Add align_corners and half_pixel_centers parameters to resize ops

Fix: 135147454
Test: NNTest_static
Change-Id: I058da4e420c31e71a02a0ea062b2deb9fda621cf

2 files changed, 739 insertions, 0 deletions

diff --git a/nn/runtime/test/specs/V1_3/resize_bilinear_v1_3.mod.py b/nn/runtime/test/specs/V1_3/resize_bilinear_v1_3.mod.py
new file mode 100644
index 000000000..c4f9b3495
--- /dev/null
+++ b/nn/runtime/test/specs/V1_3/resize_bilinear_v1_3.mod.py
@@ -0,0 +1,532 @@
+#
+# 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,
+         add_quant_variations=True):
+  model = Model().Operation("RESIZE_BILINEAR", 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],
+  })
+  variations = ["float16", "relaxed"]
+  if add_quant_variations:
+    variations.extend([quant8, quant8_signed])
+
+  example = Example({
+      input0: input0_data,
+      output0: output_data,
+  },
+                    model=model,
+                    name=name).AddVariations(*variations)
+
+
+test(
+    name="half_pixel_centers_2x2x2x1_to_2x3x3x1",
+    input0=Input("input0", "TENSOR_FLOAT32", "{2, 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", "{2, 3, 3, 1}"),
+    input0_data=[1, 2, 3, 4, 1, 2, 3, 4],
+    output_data=[
+        1, 1.5, 2, 2, 2.5, 3, 3, 3.5, 4, 1, 1.5, 2, 2, 2.5, 3, 3, 3.5, 4
+    ],
+)
+
+test(
+    name="half_pixel_centers_2x14x13x3_to_2x6x7x3",
+    input0=Input("input0", "TENSOR_FLOAT32", "{2, 14, 13, 3}"),
+    output_width=Int32Scalar("output_width", 7),
+    output_height=Int32Scalar("output_height", 6),
+    layout=BoolScalar("layout", False),
+    align_corners=BoolScalar("align_corners", False),
+    half_pixel_centers=BoolScalar("half_pixel_centers", True),
+    output0=Output("output0", "TENSOR_FLOAT32", "{2, 6, 7, 3}"),
+    input0_data=[
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+    ],
+    output_data=[
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+        0.318251, 0.275884, 0.514905, 0.67128, 0.102982, 0.405996, 0.124694,
+        0.624429, 0.946997, 0.570262, 0.709512, 0.0355313, 0.202523, 0.192552,
+        0.495778, 0.406154, 0.533116, 0.521298, 0.386251, 0.589013, 0.648783,
+        0.58227, 0.38448, 0.527951, 0.410309, 0.288544, 0.524046, 0.411198,
+        0.244783, 0.473302, 0.327214, 0.223712, 0.517082, 0.524867, 0.477849,
+        0.207615, 0.625594, 0.597887, 0.716884, 0.722997, 0.564202, 0.543249,
+        0.722101, 0.585117, 0.459117, 0.301243, 0.723311, 0.668172, 0.551366,
+        0.772401, 0.38182, 0.361603, 0.342651, 0.717503, 0.541077, 0.491924,
+        0.402338, 0.371644, 0.490079, 0.398466, 0.346357, 0.547003, 0.447432,
+        0.44314, 0.47806, 0.52583, 0.745239, 0.737669, 0.555497, 0.905609,
+        0.293674, 0.130434, 0.80983, 0.146657, 0.313562, 0.527374, 0.103938,
+        0.34818, 0.448853, 0.375606, 0.178143, 0.643709, 0.370183, 0.579374,
+        0.40045, 0.568371, 0.602847, 0.34384, 0.39982, 0.433225, 0.317636,
+        0.519918, 0.418223, 0.519364, 0.686467, 0.46176, 0.357139, 0.597116,
+        0.428639, 0.372748, 0.447356, 0.728054, 0.466077, 0.223182, 0.471054,
+        0.662586, 0.376077, 0.36661, 0.680342, 0.708627, 0.426894, 0.781609,
+        0.413516, 0.569702, 0.51284, 0.513902, 0.293259, 0.20138, 0.303709,
+        0.335072, 0.613278, 0.578262, 0.595837, 0.653285, 0.442471, 0.545559,
+        0.480946, 0.689819, 0.292554, 0.722947, 0.297008, 0.735673, 0.100418,
