text: # coding=utf-8
  # Copyright 2022 The Google Research Authors.
  #
  # 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.
  
  """Trivial model configuration."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow.compat.v1 as tf
  from cnn_quantization.tf_cnn_benchmarks.models import model
  
  
  class TrivialModel(model.CNNModel):
    """Trivial model configuration."""
  
    def __init__(self, params=None):
      super(TrivialModel, self).__init__(
          'trivial', 224 + 3, 32, 0.005, params=params)
  
    def add_inference(self, cnn):
      cnn.reshape([-1, 227 * 227 * 3])
      cnn.affine(1)
      cnn.affine(4096)
  
  
  class TrivialCifar10Model(model.CNNModel):
    """Trivial cifar10 model configuration."""
  
    def __init__(self, params=None):
      super(TrivialCifar10Model, self).__init__(
          'trivial', 32, 32, 0.005, params=params)
  
    def add_inference(self, cnn):
      cnn.reshape([-1, 32 * 32 * 3])
      cnn.affine(1)
      cnn.affine(4096)
  
  
  class TrivialSSD300Model(model.CNNModel):
    """Trivial SSD300 model configuration."""
  
    def __init__(self, params=None):
      super(TrivialSSD300Model, self).__init__(
          'trivial', 300, 32, 0.005, params=params)
  
    def add_inference(self, cnn):
      cnn.reshape([-1, 300 * 300 * 3])
      cnn.affine(1)
      cnn.affine(4096)
  
    def get_input_shapes(self, subset):
      return [[32, 300, 300, 3], [32, 8732, 4], [32, 8732, 1], [32]]
  
    def loss_function(self, inputs, build_network_result):
      images, _, _, labels = inputs
      labels = tf.cast(labels, tf.int32)
      return super(TrivialSSD300Model, self).loss_function(
          (images, labels), build_network_result)
  