official/projects/qat/vision/modeling/layers/nn_blocks_test.py
# Copyright 2024 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 nn_blocks."""
from typing import Any, Iterable, Tuple
# Import libraries
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import strategy_combinations
from official.projects.qat.vision.modeling.layers import nn_blocks
def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
"""Returns the combinations of end-to-end tests to run."""
return combinations.combine(
distribution=[
strategy_combinations.default_strategy,
strategy_combinations.cloud_tpu_strategy,
strategy_combinations.one_device_strategy_gpu,
],
)
class NNBlocksTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(nn_blocks.BottleneckBlockQuantized, 1, False, 0.0, None),
(nn_blocks.BottleneckBlockQuantized, 2, True, 0.2, 0.25),
)
def test_bottleneck_block_creation(self, block_fn, strides, use_projection,
stochastic_depth_drop_rate, se_ratio):
input_size = 128
filter_size = 256
inputs = tf_keras.Input(
shape=(input_size, input_size, filter_size * 4), batch_size=1)
block = block_fn(
filter_size,
strides,
use_projection=use_projection,
se_ratio=se_ratio,
stochastic_depth_drop_rate=stochastic_depth_drop_rate)
features = block(inputs)
self.assertAllEqual(
[1, input_size // strides, input_size // strides, filter_size * 4],
features.shape.as_list())
@parameterized.parameters(
(nn_blocks.InvertedBottleneckBlockQuantized, 1, 1, None, None),
(nn_blocks.InvertedBottleneckBlockQuantized, 6, 1, None, None),
(nn_blocks.InvertedBottleneckBlockQuantized, 1, 2, None, None),
(nn_blocks.InvertedBottleneckBlockQuantized, 1, 1, 0.2, None),
(nn_blocks.InvertedBottleneckBlockQuantized, 1, 1, None, 0.2),
)
def test_invertedbottleneck_block_creation(
self, block_fn, expand_ratio, strides, se_ratio,
stochastic_depth_drop_rate):
input_size = 128
in_filters = 24
out_filters = 40
inputs = tf_keras.Input(
shape=(input_size, input_size, in_filters), batch_size=1)
block = block_fn(
in_filters=in_filters,
out_filters=out_filters,
expand_ratio=expand_ratio,
strides=strides,
se_ratio=se_ratio,
stochastic_depth_drop_rate=stochastic_depth_drop_rate,
output_intermediate_endpoints=False)
features = block(inputs)
self.assertAllEqual(
[1, input_size // strides, input_size // strides, out_filters],
features.shape.as_list())
@parameterized.parameters(
(2, True, 0, 5, 0, 12, 12, 2),
(2, False, 5, 0, 0, 12, 18, 4),
(1, True, 0, 0, 0, 12, 12, 6),
(1, True, 3, 0, 0, 12, 18, 2),
(1, True, 3, 3, 0, 12, 12, 4),
(1, True, 3, 3, 3, 12, 18, 6),
(1, True, 0, 3, 3, 12, 12, 2),
(1, True, 0, 0, 3, 12, 18, 4),
(1, True, 3, 0, 3, 12, 12, 6),
)
def test_maybedwinvertedbottleneck_block_creation(
self,
strides,
middle_dw_downsample,
start_dw_kernel_size,
middle_dw_kernel_size,
end_dw_kernel_size,
in_filters,
out_filters,
expand_ratio,
):
input_size = 128
inputs = tf_keras.Input(
shape=(input_size, input_size, in_filters), batch_size=1
)
block = nn_blocks.MaybeDwInvertedBottleneckBlockQuantized(
in_filters=in_filters,
out_filters=out_filters,
expand_ratio=expand_ratio,
strides=strides,
middle_dw_downsample=middle_dw_downsample,
start_dw_kernel_size=start_dw_kernel_size,
middle_dw_kernel_size=middle_dw_kernel_size,
end_dw_kernel_size=end_dw_kernel_size,
)
features = block(inputs)
self.assertAllEqual(
[1, input_size // strides, input_size // strides, out_filters],
