Build Functional Model, remove subclassed model
Functional models are way easier to work with, and I don't need any advanced features that would require model subclassing
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Piv
@@ -5,59 +5,53 @@ Functional version of fastdepth model. Note that this doesn't work at the moment
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'''
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def _depthwise_conv_block(inputs,
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pointwise_conv_filters,
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depth_multiplier=1,
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strides=(1, 1),
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block_id=1):
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channel_axis = 1 if keras.backend.image_data_format() == 'channels_first' else -1
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pointwise_conv_filters = int(pointwise_conv_filters)
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if strides == (1, 1):
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x = inputs
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else:
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x = keras.layers.ZeroPadding2D(((0, 1), (0, 1)), name='conv_pad_%d' % block_id)(
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inputs)
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x = keras.layers.DepthwiseConv2D((3, 3),
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padding='same' if strides == (1, 1) else 'valid',
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depth_multiplier=depth_multiplier,
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strides=strides,
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use_bias=False,
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name='conv_dw_%d' % block_id)(
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x)
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x = keras.layers.BatchNormalization(
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axis=channel_axis, name='conv_dw_%d_bn' % block_id)(
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x)
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x = keras.layers.ReLU(6., name='conv_dw_%d_relu' % block_id)(x)
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x = keras.layers.Conv2D(
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pointwise_conv_filters, (1, 1),
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padding='same',
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use_bias=False,
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strides=(1, 1),
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name='conv_pw_%d' % block_id)(
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x)
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x = keras.layers.BatchNormalization(
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axis=channel_axis, name='conv_pw_%d_bn' % block_id)(
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x)
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def FDDepthwiseBlock(inputs,
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out_channels,
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block_id=1):
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x = keras.layers.DepthwiseConv2D(5, padding='same')(inputs)
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x = keras.layers.BatchNormalization()(x)
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x = keras.layers.ReLU(6.)(x)
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x = keras.layers.Conv2D(out_channels, 1, padding='same')(x)
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x = keras.layers.BatchNormalization()(x)
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return keras.layers.ReLU(6., name='conv_pw_%d_relu' % block_id)(x)
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def make_fastdepth_functional():
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# This doesn't work, at least right now...
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mobilenet = keras.applications.MobileNet(include_top=False)
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input = keras.layers.Input(shape=(224, 224, 3))
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x = mobilenet(input)
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x = _depthwise_conv_block(x, 512, block_id=14)
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x = _depthwise_conv_block(x, 256, block_id=15)
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x = keras.layers.Add()([x, mobilenet.get_layer('conv_pw_5_relu').output])
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x = _depthwise_conv_block(x, 128, block_id=16)
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x = keras.layers.Add()([x, mobilenet.get_layer('conv_pw_3_relu').output])
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x = _depthwise_conv_block(x, 64, block_id=17)
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x = keras.layers.Add()([x, mobilenet.get_layer('conv_pw_1_relu').output])
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x = _depthwise_conv_block(x, 32, block_id=18)
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x = input
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mobilenet = keras.applications.MobileNet(include_top=False, weights=None)
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for layer in mobilenet.layers:
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x = layer(x)
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if layer.name == 'conv_pw_5_relu':
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conv5 = x
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elif layer.name == 'conv_pw_3_relu':
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conv3 = x
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elif layer.name == 'conv_pw_1_relu':
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conv1 = x
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x = keras.layers.Conv2D(1, 1)(x)
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x = FDDepthwiseBlock(x, 512, block_id=14)
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# TODO: Bilinear interpolation
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# x = keras.layers.experimental.preprocessing.Resizing(14, 14)
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# Nearest neighbour interpolation, used by fast depth paper
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x = keras.layers.experimental.preprocessing.Resizing(14, 14, interpolation='nearest')(x)
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x = FDDepthwiseBlock(x, 256, block_id=15)
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x = keras.layers.experimental.preprocessing.Resizing(28, 28, interpolation='nearest')(x)
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x = keras.layers.Add()([x, conv5])
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x = FDDepthwiseBlock(x, 128, block_id=16)
