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, 104 insertions, 0 deletions

diff --git a/nn/runtime/test/specs/V1_3/relu1_quant8_signed.mod.py b/nn/runtime/test/specs/V1_3/relu1_quant8_signed.mod.py
new file mode 100644
index 000000000..40a48c371
--- /dev/null
+++ b/nn/runtime/test/specs/V1_3/relu1_quant8_signed.mod.py
@@ -0,0 +1,104 @@
+#
+# 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
+o = Output("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 2, 2, 1}, 0.5f, -128") # output 0
+model = model.Operation("RELU1", i1).To(o)
+
+# Example 1. Input in operand 0,
+input0 = {i1: # input 0
+          [-128, -127, -126, -125]}
+output0 = {o: # output 0
+          [-128, -127, -126, -126]}
+
+# Instantiate one example
+Example((input0, output0))
+
+#######################################################
+
+# Example 2. Input in operand 0,
+input1 = {i1: # input 0
+          [-124, -118, -28, 127]}
+output1 = {o: # output 0
+          [-126, -126, -126, -126]}
+
+# Instantiate another example
+Example((input1, output1))
+
+#######################################################
+
+model = Model()
+
+d0 = 2
+d1 = 64
+d2 = 64
+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("RELU1", 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 = [127 if x < 127 else 129 if x > 129 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))
+
+#######################################################
+# 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)
+
+# RELU1 op with numBatches = 0.
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 1}") # out
+model = model.Operation("RELU1", 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)