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# coding=utf-8
# Copyright 2021 The Deeplab2 Authors.
#
# 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 mobilenet."""
from absl.testing import parameterized
import tensorflow as tf
from deeplab2.model import test_utils
from deeplab2.model.encoder import mobilenet
class MobilenetTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.parameters('MobileNetV3Small', 'MobileNetV3Large')
def test_mobilenetv3_construct_graph(self, model_name):
tf.keras.backend.set_image_data_format('channels_last')
input_size = 128
mobilenet_models = {
'MobileNetV3Small': mobilenet.MobileNetV3Small,
'MobileNetV3Large': mobilenet.MobileNetV3Large,
}
mobilenet_channels = {
# The number of filters of layers having outputs been collected
# for filter_size_scale = 1.0
'MobileNetV3Small': [16, 24, 48, 576],
'MobileNetV3Large': [24, 40, 112, 960],
}
network = mobilenet_models[str(model_name)](width_multiplier=1.0)
inputs = tf.ones([1, input_size, input_size, 3])
endpoints = network(inputs)
for idx, num_filter in enumerate(mobilenet_channels[model_name]):
self.assertAllEqual(
[1, input_size / 2 ** (idx+2), input_size / 2 ** (idx+2), num_filter],
endpoints['res'+str(idx+2)].shape.as_list())
@parameterized.product(
model_name=['MobileNetV3Small', 'MobileNetV3Large'],
output_stride=[4, 8, 16, 32])
def test_mobilenetv3_atrous_endpoint_shape(self, model_name, output_stride):
tf.keras.backend.set_image_data_format('channels_last')
input_size = 321
batch_size = 2
mobilenet_models = {
'MobileNetV3Small': mobilenet.MobileNetV3Small,
'MobileNetV3Large': mobilenet.MobileNetV3Large,
}
stride_spatial_shapes_map = {
4: [81, 81, 81, 81],
8: [81, 41, 41, 41],
16: [81, 41, 21, 21],
32: [81, 41, 21, 11],
}
mobilenet_channels = {
# The number of filters of layers having outputs been collected
# for filter_size_scale = 1.0
'MobileNetV3Small': [16, 24, 48, 576],
'MobileNetV3Large': [24, 40, 112, 960],
}
network = mobilenet_models[str(model_name)](
width_multiplier=1.0,
output_stride=output_stride)
spatial_shapes = stride_spatial_shapes_map[output_stride]
inputs = tf.ones([batch_size, input_size, input_size, 3])
endpoints = network(inputs)
for idx, num_filters in enumerate(mobilenet_channels[model_name]):
expected_shape = [
batch_size, spatial_shapes[idx], spatial_shapes[idx], num_filters
]
self.assertAllEqual(
expected_shape,
endpoints['res'+str(idx+2)].shape.as_list())
@parameterized.parameters('MobileNetV3Small', 'MobileNetV3Large')
def test_mobilenet_reload_weights(self, model_name):
tf.keras.backend.set_image_data_format('channels_last')
mobilenet_models = {
'MobileNetV3Small': mobilenet.MobileNetV3Small,
'MobileNetV3Large': mobilenet.MobileNetV3Large,
}
tf.random.set_seed(0)
pixel_inputs = test_utils.create_test_input(1, 320, 320, 3)
network1 = mobilenet_models[model_name](
width_multiplier=1.0,
output_stride=32,
name='m1')
network1(pixel_inputs, False)
outputs1 = network1(pixel_inputs, False)
pixel_outputs = outputs1['res5']
# Feature extraction at the normal network rate.
network2 = mobilenet_models[model_name](
width_multiplier=1.0,
output_stride=32,
name='m2')
network2(pixel_inputs, False)
# Make the two networks use the same weights.
network2.set_weights(network1.get_weights())
outputs2 = network2(pixel_inputs, False)
expected = outputs2['res5']
self.assertAllClose(network1.get_weights(), network2.get_weights(),
atol=1e-4, rtol=1e-4)
self.assertAllClose(pixel_outputs, expected, atol=1e-4, rtol=1e-4)
if __name__ == '__main__':
tf.test.main()
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