def unet2D_bn_modified(images, training, nlabels): images_padded = tf.pad(images, [[0,0], [92, 92], [92, 92], [0,0]], 'CONSTANT') conv1_1 = layers.conv2D_layer_bn(images_padded, 'conv1_1', num_filters=64, training=training, padding='VALID') conv1_2 = layers.conv2D_layer_bn(conv1_1, 'conv1_2', num_filters=64, training=training, padding='VALID') pool1 = layers.max_pool_layer2d(conv1_2) conv2_1 = layers.conv2D_layer_bn(pool1, 'conv2_1', num_filters=128, training=training, padding='VALID') conv2_2 = layers.conv2D_layer_bn(conv2_1, 'conv2_2', num_filters=128, training=training, padding='VALID') pool2 = layers.max_pool_layer2d(conv2_2) conv3_1 = layers.conv2D_layer_bn(pool2, 'conv3_1', num_filters=256, training=training, padding='VALID') conv3_2 = layers.conv2D_layer_bn(conv3_1, 'conv3_2', num_filters=256, training=training, padding='VALID') pool3 = layers.max_pool_layer2d(conv3_2) conv4_1 = layers.conv2D_layer_bn(pool3, 'conv4_1', num_filters=512, training=training, padding='VALID') conv4_2 = layers.conv2D_layer_bn(conv4_1, 'conv4_2', num_filters=512, training=training, padding='VALID') pool4 = layers.max_pool_layer2d(conv4_2) conv5_1 = layers.conv2D_layer_bn(pool4, 'conv5_1', num_filters=1024, training=training, padding='VALID') conv5_2 = layers.conv2D_layer_bn(conv5_1, 'conv5_2', num_filters=1024, training=training, padding='VALID') upconv4 = layers.deconv2D_layer_bn(conv5_2, name='upconv4', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat4 = layers.crop_and_concat_layer([upconv4, conv4_2], axis=3) conv6_1 = layers.conv2D_layer_bn(concat4, 'conv6_1', num_filters=512, training=training, padding='VALID') conv6_2 = layers.conv2D_layer_bn(conv6_1, 'conv6_2', num_filters=512, training=training, padding='VALID') upconv3 = layers.deconv2D_layer_bn(conv6_2, name='upconv3', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat3 = layers.crop_and_concat_layer([upconv3, conv3_2], axis=3) conv7_1 = layers.conv2D_layer_bn(concat3, 'conv7_1', num_filters=256, training=training, padding='VALID') conv7_2 = layers.conv2D_layer_bn(conv7_1, 'conv7_2', num_filters=256, training=training, padding='VALID') upconv2 = layers.deconv2D_layer_bn(conv7_2, name='upconv2', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat2 = layers.crop_and_concat_layer([upconv2, conv2_2], axis=3) conv8_1 = layers.conv2D_layer_bn(concat2, 'conv8_1', num_filters=128, training=training, padding='VALID') conv8_2 = layers.conv2D_layer_bn(conv8_1, 'conv8_2', num_filters=128, training=training, padding='VALID') upconv1 = layers.deconv2D_layer_bn(conv8_2, name='upconv1', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat1 = layers.crop_and_concat_layer([upconv1, conv1_2], axis=3) conv9_1 = layers.conv2D_layer_bn(concat1, 'conv9_1', num_filters=64, training=training, padding='VALID') conv9_2 = layers.conv2D_layer_bn(conv9_1, 'conv9_2', num_filters=64, training=training, padding='VALID') pred = layers.conv2D_layer_bn(conv9_2, 'pred', num_filters=nlabels, kernel_size=(1,1), activation=tf.identity, training=training, padding='VALID') return pred
