Conv2D(128, 1, activation='relu', padding='same')
+        self.conv9_2 = tf.keras.layers.Conv2D(256, 3, strides=2, activation='relu', padding='same')
+        # conv10
+        self.conv10_1 = tf.keras.layers.Conv2D(128, 1, activation='relu', padding='same')
+        self.conv10_2 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='valid')
+        # conv11
+        self.conv11_1 = tf.keras.layers.Conv2D(128, 1, activation='relu', padding='same')
+        self.conv11_2 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='valid')
 
 
 
@@ -71,18 +82,33 @@ def call(self, x, training=False):
         h = self.conv4_1(h)
         h = self.conv4_2(h)
         h = self.conv4_3(h)
+        print(h.shape)
         h = self.pool4(h)
 
         h = self.conv5_1(h)
         h = self.conv5_2(h)
         h = self.conv5_3(h)
-        print(h.shape)
         h = self.pool5(h)
+
+        h = self.fc6(h)     # [1,19,19,1024]
+        h = self.fc7(h)     # [1,19,19,1024]
+        print(h.shape)
+
+        h = self.conv8_1(h)
+        h = self.conv8_2(h) # [1,10,10, 512]
         print(h.shape)
-        h = self.fc6(h)
+
+        h = self.conv9_1(h)
+        h = self.conv9_2(h) # [1, 5, 5, 256]
         print(h.shape)
-        h = self.fc7(h)
 
+        h = self.conv10_1(h)
+        h = self.conv10_2(h) # [1, 3, 3, 256]
+        print(h.shape)
+
+        h = self.conv11_1(h)
+        h = self.conv11_2(h) # [1, 1, 1, 256]
+        print(h.shape)
         return h
 
 model = SSD(21)