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raw | patch | inline | side by side (parent: 8e139a7)
| author | Slava Shklyaev <slavash@google.com> | |
| Wed, 12 Sep 2018 13:52:02 +0000 (14:52 +0100) | ||
| committer | Przemyslaw Szczepaniak <pszczepaniak@google.com> | |
| Fri, 21 Sep 2018 13:46:24 +0000 (14:46 +0100) |
This is a copy the v1.1 tests since we don't have any new ops
implemented in v1.2 yet.
Bug: 114365802
Test: mm
Test: NNAPI VTS
Change-Id: Ida7525fcd3ae0fd6f88ff9591e06aba922bdae64
Merged-In: Ida7525fcd3ae0fd6f88ff9591e06aba922bdae64
(cherry-picked from 871be9477032e595f685e02f256b2909ea524fc0)
implemented in v1.2 yet.
Bug: 114365802
Test: mm
Test: NNAPI VTS
Change-Id: Ida7525fcd3ae0fd6f88ff9591e06aba922bdae64
Merged-In: Ida7525fcd3ae0fd6f88ff9591e06aba922bdae64
(cherry-picked from 871be9477032e595f685e02f256b2909ea524fc0)
13 files changed:
diff --git a/neuralnetworks/1.0/vts/functional/Android.bp b/neuralnetworks/1.0/vts/functional/Android.bp
index e28113bcdc2096d017ddb516a5743d0ce2aab90d..18f35c1a167174ce18a19a4e67a9bcb802e64da7 100644 (file)
static_libs: [
"android.hardware.neuralnetworks@1.0",
"android.hardware.neuralnetworks@1.1",
+ "android.hardware.neuralnetworks@1.2",
"android.hidl.allocator@1.0",
"android.hidl.memory@1.0",
"libhidlmemory",
],
defaults: ["VtsHalTargetTestDefaults"],
static_libs: [
- "android.hardware.neuralnetworks@1.1",
"android.hardware.neuralnetworks@1.0",
+ "android.hardware.neuralnetworks@1.1",
+ "android.hardware.neuralnetworks@1.2",
"android.hidl.allocator@1.0",
"android.hidl.memory@1.0",
"libhidlmemory",
diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
index 64495cf7639c0f48bf82c7f5bdf9d5cedc85b7fd..b8046c79b2365499300ea684e6abe1ef778fd59f 100644 (file)
@@ -275,6 +275,58 @@ void Execute(const sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> c
EvaluatePreparedModel(preparedModel, is_ignored, examples, fpAtol, fpRtol);
}
+// TODO: Reduce code duplication.
+void Execute(const sp<V1_2::IDevice>& device, std::function<V1_2::Model(void)> create_model,
+ std::function<bool(int)> is_ignored,
+ const std::vector<MixedTypedExampleType>& examples) {
+ V1_2::Model model = create_model();
+
+ // see if service can handle model
+ bool fullySupportsModel = false;
+ Return<void> supportedCall = device->getSupportedOperations_1_2(
+ model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
+ ASSERT_EQ(ErrorStatus::NONE, status);
+ ASSERT_NE(0ul, supported.size());
+ fullySupportsModel =
+ std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
+ });
+ ASSERT_TRUE(supportedCall.isOk());
+
+ // launch prepare model
+ sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+ ASSERT_NE(nullptr, preparedModelCallback.get());
+ Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_2(
+ model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk());
+ ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+ // retrieve prepared model
+ preparedModelCallback->wait();
+ ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
+
+ // early termination if vendor service cannot fully prepare model
+ if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+ ASSERT_EQ(nullptr, preparedModel.get());
+ LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
+ "prepare model that it does not support.";
+ std::cout << "[ ] Early termination of test because vendor service cannot "
+ "prepare model that it does not support."
+ << std::endl;
+ return;
+ }
+ EXPECT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+ ASSERT_NE(nullptr, preparedModel.get());
+
+ // TODO: Adjust the error limit based on testing.
+ // If in relaxed mode, set the absolute tolerance to be 5ULP of FP16.
+ float fpAtol = !model.relaxComputationFloat32toFloat16 ? 1e-5f : 5.0f * 0.0009765625f;
+ // Set the relative tolerance to be 5ULP of the corresponding FP precision.
+ float fpRtol = !model.relaxComputationFloat32toFloat16 ? 5.0f * 1.1920928955078125e-7f
+ : 5.0f * 0.0009765625f;
+ EvaluatePreparedModel(preparedModel, is_ignored, examples, fpAtol, fpRtol);
+}
+
} // namespace generated_tests
} // namespace neuralnetworks
diff --git a/neuralnetworks/1.1/vts/functional/Android.bp b/neuralnetworks/1.1/vts/functional/Android.bp
index f755c20be5a9bdd531909105f3c3a88a67e5e43a..52a804a8a3aa37d98f9180e33eab749ac873ff05 100644 (file)
static_libs: [
"android.hardware.neuralnetworks@1.0",
"android.hardware.neuralnetworks@1.1",
+ "android.hardware.neuralnetworks@1.2",
"android.hidl.allocator@1.0",
"android.hidl.memory@1.0",
"libhidlmemory",
diff --git a/neuralnetworks/1.2/vts/OWNERS b/neuralnetworks/1.2/vts/OWNERS
--- /dev/null
@@ -0,0 +1,14 @@
+# Neuralnetworks team
+butlermichael@google.com
+dgross@google.com
+jeanluc@google.com
+levp@google.com
+miaowang@google.com
+mikie@google.com
+mks@google.com
+pszczepaniak@google.com
+slavash@google.com
+
+# VTS team
+yim@google.com
+yuexima@google.com
diff --git a/neuralnetworks/1.2/vts/functional/Android.bp b/neuralnetworks/1.2/vts/functional/Android.bp
--- /dev/null
@@ -0,0 +1,52 @@
+//
+// 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.
+//
+
+cc_test {
+ name: "VtsHalNeuralnetworksV1_2TargetTest",
+ srcs: [
+ "BasicTests.cpp",
+ "GeneratedTests.cpp",
+ "ValidateModel.cpp",
+ "ValidateRequest.cpp",
+ "ValidationTests.cpp",
+ "VtsHalNeuralnetworks.cpp",
+ ],
+ defaults: ["VtsHalTargetTestDefaults"],
+ static_libs: [
+ "android.hardware.neuralnetworks@1.0",
+ "android.hardware.neuralnetworks@1.1",
+ "android.hardware.neuralnetworks@1.2",
+ "android.hidl.allocator@1.0",
+ "android.hidl.memory@1.0",
+ "libhidlmemory",
+ "libneuralnetworks_utils",
+ "VtsHalNeuralnetworksTest_utils",
+ ],
+ header_libs: [
+ "libneuralnetworks_headers",
+ "libneuralnetworks_generated_test_harness_headers",
+ "libneuralnetworks_generated_tests",
+ ],
+ // Bug: http://b/74200014 - Disable arm32 asan since it triggers internal
+ // error in ld.gold.
