1 files changed, 89 insertions, 0 deletions
diff --git a/tests/nnapi/specs/V1_2/tanh_v1_2.mod.py b/tests/nnapi/specs/V1_2/tanh_v1_2.mod.py
new file mode 100755
index 000000000..c65d09fdb
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+++ b/tests/nnapi/specs/V1_2/tanh_v1_2.mod.py
@@ -0,0 +1,89 @@
+#
+# 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.
+#
+
+# TEST 1
+input0 = Input("input0", "TENSOR_FLOAT16", "{1, 2, 2, 1}")
+output0 = Output("output0", "TENSOR_FLOAT16", "{1, 2, 2, 1}")
+
+model = Model().Operation("TANH", input0).To(output0)
+
+Example({
+    input0: [-1, 0, 1, 10],
+    output0: [-.761594156, 0, .761594156, 0.999999996],
+})
+
+
+# TEST 2
+input_scale, input_offset = 0.05, 100
+output_scale, output_offset = 1.0 / 128, 128  # Required.
+
+def dequantize(x):
+  return (x - input_offset) * input_scale
+
+def quantize(x):
+  return max(0, min(255, int(round(x / output_scale)) + output_offset))
+
+input0 = Input("input0", "TENSOR_QUANT8_ASYMM", "{256}, %g, %d" % (input_scale, input_offset))
+output0 = Output("output0", "TENSOR_QUANT8_ASYMM", "{256}, %g, %d" % (output_scale, output_offset))
+model = Model().Operation("TANH", input0).To(output0)
+
+input_values = list(range(256))
+output_values = [quantize(math.tanh(dequantize(x))) for x in input_values]
+
+Example({
+    input0: input_values,
+    output0: output_values,
+})
+
+
+# TEST 3: 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)
+
+# TANH op with numBatches = 0.
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 1}") # out
+model = model.Operation("TANH", zero_sized).To(o3)
+
+quant8 = DataTypeConverter().Identify({
+    p1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+    p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+    tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
+    i1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+    zero_sized: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+    o3: ("TENSOR_QUANT8_ASYMM", 1.0 / 128, 128)
+})
+
+# Create test case with dummy values.
+Example({
+    i1: [1],
+    o1: [0],
+    o2: [0],
+    o3: [0],
+}).AddVariations("relaxed", quant8, "float16")