95 lines
4.1 KiB
Python
95 lines
4.1 KiB
Python
import tensorflow.keras as keras
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import tensorflow_datasets as tfds
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import fast_depth_functional as fd
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def dense_upsample_block(input, out_channels, skip_connection):
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"""
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Upsample block as described by dense depth in https://arxiv.org/pdf/1812.11941.pdf
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"""
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x = keras.layers.UpSampling2D(interpolation='bilinear')(input)
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x = keras.layers.Concatenate()([x, skip_connection])
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x = keras.layers.Conv2D(filters=out_channels,
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kernel_size=3, strides=1, padding='same')(x)
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x = keras.layers.Conv2D(filters=out_channels,
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kernel_size=3, strides=1, padding='same')(x)
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return keras.layers.LeakyReLU(alpha=0.2)(x)
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def dense_depth(size, weights=None, shape=(224, 224, 3)):
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input = keras.layers.Input(shape=shape)
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densenet = dense_net(input, size, weights, shape)
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densenet_output_channels = densenet.layers[-1].output.shape[-1]
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# Reduce the feature set (pointwise)
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decoder = keras.layers.Conv2D(
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filters=densenet_output_channels, kernel_size=1, padding='same')(densenet.output)
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# The actual decoder
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decoder = dense_upsample_block(
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decoder, densenet_output_channels // 2, densenet.get_layer('pool3_pool').output)
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decoder = dense_upsample_block(
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decoder, densenet_output_channels // 4, densenet.get_layer('pool2_pool').output)
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decoder = dense_upsample_block(
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decoder, densenet_output_channels // 8, densenet.get_layer('pool1').output)
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decoder = dense_upsample_block(
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decoder, densenet_output_channels // 16, densenet.get_layer('conv1/relu').output)
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conv3 = keras.layers.Conv2D(
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filters=1, kernel_size=3, strides=1, padding='same', name='conv3')(decoder)
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return keras.Model(inputs=input, outputs=conv3, name='dense_depth')
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def dense_net(input, size, weights=None, shape=(224, 224, 3)):
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if size == 121:
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densenet = keras.applications.DenseNet121(input_tensor=input, input_shape=shape, weights=weights,
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include_top=False)
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elif size == 169:
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densenet = keras.applications.DenseNet169(input_tensor=input, input_shape=shape, weights=weights,
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include_top=False)
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else:
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densenet = keras.applications.DenseNet201(input_tensor=input, input_shape=shape, weights=weights,
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include_top=False)
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for layer in densenet.layers:
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layer.trainable = True
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return densenet
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def dense_nnconv5(size, weights=None, shape=(224, 224, 3), half_features=True):
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input = keras.layers.Input(shape=shape)
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densenet = dense_net(input, size, weights, shape)
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densenet_output_shape = densenet.layers[-1].output.shape
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# Reduce the feature set (pointwise)
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decoder = keras.layers.Conv2D(filters=int(densenet_output_shape[-1]), kernel_size=1, padding='same',
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input_shape=densenet_output_shape, name='conv2')(densenet.output)
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# TODO: More intermediate layers here?
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# Fast Depth Decoder
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decoder = fd.nnconv5(decoder, densenet.get_layer('pool3_pool').output_shape[3], 1,
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skip_connection=densenet.get_layer('pool3_pool').output)
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decoder = fd.nnconv5(decoder, densenet.get_layer('pool2_pool').output_shape[3], 2,
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skip_connection=densenet.get_layer('pool2_pool').output)
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decoder = fd.nnconv5(decoder, densenet.get_layer('pool1').output_shape[3], 3,
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skip_connection=densenet.get_layer('pool1').output)
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decoder = fd.nnconv5(decoder, densenet.get_layer('conv1/relu').output_shape[3], 4,
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skip_connection=densenet.get_layer('conv1/relu').output)
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# Final Pointwise for depth extraction
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decoder = keras.layers.Conv2D(1, 1, padding='same')(decoder)
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decoder = keras.layers.BatchNormalization()(decoder)
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decoder = keras.layers.ReLU(6.)(decoder)
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return keras.Model(inputs=input, outputs=decoder, name="fast_dense_depth")
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if __name__ == '__main__':
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model = dense_depth(169, 'imagenet')
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model.summary()
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# model = dense_nnconv5(169, 'imagenet')
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# model.summary()
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