diff --git a/WORKSPACE b/WORKSPACE index 046da997..702d1899 100644 --- a/WORKSPACE +++ b/WORKSPACE @@ -177,7 +177,7 @@ http_archive( "//third_party:com_google_sentencepiece_no_gflag_no_gtest.diff", ], patch_args = ["-p1"], - repo_mapping = {"@com_google_glog" : "@com_github_glog_glog"}, + repo_mapping = {"@com_google_glog" : "@com_github_glog_glog_no_gflags"}, ) http_archive( diff --git a/docs/build_java_api_docs.py b/docs/build_java_api_docs.py index 7b44e79f..e96e1fd8 100644 --- a/docs/build_java_api_docs.py +++ b/docs/build_java_api_docs.py @@ -45,6 +45,12 @@ def main(_) -> None: while (mp_root := mp_root.parent).name != 'mediapipe': # Find the nearest `mediapipe` dir. pass + + # Externally, parts of the repo are nested inside a mediapipe/ directory + # that does not exist internally. Support both. + if (mp_root / 'mediapipe').exists(): + mp_root = mp_root / 'mediapipe' + java_root = mp_root / 'tasks/java' gen_java.gen_java_docs( diff --git a/mediapipe/calculators/core/flow_limiter_calculator_test.cc b/mediapipe/calculators/core/flow_limiter_calculator_test.cc index 8a8cc965..45bace27 100644 --- a/mediapipe/calculators/core/flow_limiter_calculator_test.cc +++ b/mediapipe/calculators/core/flow_limiter_calculator_test.cc @@ -79,21 +79,25 @@ std::vector PacketValues(const std::vector& packets) { return result; } -template -std::vector MakePackets(std::vector> contents) { - std::vector result; - for (auto& entry : contents) { - result.push_back(MakePacket(entry.second).At(entry.first)); - } - return result; -} - std::string SourceString(Timestamp t) { return (t.IsSpecialValue()) ? t.DebugString() : absl::StrCat("Timestamp(", t.DebugString(), ")"); } +template +std::string SourceString(Packet packet) { + std::ostringstream oss; + if (packet.IsEmpty()) { + oss << "Packet()"; + } else { + oss << "MakePacket<" << MediaPipeTypeStringOrDemangled() << ">(" + << packet.Get() << ")"; + } + oss << ".At(" << SourceString(packet.Timestamp()) << ")"; + return oss.str(); +} + template class PacketsEqMatcher : public ::testing::MatcherInterface { @@ -123,8 +127,9 @@ class PacketsEqMatcher } for (auto i1 = c1.begin(), i2 = c2.begin(); i1 != c1.end(); ++i1, ++i2) { Packet p1 = *i1, p2 = *i2; - if (p1.Timestamp() != p2.Timestamp() || - p1.Get() != p2.Get()) { + if (p1.Timestamp() != p2.Timestamp() || p1.IsEmpty() != p2.IsEmpty() || + (!p1.IsEmpty() && + p1.Get() != p2.Get())) { return false; } } @@ -133,10 +138,9 @@ class PacketsEqMatcher void Print(const PacketContainer& packets, ::std::ostream* os) const { for (auto it = packets.begin(); it != packets.end(); ++it) { const Packet& packet = *it; - *os << (it == packets.begin() ? "{" : "") << "{" - << SourceString(packet.Timestamp()) << ", " - << packet.Get() << "}" - << (std::next(it) == packets.end() ? "}" : ", "); + *os << (it == packets.begin() ? "{" : ""); + *os << SourceString(packet); + *os << (std::next(it) == packets.end() ? "}" : ", "); } } @@ -144,7 +148,7 @@ class PacketsEqMatcher }; template -::testing::Matcher PackestEq( +::testing::Matcher PacketsEq( const PacketContainer& packets) { return MakeMatcher( new PacketsEqMatcher(packets)); @@ -739,8 +743,8 @@ TEST_F(FlowLimiterCalculatorTest, TwoInputStreams) { // The processing time "sleep_time" is reduced from 22ms to 12ms to create // the same frame rate as FlowLimiterCalculatorTest::TwoInputStreams. TEST_F(FlowLimiterCalculatorTest, ZeroQueue) { - auto BoolPackestEq = PackestEq, bool>; - auto IntPackestEq = PackestEq, int>; + auto BoolPacketsEq = PacketsEq, bool>; + auto IntPacketsEq = PacketsEq, int>; // Configure the test. SetUpInputData(); @@ -835,52 +839,86 @@ TEST_F(FlowLimiterCalculatorTest, ZeroQueue) { input_packets_[0], input_packets_[2], input_packets_[15], input_packets_[17], input_packets_[19], }; - EXPECT_THAT(out_1_packets_, IntPackestEq(expected_output)); + EXPECT_THAT(out_1_packets_, IntPacketsEq(expected_output)); // Exactly the timestamps released by FlowLimiterCalculator for in_1_sampled. std::vector expected_output_2 = { input_packets_[0], input_packets_[2], input_packets_[4], input_packets_[15], input_packets_[17], input_packets_[19], }; - EXPECT_THAT(out_2_packets, IntPackestEq(expected_output_2)); + EXPECT_THAT(out_2_packets, IntPacketsEq(expected_output_2)); // Validate the ALLOW stream output. - std::vector expected_allow = MakePackets( // - {{Timestamp(0), true}, {Timestamp(10000), false}, - {Timestamp(20000), true}, {Timestamp(30000), false}, - {Timestamp(40000), true}, {Timestamp(50000), false}, - {Timestamp(60000), false}, {Timestamp(70000), false}, - {Timestamp(80000), false}, {Timestamp(90000), false}, - {Timestamp(100000), false}, {Timestamp(110000), false}, - {Timestamp(120000), false}, {Timestamp(130000), false}, - {Timestamp(140000), false}, {Timestamp(150000), true}, - {Timestamp(160000), false}, {Timestamp(170000), true}, - {Timestamp(180000), false}, {Timestamp(190000), true}, - {Timestamp(200000), false}}); - EXPECT_THAT(allow_packets_, BoolPackestEq(expected_allow)); + std::vector expected_allow = { + MakePacket(true).At(Timestamp(0)), + MakePacket(false).At(Timestamp(10000)), + MakePacket(true).At(Timestamp(20000)), + MakePacket(false).At(Timestamp(30000)), + MakePacket(true).At(Timestamp(40000)), + MakePacket(false).At(Timestamp(50000)), + MakePacket(false).At(Timestamp(60000)), + MakePacket(false).At(Timestamp(70000)), + MakePacket(false).At(Timestamp(80000)), + MakePacket(false).At(Timestamp(90000)), + MakePacket(false).At(Timestamp(100000)), + MakePacket(false).At(Timestamp(110000)), + MakePacket(false).At(Timestamp(120000)), + MakePacket(false).At(Timestamp(130000)), + MakePacket(false).At(Timestamp(140000)), + MakePacket(true).At(Timestamp(150000)), + MakePacket(false).At(Timestamp(160000)), + MakePacket(true).At(Timestamp(170000)), + MakePacket(false).At(Timestamp(180000)), + MakePacket(true).At(Timestamp(190000)), + MakePacket(false).At(Timestamp(200000)), + }; + EXPECT_THAT(allow_packets_, BoolPacketsEq(expected_allow)); +} + +std::vector StripBoundsUpdates(const std::vector& packets, + Timestamp begin = Timestamp::Min(), + Timestamp end = Timestamp::Max()) { + std::vector result; + for (const auto& packet : packets) { + Timestamp ts = packet.Timestamp(); + if (packet.IsEmpty() && ts >= begin && ts < end) { + continue; + } + result.push_back(packet); + } + return result; } // Shows how FlowLimiterCalculator releases auxiliary input packets. // In this test, auxiliary input packets arrive at twice the primary rate. TEST_F(FlowLimiterCalculatorTest, AuxiliaryInputs) { - auto BoolPackestEq = PackestEq, bool>; - auto IntPackestEq = PackestEq, int>; + auto BoolPacketsEq = PacketsEq, bool>; + auto IntPacketsEq = PacketsEq, int>; // Configure the test. SetUpInputData(); SetUpSimulationClock(); CalculatorGraphConfig graph_config = ParseTextProtoOrDie(R"pb( - input_stream: 'in_1' - input_stream: 'in_2' + input_stream: 'input_1' + input_stream: 'auxiliary_input_2' + input_stream: 'auxiliary_input_3' node { calculator: 'FlowLimiterCalculator' - input_side_packet: 'OPTIONS:limiter_options' - input_stream: 'in_1' - input_stream: 'in_2' + options { + [mediapipe.FlowLimiterCalculatorOptions.ext] { + max_in_flight: 1 + max_in_queue: 0 + in_flight_timeout: 1000000 # 1s + } + } + input_stream: 'input_1' + input_stream: 'auxiliary_input_2' + input_stream: 'auxiliary_input_3' input_stream: 'FINISHED:out_1' input_stream_info: { tag_index: 'FINISHED' back_edge: true } - output_stream: 'in_1_sampled' - output_stream: 'in_2_sampled' + output_stream: 'input_1_sampled' + output_stream: 'auxiliary_input_2_sampled' + output_stream: 'auxiliary_input_3_sampled' output_stream: 'ALLOW:allow' } node { @@ -888,49 +926,75 @@ TEST_F(FlowLimiterCalculatorTest, AuxiliaryInputs) { input_side_packet: 'WARMUP_TIME:warmup_time' input_side_packet: 'SLEEP_TIME:sleep_time' input_side_packet: 'CLOCK:clock' - input_stream: 'PACKET:in_1_sampled' + input_stream: 'PACKET:input_1_sampled' output_stream: 'PACKET:out_1' } )pb"); - auto limiter_options = ParseTextProtoOrDie( - R"pb( - max_in_flight: 1 max_in_queue: 0 in_flight_timeout: 1000000 # 1s - )pb"); std::map side_packets = { - {"limiter_options", - MakePacket(limiter_options)}, + // Fake processing lazy initialization time in microseconds. {"warmup_time", MakePacket(22000)}, + // Fake processing duration in microseconds. {"sleep_time", MakePacket(22000)}, + // The SimulationClock to count virtual elapsed time. {"clock", MakePacket(clock_)}, }; // Start the graph. MP_ASSERT_OK(graph_.Initialize(graph_config)); - MP_EXPECT_OK(graph_.ObserveOutputStream("out_1", [this](Packet p) { - out_1_packets_.push_back(p); - return absl::OkStatus(); - })); - std::vector out_2_packets; - MP_EXPECT_OK(graph_.ObserveOutputStream("in_2_sampled", [&](Packet p) { - out_2_packets.push_back(p); - return absl::OkStatus(); - })); - MP_EXPECT_OK(graph_.ObserveOutputStream("allow", [this](Packet p) { - allow_packets_.push_back(p); - return absl::OkStatus(); - })); + MP_EXPECT_OK(graph_.ObserveOutputStream( + "out_1", + [this](Packet p) { + out_1_packets_.push_back(p); + return absl::OkStatus(); + }, + true)); + std::vector out_2_packets, out_3_packets; + MP_EXPECT_OK(graph_.ObserveOutputStream( + "auxiliary_input_2_sampled", + [&](Packet p) { + out_2_packets.push_back(p); + return absl::OkStatus(); + }, + true)); + MP_EXPECT_OK(graph_.ObserveOutputStream( + "auxiliary_input_3_sampled", + [&](Packet p) { + out_3_packets.push_back(p); + return absl::OkStatus(); + }, + true)); + MP_EXPECT_OK(graph_.ObserveOutputStream( + "allow", + [this](Packet p) { + allow_packets_.push_back(p); + return absl::OkStatus(); + }, + true)); simulation_clock_->ThreadStart(); MP_ASSERT_OK(graph_.StartRun(side_packets)); - // Add packets 2,4,6,8 to stream in_1 and 1..9 to stream in_2. - clock_->Sleep(absl::Microseconds(10000)); + // Add packets 1..9 to auxiliary_input_3, early. + for (int i = 1; i < 10; ++i) { + MP_EXPECT_OK(graph_.AddPacketToInputStream( + "auxiliary_input_3", MakePacket(i).At(Timestamp(i * 10000)))); + } + + // The total count of out_2_packets after each input packet. + // std::vector sizes_2 = {0, 0, 2, 2, 3, 3, 4, 4, 5, 5}; + std::vector sizes_2 = {0, 1, 3, 4, 6, 7, 9, 10, 12, 13}; + + // Add packets 2,4,6,8 to stream input_1. + // Add packets 1..9 to auxiliary_input_2. for (int i = 1; i < 10; ++i) { if (i % 2 == 0) { - MP_EXPECT_OK(graph_.AddPacketToInputStream("in_1", input_packets_[i])); + MP_EXPECT_OK(graph_.AddPacketToInputStream( + "input_1", MakePacket(i).At(Timestamp(i * 10000)))); } - MP_EXPECT_OK(graph_.AddPacketToInputStream("in_2", input_packets_[i])); + MP_EXPECT_OK(graph_.AddPacketToInputStream( + "auxiliary_input_2", MakePacket(i).At(Timestamp(i * 