+ arch: {
+ arm: {
+ sanitize: {
+ never: true,
+ },
+ },
+ },
+}
diff --git a/neuralnetworks/1.2/vts/functional/BasicTests.cpp b/neuralnetworks/1.2/vts/functional/BasicTests.cpp
--- /dev/null
@@ -0,0 +1,45 @@
+/*
+ * 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.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_2 {
+namespace vts {
+namespace functional {
+
+using V1_1::Capabilities;
+
+// create device test
+TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
+
+// status test
+TEST_F(NeuralnetworksHidlTest, StatusTest) {
+ Return<DeviceStatus> status = device->getStatus();
+ ASSERT_TRUE(status.isOk());
+ EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
+}
+
+} // namespace functional
+} // namespace vts
+} // namespace V1_2
+} // namespace neuralnetworks
+} // namespace hardware
+} // namespace android
diff --git a/neuralnetworks/1.2/vts/functional/GeneratedTests.cpp b/neuralnetworks/1.2/vts/functional/GeneratedTests.cpp
--- /dev/null
@@ -0,0 +1,60 @@
+/*
+ * 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.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+#include "TestHarness.h"
+#include "Utils.h"
+
+#include <android-base/logging.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+
+namespace generated_tests {
+using ::test_helper::MixedTypedExampleType;
+extern void Execute(const sp<V1_2::IDevice>&, std::function<V1_2::Model(void)>,
+ std::function<bool(int)>, const std::vector<MixedTypedExampleType>&);
+} // namespace generated_tests
+
+namespace V1_2 {
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+using ::android::nn::allocateSharedMemory;
+
+// Mixed-typed examples
+typedef generated_tests::MixedTypedExampleType MixedTypedExample;
+
+// in frameworks/ml/nn/runtime/tests/generated/
+#include "all_generated_V1_0_vts_tests.cpp"
+#include "all_generated_V1_1_vts_tests.cpp"
+#include "all_generated_V1_2_vts_tests.cpp"
+
+} // namespace functional
+} // namespace vts
+} // namespace V1_2
+} // namespace neuralnetworks
+} // namespace hardware
+} // namespace android
diff --git a/neuralnetworks/1.2/vts/functional/Models.h b/neuralnetworks/1.2/vts/functional/Models.h
--- /dev/null
@@ -0,0 +1,378 @@
+/*
+ * 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.
+ */
+
+#ifndef VTS_HAL_NEURALNETWORKS_V1_2_VTS_FUNCTIONAL_MODELS_H
+#define VTS_HAL_NEURALNETWORKS_V1_2_VTS_FUNCTIONAL_MODELS_H
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "TestHarness.h"
+
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/types.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_2 {
+namespace vts {
+namespace functional {
+
+using MixedTypedExample = test_helper::MixedTypedExampleType;
+
+#define FOR_EACH_TEST_MODEL(FN) \
+ FN(add) \
+ FN(add_broadcast_quant8) \
+ FN(add_quant8) \
+ FN(add_relaxed) \
+ FN(avg_pool_float_1) \
+ FN(avg_pool_float_1_relaxed) \
+ FN(avg_pool_float_2) \
+ FN(avg_pool_float_2_relaxed) \
+ FN(avg_pool_float_3) \
+ FN(avg_pool_float_3_relaxed) \
+ FN(avg_pool_float_4) \
+ FN(avg_pool_float_4_relaxed) \
+ FN(avg_pool_float_5) \
+ FN(avg_pool_float_5_relaxed) \
+ FN(avg_pool_quant8_1) \
+ FN(avg_pool_quant8_2) \
+ FN(avg_pool_quant8_3) \
+ FN(avg_pool_quant8_4) \
+ FN(avg_pool_quant8_5) \
+ FN(batch_to_space) \
+ FN(batch_to_space_float_1) \
+ FN(batch_to_space_float_1_relaxed) \
+ FN(batch_to_space_quant8_1) \
+ FN(batch_to_space_relaxed) \
+ FN(concat_float_1) \
+ FN(concat_float_1_relaxed) \
+ FN(concat_float_2) \
+ FN(concat_float_2_relaxed) \
+ FN(concat_float_3) \
+ FN(concat_float_3_relaxed) \
+ FN(concat_quant8_1) \
+ FN(concat_quant8_2) \
+ FN(concat_quant8_3) \
+ FN(conv_1_h3_w2_SAME) \
+ FN(conv_1_h3_w2_SAME_relaxed) \
+ FN(conv_1_h3_w2_VALID) \
+ FN(conv_1_h3_w2_VALID_relaxed) \
+ FN(conv_3_h3_w2_SAME) \
+ FN(conv_3_h3_w2_SAME_relaxed) \
+ FN(conv_3_h3_w2_VALID) \
+ FN(conv_3_h3_w2_VALID_relaxed) \
+ FN(conv_float) \
+ FN(conv_float_2) \
+ FN(conv_float_2_relaxed) \
+ FN(conv_float_channels) \
+ FN(conv_float_channels_relaxed) \
+ FN(conv_float_channels_weights_as_inputs) \
+ FN(conv_float_channels_weights_as_inputs_relaxed) \
+ FN(conv_float_large) \
+ FN(conv_float_large_relaxed) \
+ FN(conv_float_large_weights_as_inputs) \
+ FN(conv_float_large_weights_as_inputs_relaxed) \
+ FN(conv_float_relaxed) \
+ FN(conv_float_weights_as_inputs) \
+ FN(conv_float_weights_as_inputs_relaxed) \
+ FN(conv_quant8) \
+ FN(conv_quant8_2) \
+ FN(conv_quant8_channels) \
+ FN(conv_quant8_channels_weights_as_inputs) \
+ FN(conv_quant8_large) \
+ FN(conv_quant8_large_weights_as_inputs) \
+ FN(conv_quant8_overflow) \
+ FN(conv_quant8_overflow_weights_as_inputs) \
+ FN(conv_quant8_weights_as_inputs) \
+ FN(depth_to_space_float_1) \
+ FN(depth_to_space_float_1_relaxed) \
+ FN(depth_to_space_float_2) \
+ FN(depth_to_space_float_2_relaxed) \
+ FN(depth_to_space_float_3) \
+ FN(depth_to_space_float_3_relaxed) \
+ FN(depth_to_space_quant8_1) \
+ FN(depth_to_space_quant8_2) \
+ FN(depthwise_conv) \
+ FN(depthwise_conv2d_float) \
+ FN(depthwise_conv2d_float_2) \
+ FN(depthwise_conv2d_float_2_relaxed) \
+ FN(depthwise_conv2d_float_large) \
+ FN(depthwise_conv2d_float_large_2) \
+ FN(depthwise_conv2d_float_large_2_relaxed) \
+ FN(depthwise_conv2d_float_large_2_weights_as_inputs) \
