from keras import backend as K
from keras.objectives import categorical_crossentropy
if K.image_dim_ordering() == 'tf':
import tensorflow as tf
lambda_rpn_regr = 1.0
lambda_rpn_class = 1.0
lambda_cls_regr = 1.0
lambda_cls_class = 1.0
epsilon = 1e-4
def rpn_loss_regr(num_anchors):
def rpn_loss_regr_fixed_num(y_true, y_pred):
if K.image_dim_ordering() == 'th':
x = y_true[:, 4 * num_anchors:, :, :] - y_pred
x_abs = K.abs(x)
x_bool = K.less_equal(x_abs, 1.0)
return lambda_rpn_regr * K.sum(
y_true[:, :4 * num_anchors, :, :] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :4 * num_anchors, :, :])
else:
x = y_true[:, :, :, 4 * num_anchors:] - y_pred
x_abs = K.abs(x)
x_bool = K.cast(K.less_equal(x_abs, 1.0), tf.float32)
return lambda_rpn_regr * K.sum(
y_true[:, :, :, :4 * num_anchors] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :, :, :4 * num_anchors])
return rpn_loss_regr_fixed_num
def rpn_loss_cls(num_anchors):
def rpn_loss_cls_fixed_num(y_true, y_pred):
if K.image_dim_ordering() == 'tf':
return lambda_rpn_class * K.sum(y_true[:, :, :, :num_anchors] * K.binary_crossentropy(y_pred[:, :, :, :], y_true[:, :, :, num_anchors:])) / K.sum(epsilon + y_true[:, :, :, :num_anchors])
else:
return lambda_rpn_class * K.sum(y_true[:, :num_anchors, :, :] * K.binary_crossentropy(y_pred[:, :, :, :], y_true[:, num_anchors:, :, :])) / K.sum(epsilon + y_true[:, :num_anchors, :, :])
return rpn_loss_cls_fixed_num
def class_loss_regr(num_classes):
def class_loss_regr_fixed_num(y_true, y_pred):
x = y_true[:, :, 4*num_classes:] - y_pred
x_abs = K.abs(x)
x_bool = K.cast(K.less_equal(x_abs, 1.0), 'float32')
return lambda_cls_regr * K.sum(y_true[:, :, :4*num_classes] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :, :4*num_classes])
return class_loss_regr_fixed_num
def class_loss_cls(y_true, y_pred):
return lambda_cls_class * K.mean(categorical_crossentropy(y_true[0, :, :], y_pred[0, :, :]))
