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import tensorflow.compat.v1 as tf |
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import tf_slim as slim |
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def relu(x): |
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return tf.nn.relu(x) |
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def upsample_and_sum(x1, x2,output_channels,in_channels): |
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pool_size = 2 |
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deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02)) |
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deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1]) |
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deconv_output = tf.add(deconv,x2) |
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return deconv_output |
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def upsample_and_concat(x1, x2, output_channels, in_channels): |
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pool_size = 2 |
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deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02)) |
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deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1]) |
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deconv_output = tf.concat([deconv, x2], 3) |
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deconv_output.set_shape([None, None, None, output_channels * 2]) |
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return deconv_output |
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def sc_net_1f(input): |
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with slim.arg_scope([slim.conv2d], weights_initializer=slim.variance_scaling_initializer(), |
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weights_regularizer=slim.l1_regularizer(0.0001),biases_initializer = None): |
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conv1 = slim.conv2d(input, 64, [3, 3], rate=1, activation_fn=relu, scope='conv1_1') |
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res_conv1 = slim.conv2d(conv1, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv1_1') |
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res_conv1 = slim.conv2d(res_conv1, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv1_2') |
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res_block1 = conv1 + res_conv1 |
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pool2 = slim.avg_pool2d(res_block1,[2,2],padding='SAME') |
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res_conv2 = slim.conv2d(pool2, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv2_1') |
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res_conv2 = slim.conv2d(res_conv2, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv2_2') |
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res_block2 = pool2 + res_conv2 |
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pool3 = slim.avg_pool2d(res_block2,[2,2],padding='SAME') |
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res_conv3 = slim.conv2d(pool3, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv3_1') |
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res_conv3 = slim.conv2d(res_conv3, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv3_2') |
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res_block3 = pool3 + res_conv3 |
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deconv1 = upsample_and_sum(res_block3, res_block2, 64, 64) |
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conv4 = slim.conv2d(deconv1, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv4_1') |
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res_conv4 = slim.conv2d(conv4, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv4_1') |
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res_conv4 = slim.conv2d(res_conv4, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv4_2') |
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res_block4 = conv4 + res_conv4 |
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deconv2 = upsample_and_sum(res_block4, res_block1, 64, 64) |
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conv5 = slim.conv2d(deconv2, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv5_1') |
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res_conv5 = slim.conv2d(conv5, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv5_1') |
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res_conv5 = slim.conv2d(res_conv5, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv5_2') |
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res_block5 = conv5 + res_conv5 |
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conv6 = slim.conv2d(res_block5, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv6_1') |
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conv7 = slim.conv2d(conv6, 4, [3, 3], rate=1, stride=1, activation_fn=None, scope='conv7_1') |
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out = conv7 |
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return out |
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def sc_net(input): |
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with slim.arg_scope([slim.conv2d], weights_initializer=slim.variance_scaling_initializer(), |
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weights_regularizer=slim.l1_regularizer(0.0001),biases_initializer = None): |
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conv1 = slim.conv2d(input, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv1_1') |
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conv1 = slim.conv2d(conv1, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv1_2') |
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pool1 = slim.max_pool2d(conv1, [2, 2], padding='SAME') |
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conv2 = slim.conv2d(pool1, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv2_1') |
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conv2 = slim.conv2d(conv2, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv2_2') |
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pool2 = slim.max_pool2d(conv2, [2, 2], padding='SAME') |
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conv3 = slim.conv2d(pool2, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv3_1') |
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conv3 = slim.conv2d(conv3, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv3_2') |
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pool3 = slim.max_pool2d(conv3, [2, 2], padding='SAME') |
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conv4 = slim.conv2d(pool3, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv4_1') |
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conv4 = slim.conv2d(conv4, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv4_2') |
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pool4 = slim.max_pool2d(conv4, [2, 2], padding='SAME') |
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conv5 = slim.conv2d(pool4, 512, [3, 3], rate=1, activation_fn=relu, scope='g_conv5_1') |
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conv5 = slim.conv2d(conv5, 512, [3, 3], rate=1, activation_fn=relu, scope='g_conv5_2') |
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up6 = upsample_and_concat(conv5, conv4, 256, 512) |
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conv6 = slim.conv2d(up6, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv6_1') |
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conv6 = slim.conv2d(conv6, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv6_2') |
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up7 = upsample_and_concat(conv6, conv3, 128, 256) |
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conv7 = slim.conv2d(up7, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv7_1') |
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conv7 = slim.conv2d(conv7, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv7_2') |
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up8 = upsample_and_concat(conv7, conv2, 64, 128) |
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conv8 = slim.conv2d(up8, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv8_1') |
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conv8 = slim.conv2d(conv8, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv8_2') |
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up9 = upsample_and_concat(conv8, conv1, 32, 64) |
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conv9 = slim.conv2d(up9, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv9_1') |
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conv9 = slim.conv2d(conv9, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv9_2') |
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conv10 = slim.conv2d(conv9, 4, [1, 1], rate=1, activation_fn=relu, scope='g_conv10') |
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out = conv10 |
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return out |
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