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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for resnet."""
# Import libraries
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.vision.modeling.layers import nn_blocks_3d
class NNBlocksTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(nn_blocks_3d.BottleneckBlock3D, 1, 1, 2, True, 0.2, 0.1),
(nn_blocks_3d.BottleneckBlock3D, 3, 2, 1, False, 0.0, 0.0),
)
def test_bottleneck_block_creation(self, block_fn, temporal_kernel_size,
temporal_strides, spatial_strides,
use_self_gating, se_ratio,
stochastic_depth):
temporal_size = 16
spatial_size = 128
filters = 256
inputs = tf_keras.Input(
shape=(temporal_size, spatial_size, spatial_size, filters * 4),
batch_size=1)
block = block_fn(
filters=filters,
temporal_kernel_size=temporal_kernel_size,
temporal_strides=temporal_strides,
spatial_strides=spatial_strides,
use_self_gating=use_self_gating,
se_ratio=se_ratio,
stochastic_depth_drop_rate=stochastic_depth)
features = block(inputs)
self.assertAllEqual([
1, temporal_size // temporal_strides, spatial_size // spatial_strides,
spatial_size // spatial_strides, filters * 4
], features.shape.as_list())
if __name__ == '__main__':
tf.test.main()
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