Fix packnet residual block and layers, refactor to support different amount of residual layers

I noticed the number of parameters didn't match up to the paper (~128 million)
Fixed this by doing the following:
 - Kernel size of 1 for 3rd conv2d in residual block
 - Use add rather than concat in residual block
 - Fixed add/concat features in decode layers
 - Fixed final layers -> this also allows features_3d == 16 to work

packnet_functional.py

@@ -22,10 +22,10 @@ def residual_layer(inputs, out_channels, stride, dropout=None):
     x = layers.Conv2D(out_channels, 3, padding='same', strides=stride)(inputs)
     x = layers.Conv2D(out_channels, 3, padding='same')(x)
     shortcut = layers.Conv2D(
-        out_channels, 3, padding='same', strides=stride)(inputs)
+        out_channels, 1, padding='same', strides=stride)(inputs)
     if dropout:
         shortcut = keras.layers.SpatialDropout2D(dropout)(shortcut)
-    x = keras.layers.Concatenate()([x, shortcut])
+    x = keras.layers.Add()([x, shortcut])
     x = group_norm.GroupNormalization(16)(x)
     return keras.layers.ELU()(x)
 
@@ -53,10 +53,10 @@ def packnet_inverse_depth(inputs, out_channels=1, min_depth=0.5):
 def pack_3d(inputs, kernel_size, r=2, features_3d=8):
     """
     Implementatino of the 3d packing block proposed here: https://arxiv.org/abs/1905.02693
-    :param inputs:
-    :param kernel_size:
-    :param r:
-    :param features_3d:
+    :param inputs: Tensor inputs
+    :param kernel_size: Conv3D kernels size
+    :param r: Packing factor
+    :param features_3d: Packing depth (increase to increase number of parameters and accuracy)
     :return:
     """
     # Data format for single image in nyu is HWC (space_to_depth uses NHWC as default)
@@ -78,7 +78,6 @@ def unpack_3d(inputs, out_channels, kernel_size, r=2, features_3d=8):
     return nn.depth_to_space(x, r)
 
 
-# TODO: Support different size packnet for scaling up/down
 # TODO: Support different channel format (right now we're supporting NHWC, we should also support NCHW)
 def make_packnet(shape=(224, 224, 3), skip_add=True, features_3d=4, dropout=None):
     """
@@ -109,42 +108,48 @@ def make_packnet(shape=(224, 224, 3), skip_add=True, features_3d=4, dropout=None
     # ================ ENCODER =================
 
     # ================ DECODER =================
-    # layer 7
-    x = unpack_3d(x, 512, 3, features_3d=features_3d)
+    # Addition requires we half the outputs so there is a matching number of channels
+    divide_factor = (2 if skip_add else 1)
+    # layer 12 - 13
+    x = unpack_3d(x, 512 // divide_factor, 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)
-    # layer 8
-    x = unpack_3d(x, 256, 3, features_3d=features_3d)
+    # layer 14 - 15
+    x = unpack_3d(x, 256 // divide_factor, 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)
     layer_8 = x
-    # layer 9
+    # layer 16
     x = packnet_inverse_depth(x, 1)
-    # layer 10
-    u_layer_8 = unpack_3d(layer_8, 128, 3, features_3d=features_3d)
+    # layer 17 - 18
+    u_layer_8 = unpack_3d(layer_8, 128 // divide_factor, 3, features_3d=features_3d)
     x = keras.layers.UpSampling2D()(x)
     x = keras.layers.Add()([u_layer_8, skip_3, x]) if skip_add else keras.layers.Concatenate()([u_layer_8, skip_3, x])
     x = packnet_conv2d(x, 128, 3, 1)
     layer_10 = x
-    # layer 11
+    # layer 19
     x = packnet_inverse_depth(x, 1)
-    # layer 12
+    # layer 20 - 21
     u_layer_10 = unpack_3d(layer_10, 64, 3, features_3d=features_3d)
     x = keras.layers.UpSampling2D()(x)
     x = keras.layers.Add()([u_layer_10, skip_2, x]) if skip_add else keras.layers.Concatenate()([u_layer_10, skip_2, x])
     x = packnet_conv2d(x, 64, 3, 1)
     layer_12 = x
-    # layer 13
+    # layer 22
     x = packnet_inverse_depth(x)
-    # layer 14
-    u_layer_12 = unpack_3d(layer_12, 32, 3, features_3d=features_3d)
+    # layer 23 - 24
+    u_layer_12 = unpack_3d(layer_12, 64, 3, features_3d=features_3d)
     x = keras.layers.UpSampling2D()(x)
     x = keras.layers.Add()([u_layer_12, skip_1, x]) if skip_add else keras.layers.Concatenate()([u_layer_12, skip_1, x])
-    x = packnet_conv2d(x, 32, 3, 1)
-    # layer 15
+    x = packnet_conv2d(x, 64, 3, 1)
+    # layer 25
     x = packnet_inverse_depth(x)
     # ================ DECODER =================
 
     return keras.Model(inputs=input, outputs=x, name="PackNet")
+
+if __name__ == '__main__':
+    # This is the implementation used by the packnet sfm paper
+    make_packnet(features_3d=8, skip_add=False).summary()