def unet2D_bn_padding_same_modified(images, training, nlabels): conv1_1 = layers.conv2D_layer_bn(images, 'conv1_1', num_filters=64, training=training) conv1_2 = layers.conv2D_layer_bn(conv1_1, 'conv1_2', num_filters=64, training=training) pool1 = layers.max_pool_layer2d(conv1_2) conv2_1 = layers.conv2D_layer_bn(pool1, 'conv2_1', num_filters=128, training=training) conv2_2 = layers.conv2D_layer_bn(conv2_1, 'conv2_2', num_filters=128, training=training) pool2 = layers.max_pool_layer2d(conv2_2) conv3_1 = layers.conv2D_layer_bn(pool2, 'conv3_1', num_filters=256, training=training) conv3_2 = layers.conv2D_layer_bn(conv3_1, 'conv3_2', num_filters=256, training=training) pool3 = layers.max_pool_layer2d(conv3_2) conv4_1 = layers.conv2D_layer_bn(pool3, 'conv4_1', num_filters=512, training=training) conv4_2 = layers.conv2D_layer_bn(conv4_1, 'conv4_2', num_filters=512, training=training) pool4 = layers.max_pool_layer2d(conv4_2) conv5_1 = layers.conv2D_layer_bn(pool4, 'conv5_1', num_filters=1024, training=training) conv5_2 = layers.conv2D_layer_bn(conv5_1, 'conv5_2', num_filters=1024, training=training) upconv4 = layers.deconv2D_layer_bn(conv5_2, name='upconv4', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat4 = tf.concat([conv4_2, upconv4], axis=3, name='concat4') conv6_1 = layers.conv2D_layer_bn(concat4, 'conv6_1', num_filters=512, training=training) conv6_2 = layers.conv2D_layer_bn(conv6_1, 'conv6_2', num_filters=512, training=training) upconv3 = layers.deconv2D_layer_bn(conv6_2, name='upconv3', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat3 = tf.concat([conv3_2, upconv3], axis=3, name='concat3') conv7_1 = layers.conv2D_layer_bn(concat3, 'conv7_1', num_filters=256, training=training) conv7_2 = layers.conv2D_layer_bn(conv7_1, 'conv7_2', num_filters=256, training=training) upconv2 = layers.deconv2D_layer_bn(conv7_2, name='upconv2', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat2 = tf.concat([conv2_2, upconv2], axis=3, name='concat2') conv8_1 = layers.conv2D_layer_bn(concat2, 'conv8_1', num_filters=128, training=training) conv8_2 = layers.conv2D_layer_bn(conv8_1, 'conv8_2', num_filters=128, training=training) upconv1 = layers.deconv2D_layer_bn(conv8_2, name='upconv1', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat1 = tf.concat([conv1_2, upconv1], axis=3, name='concat1') conv9_1 = layers.conv2D_layer_bn(concat1, 'conv9_1', num_filters=64, training=training) conv9_2 = layers.conv2D_layer_bn(conv9_1, 'conv9_2', num_filters=64, training=training) pred = layers.conv2D_layer_bn(conv9_2, 'pred', num_filters=nlabels, kernel_size=(1,1), activation=tf.identity, training=training) return pred
def VGG16_FCN_8(images, training, nlabels): conv1_1 = layers.conv2D_layer(images, 'conv1_1', num_filters=64) conv1_2 = layers.conv2D_layer(conv1_1, 'conv1_2', num_filters=64) pool1 = layers.max_pool_layer2d(conv1_2) conv2_1 = layers.conv2D_layer(pool1, 'conv2_1', num_filters=128) conv2_2 = layers.conv2D_layer(conv2_1, 'conv2_2', num_filters=128) pool2 = layers.max_pool_layer2d(conv2_2) conv3_1 = layers.conv2D_layer(pool2, 'conv3_1', num_filters=256) conv3_2 = layers.conv2D_layer(conv3_1, 'conv3_2', num_filters=256) conv3_3 = layers.conv2D_layer(conv3_2, 'conv3_3', num_filters=256) pool3 = layers.max_pool_layer2d(conv3_3) conv4_1 = layers.conv2D_layer(pool3, 'conv4_1', num_filters=512) conv4_2 = layers.conv2D_layer(conv4_1, 'conv4_2', num_filters=512) conv4_3 = layers.conv2D_layer(conv4_2, 'conv4_3', num_filters=512) pool4 = layers.max_pool_layer2d(conv4_3) conv5_1 = layers.conv2D_layer(pool4, 'conv5_1', num_filters=512) conv5_2 = layers.conv2D_layer(conv5_1, 'conv5_2', num_filters=512) conv5_3 = layers.conv2D_layer(conv5_2, 'conv5_3', num_filters=512) pool5 = layers.max_pool_layer2d(conv5_3) conv6 = layers.conv2D_layer(pool5, 'conv6', num_filters=4096, kernel_size=(3,3)) conv7= layers.conv2D_layer(conv6, 'conv7', num_filters=4096, kernel_size=(1,1)) score5 = layers.conv2D_layer(conv7, 'score5', num_filters=nlabels, kernel_size=(1,1)) score4 = layers.conv2D_layer(pool4, 'score4', num_filters=nlabels, kernel_size=(1,1)) score3 = layers.conv2D_layer(pool3, 