1 files changed, 186 insertions, 0 deletions
diff --git a/tests/nnapi/specs/skip/V1_2/max_pool_v1_2.mod.py b/tests/nnapi/specs/skip/V1_2/max_pool_v1_2.mod.py
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index 000000000..979cf2ea3
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+++ b/tests/nnapi/specs/skip/V1_2/max_pool_v1_2.mod.py
@@ -0,0 +1,186 @@
+#
+# 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.
+#
+
+layout = BoolScalar("layout", False) # NHWC
+
+# TEST 1: MAX_POOL_2D_NCHW_1, pad = 0, stride = 1, filter = 1, act = none
+i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
+o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
+Model().Operation("MAX_POOL_2D", i1, 0, 0, 0, 0, 1, 1, 1, 1, 0, layout).To(o1)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM", 0.5, 0),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.5, 0)
+})
+
+# Instantiate an example
+example = Example({
+    i1: [1.0, 2.0, 3.0, 4.0],
+    o1: [1.0, 2.0, 3.0, 4.0]
+}).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 2: MAX_POOL_2D_NCHW_2, act = none
+bat = 5
+row = 50
+col = 70
+chn = 3
+std = 20
+flt = 20
+pad = 0
+output_row = (row + 2 * pad - flt + std) // std
+output_col = (col + 2 * pad - flt + std) // std
+
+i2 = Input("op1", ("TENSOR_FLOAT32", [bat, row, col, chn]))
+o2 = Output("op4", ("TENSOR_FLOAT32", [bat, output_row, output_col, chn]))
+Model().Operation("MAX_POOL_2D", i2, pad, pad, pad, pad, std, std, flt, flt, 0, layout).To(o2)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i2: ("TENSOR_QUANT8_ASYMM", 0.5, 0),
+    o2: ("TENSOR_QUANT8_ASYMM", 0.5, 0)
+})
+
+# Instantiate an example
+example = Example({
+    i2: [x % std + 1 for x in range(bat * row * col * chn)],
+    o2: [std for _ in range(bat * output_row * output_col * chn)]
+}).AddNchw(i2, o2, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 3: MAX_POOL_2D_NCHW_3, act = relu6
+bat = 5
+row = 50
+col = 70
+chn = 3
+std = 20
+flt = 20
+pad = 0
+output_row = (row + 2 * pad - flt + std) // std
+output_col = (col + 2 * pad - flt + std) // std
+
+i3 = Input("op1", ("TENSOR_FLOAT32", [bat, row, col, chn]))
+o3 = Output("op4", ("TENSOR_FLOAT32", [bat, output_row, output_col, chn]))
+Model().Operation("MAX_POOL_2D", i3, pad, pad, pad, pad, std, std, flt, flt, 3, layout).To(o3)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i3: ("TENSOR_QUANT8_ASYMM", 0.25, 0),
+    o3: ("TENSOR_QUANT8_ASYMM", 0.25, 0)
+})
+
+# Instantiate an example
+example = Example({
+    i3: [x % std + 1 for x in range(bat * row * col * chn)],
+    o3: [6 for _ in range(bat * output_row * output_col * chn)]
+}).AddNchw(i3, o3, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 4: MAX_POOL_2D_NCHW_4, pad = same, stride = 2, filter = 2, act = none
+i4 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 4, 1}")
+o4 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 2, 1}")
+Model().Operation("MAX_POOL_2D", i4, 1, 2, 2, 2, 2, 0, layout).To(o4)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i4: ("TENSOR_QUANT8_ASYMM", 0.25, 0),
+    o4: ("TENSOR_QUANT8_ASYMM", 0.25, 0)
+})
+
+# Instantiate an example
+example = Example({
+    i4: [0, 6, 2, 4, 3, 2, 10, 7],
+    o4: [6, 10]
+}).AddNchw(i4, o4, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 5: zero-sized input, explicit padding
+
+# 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.
+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)
+
+# MAX_POOL_2D op with numBatches = 0.
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 1, 1, 1}") # out
+model = model.Operation("MAX_POOL_2D", zero_sized, 0, 0, 0, 0, 1, 1, 2, 2, 0, layout).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],
+}).AddNchw(i1, zero_sized, o3, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 6: zero-sized input, implicit padding
+
+# 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.
+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)
+
+# MAX_POOL_2D op with numBatches = 0.
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 1}") # out
+model = model.Operation("MAX_POOL_2D", zero_sized, 1, 1, 1, 2, 2, 0, layout).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],
+}).AddNchw(i1, zero_sized, o3, layout).AddVariations("relaxed", quant8, "float16")