Imported Upstream version 1.11.0upstream/1.11.0

1 files changed, 86 insertions, 0 deletions

diff --git a/tests/nnapi/specs/skip/V1_2/fully_connected_v1_2.mod.py b/tests/nnapi/specs/skip/V1_2/fully_connected_v1_2.mod.py
new file mode 100644
index 000000000..13b45fa92
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+++ b/tests/nnapi/specs/skip/V1_2/fully_connected_v1_2.mod.py
@@ -0,0 +1,86 @@
+#
+# Copyright (C) 2018 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: FULLY_CONNECTED
+model = Model()
+in0 = Input("op1", "TENSOR_FLOAT32", "{3, 1}")
+weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 1}", [2])
+bias = Parameter("b0", "TENSOR_FLOAT32", "{1}", [4])
+out0 = Output("op3", "TENSOR_FLOAT32", "{3, 1}")
+act = Int32Scalar("act", 0)
+model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
+
+quant8_mult_gt_1 = DataTypeConverter(name="quant8_mult_gt_1").Identify({
+    in0: ("TENSOR_QUANT8_ASYMM", 0.5, 127),
+    weights: ("TENSOR_QUANT8_ASYMM", 0.5, 120),
+    bias: ("TENSOR_INT32", 0.25, 0),
+    out0: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+})
+
+# Example 1. Input in operand 0,
+input0 = {in0: # input 0
+          [2, 32, 16]}
+output0 = {out0: # output 0
+               [8, 68, 36]}
+
+# Instantiate an example
+Example((input0, output0)).AddVariations("relaxed", "float16", quant8_mult_gt_1)
+
+# FULLY_CONNECTED of data type TENSOR_FLOAT32 is introduced in V1_0.
+Example.SetVersion("V1_0", "fully_connected_v1_2")
+
+# TEST 2: FULLY_CONNECTED, 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, 3}")
+zero_sized = Internal("featureMap", "TENSOR_FLOAT32", "{0, 2, 2, 3}")
+model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 2, 2, 2.0, 2.0, 4, 4, layout).To(zero_sized)
+
+# FULLY_CONNECTED op with numBatches = 0.
+w = Parameter("weights", "TENSOR_FLOAT32", "{1, 3}", [1, 2, 3]) # weights
+b = Parameter("bias", "TENSOR_FLOAT32", "{1}", [1]) # bias
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 1}") # out
+model = model.Operation("FULLY_CONNECTED", zero_sized, w, b, 0).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),
+    w: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+    b: ("TENSOR_INT32", 0.01, 0),
+    o3: ("TENSOR_QUANT8_ASYMM", 0.1, 128)
+})
+
+# Create test case with dummy values.
+Example({
+    i1: [1, 2, 3],
+    o1: [0],
+    o2: [0],
+    o3: [0],
+}).AddNchw(i1, zero_sized, layout).AddVariations("relaxed", quant8, "float16")