'score3', num_filters=nlabels, kernel_size=(1,1)) upscore1 = layers.deconv2D_layer(score5, name='upscore1', kernel_size=(4,4), strides=(2,2), num_filters=nlabels, weight_init='bilinear') sum1 = tf.add(upscore1, score4) upscore2 = layers.deconv2D_layer(sum1, name='upscore2', kernel_size=(4,4), strides=(2,2), num_filters=nlabels, weight_init='bilinear') sum2 = tf.add(upscore2, score3) upscore3 = layers.deconv2D_layer(sum2, name='upscore3', kernel_size=(16,16), strides=(8,8), num_filters=nlabels, weight_init='bilinear', activation=tf.identity) return upscore3
def unet2D_padding_same_shallow(images, training, nlabels): conv1_1 = layers.conv2D_layer(images, 'conv1_1', num_filters=64) conv1_2 = layers.conv2D_layer(conv1_1, 'conv1_2', num_filters=64) pool1 = layers.max_pool_layer2d(conv1_2) conv2_1 = layers.conv2D_layer(pool1, 'conv2_1', num_filters=128) conv2_2 = layers.conv2D_layer(conv2_1, 'conv2_2', num_filters=128) pool2 = layers.max_pool_layer2d(conv2_2) conv3_1 = layers.conv2D_layer(pool2, 'conv3_1', num_filters=256) conv3_2 = layers.conv2D_layer(conv3_1, 'conv3_2', num_filters=256) pool3 = layers.max_pool_layer2d(conv3_2) conv4_1 = layers.conv2D_layer(pool3, 'conv4_1', num_filters=512) conv4_2 = layers.conv2D_layer(conv4_1, 'conv4_2', num_filters=512) upconv3 = layers.deconv2D_layer(conv4_2, name='upconv3', kernel_size=(4, 4), strides=(2, 2), num_filters=256) concat3 = tf.concat([conv3_2, upconv3], axis=3, name='concat3') conv7_1 = layers.conv2D_layer(concat3, 'conv7_1', num_filters=256) conv7_2 = layers.conv2D_layer(conv7_1, 'conv7_2', num_filters=256) upconv2 = layers.deconv2D_layer(conv7_2, name='upconv2', kernel_size=(4, 4), strides=(2, 2), num_filters=128) concat2 = tf.concat([conv2_2, upconv2], axis=3, name='concat2') conv8_1 = layers.conv2D_layer(concat2, 'conv8_1', num_filters=128) conv8_2 = layers.conv2D_layer(conv8_1, 'conv8_2', num_filters=128) upconv1 = layers.deconv2D_layer(conv8_2, name='upconv1', kernel_size=(4, 4), strides=(2, 2), num_filters=64) concat1 = tf.concat([conv1_2, upconv1], axis=3, name='concat1') conv9_1 = layers.conv2D_layer(concat1, 'conv9_1', num_filters=64) conv9_2 = layers.conv2D_layer(conv9_1, 'conv9_2', num_filters=64) pred = layers.conv2D_layer(conv9_2, 'pred', num_filters=nlabels, kernel_size=(1,1), activation=tf.identity) return pred
def discriminator(features, training_pl, scope_name = 'discriminator', scope_reuse = False): with tf.variable_scope(scope_name, reuse = scope_reuse): out = features num_layers = 5 n0 = 16 for l in range(num_layers): out = tf.layers.conv2d(inputs=out, filters=(l+1)*n0, kernel_size=3, padding='SAME', name='D_conv_'+str(l+1) + '_1', use_bias=True, activation=None) out = tf.layers.batch_normalization(inputs=out, name = 'D_conv_' + str(l+1) + '_1' + '_bn', training = training_pl) out = tf.nn.relu(out) out = tf.layers.conv2d(inputs=out, filters=(l+1)*n0, kernel_size=3, padding='SAME', name='D_conv_'+str(l+1) + '_2', use_bias=True, activation=None) out = tf.layers.batch_normalization(inputs=out, name = 'D_conv_' + str(l+1) + '_2' + '_bn', training = training_pl) out = tf.nn.relu(out) out = layers.max_pool_layer2d(out) batch_size = out.get_shape()[0].value out = tf.reshape(out, [batch_size, -1]) logits = tf.layers.dense(out, 1, name = 'D_logits') # outputs = tf.nn.sigmoid(logits) return logits