10000)))); clock_->Sleep(absl::Microseconds(10000)); + EXPECT_EQ(out_2_packets.size(), sizes_2[i]); } // Finish the graph run. @@ -942,24 +1006,46 @@ TEST_F(FlowLimiterCalculatorTest, AuxiliaryInputs) { // Validate the output. // Input packets 4 and 8 are dropped due to max_in_flight. std::vector expected_output = { - input_packets_[2], - input_packets_[6], + MakePacket(2).At(Timestamp(20000)), + Packet().At(Timestamp(40000)), + MakePacket(6).At(Timestamp(60000)), + Packet().At(Timestamp(80000)), }; - EXPECT_THAT(out_1_packets_, IntPackestEq(expected_output)); + EXPECT_THAT(out_1_packets_, IntPacketsEq(expected_output)); + // Packets following input packets 2 and 6, and not input packets 4 and 8. - std::vector expected_output_2 = { - input_packets_[1], input_packets_[2], input_packets_[3], - input_packets_[6], input_packets_[7], + std::vector expected_auxiliary_output = { + Packet().At(Timestamp(9999)), + MakePacket(1).At(Timestamp(10000)), + MakePacket(2).At(Timestamp(20000)), + Packet().At(Timestamp(29999)), + MakePacket(3).At(Timestamp(30000)), + Packet().At(Timestamp(40000)), + Packet().At(Timestamp(49999)), + Packet().At(Timestamp(50000)), + MakePacket(6).At(Timestamp(60000)), + Packet().At(Timestamp(69999)), + MakePacket(7).At(Timestamp(70000)), + Packet().At(Timestamp(80000)), + Packet().At(Timestamp(89999)), }; - EXPECT_THAT(out_2_packets, IntPackestEq(expected_output_2)); + std::vector actual_2 = + StripBoundsUpdates(out_2_packets, Timestamp(90000)); + EXPECT_THAT(actual_2, IntPacketsEq(expected_auxiliary_output)); + std::vector expected_3 = + StripBoundsUpdates(expected_auxiliary_output, Timestamp(39999)); + std::vector actual_3 = + StripBoundsUpdates(out_3_packets, Timestamp(39999)); + EXPECT_THAT(actual_3, IntPacketsEq(expected_3)); // Validate the ALLOW stream output. - std::vector expected_allow = - MakePackets({{Timestamp(20000), 1}, - {Timestamp(40000), 0}, - {Timestamp(60000), 1}, - {Timestamp(80000), 0}}); - EXPECT_THAT(allow_packets_, BoolPackestEq(expected_allow)); + std::vector expected_allow = { + MakePacket(true).At(Timestamp(20000)), + MakePacket(false).At(Timestamp(40000)), + MakePacket(true).At(Timestamp(60000)), + MakePacket(false).At(Timestamp(80000)), + }; + EXPECT_THAT(allow_packets_, BoolPacketsEq(expected_allow)); } } // anonymous namespace diff --git a/mediapipe/calculators/image/set_alpha_calculator.cc b/mediapipe/calculators/image/set_alpha_calculator.cc index 04c3b2cf..f5ed703e 100644 --- a/mediapipe/calculators/image/set_alpha_calculator.cc +++ b/mediapipe/calculators/image/set_alpha_calculator.cc @@ -46,6 +46,40 @@ constexpr char kOutputFrameTagGpu[] = "IMAGE_GPU"; constexpr int kNumChannelsRGBA = 4; enum { ATTRIB_VERTEX, ATTRIB_TEXTURE_POSITION, NUM_ATTRIBUTES }; + +// Combines an RGB cv::Mat and a single-channel alpha cv::Mat of the same +// dimensions into an RGBA cv::Mat. Alpha may be read as uint8 or as another +// numeric type; in the latter case, it is upscaled to values between 0 and 255 +// from an assumed input range of [0, 1). RGB and RGBA Mat's must be uchar. +template +absl::Status MergeRGBA8Image(const cv::Mat input_mat, const cv::Mat& alpha_mat, + cv::Mat& output_mat) { + RET_CHECK_EQ(input_mat.rows, alpha_mat.rows); + RET_CHECK_EQ(input_mat.cols, alpha_mat.cols); + RET_CHECK_EQ(input_mat.rows, output_mat.rows); + RET_CHECK_EQ(input_mat.cols, output_mat.cols); + + for (int i = 0; i < output_mat.rows; ++i) { + const uchar* in_ptr = input_mat.ptr(i); + const AlphaType* alpha_ptr = alpha_mat.ptr(i); + uchar* out_ptr = output_mat.ptr(i); + for (int j = 0; j < output_mat.cols; ++j) { + const int out_idx = j * kNumChannelsRGBA; + const int in_idx = j * input_mat.channels(); + const int alpha_idx = j * alpha_mat.channels(); + out_ptr[out_idx + 0] = in_ptr[in_idx + 0]; + out_ptr[out_idx + 1] = in_ptr[in_idx + 1]; + out_ptr[out_idx + 2] = in_ptr[in_idx + 2]; + if constexpr (std::is_same::value) { + out_ptr[out_idx + 3] = alpha_ptr[alpha_idx + 0]; + } else { + const AlphaType alpha = alpha_ptr[alpha_idx + 0]; + out_ptr[out_idx + 3] = static_cast(round(alpha * 255.0f)); + } + } + } + return absl::OkStatus(); +} } // namespace // A calculator for setting the alpha channel of an RGBA image. @@ -250,28 +284,22 @@ absl::Status SetAlphaCalculator::RenderCpu(CalculatorContext* cc) { const bool has_alpha_mask = cc->Inputs().HasTag(kInputAlphaTag) && !cc->Inputs().Tag(kInputAlphaTag).IsEmpty(); - const bool use_alpa_mask = alpha_value_ < 0 && has_alpha_mask; + const bool use_alpha_mask = alpha_value_ < 0 && has_alpha_mask; // Setup alpha image and Update image in CPU. - if (use_alpa_mask) { + if (use_alpha_mask) { const auto& alpha_mask = cc->Inputs().Tag(kInputAlphaTag).Get(); cv::Mat alpha_mat = mediapipe::formats::MatView(&alpha_mask); - RET_CHECK_EQ(input_mat.rows, alpha_mat.rows); - RET_CHECK_EQ(input_mat.cols, alpha_mat.cols); - for (int i = 0; i < output_mat.rows; ++i) { - const uchar* in_ptr = input_mat.ptr(i); - uchar* alpha_ptr = alpha_mat.ptr(i); - uchar* out_ptr = output_mat.ptr(i); - for (int j = 0; j < output_mat.cols; ++j) { - const int out_idx = j * kNumChannelsRGBA; - const int in_idx = j * input_mat.channels(); - const int alpha_idx = j * alpha_mat.channels(); - out_ptr[out_idx + 0] = in_ptr[in_idx + 0]; - out_ptr[out_idx + 1] = in_ptr[in_idx + 1]; - out_ptr[out_idx + 2] = in_ptr[in_idx + 2]; - out_ptr[out_idx + 3] = alpha_ptr[alpha_idx + 0]; // channel 0 of mask - } + const bool alpha_is_float = alpha_mat.type() == CV_32FC1; + RET_CHECK(alpha_is_float || alpha_mat.type() == CV_8UC1); + + if (alpha_is_float) { + MP_RETURN_IF_ERROR( + MergeRGBA8Image(input_mat, alpha_mat, output_mat)); + } else { + MP_RETURN_IF_ERROR( + MergeRGBA8Image(input_mat, alpha_mat, output_mat)); } } else { const uchar alpha_value = std::min(std::max(0.0f, alpha_value_), 255.0f); diff --git a/mediapipe/calculators/tensor/BUILD b/mediapipe/calculators/tensor/BUILD index 3385bcf4..3f127839 100644 --- a/mediapipe/calculators/tensor/BUILD +++ b/mediapipe/calculators/tensor/BUILD @@ -266,6 +266,7 @@ cc_test( "@com_google_absl//absl/status", "@com_google_absl//absl/status:statusor", "@com_google_absl//absl/strings", + "@com_google_sentencepiece//src:sentencepiece_processor", ], ) @@ -321,6 +322,7 @@ cc_test( "@com_google_absl//absl/status", "@com_google_absl//absl/status:statusor", "@com_google_absl//absl/strings", + "@com_google_sentencepiece//src:sentencepiece_processor", ], ) diff --git a/mediapipe/calculators/tensorflow/BUILD b/mediapipe/calculators/tensorflow/BUILD index 4037d89c..d0dfc12a 100644 --- a/mediapipe/calculators/tensorflow/BUILD +++ b/mediapipe/calculators/tensorflow/BUILD @@ -613,6 +613,7 @@ cc_library( deps = [ ":tensorflow_session", ":tensorflow_session_from_saved_model_generator_cc_proto", + "@com_google_absl//absl/status", "//mediapipe/framework:packet_generator", "//mediapipe/framework:packet_type", "//mediapipe/framework/tool:status_util", diff --git a/mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_generator.cc b/mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_generator.cc index 97c67592..d5236f1c 100644 --- a/mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_generator.cc +++ b/mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_generator.cc @@ -14,6 +14,8 @@ #include +#include "absl/status/status.h" + #if !defined(__ANDROID__) #include "mediapipe/framework/port/file_helpers.h" #endif @@ -38,6 +40,8 @@ constexpr char kSessionTag[] = "SESSION"; static constexpr char kStringSavedModelPath[] = "STRING_SAVED_MODEL_PATH"; +static constexpr char kStringSignatureName[] = "STRING_SIGNATURE_NAME"; + // Given the path to a directory containing multiple tensorflow saved models // in subdirectories, replaces path with the alphabetically last subdirectory. absl::Status GetLatestDirectory(std::string* path) { @@ -104,6 +108,10 @@ class TensorFlowSessionFromSavedModelGenerator : public PacketGenerator { if (input_side_packets->HasTag(kStringSavedModelPath)) { input_side_packets->Tag(kStringSavedModelPath).Set(); } + // Set Signature_def. + if (input_side_packets->HasTag(kStringSignatureName)) { + input_side_packets->Tag(kStringSignatureName).Set(); + } // A TensorFlow model loaded and ready for use along with tensor output_side_packets->Tag(kSessionTag).Set(); return absl::OkStatus(); @@ -146,9 +154,19 @@ class TensorFlowSessionFromSavedModelGenerator : public PacketGenerator { auto session = absl::make_unique(); session->session = std::move(saved_model->session); - RET_CHECK(!options.signature_name().empty()); + // Use input side packet to overwrite signature name in options. + std::string signature_name = + input_side_packets.HasTag(kStringSignatureName) + ? input_side_packets.Tag(kStringSignatureName).Get() + : options.signature_name(); + RET_CHECK(!signature_name.empty()); const auto& signature_def_map = saved_model->meta_graph_def.signature_def(); - const auto& signature_def = signature_def_map.at(options.signature_name()); + if (signature_def_map.find(signature_name) == signature_def_map.end()) { + return absl::NotFoundError(absl::StrFormat( + "Signature name '%s' does not exist in the loaded signature def", + signature_name)); + } + const auto& signature_def = signature_def_map.at(signature_name); for (const auto& input_signature : signature_def.inputs()) { session->tag_to_tensor_map[MaybeConvertSignatureToTag( input_signature.first, options)] = input_signature.second.name(); diff --git a/mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_generator_test.cc b/mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_generator_test.cc index 5c6de3e8..c002b1bd 100644 --- a/mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_generator_test.cc +++ b/mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_generator_test.cc @@ -30,11 +30,13 @@ namespace mediapipe { +using ::testing::status::StatusIs; namespace { namespace tf = ::tensorflow; constexpr char kStringSavedModelPathTag[] = "STRING_SAVED_MODEL_PATH"; +constexpr char kStringSignatureNameTag[] = "STRING_SIGNATURE_NAME"; constexpr char kSessionTag[] = "SESSION"; std::string GetSavedModelDir() { @@ -124,6 +126,48 @@ TEST_F(TensorFlowSessionFromSavedModelGeneratorTest, ASSERT_NE(session.session, nullptr); } +TEST_F(TensorFlowSessionFromSavedModelGeneratorTest, + CreateSessionFromSidePacketWithCorrectSignatureName) { + generator_options_->clear_saved_model_path(); + PacketSet input_side_packets( + tool::CreateTagMap({"STRING_SAVED_MODEL_PATH:saved_model_dir", + "STRING_SIGNATURE_NAME:signature_name"}) + .value()); + input_side_packets.Tag(kStringSavedModelPathTag) = + Adopt(new std::string(GetSavedModelDir())); + input_side_packets.Tag(kStringSignatureNameTag) = + Adopt(new