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#
# Copyright (C) 2019 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.
#
model = Model()
i1 = Input("input", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 4}, 0.5f, -128") # batch = 1, depth = 1
beta = Float32Scalar("beta", 0.00001) # close to 0
output = Output("output", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 4}, 0.00390625f, -128")
# model 1
model = model.Operation("SOFTMAX", i1, beta).To(output)
# Example 1. Input in operand 0,
input0 = {i1: [-127, -126, -118, -108]}
output0 = {output: [-64, -64, -64, -64]}
# Instantiate an example
Example((input0, output0))
#######################################################
model = Model()
i1 = Input("input", "TENSOR_QUANT8_ASYMM_SIGNED", "{2, 5}, 0.5f, -128") # batch = 2, depth = 5
beta = Float32Scalar("beta", 1.)
output = Output("output", "TENSOR_QUANT8_ASYMM_SIGNED", "{2, 5}, 0.00390625f, -128")
# model 1
model = model.Operation("SOFTMAX", i1, beta).To(output)
# Example 1. Input in operand 0,
input0 = {i1:
[-127, -126, -125, -124, -123,
127, 126, 125, 124, 123]}
output0 = {output:
[-113, -104, -88, -61, -18,
-18, -61, -88, -104, -113]}
# Instantiate an example
Example((input0, output0))
#######################################################
i = Input("op1", "TENSOR_FLOAT32", "{2, 2, 2, 5}") # input 0
o = Output("op2", "TENSOR_FLOAT32", "{2, 2, 2, 5}") # output 0
axis = Int32Scalar("axis", -1) # last axis
# Additional data type
quant8_signed = DataTypeConverter().Identify({
i: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, 0),
o: ("TENSOR_QUANT8_ASYMM_SIGNED", 1./256, -128)
})
example1 = {
i: [17., 16., 15., 14., 1.,
-1., -2., -3., -4., -17.] * 4,
o: [0.643914213228014,
0.236882800924671,
0.087144312427294,
0.032058600957022,
7.246299848982885e-08] * 8
}
example2 = {
i: [1., 2., 3., 4., 5., -1., -2., -3., -4., -5.] * 4,
o: [0.2] * 40
}
# All dimensions other than 2 or 4, without axis parameter
# beta = 1.0
Model().Operation("SOFTMAX", i, 1.0).To(o)
Example(example1).AddVariations(quant8_signed, includeDefault=False).AddDims([1, 3], i, o)
# beta = 0.000001
Model().Operation("SOFTMAX", i, 0.000001).To(o)
Example(example2).AddVariations(quant8_signed, includeDefault=False).AddDims([1, 3], i, o)
#######################################################
# All dimensions, with all possible axis parameter
# beta = 1.0
Model("axis").Operation("SOFTMAX", i, 1.0, axis).To(o)
Example(example1).AddVariations(quant8_signed, includeDefault=False).AddAllDimsAndAxis(i, o, axis)
# beta = 0.000001
Model("axis").Operation("SOFTMAX", i, 0.000001, axis).To(o)
Example(example2).AddVariations(quant8_signed, includeDefault=False).AddAllDimsAndAxis(i, o, axis)
#######################################################
# zero-sized input
# 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.
layout = BoolScalar("layout", False) # NHWC
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)
# SOFTMAX op with numBatches = 0.
o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 1}") # out
model = model.Operation("SOFTMAX", zero_sized, 1.0).To(o3)
quant8_signed = DataTypeConverter().Identify({
p1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
zero_sized: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
o3: ("TENSOR_QUANT8_ASYMM_SIGNED", 1./256, -128)
})
Example({
i1: [1],
o1: [],
o2: [],
o3: [],
}).AddVariations(quant8_signed, includeDefault=False)
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