xref: /frameworks/ml/nn/runtime/test/specs/V1_0/conv_quant8.mod.py

#
# Copyright (C) 2017 The Android Open Source Project
#
# 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.

# conv_quant8.mod.py with biases and filter being constants

model = Model()
i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 3, 3, 1}, 0.5f, 0")
f1 = Parameter("op2", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 0.5f, 0",
               [2, 2, 2, 2])
b1 = Parameter("op3", "TENSOR_INT32", "{1}, 0.25f, 0", [4])
pad0 = Int32Scalar("pad0", 0)
act = Int32Scalar("act", 0)
stride = Int32Scalar("stride", 1)
# output dimension:
#     (i1.height - f1.height + 1) x (i1.width - f1.width + 1)
output = Output("op4", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 1.f, 0")

model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride,
                        stride, act).To(output)

# Example 1. Input in operand 0,
input0 = {
    i1:  # input 0
        [8, 8, 8, 8, 4, 8, 8, 8, 8]
}
# (i1 (conv) f1) + b1
output0 = {
    output:  # output 0
        [15, 15, 15, 15]
}

# Instantiate an example
Example((input0, output0))