+ FN(depthwise_conv2d_float_large_2_weights_as_inputs_relaxed) \
+ FN(depthwise_conv2d_float_large_relaxed) \
+ FN(depthwise_conv2d_float_large_weights_as_inputs) \
+ FN(depthwise_conv2d_float_large_weights_as_inputs_relaxed) \
+ FN(depthwise_conv2d_float_relaxed) \
+ FN(depthwise_conv2d_float_weights_as_inputs) \
+ FN(depthwise_conv2d_float_weights_as_inputs_relaxed) \
+ FN(depthwise_conv2d_quant8) \
+ FN(depthwise_conv2d_quant8_2) \
+ FN(depthwise_conv2d_quant8_large) \
+ FN(depthwise_conv2d_quant8_large_weights_as_inputs) \
+ FN(depthwise_conv2d_quant8_weights_as_inputs) \
+ FN(depthwise_conv_relaxed) \
+ FN(dequantize) \
+ FN(dequantize_relaxed) \
+ FN(div) \
+ FN(div_broadcast_float) \
+ FN(div_broadcast_float_relaxed) \
+ FN(div_relaxed) \
+ FN(embedding_lookup) \
+ FN(embedding_lookup_relaxed) \
+ FN(floor) \
+ FN(floor_relaxed) \
+ FN(fully_connected_float) \
+ FN(fully_connected_float_2) \
+ FN(fully_connected_float_2_relaxed) \
+ FN(fully_connected_float_4d_simple) \
+ FN(fully_connected_float_4d_simple_relaxed) \
+ FN(fully_connected_float_large) \
+ FN(fully_connected_float_large_relaxed) \
+ FN(fully_connected_float_large_weights_as_inputs) \
+ FN(fully_connected_float_large_weights_as_inputs_relaxed) \
+ FN(fully_connected_float_relaxed) \
+ FN(fully_connected_float_weights_as_inputs) \
+ FN(fully_connected_float_weights_as_inputs_relaxed) \
+ FN(fully_connected_quant8) \
+ FN(fully_connected_quant8_2) \
+ FN(fully_connected_quant8_large) \
+ FN(fully_connected_quant8_large_weights_as_inputs) \
+ FN(fully_connected_quant8_weights_as_inputs) \
+ FN(hashtable_lookup_float) \
+ FN(hashtable_lookup_float_relaxed) \
+ FN(hashtable_lookup_quant8) \
+ FN(l2_normalization) \
+ FN(l2_normalization_2) \
+ FN(l2_normalization_2_relaxed) \
+ FN(l2_normalization_large) \
+ FN(l2_normalization_large_relaxed) \
+ FN(l2_normalization_relaxed) \
+ FN(l2_pool_float) \
+ FN(l2_pool_float_2) \
+ FN(l2_pool_float_2_relaxed) \
+ FN(l2_pool_float_large) \
+ FN(l2_pool_float_large_relaxed) \
+ FN(l2_pool_float_relaxed) \
+ FN(local_response_norm_float_1) \
+ FN(local_response_norm_float_1_relaxed) \
+ FN(local_response_norm_float_2) \
+ FN(local_response_norm_float_2_relaxed) \
+ FN(local_response_norm_float_3) \
+ FN(local_response_norm_float_3_relaxed) \
+ FN(local_response_norm_float_4) \
+ FN(local_response_norm_float_4_relaxed) \
+ FN(logistic_float_1) \
+ FN(logistic_float_1_relaxed) \
+ FN(logistic_float_2) \
+ FN(logistic_float_2_relaxed) \
+ FN(logistic_quant8_1) \
+ FN(logistic_quant8_2) \
+ FN(lsh_projection) \
+ FN(lsh_projection_2) \
+ FN(lsh_projection_2_relaxed) \
+ FN(lsh_projection_relaxed) \
+ FN(lsh_projection_weights_as_inputs) \
+ FN(lsh_projection_weights_as_inputs_relaxed) \
+ FN(lstm) \
+ FN(lstm2) \
+ FN(lstm2_relaxed) \
+ FN(lstm2_state) \
+ FN(lstm2_state2) \
+ FN(lstm2_state2_relaxed) \
+ FN(lstm2_state_relaxed) \
+ FN(lstm3) \
+ FN(lstm3_relaxed) \
+ FN(lstm3_state) \
+ FN(lstm3_state2) \
+ FN(lstm3_state2_relaxed) \
+ FN(lstm3_state3) \
+ FN(lstm3_state3_relaxed) \
+ FN(lstm3_state_relaxed) \
+ FN(lstm_relaxed) \
+ FN(lstm_state) \
+ FN(lstm_state2) \
+ FN(lstm_state2_relaxed) \
+ FN(lstm_state_relaxed) \
+ FN(max_pool_float_1) \
+ FN(max_pool_float_1_relaxed) \
+ FN(max_pool_float_2) \
+ FN(max_pool_float_2_relaxed) \
+ FN(max_pool_float_3) \
+ FN(max_pool_float_3_relaxed) \
+ FN(max_pool_float_4) \
+ FN(max_pool_float_4_relaxed) \
+ FN(max_pool_quant8_1) \
+ FN(max_pool_quant8_2) \
+ FN(max_pool_quant8_3) \
+ FN(max_pool_quant8_4) \
+ FN(mean) \
+ FN(mean_float_1) \
+ FN(mean_float_1_relaxed) \
+ FN(mean_float_2) \
+ FN(mean_float_2_relaxed) \
+ FN(mean_quant8_1) \
+ FN(mean_quant8_2) \
+ FN(mean_relaxed) \
+ FN(mobilenet_224_gender_basic_fixed) \
+ FN(mobilenet_224_gender_basic_fixed_relaxed) \
+ FN(mobilenet_quantized) \
+ FN(mul) \
+ FN(mul_broadcast_quant8) \
+ FN(mul_quant8) \
+ FN(mul_relaxed) \
+ FN(mul_relu) \
+ FN(mul_relu_relaxed) \
+ FN(pad) \
+ FN(pad_float_1) \
+ FN(pad_float_1_relaxed) \
+ FN(pad_relaxed) \
+ FN(relu1_float_1) \
+ FN(relu1_float_1_relaxed) \
+ FN(relu1_float_2) \
+ FN(relu1_float_2_relaxed) \
+ FN(relu1_quant8_1) \
+ FN(relu1_quant8_2) \
+ FN(relu6_float_1) \
+ FN(relu6_float_1_relaxed) \
+ FN(relu6_float_2) \
+ FN(relu6_float_2_relaxed) \
+ FN(relu6_quant8_1) \
+ FN(relu6_quant8_2) \
+ FN(relu_float_1) \
+ FN(relu_float_1_relaxed) \
+ FN(relu_float_2) \
+ FN(relu_float_2_relaxed) \
+ FN(relu_quant8_1) \
+ FN(relu_quant8_2) \
+ FN(reshape) \
+ FN(reshape_quant8) \
+ FN(reshape_quant8_weights_as_inputs) \
+ FN(reshape_relaxed) \
+ FN(reshape_weights_as_inputs) \
+ FN(reshape_weights_as_inputs_relaxed) \
+ FN(resize_bilinear) \
+ FN(resize_bilinear_2) \
+ FN(resize_bilinear_2_relaxed) \
+ FN(resize_bilinear_relaxed) \
+ FN(rnn) \
+ FN(rnn_relaxed) \
+ FN(rnn_state) \
+ FN(rnn_state_relaxed) \
+ FN(softmax_float_1) \
+ FN(softmax_float_1_relaxed) \
+ FN(softmax_float_2) \
+ FN(softmax_float_2_relaxed) \
+ FN(softmax_quant8_1) \
+ FN(softmax_quant8_2) \
+ FN(space_to_batch) \
+ FN(space_to_batch_float_1) \
+ FN(space_to_batch_float_1_relaxed) \
+ FN(space_to_batch_float_2) \
+ FN(space_to_batch_float_2_relaxed) \
+ FN(space_to_batch_float_3) \
+ FN(space_to_batch_float_3_relaxed) \