+        0.801456, 0.570554, 0.686329, 0.519124, 0.214197, 0.150897, 0.629145,
+        0.501524, 0.179417, 0.47335, 0.706731, 0.611372, 0.677365, 0.634654,
+        0.481956, 0.525163, 0.463365, 0.502, 0.43508, 0.565645, 0.266055,
+        0.70408, 0.55236, 0.156929, 0.588925, 0.399344, 0.475003, 0.356185,
+        0.59649, 0.562223, 0.526428, 0.439555, 0.492657, 0.323885, 0.765127,
+        0.429603, 0.466623, 0.327579, 0.381093, 0.570198, 0.257691, 0.463234,
+        0.879136, 0.894351, 0.307519, 0.504116, 0.415028, 0.287617, 0.0813091,
+        0.632005, 0.578317, 0.746861, 0.448167, 0.365736, 0.404652, 0.543348,
+        0.708017, 0.363659, 0.691263, 0.805467, 0.260183, 0.224962, 0.514807,
+        0.0318816, 0.350329, 0.746008, 0.317472, 0.684392, 0.859385, 0.463334,
+        0.449842, 0.329192, 0.696575, 0.728967, 0.90306, 0.203413, 0.465313,
+        0.428212, 0.540184, 0.911023, 0.760282, 0.628308, 0.512895, 0.484829,
+        0.43916, 0.453882, 0.298958, 0.352472, 0.242515, 0.563185, 0.635929,
+        0.530235, 0.575659, 0.548754, 0.36042, 0.441017, 0.684306, 0.700217
+    ],
+    add_quant_variations=False,
+)
+
+test(
+    name="half_pixel_centers_2x6x7x3_to_2x14x13x3",
+    input0=Input("input0", "TENSOR_FLOAT32", "{2, 6, 7, 3}"),
+    output_width=Int32Scalar("output_width", 13),
+    output_height=Int32Scalar("output_height", 14),
+    layout=BoolScalar("layout", False),
+    align_corners=BoolScalar("align_corners", False),
+    half_pixel_centers=BoolScalar("half_pixel_centers", True),
+    output0=Output("output0", "TENSOR_FLOAT32", "{2, 14, 13, 3}"),
+    input0_data=[
+        0.597099, 0.717064, 0.696989, 0.37401, 0.587701, 0.52613, 0.45983,
+        0.834707, 0.825826, 0.188192, 0.0581832, 0.489487, 0.897472, 0.912709,
+        0.338215, 0.114233, 0.975378, 0.189915, 0.885871, 0.0123631, 0.682109,
+        0.0637571, 0.021166, 0.990439, 0.405466, 0.245107, 0.235285, 0.670532,
+        0.721946, 0.561132, 0.00936949, 0.0526415, 0.447197, 0.755065, 0.597531,
+        0.195151, 0.269797, 0.551208, 0.954087, 0.223795, 0.397895, 0.924789,
+        0.635592, 0.297786, 0.0237268, 0.101017, 0.491771, 0.715934, 0.546113,
+        0.782431, 0.822342, 0.454632, 0.584726, 0.427173, 0.130061, 0.63263,
+        0.0130227, 0.469195, 0.689757, 0.02537, 0.630514, 0.985893, 0.257825,
+        0.0865368, 0.465077, 0.950239, 0.221726, 0.349166, 0.799219, 0.181399,
+        0.312473, 0.0402693, 0.533386, 0.581182, 0.600382, 0.186105, 0.322734,
+        0.460414, 0.37009, 0.452929, 0.387669, 0.883785, 0.291214, 0.403792,
+        0.288797, 0.114932, 0.441901, 0.369459, 0.00886202, 0.577986, 0.457492,
+        0.488561, 0.845964, 0.0515606, 0.210285, 0.14478, 0.935541, 0.874223,
+        0.0491093, 0.81723, 0.35612, 0.0562574, 0.763055, 0.364821, 0.731659,
+        0.745522, 0.64809, 0.510889, 0.430949, 0.161836, 0.735475, 0.264182,
+        0.476501, 0.465461, 0.258138, 0.730769, 0.899318, 0.174359, 0.86886,
+        0.756424, 0.685789, 0.229707, 0.443774, 0.275146, 0.658326, 0.0486892,
+        0.0457834, 0.787678, 0.663175, 0.481761, 0.573898, 0.439558, 0.56098,
+        0.98622, 0.807752, 0.34172, 0.972262, 0.284004, 0.396007, 0.440433,
+        0.20663, 0.121019, 0.0240191, 0.0363696, 0.93515, 0.283339, 0.90816,
+        0.694879, 0.28891, 0.7681, 0.52112, 0.473317, 0.933415, 0.149368,
+        0.179469, 0.922061, 0.625879, 0.916547, 0.112067, 0.974693, 0.553303,
