Test models for testing quantization

This directory contains test models for testing quantization.

Models

• single_conv_weights_min_0_max_plus_10.bin
  A floating point model with single convolution where all weights are integers between [0, 10] weights are randomly distributed. It is not guaranteed that min max for weights are going to appear in each channel. All activations have min maxes and activations are in range [0,10].
• single_conv_weights_min_minus_127_max_plus_127.bin
  A floating point model with a single convolution where weights of the model are all integers that lie in range[-127, 127]. The weights have been put in such a way that each channel has at least one weight as -127 and one weight as 127. The activations are all in range: [-128, 127]. This means all bias computations should result in 1.0 scale.
• single_softmax_min_minus_5_max_5.bin
  A floating point model with a single softmax. The input tensor has min and max in range [-5, 5], not necessarily -5 or +5.
• single_avg_pool_input_min_minus_5_max_5.bin
  A floating point model with a single average pool. The input tensor has min and max in range [-5, 5], not necessarily -5 or +5.
• weight_shared_between_convs.bin
  A floating point model with two convs that have a use the same weight tensor.
• multi_input_add_reshape.bin
  A floating point model with two inputs with an add followed by a reshape.
• quantized_with_gather.bin
  A floating point model with an input with a gather, modeling a situation of mapping categorical input to embeddings.