Internal cleanup for text-domain unit tests.

PiperOrigin-RevId: 482241566
2022-10-19 10:29:57 -07:00 · 2022-10-19 10:29:57 -07:00 · 8426428de3
commit 8426428de3
parent a18f91e04f
1 changed files with 130 additions and 0 deletions
--- a/mediapipe/calculators/tensor/regex_preprocessor_calculator_test.cc
+++ b/mediapipe/calculators/tensor/regex_preprocessor_calculator_test.cc
@ -0,0 +1,130 @@
+// Copyright 2022 The MediaPipe Authors.
+//
+// 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.
+
+#include <memory>
+#include <sstream>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "absl/status/status.h"
+#include "absl/status/statusor.h"
+#include "absl/strings/string_view.h"
+#include "absl/strings/substitute.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/tensor.h"
+#include "mediapipe/framework/port/gmock.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/framework/port/parse_text_proto.h"
+#include "mediapipe/framework/port/status_matchers.h"
+#include "mediapipe/framework/tool/sink.h"
+#include "mediapipe/tasks/cc/core/utils.h"
+#include "mediapipe/tasks/cc/metadata/metadata_extractor.h"
+
+namespace mediapipe {
+namespace {
+
+using ::mediapipe::tasks::metadata::ModelMetadataExtractor;
+using ::testing::ElementsAreArray;
+
+constexpr int kMaxSeqLen = 256;
+constexpr char kTestModelPath[] =
+    "mediapipe/tasks/testdata/text/"
+    "test_model_text_classifier_with_regex_tokenizer.tflite";
+
+absl::StatusOr<std::vector<int>> RunRegexPreprocessorCalculator(
+    absl::string_view text) {
+  auto graph_config =
+      ParseTextProtoOrDie<CalculatorGraphConfig>(absl::Substitute(
+          R"pb(
+            input_stream: "text"
+            output_stream: "tensors"
+            node {
+              calculator: "RegexPreprocessorCalculator"
+              input_stream: "TEXT:text"
+              input_side_packet: "METADATA_EXTRACTOR:metadata_extractor"
+              output_stream: "TENSORS:tensors"
+              options {
+                [mediapipe.RegexPreprocessorCalculatorOptions.ext] {
+                  max_seq_len: $0
+                }
+              }
+            }
+          )pb",
+          kMaxSeqLen));
+  std::vector<Packet> output_packets;
+  tool::AddVectorSink("tensors", &graph_config, &output_packets);
+
+  std::string model_buffer = tasks::core::LoadBinaryContent(kTestModelPath);
+  ASSIGN_OR_RETURN(std::unique_ptr<ModelMetadataExtractor> metadata_extractor,
+                   ModelMetadataExtractor::CreateFromModelBuffer(
+                       model_buffer.data(), model_buffer.size()));
+  // Run the graph.
+  CalculatorGraph graph;
+  MP_RETURN_IF_ERROR(graph.Initialize(
+      graph_config,
+      {{"metadata_extractor",
+        MakePacket<ModelMetadataExtractor>(std::move(*metadata_extractor))}}));
+  MP_RETURN_IF_ERROR(graph.StartRun({}));
+  MP_RETURN_IF_ERROR(graph.AddPacketToInputStream(
+      "text", MakePacket<std::string>(text).At(Timestamp(0))));
+  MP_RETURN_IF_ERROR(graph.WaitUntilIdle());
+
+  if (output_packets.size() != 1) {
+    return absl::InvalidArgumentError(absl::Substitute(
+        "output_packets has size $0, expected 1", output_packets.size()));
+  }
+  const std::vector<Tensor>& tensor_vec =
+      output_packets[0].Get<std::vector<Tensor>>();
+  if (tensor_vec.size() != 1) {
+    return absl::InvalidArgumentError(absl::Substitute(
+        "tensor_vec has size $0, expected $1", tensor_vec.size(), 1));
+  }
+  if (tensor_vec[0].element_type() != Tensor::ElementType::kInt32) {
+    return absl::InvalidArgumentError("Expected tensor element type kInt32");
+  }
+  auto* buffer = tensor_vec[0].GetCpuReadView().buffer<int>();
+  std::vector<int> result(buffer, buffer + kMaxSeqLen);
+  MP_RETURN_IF_ERROR(graph.CloseAllPacketSources());
+  MP_RETURN_IF_ERROR(graph.WaitUntilDone());
+  return result;
+}
+
+TEST(RegexPreprocessorCalculatorTest, TextClassifierModel) {
+  MP_ASSERT_OK_AND_ASSIGN(
+      std::vector<int> processed_tensor_values,
+      RunRegexPreprocessorCalculator("This is the best movie I’ve seen in "
+                                     "recent years. Strongly recommend it!"));
+  static const int expected_result[kMaxSeqLen] = {
+      1, 2, 9, 4, 118, 20, 2, 2, 110, 11, 1136, 153, 2, 386, 12};
+  EXPECT_THAT(processed_tensor_values, ElementsAreArray(expected_result));
+}
+
+TEST(RegexPreprocessorCalculatorTest, LongInput) {
+  std::stringstream long_input;
+  long_input << "This is the best";
+  for (int i = 0; i < kMaxSeqLen; ++i) {
+    long_input << " best";
+  }
+  long_input << "movie I’ve seen in recent years. Strongly recommend it!";
+  MP_ASSERT_OK_AND_ASSIGN(std::vector<int> processed_tensor_values,
+                          RunRegexPreprocessorCalculator(long_input.str()));
+  std::vector<int> expected_result = {1, 2, 9, 4, 118};
+  // "best" id
+  expected_result.resize(kMaxSeqLen, 118);
+  EXPECT_THAT(processed_tensor_values, ElementsAreArray(expected_result));
+}
+
+}  // namespace
+}  // namespace mediapipe