+        0.44083, 0.772933, 0.423641, 0.0963937, 0.937332, 0.301141, 0.164467,
+        0.977361, 0.224354, 0.84855, 0.728315, 0.564487, 0.733492, 0.911596,
+        0.794423, 0.175493, 0.156265, 0.511936, 0.574377, 0.394498, 0.522894,
+        0.0984874, 0.921798, 0.850203, 0.596674, 0.0368204, 0.326229, 0.281598,
+        0.939939, 0.487391, 0.596167, 0.549989, 0.0975398, 0.983239, 0.993055,
+        0.0635948, 0.749972, 0.177322, 0.771397, 0.777728, 0.531868, 0.31498,
+        0.334786, 0.633789, 0.959249, 0.573456, 0.429871, 0.238746, 0.376446,
+        0.0555074, 0.506892, 0.565241, 0.897762, 0.409047, 0.890007, 0.394771,
+        0.923041, 0.0133429, 0.940546, 0.601826, 0.117889, 0.341266, 0.77822,
+        0.948948, 0.675557, 0.620504, 0.274183, 0.697336, 0.170604, 0.726621,
+        0.583086, 0.0914261, 0.621153, 0.158024, 0.481524, 0.801537, 0.682243,
+        0.469472, 0.0622474, 0.0587367, 0.986715, 0.24073, 0.454288, 0.199331,
+        0.830628, 0.866293, 0.504453, 0.62714, 0.760198, 0.848832, 0.467382
+    ],
+    output_data=[
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+        0.607603, 0.552416, 0.407018, 0.682704, 0.641398, 0.453228, 0.815707,
+        0.802773, 0.334458, 0.476311, 0.670593, 0.188192, 0.0581834, 0.489487,
+        0.570112, 0.518313, 0.408033, 0.837223, 0.91753, 0.326807, 0.415478,
+        0.951275, 0.246953, 0.232946, 0.827222, 0.265637, 0.648444, 0.308675,
+        0.530665, 0.885871, 0.0123631, 0.682109, 0.520907, 0.61765, 0.738911,
+        0.477091, 0.593376, 0.660655, 0.400412, 0.550896, 0.523708, 0.42136,
+        0.64639, 0.601285, 0.481359, 0.797072, 0.764672, 0.338876, 0.467272,
+        0.647443, 0.162646, 0.0573917, 0.483445, 0.547367, 0.493703, 0.394239,
+        0.820153, 0.871307, 0.316339, 0.42133, 0.896668, 0.306273, 0.2372,
+        0.784422, 0.363343, 0.589802, 0.32816, 0.588256, 0.791288, 0.067439,
+        0.716777, 0.292332, 0.319408, 0.864675, 0.322995, 0.341724, 0.70937,
+        0.376654, 0.380775, 0.437585, 0.464387, 0.537448, 0.480948, 0.56575,
+        0.741169, 0.650369, 0.352128, 0.440154, 0.577995, 0.0860078, 0.0550167,
+        0.465321, 0.479133, 0.419873, 0.352859, 0.768945, 0.732637, 0.284935,
+        0.438884, 0.732846, 0.484231, 0.24996, 0.656022, 0.656461, 0.413876,
+        0.386614, 0.76103, 0.507542, 0.232667, 0.820783, 0.0637571, 0.021166,
+        0.990439, 0.168898, 0.0900708, 0.758084, 0.352896, 0.210654, 0.351462,
+        0.507414, 0.428506, 0.360611, 0.650142, 0.685266, 0.536067, 0.36538,
+        0.413036, 0.508547, 0.00936967, 0.0526416, 0.447197, 0.410898, 0.346044,
+        0.31148, 0.717737, 0.593968, 0.253531, 0.456438, 0.569025, 0.662189,
+        0.262719, 0.527622, 0.94958, 0.237949, 0.445068, 0.933804, 0.223795,
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+        0.590885, 0.717326, 0.655248, 0.424742, 0.532242, 0.564865, 0.200196,
+        0.280678, 0.438615, 0.35474, 0.459391, 0.265489, 0.477056, 0.612421,
+        0.150863, 0.406004, 0.611351, 0.387231, 0.361845, 0.616633, 0.568817,
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+        0.625918, 0.915062, 0.573727, 0.375416, 0.578291, 0.585824, 0.474791,
+        0.162281, 0.586067, 0.632477, 0.308455, 0.520064, 0.381298, 0.464959,
+        0.495634, 0.19862, 0.683447, 0.720641, 0.426949, 0.733296, 0.792109,