def forward(images, training, nlabels): conv1_1 = layers.conv2D_layer_bn(images, 'conv1_1', num_filters=64, training=training, padding='SAME') conv1_2 = layers.conv2D_layer_bn(conv1_1, 'conv1_2', num_filters=64, training=training, padding='SAME') pool1 = layers.max_pool_layer2d(conv1_2, 'pool_1') conv2_1 = layers.conv2D_layer_bn(pool1, 'conv2_1', num_filters=128, training=training, padding='SAME') conv2_2 = layers.conv2D_layer_bn(conv2_1, 'conv2_2', num_filters=128, training=training, padding='SAME') pool2 = layers.max_pool_layer2d(conv2_2, 'pool_2') dout2 = layers.dropout_layer(pool2, 'dropout_2', training) conv3_1 = layers.conv2D_layer_bn(dout2, 'conv3_1', num_filters=256, training=training, padding='SAME') conv3_2 = layers.conv2D_layer_bn(conv3_1, 'conv3_2', num_filters=256, training=training, padding='SAME') pool3 = layers.max_pool_layer2d(conv3_2, 'pool_3') dout3 = layers.dropout_layer(pool3, 'dropout_3', training) conv4_1 = layers.conv2D_layer_bn(dout3, 'conv4_1', num_filters=512, training=training, padding='SAME') conv4_2 = layers.conv2D_layer_bn(conv4_1, 'conv4_2', num_filters=512, training=training, padding='SAME') pool4 = layers.max_pool_layer2d(conv4_2, 'pool_4') dout4 = layers.dropout_layer(pool4, 'dropout_4', training) conv5_1 = layers.conv2D_layer_bn(dout4, 'conv5_1', num_filters=1024, training=training, padding='SAME') conv5_2 = layers.conv2D_layer_bn(conv5_1, 'conv5_2', num_filters=1024, training=training, padding='SAME') upconv4 = layers.deconv2D_layer_bn(conv5_2, name='upconv4', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat4 = layers.crop_and_concat_layer([upconv4, conv4_2], 'crop_concat_4', axis=3) dout5 = layers.dropout_layer(concat4, 'dropout_5', training) conv6_1 = layers.conv2D_layer_bn(dout5, 'conv6_1', num_filters=512, training=training, padding='SAME') conv6_2 = layers.conv2D_layer_bn(conv6_1, 'conv6_2', num_filters=512, training=training, padding='SAME') upconv3 = layers.deconv2D_layer_bn(conv6_2, name='upconv3', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat3 = layers.crop_and_concat_layer([upconv3, conv3_2], 'crop_concat_3', axis=3) dout6 = layers.dropout_layer(concat3, 'dropout_6', training) conv7_1 = layers.conv2D_layer_bn(dout6, 'conv7_1', num_filters=256, training=training, padding='SAME') conv7_2 = layers.conv2D_layer_bn(conv7_1, 'conv7_2', num_filters=256, training=training, padding='SAME') upconv2 = layers.deconv2D_layer_bn(conv7_2, name='upconv2', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat2 = layers.crop_and_concat_layer([upconv2, conv2_2], 'crop_concat_2', axis=3) dout7 = layers.dropout_layer(concat2, 'dropout_7', training) conv8_1 = layers.conv2D_layer_bn(dout7, 'conv8_1', num_filters=128, training=training, padding='SAME') conv8_2 = layers.conv2D_layer_bn(conv8_1, 'conv8_2', num_filters=128, training=training, padding='SAME') upconv1 = layers.deconv2D_layer_bn(conv8_2, name='upconv1', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, training=training) concat1 = layers.crop_and_concat_layer([upconv1, conv1_2], 'crop_concat_1', axis=3) conv9_1 = layers.conv2D_layer_bn(concat1, 'conv9_1', num_filters=64, training=training, padding='SAME') conv9_2 = layers.conv2D_layer_bn(conv9_1, 'conv9_2', num_filters=64, training=training, padding='SAME') pred_1 = layers.conv2D_layer_bn(conv9_2, 'pred', num_filters=nlabels, kernel_size=(1, 1), activation=tf.identity, training=training, padding='SAME') # Deep supervision ds1_1 = layers.conv2D_layer(conv7_2, 'ds_1', num_filters=nlabels, kernel_size=(1, 1), activation=tf.identity, padding='SAME') ds1_2 = layers.deconv2D_layer(ds1_1, 'ds_2', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, padding='SAME') ds2_1 = layers.conv2D_layer(conv8_2, 'ds_3', num_filters=nlabels, kernel_size=(1, 1), activation=tf.identity, padding='SAME') ds1_ds2 = tf.add(ds1_2, ds2_1) ds = layers.deconv2D_layer(ds1_ds2, 'ds_4', kernel_size=(4, 4), strides=(2, 2), num_filters=nlabels, padding='SAME') pred_2 = tf.add(pred_1, ds) return pred_2
