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author | Lev Proleev <levp@google.com> | 2019-12-19 15:18:50 +0000 |
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committer | Lev Proleev <levp@google.com> | 2020-01-03 16:01:32 +0000 |
commit | f40dc3a495a069d70d95187c7e2eb68e22a514bd (patch) | |
tree | 5ce61595f8fad9ead7741652c2b26f1f8ed63e5d /nn/runtime/test/specs/V1_3/relu6_quant8_signed.mod.py | |
parent | 21d5907e55f6ed8a3e08c240efb9cf5d4a644fd8 (diff) | |
download | ml-f40dc3a495a069d70d95187c7e2eb68e22a514bd.tar.gz |
Add quant8 signed generated tests
The tests are written semi-automatically by joining all of the 1.0-1.2
tests with TENSOR_QUANT8_ASYMM operands and converting them to
TENSOR_QUANT8_ASYMM_SIGNED.
Also:
* Fix implementation of CONCATENATION op for zero-sized tensors
* Add support for TENSOR_QUANT8_ASYMM_SIGNED in test generator
Bug: 136735770
Test: NNTest_static and VtsHalNeuralnetworksV1_3TargetTest
Change-Id: I250dbe85684aa594892494eb53e6312c1cacb6f3
Diffstat (limited to 'nn/runtime/test/specs/V1_3/relu6_quant8_signed.mod.py')
-rw-r--r-- | nn/runtime/test/specs/V1_3/relu6_quant8_signed.mod.py | 103 |
1 files changed, 103 insertions, 0 deletions
diff --git a/nn/runtime/test/specs/V1_3/relu6_quant8_signed.mod.py b/nn/runtime/test/specs/V1_3/relu6_quant8_signed.mod.py new file mode 100644 index 000000000..eb3407135 --- /dev/null +++ b/nn/runtime/test/specs/V1_3/relu6_quant8_signed.mod.py @@ -0,0 +1,103 @@ +# +# 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("op1", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 2, 2, 1}, 0.5f, -128") # input 0 +i2 = Output("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 2, 2, 1}, 0.5f, -128") # output 0 +model = model.Operation("RELU6", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [-128, -127, -117, -116]} +output0 = {i2: # output 0 + [-128, -127, -117, -116]} +# Instantiate an example +Example((input0, output0)) + +####################################################### + +# Example 2. Input in operand 0, +input1 = {i1: # input 0 + [-115, -114, 126, 127]} +output1 = {i2: # output 0 + [-116, -116, -116, -116]} +# Instantiate an example +Example((input1, output1)) + +####################################################### + +model = Model() + +d0 = 2 +d1 = 128 +d2 = 20 +d3 = 2 + +i0 = Input("input", "TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d, %d, %d}, 1.f, 0" % (d0, d1, d2, d3)) + +output = Output("output", "TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d, %d, %d}, 1.f, 0" % (d0, d1, d2, d3)) + +model = model.Operation("RELU6", i0).To(output) + +# Example 1. Input in operand 0, +rng = d0 * d1 * d2 * d3 +input_values = (lambda r = rng: [x % 256 for x in range(r)])() +output_values = [128 if x < 128 else 134 if x > 134 else x for x in input_values] + +input0 = {i0: [value - 128 for value in input_values]} +output0 = {output: [value - 128 for value in output_values]} + +# Instantiate an example +Example((input0, output0)) + +####################################################### +# 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) + +# RELU6 op with numBatches = 0. +o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 1}") # out +model = model.Operation("RELU6", zero_sized).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", 0.1, 0) +}) + +Example({ + i1: [1], + o1: [], + o2: [], + o3: [], +}).AddVariations(quant8_signed, includeDefault=False) |