| # |
| # Copyright (C) 2018 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. |
| # |
| import collections |
| |
| TestCase = collections.namedtuple("TestCase", [ |
| "inp", "inp_data", "begin", "begin_data", "size", "size_data", "output", |
| "output_data" |
| ]) |
| |
| test_cases = [ |
| TestCase( |
| inp=Input("input", "TENSOR_FLOAT32", "{4}"), |
| inp_data=[1, 2, 3, 4], |
| begin=Input("begin", "TENSOR_INT32", "{1}"), |
| begin_data=[1], |
| size=Input("size", "TENSOR_INT32", "{1}"), |
| size_data=[2], |
| output=Output("output", "TENSOR_FLOAT32", "{2}"), |
| output_data=[2, 3]), |
| TestCase( |
| inp=Input("input", "TENSOR_FLOAT32", "{2,3}"), |
| inp_data=[1, 2, 3, 4, 5, 6], |
| begin=Input("begin", "TENSOR_INT32", "{2}"), |
| begin_data=[1, 0], |
| size=Input("size", "TENSOR_INT32", "{2}"), |
| size_data=[1, 2], |
| output=Output("output", "TENSOR_FLOAT32", "{1, 2}"), |
| output_data=[4, 5]), |
| TestCase( |
| inp=Input("input", "TENSOR_FLOAT32", "{2,3,2}"), |
| inp_data=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], |
| begin=Input("begin", "TENSOR_INT32", "{3}"), |
| begin_data=[0, 0, 0], |
| size=Input("size", "TENSOR_INT32", "{3}"), |
| size_data=[2, 3, 2], |
| output=Output("output", "TENSOR_FLOAT32", "{2, 3, 2}"), |
| output_data=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), |
| TestCase( |
| inp=Input("input", "TENSOR_FLOAT32", "{4, 1, 1, 1}"), |
| inp_data=[1, 2, 3, 4], |
| begin=Input("begin", "TENSOR_INT32", "{4}"), |
| begin_data=[1, 0, 0, 0], |
| size=Input("size", "TENSOR_INT32", "{4}"), |
| size_data=[3, 1, 1, 1], |
| output=Output("output", "TENSOR_FLOAT32", "{3, 1, 1, 1}"), |
| output_data=[2, 3, 4]), |
| TestCase( |
| inp=Input("input", "TENSOR_INT32", "{3, 2, 3, 1}"), |
| inp_data=[1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6], |
| begin=Input("begin", "TENSOR_INT32", "{4}"), |
| begin_data=[1, 0, 0, 0], |
| size=Input("size", "TENSOR_INT32", "{4}"), |
| size_data=[1, 1, 3, 1], |
| output=Output("output", "TENSOR_INT32", "{1, 1, 3, 1}"), |
| output_data=[3, 3, 3]), |
| TestCase( |
| inp=Input("input", "TENSOR_INT32", "{3, 2, 3, 1}"), |
| inp_data=[1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6], |
| begin=Input("begin", "TENSOR_INT32", "{4}"), |
| begin_data=[1, 0, 0, 0], |
| size=Input("size", "TENSOR_INT32", "{4}"), |
| size_data=[2, 1, 3, 1], |
| output=Output("output", "TENSOR_INT32", "{2, 1, 3, 1}"), |
| output_data=[3, 3, 3, 5, 5, 5]), |
| TestCase( |
| inp=Input("input", "TENSOR_QUANT8_ASYMM", "{3, 2, 3, 1}, 2.0, 128"), |
| inp_data=[1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6], |
| begin=Input("begin", "TENSOR_INT32", "{4}"), |
| begin_data=[1, 0, 0, 0], |
| size=Input("size", "TENSOR_INT32", "{4}"), |
| size_data=[2, 1, 3, 1], |
| output=Output("output", "TENSOR_QUANT8_ASYMM", "{2, 1, 3, 1}, 2.0, 128"), |
| output_data=[3, 3, 3, 5, 5, 5]), |
| TestCase( |
| inp=Input("input", "TENSOR_INT32", "{3, 2, 3, 1}"), |
| inp_data=[1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6], |
| begin=Input("begin", "TENSOR_INT32", "{4}"), |
| begin_data=[1, 0, 0, 0], |
| size=Input("size", "TENSOR_INT32", "{4}"), |
| size_data=[2, 1, -1, 1], |
| output=Output("output", "TENSOR_INT32", "{2, 1, 3, 1}"), |
| output_data=[3, 3, 3, 5, 5, 5]), |
| ] |
| |
| for test_case in test_cases: |
| model = Model().Operation("SLICE", test_case.inp, test_case.begin, |
| test_case.size).To(test_case.output) |
| Example({ |
| test_case.inp: test_case.inp_data, |
| test_case.begin: test_case.begin_data, |
| test_case.size: test_case.size_data, |
| test_case.output: test_case.output_data, |
| }, |
| model=model).AddVariations("relaxed", "float16") |
| |
| |
| # zero-sized input |
| |
| # Use BOX_WITH_NMS_LIMIT op to generate a zero-sized internal tensor for box cooridnates. |
| p1 = Parameter("scores", "TENSOR_FLOAT32", "{1, 2}", [0.90, 0.10]) # scores |
| p2 = Parameter("roi", "TENSOR_FLOAT32", "{1, 8}", [1, 1, 10, 10, 0, 0, 10, 10]) # roi |
| o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out |
| o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out |
| tmp1 = Internal("roiOut", "TENSOR_FLOAT32", "{0, 4}") # roi out |
| tmp2 = Internal("batchSplitOut", "TENSOR_INT32", "{0}") # batch split out |
| model = Model("zero_sized").Operation("BOX_WITH_NMS_LIMIT", p1, p2, [0], 0.3, -1, 0, 0.4, 1.0, 0.3).To(o1, tmp1, o2, tmp2) |
| |
| # Use ROI_ALIGN op to convert into zero-sized feature map. |
| layout = BoolScalar("layout", False) # NHWC |
| i1 = Input("in", "TENSOR_FLOAT32", "{1, 1, 1, 1}") |
| zero_sized = Internal("featureMap", "TENSOR_FLOAT32", "{0, 2, 2, 1}") |
| model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 2, 2, 2.0, 2.0, 4, 4, layout).To(zero_sized) |
| |
| # SLICE op with numBatches = 0. |
| o3 = Output("out", "TENSOR_FLOAT32", "{0, 1, 1, 1}") # out |
| model = model.Operation("SLICE", zero_sized, [0, 1, 1, 0], [-1, 1, -1, 1]).To(o3) |
| |
| quant8 = DataTypeConverter().Identify({ |
| p1: ("TENSOR_QUANT8_ASYMM", 0.1, 128), |
| p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0), |
| o1: ("TENSOR_QUANT8_ASYMM", 0.1, 128), |
| tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0), |
| i1: ("TENSOR_QUANT8_ASYMM", 0.1, 128), |
| zero_sized: ("TENSOR_QUANT8_ASYMM", 0.1, 128), |
| o3: ("TENSOR_QUANT8_ASYMM", 0.1, 128) |
| }) |
| |
| # Create test case with dummy values. |
| Example({ |
| i1: [1], |
| o1: [0], |
| o2: [0], |
| o3: [0], |
| }).AddVariations("relaxed", quant8, "float16") |