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Merge branch 'master' into text-embedder-python

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Kinar R 2022-11-12 21:08:38 +05:30 committed by GitHub
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33 changed files with 1199 additions and 292 deletions
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2
WORKSPACE
View File

@ -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(

6
docs/build_java_api_docs.py
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@ -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(

244
mediapipe/calculators/core/flow_limiter_calculator_test.cc
View File

@ -79,21 +79,25 @@ std::vector<T> PacketValues(const std::vector<Packet>& packets) {
return result;
}
template <typename T>
std::vector<Packet> MakePackets(std::vector<std::pair<Timestamp, T>> contents) {
std::vector<Packet> result;
for (auto& entry : contents) {
result.push_back(MakePacket<T>(entry.second).At(entry.first));
}
return result;
}
std::string SourceString(Timestamp t) {
return (t.IsSpecialValue())
? t.DebugString()
: absl::StrCat("Timestamp(", t.DebugString(), ")");
}
template <typename T>
std::string SourceString(Packet packet) {
std::ostringstream oss;
if (packet.IsEmpty()) {
oss << "Packet()";
} else {
oss << "MakePacket<" << MediaPipeTypeStringOrDemangled<T>() << ">("
<< packet.Get<T>() << ")";
}
oss << ".At(" << SourceString(packet.Timestamp()) << ")";
return oss.str();
}
template <typename PacketContainer, typename PacketContent>
class PacketsEqMatcher
: public ::testing::MatcherInterface<const PacketContainer&> {
@ -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<PacketContent>() != p2.Get<PacketContent>()) {
if (p1.Timestamp() != p2.Timestamp() || p1.IsEmpty() != p2.IsEmpty() ||
(!p1.IsEmpty() &&
p1.Get<PacketContent>() != p2.Get<PacketContent>())) {
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<PacketContent>() << "}"
<< (std::next(it) == packets.end() ? "}" : ", ");
*os << (it == packets.begin() ? "{" : "");
*os << SourceString<PacketContent>(packet);
*os << (std::next(it) == packets.end() ? "}" : ", ");
}
}
@ -144,7 +148,7 @@ class PacketsEqMatcher
};
template <typename PacketContainer, typename PacketContent>
::testing::Matcher<const PacketContainer&> PackestEq(
::testing::Matcher<const PacketContainer&> PacketsEq(
const PacketContainer& packets) {
return MakeMatcher(
new PacketsEqMatcher<PacketContainer, PacketContent>(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<std::vector<Packet>, bool>;
auto IntPackestEq = PackestEq<std::vector<Packet>, int>;
auto BoolPacketsEq = PacketsEq<std::vector<Packet>, bool>;
auto IntPacketsEq = PacketsEq<std::vector<Packet>, 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<Packet> 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<Packet> expected_allow = MakePackets<bool>( //
{{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<Packet> expected_allow = {
MakePacket<bool>(true).At(Timestamp(0)),
MakePacket<bool>(false).At(Timestamp(10000)),
MakePacket<bool>(true).At(Timestamp(20000)),
MakePacket<bool>(false).At(Timestamp(30000)),
MakePacket<bool>(true).At(Timestamp(40000)),
MakePacket<bool>(false).At(Timestamp(50000)),
MakePacket<bool>(false).At(Timestamp(60000)),
MakePacket<bool>(false).At(Timestamp(70000)),
MakePacket<bool>(false).At(Timestamp(80000)),
MakePacket<bool>(false).At(Timestamp(90000)),
MakePacket<bool>(false).At(Timestamp(100000)),
MakePacket<bool>(false).At(Timestamp(110000)),
MakePacket<bool>(false).At(Timestamp(120000)),
MakePacket<bool>(false).At(Timestamp(130000)),
MakePacket<bool>(false).At(Timestamp(140000)),
MakePacket<bool>(true).At(Timestamp(150000)),
MakePacket<bool>(false).At(Timestamp(160000)),
MakePacket<bool>(true).At(Timestamp(170000)),
MakePacket<bool>(false).At(Timestamp(180000)),