std::string("serving_default")); + PacketSet output_side_packets( + tool::CreateTagMap({"SESSION:session"}).value()); + absl::Status run_status = tool::RunGenerateAndValidateTypes( + "TensorFlowSessionFromSavedModelGenerator", extendable_options_, + input_side_packets, &output_side_packets); + MP_EXPECT_OK(run_status) << run_status.message(); + const TensorFlowSession& session = + output_side_packets.Tag(kSessionTag).Get(); + // Session must be set. + ASSERT_NE(session.session, nullptr); +} + +TEST_F(TensorFlowSessionFromSavedModelGeneratorTest, + CreateSessionFromSidePacketWithWrongSignatureName) { + generator_options_->clear_saved_model_path(); + PacketSet input_side_packets( + tool::CreateTagMap({"STRING_SAVED_MODEL_PATH:saved_model_dir", + "STRING_SIGNATURE_NAME:signature_name"}) + .value()); + input_side_packets.Tag(kStringSavedModelPathTag) = + Adopt(new std::string(GetSavedModelDir())); + input_side_packets.Tag(kStringSignatureNameTag) = + Adopt(new std::string("wrong_signature_name")); + PacketSet output_side_packets( + tool::CreateTagMap({"SESSION:session"}).value()); + absl::Status run_status = tool::RunGenerateAndValidateTypes( + "TensorFlowSessionFromSavedModelGenerator", extendable_options_, + input_side_packets, &output_side_packets); + EXPECT_THAT(run_status, StatusIs(absl::StatusCode::kNotFound)); +} + // Integration test. Verifies that TensorFlowInferenceCalculator correctly // consumes the Packet emitted by this factory. TEST_F(TensorFlowSessionFromSavedModelGeneratorTest, diff --git a/mediapipe/model_maker/python/text/text_classifier/BUILD b/mediapipe/model_maker/python/text/text_classifier/BUILD index 35726367..0c35e796 100644 --- a/mediapipe/model_maker/python/text/text_classifier/BUILD +++ b/mediapipe/model_maker/python/text/text_classifier/BUILD @@ -21,6 +21,19 @@ package( licenses(["notice"]) +py_library( + name = "text_classifier_import", + srcs = ["__init__.py"], + deps = [ + ":dataset", + ":model_options", + ":model_spec", + ":text_classifier", + ":text_classifier_options", + "//mediapipe/model_maker/python/core:hyperparameters", + ], +) + py_library( name = "model_options", srcs = ["model_options.py"], @@ -114,12 +127,7 @@ py_test( ], tags = ["requires-net:external"], deps = [ - ":dataset", - ":model_options", - ":model_spec", - ":text_classifier", - ":text_classifier_options", - "//mediapipe/model_maker/python/core:hyperparameters", + ":text_classifier_import", "//mediapipe/tasks/python/test:test_utils", ], ) @@ -128,11 +136,7 @@ py_library( name = "text_classifier_demo_lib", srcs = ["text_classifier_demo.py"], deps = [ - ":dataset", - ":model_spec", - ":text_classifier", - ":text_classifier_options", - "//mediapipe/model_maker/python/core:hyperparameters", + ":text_classifier_import", "//mediapipe/model_maker/python/core/utils:quantization", ], ) diff --git a/mediapipe/model_maker/python/text/text_classifier/__init__.py b/mediapipe/model_maker/python/text/text_classifier/__init__.py index 7ca2f921..5f34fe86 100644 --- a/mediapipe/model_maker/python/text/text_classifier/__init__.py +++ b/mediapipe/model_maker/python/text/text_classifier/__init__.py @@ -11,3 +11,21 @@ # 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. +"""MediaPipe Public Python API for Text Classifier.""" + +from mediapipe.model_maker.python.core import hyperparameters +from mediapipe.model_maker.python.text.text_classifier import dataset +from mediapipe.model_maker.python.text.text_classifier import model_options +from mediapipe.model_maker.python.text.text_classifier import model_spec +from mediapipe.model_maker.python.text.text_classifier import text_classifier +from mediapipe.model_maker.python.text.text_classifier import text_classifier_options + +HParams = hyperparameters.BaseHParams +CSVParams = dataset.CSVParameters +Dataset = dataset.Dataset +AverageWordEmbeddingClassifierModelOptions = ( + model_options.AverageWordEmbeddingClassifierModelOptions) +BertClassifierModelOptions = model_options.BertClassifierModelOptions +SupportedModels = model_spec.SupportedModels +TextClassifier = text_classifier.TextClassifier +TextClassifierOptions = text_classifier_options.TextClassifierOptions diff --git a/mediapipe/model_maker/python/text/text_classifier/model_options.py b/mediapipe/model_maker/python/text/text_classifier/model_options.py index b48e38da..3dfce316 100644 --- a/mediapipe/model_maker/python/text/text_classifier/model_options.py +++ b/mediapipe/model_maker/python/text/text_classifier/model_options.py @@ -18,12 +18,12 @@ from typing import Union from mediapipe.model_maker.python.text.core import bert_model_options -# BERT text classifier options inherited from BertModelOptions. -BertClassifierOptions = bert_model_options.BertModelOptions +# BERT text classifier model options inherited from BertModelOptions. +BertClassifierModelOptions = bert_model_options.BertModelOptions @dataclasses.dataclass -class AverageWordEmbeddingClassifierOptions: +class AverageWordEmbeddingClassifierModelOptions: """Configurable model options for an Average Word Embedding classifier. Attributes: @@ -41,5 +41,5 @@ class AverageWordEmbeddingClassifierOptions: dropout_rate: float = 0.2 -TextClassifierModelOptions = Union[AverageWordEmbeddingClassifierOptions, - BertClassifierOptions] +TextClassifierModelOptions = Union[AverageWordEmbeddingClassifierModelOptions, + BertClassifierModelOptions] diff --git a/mediapipe/model_maker/python/text/text_classifier/model_spec.py b/mediapipe/model_maker/python/text/text_classifier/model_spec.py index c2694786..1e215c52 100644 --- a/mediapipe/model_maker/python/text/text_classifier/model_spec.py +++ b/mediapipe/model_maker/python/text/text_classifier/model_spec.py @@ -38,8 +38,8 @@ class AverageWordEmbeddingClassifierSpec: # `learning_rate` is unused for the average word embedding model hparams: hp.BaseHParams = hp.BaseHParams( epochs=10, batch_size=32, learning_rate=0) - model_options: mo.AverageWordEmbeddingClassifierOptions = ( - mo.AverageWordEmbeddingClassifierOptions()) + model_options: mo.AverageWordEmbeddingClassifierModelOptions = ( + mo.AverageWordEmbeddingClassifierModelOptions()) name: str = 'AverageWordEmbedding' diff --git a/mediapipe/model_maker/python/text/text_classifier/model_spec_test.py b/mediapipe/model_maker/python/text/text_classifier/model_spec_test.py index 118b84fd..6cd5408b 100644 --- a/mediapipe/model_maker/python/text/text_classifier/model_spec_test.py +++ b/mediapipe/model_maker/python/text/text_classifier/model_spec_test.py @@ -40,7 +40,7 @@ class ModelSpecTest(tf.test.TestCase): }) self.assertEqual( model_spec_obj.model_options, - classifier_model_options.BertClassifierOptions( + classifier_model_options.BertClassifierModelOptions( seq_len=128, do_fine_tuning=True, dropout_rate=0.1)) self.assertEqual( model_spec_obj.hparams, @@ -57,7 +57,7 @@ class ModelSpecTest(tf.test.TestCase): self.assertEqual(model_spec_obj.name, 'AverageWordEmbedding') self.assertEqual( model_spec_obj.model_options, - classifier_model_options.AverageWordEmbeddingClassifierOptions( + classifier_model_options.AverageWordEmbeddingClassifierModelOptions( seq_len=256, wordvec_dim=16, do_lower_case=True, @@ -77,7 +77,7 @@ class ModelSpecTest(tf.test.TestCase): def test_custom_bert_spec(self): custom_bert_classifier_options = ( - classifier_model_options.BertClassifierOptions( + classifier_model_options.BertClassifierModelOptions( seq_len=512, do_fine_tuning=False, dropout_rate=0.3)) model_spec_obj = ( ms.SupportedModels.MOBILEBERT_CLASSIFIER.value( @@ -97,7 +97,7 @@ class ModelSpecTest(tf.test.TestCase): num_gpus=3, tpu='tpu/address') custom_average_word_embedding_model_options = ( - classifier_model_options.AverageWordEmbeddingClassifierOptions( + classifier_model_options.AverageWordEmbeddingClassifierModelOptions( seq_len=512, wordvec_dim=32, do_lower_case=False, diff --git a/mediapipe/model_maker/python/text/text_classifier/text_classifier.py b/mediapipe/model_maker/python/text/text_classifier/text_classifier.py index 919277b8..a6ad9ab5 100644 --- a/mediapipe/model_maker/python/text/text_classifier/text_classifier.py +++ b/mediapipe/model_maker/python/text/text_classifier/text_classifier.py @@ -51,12 +51,12 @@ def _validate(options: text_classifier_options.TextClassifierOptions): return if (isinstance(options.model_options, - mo.AverageWordEmbeddingClassifierOptions) and + mo.AverageWordEmbeddingClassifierModelOptions) and (options.supported_model != ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER)): raise ValueError("Expected AVERAGE_WORD_EMBEDDING_CLASSIFIER," f" got {options.supported_model}") - if (isinstance(options.model_options, mo.BertClassifierOptions) and + if (isinstance(options.model_options, mo.BertClassifierModelOptions) and (options.supported_model != ms.SupportedModels.MOBILEBERT_CLASSIFIER)): raise ValueError( f"Expected MOBILEBERT_CLASSIFIER, got {options.supported_model}") @@ -194,7 +194,7 @@ class _AverageWordEmbeddingClassifier(TextClassifier): _DELIM_REGEX_PATTERN = r"[^\w\']+" def __init__(self, model_spec: ms.AverageWordEmbeddingClassifierSpec, - model_options: mo.AverageWordEmbeddingClassifierOptions, + model_options: mo.AverageWordEmbeddingClassifierModelOptions, hparams: hp.BaseHParams, label_names: Sequence[str]): super().__init__(model_spec, hparams, label_names) self._model_options = model_options @@ -304,8 +304,8 @@ class _BertClassifier(TextClassifier): _INITIALIZER_RANGE = 0.02 def __init__(self, model_spec: ms.BertClassifierSpec, - model_options: mo.BertClassifierOptions, hparams: hp.BaseHParams, - label_names: Sequence[str]): + model_options: mo.BertClassifierModelOptions, + hparams: hp.BaseHParams, label_names: Sequence[str]): super().__init__(model_spec, hparams, label_names) self._model_options = model_options self._loss_function = tf.keras.losses.SparseCategoricalCrossentropy() diff --git a/mediapipe/model_maker/python/text/text_classifier/text_classifier_demo.py b/mediapipe/model_maker/python/text/text_classifier/text_classifier_demo.py index de6b8575..08f4c2ad 100644 --- a/mediapipe/model_maker/python/text/text_classifier/text_classifier_demo.py +++ b/mediapipe/model_maker/python/text/text_classifier/text_classifier_demo.py @@ -23,12 +23,8 @@ from absl import flags from absl import logging import tensorflow as tf -from mediapipe.model_maker.python.core import hyperparameters as hp from mediapipe.model_maker.python.core.utils import quantization -from mediapipe.model_maker.python.text.text_classifier import dataset as text_ds -from mediapipe.model_maker.python.text.text_classifier import model_spec as ms -from mediapipe.model_maker.python.text.text_classifier import text_classifier -from mediapipe.model_maker.python.text.text_classifier import text_classifier_options +from mediapipe.model_maker.python.text import text_classifier FLAGS = flags.FLAGS @@ -53,31 +49,34 @@ def download_demo_data(): def run(data_dir, export_dir=tempfile.mkdtemp(), - supported_model=ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER): + supported_model=( + text_classifier.