+ FN(space_to_batch_quant8_1) \
+ FN(space_to_batch_quant8_2) \
+ FN(space_to_batch_quant8_3) \
+ FN(space_to_batch_relaxed) \
+ FN(space_to_depth_float_1) \
+ FN(space_to_depth_float_1_relaxed) \
+ FN(space_to_depth_float_2) \
+ FN(space_to_depth_float_2_relaxed) \
+ FN(space_to_depth_float_3) \
+ FN(space_to_depth_float_3_relaxed) \
+ FN(space_to_depth_quant8_1) \
+ FN(space_to_depth_quant8_2) \
+ FN(squeeze) \
+ FN(squeeze_float_1) \
+ FN(squeeze_float_1_relaxed) \
+ FN(squeeze_quant8_1) \
+ FN(squeeze_relaxed) \
+ FN(strided_slice) \
+ FN(strided_slice_float_1) \
+ FN(strided_slice_float_10) \
+ FN(strided_slice_float_10_relaxed) \
+ FN(strided_slice_float_11) \
+ FN(strided_slice_float_11_relaxed) \
+ FN(strided_slice_float_1_relaxed) \
+ FN(strided_slice_float_2) \
+ FN(strided_slice_float_2_relaxed) \
+ FN(strided_slice_float_3) \
+ FN(strided_slice_float_3_relaxed) \
+ FN(strided_slice_float_4) \
+ FN(strided_slice_float_4_relaxed) \
+ FN(strided_slice_float_5) \
+ FN(strided_slice_float_5_relaxed) \
+ FN(strided_slice_float_6) \
+ FN(strided_slice_float_6_relaxed) \
+ FN(strided_slice_float_7) \
+ FN(strided_slice_float_7_relaxed) \
+ FN(strided_slice_float_8) \
+ FN(strided_slice_float_8_relaxed) \
+ FN(strided_slice_float_9) \
+ FN(strided_slice_float_9_relaxed) \
+ FN(strided_slice_qaunt8_10) \
+ FN(strided_slice_qaunt8_11) \
+ FN(strided_slice_quant8_1) \
+ FN(strided_slice_quant8_2) \
+ FN(strided_slice_quant8_3) \
+ FN(strided_slice_quant8_4) \
+ FN(strided_slice_quant8_5) \
+ FN(strided_slice_quant8_6) \
+ FN(strided_slice_quant8_7) \
+ FN(strided_slice_quant8_8) \
+ FN(strided_slice_quant8_9) \
+ FN(strided_slice_relaxed) \
+ FN(sub) \
+ FN(sub_broadcast_float) \
+ FN(sub_broadcast_float_relaxed) \
+ FN(sub_relaxed) \
+ FN(svdf) \
+ FN(svdf2) \
+ FN(svdf2_relaxed) \
+ FN(svdf_relaxed) \
+ FN(svdf_state) \
+ FN(svdf_state_relaxed) \
+ FN(tanh) \
+ FN(tanh_relaxed) \
+ FN(transpose) \
+ FN(transpose_float_1) \
+ FN(transpose_float_1_relaxed) \
+ FN(transpose_quant8_1) \
+ FN(transpose_relaxed)
+
+#define FORWARD_DECLARE_GENERATED_OBJECTS(function) \
+ namespace function { \
+ extern std::vector<MixedTypedExample> examples; \
+ Model createTestModel(); \
+ }
+
+FOR_EACH_TEST_MODEL(FORWARD_DECLARE_GENERATED_OBJECTS)
+
+#undef FORWARD_DECLARE_GENERATED_OBJECTS
+
+} // namespace functional
+} // namespace vts
+} // namespace V1_2
+} // namespace neuralnetworks
+} // namespace hardware
+} // namespace android
+
+#endif // VTS_HAL_NEURALNETWORKS_V1_2_VTS_FUNCTIONAL_MODELS_H
diff --git a/neuralnetworks/1.2/vts/functional/ValidateModel.cpp b/neuralnetworks/1.2/vts/functional/ValidateModel.cpp
--- /dev/null
@@ -0,0 +1,538 @@
+/*
+ * 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.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_2 {
+
+using V1_0::IPreparedModel;
+using V1_0::Operand;
+using V1_0::OperandLifeTime;
+using V1_0::OperandType;
+using V1_1::ExecutionPreference;
+
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void validateGetSupportedOperations(const sp<IDevice>& device, const std::string& message,
+ const Model& model) {
+ SCOPED_TRACE(message + " [getSupportedOperations_1_2]");
+
+ Return<void> ret =
+ device->getSupportedOperations_1_2(model, [&](ErrorStatus status, const hidl_vec<bool>&) {
+ EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
+ });
+ EXPECT_TRUE(ret.isOk());
+}
+
+static void validatePrepareModel(const sp<IDevice>& device, const std::string& message,
+ const Model& model, ExecutionPreference preference) {
+ SCOPED_TRACE(message + " [prepareModel_1_2]");
+
+ sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+ ASSERT_NE(nullptr, preparedModelCallback.get());
+ Return<ErrorStatus> prepareLaunchStatus =
+ device->prepareModel_1_2(model, preference, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk());
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+ preparedModelCallback->wait();
+ ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
+ sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
+ ASSERT_EQ(nullptr, preparedModel.get());
+}
+
+static bool validExecutionPreference(ExecutionPreference preference) {
+ return preference == ExecutionPreference::LOW_POWER ||
+ preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
+ preference == ExecutionPreference::SUSTAINED_SPEED;
+}
+
+// Primary validation function. This function will take a valid model, apply a
+// mutation to it to invalidate the model, then pass it to interface calls that
+// use the model. Note that the model here is passed by value, and any mutation
+// to the model does not leave this function.
+static void validate(const sp<IDevice>& device, const std::string& message, Model model,
+ const std::function<void(Model*)>& mutation,
+ ExecutionPreference preference = ExecutionPreference::FAST_SINGLE_ANSWER) {
+ mutation(&model);
+ if (validExecutionPreference(preference)) {
+ validateGetSupportedOperations(device, message, model);
+ }
+ validatePrepareModel(device, message, model, preference);
+}
+
+// Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,
+// so this is efficiently accomplished by moving the element to the end and
+// resizing the hidl_vec to one less.
