Merge cherrypicks of [4315359, 4315389, 4315390, 4315519, 4315520, 4315360, 4317665, 4317701, 4315391, 4316085] into pi-release
Change-Id: Ib93be30376ce88151abf82f39ee8c99c39a8503b
diff --git a/nn/common/operations/FullyConnected.cpp b/nn/common/operations/FullyConnected.cpp
index 4d2008d..4e2deff 100644
--- a/nn/common/operations/FullyConnected.cpp
+++ b/nn/common/operations/FullyConnected.cpp
@@ -18,6 +18,7 @@
#include "CpuOperationUtils.h"
#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
+#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
namespace android {
namespace nn {
@@ -36,13 +37,25 @@
CalculateActivationRangeFloat(activation, &output_activation_min,
&output_activation_max);
- tflite::optimized_ops::FullyConnected(
- inputData, convertShapeToDims(inputShape),
- weightsData, convertShapeToDims(weightsShape),
- biasData, convertShapeToDims(biasShape),
- output_activation_min, output_activation_max,
- outputData, convertShapeToDims(outputShape));
-
+ // b/80425683, optimized implementation produces incorrect results when the
+ // number of input elements is the squre of batch_size.
+ uint32_t batch_size = getSizeOfDimension(outputShape, 0);
+ uint32_t input_n_elements = getNumberOfElements(inputShape);
+ if (batch_size * batch_size == input_n_elements) {
+ tflite::reference_ops::FullyConnected(
+ inputData, convertShapeToDims(inputShape),
+ weightsData, convertShapeToDims(weightsShape),
+ biasData, convertShapeToDims(biasShape),
+ output_activation_min, output_activation_max,
+ outputData, convertShapeToDims(outputShape));
+ } else {
+ tflite::optimized_ops::FullyConnected(
+ inputData, convertShapeToDims(inputShape),
+ weightsData, convertShapeToDims(weightsShape),
+ biasData, convertShapeToDims(biasShape),
+ output_activation_min, output_activation_max,
+ outputData, convertShapeToDims(outputShape));
+ }
return true;
}
diff --git a/nn/runtime/test/generated/examples/fully_connected_float_3.example.cpp b/nn/runtime/test/generated/examples/fully_connected_float_3.example.cpp
new file mode 100644
index 0000000..14ee46d
--- /dev/null
+++ b/nn/runtime/test/generated/examples/fully_connected_float_3.example.cpp
@@ -0,0 +1,22 @@
+// Generated file (from: fully_connected_float_3.mod.py). Do not edit
+// Begin of an example
+{
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+ // int -> FLOAT32 map
+ {{0, {1, 2, 2, 1}}},
+ // int -> INT32 map
+ {},
+ // int -> QUANT8_ASYMM map
+ {}
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+ // int -> FLOAT32 map
+ {{0, {11, 9}}},
+ // int -> INT32 map
+ {},
+ // int -> QUANT8_ASYMM map
+ {}
+}
+}, // End of an example
diff --git a/nn/runtime/test/generated/models/fully_connected_float_3.model.cpp b/nn/runtime/test/generated/models/fully_connected_float_3.model.cpp
new file mode 100644
index 0000000..1527525
--- /dev/null
+++ b/nn/runtime/test/generated/models/fully_connected_float_3.model.cpp
@@ -0,0 +1,32 @@
+// Generated file (from: fully_connected_float_3.mod.py). Do not edit
+void CreateModel(Model *model) {
+ OperandType type4(Type::INT32, {});
+ OperandType type1(Type::TENSOR_FLOAT32, {1, 2});
+ OperandType type2(Type::TENSOR_FLOAT32, {1});
+ OperandType type3(Type::TENSOR_FLOAT32, {2, 1});
+ OperandType type0(Type::TENSOR_FLOAT32, {2, 2});
+ // Phase 1, operands
+ auto op1 = model->addOperand(&type0);
+ auto op2 = model->addOperand(&type1);
+ auto b0 = model->addOperand(&type2);
+ auto op3 = model->addOperand(&type3);
+ auto act = model->addOperand(&type4);
+ // Phase 2, operations
+ static float op2_init[] = {2.0f, 4.0f};
+ model->setOperandValue(op2, op2_init, sizeof(float) * 2);
+ static float b0_init[] = {1.0f};
+ model->setOperandValue(b0, b0_init, sizeof(float) * 1);
+ static int32_t act_init[] = {0};
+ model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
+ model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3});
+ // Phase 3, inputs and outputs
+ model->identifyInputsAndOutputs(
+ {op1},
+ {op3});
+ assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+ static std::set<int> ignore = {};
+ return ignore.find(i) != ignore.end();
+}
diff --git a/nn/runtime/test/generated/tests/fully_connected_float_3.mod.py.cpp b/nn/runtime/test/generated/tests/fully_connected_float_3.mod.py.cpp
new file mode 100644
index 0000000..eef737e
--- /dev/null
+++ b/nn/runtime/test/generated/tests/fully_connected_float_3.mod.py.cpp
@@ -0,0 +1,17 @@
+// DO NOT EDIT;
+// Generated by ml/nn/runtime/test/specs/generate_test.sh
+#include "../../TestGenerated.h"
+
+namespace fully_connected_float_3 {
+std::vector<MixedTypedExample> examples = {
+// Generated fully_connected_float_3 test
+#include "generated/examples/fully_connected_float_3.example.cpp"
+};
+// Generated model constructor
+#include "generated/models/fully_connected_float_3.model.cpp"
+} // namespace fully_connected_float_3
+TEST_F(GeneratedTests, fully_connected_float_3) {
+ execute(fully_connected_float_3::CreateModel,
+ fully_connected_float_3::is_ignored,
+ fully_connected_float_3::examples);
+}
diff --git a/nn/runtime/test/specs/V1_0/fully_connected_float_3.mod.py b/nn/runtime/test/specs/V1_0/fully_connected_float_3.mod.py
new file mode 100644
index 0000000..804f812
--- /dev/null
+++ b/nn/runtime/test/specs/V1_0/fully_connected_float_3.mod.py
@@ -0,0 +1,32 @@
+#
+# 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.
+#
+
+model = Model()
+in0 = Input("op1", "TENSOR_FLOAT32", "{2, 2}")
+weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 2}", [2, 4])
+bias = Parameter("b0", "TENSOR_FLOAT32", "{1}", [1])
+out0 = Output("op3", "TENSOR_FLOAT32", "{2, 1}")
+act = Int32Scalar("act", 0)
+model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
+
+# Example 1. Input in operand 0,
+input0 = {in0: # input 0
+ [1, 2, 2, 1]}
+output0 = {out0: # output 0
+ [11, 9]}
+
+# Instantiate an example
+Example((input0, output0))
