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#
# Copyright (C) 2020 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, output_width, output_height, layout, align_corners,
half_pixel_centers, output0, input0_data, output_data):
model = Model().Operation("RESIZE_NEAREST_NEIGHBOR", input0, output_width,
output_height, layout, align_corners,
half_pixel_centers).To(output0)
quant8 = DataTypeConverter().Identify({
input0: ["TENSOR_QUANT8_ASYMM", 0.5, 128],
output0: ["TENSOR_QUANT8_ASYMM", 0.5, 128],
})
quant8_signed = DataTypeConverter().Identify({
input0: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.5, 0],
output0: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.5, 0],
})
example = Example({
input0: input0_data,
output0: output_data,
},
model=model,
name=name).AddVariations("float16", "relaxed", quant8)
test(
name="half_pixel_centers_5x2_to_2x2",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 2, 5, 1}"),
output_width=Int32Scalar("output_width", 2),
output_height=Int32Scalar("output_height", 2),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", False),
half_pixel_centers=BoolScalar("half_pixel_centers", True),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
input0_data=[1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
output_data=[2, 4, 2, 4],
)
test(
name="half_pixel_centers_2x2_to_1x1",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
output_width=Int32Scalar("output_width", 1),
output_height=Int32Scalar("output_height", 1),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", False),
half_pixel_centers=BoolScalar("half_pixel_centers", True),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 1, 1, 1}"),
input0_data=[1, 2, 3, 4],
output_data=[4],
)
test(
name="half_pixel_centers_2x2_to_3x3",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
output_width=Int32Scalar("output_width", 3),
output_height=Int32Scalar("output_height", 3),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", False),
half_pixel_centers=BoolScalar("half_pixel_centers", True),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 3, 3, 1}"),
input0_data=[1, 2, 3, 4],
output_data=[1, 2, 2, 3, 4, 4, 3, 4, 4],
)
test(
name="half_pixel_centers_2x2_to_2x5",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
output_width=Int32Scalar("output_width", 5),
output_height=Int32Scalar("output_height", 2),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", False),
half_pixel_centers=BoolScalar("half_pixel_centers", True),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 2, 5, 1}"),
input0_data=[1, 2, 3, 4],
output_data=[1, 1, 2, 2, 2, 3, 3, 4, 4, 4],
)
test(
name="half_pixel_centers_4x4_to_3x3",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 4, 4, 1}"),
output_width=Int32Scalar("output_width", 3),
output_height=Int32Scalar("output_height", 3),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", False),
half_pixel_centers=BoolScalar("half_pixel_centers", True),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 3, 3, 1}"),
input0_data=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
output_data=[1, 3, 4, 9, 11, 12, 13, 15, 16],
)
test(
name="half_pixel_centers_2x2_to_5x2",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
output_width=Int32Scalar("output_width", 2),
output_height=Int32Scalar("output_height", 5),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", False),
half_pixel_centers=BoolScalar("half_pixel_centers", True),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 5, 2, 1}"),
input0_data=[1, 2, 3, 4],
output_data=[1, 2, 1, 2, 3, 4, 3, 4, 3, 4],
)
test(
name="half_pixel_centers_2x2_to_4x4",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
output_width=Int32Scalar("output_width", 4),
output_height=Int32Scalar("output_height", 4),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", False),
half_pixel_centers=BoolScalar("half_pixel_centers", True),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 4, 4, 1}"),
input0_data=[1, 2, 3, 4],
output_data=[1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4],
)
test(
name="half_pixel_centers_2x2x2x2_to_2x3x3x2",
input0=Input("input0", "TENSOR_FLOAT32", "{2, 2, 2, 2}"),
output_width=Int32Scalar("output_width", 3),
output_height=Int32Scalar("output_height", 3),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", False),
half_pixel_centers=BoolScalar("half_pixel_centers", True),
output0=Output("output0", "TENSOR_FLOAT32", "{2, 3, 3, 2}"),
input0_data=[1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8],
output_data=[
1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 5, 5, 6, 6, 6, 6,
7, 7, 8, 8, 8, 8, 7, 7, 8, 8, 8, 8
],
)
test(
name="align_corners_2x2_to_1x1",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
output_width=Int32Scalar("output_width", 1),
output_height=Int32Scalar("output_height", 1),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", True),
half_pixel_centers=BoolScalar("half_pixel_centers", False),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 1, 1, 1}"),
