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Diffstat (limited to 'tests/nnapi/specs/V1_2/resize_nearest_neighbor.mod.py')
-rw-r--r-- | tests/nnapi/specs/V1_2/resize_nearest_neighbor.mod.py | 264 |
1 files changed, 0 insertions, 264 deletions
diff --git a/tests/nnapi/specs/V1_2/resize_nearest_neighbor.mod.py b/tests/nnapi/specs/V1_2/resize_nearest_neighbor.mod.py deleted file mode 100644 index 04102c5ed..000000000 --- a/tests/nnapi/specs/V1_2/resize_nearest_neighbor.mod.py +++ /dev/null @@ -1,264 +0,0 @@ -# -# 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") |