+        0.546757, 0.361548, 0.479734, 0.395264, 0.149121, 0.301234, 0.308697,
+        0.950631, 0.412237, 0.668276, 0.843034, 0.356458, 0.743232, 0.654738,
+        0.258843, 0.874405, 0.742802, 0.246895, 0.78648, 0.941409, 0.269214,
+        0.610916, 0.601254, 0.468493, 0.647586, 0.171306, 0.697265, 0.719628,
+        0.344321, 0.523406, 0.476052, 0.509808, 0.416977, 0.291335, 0.630132,
+        0.715128, 0.459775, 0.654768, 0.825885, 0.544591, 0.440188, 0.468157,
+        0.420344, 0.317571, 0.263741, 0.349346, 0.813592, 0.490177, 0.591749,
+        0.74777, 0.383058, 0.708109, 0.632582, 0.1956, 0.91174, 0.7181,
+        0.159092, 0.844851, 0.8839, 0.182963, 0.669753, 0.620864, 0.445203,
+        0.662613, 0.286354, 0.747172, 0.683465, 0.403892, 0.550087, 0.542958,
+        0.515033, 0.421903, 0.430845, 0.587788, 0.707126, 0.489095, 0.613344,
+        0.805958, 0.514667, 0.520904, 0.43881, 0.458542, 0.468081, 0.229012,
+        0.426471, 0.434549, 0.498534, 0.578495, 0.531216, 0.429922, 0.686639,
+        0.700383, 0.30985, 0.875891, 0.711202, 0.382914, 0.698839, 0.658681,
+        0.533233, 0.375266, 0.633765, 0.609996, 0.362104, 0.61345, 0.674499,
+        0.400677, 0.510875, 0.623447, 0.55417, 0.441007, 0.593996, 0.663944,
+        0.567386, 0.694145, 0.511401, 0.646126, 0.678623, 0.429228, 0.605769,
+        0.373922, 0.522976, 0.582708, 0.199808, 0.576546, 0.0555074, 0.506892,
+        0.565241, 0.314663, 0.476786, 0.665169, 0.768185, 0.4241, 0.840043,
+        0.704304, 0.606737, 0.552828, 0.433463, 0.883503, 0.0807784, 0.646667,
+        0.774788, 0.0615947, 0.940546, 0.601826, 0.117889, 0.617857, 0.696808,
+        0.565382, 0.366981, 0.766088, 0.897043, 0.546984, 0.681164, 0.533708,
+        0.678907, 0.551288, 0.343789, 0.690635, 0.309035, 0.587409, 0.697336,
+        0.170604, 0.726621, 0.281613, 0.328835, 0.589203, 0.373649, 0.363073,
+        0.670092, 0.534713, 0.42299, 0.811647, 0.556594, 0.551088, 0.537557,
+        0.522801, 0.706459, 0.0972087, 0.538583, 0.74625, 0.0971787, 0.562627,
+        0.766779, 0.170535, 0.469515, 0.63935, 0.562376, 0.417981, 0.533251,
+        0.861871, 0.615918, 0.555136, 0.607292, 0.75222, 0.553922, 0.454692,
+        0.734438, 0.494964, 0.557036, 0.724277, 0.461273, 0.615519, 0.507718,
+        0.150778, 0.613165, 0.432636, 0.249361, 0.675015, 0.301242, 0.42188,
+        0.783251, 0.408884, 0.495439, 0.522285, 0.612139, 0.529414, 0.113639,
+        0.430499, 0.717713, 0.132763, 0.184709, 0.931731, 0.223181, 0.321173,
+        0.581892, 0.559369, 0.468981, 0.300414, 0.826699, 0.684852, 0.429108,
+        0.680876, 0.825533, 0.556557, 0.565594, 0.778242, 0.680893, 0.526663,
+        0.751218, 0.751942, 0.504416, 0.583086, 0.0914261, 0.621153, 0.452298,
+        0.211456, 0.676656, 0.223418, 0.421509, 0.773785, 0.359647, 0.476889,
+        0.517195, 0.641919, 0.470399, 0.119116, 0.394471, 0.7082, 0.144624,
+        0.0587368, 0.986715, 0.24073, 0.271726, 0.562739, 0.558367, 0.485981,
+        0.222802, 0.814975, 0.70783, 0.387099, 0.705405, 0.849971, 0.557435,
+        0.602562, 0.792843, 0.742869, 0.516539, 0.760198, 0.848832, 0.467382
+    ],
+    add_quant_variations=False,
+)
+
+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, 1.5, 2, 2, 2.5, 3, 3, 3.5, 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, 2.5, 4, 7, 8.5, 10, 13, 14.5, 16],
+)
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],
+)