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x = keras.layers.experimental.preprocessing.Resizing(56, 56, interpolation='nearest')(x)
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x = keras.layers.Add()([x, conv3])
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x = FDDepthwiseBlock(x, 64, block_id=17)
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x = keras.layers.experimental.preprocessing.Resizing(112, 112, interpolation='nearest')(x)
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x = keras.layers.Add()([x, conv1])
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x = FDDepthwiseBlock(x, 32, block_id=18)
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x = keras.layers.experimental.preprocessing.Resizing(224, 224, interpolation='nearest')(x)
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x = keras.layers.Conv2D(1, 1, padding='same')(x)
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x = keras.layers.BatchNormalization()(x)
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x = keras.layers.ReLU()(x)
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return keras.Model(input, x, name="fast_depth")
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x = keras.layers.ReLU(6.)(x)
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return keras.Model(inputs=input, outputs=x, name="fast_depth")
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if __name__ == '__main__':
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make_fastdepth_functional().summary()
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64
main.py
64
main.py
@@ -1,70 +1,6 @@
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import tensorflow as tf
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from tensorflow import keras
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import tensorflow_datasets as tfds
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class DecodeConv(keras.layers.Layer):
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def __init__(self, out_filters, **kwargs):
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super().__init__(**kwargs)
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# Should be depthwise followed by batchnorm and relu.
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self.depthwise = keras.layers.DepthwiseConv2D(5)
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self.batch_norm = keras.layers.BatchNormalization()
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self.relu = keras.layers.ReLU(6.)
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self.pointwise = keras.layers.Conv2D(out_filters, 1)
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self.pointwise_bn = keras.layers.BatchNormalization()
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self.pointwise_rl = keras.layers.ReLU(6.)
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def call(self, inputs, **kwargs):
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inputs = self.depthwise(inputs, **kwargs)
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inputs = self.batch_norm(inputs, **kwargs)
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inputs = self.relu(inputs, **kwargs)
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inputs = self.pointwise(inputs, **kwargs)
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inputs = self.pointwise_bn(inputs, **kwargs)
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return self.pointwise_rl(inputs, **kwargs)
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class FastDepth(keras.Model):
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def get_config(self):
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# TODO: What to put here?
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pass
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.mobile_net = keras.applications.MobileNet(include_top=False)
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# TODO: Try keras.layers.SeparableConv2D as well, should do the same thing if relu is used as activation
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# It probably doesn't, since
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self.decode_conv1 = DecodeConv(512)
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self.decode_conv2 = DecodeConv(256)
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self.decode_conv3 = DecodeConv(128)
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self.decode_conv4 = DecodeConv(64)
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self.decode_conv5 = DecodeConv(32)
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self.final_pointwise = keras.layers.Conv2D(1, 1)
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self.final_pointwise_bn = keras.layers.BatchNormalization()
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self.final_pointwise_relu = keras.layers.ReLU()
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def call(self, inputs, is_training=False, **kwargs):
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# Go through mobilenet, then each decode layer, including skip connections using:
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# keras.layers.Add()
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inputs = self.mobile_net(inputs, is_training=is_training, **kwargs)
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# FastDepth Additive Decoder
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inputs = self.decode_conv1(inputs, is_training=is_training, **kwargs)
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inputs = self.decode_conv2(inputs, is_training=is_training, **kwargs)
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inputs = inputs + self.mobile_net.get_layer('conv_pw_5_relu').output
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inputs = self.decode_conv3(inputs, is_training=is_training, **kwargs)
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inputs = inputs + self.mobile_net.get_layer('conv_pw_3_relu').output
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inputs = self.decode_conv4(inputs, is_training=is_training, **kwargs)
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inputs = inputs + self.mobile_net.get_layer('conv_pw_1_relu').output
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inputs = self.decode_conv5(inputs, is_training=is_training, **kwargs)
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inputs = self.final_pointwise(inputs, is_training=is_training, **kwargs)
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inputs = self.final_pointwise_bn(inputs, is_training=is_training, **kwargs)
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return self.final_pointwise_relu(inputs, is_training=is_training, **kwargs)
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def load_nyu():
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builder = tfds.builder('nyu_depth_v2')
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builder.download_and_prepare(download_dir='../nyu')
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