(image, keep_prob)
return tf.expand_dims(annotation_pred, dim=3), conv_t3
Semantic segmentation network definition
    :param image: input image. Should have values in range 0-255
    :param keep_prob:
    :return:
Semantic segmentation network definition
    :param image: input image. Should have values in range 0-255
    :param keep_prob:
    :return:
Semantic segmentation network definition : param image : input image . Should have values in range 0 - 255 : param keep_prob : : return :
def inference(image, keep_prob):
    """
    Semantic segmentation network definition
    :param image: input image. Should have values in range 0-255
    :param keep_prob:
    :return:
    """
    print("setting up vgg initialized conv layers ...")
    model_data = utils.get_model_data(FLAGS.model_dir, MODEL_URL)

    mean = model_data['normalization'][0][0][0]
    mean_pixel = np.mean(mean, axis=(0, 1))

    weights = np.squeeze(model_data['layers'])

    #processed_image = utils.process_image(image, mean_pixel)

    with tf.variable_scope("inference"):
        image_net = vgg_net(weights, image)
        conv_final_layer = image_net["conv5_3"]

        pool5 = utils.max_pool_2x2(conv_final_layer)

        W6 = utils.weight_variable([7, 7, 512, 4096], name="W6")
        b6 = utils.bias_variable([4096], name="b6")
        conv6 = utils.conv2d_basic(pool5, W6, b6)
        relu6 = tf.nn.relu(conv6, name="relu6")
        if FLAGS.debug:
            utils.add_activation_summary(relu6)
        relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob)

        W7 = utils.weight_variable([1, 1, 4096, 4096], name="W7")
        b7 = utils.bias_variable([4096], name="b7")
        conv7 = utils.conv2d_basic(relu_dropout6, W7, b7)
        relu7 = tf.nn.relu(conv7, name="relu7")
        if FLAGS.debug:
            utils.add_activation_summary(relu7)
        relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob)

        W8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSESS], name="W8")
        b8 = utils.bias_variable([NUM_OF_CLASSESS], name="b8")
        conv8 = utils.conv2d_basic(relu_dropout7, W8, b8)
        # annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1")

        # now to upscale to actual image size
        deconv_shape1 = image_net["pool4"].get_shape()
        W_t1 = utils.weight_variable([4, 4, deconv_shape1[3].value, NUM_OF_CLASSESS], name="W_t1")
        b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1")
        conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=tf.shape(image_net["pool4"]))
        fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1")

        deconv_shape2 = image_net["pool3"].get_shape()
        W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2")
        b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
        conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(image_net["pool3"]))
        fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2")

        shape = tf.shape(image)
        deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSESS])
        W_t3 = utils.weight_variable([16, 16, NUM_OF_CLASSESS, deconv_shape2[3].value], name="W_t3")
        b_t3 = utils.bias_variable([NUM_OF_CLASSESS], name="b_t3")
        conv_t3 = utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=deconv_shape3, stride=8)

        annotation_pred = tf.argmax(conv_t3, dimension=3, name="prediction")

    return tf.expand_dims(annotation_pred, dim=3), conv_t3
def inference ( image , keep_prob ) : print ( "setting up vgg initialized conv layers ..." ) model_data = utils . get_model_data ( FLAGS . model_dir , MODEL_URL ) mean = model_data [ 'normalization' ] [ 0 ] [ 0 ] [ 0 ] mean_pixel = np . mean ( mean , axis = ( 0 , 1 ) ) weights = np . squeeze ( model_data [ 'layers' ] ) #processed_image = utils.process_image(image, mean_pixel) with tf . variable_scope ( "inference" ) : image_net = vgg_net ( weights , image ) conv_final_layer = image_net [ "conv5_3" ] pool5 = utils . max_pool_2x2 ( conv_final_layer ) W6 = utils . weight_variable ( [ 7 , 7 , 512 , 4096 ] , name = "W6" ) b6 = utils . bias_variable ( [ 4096 ] , name = "b6" ) conv6 = utils . conv2d_basic ( pool5 , W6 , b6 ) relu6 = tf . nn . relu ( conv6 , name = "relu6" ) if FLAGS . debug : utils . add_activation_summary ( relu6 ) relu_dropout6 = tf . nn . dropout ( relu6 , keep_prob = keep_prob ) W7 = utils . weight_variable ( [ 1 , 1 , 4096 , 4096 ] , name = "W7" ) b7 = utils . bias_variable ( [ 4096 ] , name = "b7" ) conv7 = utils . conv2d_basic ( relu_dropout6 , W7 , b7 ) relu7 = tf . nn . relu ( conv7 , name = "relu7" ) if FLAGS . debug : utils . add_activation_summary ( relu7 ) relu_dropout7 = tf . nn . dropout ( relu7 , keep_prob = keep_prob ) W8 = utils . weight_variable ( [ 1 , 1 , 4096 , NUM_OF_CLASSESS ] , name = "W8" ) b8 = utils . bias_variable ( [ NUM_OF_CLASSESS ] , name = "b8" ) conv8 = utils . conv2d_basic ( relu_dropout7 , W8 , b8 ) # annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1") # now to upscale to actual image size deconv_shape1 = image_net [ "pool4" ] . get_shape ( ) W_t1 = utils . weight_variable ( [ 4 , 4 , deconv_shape1 [ 3 ] . value , NUM_OF_CLASSESS ] , name = "W_t1" ) b_t1 = utils . bias_variable ( [ deconv_shape1 [ 3 ] . value ] , name = "b_t1" ) conv_t1 = utils . conv2d_transpose_strided ( conv8 , W_t1 , b_t1 , output_shape = tf . shape ( image_net [ "pool4" ] ) ) fuse_1 = tf . add ( conv_t1 , image_net [ "pool4" ] , name = "fuse_1" ) deconv_shape2 = image_net [ "pool3" ] . get_shape ( ) W_t2 = utils . weight_variable ( [ 4 , 4 , deconv_shape2 [ 3 ] . value , deconv_shape1 [ 3 ] . value ] , name = "W_t2" ) b_t2 = utils . bias_variable ( [ deconv_shape2 [ 3 ] . value ] , name = "b_t2" ) conv_t2 = utils . conv2d_transpose_strided ( fuse_1 , W_t2 , b_t2 , output_shape = tf . shape ( image_net [ "pool3" ] ) ) fuse_2 = tf . add ( conv_t2 , image_net [ "pool3" ] , name = "fuse_2" ) shape = tf . shape ( image ) deconv_shape3 = tf . stack ( [ shape [ 0 ] , shape [ 1 ] , shape [ 2 ] , NUM_OF_CLASSESS ] ) W_t3 = utils . weight_variable ( [ 16 , 16 , NUM_OF_CLASSESS , deconv_shape2 [ 3 ] . value ] , name = "W_t3" ) b_t3 = utils . bias_variable ( [ NUM_OF_CLASSESS ] , name = "b_t3" ) conv_t3 = utils . conv2d_transpose_strided ( fuse_2 , W_t3 , b_t3 , output_shape = deconv_shape3 , stride = 8 ) annotation_pred = tf . argmax ( conv_t3 , dimension = 3 , name = "prediction" ) return tf . expand_dims ( annotation_pred , dim = 3 ) , conv_t3