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fcn8s.py
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fcn8s.py
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#! /usr/bin/env python
# coding=utf-8
#================================================================
# Copyright (C) 2019 * Ltd. All rights reserved.
#
# Editor : VIM
# File name : fcn8s.py
# Author : YunYang1994
# Created date: 2019-10-12 15:42:20
# Description :
#
#================================================================
import tensorflow as tf
class FCN8s(tf.keras.Model):
def __init__(self, n_class=21):
super(FCN8s, self).__init__()
# conv1
self.conv1_1 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='valid')
self.conv1_2 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same')
self.pool1 = tf.keras.layers.MaxPooling2D(2, strides=2, padding='same') # 1/2
# conv2
self.conv2_1 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same')
self.conv2_2 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same')
self.pool2 = tf.keras.layers.MaxPooling2D(2, strides=2, padding='same') # 1/4
# conv3
self.conv3_1 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same')
self.conv3_2 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same')
self.conv3_3 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same')
self.pool3 = tf.keras.layers.MaxPooling2D(2, strides=2, padding='same') # 1/8
# conv4
self.conv4_1 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same')
self.conv4_2 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same')
self.conv4_3 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same')
self.pool4 = tf.keras.layers.MaxPooling2D(2, strides=2, padding='same') # 1/16
# conv5
self.conv5_1 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same')
self.conv5_2 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same')
self.conv5_3 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same')
self.pool5 = tf.keras.layers.MaxPooling2D(2, strides=2, padding='same') # 1/32
# fc6
self.fc6 = tf.keras.layers.Conv2D(4096, 7, activation='relu', padding='valid')
self.drop6 = tf.keras.layers.Dropout(0.5)
# fc7
self.fc7 = tf.keras.layers.Conv2D(4096, 1, activation='relu', padding='valid')
self.drop7 = tf.keras.layers.Dropout(0.5)
self.socre_fr = tf.keras.layers.Conv2D(n_class, 1)
self.score_pool3 = tf.keras.layers.Conv2D(n_class, 1)
self.score_pool4 = tf.keras.layers.Conv2D(n_class, 1)
self.upscore2 = tf.keras.layers.Conv2DTranspose(
n_class, 4, strides=2, padding='valid', use_bias=False)
self.upscore8 = tf.keras.layers.Conv2DTranspose(
n_class, 16, strides=8, padding='valid', use_bias=False)
self.upscore_pool4 = tf.keras.layers.Conv2DTranspose(
n_class, 4, strides=2, padding='valid', use_bias=False)
def call(self, x, training=False):
h = x
h = self.conv1_1(tf.keras.layers.ZeroPadding2D(padding=(100, 100))(h))
h = self.conv1_2(h)
h = self.pool1(h)
h = self.conv2_1(h)
h = self.conv2_2(h)
h = self.pool2(h)
h = self.conv3_1(h)
h = self.conv3_2(h)
h = self.conv3_3(h)
h = self.pool3(h)
pool3 = h # 1/8
h = self.conv4_1(h)
h = self.conv4_2(h)
h = self.conv4_3(h)
h = self.pool4(h)
pool4 = h # 1/16
h = self.conv5_1(h)
h = self.conv5_2(h)
h = self.conv5_3(h)
h = self.pool5(h)
h = self.fc6(h)
h = self.drop6(h, training)
h = self.fc7(h)
h = self.drop7(h, training)
h = self.socre_fr(h)
h = self.upscore2(h)
upscore2 = h # 1/16
# print(upscore2.shape)
h = self.score_pool4(pool4 * 0.01) # XXX: scaling to train at onece
h = h[:, 5:5+upscore2.shape[1], 5:5+upscore2.shape[2], :] # channel last
score_pool4c = h # 1/16
h = upscore2 + score_pool4c # 1/16
h = self.upscore_pool4(h)
upscore_pool4 = h # 1/8
h = self.score_pool3(pool3 * 0.0001) # XXX: scaling to train at onece
h = h[:,
9:9+upscore_pool4.shape[1],
9:9+upscore_pool4.shape[2], :] # channel last
score_pool3c = h # 1/8
h = upscore_pool4 + score_pool3c # 1/8
h = self.upscore8(h)
h = h[:, 31:31+x.shape[1], 31:31+x.shape[2], :] # channel last
return tf.nn.softmax(h, axis=-1)