Imported Upstream version 1.1.0upstream/1.1.0submit/tizen/20200304.094649submit/tizen/20200304.093946submit/tizen/20200304.092919accepted/tizen/unified/20200305.051107

1 files changed, 264 insertions, 0 deletions

diff --git a/tests/nnapi/specs/skip/V1_2/resize_nearest_neighbor.mod.py b/tests/nnapi/specs/skip/V1_2/resize_nearest_neighbor.mod.py
new file mode 100644
index 000000000..04102c5ed
--- /dev/null
+++ b/tests/nnapi/specs/skip/V1_2/resize_nearest_neighbor.mod.py
@@ -0,0 +1,264 @@
+#
+# 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: RESIZE_NEAREST_NEIGHBOR_1, w = 1, h = 1
+i1 = Input("in", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # input 0
+o1 = Output("out", "TENSOR_FLOAT32", "{1, 1, 1, 1}") # output 0
+model_shape = Model("shape").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 1, 1, layout).To(o1)
+model_scale = Model("scale").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 0.5, 0.5, layout).To(o1)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 128),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.25, 128)
+})
+
+test1 = {
+    i1: [1, 2, 3, 4],
+    o1: [1]
+}
+
+Example(test1, model=model_shape).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+Example(test1, model=model_scale).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 2: RESIZE_NEAREST_NEIGHBOR_2, w = 3, h = 3
+i1 = Input("in", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # input 0
+o1 = Output("out", "TENSOR_FLOAT32", "{1, 3, 3, 1}") # output 0
+model_shape = Model("shape").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 3, 3, layout).To(o1)
+model_scale = Model("scale").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 1.5, 1.5, layout).To(o1)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 0),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.25, 0)
+})
+
+test2 = {
+    i1: [1, 2, 3, 4],
+    o1: [1, 1, 2, 1, 1, 2, 3, 3, 4]
+}
+
+Example(test2, model=model_shape).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+Example(test2, model=model_scale).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 3: RESIZE_NEAREST_NEIGHBOR_3, w = 2, h = 2
+i1 = Input("in", "TENSOR_FLOAT32", "{1, 3, 3, 1}") # input 0
+o1 = Output("out", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # output 0
+model_shape = Model("shape").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 2, 2, layout).To(o1)
+model_scale = Model("scale").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 0.8, 0.8, layout).To(o1)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.25, 100)
+})
+
+test3 = {
+    i1: [1, 2, 3, 4, 5, 6, 7, 8, 9],
+    o1: [1, 2, 4, 5]
+}
+
+Example(test3, model=model_shape).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+Example(test3, model=model_scale).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 4: RESIZE_NEAREST_NEIGHBOR_4, w = 5, h = 2
+i1 = Input("in", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # input 0
+o1 = Output("out", "TENSOR_FLOAT32", "{1, 2, 5, 1}") # output 0
+model_shape = Model("shape").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 5, 2, layout).To(o1)
+model_scale = Model("scale").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 2.6, 1.1, layout).To(o1)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.25, 100)
+})
+
+test4 = {
+    i1: [1, 2, 3, 4],
+    o1: [1, 1, 1, 2, 2, 3, 3, 3, 4, 4]
+}
+
+Example(test4, model=model_shape).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+Example(test4, model=model_scale).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 5: RESIZE_NEAREST_NEIGHBOR_5, w = 3, h = 3
+i1 = Input("in", "TENSOR_FLOAT32", "{1, 4, 4, 1}") # input 0
+o1 = Output("out", "TENSOR_FLOAT32", "{1, 3, 3, 1}") # output 0
+model_shape = Model("shape").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 3, 3, layout).To(o1)
+model_scale = Model("scale").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 0.9, 0.9, layout).To(o1)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.25, 100)
+})
+
+test5 = {
+    i1: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
+    o1: [1, 2, 3, 5, 6, 7, 9, 10, 11]
+}
+
+Example(test5, model=model_shape).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+Example(test5, model=model_scale).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 6: RESIZE_NEAREST_NEIGHBOR_6, w = 2, h = 5
+i1 = Input("in", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # input 0
+o1 = Output("out", "TENSOR_FLOAT32", "{1, 5, 2, 1}") # output 0
+model_shape = Model("shape").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 2, 5, layout).To(o1)
+model_scale = Model("scale").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 1.4, 2.8, layout).To(o1)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.25, 100)
+})
+
+test6 = {
+    i1: [1, 2, 3, 4],
+    o1: [1, 2, 1, 2, 1, 2, 3, 4, 3, 4]
+}
+
+Example(test6, model=model_shape).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+Example(test6, model=model_scale).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 7: RESIZE_NEAREST_NEIGHBOR_7, w = 4, h = 4
+i1 = Input("in", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # input 0
+o1 = Output("out", "TENSOR_FLOAT32", "{1, 4, 4, 1}") # output 0
+model_shape = Model("shape").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 4, 4, layout).To(o1)
+model_scale = Model("scale").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 2.0, 2.0, layout).To(o1)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.25, 100)
+})
+
+test7 = {
+    i1: [1, 2, 3, 4],
+    o1: [1, 1, 2, 2, 1, 1, 2, 2, 3, 3, 4, 4, 3, 3, 4, 4]
+}
+
+Example(test7, model=model_shape).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+Example(test7, model=model_scale).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 8: RESIZE_NEAREST_NEIGHBOR_8, w = 3, h = 3
+i1 = Input("in", "TENSOR_FLOAT32", "{2, 2, 2, 2}") # input 0
+o1 = Output("out", "TENSOR_FLOAT32", "{2, 3, 3, 2}") # output 0
+model_shape = Model("shape").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 3, 3, layout).To(o1)
+model_scale = Model("scale").Operation("RESIZE_NEAREST_NEIGHBOR", i1, 1.6, 1.8, layout).To(o1)
+
+# Additional data type
+quant8 = DataTypeConverter().Identify({
+    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.25, 100)
+})
+
+test8 = {
+    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(test8, model=model_shape).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+Example(test8, model=model_scale).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 8: zero-sized input, resize by output shape
+
+# 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)
+
+# RESIZE_NEAREST_NEIGHBOR op with numBatches = 0.
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 3, 3, 1}") # out
+model = model.Operation("RESIZE_NEAREST_NEIGHBOR", zero_sized, 3, 3, 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 9: zero-sized input, resize by scale
+
+# 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)
+
+# RESIZE_NEAREST_NEIGHBOR op with numBatches = 0.
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 3, 3, 1}") # out
+model = model.Operation("RESIZE_NEAREST_NEIGHBOR", zero_sized, 1.6, 1.6, 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")