MakePacket<bool>(true).At(Timestamp(190000)),
MakePacket<bool>(false).At(Timestamp(200000)),
};
EXPECT_THAT(allow_packets_, BoolPacketsEq(expected_allow));
}
std::vector<Packet> StripBoundsUpdates(const std::vector<Packet>& packets,
Timestamp begin = Timestamp::Min(),
Timestamp end = Timestamp::Max()) {
std::vector<Packet> 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<std::vector<Packet>, bool>;
auto IntPackestEq = PackestEq<std::vector<Packet>, int>;
auto BoolPacketsEq = PacketsEq<std::vector<Packet>, bool>;
auto IntPacketsEq = PacketsEq<std::vector<Packet>, int>;
// Configure the test.
SetUpInputData();
SetUpSimulationClock();
CalculatorGraphConfig graph_config =
ParseTextProtoOrDie<CalculatorGraphConfig>(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<FlowLimiterCalculatorOptions>(
R"pb(
max_in_flight: 1 max_in_queue: 0 in_flight_timeout: 1000000 # 1s
)pb");
std::map<std::string, Packet> side_packets = {
{"limiter_options",
MakePacket<FlowLimiterCalculatorOptions>(limiter_options)},
// Fake processing lazy initialization time in microseconds.
{"warmup_time", MakePacket<int64>(22000)},
// Fake processing duration in microseconds.
{"sleep_time", MakePacket<int64>(22000)},
// The SimulationClock to count virtual elapsed time.
{"clock", MakePacket<mediapipe::Clock*>(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<Packet> 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<Packet> 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<int>(i).At(Timestamp(i * 10000))));
}
// The total count of out_2_packets after each input packet.
// std::vector<int> sizes_2 = {0, 0, 2, 2, 3, 3, 4, 4, 5, 5};
std::vector<int> 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<int>(i).At(Timestamp(i * 10000))));
}
MP_EXPECT_OK(graph_.AddPacketToInputStream("in_2", input_packets_[i]));
MP_EXPECT_OK(graph_.AddPacketToInputStream(
"auxiliary_input_2", MakePacket<int>(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<Packet> expected_output = {
input_packets_[2],
input_packets_[6],
MakePacket<int>(2).At(Timestamp(20000)),
Packet().At(Timestamp(40000)),
MakePacket<int>(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<Packet> expected_output_2 = {
input_packets_[1], input_packets_[2], input_packets_[3],
input_packets_[6], input_packets_[7],
std::vector<Packet> expected_auxiliary_output = {
Packet().At(Timestamp(9999)),
MakePacket<int>(1).At(Timestamp(10000)),
MakePacket<int>(2).At(Timestamp(20000)),
Packet().At(Timestamp(29999)),
MakePacket<int>(3).At(Timestamp(30000)),
Packet().At(Timestamp(40000)),
Packet().At(Timestamp(49999)),
Packet().At(Timestamp(50000)),
MakePacket<int>(6).At(Timestamp(60000)),
Packet().At(Timestamp(69999)),
MakePacket<int>(7).At(Timestamp(70000)),
Packet().At(Timestamp(80000)),
Packet().At(Timestamp(89999)),
};
EXPECT_THAT(out_2_packets, IntPackestEq(expected_output_2));
std::vector<Packet> actual_2 =
StripBoundsUpdates(out_2_packets, Timestamp(90000));
EXPECT_THAT(actual_2, IntPacketsEq(expected_auxiliary_output));
std::vector<Packet> expected_3 =
StripBoundsUpdates(expected_auxiliary_output, Timestamp(39999));
std::vector<Packet> actual_3 =
StripBoundsUpdates(out_3_packets, Timestamp(39999));
EXPECT_THAT(actual_3, IntPacketsEq(expected_3));
// Validate the ALLOW stream output.
std::vector<Packet> expected_allow =
MakePackets<bool>({{Timestamp(20000), 1},
{Timestamp(40000), 0},
{Timestamp(60000), 1},
{Timestamp(80000), 0}});
EXPECT_THAT(allow_packets_, BoolPackestEq(expected_allow));
std::vector<Packet> expected_allow = {
MakePacket<bool>(true).At(Timestamp(20000)),
MakePacket<bool>(false).At(Timestamp(40000)),
MakePacket<bool>(true).At(Timestamp(60000)),
MakePacket<bool>(false).At(Timestamp(80000)),
};
EXPECT_THAT(allow_packets_, BoolPacketsEq(expected_allow));
}
} // anonymous namespace

