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