Format pep8, include pass shaped to mobilenet

fast_depth_functional.py

@@ -22,7 +22,8 @@ def fix_windows_gpu():
             for gpu in gpus:
                 tf.config.experimental.set_memory_growth(gpu, True)
             logical_gpus = tf.config.experimental.list_logical_devices('GPU')
-            print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
+            print(len(gpus), "Physical GPUs,", len(
+                logical_gpus), "Logical GPUs")
         except RuntimeError as e:
             # Memory growth must be set before GPUs have been initialized
             print(e)
@@ -47,7 +48,8 @@ def mobilenet_nnconv5(weights=None, shape=(224, 224, 3)):
     :return: FastDepth keras Model
     """
     input = keras.layers.Input(shape=shape)
-    mobilenet = keras.applications.MobileNet(input_tensor=input, include_top=False, weights=weights)
+    mobilenet = keras.applications.MobileNet(
+        input_shape=shape, input_tensor=input, include_top=False, weights=weights)
     for layer in mobilenet.layers:
         layer.trainable = True
 
@@ -57,13 +59,16 @@ def mobilenet_nnconv5(weights=None, shape=(224, 224, 3)):
     x = keras.layers.UpSampling2D()(x)
     x = FDDepthwiseBlock(x, 256, block_id=15)
     x = keras.layers.UpSampling2D()(x)
-    x = keras.layers.Add()([x, mobilenet.get_layer(name="conv_pw_5_relu").output])
+    x = keras.layers.Add()(
+        [x, mobilenet.get_layer(name="conv_pw_5_relu").output])
     x = FDDepthwiseBlock(x, 128, block_id=16)
     x = keras.layers.UpSampling2D()(x)
-    x = keras.layers.Add()([x, mobilenet.get_layer(name="conv_pw_3_relu").output])
+    x = keras.layers.Add()(
+        [x, mobilenet.get_layer(name="conv_pw_3_relu").output])
     x = FDDepthwiseBlock(x, 64, block_id=17)
     x = keras.layers.UpSampling2D()(x)
-    x = keras.layers.Add()([x, mobilenet.get_layer(name="conv_pw_1_relu").output])
+    x = keras.layers.Add()(
+        [x, mobilenet.get_layer(name="conv_pw_1_relu").output])
     x = FDDepthwiseBlock(x, 32, block_id=18)
     x = keras.layers.UpSampling2D()(x)
 
@@ -163,8 +168,10 @@ def crop_and_resize(x):
 
     def layer():
         return keras.Sequential([
-            keras.layers.experimental.preprocessing.CenterCrop(shape[1], shape[2]),
+            keras.layers.experimental.preprocessing.CenterCrop(
-            keras.layers.experimental.preprocessing.Resizing(224, 224, interpolation='nearest')
+                shape[1], shape[2]),
+            keras.layers.experimental.preprocessing.Resizing(
+                224, 224, interpolation='nearest')
         ])
 
     # Reshape label to 4d, can't use array unwrap as it's unsupported by tensorflow

train.ipynb

@@ -15,7 +15,7 @@
   "orig_nbformat": 2,
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3.8.8 64-bit",
+   "display_name": "Python 3.8.8 64-bit ('tensorflow2': conda)",
    "metadata": {
     "interpreter": {
      "hash": "ee99f7bd678359d45d92ad289bdab8f6bcfaae579cfd1bff07d2bb16d7ba024f"
@@ -70,15 +70,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
-     ]
-    }
-   ],
    "source": [
     "model = fd.mobilenet_nnconv5(weights='imagenet')\n",
     "fd.compile(model)"
@@ -102,7 +94,7 @@
   },
   {
    "source": [
-    "## Evaluate the model"
+    "## Evaluate the trained model"
    ],
    "cell_type": "markdown",
    "metadata": {}