# Copyright 2017 The TensorFlow 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.
# ==============================================================================
"""Model class for Cifar10 Dataset."""
from __future__ import division, print_function

import model_base
import tensorflow as tf


class ResNetCifar10(model_base.ResNet):
    """Cifar10 model with ResNetV1 and basic residual block."""

    def __init__(
        self,
        num_layers,
        is_training,
        batch_norm_decay,
        batch_norm_epsilon,
        data_format="channels_first",
    ):
        super(ResNetCifar10, self).__init__(is_training, data_format, batch_norm_decay, batch_norm_epsilon)
        self.n = (num_layers - 2) // 6
        # Add one in case label starts with 1. No impact if label starts with 0.
        self.num_classes = 10 + 1
        self.filters = [16, 16, 32, 64]
        self.strides = [1, 2, 2]

    def forward_pass(self, x, input_data_format="channels_last"):
        """Build the core model within the graph."""
        if self._data_format != input_data_format:
            if input_data_format == "channels_last":
                # Computation requires channels_first.
                x = tf.transpose(x, [0, 3, 1, 2])
            else:
                # Computation requires channels_last.
                x = tf.transpose(x, [0, 2, 3, 1])

        # Image standardization.
        x = x / 128 - 1

        x = self._conv(x, 3, 16, 1)
        x = self._batch_norm(x)
        x = self._relu(x)

        # Use basic (non-bottleneck) block and ResNet V1 (post-activation).
        res_func = self._residual_v1

        # 3 stages of block stacking.
        for i in range(3):
            with tf.name_scope("stage"):
                for j in range(self.n):
                    if j == 0:
                        # First block in a stage, filters and strides may change.
                        x = res_func(x, 3, self.filters[i], self.filters[i + 1], self.strides[i])
                    else:
                        # Following blocks in a stage, constant filters and unit stride.
                        x = res_func(x, 3, self.filters[i + 1], self.filters[i + 1], 1)

        x = self._global_avg_pool(x)
        x = self._fully_connected(x, self.num_classes)

        return x
