Add test for a issue with reshape truncating output values.

Test: NeuralNetworksTest_static
Bug: 138286301
Bug: 138150365
Change-Id: I6ef1eda244e71c8cc9dec01f76bdf233d0999849
Merged-In: I6ef1eda244e71c8cc9dec01f76bdf233d0999849
(cherry picked from commit 540fe8bc83628c8bff9f654d02224ae10d31e70d)

2 files changed, 175 insertions, 0 deletions

diff --git a/nn/runtime/test/generated/spec_V1_1/reshape_b138150365.example.cpp b/nn/runtime/test/generated/spec_V1_1/reshape_b138150365.example.cpp
new file mode 100644
index 000000000..3f0f636ad
--- /dev/null
+++ b/nn/runtime/test/generated/spec_V1_1/reshape_b138150365.example.cpp
@@ -0,0 +1,148 @@
+// Generated from reshape_b138150365.mod.py
+// DO NOT EDIT
+// clang-format off
+#include "TestHarness.h"
+using namespace test_helper;
+
+namespace generated_tests::reshape_b138150365 {
+
+const TestModel& get_test_model() {
+    static TestModel model = {
+        .expectFailure = false,
+        .expectedMultinomialDistributionTolerance = 0,
+        .inputIndexes = {0},
+        .isRelaxed = true,
+        .minSupportedVersion = TestHalVersion::UNKNOWN,
+        .operands = {{
+                .channelQuant = {},
+                .data = TestBuffer::createFromVector<float>({-1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, 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-1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f}),
+                .dimensions = {1, 1, 1, 1001},
+                .isIgnored = false,
+                .lifetime = TestOperandLifeTime::MODEL_INPUT,
+                .numberOfConsumers = 1,
+                .scale = 0.0f,
+                .type = TestOperandType::TENSOR_FLOAT32,
+                .zeroPoint = 0
+            }, {
+                .channelQuant = {},
+                .data = TestBuffer::createFromVector<int32_t>({1, 1001}),
+                .dimensions = {2},
+                .isIgnored = false,
+                .lifetime = TestOperandLifeTime::CONSTANT_COPY,
+                .numberOfConsumers = 1,
+                .scale = 0.0f,
+                .type = TestOperandType::TENSOR_INT32,
+                .zeroPoint = 0
+            }, {
+                .channelQuant = {},
+                .data = TestBuffer::createFromVector<float>({-1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, 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-1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f}),
+                .dimensions = {1, 1001},
+                .isIgnored = false,
+                .lifetime = TestOperandLifeTime::MODEL_OUTPUT,
+                .numberOfConsumers = 0,
+                .scale = 0.0f,
+                .type = TestOperandType::TENSOR_FLOAT32,
+                .zeroPoint = 0
+            }},
+        .operations = {{
+                .inputs = {0, 1},
+                .outputs = {2},
+                .type = TestOperationType::RESHAPE
+            }},
+        .outputIndexes = {2}
+    };
+    return model;
+}
+
+const auto dummy_test_model = TestModelManager::get().add("reshape_b138150365", get_test_model());
+
+}  // namespace generated_tests::reshape_b138150365
+
+namespace generated_tests::reshape_b138150365 {
+
+const TestModel& get_test_model_all_inputs_as_internal() {
+    static TestModel model = {
+        .expectFailure = false,
+        .expectedMultinomialDistributionTolerance = 0,
+        .inputIndexes = {3},
+        .isRelaxed = true,
+        .minSupportedVersion = TestHalVersion::UNKNOWN,
+        .operands = {{
+                .channelQuant = {},
+                .data = TestBuffer::createFromVector<float>({}),
+                .dimensions = {1, 1, 1, 1001},
+                .isIgnored = false,
+                .lifetime = TestOperandLifeTime::TEMPORARY_VARIABLE,
+                .numberOfConsumers = 1,
+                .scale = 0.0f,
+                .type = TestOperandType::TENSOR_FLOAT32,