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER)): """Runs demo.""" # Gets training data and validation data. - csv_params = text_ds.CSVParameters( + csv_params = text_classifier.CSVParams( text_column='sentence', label_column='label', delimiter='\t') - train_data = text_ds.Dataset.from_csv( + train_data = text_classifier.Dataset.from_csv( filename=os.path.join(os.path.join(data_dir, 'train.tsv')), csv_params=csv_params) - validation_data = text_ds.Dataset.from_csv( + validation_data = text_classifier.Dataset.from_csv( filename=os.path.join(os.path.join(data_dir, 'dev.tsv')), csv_params=csv_params) quantization_config = None - if supported_model == ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER: - hparams = hp.BaseHParams( + if (supported_model == + text_classifier.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER): + hparams = text_classifier.HParams( epochs=10, batch_size=32, learning_rate=0, export_dir=export_dir) # Warning: This takes extremely long to run on CPU - elif supported_model == ms.SupportedModels.MOBILEBERT_CLASSIFIER: + elif ( + supported_model == text_classifier.SupportedModels.MOBILEBERT_CLASSIFIER): quantization_config = quantization.QuantizationConfig.for_dynamic() - hparams = hp.BaseHParams( + hparams = text_classifier.HParams( epochs=3, batch_size=48, learning_rate=3e-5, export_dir=export_dir) # Fine-tunes the model. - options = text_classifier_options.TextClassifierOptions( + options = text_classifier.TextClassifierOptions( supported_model=supported_model, hparams=hparams) model = text_classifier.TextClassifier.create(train_data, validation_data, options) @@ -96,9 +95,10 @@ def main(_): export_dir = os.path.expanduser(FLAGS.export_dir) if FLAGS.supported_model == 'average_word_embedding': - supported_model = ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER + supported_model = ( + text_classifier.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER) elif FLAGS.supported_model == 'bert': - supported_model = ms.SupportedModels.MOBILEBERT_CLASSIFIER + supported_model = text_classifier.SupportedModels.MOBILEBERT_CLASSIFIER run(data_dir, export_dir, supported_model) diff --git a/mediapipe/model_maker/python/text/text_classifier/text_classifier_test.py b/mediapipe/model_maker/python/text/text_classifier/text_classifier_test.py index 41dbb464..55ffd6a7 100644 --- a/mediapipe/model_maker/python/text/text_classifier/text_classifier_test.py +++ b/mediapipe/model_maker/python/text/text_classifier/text_classifier_test.py @@ -18,12 +18,7 @@ import os import tensorflow as tf -from mediapipe.model_maker.python.core import hyperparameters as hp -from mediapipe.model_maker.python.text.text_classifier import dataset -from mediapipe.model_maker.python.text.text_classifier import model_options as mo -from mediapipe.model_maker.python.text.text_classifier import model_spec as ms -from mediapipe.model_maker.python.text.text_classifier import text_classifier -from mediapipe.model_maker.python.text.text_classifier import text_classifier_options +from mediapipe.model_maker.python.text import text_classifier from mediapipe.tasks.python.test import test_utils @@ -43,18 +38,23 @@ class TextClassifierTest(tf.test.TestCase): writer.writeheader() for label, text in labels_and_text: writer.writerow({'text': text, 'label': label}) - csv_params = dataset.CSVParameters(text_column='text', label_column='label') - all_data = dataset.Dataset.from_csv( + csv_params = text_classifier.CSVParams( + text_column='text', label_column='label') + all_data = text_classifier.Dataset.from_csv( filename=csv_file, csv_params=csv_params) return all_data.split(0.5) def test_create_and_train_average_word_embedding_model(self): train_data, validation_data = self._get_data() - options = text_classifier_options.TextClassifierOptions( - supported_model=ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER, - hparams=hp.BaseHParams(epochs=1, batch_size=1, learning_rate=0)) - average_word_embedding_classifier = text_classifier.TextClassifier.create( - train_data, validation_data, options) + options = ( + text_classifier.TextClassifierOptions( + supported_model=(text_classifier.SupportedModels + .AVERAGE_WORD_EMBEDDING_CLASSIFIER), + hparams=text_classifier.HParams( + epochs=1, batch_size=1, learning_rate=0))) + average_word_embedding_classifier = ( + text_classifier.TextClassifier.create(train_data, validation_data, + options)) _, accuracy = average_word_embedding_classifier.evaluate(validation_data) self.assertGreaterEqual(accuracy, 0.0) @@ -77,10 +77,11 @@ class TextClassifierTest(tf.test.TestCase): def test_create_and_train_bert(self): train_data, validation_data = self._get_data() - options = text_classifier_options.TextClassifierOptions( - supported_model=ms.SupportedModels.MOBILEBERT_CLASSIFIER, - model_options=mo.BertClassifierOptions(do_fine_tuning=False, seq_len=2), - hparams=hp.BaseHParams( + options = text_classifier.TextClassifierOptions( + supported_model=text_classifier.SupportedModels.MOBILEBERT_CLASSIFIER, + model_options=text_classifier.BertClassifierModelOptions( + do_fine_tuning=False, seq_len=2), + hparams=text_classifier.HParams( epochs=1, batch_size=1, learning_rate=3e-5, @@ -94,12 +95,13 @@ class TextClassifierTest(tf.test.TestCase): def test_label_mismatch(self): options = ( - text_classifier_options.TextClassifierOptions( - supported_model=ms.SupportedModels.MOBILEBERT_CLASSIFIER)) + text_classifier.TextClassifierOptions( + supported_model=( + text_classifier.SupportedModels.MOBILEBERT_CLASSIFIER))) train_tf_dataset = tf.data.Dataset.from_tensor_slices([[0]]) - train_data = dataset.Dataset(train_tf_dataset, 1, ['foo']) + train_data = text_classifier.Dataset(train_tf_dataset, 1, ['foo']) validation_tf_dataset = tf.data.Dataset.from_tensor_slices([[0]]) - validation_data = dataset.Dataset(validation_tf_dataset, 1, ['bar']) + validation_data = text_classifier.Dataset(validation_tf_dataset, 1, ['bar']) with self.assertRaisesRegex( ValueError, 'Training data label names .* not equal to validation data label names' @@ -111,9 +113,11 @@ class TextClassifierTest(tf.test.TestCase): train_data, validation_data = self._get_data() avg_options = ( - text_classifier_options.TextClassifierOptions( - supported_model=ms.SupportedModels.MOBILEBERT_CLASSIFIER, - model_options=mo.AverageWordEmbeddingClassifierOptions())) + text_classifier.TextClassifierOptions( + supported_model=( + text_classifier.SupportedModels.MOBILEBERT_CLASSIFIER), + model_options=( + text_classifier.AverageWordEmbeddingClassifierModelOptions()))) with self.assertRaisesRegex( ValueError, 'Expected AVERAGE_WORD_EMBEDDING_CLASSIFIER, got' ' SupportedModels.MOBILEBERT_CLASSIFIER'): @@ -121,10 +125,10 @@ class TextClassifierTest(tf.test.TestCase): avg_options) bert_options = ( - text_classifier_options.TextClassifierOptions( - supported_model=( - ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER), - model_options=mo.BertClassifierOptions())) + text_classifier.TextClassifierOptions( + supported_model=(text_classifier.SupportedModels + .AVERAGE_WORD_EMBEDDING_CLASSIFIER), + model_options=text_classifier.BertClassifierModelOptions())) with self.assertRaisesRegex( ValueError, 'Expected MOBILEBERT_CLASSIFIER, got' ' SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER'): diff --git a/mediapipe/python/BUILD b/mediapipe/python/BUILD index a0f416ba..dc8561da 100644 --- a/mediapipe/python/BUILD +++ b/mediapipe/python/BUILD @@ -89,6 +89,7 @@ cc_library( deps = [ "//mediapipe/tasks/cc/vision/gesture_recognizer:gesture_recognizer_graph", "//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph", + "//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph", "//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph", "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph", ] + select({ diff --git a/mediapipe/tasks/cc/text/text_classifier/BUILD b/mediapipe/tasks/cc/text/text_classifier/BUILD index b2e1bed2..52b0c0e4 100644 --- a/mediapipe/tasks/cc/text/text_classifier/BUILD +++ b/mediapipe/tasks/cc/text/text_classifier/BUILD @@ -85,6 +85,7 @@ cc_test( "@com_google_absl//absl/status:statusor", "@com_google_absl//absl/strings", "@com_google_absl//absl/strings:cord", + "@com_google_sentencepiece//src:sentencepiece_processor", "@org_tensorflow//tensorflow/lite/core/shims:cc_shims_test_util", ], ) diff --git a/mediapipe/tasks/cc/text/text_embedder/BUILD b/mediapipe/tasks/cc/text/text_embedder/BUILD index 33190236..e2e16c9c 100644 --- a/mediapipe/tasks/cc/text/text_embedder/BUILD +++ b/mediapipe/tasks/cc/text/text_embedder/BUILD @@ -82,6 +82,7 @@ cc_test( "@com_google_absl//absl/flags:flag", "@com_google_absl//absl/status", "@com_google_absl//absl/status:statusor", + "@com_google_sentencepiece//src:sentencepiece_processor", "@org_tensorflow//tensorflow/lite/core/shims:cc_shims_test_util", ], ) diff --git a/mediapipe/tasks/cc/text/tokenizers/BUILD b/mediapipe/tasks/cc/text/tokenizers/BUILD index 5ce08b2d..7f1ea284 100644 --- a/mediapipe/tasks/cc/text/tokenizers/BUILD +++ b/mediapipe/tasks/cc/text/tokenizers/BUILD @@ -83,6 +83,7 @@ cc_test( ":sentencepiece_tokenizer", "//mediapipe/framework/port:gtest_main", "//mediapipe/tasks/cc/core:utils", + "@com_google_sentencepiece//src:sentencepiece_processor", ], ) @@ -132,6 +133,7 @@ cc_test( "@com_google_absl//absl/status:statusor", "@com_google_absl//absl/strings", "@com_google_absl//absl/strings:cord", + "@com_google_sentencepiece//src:sentencepiece_processor", ], ) diff --git a/mediapipe/tasks/python/components/containers/embedding_result.py b/mediapipe/tasks/python/components/containers/embedding_result.py index 35882b9f..8ddbb3ae 100644 --- a/mediapipe/tasks/python/components/containers/embedding_result.py +++ b/mediapipe/tasks/python/components/containers/embedding_result.py @@ -14,7 +14,7 @@ """Embeddings data class.""" import dataclasses -from typing import Any, Optional, List +from typing import Optional, List import numpy as np from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2 @@ -36,29 +36,11 @@ class FloatEmbedding: values: np.ndarray - @doc_controls.do_not_generate_docs - def to_pb2(self) -> _FloatEmbeddingProto: - """Generates a FloatEmbedding protobuf object.""" - return _FloatEmbeddingProto(values=self.values) - @classmethod @doc_controls.do_not_generate_docs - def create_from_pb2( - cls, pb2_obj: _FloatEmbeddingProto) -> 'FloatEmbedding': + def create_from_pb2(cls, pb2_obj: _FloatEmbeddingProto) -> 'FloatEmbedding': """Creates a `FloatEmbedding` object from the given protobuf object.""" - return FloatEmbedding(values=np.array(pb2_obj.value_float, dtype=float)) - - def __eq__(self, other: Any) -> bool: - """Checks if this object is equal to the given object. - Args: - other: The object to be compared with. - Returns: - True if the objects are equal. - """ - if not isinstance(other, FloatEmbedding): - return False - - return self.to_pb2().