Add pack layer tests, fix unpack_3d layer

packnet_functional.py

@@ -68,12 +68,12 @@ def pack_3d(inputs, kernel_size, r=2, features_3d=8):
     return packnet_conv2d(x, inputs.shape[3], kernel_size, 1)
 
 
-def unpack_3d(inputs, out_channels, kernel_size, r=3, features_3d=8):
+def unpack_3d(inputs, out_channels, kernel_size, r=2, features_3d=8):
     x = packnet_conv2d(inputs, out_channels * (r ** 2) //
                        features_3d, kernel_size, 1)
     x = tf.expand_dims(x, 4)  # B x H/2 x W/2 x 4(out)/D x D
-    x = keras.layers.Conv3D(features_3d, kernel_size=3, padding='same')
+    x = keras.layers.Conv3D(features_3d, kernel_size=3, padding='same')(x)
-    b, c, d, h, w = x.shape
+    b, h, w, c, d = x.shape
     x = tf.reshape(x, [b, h, w, c * d])
     return nn.depth_to_space(x, r)
 

packnet_tests.py

@@ -0,0 +1,33 @@
+import unittest
+
+import tensorflow as tf
+
+import packnet_functional as p
+
+
+class PacknetTests(unittest.TestCase):
+
+    def test_pack_3d_layer(self):
+        # 3d packing expects a multiple of 16 for channels due to using 16 groups in group normalisation
+        test_input = tf.random.normal([4, 224, 224, 32])
+        y = p.pack_3d(test_input, 3, features_3d=4)
+        out_shape = [i for i in test_input.shape]
+        out_shape[1] = out_shape[1] // 2
+        out_shape[2] = out_shape[2] // 2
+        # TODO: Anything else we can test here for validity?
+        self.assertEqual(y.shape, out_shape)
+
+    def test_unpack_3d_layer(self):
+        num_output_channels = 32
+        test_input = tf.random.normal([4, 112, 112, 64])
+        y = p.unpack_3d(test_input, num_output_channels, 3, features_3d=4)
+        out_shape = [i for i in test_input.shape]
+        out_shape[1] = out_shape[1] * 2
+        out_shape[2] = out_shape[2] * 2
+        out_shape[3] = num_output_channels
+        # TODO: Anything else we can test here for validity?
+        self.assertEqual(y.shape, out_shape)
+
+
+if __name__ == '__main__':
+    unittest.main()