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#
# 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")
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