Add model with no batch normalisation, use actual mobilnet model rather than extracting layers

Found from DenseDepth, each layer can be set to trainable in the encoder, then the outputs of the
model and the required layers for skip connections can be used directly. Ends up being much cleaner

fast_depth_functional.py

@@ -35,34 +35,34 @@ def FDDepthwiseBlock(inputs,
     return keras.layers.ReLU(6., name='conv_pw_%d_relu' % block_id)(x)
 
 
+def FDDepthwiseBlockNoBN(inputs, out_channels, block_id=1):
+    x = keras.layers.DepthwiseConv2D(5, padding='same')(inputs)
+    x = keras.layers.ReLU(6.)(x)
+    x = keras.layers.Conv2D(out_channels, 1, padding='same')(x)
+    return keras.layers.ReLU(6., name='conv_pw_%d_relu' % block_id)(x)
+
+
 def make_mobilenet_nnconv5(weights=None, shape=(224, 224, 3)):
     input = keras.layers.Input(shape=shape)
-    x = input
+    mobilenet = keras.applications.MobileNet(input_tensor=input, include_top=False, weights=weights)
-    mobilenet = keras.applications.MobileNet(input_tensor=x, include_top=False, weights=weights)
     for layer in mobilenet.layers:
-        x = layer(x)
+        layer.trainable = True
-        if layer.name == 'conv_pw_5_relu':
-            conv5 = x
-        elif layer.name == 'conv_pw_3_relu':
-            conv3 = x
-        elif layer.name == 'conv_pw_1_relu':
-            conv1 = x
 
     # Fast depth decoder
-    x = FDDepthwiseBlock(x, 512, block_id=14)
+    x = FDDepthwiseBlock(mobilenet.output, 512, block_id=14)
     # TODO: Bilinear interpolation
-    #     x = keras.layers.experimental.preprocessing.Resizing(14, 14)
+    #     x = keras.layers.experimental.preprocessing.Resizing(14, 14, interpolation='bilinear')
     # Nearest neighbour interpolation, used by fast depth paper
     x = keras.layers.experimental.preprocessing.Resizing(14, 14, interpolation='nearest')(x)
     x = FDDepthwiseBlock(x, 256, block_id=15)
     x = keras.layers.experimental.preprocessing.Resizing(28, 28, interpolation='nearest')(x)
-    x = keras.layers.Add()([x, conv5])
+    x = keras.layers.Add()([x, mobilenet.get_layer(name="conv_pw_5_relu").output])
     x = FDDepthwiseBlock(x, 128, block_id=16)
     x = keras.layers.experimental.preprocessing.Resizing(56, 56, interpolation='nearest')(x)
-    x = keras.layers.Add()([x, conv3])
+    x = keras.layers.Add()([x, mobilenet.get_layer(name="conv_pw_3_relu").output])
     x = FDDepthwiseBlock(x, 64, block_id=17)
     x = keras.layers.experimental.preprocessing.Resizing(112, 112, interpolation='nearest')(x)
-    x = keras.layers.Add()([x, conv1])
+    x = keras.layers.Add()([x, mobilenet.get_layer(name="conv_pw_1_relu").output])
     x = FDDepthwiseBlock(x, 32, block_id=18)
     x = keras.layers.experimental.preprocessing.Resizing(224, 224, interpolation='nearest')(x)
 
@@ -72,6 +72,33 @@ def make_mobilenet_nnconv5(weights=None, shape=(224, 224, 3)):
     return keras.Model(inputs=input, outputs=x, name="fast_depth")
 
 
+def make_mobilenet_nnconv5_no_bn(weights=None, shape=(224, 224, 3)):
+    input = keras.layers.Input(shape=shape)
+    mobilenet = keras.applications.MobileNet(input_tensor=input, include_top=False, weights=weights)
+    for layer in mobilenet.layers:
+        layer.trainable = True
+
+    # Fast depth decoder
+    x = FDDepthwiseBlockNoBN(mobilenet.output, 512, block_id=14)
+    # Nearest neighbour interpolation, used by fast depth paper
+    x = keras.layers.experimental.preprocessing.Resizing(14, 14, interpolation='bilinear')(x)
+    x = FDDepthwiseBlockNoBN(x, 256, block_id=15)
+    x = keras.layers.experimental.preprocessing.Resizing(28, 28, interpolation='bilinear')(x)
+    x = keras.layers.Add()([x, mobilenet.get_layer(name="conv_pw_5_relu").output])
+    x = FDDepthwiseBlockNoBN(x, 128, block_id=16)
+    x = keras.layers.experimental.preprocessing.Resizing(56, 56, interpolation='bilinear')(x)
+    x = keras.layers.Add()([x, mobilenet.get_layer(name="conv_pw_3_relu").output])
+    x = FDDepthwiseBlockNoBN(x, 64, block_id=17)
+    x = keras.layers.experimental.preprocessing.Resizing(112, 112, interpolation='bilinear')(x)
+    x = keras.layers.Add()([x, mobilenet.get_layer(name="conv_pw_1_relu").output])
+    x = FDDepthwiseBlockNoBN(x, 32, block_id=18)
+    x = keras.layers.experimental.preprocessing.Resizing(224, 224, interpolation='bilinear')(x)
+
+    x = keras.layers.Conv2D(1, 1, padding='same')(x)
+    x = keras.layers.ReLU(6.)(x)
+    return keras.Model(inputs=input, outputs=x, name="fast_depth")
+
+
 # TODO: Fix these, float doesn't work same as pytorch
 def delta1_metric(y_true, y_pred):
     maxRatio = tf.maximum(y_pred / y_true, y_true / y_pred)