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path: root/runtimes/tests/neural_networks_test/specs/V1_0/conv_float_channels.mod.py
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#
# 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, 1, 1, 3}")
f1 = Parameter("op2", "TENSOR_FLOAT32", "{3, 1, 1, 3}", [1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0])
b1 = Parameter("op3", "TENSOR_FLOAT32", "{3}", [0., 0., 0.])
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_FLOAT32", "{1, 1, 1, 3}")

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
          [99.0, 99.0, 99.0]}

output0 = {output: # output 0
           [297., 594., 891.]}

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