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

1 files changed, 216 insertions, 0 deletions

diff --git a/nn/runtime/test/specs/V1_3/concat_quant8_signed.mod.py b/nn/runtime/test/specs/V1_3/concat_quant8_signed.mod.py
new file mode 100644
index 000000000..d311b43d2
--- /dev/null
+++ b/nn/runtime/test/specs/V1_3/concat_quant8_signed.mod.py
@@ -0,0 +1,216 @@
+#
+# 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.
+#
+
+# Adapted from tensorflow/lite/kernels/concatenation_test.cc
+
+input0 = Input("input0", "TENSOR_FLOAT32", "{2, 1, 2}")
+input1 = Input("input1", "TENSOR_FLOAT32", "{2, 1, 2}")
+input2 = Input("input2", "TENSOR_FLOAT32", "{2, 1, 2}")
+input3 = Input("input3", "TENSOR_FLOAT32", "{2, 1, 2}")
+axis = 2
+output0 = Output("output0", "TENSOR_FLOAT32", "{2, 1, 8}")
+
+model = Model().Operation("CONCATENATION", input0, input1, input2, input3, axis).To(output0)
+
+# FourInputsQuantizedMixedRange
+Example({
+    input0: [1.0, -3.0, -4.0, -7.0],
+    input1: [1.1, 3.1, 4.1, 7.1],
+    input2: [1.2, -3.2, -4.2, 7.2],
+    input3: [1.3, 3.3, 4.3, 7.3],
+    output0: [1.0, -3.0, 1.1, 3.1, 1.2, -3.2, 1.3, 3.3, -4.0, -7.0, 4.1, 7.1, -4.2, 7.2, 4.3, 7.3],
+}).AddVariations(DataTypeConverter().Identify({
+    input0: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.084, -1],
+    input1: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.05, -128],
+    input2: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.089, -5],
+    input3: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.029, -128],
+    output0: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.1, -1],
+}), includeDefault=False)
+
+# FourInputsQuantizedMixedRangeClampingLogic
+Example({
+    input0: [1.0, -3.0, -4.0, -7.0],
+    input1: [1.1, 3.1, 4.1, 7.1],
+    input2: [1.2, -3.2, -4.2, 7.2],
+    input3: [1.3, 3.3, 4.3, 7.3],
+    output0: [1.0, -1.0, 1.0, 1.0, 1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 1.0, 1.0, -1.0, 1.0, 1.0, 1.0]
+}).AddVariations(DataTypeConverter().Identify({
+    input0: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.084, -1],
+    input1: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.05, -128],
+    input2: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.089, -5],
+    input3: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.029, -128],
+    output0: ["TENSOR_QUANT8_ASYMM_SIGNED", 0.0078125, -1],
+}), includeDefault=False)
+
+#######################################################
+
+model = Model()
+i1 = Input("op1", "TENSOR_QUANT8_ASYMM_SIGNED", "{2, 3}, 0.5f, -128") # input 0
+i2 = Input("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{2, 3}, 0.5f, -128") # input 1
+axis1 = Int32Scalar("axis1", 1)
+r = Output("result", "TENSOR_QUANT8_ASYMM_SIGNED", "{2, 6}, 0.5f, -128") # output
+model = model.Operation("CONCATENATION", i1, i2, axis1).To(r)
+
+# Example 1.
+input0 = {i1: [1, 2, 3, 4, 5, 6],
+          i2: [7, 8, 9, 10, 11, 12]}
+output0 = {r: [1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12]}
+
+# Instantiate an example
+Example((input0, output0))
+
+#######################################################
+
+model = Model()
+
+row1 = 52
+row2 = 40
+col = 300
+output_row = row1 + row2
+
+input1 = Input("input1", "TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}, 0.5f, -128" % (row1, col))
+input2 = Input("input2", "TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}, 0.5f, -128" % (row2, col))
+axis0 = Int32Scalar("axis0", 0)
+output = Output("output", "TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}, 0.5f, -128" % (output_row, col))
+model = model.Operation("CONCATENATION", input1, input2, axis0).To(output)
+
+# Example 1.
+input1_values = [x % 256 for x in range(row1 * col)]
+input2_values = (lambda s1 = row1 * col, s2 = row2 * col:
+                 [(x + s1) % 256 for x in range(s2)])()
+input0 = {input1: [x - 128 for x in input1_values],
+          input2: [x - 128 for x in input2_values]}
+output_values = [x % 256 - 128 for x in range(output_row * col)]
+output0 = {output: output_values}
+
+# Instantiate an example
+Example((input0, output0))
+
+#######################################################
+
+model = Model()
+
+row = 400
+col1 = 60
+col2 = 30
+output_col = col1 + col2
+
+input1 = Input("input1", "TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}, 0.5f, -128" % (row, col1))
+input2 = Input("input2", "TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}, 0.5f, -128" % (row, col2))
+axis1 = Int32Scalar("axis1", 1)
+output = Output("output", "TENSOR_QUANT8_ASYMM_SIGNED", "{%d, %d}, 0.5f, -128" % (row, output_col))
+model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
+
+# Example 1.
+input1_values = [(x % 128) for x in range(row * col1)]
+input2_values = [x % 128 - 128 for x in range(row * col2)]
+input0 = {input1: input1_values,
+          input2: input2_values}
+
+output_values = [x for x in range(row * output_col)]
+for r in range(row):
+  for c1 in range(col1):
+    output_values[r * output_col + c1] = input1_values[r * col1 + c1]
+  for c2 in range(col2):
+    output_values[r * output_col + col1 + c2] = input2_values[r * col2 + c2]
+
+output0 = {output: output_values}
+
+# Instantiate an example
+Example((input0, output0))
+
+#######################################################
+
+# Zero-sized input: zero dimension is not "axis"
+
+# 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().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)
+
+# CONCATENATION op with numBatches = 0.
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 2}") # out
+model = model.Operation("CONCATENATION", zero_sized, zero_sized, 3).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)
+
+#######################################################
+
+# Zero-sized input: zero dimension is "axis"
+
+# 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().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)
+
+# CONCATENATION op with numBatches = 0.
+i2 = Input("in", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
+o3 = Output("out", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # out
+model = model.Operation("CONCATENATION", zero_sized, i2, 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),
+    i2: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.2, 0),
+    o3: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0)
+})
+
+Example({
+    i1: [1],
+    i2: [1, 2, 3, 4],
+    o1: [],
+    o2: [],
+    o3: [1, 2, 3, 4],
+}).AddVariations(quant8_signed, includeDefault=False)