official/projects/volumetric_models/modeling/nn_blocks_3d_test.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""Tests for 3D volumeric convoluion blocks."""
# Import libraries
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.projects.volumetric_models.modeling import nn_blocks_3d
class NNBlocks3DTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters((128, 128, 32, 1), (256, 256, 16, 2))
def test_bottleneck_block_3d_volume_creation(self, spatial_size, volume_size,
filters, strides):
inputs = tf_keras.Input(
shape=(spatial_size, spatial_size, volume_size, filters * 4),
batch_size=1)
block = nn_blocks_3d.BottleneckBlock3DVolume(
filters=filters,
strides=strides,
use_projection=True,
se_ratio=0.2,
stochastic_depth_drop_rate=0.2)
features = block(inputs)
self.assertAllEqual([
1, spatial_size // strides, spatial_size // strides,
volume_size // strides, filters * 4
], features.shape.as_list())
@parameterized.parameters((128, 128, 32, 1), (256, 256, 64, 2))
def test_residual_block_3d_volume_creation(self, spatial_size, volume_size,
filters, strides):
inputs = tf_keras.Input(
shape=(spatial_size, spatial_size, volume_size, filters), batch_size=1)
block = nn_blocks_3d.ResidualBlock3DVolume(
filters=filters,
strides=strides,
use_projection=True,
se_ratio=0.2,
stochastic_depth_drop_rate=0.2)
features = block(inputs)
self.assertAllEqual([
1, spatial_size // strides, spatial_size // strides,
volume_size // strides, filters
], features.shape.as_list())
@parameterized.parameters((128, 128, 64, 1, 3), (256, 256, 128, 2, 1))
def test_basic_block_3d_volume_creation(self, spatial_size, volume_size,
filters, strides, kernel_size):
inputs = tf_keras.Input(
shape=(spatial_size, spatial_size, volume_size, filters), batch_size=1)
block = nn_blocks_3d.BasicBlock3DVolume(
filters=filters, strides=strides, kernel_size=kernel_size)
features = block(inputs)
self.assertAllEqual([
1, spatial_size // strides, spatial_size // strides,
volume_size // strides, filters
], features.shape.as_list())
if __name__ == '__main__':
tf.test.main()