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#
# Copyright (C) 2019 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.
#
def test(name, input0, input1, output0, input0_data, input1_data, output_data):
model = Model().Operation("MAXIMUM", input0, input1).To(output0)
quant8_signed = DataTypeConverter().Identify({
input0: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -1],
input1: ["TENSOR_QUANT8_ASYMM_SIGNED", 1.0, -28],
output0: ["TENSOR_QUANT8_ASYMM_SIGNED", 2.0, -48],
})
Example({
input0: input0_data,
input1: input1_data,
output0: output_data,
}, model=model, name=name).AddVariations(quant8_signed, includeDefault=False)
test(
name="simple",
input0=Input("input0", "TENSOR_FLOAT32", "{3, 1, 2}"),
input1=Input("input1", "TENSOR_FLOAT32", "{3, 1, 2}"),
output0=Output("output0", "TENSOR_FLOAT32", "{3, 1, 2}"),
input0_data=[1.0, 0.0, -1.0, 11.0, -2.0, -1.44],
input1_data=[-1.0, 0.0, 1.0, 12.0, -3.0, -1.43],
output_data=[1.0, 0.0, 1.0, 12.0, -2.0, -1.43],
)
test(
name="broadcast",
input0=Input("input0", "TENSOR_FLOAT32", "{3, 1, 2}"),
input1=Input("input1", "TENSOR_FLOAT32", "{2}"),
output0=Output("output0", "TENSOR_FLOAT32", "{3, 1, 2}"),
input0_data=[1.0, 0.0, -1.0, -2.0, -1.44, 11.0],
input1_data=[0.5, 2.0],
output_data=[1.0, 2.0, 0.5, 2.0, 0.5, 11.0],
)
# Test overflow and underflow.
input0 = Input("input0", "TENSOR_QUANT8_ASYMM_SIGNED", "{2}, 1.0f, 0")
input1 = Input("input1", "TENSOR_QUANT8_ASYMM_SIGNED", "{2}, 1.0f, 0")
output0 = Output("output0", "TENSOR_QUANT8_ASYMM_SIGNED", "{2}, 0.5f, 0")
model = Model().Operation("MAXIMUM", input0, input1).To(output0)
Example({
input0: [-68, 0],
input1: [0, 72],
output0: [0, 127],
}, model=model, name="overflow")
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