# Copyright (c) 2019 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.

from __future__ import print_function
import seresnext_net
import paddle.fluid.core as core
from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
from parallel_executor_test_base import DeviceType
import numpy as np


class TestResnetBase(TestParallelExecutorBase):
    def _compare_result_with_origin_model(self,
                                          check_func,
                                          use_device,
                                          delta2=1e-5,
                                          compare_seperately=True):
        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
            return

        func_1_first_loss, func_1_last_loss = self.check_network_convergence(
            seresnext_net.model,
            feed_dict=seresnext_net.feed_dict(use_device),
            iter=seresnext_net.iter(use_device),
            batch_size=seresnext_net.batch_size(use_device),
            use_device=use_device,
            use_reduce=False,
            optimizer=seresnext_net.optimizer)

        func_2_first_loss, func_2_last_loss = check_func(
            seresnext_net.model,
            feed_dict=seresnext_net.feed_dict(use_device),
            iter=seresnext_net.iter(use_device),
            batch_size=seresnext_net.batch_size(use_device),
            use_device=use_device)

        if compare_seperately:
            for loss in zip(func_1_first_loss, func_2_first_loss):
                self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
            for loss in zip(func_1_last_loss, func_2_last_loss):
                self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
        else:
            self.assertAlmostEquals(
                np.mean(func_1_first_loss), func_2_first_loss[0], delta=1e-5)
            self.assertAlmostEquals(
                np.mean(func_1_last_loss), func_2_last_loss[0], delta=delta2)