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#
# 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: L2_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("L2_POOL_2D", i1, 0, 0, 0, 0, 1, 1, 1, 1, 0, layout).To(o1)
# 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).AddRelaxed().AddVariations("float16")
# TEST 2: L2_POOL_2D_NCHW_2, pad = same, stride = 2, filter = 2, act = none
i2 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 4, 1}")
o2 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 2, 1}")
Model().Operation("L2_POOL_2D", i2, 1, 2, 2, 2, 2, 0, layout).To(o2)
# Instantiate an example
example = Example({
i2: [0, 6, 2, 4, 3, 2, 10, 7],
o2: [3.5, 6.5]
}).AddNchw(i2, o2, layout).AddRelaxed().AddVariations("float16")
# TEST 3: L2_POOL_2D_NCHW_LARGE, pad = 0, stride = 1, filter = 2, act = none
i3 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 3}")
o3 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 1, 3}")
Model("large").Operation("L2_POOL_2D", i3, 0, 0, 0, 0, 1, 1, 2, 2, 0, layout).To(o3)
# Instantiate an example
example = Example({
i3: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0],
o3: [6.442049503326416, 7.3143692016601562, 8.2158384323120117]
}).AddNchw(i3, o3, layout).AddRelaxed().AddVariations("float16")
# TEST 4: 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)
# L2_POOL_2D op with numBatches = 0.
o3 = Output("out", "TENSOR_FLOAT32", "{0, 1, 1, 1}") # out
model = model.Operation("L2_POOL_2D", zero_sized, 0, 0, 0, 0, 1, 1, 2, 2, 0, layout).To(o3)
# Create test case with dummy values.
Example({
i1: [1],
o1: [0],
o2: [0],
o3: [0],
}).AddNchw(i1, zero_sized, o3, layout).AddVariations("relaxed", "float16")
# TEST 5: 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)
# L2_POOL_2D op with numBatches = 0.
o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 1}") # out
model = model.Operation("L2_POOL_2D", zero_sized, 1, 1, 1, 2, 2, 0, layout).To(o3)
# Create test case with dummy values.
Example({
i1: [1],
o1: [0],
o2: [0],
o3: [0],
}).AddNchw(i1, zero_sized, o3, layout).AddVariations("relaxed", "float16")
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