More packnet implementation

packnet_functional.py

@@ -30,8 +30,11 @@ def residual_layer(inputs, out_channels, stride, dropout=None):
 
 
 # Packnet usually expects more than one layer per block (2,2,3,3)
-def residual_bock(inputs, out_channels, residual_layers, stride, dropout=None):
-    pass
+def residual_block(inputs, out_channels, residual_layers, stride, dropout=None):
+    x = inputs
+    for i in range(0, residual_layers):
+        x = residual_layer(x, out_channels, stride, dropout)
+    return x
 
 
 def packnet_conv2d(inputs, out_channels, kernel_size, stride):
@@ -73,19 +76,43 @@ def unpack_3d(inputs, out_channels, kernel_size, r=3, features_3d=8):
 
 
 # TODO: Support different size packnet for scaling up/down
-def make_packnet(shape=(224, 224, 3), skip_add=True, features_3d=4):
+def make_packnet(shape=(224, 224, 3), skip_add=True, features_3d=4, dropout=None):
     """
     Make the PackNet depth network.
     :param shape: Input shape of the image
     :param skip_add: Set to use add rather than concat skip connections, defaults to True
     :return:
     """
+
+    # ================ ENCODER =================
     input = keras.layers.Input(shape=shape)
     x = packnet_conv2d(input, 32, 5, 1)
     skip_1 = x
-    x = packnet_conv2d(input, 32, 7, 1)
-    x = pack_3d(x, 5, features_3d)
-    x = residual_layer(x, 64, )
+    x = packnet_conv2d(x, 32, 7, 1)
+    x = pack_3d(x, 5, features_3d=features_3d)
+    skip_2 = x
+    x = residual_block(x, 64, 2, 1, dropout)
+    x = pack_3d(x, 3, features_3d=features_3d)
+    skip_3 = x
+    x = residual_block(x, 128, 2, 1, dropout)
+    x = pack_3d(x, 3, features_3d=features_3d)
+    skip_4 = x
+    x = residual_block(x, 256, 3, 1, dropout)
+    x = pack_3d(x, 3, features_3d=features_3d)
+    skip_5 = x
+    x = residual_block(x, 512, 3, 1, dropout)
+    x = pack_3d(x, 3, features_3d=features_3d)
+    # ================ ENCODER =================
+
+    x = unpack_3d(x, 512, 3, features_3d=features_3d)
+    x = keras.layers.Add([x, skip_5]) if skip_add else keras.layers.Concatenate([x, skip_5])
+    x = packnet_conv2d(x, 512, 3, 1)
+    x = unpack_3d(x, 256, 3, features_3d=features_3d)
+    x = keras.layers.Add([x, skip_4]) if skip_add else keras.layers.Concatenate([x, skip_4])
+    x = packnet_conv2d(x, 256, 3, 1)
+    # TODO: This is wrong, look at the paper
+    x = packnet_inverse_depth(x, 1)
+    x = keras.layers.UpSampling2D()
 
     # TODO: Skip connection
     if skip_add: