61b2f77f15a3d04f779bf4ccaf998a84eaf08bb5,keras_retinanet/models/resnet.py,,RetinaNet,#Any#Any#Any#Any#,49

Before Change

		// run the regression subnet
		reg = p
		for l in regression_layers:
			reg = l(reg)

		reg        = keras.layers.Reshape((-1, 4), name="boxes_reshaped_{}".format(i))(reg)
		regression = reg if regression == None else keras.layers.Concatenate(axis=1)([regression, reg])

	// compute classification and regression losses
	classification = keras.layers.Lambda(lambda x: keras.backend.softmax(x), name="classification_softmax")(classification)

After Change

		// compute labels and bbox_reg_targets
		lb, r, a          = keras_retinanet.layers.AnchorTarget(stride=s, name="boxes_{}".format(i))([cls, im_info, gt_boxes])
		anchors           = a if anchors == None else keras.layers.Concatenate(axis=0)([anchors, a])
		labels            = lb if labels == None else keras.layers.Concatenate(axis=0)([labels, lb])
		regression_target = r if regression_target == None else keras.layers.Concatenate(axis=0)([regression_target, r])

Instances