def unet2D_i2l(images, nlabels, training_pl, scope_reuse = False): n0 = 16 n1, n2, n3, n4 = 1*n0, 2*n0, 4*n0, 8*n0 with tf.variable_scope('i2l_mapper') as scope: if scope_reuse: scope.reuse_variables() # ==================================== # 1st Conv block - two conv layers, followed by max-pooling # ==================================== conv1_1 = layers.conv2D_layer_bn(x=images, name='conv1_1', num_filters=n1, training = training_pl) conv1_2 = layers.conv2D_layer_bn(x=conv1_1, name='conv1_2', num_filters=n1, training = training_pl) pool1 = layers.max_pool_layer2d(conv1_2) # ==================================== # 2nd Conv block # ==================================== conv2_1 = layers.conv2D_layer_bn(x=pool1, name='conv2_1', num_filters=n2, training = training_pl) conv2_2 = layers.conv2D_layer_bn(x=conv2_1, name='conv2_2', num_filters=n2, training = training_pl) pool2 = layers.max_pool_layer2d(conv2_2) # ==================================== # 3rd Conv block # ==================================== conv3_1 = layers.conv2D_layer_bn(x=pool2, name='conv3_1', num_filters=n3, training = training_pl) conv3_2 = layers.conv2D_layer_bn(x=conv3_1, name='conv3_2', num_filters=n3, training = training_pl) pool3 = layers.max_pool_layer2d(conv3_1) # ==================================== # 4th Conv block # ==================================== conv4_1 = layers.conv2D_layer_bn(x=pool3, name='conv4_1', num_filters=n4, training = training_pl) conv4_2 = layers.conv2D_layer_bn(x=conv4_1, name='conv4_2', num_filters=n4, training = training_pl) # ==================================== # Upsampling via bilinear upsampling, concatenation (skip connection), followed by 2 conv layers # ==================================== deconv3 = layers.bilinear_upsample2D(conv4_2, size = (tf.shape(conv3_2)[1],tf.shape(conv3_2)[2]), name='upconv3') concat3 = tf.concat([deconv3, conv3_2], axis=-1) conv5_1 = layers.conv2D_layer_bn(x=concat3, name='conv5_1', num_filters=n3, training = training_pl) conv5_2 = layers.conv2D_layer_bn(x=conv5_1, name='conv5_2', num_filters=n3, training = training_pl) # ==================================== # Upsampling via bilinear upsampling, concatenation (skip connection), followed by 2 conv layers # ==================================== deconv2 = layers.bilinear_upsample2D(conv5_2, size = (tf.shape(conv2_2)[1],tf.shape(conv2_2)[2]), name='upconv2') concat2 = tf.concat([deconv2, conv2_2], axis=-1) conv6_1 = layers.conv2D_layer_bn(x=concat2, name='conv6_1', num_filters=n2, training = training_pl) conv6_2 = layers.conv2D_layer_bn(x=conv6_1, name='conv6_2', num_filters=n2, training = training_pl) # ==================================== # Upsampling via bilinear upsampling, concatenation (skip connection), followed by 2 conv layers # ==================================== deconv1 = layers.bilinear_upsample2D(conv6_2, size = (tf.shape(conv1_2)[1],tf.shape(conv1_2)[2]), name='upconv1') concat1 = tf.concat([deconv1, conv1_2], axis=-1) conv7_1 = layers.conv2D_layer_bn(x=concat1, name='conv7_1', num_filters=n1, training = training_pl) conv7_2 = layers.conv2D_layer_bn(x=conv7_1, name='conv7_2', num_filters=n1, training = training_pl) # ==================================== # Final conv layer - without batch normalization or activation # ==================================== pred = layers.conv2D_layer(x=conv7_2, name='pred', num_filters=nlabels, kernel_size=1) return pool1, pool2, pool3, conv4_2, conv5_2, conv6_2, conv7_2, pred