基于Deeplab-v3算法实现对遥感图像的语义分割python源码(带详细注释+项目说明+数据集+模型).zip

"""ResNet model. Related papers: https://arxiv.org/pdf/1603.05027v2.pdf https://arxiv.org/pdf/1512.03385v1.pdf https://arxiv.org/pdf/1605.07146v1.pdf """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.training import moving_averages import tensorflow as tf # 为了finetune resnet_v2_50 对数据每个通道中心化 _R_MEAN = 123.68 _G_MEAN = 116.78 _B_MEAN = 103.94 class Deeplab_v3(): def __init__(self, batch_norm_decay=0.99, batch_norm_epsilon=1e-3,): self._batch_norm_decay = batch_norm_decay self._batch_norm_epsilon = batch_norm_epsilon # 模型训练开关占位符 self._is_training = tf.placeholder(tf.bool, name='is_training') self.num_class = 5 self.filters = [64, 256, 512, 1024, 2048] self.strides = [2, 2, 1, 1] self.n = [3, 4, 6, 3] def forward_pass(self, x): """Build the core model within the graph""" with tf.variable_scope('resnet_v2_50', reuse=tf.AUTO_REUSE): size = tf.shape(x)[1:3] x = x - [_R_MEAN, _G_MEAN, _B_MEAN] x = self._conv(x, 7, 64, 2, 'conv1', False, False) x = self._max_pool(x, 3, 2, 'max') res_func = self._bottleneck_residual_v2 for i in range(4): with tf.variable_scope('block%d' % (i + 1)): for j in range(self.n[i]): with tf.variable_scope('unit_%d' % (j + 1)): if j == 0: x = res_func(x, self.filters[i], self.filters[i+1], 1) elif j == self.n[i] - 1: x = res_func(x, self.filters[i+1], self.filters[i+1], self.strides[i]) else: x = res_func(x, self.filters[i+1], self.filters[i+1], 1) tf.logging.info('the shape of features after block%d is %s' % (i+1, x.get_shape())) # DeepLab_v3的部分 with tf.variable_scope('DeepLab_v3', reuse=tf.AUTO_REUSE): x = self._atrous_spatial_pyramid_pooling(x) x = self._conv(x, 1, 5, 1, 'logits', False, False) x = tf.image.resize_bilinear(x, size) return x def _A_ASPP(self): pass def _atrous_spatial_pyramid_pooling(self, x): """空洞空间金字塔池化 """ with tf.variable_scope('ASSP_layers'): feature_map_size = tf.shape(x) image_level_features = tf.reduce_mean(x, [1, 2], keep_dims=True) image_level_features = self._conv(image_level_features, 1, 256, 1, 'global_avg_pool', True) image_level_features = tf.image.resize_bilinear(image_level_features, (feature_map_size[1], feature_map_size[2])) at_pool1x1 = self._conv(x, kernel_size=1, filters=256, strides=1, scope='assp1', batch_norm=True) at_pool3x3_1 = self._conv(x, kernel_size=3, filters=256, strides=1, scope='assp2', batch_norm=True, rate=6) at_pool3x3_2 = self._conv(x, kernel_size=3, filters=256, strides=1, scope='assp3', batch_norm=True, rate=12) at_pool3x3_3 = self._conv(x, kernel_size=3, filters=256, strides=1, scope='assp4', batch_norm=True, rate=18) net = tf.concat((image_level_features, at_pool1x1, at_pool3x3_1, at_pool3x3_2, at_pool3x3_3), axis=3) net = self._conv(net, kernel_size=1, filters=256, strides=1, scope='concat', batch_norm=True) return net def _bottleneck_residual_v2(self, x, in_filter, out_filter, stride,): """Bottleneck residual unit with 3 sub layers, plan B shortcut.""" with tf.variable_scope('bottleneck_v2'): origin_x = x with tf.variable_scope('preact'): preact = self._batch_norm(x) preact = self._relu(preact) residual = self._conv(preact, 1, out_filter // 4, stride, 'conv1', True, True) residual = self._conv(residual, 3, out_filter // 4, 1, 'conv2', True, True) residual = self._conv(residual, 1, out_filter, 1, 'conv3', False, False) if in_filter != out_filter: short_cut = self._conv(preact, 1, out_filter, stride, 'shortcut', False, False) else: short_cut = self._subsample(origin_x, stride, 'shortcut') x = tf.add(residual, short_cut) return x def _conv(self, x, kernel_size, filters, strides, scope, batch_norm=False, activation=False, rate=None ): """Convolution.""" with tf.variable_scope(scope): x_shape = x.get_shape().as_list() w = tf.get_variable(name='weights', shape=[kernel_size, kernel_size, x_shape[3], filters]) if rate == None: x = tf.nn.conv2d(input=x, filter=w, padding='SAME', strides=[1, strides, strides, 1], name='conv', ) else: x = tf.nn.atrous_conv2d(value=x, filters=w, padding='SAME', name='conv', rate=rate) if batch_norm: with tf.variable_scope('BatchNorm'): x = self._batch_norm(x) else: b = tf.get_variable(name='biases', shape=[filters]) x = x + b if activation: x = tf.nn.relu(x) return x def _batch_norm(self, x): x_shape = x.get_shape() params_shape = x_shape[-1:] axis = list(range(len(x_shape) - 1)) beta = tf.get_variable(name='beta', shape=params_shape, initializer=tf.zeros_initializer) gamma = tf.get_variable(name='gamma', shape=params_shape, initializer=tf.ones_initializer) moving_mean = tf.get_variable(name='moving_mean', shape=params_shape, initializer=tf.zeros_initializer, trainable=False) moving_variance = tf.get_variable(name='moving_variance', shape=params_shape, initializer=tf.ones_initializer, trainable=False) tf.add_to_collection('BN_MEAN_VARIANCE', moving_mean) tf.add_to_collection('BN_MEAN_VARIANCE', moving_variance) # These ops will only be preformed when training. mean, variance = tf.nn.moments(x, axis) update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, self._batch_norm_decay, name='MovingAvgMean') update_moving_variance = moving_averages.assign_moving_average(moving_variance, variance, self._batch_norm_decay, name='MovingAvgVariance')