+template <typename Type>
+static void hidl_vec_removeAt(hidl_vec<Type>* vec, uint32_t index) {
+ if (vec) {
+ std::rotate(vec->begin() + index, vec->begin() + index + 1, vec->end());
+ vec->resize(vec->size() - 1);
+ }
+}
+
+template <typename Type>
+static uint32_t hidl_vec_push_back(hidl_vec<Type>* vec, const Type& value) {
+ // assume vec is valid
+ const uint32_t index = vec->size();
+ vec->resize(index + 1);
+ (*vec)[index] = value;
+ return index;
+}
+
+static uint32_t addOperand(Model* model) {
+ return hidl_vec_push_back(&model->operands,
+ {
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .numberOfConsumers = 0,
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::MODEL_INPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ });
+}
+
+static uint32_t addOperand(Model* model, OperandLifeTime lifetime) {
+ uint32_t index = addOperand(model);
+ model->operands[index].numberOfConsumers = 1;
+ model->operands[index].lifetime = lifetime;
+ return index;
+}
+
+///////////////////////// VALIDATE MODEL OPERAND TYPE /////////////////////////
+
+static const int32_t invalidOperandTypes[] = {
+ static_cast<int32_t>(OperandType::FLOAT32) - 1, // lower bound fundamental
+ static_cast<int32_t>(OperandType::TENSOR_QUANT8_ASYMM) + 1, // upper bound fundamental
+ static_cast<int32_t>(OperandType::OEM) - 1, // lower bound OEM
+ static_cast<int32_t>(OperandType::TENSOR_OEM_BYTE) + 1, // upper bound OEM
+};
+
+static void mutateOperandTypeTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ for (int32_t invalidOperandType : invalidOperandTypes) {
+ const std::string message = "mutateOperandTypeTest: operand " +
+ std::to_string(operand) + " set to value " +
+ std::to_string(invalidOperandType);
+ validate(device, message, model, [operand, invalidOperandType](Model* model) {
+ model->operands[operand].type = static_cast<OperandType>(invalidOperandType);
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE OPERAND RANK /////////////////////////
+
+static uint32_t getInvalidRank(OperandType type) {
+ switch (type) {
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ return 1;
+ case OperandType::TENSOR_FLOAT32:
+ case OperandType::TENSOR_INT32:
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ return 0;
+ default:
+ return 0;
+ }
+}
+
+static void mutateOperandRankTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ const uint32_t invalidRank = getInvalidRank(model.operands[operand].type);
+ const std::string message = "mutateOperandRankTest: operand " + std::to_string(operand) +
+ " has rank of " + std::to_string(invalidRank);
+ validate(device, message, model, [operand, invalidRank](Model* model) {
+ model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0);
+ });
+ }
+}
+
+///////////////////////// VALIDATE OPERAND SCALE /////////////////////////
+
+static float getInvalidScale(OperandType type) {
+ switch (type) {
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::TENSOR_FLOAT32:
+ return 1.0f;
+ case OperandType::TENSOR_INT32:
+ return -1.0f;
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ return 0.0f;
+ default:
+ return 0.0f;
+ }
+}
+
+static void mutateOperandScaleTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ const float invalidScale = getInvalidScale(model.operands[operand].type);
+ const std::string message = "mutateOperandScaleTest: operand " + std::to_string(operand) +
+ " has scale of " + std::to_string(invalidScale);
+ validate(device, message, model, [operand, invalidScale](Model* model) {
+ model->operands[operand].scale = invalidScale;
+ });
+ }
+}
+
+///////////////////////// VALIDATE OPERAND ZERO POINT /////////////////////////
+
+static std::vector<int32_t> getInvalidZeroPoints(OperandType type) {
+ switch (type) {
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::TENSOR_FLOAT32:
+ case OperandType::TENSOR_INT32:
+ return {1};
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ return {-1, 256};
+ default:
+ return {};
+ }
+}
+
+static void mutateOperandZeroPointTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ const std::vector<int32_t> invalidZeroPoints =
+ getInvalidZeroPoints(model.operands[operand].type);
+ for (int32_t invalidZeroPoint : invalidZeroPoints) {
+ const std::string message = "mutateOperandZeroPointTest: operand " +
+ std::to_string(operand) + " has zero point of " +
+ std::to_string(invalidZeroPoint);
+ validate(device, message, model, [operand, invalidZeroPoint](Model* model) {
+ model->operands[operand].zeroPoint = invalidZeroPoint;
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE EXTRA ??? /////////////////////////
+
+// TODO: Operand::lifetime
+// TODO: Operand::location
+
+///////////////////////// VALIDATE OPERATION OPERAND TYPE /////////////////////////
+
+static void mutateOperand(Operand* operand, OperandType type) {
+ Operand newOperand = *operand;
+ newOperand.type = type;
+ switch (type) {
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ newOperand.dimensions = hidl_vec<uint32_t>();
+ newOperand.scale = 0.0f;
+ newOperand.zeroPoint = 0;
+ break;
+ case OperandType::TENSOR_FLOAT32:
+ newOperand.dimensions =
+ operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+ newOperand.scale = 0.0f;
+ newOperand.zeroPoint = 0;
+ break;
+ case OperandType::TENSOR_INT32:
+ newOperand.dimensions =
+ operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+ newOperand.zeroPoint = 0;
+ break;
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ newOperand.dimensions =
+ operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+ newOperand.scale = operand->scale != 0.0f ? operand->scale : 1.0f;
+ break;
+ case OperandType::OEM:
+ case OperandType::TENSOR_OEM_BYTE:
+ default:
+ break;
+ }
+ *operand = newOperand;
+}
+
+static bool mutateOperationOperandTypeSkip(size_t operand, const Model& model) {
+ // LSH_PROJECTION's second argument is allowed to have any type. This is the
+ // only operation that currently has a type that can be anything independent
+ // from any other type. Changing the operand type to any other type will
+ // result in a valid model for LSH_PROJECTION. If this is the case, skip the
+ // test.
