Initial Commit

fast_depth_functional.py

@@ -0,0 +1,63 @@
+import tensorflow.keras as keras
+
+'''
+Functional version of fastdepth model. Note that this doesn't work at the moment
+'''
+
+
+def _depthwise_conv_block(inputs,
+                          pointwise_conv_filters,
+                          depth_multiplier=1,
+                          strides=(1, 1),
+                          block_id=1):
+    channel_axis = 1 if keras.backend.image_data_format() == 'channels_first' else -1
+    pointwise_conv_filters = int(pointwise_conv_filters)
+
+    if strides == (1, 1):
+        x = inputs
+    else:
+        x = keras.layers.ZeroPadding2D(((0, 1), (0, 1)), name='conv_pad_%d' % block_id)(
+            inputs)
+    x = keras.layers.DepthwiseConv2D((3, 3),
+                                     padding='same' if strides == (1, 1) else 'valid',
+                                     depth_multiplier=depth_multiplier,
+                                     strides=strides,
+                                     use_bias=False,
+                                     name='conv_dw_%d' % block_id)(
+        x)
+    x = keras.layers.BatchNormalization(
+        axis=channel_axis, name='conv_dw_%d_bn' % block_id)(
+        x)
+    x = keras.layers.ReLU(6., name='conv_dw_%d_relu' % block_id)(x)
+
+    x = keras.layers.Conv2D(
+        pointwise_conv_filters, (1, 1),
+        padding='same',
+        use_bias=False,
+        strides=(1, 1),
+        name='conv_pw_%d' % block_id)(
+        x)
+    x = keras.layers.BatchNormalization(
+        axis=channel_axis, name='conv_pw_%d_bn' % block_id)(
+        x)
+    return keras.layers.ReLU(6., name='conv_pw_%d_relu' % block_id)(x)
+
+
+def make_fastdepth_functional():
+    # This doesn't work, at least right now...
+    mobilenet = keras.applications.MobileNet(include_top=False)
+    input = keras.layers.Input(shape=(224, 224, 3))
+    x = mobilenet(input)
+    x = _depthwise_conv_block(x, 512, block_id=14)
+    x = _depthwise_conv_block(x, 256, block_id=15)
+    x = keras.layers.Add()([x, mobilenet.get_layer('conv_pw_5_relu').output])
+    x = _depthwise_conv_block(x, 128, block_id=16)
+    x = keras.layers.Add()([x, mobilenet.get_layer('conv_pw_3_relu').output])
+    x = _depthwise_conv_block(x, 64, block_id=17)
+    x = keras.layers.Add()([x, mobilenet.get_layer('conv_pw_1_relu').output])
+    x = _depthwise_conv_block(x, 32, block_id=18)
+
+    x = keras.layers.Conv2D(1, 1)(x)
+    x = keras.layers.BatchNormalization()(x)
+    x = keras.layers.ReLU()(x)
+    return keras.Model(input, x, name="fast_depth")