__eq__(other.to_pb2()) + return FloatEmbedding(values=np.array(pb2_obj.values, dtype=float)) @dataclasses.dataclass @@ -71,30 +53,13 @@ class QuantizedEmbedding: values: np.ndarray - @doc_controls.do_not_generate_docs - def to_pb2(self) -> _QuantizedEmbeddingProto: - """Generates a QuantizedEmbedding protobuf object.""" - return _QuantizedEmbeddingProto(values=self.values) - @classmethod @doc_controls.do_not_generate_docs def create_from_pb2( cls, pb2_obj: _QuantizedEmbeddingProto) -> 'QuantizedEmbedding': """Creates a `QuantizedEmbedding` object from the given protobuf object.""" return QuantizedEmbedding( - values=np.array(bytearray(pb2_obj.value_string), dtype=np.uint8)) - - def __eq__(self, other: Any) -> bool: - """Checks if this object is equal to the given object. - Args: - other: The object to be compared with. - Returns: - True if the objects are equal. - """ - if not isinstance(other, QuantizedEmbedding): - return False - - return self.to_pb2().__eq__(other.to_pb2()) + values=np.array(bytearray(pb2_obj.values), dtype=np.uint8)) @dataclasses.dataclass @@ -113,58 +78,31 @@ class Embedding: head_index: Optional[int] = None head_name: Optional[str] = None - @doc_controls.do_not_generate_docs - def to_pb2(self) -> _EmbeddingProto: - """Generates a Embedding protobuf object.""" - - if self.embedding.dtype == float: - return _EmbeddingProto(float_embedding=self.embedding, - head_index=self.head_index, - head_name=self.head_name) - - elif self.embedding.dtype == np.uint8: - return _EmbeddingProto(quantized_embedding=bytes(self.embedding), - head_index=self.head_index, - head_name=self.head_name) - - else: - raise ValueError("Invalid dtype. Only float and np.uint8 are supported.") - @classmethod @doc_controls.do_not_generate_docs - def create_from_pb2( - cls, pb2_obj: _EmbeddingProto) -> 'Embedding': + def create_from_pb2(cls, pb2_obj: _EmbeddingProto) -> 'Embedding': """Creates a `Embedding` object from the given protobuf object.""" quantized_embedding = np.array( bytearray(pb2_obj.quantized_embedding.values)) float_embedding = np.array(pb2_obj.float_embedding.values, dtype=float) - if len(quantized_embedding) == 0: - return Embedding(embedding=float_embedding, - head_index=pb2_obj.head_index, - head_name=pb2_obj.head_name) + if not quantized_embedding: + return Embedding( + embedding=float_embedding, + head_index=pb2_obj.head_index, + head_name=pb2_obj.head_name) else: - return Embedding(embedding=quantized_embedding, - head_index=pb2_obj.head_index, - head_name=pb2_obj.head_name) - - def __eq__(self, other: Any) -> bool: - """Checks if this object is equal to the given object. - Args: - other: The object to be compared with. - Returns: - True if the objects are equal. - """ - if not isinstance(other, Embedding): - return False - - return self.to_pb2().__eq__(other.to_pb2()) + return Embedding( + embedding=quantized_embedding, + head_index=pb2_obj.head_index, + head_name=pb2_obj.head_name) @dataclasses.dataclass class EmbeddingResult: """Embedding results for a given embedder model. + Attributes: embeddings: A list of `Embedding` objects. timestamp_ms: The optional timestamp (in milliseconds) of the start of the @@ -178,33 +116,10 @@ class EmbeddingResult: embeddings: List[Embedding] timestamp_ms: Optional[int] = None - @doc_controls.do_not_generate_docs - def to_pb2(self) -> _EmbeddingResultProto: - """Generates a EmbeddingResult protobuf object.""" - return _EmbeddingResultProto( - embeddings=[ - embedding.to_pb2() for embedding in self.embeddings - ]) - @classmethod @doc_controls.do_not_generate_docs - def create_from_pb2( - cls, pb2_obj: _EmbeddingResultProto) -> 'EmbeddingResult': + def create_from_pb2(cls, pb2_obj: _EmbeddingResultProto) -> 'EmbeddingResult': """Creates a `EmbeddingResult` object from the given protobuf object.""" - return EmbeddingResult( - embeddings=[ - Embedding.create_from_pb2(embedding) - for embedding in pb2_obj.embeddings - ]) - - def __eq__(self, other: Any) -> bool: - """Checks if this object is equal to the given object. - Args: - other: The object to be compared with. - Returns: - True if the objects are equal. - """ - if not isinstance(other, EmbeddingResult): - return False - - return self.to_pb2().__eq__(other.to_pb2()) + return EmbeddingResult(embeddings=[ + Embedding.create_from_pb2(embedding) for embedding in pb2_obj.embeddings + ]) diff --git a/mediapipe/tasks/python/components/processors/embedder_options.py b/mediapipe/tasks/python/components/processors/embedder_options.py index dcd316dc..c86a9110 100644 --- a/mediapipe/tasks/python/components/processors/embedder_options.py +++ b/mediapipe/tasks/python/components/processors/embedder_options.py @@ -44,16 +44,14 @@ class EmbedderOptions: def to_pb2(self) -> _EmbedderOptionsProto: """Generates a EmbedderOptions protobuf object.""" return _EmbedderOptionsProto( - l2_normalize=self.l2_normalize, - quantize=self.quantize) + l2_normalize=self.l2_normalize, quantize=self.quantize) @classmethod @doc_controls.do_not_generate_docs def create_from_pb2(cls, pb2_obj: _EmbedderOptionsProto) -> 'EmbedderOptions': """Creates a `EmbedderOptions` object from the given protobuf object.""" return EmbedderOptions( - l2_normalize=pb2_obj.l2_normalize, - quantize=pb2_obj.quantize) + l2_normalize=pb2_obj.l2_normalize, quantize=pb2_obj.quantize) def __eq__(self, other: Any) -> bool: """Checks if this object is equal to the given object. diff --git a/mediapipe/tasks/python/components/utils/BUILD b/mediapipe/tasks/python/components/utils/BUILD index 50d4094c..b64d04c7 100644 --- a/mediapipe/tasks/python/components/utils/BUILD +++ b/mediapipe/tasks/python/components/utils/BUILD @@ -14,6 +14,8 @@ # Placeholder for internal Python strict library compatibility macro. +# Placeholder for internal Python strict library and test compatibility macro. + package(default_visibility = ["//mediapipe/tasks:internal"]) licenses(["notice"]) diff --git a/mediapipe/tasks/python/components/utils/cosine_similarity.py b/mediapipe/tasks/python/components/utils/cosine_similarity.py index d6102f0b..486c02ec 100644 --- a/mediapipe/tasks/python/components/utils/cosine_similarity.py +++ b/mediapipe/tasks/python/components/utils/cosine_similarity.py @@ -23,6 +23,8 @@ _EmbedderOptions = embedder_options.EmbedderOptions def _compute_cosine_similarity(u, v): + """Computes cosine similarity between two embeddings.""" + if len(u.embedding) <= 0: raise ValueError("Cannot compute cosing similarity on empty embeddings.") @@ -38,6 +40,7 @@ def _compute_cosine_similarity(u, v): def cosine_similarity(u: _Embedding, v: _Embedding) -> float: """Utility function to compute cosine similarity between two embedding. + May return an InvalidArgumentError if e.g. the feature vectors are of different types (quantized vs. float), have different sizes, or have an L2-norm of 0. @@ -45,6 +48,9 @@ def cosine_similarity(u: _Embedding, v: _Embedding) -> float: Args: u: An embedding. v: An embedding. + + Returns: + Cosine similarity value. """ if len(u.embedding) != len(v.embedding): raise ValueError(f"Cannot compute cosine similarity between embeddings " diff --git a/mediapipe/tasks/python/test/text/text_classifier_test.py b/mediapipe/tasks/python/test/text/text_classifier_test.py index e864cc02..8678d219 100644 --- a/mediapipe/tasks/python/test/text/text_classifier_test.py +++ b/mediapipe/tasks/python/test/text/text_classifier_test.py @@ -125,7 +125,7 @@ class ModelFileType(enum.Enum): FILE_NAME = 2 -class ImageClassifierTest(parameterized.TestCase): +class TextClassifierTest(parameterized.TestCase): def setUp(self): super().setUp() diff --git a/mediapipe/tasks/python/test/vision/BUILD b/mediapipe/tasks/python/test/vision/BUILD index a58474aa..06610742 100644 --- a/mediapipe/tasks/python/test/vision/BUILD +++ b/mediapipe/tasks/python/test/vision/BUILD @@ -58,6 +58,26 @@ py_test( ], ) +py_test( + name = "image_embedder_test", + srcs = ["image_embedder_test.py"], + data = [ + "//mediapipe/tasks/testdata/vision:test_images", + "//mediapipe/tasks/testdata/vision:test_models", + ], + deps = [ + "//mediapipe/python:_framework_bindings", + "//mediapipe/tasks/python/components/containers:embedding_result", + "//mediapipe/tasks/python/components/containers:rect", + "//mediapipe/tasks/python/components/processors:embedder_options", + "//mediapipe/tasks/python/core:base_options", + "//mediapipe/tasks/python/test:test_utils", + "//mediapipe/tasks/python/vision:image_embedder", + "//mediapipe/tasks/python/vision/core:image_processing_options", + "//mediapipe/tasks/python/vision/core:vision_task_running_mode", + ], +) + py_test( name = "image_segmenter_test", srcs = ["image_segmenter_test.py"], diff --git a/mediapipe/tasks/python/test/vision/image_embedder_test.py b/mediapipe/tasks/python/test/vision/image_embedder_test.py new file mode 100644 index 00000000..d28320d7 --- /dev/null +++ b/mediapipe/tasks/python/test/vision/image_embedder_test.py @@ -0,0 +1,402 @@ +# Copyright 2022 The MediaPipe Authors. All Rights Reserved. +# +# 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. +"""Tests for image embedder.""" + +import enum +import os +from unittest import mock + +from absl.testing import absltest +from absl.testing import parameterized +import numpy as np + +from mediapipe.python._framework_bindings import image as image_module +from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module +from mediapipe.tasks.python.components.containers import rect +from mediapipe.tasks.python.components.processors import embedder_options as embedder_options_module +from mediapipe.tasks.python.core import base_options as base_options_module +from mediapipe.tasks.python.test import test_utils +from mediapipe.tasks.python.vision import image_embedder +from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module +from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module + +ImageEmbedderResult = embedding_result_module.EmbeddingResult +_Rect = rect.Rect +_BaseOptions = base_options_module.BaseOptions +_EmbedderOptions = embedder_options_module.EmbedderOptions +_FloatEmbedding = embedding_result_module.FloatEmbedding +_QuantizedEmbedding = embedding_result_module.QuantizedEmbedding +_Embedding = embedding_result_module.Embedding +_Image = image_module.Image +_ImageEmbedder = image_embedder.ImageEmbedder +_ImageEmbedderOptions = image_embedder.ImageEmbedderOptions +_RUNNING_MODE = running_mode_module.VisionTaskRunningMode +_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions + +_MODEL_FILE = 'mobilenet_v3_small_100_224_embedder.tflite' +_BURGER_IMAGE_FILE = 'burger.jpg' +_BURGER_CROPPED_IMAGE_FILE = 'burger_crop.jpg' +_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision' +# Tolerance for embedding vector coordinate values. +_EPSILON = 1e-4 +# Tolerance for cosine similarity evaluation. +_SIMILARITY_TOLERANCE = 1e-6 + + +class ModelFileType(enum.Enum): + FILE_CONTENT = 1 + FILE_NAME = 2 + + +class ImageEmbedderTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.test_image = _Image.create_from_file( + test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, _BURGER_IMAGE_FILE))) + self.test_cropped_image = _Image.create_from_file( + test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, _BURGER_CROPPED_IMAGE_FILE))) + self.model_path = test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, _MODEL_FILE)) + + def test_create_from_file_succeeds_with_valid_model_path(self): + # Creates with default option and valid model file successfully. + with _ImageEmbedder.create_from_model_path(self.model_path) as embedder: + self.assertIsInstance(embedder, _ImageEmbedder) + + def test_create_from_options_succeeds_with_valid_model_path(self): + # Creates with options containing model file successfully. + base_options = _BaseOptions(model_asset_path=self.model_path) + options = _ImageEmbedderOptions(base_options=base_options) + with _ImageEmbedder.create_from_options(options) as embedder: + self.assertIsInstance(embedder, _ImageEmbedder) + + def test_create_from_options_fails_with_invalid_model_path(self): + with self.assertRaisesRegex( + RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'): + base_options = _BaseOptions( + model_asset_path='/path/to/invalid/model.tflite') + options = _ImageEmbedderOptions(base_options=base_options) + _ImageEmbedder.create_from_options(options) + + def test_create_from_options_succeeds_with_valid_model_content(self): + # Creates with options containing model content successfully. + with open(self.model_path, 'rb') as f: + base_options = _BaseOptions(model_asset_buffer=f.read()) + options = _ImageEmbedderOptions(base_options=base_options) + embedder = _ImageEmbedder.create_from_options(options) + self.assertIsInstance(embedder, _ImageEmbedder) + + def _check_embedding_value(self, result, expected_first_value): + # Check embedding first value. + self.assertAlmostEqual( + result.embeddings[0].embedding[0], expected_first_value, delta=_EPSILON) + + def _check_embedding_size(self, result, quantize, expected_embedding_size): + # Check embedding size. + self.assertLen(result.embeddings, 1) + embedding_result = result.embeddings[0] + self.assertLen(embedding_result.embedding, expected_embedding_size) + if quantize: + self.assertEqual(embedding_result.embedding.dtype, np.uint8) + else: + self.assertEqual(embedding_result.embedding.dtype, float) + + def _check_cosine_similarity(self, result0, result1, expected_similarity): + # Checks cosine similarity. + similarity = _ImageEmbedder.cosine_similarity(result0.embeddings[0], + result1.embeddings[0]) + self.assertAlmostEqual( + similarity, expected_similarity, delta=_SIMILARITY_TOLERANCE) + + @parameterized.parameters( + (False, False, False, ModelFileType.FILE_NAME, 0.925519, 1024, + (-0.2101883, -0.193027)), + (True, False, False, ModelFileType.FILE_NAME, 0.925519, 1024, + (-0.0142344, -0.0131606)), + # (False, True, False, ModelFileType.FILE_NAME, + # 0.926791, 1024, (229, 231)), + (False, False, True, ModelFileType.FILE_CONTENT, 0.999931, 1024, + (-0.195062, -0.193027))) + def test_embed(self, l2_normalize, quantize, with_roi, model_file_type, + expected_similarity, expected_size, expected_first_values): + # Creates embedder. + if model_file_type is ModelFileType.FILE_NAME: + base_options = _BaseOptions(model_asset_path=self.model_path) + elif model_file_type is ModelFileType.FILE_CONTENT: + with open(self.model_path, 'rb') as f: + model_content = f.read() + base_options = _BaseOptions(model_asset_buffer=model_content) + else: + # Should never happen + raise ValueError('model_file_type is invalid.') + + embedder_options = _EmbedderOptions( + l2_normalize=l2_normalize, quantize=quantize) + options = _ImageEmbedderOptions( + base_options=base_options, embedder_options=embedder_options) + embedder = _ImageEmbedder.create_from_options(options) + + image_processing_options = None + if with_roi: + # Region-of-interest in "burger.jpg" corresponding to "burger_crop.jpg". + roi = _Rect(left=0, top=0, right=0.833333, bottom=1) + image_processing_options = _ImageProcessingOptions(roi) + + # Extracts both embeddings. + image_result = embedder.embed(self.test_image, image_processing_options) + crop_result = embedder.embed(self.test_cropped_image) + + # Checks embeddings and cosine similarity. + expected_result0_value, expected_result1_value = expected_first_values + self._check_embedding_size(image_result, quantize, expected_size) + self._check_embedding_size(crop_result, quantize, expected_size) + self._check_embedding_value(image_result, expected_result0_value) + self._check_embedding_value(crop_result, expected_result1_value) + self._check_cosine_similarity(image_result, crop_result, + expected_similarity) + # Closes the embedder explicitly when the embedder is not used in + # a context. + embedder.close() + + @parameterized.parameters( + (False, False, ModelFileType.FILE_NAME, 0.925519), + (False, False, ModelFileType.FILE_CONTENT, 0.925519)) + def test_embed_in_context(self, l2_normalize, quantize, model_file_type, + expected_similarity): + # Creates embedder. + if model_file_type is ModelFileType.FILE_NAME: + base_options = _BaseOptions(model_asset_path=self.model_path) + elif model_file_type is ModelFileType.FILE_CONTENT: + with open(self.model_path, 'rb') as f: + model_content = f.read() + base_options = _BaseOptions(model_asset_buffer=model_content) + else: + # Should never happen + raise ValueError('model_file_type is invalid.') + + embedder_options = _EmbedderOptions( + l2_normalize=l2_normalize, quantize=quantize) + options = _ImageEmbedderOptions( + base_options=base_options, embedder_options=embedder_options) + + with _ImageEmbedder.create_from_options(options) as embedder: + # Extracts both embeddings. + image_result = embedder.embed(self.test_image) + crop_result = embedder.embed(self.test_cropped_image) + + # Checks cosine similarity. + self._check_cosine_similarity(image_result, crop_result, + expected_similarity) + + def test_missing_result_callback(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM) + with self.assertRaisesRegex(ValueError, + r'result callback must be provided'): + with _ImageEmbedder.create_from_options(options) as unused_embedder: + pass + + @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO)) + def test_illegal_result_callback(self, running_mode): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=running_mode, + result_callback=mock.MagicMock()) + with self.assertRaisesRegex(ValueError, + r'result callback should not be provided'): + with _ImageEmbedder.create_from_options(options) as unused_embedder: + pass + + def test_calling_embed_for_video_in_image_mode(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.IMAGE) + with _ImageEmbedder.create_from_options(options) as embedder: + with self.assertRaisesRegex(ValueError, + r'not initialized with the video mode'): + embedder.embed_for_video(self.test_image, 0) + + def test_calling_embed_async_in_image_mode(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.IMAGE) + with _ImageEmbedder.create_from_options(options) as embedder: + with self.assertRaisesRegex(ValueError, + r'not initialized with the live stream mode'): + embedder.embed_async(self.test_image, 0) + + def test_calling_embed_in_video_mode(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _ImageEmbedder.create_from_options(options) as embedder: + with self.assertRaisesRegex(ValueError, + r'not initialized with the image mode'): + embedder.embed(self.test_image) + + def test_calling_embed_async_in_video_mode(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _ImageEmbedder.create_from_options(options) as embedder: + with self.assertRaisesRegex(ValueError, + r'not initialized with the live stream mode'): + embedder.embed_async(self.test_image, 0) + + def test_embed_for_video_with_out_of_order_timestamp(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _ImageEmbedder.create_from_options(options) as embedder: + unused_result = embedder.embed_for_video(self.test_image, 1) + with self.assertRaisesRegex( + ValueError, r'Input timestamp must be monotonically increasing'): + embedder.embed_for_video(self.test_image, 0) + + def test_embed_for_video(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _ImageEmbedder.create_from_options(options) as embedder0, \ + _ImageEmbedder.create_from_options(options) as embedder1: + for timestamp in range(0, 300, 30): + # Extracts both embeddings. + image_result = embedder0.embed_for_video(self.test_image, timestamp) + crop_result = embedder1.embed_for_video(self.test_cropped_image, + timestamp) + # Checks cosine similarity. + self._check_cosine_similarity( + image_result, crop_result, expected_similarity=0.925519) + + def test_embed_for_video_succeeds_with_region_of_interest(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _ImageEmbedder.create_from_options(options) as embedder0, \ + _ImageEmbedder.create_from_options(options) as embedder1: + # Region-of-interest in "burger.jpg" corresponding to "burger_crop.jpg". + roi = _Rect(left=0, top=0, right=0.833333, bottom=1) + image_processing_options = _ImageProcessingOptions(roi) + + for timestamp in range(0, 300, 30): + # Extracts both embeddings. + image_result = embedder0.embed_for_video(self.test_image, timestamp, + image_processing_options) + crop_result = embedder1.embed_for_video(self.test_cropped_image, + timestamp) + + # Checks cosine similarity. + self._check_cosine_similarity( + image_result, crop_result, expected_similarity=0.999931) + + def test_calling_embed_in_live_stream_mode(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock()) + with _ImageEmbedder.create_from_options(options) as embedder: + with self.assertRaisesRegex(ValueError, + r'not initialized with the image mode'): + embedder.embed(self.test_image) + + def test_calling_embed_for_video_in_live_stream_mode(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock()) + with _ImageEmbedder.create_from_options(options) as embedder: + with self.assertRaisesRegex(ValueError, + r'not initialized with the video mode'): + embedder.embed_for_video(self.test_image, 0) + + def test_embed_async_calls_with_illegal_timestamp(self): + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock()) + with _ImageEmbedder.create_from_options(options) as embedder: + embedder.embed_async(self.test_image, 100) + with self.assertRaisesRegex( + ValueError, r'Input timestamp must be monotonically increasing'): + embedder.embed_async(self.test_image, 0) + + def test_embed_async_calls(self): + # Get the embedding result for the cropped image. + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.IMAGE) + with _ImageEmbedder.create_from_options(options) as embedder: + crop_result = embedder.embed(self.test_cropped_image) + + observed_timestamp_ms = -1 + + def check_result(result: ImageEmbedderResult, output_image: _Image, + timestamp_ms: int): + # Checks cosine similarity. + self._check_cosine_similarity( + result, crop_result, expected_similarity=0.925519) + self.assertTrue( + np.array_equal(output_image.numpy_view(), + self.test_image.numpy_view())) + self.assertLess(observed_timestamp_ms, timestamp_ms) + self.observed_timestamp_ms = timestamp_ms + + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=check_result) + with _ImageEmbedder.create_from_options(options) as embedder: + for timestamp in range(0, 300, 30): + embedder.embed_async(self.test_image, timestamp) + + def test_embed_async_succeeds_with_region_of_interest(self): + # Get the embedding result for the cropped image. + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.IMAGE) + with _ImageEmbedder.create_from_options(options) as embedder: + crop_result = embedder.embed(self.test_cropped_image) + + # Region-of-interest in "burger.jpg" corresponding to "burger_crop.jpg". + roi = _Rect(left=0, top=0, right=0.833333, bottom=1) + image_processing_options = _ImageProcessingOptions(roi) + observed_timestamp_ms = -1 + + def check_result(result: ImageEmbedderResult, output_image: _Image, + timestamp_ms: int): + # Checks cosine similarity. + self._check_cosine_similarity( + result, crop_result, expected_similarity=0.999931) + self.assertTrue( + np.array_equal(output_image.numpy_view(), + self.test_image.numpy_view())) + self.assertLess(observed_timestamp_ms, timestamp_ms) + self.observed_timestamp_ms = timestamp_ms + + options = _ImageEmbedderOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=check_result) + with _ImageEmbedder.create_from_options(options) as embedder: + for timestamp in range(0, 300, 30): + embedder.embed_async(self.test_image, timestamp, + image_processing_options) + + +if __name__ == '__main__': + absltest.main() diff --git a/mediapipe/tasks/python/vision/BUILD b/mediapipe/tasks/python/vision/BUILD index 97b92f2b..e94507ee 100644 --- a/mediapipe/tasks/python/vision/BUILD +++ b/mediapipe/tasks/python/vision/BUILD @@ -79,6 +79,29 @@ py_library( ], ) +py_library( + name = "image_embedder", + srcs = [ + "image_embedder.py", + ], + deps = [ + "//mediapipe/python:_framework_bindings", + "//mediapipe/python:packet_creator", + "//mediapipe/python:packet_getter", + "//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2", + "//mediapipe/tasks/cc/vision/image_embedder/proto:image_embedder_graph_options_py_pb2", + "//mediapipe/tasks/python/components/containers:embedding_result", + "//mediapipe/tasks/python/components/processors:embedder_options", + "//mediapipe/tasks/python/components/utils:cosine_similarity", + "//mediapipe/tasks/python/core:base_options", + "//mediapipe/tasks/python/core:optional_dependencies", + "//mediapipe/tasks/python/core:task_info", + "//mediapipe/tasks/python/vision/core:base_vision_task_api", + "//mediapipe/tasks/python/vision/core:image_processing_options", + "//mediapipe/tasks/python/vision/core:vision_task_running_mode", + ], +) + py_library( name = "gesture_recognizer", srcs = [ diff --git a/mediapipe/tasks/python/vision/__init__.py b/mediapipe/tasks/python/vision/__init__.py index 17593357..cf19813c 100644 --- a/mediapipe/tasks/python/vision/__init__.py +++ b/mediapipe/tasks/python/vision/__init__.py @@ -18,6 +18,7 @@ import mediapipe.tasks.python.vision.core import mediapipe.tasks.python.vision.gesture_recognizer import mediapipe.tasks.python.vision.hand_landmarker import mediapipe.tasks.python.vision.image_classifier +import mediapipe.tasks.python.vision.image_embedder import mediapipe.tasks.python.vision.image_segmenter import mediapipe.tasks.python.vision.object_detector @@ -29,6 +30,10 @@ HandLandmarkerOptions = hand_landmarker.HandLandmarkerOptions HandLandmarkerResult = hand_landmarker.HandLandmarkerResult ImageClassifier = image_classifier.ImageClassifier ImageClassifierOptions = image_classifier.ImageClassifierOptions +ImageClassifierResult = image_classifier.ImageClassifierResult +ImageEmbedder = image_embedder.ImageEmbedder +ImageEmbedderOptions = image_embedder.ImageEmbedderOptions +ImageEmbedderResult = image_embedder.ImageEmbedderResult ImageSegmenter = image_segmenter.ImageSegmenter ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions ObjectDetector = object_detector.ObjectDetector @@ -40,6 +45,7 @@ del core del gesture_recognizer del hand_landmarker del image_classifier +del image_embedder del image_segmenter del object_detector del mediapipe diff --git a/mediapipe/tasks/python/vision/image_embedder.py b/mediapipe/tasks/python/vision/image_embedder.py new file mode 100644 index 00000000..92204039 --- /dev/null +++ b/mediapipe/tasks/python/vision/image_embedder.py @@ -0,0 +1,309 @@ +# Copyright 2022 The MediaPipe Authors. All Rights Reserved. +# +# 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. +"""MediaPipe image embedder task.""" + +import dataclasses +from typing import Callable, Mapping, Optional + +from mediapipe.python import packet_creator +from mediapipe.python import packet_getter +from mediapipe.python._framework_bindings import image as image_module +from mediapipe.python._framework_bindings import packet as packet_module +from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2 +from mediapipe.tasks.cc.vision.image_embedder.proto import image_embedder_graph_options_pb2 +from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module +from mediapipe.tasks.python.components.processors import embedder_options +from mediapipe.tasks.python.components.utils import cosine_similarity +from mediapipe.tasks.python.core import base_options as base_options_module +from mediapipe.tasks.python.core import task_info as task_info_module +from mediapipe.tasks.python.core.optional_dependencies import doc_controls +from mediapipe.tasks.python.vision.core import base_vision_task_api +from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module +from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module + +ImageEmbedderResult = embedding_result_module.EmbeddingResult +_BaseOptions = base_options_module.BaseOptions +_ImageEmbedderGraphOptionsProto = image_embedder_graph_options_pb2.ImageEmbedderGraphOptions +_EmbedderOptions = embedder_options.EmbedderOptions +_RunningMode = running_mode_module.VisionTaskRunningMode +_TaskInfo = task_info_module.TaskInfo +_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions + +_EMBEDDINGS_OUT_STREAM_NAME = 'embeddings_out' +_EMBEDDINGS_TAG = 'EMBEDDINGS' +_IMAGE_IN_STREAM_NAME = 'image_in' +_IMAGE_OUT_STREAM_NAME = 'image_out' +_IMAGE_TAG = 'IMAGE' +_NORM_RECT_STREAM_NAME = 'norm_rect_in' +_NORM_RECT_TAG = 'NORM_RECT' +_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_embedder.ImageEmbedderGraph' +_MICRO_SECONDS_PER_MILLISECOND = 1000 + + +@dataclasses.dataclass +class ImageEmbedderOptions: + """Options for the image embedder task. + + Attributes: + base_options: Base options for the image embedder task. + running_mode: The running mode of the task. Default to the image mode. Image + embedder task has three running modes: 1) The image mode for embedding + image on single image inputs. 2) The video mode for embedding image on the + decoded frames of a video. 3) The live stream mode for embedding image on + a live stream of input data, such as from camera. + embedder_options: Options for the image embedder task. + result_callback: The user-defined result callback for processing live stream + data. The result callback should only be specified when the running mode + is set to the live stream mode. + """ + base_options: _BaseOptions + running_mode: _RunningMode = _RunningMode.IMAGE + embedder_options: _EmbedderOptions = _EmbedderOptions() + result_callback: Optional[Callable[ + [ImageEmbedderResult, image_module.Image, int], None]] = None + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _ImageEmbedderGraphOptionsProto: + """Generates an ImageEmbedderOptions protobuf object.""" + base_options_proto = self.base_options.to_pb2() + base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True + embedder_options_proto = self.embedder_options.to_pb2() + + return _ImageEmbedderGraphOptionsProto( + base_options=base_options_proto, + embedder_options=embedder_options_proto) + + +class ImageEmbedder(base_vision_task_api.BaseVisionTaskApi): + """Class that performs embedding extraction on images.""" + + @classmethod + def create_from_model_path(cls, model_path: str) -> 'ImageEmbedder': + """Creates an `ImageEmbedder` object from a TensorFlow Lite model and the default `ImageEmbedderOptions`. + + Note that the created `ImageEmbedder` instance is in image mode, for + embedding image on single image inputs. + + Args: + model_path: Path to the model. + + Returns: + `ImageEmbedder` object that's created from the model file and the default + `ImageEmbedderOptions`. + + Raises: + ValueError: If failed to create `ImageEmbedder` object from the provided + file such as invalid file path. + RuntimeError: If other types of error occurred. + """ + base_options = _BaseOptions(model_asset_path=model_path) + options = ImageEmbedderOptions( + base_options=base_options, running_mode=_RunningMode.IMAGE) + return cls.create_from_options(options) + + @classmethod + def create_from_options(cls, + options: ImageEmbedderOptions) -> 'ImageEmbedder': + """Creates the `ImageEmbedder` object from image embedder options. + + Args: + options: Options for the image embedder task. + + Returns: + `ImageEmbedder` object that's created from `options`. + + Raises: + ValueError: If failed to create `ImageEmbedder` object from + `ImageEmbedderOptions` such as missing the model. + RuntimeError: If other types of error occurred. + """ + + def packets_callback(output_packets: