#
# 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.
#

model = Model()
i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 2}") # depth_in = 2
f1 = Parameter("op2", "TENSOR_FLOAT32", "{1, 2, 2, 2}", [.25, 0, .25, 1, .25, 0, .25, 1]) # depth_out = 2
b1 = Parameter("op3", "TENSOR_FLOAT32", "{2}", [100, 200]) # depth_out = 2
pad0 = Int32Scalar("pad0", 0)
act = Int32Scalar("act", 0)
stride = Int32Scalar("stride", 1)
cm = Int32Scalar("channelMultiplier", 1)
output = Output("op4", "TENSOR_FLOAT32", "{1, 1, 1, 2}")

model = model.Operation("DEPTHWISE_CONV_2D",
                        i1, f1, b1,
                        pad0, pad0, pad0, pad0,
                        stride, stride,
                        cm, act).To(output)

# Example 1. Input in operand 0,
input0 = {
    i1: [ # input 0
     10, 21, 10, 22,
     10, 23, 10, 24],
  }
# (i1 (conv) f1) + b1
output0 = {output: # output 0
           [110, 246]}

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