62
mediapipe/calculators/image/set_alpha_calculator.cc
View File

@ -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 <typename AlphaType>
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<uchar>(i);
const AlphaType* alpha_ptr = alpha_mat.ptr<AlphaType>(i);
uchar* out_ptr = output_mat.ptr<uchar>(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<AlphaType, uchar>::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<uchar>(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<ImageFrame>();
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<uchar>(i);
uchar* alpha_ptr = alpha_mat.ptr<uchar>(i);
uchar* out_ptr = output_mat.ptr<uchar>(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<float>(input_mat, alpha_mat, output_mat));
} else {
MP_RETURN_IF_ERROR(
MergeRGBA8Image<uchar>(input_mat, alpha_mat, output_mat));
}
} else {
const uchar alpha_value = std::min(std::max(0.0f, alpha_value_), 255.0f);

2
mediapipe/calculators/tensor/BUILD
View File

@ -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",
],
)

1
mediapipe/calculators/tensorflow/BUILD
View File

@ -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",

22
mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_generator.cc
View File

@ -14,6 +14,8 @@
#include <algorithm>
#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<std::string>();
}
// Set Signature_def.
if (input_side_packets->HasTag(kStringSignatureName)) {
input_side_packets->Tag(kStringSignatureName).Set<std::string>();
}
// A TensorFlow model loaded and ready for use along with tensor
output_side_packets->Tag(kSessionTag).Set<TensorFlowSession>();
return absl::OkStatus();
@ -146,9 +154,19 @@ class TensorFlowSessionFromSavedModelGenerator : public PacketGenerator {
auto session = absl::make_unique<TensorFlowSession>();
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<std::string>()
: 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();

44
mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_generator_test.cc
View File

@ -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<TensorFlowSession>();
// 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,

26
mediapipe/model_maker/python/text/text_classifier/BUILD
View File

@ -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",
],
)

18
mediapipe/model_maker/python/text/text_classifier/__init__.py
View File

@ -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

10
mediapipe/model_maker/python/text/text_classifier/model_options.py
View File

@ -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]

4
mediapipe/model_maker/python/text/text_classifier/model_spec.py
View File

@ -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'

8
mediapipe/model_maker/python/text/text_classifier/model_spec_test.py
View File

@ -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,

10
mediapipe/model_maker/python/text/text_classifier/text_classifier.py
View File

@ -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()

32
mediapipe/model_maker/python/text/text_classifier/text_classifier_demo.py
View File

@ -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)

60
mediapipe/model_maker/python/text/text_classifier/text_classifier_test.py
View File

@ -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'):

1
mediapipe/python/BUILD
View File

@ -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({

1
mediapipe/tasks/cc/text/text_classifier/BUILD
View File

@ -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",
],
)

1
mediapipe/tasks/cc/text/text_embedder/BUILD
View File

@ -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",
],
)

2
mediapipe/tasks/cc/text/tokenizers/BUILD
View File

@ -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",
],
)

123
mediapipe/tasks/python/components/containers/embedding_result.py
View File

@ -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
])

6
mediapipe/tasks/python/components/processors/embedder_options.py
View File

@ -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.

2
mediapipe/tasks/python/components/utils/BUILD
View File

@ -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"])

6
mediapipe/tasks/python/components/utils/cosine_similarity.py
View File

@ -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 "

2
mediapipe/tasks/python/test/text/text_classifier_test.py
View File

@ -125,7 +125,7 @@ class ModelFileType(enum.Enum):
FILE_NAME = 2
class ImageClassifierTest(parameterized.TestCase):
class TextClassifierTest(parameterized.TestCase):
def setUp(self):
super().setUp()

20
mediapipe/tasks/python/test/vision/BUILD
View File

@ -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"],

402
mediapipe/tasks/python/test/vision/image_embedder_test.py Normal file
View File

@ -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()

23
mediapipe/tasks/python/vision/BUILD
View File

@ -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 = [

6
mediapipe/tasks/python/vision/__init__.py
View File

@ -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

309
mediapipe/tasks/python/vision/image_embedder.py Normal file
View File

@ -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)

3
mediapipe/tasks/web/BUILD
View File

@ -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",

11
mediapipe/tasks/web/package.json
View File

@ -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"
},

22
mediapipe/util/android/asset_manager_util.h
View File

@ -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<std::string> CachedFileFromAsset(
const std::string& asset_path);