features.shape.as_list(),
)
@parameterized.parameters(
(2, True, 0, 5, 0, 12, 12, 2),
(2, False, 5, 0, 0, 12, 18, 4),
(1, True, 0, 0, 0, 12, 12, 6),
(1, True, 3, 0, 0, 12, 18, 2),
(1, True, 3, 3, 0, 12, 12, 4),
(1, True, 3, 3, 3, 12, 18, 6),
(1, True, 0, 3, 3, 12, 12, 2),
(1, True, 0, 0, 3, 12, 18, 4),
(1, True, 3, 0, 3, 12, 12, 6),
)
def test_maybedwinvertedbottleneck_block_forward_pass_no_nans(
self,
strides,
middle_dw_downsample,
start_dw_kernel_size,
middle_dw_kernel_size,
end_dw_kernel_size,
in_filters,
out_filters,
expand_ratio,
):
tf.random.set_seed(42)
input_size = 128
input_shape = (input_size, input_size, in_filters)
output_shape = [
1,
input_size // strides,
input_size // strides,
out_filters,
]
inputs = tf_keras.Input(shape=input_shape, batch_size=1)
block = nn_blocks.MaybeDwInvertedBottleneckBlockQuantized(
in_filters=in_filters,
out_filters=out_filters,
expand_ratio=expand_ratio,
strides=strides,
middle_dw_downsample=middle_dw_downsample,
start_dw_kernel_size=start_dw_kernel_size,
middle_dw_kernel_size=middle_dw_kernel_size,
end_dw_kernel_size=end_dw_kernel_size,
)
features = block(inputs)
self.assertAllEqual(features.shape.as_list(), output_shape)
model = tf_keras.Model(inputs=inputs, outputs=features)
input_data = tf.random.uniform(
(1, input_size, input_size, in_filters), minval=-1.0, maxval=1.0
)
predicted_outputs = model.predict(input_data)
self.assertAllEqual(
tf.math.is_nan(predicted_outputs),
tf.constant(False, shape=output_shape),
)
@parameterized.parameters(
(2, True, 0, 5, 0, 12, 12, 2),
(2, False, 5, 0, 0, 12, 18, 4),
(1, True, 0, 0, 0, 12, 12, 6),
(1, True, 3, 0, 0, 12, 18, 2),
(1, True, 3, 3, 0, 12, 12, 4),
(1, True, 3, 3, 3, 12, 18, 6),
(1, True, 0, 3, 3, 12, 12, 2),
(1, True, 0, 0, 3, 12, 18, 4),
(1, True, 3, 0, 3, 12, 12, 6),
)
def test_maybedwinvertedbottleneck_block_backward_pass_no_nans(
self,
strides,
middle_dw_downsample,
start_dw_kernel_size,
middle_dw_kernel_size,
end_dw_kernel_size,
in_filters,
out_filters,
expand_ratio,
):
tf.random.set_seed(42)
input_size = 128
inputs = tf_keras.Input(
shape=(input_size, input_size, in_filters), batch_size=1
)
output_shape = [
1,
input_size // strides,
input_size // strides,
out_filters,
]
block = nn_blocks.MaybeDwInvertedBottleneckBlockQuantized(
in_filters=in_filters,
out_filters=out_filters,
expand_ratio=expand_ratio,
strides=strides,
middle_dw_downsample=middle_dw_downsample,
start_dw_kernel_size=start_dw_kernel_size,
middle_dw_kernel_size=middle_dw_kernel_size,
end_dw_kernel_size=end_dw_kernel_size,
)
features = block(inputs)
self.assertAllEqual(features.shape.as_list(), output_shape)
model = tf_keras.Model(inputs=inputs, outputs=features)
model.compile(
optimizer=tf_keras.optimizers.Adam(),
loss=tf_keras.losses.MeanSquaredError(),
metrics=[tf_keras.metrics.MeanSquaredError()],
)
input_train = tf.random.uniform(
(1, input_size, input_size, in_filters), minval=-1.0, maxval=1.0
)
output_train = tf.random.uniform(output_shape, minval=-1.0, maxval=1.0)
input_valid = tf.random.uniform(
(1, input_size, input_size, in_filters), minval=-1.0, maxval=1.0
)
output_valid = tf.random.uniform(output_shape, minval=-1.0, maxval=1.0)
model.fit(
input_train,
output_train,
batch_size=1,
epochs=1,
validation_data=(input_valid, output_valid),
)
if __name__ == '__main__':
tf.test.main()