Initial Commit

fast_depth_functional.py

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+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")

main.py

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+import tensorflow as tf
+from tensorflow import keras
+
+import tensorflow_datasets as tfds
+
+
+class DecodeConv(keras.layers.Layer):
+    def __init__(self, out_filters, **kwargs):
+        super().__init__(**kwargs)
+        # Should be depthwise followed by batchnorm and relu.
+        self.depthwise = keras.layers.DepthwiseConv2D(5)
+        self.batch_norm = keras.layers.BatchNormalization()
+        self.relu = keras.layers.ReLU(6.)
+        self.pointwise = keras.layers.Conv2D(out_filters, 1)
+        self.pointwise_bn = keras.layers.BatchNormalization()
+        self.pointwise_rl = keras.layers.ReLU(6.)
+
+    def call(self, inputs, **kwargs):
+        inputs = self.depthwise(inputs, **kwargs)
+        inputs = self.batch_norm(inputs, **kwargs)
+        inputs = self.relu(inputs, **kwargs)
+        inputs = self.pointwise(inputs, **kwargs)
+        inputs = self.pointwise_bn(inputs, **kwargs)
+        return self.pointwise_rl(inputs, **kwargs)
+
+
+class FastDepth(keras.Model):
+    def get_config(self):
+        # TODO: What to put here?
+        pass
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.mobile_net = keras.applications.MobileNet(include_top=False)
+
+        # TODO: Try keras.layers.SeparableConv2D as well, should do the same thing if relu is used as activation
+        # It probably doesn't, since
+        self.decode_conv1 = DecodeConv(512)
+        self.decode_conv2 = DecodeConv(256)
+        self.decode_conv3 = DecodeConv(128)
+        self.decode_conv4 = DecodeConv(64)
+        self.decode_conv5 = DecodeConv(32)
+
+        self.final_pointwise = keras.layers.Conv2D(1, 1)
+        self.final_pointwise_bn = keras.layers.BatchNormalization()
+        self.final_pointwise_relu = keras.layers.ReLU()
+
+    def call(self, inputs, is_training=False, **kwargs):
+        # Go through mobilenet, then each decode layer, including skip connections using:
+        # keras.layers.Add()
+        inputs = self.mobile_net(inputs, is_training=is_training, **kwargs)
+
+        # FastDepth Additive Decoder
+        inputs = self.decode_conv1(inputs, is_training=is_training, **kwargs)
+        inputs = self.decode_conv2(inputs, is_training=is_training, **kwargs)
+        inputs = inputs + self.mobile_net.get_layer('conv_pw_5_relu').output
+        inputs = self.decode_conv3(inputs, is_training=is_training, **kwargs)
+        inputs = inputs + self.mobile_net.get_layer('conv_pw_3_relu').output
+        inputs = self.decode_conv4(inputs, is_training=is_training, **kwargs)
+        inputs = inputs + self.mobile_net.get_layer('conv_pw_1_relu').output
+        inputs = self.decode_conv5(inputs, is_training=is_training, **kwargs)
+
+        inputs = self.final_pointwise(inputs, is_training=is_training, **kwargs)
+        inputs = self.final_pointwise_bn(inputs, is_training=is_training, **kwargs)
+        return self.final_pointwise_relu(inputs, is_training=is_training, **kwargs)
+
+
+def load_nyu():
+    builder = tfds.builder('nyu_depth_v2')
+    builder.download_and_prepare(download_dir='../nyu')
+    return builder.as_dataset(split='train', shuffle_files=True)
+
+
+def print_hi(name):
+    # Use a breakpoint in the code line below to debug your script.
+    print(f'Hi, {name}')  # Press Ctrl+F8 to toggle the breakpoint.
+
+
+# Press the green button in the gutter to run the script.
+if __name__ == '__main__':
+    load_nyu()
+
+# See PyCharm help at https://www.jetbrains.com/help/pycharm/