Mapping[str, packet_module.Packet]): + if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty(): + return + + embedding_result_proto = embeddings_pb2.EmbeddingResult() + embedding_result_proto.CopyFrom( + packet_getter.get_proto(output_packets[_EMBEDDINGS_OUT_STREAM_NAME])) + + image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME]) + timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp + options.result_callback( + ImageEmbedderResult.create_from_pb2(embedding_result_proto), image, + timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) + + task_info = _TaskInfo( + task_graph=_TASK_GRAPH_NAME, + input_streams=[ + ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]), + ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]), + ], + output_streams=[ + ':'.join([_EMBEDDINGS_TAG, _EMBEDDINGS_OUT_STREAM_NAME]), + ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]) + ], + task_options=options) + return cls( + task_info.generate_graph_config( + enable_flow_limiting=options.running_mode == + _RunningMode.LIVE_STREAM), options.running_mode, + packets_callback if options.result_callback else None) + + def embed( + self, + image: image_module.Image, + image_processing_options: Optional[_ImageProcessingOptions] = None + ) -> ImageEmbedderResult: + """Performs image embedding extraction on the provided MediaPipe Image. + + Extraction is performed on the region of interest specified by the `roi` + argument if provided, or on the entire image otherwise. + + Args: + image: MediaPipe Image. + image_processing_options: Options for image processing. + + Returns: + An embedding result object that contains a list of embeddings. + + Raises: + ValueError: If any of the input arguments is invalid. + RuntimeError: If image embedder failed to run. + """ + normalized_rect = self.convert_to_normalized_rect(image_processing_options) + output_packets = self._process_image_data({ + _IMAGE_IN_STREAM_NAME: + packet_creator.create_image(image), + _NORM_RECT_STREAM_NAME: + packet_creator.create_proto(normalized_rect.to_pb2()) + }) + + embedding_result_proto = embeddings_pb2.EmbeddingResult() + embedding_result_proto.CopyFrom( + packet_getter.get_proto(output_packets[_EMBEDDINGS_OUT_STREAM_NAME])) + + return ImageEmbedderResult.create_from_pb2(embedding_result_proto) + + def embed_for_video( + self, + image: image_module.Image, + timestamp_ms: int, + image_processing_options: Optional[_ImageProcessingOptions] = None + ) -> ImageEmbedderResult: + """Performs image embedding extraction on the provided video frames. + + Extraction is performed on the region of interested specified by the `roi` + argument if provided, or on the entire image otherwise. + + Only use this method when the ImageEmbedder is created with the video + running mode. It's required to provide the video frame's timestamp (in + milliseconds) along with the video frame. The input timestamps should be + monotonically increasing for adjacent calls of this method. + + Args: + image: MediaPipe Image. + timestamp_ms: The timestamp of the input video frame in milliseconds. + image_processing_options: Options for image processing. + + Returns: + An embedding result object that contains a list of embeddings. + + Raises: + ValueError: If any of the input arguments is invalid. + RuntimeError: If image embedder failed to run. + """ + normalized_rect = self.convert_to_normalized_rect(image_processing_options) + output_packets = self._process_video_data({ + _IMAGE_IN_STREAM_NAME: + packet_creator.create_image(image).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND), + _NORM_RECT_STREAM_NAME: + packet_creator.create_proto(normalized_rect.to_pb2()).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND) + }) + embedding_result_proto = embeddings_pb2.EmbeddingResult() + embedding_result_proto.CopyFrom( + packet_getter.get_proto(output_packets[_EMBEDDINGS_OUT_STREAM_NAME])) + + return ImageEmbedderResult.create_from_pb2(embedding_result_proto) + + def embed_async( + self, + image: image_module.Image, + timestamp_ms: int, + image_processing_options: Optional[_ImageProcessingOptions] = None + ) -> None: + """Sends live image data to embedder. + + The results will be available via the "result_callback" provided in the + ImageEmbedderOptions. Embedding extraction is performed on the region of + interested specified by the `roi` argument if provided, or on the entire + image otherwise. + + Only use this method when the ImageEmbedder is created with the live + stream running mode. The input timestamps should be monotonically increasing + for adjacent calls of this method. This method will return immediately after + the input image is accepted. The results will be available via the + `result_callback` provided in the `ImageEmbedderOptions`. The + `embed_async` method is designed to process live stream data such as + camera input. To lower the overall latency, image embedder may drop the + input images if needed. In other words, it's not guaranteed to have output + per input image. + + The `result_callback` provides: + - An embedding result object that contains a list of embeddings. + - The input image that the image embedder runs on. + - The input timestamp in milliseconds. + + Args: + image: MediaPipe Image. + timestamp_ms: The timestamp of the input image in milliseconds. + image_processing_options: Options for image processing. + + Raises: + ValueError: If the current input timestamp is smaller than what the image + embedder has already processed. + """ + normalized_rect = self.convert_to_normalized_rect(image_processing_options) + self._send_live_stream_data({ + _IMAGE_IN_STREAM_NAME: + packet_creator.create_image(image).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND), + _NORM_RECT_STREAM_NAME: + packet_creator.create_proto(normalized_rect.to_pb2()).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND) + }) + + @classmethod + def cosine_similarity(cls, u: embedding_result_module.Embedding, + v: embedding_result_module.Embedding) -> float: + """Utility function to compute cosine similarity between two embedding entries. + + May return an InvalidArgumentError if e.g. the feature vectors are of + different types (quantized vs. float), have different sizes, or have an + L2-norm of 0. + + Args: + u: An embedding entry. + v: An embedding entry. + + Returns: + The cosine similarity for the two embeddings. + + Raises: + ValueError: May return an error if e.g. the feature vectors are of + different types (quantized vs. float), have different sizes, or have + an L2-norm of 0. + """ + return cosine_similarity.cosine_similarity(u, v) diff --git a/mediapipe/tasks/web/BUILD b/mediapipe/tasks/web/BUILD index 8fe5f7fd..2c0ea57e 100644 --- a/mediapipe/tasks/web/BUILD +++ b/mediapipe/tasks/web/BUILD @@ -31,6 +31,7 @@ rollup_bundle( name = "audio_bundle", config_file = "rollup.config.mjs", entry_point = "audio.ts", + format = "cjs", output_dir = False, deps = [ ":audio_lib", @@ -67,6 +68,7 @@ rollup_bundle( name = "text_bundle", config_file = "rollup.config.mjs", entry_point = "text.ts", + format = "cjs", output_dir = False, deps = [ ":text_lib", @@ -103,6 +105,7 @@ rollup_bundle( name = "vision_bundle", config_file = "rollup.config.mjs", entry_point = "vision.ts", + format = "cjs", output_dir = False, deps = [ ":vision_lib", diff --git a/mediapipe/tasks/web/package.json b/mediapipe/tasks/web/package.json index 7726f1cb..d7d484ca 100644 --- a/mediapipe/tasks/web/package.json +++ b/mediapipe/tasks/web/package.json @@ -2,16 +2,15 @@ "name": "@mediapipe/tasks-__NAME__", "version": "__VERSION__", "description": "__DESCRIPTION__", - "main": "__NAME__bundle.js", - "module": "__NAME__bundle.js", + "main": "__NAME___bundle.js", + "module": "__NAME___bundle.js", "exports": { - ".": "./__NAME__bundle.js", - "./loader": "./wasm/__NAME__wasm_internal.js", - "./wasm": "./wasm/__NAME__wasm_internal.wasm" + ".": "./__NAME___bundle.js", + "./loader": "./wasm/__NAME___wasm_internal.js", + "./wasm": "./wasm/__NAME___wasm_internal.wasm" }, "author": "mediapipe@google.com", "license": "Apache-2.0", - "type": "module", "dependencies": { "google-protobuf": "^3.21.2" }, diff --git a/mediapipe/util/android/asset_manager_util.h b/mediapipe/util/android/asset_manager_util.h index 2d2582c2..5c963f92 100644 --- a/mediapipe/util/android/asset_manager_util.h +++ b/mediapipe/util/android/asset_manager_util.h @@ -32,17 +32,23 @@ namespace mediapipe { // Thin wrapper over AAssetManager provided by JNI. This class is meant to be // used as a singleton. -// Usage: Call InitializeFromActivity from a JNI function that has access to the -// Java activity in the Android application. This initializes the asset manager -// and now files bundled in the assets folder can be read using ReadFile(). +// +// Usage: Call one of Initialize* functions from a JNI function that has access +// to a context/activity/etc. This initializes the asset manager and now files +// bundled in the assets folder can be read using ReadFile(). +// +// NOTE: initialization should happen strictly once and guaranteed to complete +// before any possible use, otherwise it cannot be used safely across multiple +// threads. class AssetManager { public: AssetManager(const AssetManager&) = delete; AssetManager& operator=(const AssetManager&) = delete; - // Returns the asset manager if it has been set by a call to - // InitializeFromActivity, otherwise returns nullptr. + // Returns the asset manager if it has been set by one of Initialize* + // functions, otherwise returns nullptr. AAssetManager* GetAssetManager(); + // Returns true if AAssetManager was successfully initialized. bool InitializeFromAssetManager(JNIEnv* env, jobject local_asset_manager, const std::string& cache_dir_path); @@ -55,7 +61,7 @@ class AssetManager { const std::string& cache_dir_path); // Returns true if AAssetManager was successfully initialized. - ABSL_DEPRECATED("Use InitializeFromActivity instead.") + ABSL_DEPRECATED("Use one of alternate Initialize* functions instead.") bool InitializeFromAssetManager(JNIEnv* env, jobject local_asset_manager); // Returns true if AAssetManager was successfully initialized. @@ -79,12 +85,14 @@ class AssetManager { std::string* output); // Returns the path to the Android cache directory. Will be empty if - // InitializeFromActivity has not been called. + // AssetManager hasn't been initialized. const std::string& GetCacheDirPath(); // Caches the contents of the given asset as a file, and returns a path to // that file. This can be used to pass an asset to APIs that require a path // to a filesystem file. + // NOTE: this is _not_ thread-safe, e.g. if two threads are requesting the + // same file absl::StatusOr CachedFileFromAsset( const std::string& asset_path);