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Refactor load/util, start fixing packnet to support NHWC format

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Piv
2021-07-19 12:32:56 +09:30
parent d8bf493999
commit 38e7ad069e
6 changed files with 85 additions and 93 deletions
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38
dense_depth_functional.py
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@@ -1,4 +1,3 @@
import tensorflow as tf
import tensorflow.keras as keras
import tensorflow_datasets as tfds
@@ -25,7 +24,8 @@ def dense_depth(size, weights=None, shape=(224, 224, 3)):
densenet_output_channels = densenet.layers[-1].output.shape[-1]
# Reduce the feature set (pointwise)
decoder = keras.layers.Conv2D(filters=densenet_output_channels, kernel_size=1, padding='same')(densenet.output)
decoder = keras.layers.Conv2D(
filters=densenet_output_channels, kernel_size=1, padding='same')(densenet.output)
# The actual decoder
decoder = dense_upsample_block(
@@ -66,19 +66,19 @@ def dense_nnconv5(size, weights=None, shape=(224, 224, 3), half_features=True):
# Reduce the feature set (pointwise)
decoder = keras.layers.Conv2D(filters=int(densenet_output_shape[-1]), kernel_size=1, padding='same',
input_shape=densenet_output_shape, name='conv2')(densenet.output)
input_shape=densenet_output_shape, name='conv2')(densenet.output)
# TODO: More intermediate layers here?
# Fast Depth Decoder
decoder = fd.nnconv5(decoder, densenet.get_layer('pool3_pool').output_shape[3], 1,
skip_connection=densenet.get_layer('pool3_pool').output)
skip_connection=densenet.get_layer('pool3_pool').output)
decoder = fd.nnconv5(decoder, densenet.get_layer('pool2_pool').output_shape[3], 2,
skip_connection=densenet.get_layer('pool2_pool').output)
skip_connection=densenet.get_layer('pool2_pool').output)
decoder = fd.nnconv5(decoder, densenet.get_layer('pool1').output_shape[3], 3,
skip_connection=densenet.get_layer('pool1').output)
skip_connection=densenet.get_layer('pool1').output)
decoder = fd.nnconv5(decoder, densenet.get_layer('conv1/relu').output_shape[3], 4,
skip_connection=densenet.get_layer('conv1/relu').output)
skip_connection=densenet.get_layer('conv1/relu').output)
# Final Pointwise for depth extraction
decoder = keras.layers.Conv2D(1, 1, padding='same')(decoder)
@@ -87,30 +87,6 @@ def dense_nnconv5(size, weights=None, shape=(224, 224, 3), half_features=True):
return keras.Model(inputs=input, outputs=decoder, name="fast_dense_depth")
def load_nyu(download_dir='../nyu'):
"""
Load the nyu_v2 dataset train split. Will be downloaded to ../nyu
:return: nyu_v2 dataset builder
"""
builder = tfds.builder('nyu_depth_v2')
builder.download_and_prepare(download_dir=download_dir)
return builder \
.as_dataset(split='train', shuffle_files=True) \
.shuffle(buffer_size=1024) \
.batch(8) \
.map(lambda x: fd.crop_and_resize(x))
def load_nyu_evaluate(download_dir='../nyu'):
"""
Load the nyu_v2 dataset validation split. Will be downloaded to ../nyu
:return: nyu_v2 dataset builder
"""
builder = tfds.builder('nyu_depth_v2')
builder.download_and_prepare(download_dir=download_dir)
return builder.as_dataset(split='validation').batch(1).map(lambda x: fd.crop_and_resize(x))
if __name__ == '__main__':
model = dense_depth(169, 'imagenet')
model.summary()