+                .zeroPoint = 0
+            }, {
+                .channelQuant = {},
+                .data = TestBuffer::createFromVector<int32_t>({1, 1001}),
+                .dimensions = {2},
+                .isIgnored = false,
+                .lifetime = TestOperandLifeTime::CONSTANT_COPY,
+                .numberOfConsumers = 1,
+                .scale = 0.0f,
+                .type = TestOperandType::TENSOR_INT32,
+                .zeroPoint = 0
+            }, {
+                .channelQuant = {},
+                .data = TestBuffer::createFromVector<float>({-1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, 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-1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f}),
+                .dimensions = {1, 1001},
+                .isIgnored = false,
+                .lifetime = TestOperandLifeTime::MODEL_OUTPUT,
+                .numberOfConsumers = 0,
+                .scale = 0.0f,
+                .type = TestOperandType::TENSOR_FLOAT32,
+                .zeroPoint = 0
+            }, {
+                .channelQuant = {},
+                .data = TestBuffer::createFromVector<float>({-1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, 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-1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f}),
+                .dimensions = {1, 1, 1, 1001},
+                .isIgnored = false,
+                .lifetime = TestOperandLifeTime::MODEL_INPUT,
+                .numberOfConsumers = 1,
+                .scale = 0.0f,
+                .type = TestOperandType::TENSOR_FLOAT32,
+                .zeroPoint = 0
+            }, {
+                .channelQuant = {},
+                .data = TestBuffer::createFromVector<float>({0.0f}),
+                .dimensions = {1},
+                .isIgnored = false,
+                .lifetime = TestOperandLifeTime::CONSTANT_COPY,
+                .numberOfConsumers = 1,
+                .scale = 0.0f,
+                .type = TestOperandType::TENSOR_FLOAT32,
+                .zeroPoint = 0
+            }, {
+                .channelQuant = {},
+                .data = TestBuffer::createFromVector<int32_t>({0}),
+                .dimensions = {},
+                .isIgnored = false,
+                .lifetime = TestOperandLifeTime::CONSTANT_COPY,
+                .numberOfConsumers = 1,
+                .scale = 0.0f,
+                .type = TestOperandType::INT32,
+                .zeroPoint = 0
+            }},
+        .operations = {{
+                .inputs = {3, 4, 5},
+                .outputs = {0},
+                .type = TestOperationType::ADD
+            }, {
+                .inputs = {0, 1},
+                .outputs = {2},
+                .type = TestOperationType::RESHAPE
+            }},
+        .outputIndexes = {2}
+    };
+    return model;
+}
+
+const auto dummy_test_model_all_inputs_as_internal = TestModelManager::get().add("reshape_b138150365_all_inputs_as_internal", get_test_model_all_inputs_as_internal());
+
+}  // namespace generated_tests::reshape_b138150365
+
diff --git a/nn/runtime/test/specs/V1_1/reshape_b138150365.mod.py b/nn/runtime/test/specs/V1_1/reshape_b138150365.mod.py
new file mode 100644
index 000000000..99b6e9042
--- /dev/null
+++ b/nn/runtime/test/specs/V1_1/reshape_b138150365.mod.py
@@ -0,0 +1,27 @@
+#
+# 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.
+#
+
+# Issue b/138150365, truncation of last values of reshape output used in mobilenet
+model = Model()
+i1 = Input("op1", "TENSOR_FLOAT32", "{1, 1, 1, 1001}")
+p2 =  Parameter("op2", "TENSOR_INT32", "{2}", [1, 1001])
+o1 = Output("op3", "TENSOR_FLOAT32", "{1, 1001}")
+model = model.Operation("RESHAPE", i1, p2).To(o1)
+model = model.RelaxedExecution(True)
+
+input0 = {i1: [-1.0] * 1001}
+output0 = {o1:[-1.0] * 1001}
+Example((input0, output0))