102 lines
3.9 KiB
Python
102 lines
3.9 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 PNet(tf.keras.Model):
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"""
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Definition of PNet (Proposal Network)
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This network takes as input an image with variable width and height, and generates two outputs:
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* The regression of the bounding boxes (x1, y1, x2, y2) with a linear activation.
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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(PNet, self).__init__(**kwargs)
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# Definir las capas
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self.conv1 = L.Conv2D(10, 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=(2,2), strides=(2,2), padding="same", name="maxpooling1")
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self.conv2 = L.Conv2D(16, 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.conv3 = L.Conv2D(32, 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.conv4_1 = L.Conv2D(4, kernel_size=(1,1), strides=(1,1), padding="valid", activation="linear", name="conv4-1")
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self.conv4_2 = L.Conv2D(2, kernel_size=(1,1), strides=(1,1), padding="valid", activation="softmax", name="conv4-2")
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def build(self, input_shape=(None, None, None, 3)):
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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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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.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.conv4_1.build(output_shape)
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self.conv4_2.build(output_shape)
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super(PNet, 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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# Third conv block
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x = self.conv3(x)
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x = self.prelu3(x)
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# Outputs
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bbox_reg = self.conv4_1(x)
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bbox_class = self.conv4_2(x)
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return [bbox_reg, bbox_class]
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