def run(self, y_true, y_pred, anchors=[90, 95, 92, 154, 139, 281]):
# 1. setup
y_pred = tf.reshape(y_pred, y_true.shape)
object_mask = tf.expand_dims(y_true[..., 4], 4)
# 2. Adjust prediction (bxy, twh)
preds = adjust_pred_tensor(y_pred)
# 3. Adjust ground truth (bxy, twh)
trues = adjust_true_tensor(y_true)
# 4. conf_delta tensor
conf_delta = conf_delta_tensor(y_true, preds, anchors,
self.ignore_thresh)
# 5. loss tensor
wh_scale = wh_scale_tensor(trues[..., 2:4], anchors, self.image_size)
loss_box = loss_coord_tensor(object_mask, preds[..., :4],
trues[..., :4], wh_scale, self.xywh_scale)
loss_conf = loss_conf_tensor(object_mask, preds[..., 4], trues[..., 4],
self.obj_scale, self.noobj_scale,
conf_delta)
loss_class = loss_class_tensor(object_mask, preds[..., 5:],
trues[..., 5], self.class_scale)
loss = loss_box + loss_conf + loss_class
return loss * self.grid_scale
def run_loss_component(self, y_true, y_pred, anchors=[66,303, 81,318, 104,337]):
# 1. setup
y_pred = tf.reshape(y_pred, y_true.shape)
# print(y_true[...,4].shape) # (1, grid, grid, 3, 15)
object_mask = tf.expand_dims(y_true[..., 4], 4) # contain 0, 1 value, indicates which bbox has object in it
# print(object_mask.shape) # (1, grid, grid, 3, 1)
# 2. Adjust prediction (bxy, twh)
preds = adjust_pred_tensor(y_pred)
# 3. Adjust ground truth (bxy, twh)
trues = adjust_true_tensor(y_true)
# 4. conf_delta tensor
conf_delta = conf_delta_tensor(y_true, preds, anchors, self.ignore_thresh)
# 5. loss tensor
wh_scale = wh_scale_tensor(trues[..., 2:4], anchors, self.image_size)
loss_box = loss_coord_tensor(object_mask, preds[..., :4], trues[..., :4], wh_scale, self.xywh_scale)
loss_conf = loss_conf_tensor(object_mask, preds[..., 4], trues[..., 4], self.obj_scale, self.noobj_scale, conf_delta)
loss_class = loss_class_tensor(object_mask, preds[..., 5:], trues[..., 5], self.class_scale)
loss = loss_box + loss_conf + loss_class
return loss * self.grid_scale, \
[loss_box * self.grid_scale, loss_conf * self.grid_scale, loss_class * self.grid_scale]