+ for (const Operation& operation : model.operations) {
+ if (operation.type == OperationType::LSH_PROJECTION && operand == operation.inputs[1]) {
+ return true;
+ }
+ }
+ return false;
+}
+
+static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ if (mutateOperationOperandTypeSkip(operand, model)) {
+ continue;
+ }
+ for (OperandType invalidOperandType : hidl_enum_range<OperandType>{}) {
+ // Do not test OEM types
+ if (invalidOperandType == model.operands[operand].type ||
+ invalidOperandType == OperandType::OEM ||
+ invalidOperandType == OperandType::TENSOR_OEM_BYTE) {
+ continue;
+ }
+ const std::string message = "mutateOperationOperandTypeTest: operand " +
+ std::to_string(operand) + " set to type " +
+ toString(invalidOperandType);
+ validate(device, message, model, [operand, invalidOperandType](Model* model) {
+ mutateOperand(&model->operands[operand], invalidOperandType);
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////
+
+static const int32_t invalidOperationTypes[] = {
+ static_cast<int32_t>(OperationType::ADD) - 1, // lower bound fundamental
+ static_cast<int32_t>(OperationType::TRANSPOSE) + 1, // upper bound fundamental
+ static_cast<int32_t>(OperationType::OEM_OPERATION) - 1, // lower bound OEM
+ static_cast<int32_t>(OperationType::OEM_OPERATION) + 1, // upper bound OEM
+};
+
+static void mutateOperationTypeTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ for (int32_t invalidOperationType : invalidOperationTypes) {
+ const std::string message = "mutateOperationTypeTest: operation " +
+ std::to_string(operation) + " set to value " +
+ std::to_string(invalidOperationType);
+ validate(device, message, model, [operation, invalidOperationType](Model* model) {
+ model->operations[operation].type =
+ static_cast<OperationType>(invalidOperationType);
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION INPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationInputOperandIndexTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ const uint32_t invalidOperand = model.operands.size();
+ for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
+ const std::string message = "mutateOperationInputOperandIndexTest: operation " +
+ std::to_string(operation) + " input " +
+ std::to_string(input);
+ validate(device, message, model, [operation, input, invalidOperand](Model* model) {
+ model->operations[operation].inputs[input] = invalidOperand;
+ });
+ }
+ }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION OUTPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationOutputOperandIndexTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ const uint32_t invalidOperand = model.operands.size();
+ for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
+ const std::string message = "mutateOperationOutputOperandIndexTest: operation " +
+ std::to_string(operation) + " output " +
+ std::to_string(output);
+ validate(device, message, model, [operation, output, invalidOperand](Model* model) {
+ model->operations[operation].outputs[output] = invalidOperand;
+ });
+ }
+ }
+}
+
+///////////////////////// REMOVE OPERAND FROM EVERYTHING /////////////////////////
+
+static void removeValueAndDecrementGreaterValues(hidl_vec<uint32_t>* vec, uint32_t value) {
+ if (vec) {
+ // remove elements matching "value"
+ auto last = std::remove(vec->begin(), vec->end(), value);
+ vec->resize(std::distance(vec->begin(), last));
+
+ // decrement elements exceeding "value"
+ std::transform(vec->begin(), vec->end(), vec->begin(),
+ [value](uint32_t v) { return v > value ? v-- : v; });
+ }
+}
+
+static void removeOperand(Model* model, uint32_t index) {
+ hidl_vec_removeAt(&model->operands, index);
+ for (Operation& operation : model->operations) {
+ removeValueAndDecrementGreaterValues(&operation.inputs, index);
+ removeValueAndDecrementGreaterValues(&operation.outputs, index);
+ }
+ removeValueAndDecrementGreaterValues(&model->inputIndexes, index);
+ removeValueAndDecrementGreaterValues(&model->outputIndexes, index);
+}
+
+static void removeOperandTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+ const std::string message = "removeOperandTest: operand " + std::to_string(operand);
+ validate(device, message, model,
+ [operand](Model* model) { removeOperand(model, operand); });
+ }
+}
+
+///////////////////////// REMOVE OPERATION /////////////////////////
+
+static void removeOperation(Model* model, uint32_t index) {
+ for (uint32_t operand : model->operations[index].inputs) {
+ model->operands[operand].numberOfConsumers--;
+ }
+ hidl_vec_removeAt(&model->operations, index);
+}
+
+static void removeOperationTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ const std::string message = "removeOperationTest: operation " + std::to_string(operation);
+ validate(device, message, model,
+ [operation](Model* model) { removeOperation(model, operation); });
+ }
+}
+
+///////////////////////// REMOVE OPERATION INPUT /////////////////////////
+
+static void removeOperationInputTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
+ const Operation& op = model.operations[operation];
+ // CONCATENATION has at least 2 inputs, with the last element being
+ // INT32. Skip this test if removing one of CONCATENATION's
+ // inputs still produces a valid model.
+ if (op.type == OperationType::CONCATENATION && op.inputs.size() > 2 &&
+ input != op.inputs.size() - 1) {
+ continue;
+ }
+ const std::string message = "removeOperationInputTest: operation " +
+ std::to_string(operation) + ", input " +
+ std::to_string(input);
+ validate(device, message, model, [operation, input](Model* model) {
+ uint32_t operand = model->operations[operation].inputs[input];
+ model->operands[operand].numberOfConsumers--;
+ hidl_vec_removeAt(&model->operations[operation].inputs, input);
+ });
+ }
+ }
+}
+
+///////////////////////// REMOVE OPERATION OUTPUT /////////////////////////
+
+static void removeOperationOutputTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
+ const std::string message = "removeOperationOutputTest: operation " +
+ std::to_string(operation) + ", output " +
+ std::to_string(output);
+ validate(device, message, model, [operation, output](Model* model) {
+ hidl_vec_removeAt(&model->operations[operation].outputs, output);
+ });
+ }
+ }
+}
+
+///////////////////////// MODEL VALIDATION /////////////////////////
+
+// TODO: remove model input
+// TODO: remove model output
+// TODO: add unused operation
+
+///////////////////////// ADD OPERATION INPUT /////////////////////////
+
+static void addOperationInputTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ const std::string message = "addOperationInputTest: operation " + std::to_string(operation);
+ validate(device, message, model, [operation](Model* model) {
+ uint32_t index = addOperand(model, OperandLifeTime::MODEL_INPUT);
+ hidl_vec_push_back(&model->operations[operation].inputs, index);
+ hidl_vec_push_back(&model->inputIndexes, index);
+ });
+ }
+}
+
+///////////////////////// ADD OPERATION OUTPUT /////////////////////////
+
+static void addOperationOutputTest(const sp<IDevice>& device, const Model& model) {
+ for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+ const std::string message =
+ "addOperationOutputTest: operation " + std::to_string(operation);
+ validate(device, message, model, [operation](Model* model) {
+ uint32_t index = addOperand(model, OperandLifeTime::MODEL_OUTPUT);
+ hidl_vec_push_back(&model->operations[operation].outputs, index);
+ hidl_vec_push_back(&model->outputIndexes, index);
+ });
+ }
+}
+
+///////////////////////// VALIDATE EXECUTION PREFERENCE /////////////////////////
+
+static const int32_t invalidExecutionPreferences[] = {
+ static_cast<int32_t>(ExecutionPreference::LOW_POWER) - 1, // lower bound
+ static_cast<int32_t>(ExecutionPreference::SUSTAINED_SPEED) + 1, // upper bound
+};
+
+static void mutateExecutionPreferenceTest(const sp<IDevice>& device, const Model& model) {
+ for (int32_t preference : invalidExecutionPreferences) {
+ const std::string message =
+ "mutateExecutionPreferenceTest: preference " + std::to_string(preference);
+ validate(device, message, model, [](Model*) {},
+ static_cast<ExecutionPreference>(preference));
+ }
+}
+
+////////////////////////// ENTRY POINT //////////////////////////////
+
+void ValidationTest::validateModel(const Model& model) {
+ mutateOperandTypeTest(device, model);
+ mutateOperandRankTest(device, model);
+ mutateOperandScaleTest(device, model);
+ mutateOperandZeroPointTest(device, model);
+ mutateOperationOperandTypeTest(device, model);
+ mutateOperationTypeTest(device, model);
+ mutateOperationInputOperandIndexTest(device, model);
+ mutateOperationOutputOperandIndexTest(device, model);
+ removeOperandTest(device, model);
+ removeOperationTest(device, model);
+ removeOperationInputTest(device, model);
+ removeOperationOutputTest(device, model);
+ addOperationInputTest(device, model);
+ addOperationOutputTest(device, model);
+ mutateExecutionPreferenceTest(device, model);
+}
+
+} // namespace functional
+} // namespace vts
+} // namespace V1_2
+} // namespace neuralnetworks
+} // namespace hardware
+} // namespace android
diff --git a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
--- /dev/null
@@ -0,0 +1,261 @@
+/*
+ * 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.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+#include "TestHarness.h"
+#include "Utils.h"
+
+#include <android-base/logging.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_2 {
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+using ::android::hidl::memory::V1_0::IMemory;
+using test_helper::for_all;
+using test_helper::MixedTyped;
+using test_helper::MixedTypedExampleType;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void createPreparedModel(const sp<IDevice>& device, const Model& model,
+ sp<IPreparedModel>* preparedModel) {
+ ASSERT_NE(nullptr, preparedModel);
+
+ // see if service can handle model
+ bool fullySupportsModel = false;
+ Return<void> supportedOpsLaunchStatus = device->getSupportedOperations_1_2(
+ model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
+ ASSERT_EQ(ErrorStatus::NONE, status);
+ ASSERT_NE(0ul, supported.size());
+ fullySupportsModel =
+ std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
+ });
+ ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
+
+ // launch prepare model
+ sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+ ASSERT_NE(nullptr, preparedModelCallback.get());
+ Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_2(
+ model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
+ ASSERT_TRUE(prepareLaunchStatus.isOk());
+ ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+ // retrieve prepared model
+ preparedModelCallback->wait();
+ ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+ *preparedModel = preparedModelCallback->getPreparedModel();
+
+ // The getSupportedOperations_1_2 call returns a list of operations that are
+ // guaranteed not to fail if prepareModel_1_2 is called, and
+ // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
+ // If a driver has any doubt that it can prepare an operation, it must
+ // return false. So here, if a driver isn't sure if it can support an
+ // operation, but reports that it successfully prepared the model, the test
+ // can continue.
