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Remove half-features from dense_depth

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Piv
2021-04-22 12:30:30 +09:30
parent acdb58396c
commit 02d8cd5810
1 changed files with 11 additions and 39 deletions
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dense_depth_functional.py
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@@ -64,55 +64,27 @@ def dense_nnconv5(size, weights=None, shape=(224, 224, 3), half_features=True):
densenet = dense_net(input, size, weights, shape)
densenet_output_shape = densenet.layers[-1].output.shape
if half_features:
decode_filters = int(int(densenet_output_shape[-1]) / 2)
else:
decode_filters = int(densenet_output_shape[-1])
# Reduce the feature set (pointwise)
x = keras.layers.Conv2D(filters=decode_filters, kernel_size=1, padding='same',
decoder = keras.layers.Conv2D(filters=int(densenet_output_shape[-1]), kernel_size=1, padding='same',
input_shape=densenet_output_shape, name='conv2')(densenet.output)
# TODO: More intermediate layers here?
# Fast Depth Decoder
x = fd.nnconv5(x, densenet.get_layer('pool3_pool').output_shape[3], 1,
decoder = fd.nnconv5(decoder, densenet.get_layer('pool3_pool').output_shape[3], 1,
skip_connection=densenet.get_layer('pool3_pool').output)
x = fd.nnconv5(x, densenet.get_layer('pool2_pool').output_shape[3], 2,
decoder = fd.nnconv5(decoder, densenet.get_layer('pool2_pool').output_shape[3], 2,
skip_connection=densenet.get_layer('pool2_pool').output)
x = fd.nnconv5(x, densenet.get_layer('pool1').output_shape[3], 3,
decoder = fd.nnconv5(decoder, densenet.get_layer('pool1').output_shape[3], 3,
skip_connection=densenet.get_layer('pool1').output)
x = fd.nnconv5(x, densenet.get_layer('conv1/relu').output_shape[3], 4,
decoder = fd.nnconv5(decoder, densenet.get_layer('conv1/relu').output_shape[3], 4,
skip_connection=densenet.get_layer('conv1/relu').output)
# Final Pointwise for depth extraction
x = keras.layers.Conv2D(1, 1, padding='same')(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.ReLU(6.)(x)
return keras.Model(inputs=input, outputs=x, name="fast_dense_depth")
def crop_and_resize(x):
shape = tf.shape(x['depth'])
def layer():
return keras.Sequential([
keras.layers.experimental.preprocessing.CenterCrop(
shape[1], shape[2]),
keras.layers.experimental.preprocessing.Resizing(
224, 224, interpolation='nearest')
])
def half_layer():
return keras.Sequential([
keras.layers.experimental.preprocessing.CenterCrop(
shape[1], shape[2]),
keras.layers.experimental.preprocessing.Resizing(
112, 112, interpolation='nearest')
])
# Reshape label to 4d, can't use array unwrap as it's unsupported by tensorflow
return layer()(x['image']), half_layer()(tf.reshape(x['depth'], [shape[0], shape[1], shape[2], 1]))
decoder = keras.layers.Conv2D(1, 1, padding='same')(decoder)
decoder = keras.layers.BatchNormalization()(decoder)
decoder = keras.layers.ReLU(6.)(decoder)
return keras.Model(inputs=input, outputs=decoder, name="fast_dense_depth")
def load_nyu():
@@ -126,7 +98,7 @@ def load_nyu():
.as_dataset(split='train', shuffle_files=True) \
.shuffle(buffer_size=1024) \
.batch(8) \
.map(lambda x: crop_and_resize(x))
.map(lambda x: fd.crop_and_resize(x))
def load_nyu_evaluate():
@@ -136,7 +108,7 @@ def load_nyu_evaluate():
"""
builder = tfds.builder('nyu_depth_v2')
builder.download_and_prepare(download_dir='../nyu')
return builder.as_dataset(split='validation').batch(1).map(lambda x: crop_and_resize(x))
return builder.as_dataset(split='validation').batch(1).map(lambda x: fd.crop_and_resize(x))
if __name__ == '__main__':