71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
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#
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# Copyright (C) 2019 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, output0, input0_data, output0_data):
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model = Model().Operation("RANK", input0).To(output0)
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quant8 = DataTypeConverter().Identify({
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input0: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
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})
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quant8_signed = DataTypeConverter().Identify({
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input0: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
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})
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example = Example({
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input0: input0_data,
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output0: output0_data,
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}, model=model, name=name).AddVariations("int32", "float16", quant8, quant8_signed)
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test(
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name="1d",
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input0=Input("input0", "TENSOR_FLOAT32", "{3}"),
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output0=Output("output0", "INT32", "{}"),
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input0_data=[5, 7, 10],
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output0_data=[1],
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)
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test(
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name="1d",
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input0=Input("input0", "TENSOR_FLOAT32", "{2, 3}"),
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output0=Output("output0", "INT32", "{}"),
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input0_data=[1, 2, 3, 4, 5, 6],
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output0_data=[2],
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)
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# b/150728111 regression test.
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# Rank is a first operation that produces a scalar output.
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# This test verifies that RANK works with a scalar output
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# that's internal graph variable (not input or output of a graph).
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def test_internal_output(name, rank_input, fill_dims, fill_output, rank_input_data,
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fill_dims_data, fill_output_data):
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internal_result = Internal("rank_internal_result", "INT32", "{}")
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model = Model()
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model = model.Operation("RANK", rank_input).To(internal_result)
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model = model.Operation("FILL", fill_dims, internal_result).To(fill_output)
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example = Example({
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rank_input: rank_input_data,
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fill_dims: fill_dims_data,
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fill_output: fill_output_data,
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}, model=model, name=name)
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test_internal_output(
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name="internal_output",
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rank_input=Input("input0", "TENSOR_FLOAT32", "{2, 3}"),
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fill_dims=Input("input0", "TENSOR_INT32", "{3}"),
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fill_output=Output("output", "TENSOR_INT32", "{2, 3, 4}"),
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rank_input_data=[1, 2, 3, 4, 5, 6],
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fill_dims_data=[2, 3, 4],
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fill_output_data=[2] * (2 * 3 * 4),
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)
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