94 lines
3.6 KiB
Python
94 lines
3.6 KiB
Python
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#
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# Copyright (C) 2021 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, axis_value, input_tensors, output_tensor, inputs_data, output_data):
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model = Model().Operation("PACK", Int32Scalar("axis", axis_value), *input_tensors).To(output_tensor)
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quant8_asymm_type = ("TENSOR_QUANT8_ASYMM", 0.5, 4)
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quant8_asymm_dict = dict(zip([*input_tensors, output_tensor], [quant8_asymm_type] * (len(input_tensors) + 1)))
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quant8_asymm = DataTypeConverter(name="quant8_asymm").Identify(quant8_asymm_dict)
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quant8_asymm_signed_type = ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, -9)
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quant8_asymm_signed_dict = dict(zip([*input_tensors, output_tensor], [quant8_asymm_signed_type] * (len(input_tensors) + 1)))
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quant8_asymm_signed = DataTypeConverter(name="quant8_asymm_signed").Identify(quant8_asymm_signed_dict)
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Example((dict(zip(input_tensors, inputs_data)), {output_tensor: output_data}), model=model, name=name).AddVariations("float16", quant8_asymm, quant8_asymm_signed, "int32")
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test(
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name="FLOAT32_unary_axis0",
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axis_value=0,
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input_tensors=[Input("in0", ("TENSOR_FLOAT32", [2]))],
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output_tensor=Output("out", ("TENSOR_FLOAT32", [1,2])),
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inputs_data=[[3, 4]],
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output_data=[3, 4],
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)
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test(
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name="FLOAT32_unary_axis1",
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axis_value=1,
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input_tensors=[Input("in0", ("TENSOR_FLOAT32", [2]))],
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output_tensor=Output("out", ("TENSOR_FLOAT32", [2,1])),
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inputs_data=[[3, 4]],
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output_data=[3, 4],
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)
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test(
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name="FLOAT32_binary_axis0",
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axis_value=0,
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input_tensors=[Input("in0", ("TENSOR_FLOAT32", [3,4])),
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Input("in1", ("TENSOR_FLOAT32", [3,4]))],
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output_tensor=Output("out", ("TENSOR_FLOAT32", [2,3,4])),
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inputs_data=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
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[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]],
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output_data=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
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12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
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)
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test(
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name="FLOAT32_binary_axis1",
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axis_value=1,
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input_tensors=[Input("in0", ("TENSOR_FLOAT32", [3,4])),
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Input("in1", ("TENSOR_FLOAT32", [3,4]))],
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output_tensor=Output("out", ("TENSOR_FLOAT32", [3,2,4])),
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inputs_data=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
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[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]],
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output_data=[0, 1, 2, 3,
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12, 13, 14, 15,
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4, 5, 6, 7,
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16, 17, 18, 19,
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8, 9, 10, 11,
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20, 21, 22, 23],
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)
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test(
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name="FLOAT32_binary_axis2",
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axis_value=2,
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input_tensors=[Input("in0", ("TENSOR_FLOAT32", [3,4])),
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Input("in1", ("TENSOR_FLOAT32", [3,4]))],
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output_tensor=Output("out", ("TENSOR_FLOAT32", [3,4,2])),
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inputs_data=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
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[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]],
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output_data=[0, 12,
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1, 13,
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2, 14,
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3, 15,
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4, 16,
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5, 17,
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6, 18,
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7, 19,
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8, 20,
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9, 21,
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10, 22,
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11, 23],
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)
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