158 lines
6.6 KiB
Python
158 lines
6.6 KiB
Python
# MIT License
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#
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# Copyright (c) 2019-2024 Iván de Paz Centeno
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# pylint: disable=duplicate-code
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import tensorflow as tf
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L = tf.keras.layers
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class ONet(tf.keras.Model):
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"""
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Definition of ONet (Output Network) for MTCNN.
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This network takes as input an image of size 48x48 with 3 channels, and outputs:
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* The regression of the bounding boxes (x1, y1, x2, y2) with a linear activation.
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* The regression of 5 facial landmarks (10 points total).
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* The classification of the area as a softmax operation ([1, 0] -> Not face; [0, 1] -> Face).
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"""
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def __init__(self, **kwargs):
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super(ONet, self).__init__(**kwargs)
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# Defining the layers according to the provided architecture
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self.conv1 = L.Conv2D(32, kernel_size=(3, 3), strides=(1, 1), padding="valid", activation="linear", name="conv1")
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self.prelu1 = L.PReLU(shared_axes=[1, 2], name="prelu1")
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self.maxpool1 = L.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same", name="maxpooling1")
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self.conv2 = L.Conv2D(64, kernel_size=(3, 3), strides=(1, 1), padding="valid", activation="linear", name="conv2")
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self.prelu2 = L.PReLU(shared_axes=[1, 2], name="prelu2")
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self.maxpool2 = L.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="valid", name="maxpooling2")
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self.conv3 = L.Conv2D(64, kernel_size=(3, 3), strides=(1, 1), padding="valid", activation="linear", name="conv3")
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self.prelu3 = L.PReLU(shared_axes=[1, 2], name="prelu3")
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self.maxpool3 = L.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding="same", name="maxpooling3")
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self.conv4 = L.Conv2D(128, kernel_size=(2, 2), strides=(1, 1), padding="valid", activation="linear", name="conv4")
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self.prelu4 = L.PReLU(shared_axes=[1, 2], name="prelu4")
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self.permute = L.Permute((2, 1, 3), name="permute")
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self.flatten = L.Flatten(name="flatten4")
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self.fc5 = L.Dense(256, activation="linear", name="fc5")
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self.prelu5 = L.PReLU(name="prelu5")
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self.fc6_1 = L.Dense(4, activation="linear", name="fc6-1") # Bounding box regression
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self.fc6_2 = L.Dense(10, activation="linear", name="fc6-2") # Landmark regression (5 landmarks, 10 points total)
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self.fc6_3 = L.Dense(2, activation="softmax", name="fc6-3") # Classification (face or not)
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def build(self, input_shape=(None, 48, 48, 3)):
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"""
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Build the network by defining the input and manually creating each layer step by step, computing output shapes.
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This method mirrors the layer initialization in the functional API.
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"""
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# Build conv1 block
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self.conv1.build(input_shape)
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output_shape = self.conv1.compute_output_shape(input_shape)
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self.prelu1.build(output_shape)
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output_shape = self.prelu1.compute_output_shape(output_shape)
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self.maxpool1.build(output_shape)
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output_shape = self.maxpool1.compute_output_shape(output_shape)
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# Build conv2 block
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self.conv2.build(output_shape)
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output_shape = self.conv2.compute_output_shape(output_shape)
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self.prelu2.build(output_shape)
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output_shape = self.prelu2.compute_output_shape(output_shape)
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self.maxpool2.build(output_shape)
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output_shape = self.maxpool2.compute_output_shape(output_shape)
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# Build conv3 block
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self.conv3.build(output_shape)
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output_shape = self.conv3.compute_output_shape(output_shape)
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self.prelu3.build(output_shape)
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output_shape = self.prelu3.compute_output_shape(output_shape)
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self.maxpool3.build(output_shape)
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output_shape = self.maxpool3.compute_output_shape(output_shape)
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# Build conv4 block
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self.conv4.build(output_shape)
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output_shape = self.conv4.compute_output_shape(output_shape)
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self.prelu4.build(output_shape)
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output_shape = self.prelu4.compute_output_shape(output_shape)
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# Permute and flatten
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self.permute.build(output_shape)
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output_shape = self.permute.compute_output_shape(output_shape)
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self.flatten.build(output_shape)
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output_shape = self.flatten.compute_output_shape(output_shape)
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# Fully connected layers
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self.fc5.build(output_shape)
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output_shape = self.fc5.compute_output_shape(output_shape)
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self.prelu5.build(output_shape)
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output_shape = self.prelu5.compute_output_shape(output_shape)
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# Outputs (classification, bounding box regression, and landmark regression)
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self.fc6_1.build(output_shape)
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self.fc6_2.build(output_shape)
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self.fc6_3.build(output_shape)
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# Call the super build to finalize the model building
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super(ONet, self).build(input_shape)
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def call(self, inputs, *args, **kwargs):
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x = inputs
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# First conv block
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x = self.conv1(x)
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x = self.prelu1(x)
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x = self.maxpool1(x)
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# Second conv block
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x = self.conv2(x)
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x = self.prelu2(x)
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x = self.maxpool2(x)
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# Third conv block
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x = self.conv3(x)
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x = self.prelu3(x)
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x = self.maxpool3(x)
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# Fourth conv block
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x = self.conv4(x)
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x = self.prelu4(x)
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# Permute, flatten, and fully connected layers
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x = self.permute(x)
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x = self.flatten(x)
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x = self.fc5(x)
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x = self.prelu5(x)
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# Outputs
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bbox_reg = self.fc6_1(x) # Regression of bounding boxes
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landmarks = self.fc6_2(x) # Regression of facial landmarks
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bbox_class = self.fc6_3(x) # Classification (face or not)
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return [bbox_reg, landmarks, bbox_class]
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