+ if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+ ASSERT_EQ(nullptr, preparedModel->get());
+ LOG(INFO) << "NN VTS: Unable to test Request validation because vendor service cannot "
+ "prepare model that it does not support.";
+ std::cout << "[ ] Unable to test Request validation because vendor service "
+ "cannot prepare model that it does not support."
+ << std::endl;
+ return;
+ }
+ ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+ ASSERT_NE(nullptr, preparedModel->get());
+}
+
+// Primary validation function. This function will take a valid request, apply a
+// mutation to it to invalidate the request, then pass it to interface calls
+// that use the request. Note that the request here is passed by value, and any
+// mutation to the request does not leave this function.
+static void validate(const sp<IPreparedModel>& preparedModel, const std::string& message,
+ Request request, const std::function<void(Request*)>& mutation) {
+ mutation(&request);
+ SCOPED_TRACE(message + " [execute]");
+
+ sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+ ASSERT_NE(nullptr, executionCallback.get());
+ Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
+ ASSERT_TRUE(executeLaunchStatus.isOk());
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
+
+ executionCallback->wait();
+ ErrorStatus executionReturnStatus = executionCallback->getStatus();
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
+}
+
+// Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,
+// so this is efficiently accomplished by moving the element to the end and
+// resizing the hidl_vec to one less.
+template <typename Type>
+static void hidl_vec_removeAt(hidl_vec<Type>* vec, uint32_t index) {
+ if (vec) {
+ std::rotate(vec->begin() + index, vec->begin() + index + 1, vec->end());
+ vec->resize(vec->size() - 1);
+ }
+}
+
+template <typename Type>
+static uint32_t hidl_vec_push_back(hidl_vec<Type>* vec, const Type& value) {
+ // assume vec is valid
+ const uint32_t index = vec->size();
+ vec->resize(index + 1);
+ (*vec)[index] = value;
+ return index;
+}
+
+///////////////////////// REMOVE INPUT ////////////////////////////////////
+
+static void removeInputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+ for (size_t input = 0; input < request.inputs.size(); ++input) {
+ const std::string message = "removeInput: removed input " + std::to_string(input);
+ validate(preparedModel, message, request,
+ [input](Request* request) { hidl_vec_removeAt(&request->inputs, input); });
+ }
+}
+
+///////////////////////// REMOVE OUTPUT ////////////////////////////////////
+
+static void removeOutputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+ for (size_t output = 0; output < request.outputs.size(); ++output) {
+ const std::string message = "removeOutput: removed Output " + std::to_string(output);
+ validate(preparedModel, message, request,
+ [output](Request* request) { hidl_vec_removeAt(&request->outputs, output); });
+ }
+}
+
+///////////////////////////// ENTRY POINT //////////////////////////////////
+
+std::vector<Request> createRequests(const std::vector<MixedTypedExampleType>& examples) {
+ const uint32_t INPUT = 0;
+ const uint32_t OUTPUT = 1;
+
+ std::vector<Request> requests;
+
+ for (auto& example : examples) {
+ const MixedTyped& inputs = example.first;
+ const MixedTyped& outputs = example.second;
+
+ std::vector<RequestArgument> inputs_info, outputs_info;
+ uint32_t inputSize = 0, outputSize = 0;
+
+ // This function only partially specifies the metadata (vector of RequestArguments).
+ // The contents are copied over below.
