65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
#
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# Copyright (C) 2018 The Android Open Source Project
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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def test(name, input0, input1, output0, input0_data, input1_data, output_data):
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model = Model().Operation("MAXIMUM", input0, input1).To(output0)
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quant8 = DataTypeConverter().Identify({
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input0: ["TENSOR_QUANT8_ASYMM", 0.5, 127],
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input1: ["TENSOR_QUANT8_ASYMM", 1.0, 100],
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output0: ["TENSOR_QUANT8_ASYMM", 2.0, 80],
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})
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Example({
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input0: input0_data,
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input1: input1_data,
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output0: output_data,
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}, model=model, name=name).AddVariations("relaxed", "float16", "int32", quant8)
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test(
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name="simple",
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input0=Input("input0", "TENSOR_FLOAT32", "{3, 1, 2}"),
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input1=Input("input1", "TENSOR_FLOAT32", "{3, 1, 2}"),
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output0=Output("output0", "TENSOR_FLOAT32", "{3, 1, 2}"),
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input0_data=[1.0, 0.0, -1.0, 11.0, -2.0, -1.44],
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input1_data=[-1.0, 0.0, 1.0, 12.0, -3.0, -1.43],
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output_data=[1.0, 0.0, 1.0, 12.0, -2.0, -1.43],
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)
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test(
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name="broadcast",
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input0=Input("input0", "TENSOR_FLOAT32", "{3, 1, 2}"),
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input1=Input("input1", "TENSOR_FLOAT32", "{2}"),
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output0=Output("output0", "TENSOR_FLOAT32", "{3, 1, 2}"),
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input0_data=[1.0, 0.0, -1.0, -2.0, -1.44, 11.0],
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input1_data=[0.5, 2.0],
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output_data=[1.0, 2.0, 0.5, 2.0, 0.5, 11.0],
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)
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# Test overflow and underflow.
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input0 = Input("input0", "TENSOR_QUANT8_ASYMM", "{2}, 1.0f, 128")
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input1 = Input("input1", "TENSOR_QUANT8_ASYMM", "{2}, 1.0f, 128")
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output0 = Output("output0", "TENSOR_QUANT8_ASYMM", "{2}, 0.5f, 128")
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model = Model().Operation("MAXIMUM", input0, input1).To(output0)
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Example({
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input0: [60, 128],
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input1: [128, 200],
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output0: [128, 255],
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}, model=model, name="overflow")
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