# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import paddle


class PostProcessor(paddle.nn.Layer):
    def __init__(self, model_type):
        super(PostProcessor, self).__init__()
        self.model_type = model_type

    def forward(self, net_outputs):
        if self.model_type == 'classifier':
            outputs = paddle.nn.functional.softmax(net_outputs, axis=1)
        elif self.model_type == 'segmenter':
            # score_map, label_map
            outputs = paddle.transpose(paddle.nn.functional.softmax(net_outputs, axis=1), perm=[0, 2, 3, 1]), \
                      paddle.transpose(paddle.argmax(net_outputs, axis=1, keepdim=True, dtype='int32'),
                                       perm=[0, 2, 3, 1])
        else:
            outputs = net_outputs
        return outputs


class InferNet(paddle.nn.Layer):
    def __init__(self, net, model_type):
        super(InferNet, self).__init__()
        self.net = net
        self.postprocessor = PostProcessor(model_type)

    def forward(self, x):
        net_outputs = self.net(x)[0]
        outputs = self.postprocessor(net_outputs)

        return outputs