Add align_corners and half_pixel_centers parameters to resize ops

Fix: 135147454
Test: NNTest_static
Change-Id: I058da4e420c31e71a02a0ea062b2deb9fda621cf

1 files changed, 207 insertions, 0 deletions

diff --git a/nn/runtime/test/specs/V1_3/resize_nearest_neighbor_v1_3.mod.py b/nn/runtime/test/specs/V1_3/resize_nearest_neighbor_v1_3.mod.py
new file mode 100644
index 000000000..2080e2223
--- /dev/null
+++ b/nn/runtime/test/specs/V1_3/resize_nearest_neighbor_v1_3.mod.py
@@ -0,0 +1,207 @@
+#
+# 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],
+)