c3a2b85b98dc441f97703c426d5bd75568b0ce10,keras_rcnn/layers/losses/_rcnn.py,RCNNRegressionLoss,compute_regression_loss,#,64

Before Change

        indices_r = a[:, :2]
        indices_c = a[:, 2:]
        indices_0 = keras.backend.concatenate([indices_r, indices_c * 4], 1)
        indices_1 = keras.backend.concatenate([indices_r, indices_c * 4 + 1], 1)
        indices_2 = keras.backend.concatenate([indices_r, indices_c * 4 + 2], 1)
        indices_3 = keras.backend.concatenate([indices_r, indices_c * 4 + 3], 1)
        indices = keras.backend.concatenate([indices_0,
                                            indices_1,
                                            indices_2,
                                            indices_3], 0)
        updates = keras.backend.ones_like(indices, dtype=keras.backend.floatx())
        labels = keras_rcnn.backend.scatter_add_tensor(keras.backend.zeros_like(output, dtype="float32"), indices, updates[:, 0])

        inside_mul = inside_weights * keras.backend.abs(output - target) * labels
        smooth_l1_sign = keras.backend.cast(keras.backend.less(inside_mul, 1.0 / sigma2), keras.backend.floatx())

        smooth_l1_option1 = (inside_mul * inside_mul) * (0.5 * sigma2)
        smooth_l1_option2 = inside_mul - (0.5 / sigma2)

        smooth_l1_result = (smooth_l1_option1 * smooth_l1_sign)
        smooth_l1_result += (smooth_l1_option2 * (1.0 - smooth_l1_sign))

        loss = outside_weights * smooth_l1_result
        epsilon = 1e-4
        b = keras.backend.sum(epsilon + labels)
        loss = tensorflow.reduce_sum(loss) / b

        return loss

After Change

        a = keras.backend.cast(a, "int32")

        rr = a[:, :2]
        cc = a[:, 2:]

        indices = [
            keras.backend.concatenate([rr, cc * 4 + 0], 1),
            keras.backend.concatenate([rr, cc * 4 + 1], 1),
            keras.backend.concatenate([rr, cc * 4 + 2], 1),
            keras.backend.concatenate([rr, cc * 4 + 3], 1)
        ]

        indices = keras.backend.concatenate(indices, 0)

        updates = keras.backend.ones_like(indices, dtype=keras.backend.floatx())
        labels = keras_rcnn.backend.scatter_add_tensor(keras.backend.zeros_like(output, dtype="float32"), indices, updates[:, 0])

        inside_mul = inside_weights * keras.backend.abs(output - target) * labels
        smooth_l1_sign = keras.backend.cast(keras.backend.less(inside_mul, 1.0 / sigma2), keras.backend.floatx())

        smooth_l1_option1 = (inside_mul * inside_mul) * (0.5 * sigma2)
        smooth_l1_option2 = inside_mul - (0.5 / sigma2)

        smooth_l1_result = (smooth_l1_option1 * smooth_l1_sign)
        smooth_l1_result += (smooth_l1_option2 * (1.0 - smooth_l1_sign))

        loss = outside_weights * smooth_l1_result
        epsilon = 1e-4
        b = keras.backend.sum(epsilon + labels)
        loss = tensorflow.reduce_sum(loss) / b

        return loss

Instances