input0_data=[1, 2, 3, 4],
output_data=[1],
)
test(
name="align_corners_2x2_to_3x3",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
output_width=Int32Scalar("output_width", 3),
output_height=Int32Scalar("output_height", 3),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", True),
half_pixel_centers=BoolScalar("half_pixel_centers", False),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 3, 3, 1}"),
input0_data=[1, 2, 3, 4],
output_data=[1, 2, 2, 3, 4, 4, 3, 4, 4],
)
test(
name="align_corners_3x3_to_2x2",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 3, 3, 1}"),
output_width=Int32Scalar("output_width", 2),
output_height=Int32Scalar("output_height", 2),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", True),
half_pixel_centers=BoolScalar("half_pixel_centers", False),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
input0_data=[1, 2, 3, 4, 5, 6, 7, 8, 9],
output_data=[1, 3, 7, 9],
)
test(
name="align_corners_4x4_to_3x3",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 4, 4, 1}"),
output_width=Int32Scalar("output_width", 3),
output_height=Int32Scalar("output_height", 3),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", True),
half_pixel_centers=BoolScalar("half_pixel_centers", False),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 3, 3, 1}"),
input0_data=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
output_data=[1, 3, 4, 9, 11, 12, 13, 15, 16],
)
test(
name="align_corners_2x2_to_1x1",
input0=Input("input0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
output_width=Int32Scalar("output_width", 1),
output_height=Int32Scalar("output_height", 1),
layout=BoolScalar("layout", False),
align_corners=BoolScalar("align_corners", True),
half_pixel_centers=BoolScalar("half_pixel_centers", False),
output0=Output("output0", "TENSOR_FLOAT32", "{1, 1, 1, 1}"),
input0_data=[1, 2, 3, 4],
output_data=[1],
)
# Test from version 1.2 to test default arguments removal
i1 = Input("in", "TENSOR_FLOAT32", "{2, 2, 2, 2}")
o1 = Output("out", "TENSOR_FLOAT32", "{2, 3, 3, 2}")
layout = BoolScalar("layout", False) # NHWC
align_corners = BoolScalar("align_corners", False)
half_pixel_centers = BoolScalar("half_pixel_centers", False)
model_shape = Model("shape").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 3, 3,
layout, align_corners,
half_pixel_centers).To(o1)
model_scale = Model("scale").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 1.6, 1.8,
layout, align_corners,
half_pixel_centers).To(o1)
quant8 = DataTypeConverter().Identify({
i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
o1: ("TENSOR_QUANT8_ASYMM", 0.25, 100)
})
values = {
i1: [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8],
o1: [
1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 6, 6,
5, 5, 5, 5, 6, 6, 7, 7, 7, 7, 8, 8
]
}
Example(
values, model=model_shape,
name="default_values").AddNchw(i1, o1, layout).AddVariations(
"relaxed", quant8, "float16")
Example(
values, model=model_scale,
name="default_values").AddNchw(i1, o1, layout).AddVariations(
"relaxed", quant8, "float16")
Example.SetVersion(
"V1_2",
"resize_nearest_neighbor_v1_3_scale_default_values_nchw",
"resize_nearest_neighbor_v1_3_scale_default_values_nchw_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_scale_default_values_nchw_float16",
"resize_nearest_neighbor_v1_3_scale_default_values_nchw_float16_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_scale_default_values_nchw_quant8",
"resize_nearest_neighbor_v1_3_scale_default_values_nchw_quant8_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_scale_default_values_nhwc",
"resize_nearest_neighbor_v1_3_scale_default_values_nhwc_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_scale_default_values_nhwc_float16",
"resize_nearest_neighbor_v1_3_scale_default_values_nhwc_float16_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_scale_default_values_nhwc_quant8",
"resize_nearest_neighbor_v1_3_scale_default_values_nhwc_quant8_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_shape_default_values_nchw",
"resize_nearest_neighbor_v1_3_shape_default_values_nchw_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_shape_default_values_nchw_float16",
"resize_nearest_neighbor_v1_3_shape_default_values_nchw_float16_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_shape_default_values_nchw_quant8",
"resize_nearest_neighbor_v1_3_shape_default_values_nchw_quant8_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_shape_default_values_nhwc",
"resize_nearest_neighbor_v1_3_shape_default_values_nhwc_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_shape_default_values_nhwc_float16",
"resize_nearest_neighbor_v1_3_shape_default_values_nhwc_float16_all_inputs_as_internal",
"resize_nearest_neighbor_v1_3_shape_default_values_nhwc_quant8",
"resize_nearest_neighbor_v1_3_shape_default_values_nhwc_quant8_all_inputs_as_internal",
)
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