+ for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) {
+ if (inputs_info.size() <= static_cast<size_t>(index)) inputs_info.resize(index + 1);
+ RequestArgument arg = {
+ .location = {.poolIndex = INPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+ .dimensions = {},
+ };
+ RequestArgument arg_empty = {
+ .hasNoValue = true,
+ };
+ inputs_info[index] = s ? arg : arg_empty;
+ inputSize += s;
+ });
+ // Compute offset for inputs 1 and so on
+ {
+ size_t offset = 0;
+ for (auto& i : inputs_info) {
+ if (!i.hasNoValue) i.location.offset = offset;
+ offset += i.location.length;
+ }
+ }
+
+ // Go through all outputs, initialize RequestArgument descriptors
+ for_all(outputs, [&outputs_info, &outputSize](int index, auto, auto s) {
+ if (outputs_info.size() <= static_cast<size_t>(index)) outputs_info.resize(index + 1);
+ RequestArgument arg = {
+ .location = {.poolIndex = OUTPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+ .dimensions = {},
+ };
+ outputs_info[index] = arg;
+ outputSize += s;
+ });
+ // Compute offset for outputs 1 and so on
+ {
+ size_t offset = 0;
+ for (auto& i : outputs_info) {
+ i.location.offset = offset;
+ offset += i.location.length;
+ }
+ }
+ std::vector<hidl_memory> pools = {nn::allocateSharedMemory(inputSize),
+ nn::allocateSharedMemory(outputSize)};
+ if (pools[INPUT].size() == 0 || pools[OUTPUT].size() == 0) {
+ return {};
+ }
+
+ // map pool
+ sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
+ if (inputMemory == nullptr) {
+ return {};
+ }
+ char* inputPtr = reinterpret_cast<char*>(static_cast<void*>(inputMemory->getPointer()));
+ if (inputPtr == nullptr) {
+ return {};
+ }
+
+ // initialize pool
+ inputMemory->update();
+ for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) {
+ char* begin = (char*)p;
+ char* end = begin + s;
+ // TODO: handle more than one input
+ std::copy(begin, end, inputPtr + inputs_info[index].location.offset);
+ });
+ inputMemory->commit();
+
+ requests.push_back({.inputs = inputs_info, .outputs = outputs_info, .pools = pools});
+ }
+
+ return requests;
+}
+
+void ValidationTest::validateRequests(const Model& model, const std::vector<Request>& requests) {
+ // create IPreparedModel
+ sp<IPreparedModel> preparedModel;
+ ASSERT_NO_FATAL_FAILURE(createPreparedModel(device, model, &preparedModel));
+ if (preparedModel == nullptr) {
+ return;
+ }
+
+ // validate each request
+ for (const Request& request : requests) {
+ removeInputTest(preparedModel, request);
+ removeOutputTest(preparedModel, request);
+ }
+}
+
+} // namespace functional
+} // namespace vts
+} // namespace V1_2
+} // namespace neuralnetworks
+} // namespace hardware
+} // namespace android
diff --git a/neuralnetworks/1.2/vts/functional/ValidationTests.cpp b/neuralnetworks/1.2/vts/functional/ValidationTests.cpp
--- /dev/null
@@ -0,0 +1,50 @@
+/*
+ * 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.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "Models.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_2 {
+namespace vts {
+namespace functional {
+
+// forward declarations
+std::vector<Request> createRequests(const std::vector<MixedTypedExample>& examples);
+
+// generate validation tests
+#define VTS_CURRENT_TEST_CASE(TestName) \
+ TEST_F(ValidationTest, TestName) { \
+ const Model model = TestName::createTestModel(); \
+ const std::vector<Request> requests = createRequests(TestName::examples); \
+ validateModel(model); \
+ validateRequests(model, requests); \
+ }
+
+FOR_EACH_TEST_MODEL(VTS_CURRENT_TEST_CASE)
+
+#undef VTS_CURRENT_TEST_CASE
+
+} // namespace functional
+} // namespace vts
+} // namespace V1_2
+} // namespace neuralnetworks
+} // namespace hardware
+} // namespace android
diff --git a/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.cpp b/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.cpp
--- /dev/null
@@ -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.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_2 {
+namespace vts {
+namespace functional {
+
+// A class for test environment setup
+NeuralnetworksHidlEnvironment::NeuralnetworksHidlEnvironment() {}
+
+NeuralnetworksHidlEnvironment::~NeuralnetworksHidlEnvironment() {}
+
+NeuralnetworksHidlEnvironment* NeuralnetworksHidlEnvironment::getInstance() {
+ // This has to return a "new" object because it is freed inside
+ // ::testing::AddGlobalTestEnvironment when the gtest is being torn down
+ static NeuralnetworksHidlEnvironment* instance = new NeuralnetworksHidlEnvironment();
+ return instance;
+}
+
+void NeuralnetworksHidlEnvironment::registerTestServices() {
+ registerTestService<IDevice>();
+}
+
+// The main test class for NEURALNETWORK HIDL HAL.
+NeuralnetworksHidlTest::NeuralnetworksHidlTest() {}
+
+NeuralnetworksHidlTest::~NeuralnetworksHidlTest() {}
+
+void NeuralnetworksHidlTest::SetUp() {
+ ::testing::VtsHalHidlTargetTestBase::SetUp();
+ device = ::testing::VtsHalHidlTargetTestBase::getService<IDevice>(
+ NeuralnetworksHidlEnvironment::getInstance());
+ ASSERT_NE(nullptr, device.get());
+}
+
+void NeuralnetworksHidlTest::TearDown() {
+ device = nullptr;
+ ::testing::VtsHalHidlTargetTestBase::TearDown();
+}
+
+} // namespace functional
+} // namespace vts
+
+::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus) {
+ return os << toString(errorStatus);
+}
+
+::std::ostream& operator<<(::std::ostream& os, DeviceStatus deviceStatus) {
+ return os << toString(deviceStatus);
+}
+
+} // namespace V1_2
+} // namespace neuralnetworks
+} // namespace hardware
+} // namespace android
+
+using android::hardware::neuralnetworks::V1_2::vts::functional::NeuralnetworksHidlEnvironment;
+
+int main(int argc, char** argv) {
+ ::testing::AddGlobalTestEnvironment(NeuralnetworksHidlEnvironment::getInstance());
+ ::testing::InitGoogleTest(&argc, argv);
+ NeuralnetworksHidlEnvironment::getInstance()->init(&argc, argv);
+
+ int status = RUN_ALL_TESTS();
+ return status;
+}
diff --git a/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.h b/neuralnetworks/1.2/vts/functional/VtsHalNeuralnetworks.h
--- /dev/null
@@ -0,0 +1,92 @@
+/*
+ * 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.
+ */
+
+#ifndef VTS_HAL_NEURALNETWORKS_V1_2_H
+#define VTS_HAL_NEURALNETWORKS_V1_2_H
+
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/types.h>
+#include <android/hardware/neuralnetworks/1.2/IDevice.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+
+#include <VtsHalHidlTargetTestBase.h>
+#include <VtsHalHidlTargetTestEnvBase.h>
+
+#include <android-base/macros.h>
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_2 {
+
+using V1_0::DeviceStatus;
+using V1_0::ErrorStatus;
+using V1_0::Request;
+
+namespace vts {
+namespace functional {
+
+// A class for test environment setup
+class NeuralnetworksHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
+ DISALLOW_COPY_AND_ASSIGN(NeuralnetworksHidlEnvironment);
+ NeuralnetworksHidlEnvironment();
+ ~NeuralnetworksHidlEnvironment() override;
+
+ public:
+ static NeuralnetworksHidlEnvironment* getInstance();
+ void registerTestServices() override;
+};
+
+// The main test class for NEURALNETWORKS HIDL HAL.
+class NeuralnetworksHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+ DISALLOW_COPY_AND_ASSIGN(NeuralnetworksHidlTest);
+
+ public:
+ NeuralnetworksHidlTest();
+ ~NeuralnetworksHidlTest() override;
+ void SetUp() override;
+ void TearDown() override;
+
+ protected:
+ sp<IDevice> device;
+};
+
+// Tag for the validation tests
+class ValidationTest : public NeuralnetworksHidlTest {
+ protected:
+ void validateModel(const Model& model);
+ void validateRequests(const Model& model, const std::vector<Request>& request);
+};
+
+// Tag for the generated tests
+class GeneratedTest : public NeuralnetworksHidlTest {};
+
+} // namespace functional
+} // namespace vts
+
+// pretty-print values for error messages
+::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus);
+::std::ostream& operator<<(::std::ostream& os, DeviceStatus deviceStatus);
+
+} // namespace V1_2
+} // namespace neuralnetworks
+} // namespace hardware
+} // namespace android
+
+#endif // VTS_HAL_NEURALNETWORKS_V1_2_H