Mkulakow/fixes

docs/using_cloud_storage.md

@@ -56,6 +56,8 @@ openvino/model_server:latest \
 Add the S3 path as the model_path and pass the credentials as environment variables to the Docker container.
 `S3_ENDPOINT` is optional for Amazon S3 storage and mandatory for MinIO and other S3-compatible storage types.
 
+When `S3_ENDPOINT` is set without a scheme, OVMS uses `http://` by default for compatibility with MinIO and other S3-compatible deployments. To force TLS, provide the endpoint as `https://host:port` explicitly.
+
 Example command with `s3://<bucket>/<model_path>:`
 
 ```bash

src/custom_nodes/common/custom_node_library_internal_manager.hpp

@@ -44,11 +44,12 @@ class CustomNodeLibraryInternalManager {
 
 template <typename T>
 bool get_buffer(ovms::custom_nodes_common::CustomNodeLibraryInternalManager* internalManager, T** buffer, const char* buffersQueueName, uint64_t byte_size) {
+    *buffer = nullptr;
     auto buffersQueue = internalManager->getBuffersQueue(buffersQueueName);
-    if (!(buffersQueue == nullptr)) {
+    if (buffersQueue != nullptr && byte_size <= buffersQueue->getSingleBufferSize()) {
         *buffer = static_cast<T*>(buffersQueue->getBuffer());
     }
-    if (*buffer == nullptr || buffersQueue == nullptr) {
+    if (*buffer == nullptr) {
         *buffer = (T*)malloc(byte_size);
         if (*buffer == nullptr) {
             return false;

src/custom_nodes/model_zoo_intel_object_detection/model_zoo_intel_object_detection.cpp

@@ -355,7 +355,9 @@ DLL_PUBLIC int execute(const struct CustomNodeTensor* inputs, int inputsCount, s
     NODE_ASSERT(boxes.size() == confidences.size(), "boxes and confidences are not equal length");
     if (boxes.size() > maxOutputBatch) {
         boxes.resize(maxOutputBatch);
+        detections.resize(maxOutputBatch);
         confidences.resize(maxOutputBatch);
+        labelIds.resize(maxOutputBatch);
     }
 
     CustomNodeLibraryInternalManager* internalManager = static_cast<CustomNodeLibraryInternalManager*>(customNodeLibraryInternalManager);

src/filesystem/s3filesystem.cpp

@@ -48,6 +48,21 @@ namespace ovms {
 namespace s3 = Aws::S3;
 namespace fs = std::filesystem;
 
+std::pair<std::string, Aws::Http::Scheme> S3FileSystem::parseEndpoint(const std::string& endpoint) {
+    std::string normalized = endpoint;
+    auto scheme = Aws::Http::Scheme::HTTP;
+
+    if (normalized.rfind("http://", 0) == 0) {
+        normalized = normalized.substr(7);
+        scheme = Aws::Http::Scheme::HTTP;
+    } else if (normalized.rfind("https://", 0) == 0) {
+        normalized = normalized.substr(8);
+        scheme = Aws::Http::Scheme::HTTPS;
+    }
+
+    return {normalized, scheme};
+}
+
 StatusCode S3FileSystem::parsePath(const std::string& path, std::string* bucket, std::string* object) {
     std::smatch sm;
 
@@ -127,12 +142,9 @@ S3FileSystem::S3FileSystem(const Aws::SDKOptions& options, const std::string& s3
         config.scheme = Aws::Http::Scheme::HTTP;
     }
     if (s3_endpoint != nullptr) {
-        std::string endpoint(s3_endpoint);
-        if (endpoint.rfind("http://") != std::string::npos) {
-            endpoint = endpoint.substr(7);
-        }
-        config.endpointOverride = Aws::String(endpoint.c_str());
-        config.scheme = Aws::Http::Scheme::HTTP;
+        auto parsed = S3FileSystem::parseEndpoint(s3_endpoint);
+        config.scheme = parsed.second;
+        config.endpointOverride = Aws::String(parsed.first.c_str());
     }
 
     if (!default_proxy.empty()) {

src/filesystem/s3filesystem.hpp

@@ -17,6 +17,7 @@
 
 #include <regex>
 #include <string>
+#include <utility>
 #include <vector>
 
 #include <aws/core/Aws.h>
@@ -29,6 +30,14 @@ namespace ovms {
 
 class S3FileSystem : public FileSystem {
 public:
+    /**
+     * @brief Resolve an S3-compatible endpoint into a clean host:port string and scheme.
+     *
+     * If no scheme is provided, HTTP is used by default for compatibility with MinIO
+     * and other S3-compatible deployments that expose plain HTTP endpoints.
+     */
+    static std::pair<std::string, Aws::Http::Scheme> parseEndpoint(const std::string& endpoint);
+
     /**
      * @brief Construct a new S3FileSystem object
      *

src/kfs_frontend/kfs_utils.cpp

@@ -16,6 +16,7 @@
 #include "kfs_utils.hpp"
 
 #include <algorithm>
+#include <cstring>
 #include <limits>
 #include <map>
 #include <memory>
@@ -323,11 +324,28 @@ Status buildShapeFromStringTensorRequest(const ::KFSRequest::InferInputTensor& s
 
 Status convertBinaryExtensionStringFromBufferToNativeOVTensor(const ::KFSRequest::InferInputTensor& src, ov::Tensor& tensor, const std::string* buffer) {
     std::vector<uint32_t> stringSizes;
-    uint32_t totalStringsLength = 0;
-    while (totalStringsLength + stringSizes.size() * sizeof(uint32_t) + sizeof(uint32_t) <= buffer->size()) {
-        uint32_t inputSize = *(reinterpret_cast<const uint32_t*>(buffer->data() + totalStringsLength + stringSizes.size() * sizeof(uint32_t)));
+    size_t totalStringsLength = 0;
+    while (true) {
+        const size_t headersLength = stringSizes.size() * sizeof(uint32_t);
+        if (headersLength > buffer->size()) {
+            break;
+        }
+        if (totalStringsLength > buffer->size() - headersLength) {
+            break;
+        }
+        const size_t currentOffset = totalStringsLength + headersLength;
+        if (buffer->size() - currentOffset < sizeof(uint32_t)) {
+            break;
+        }
+        uint32_t inputSize = 0;
+        std::memcpy(&inputSize, buffer->data() + currentOffset, sizeof(inputSize));
+        const size_t remainingStringBytes = buffer->size() - currentOffset - sizeof(uint32_t);
+        if (static_cast<size_t>(inputSize) > remainingStringBytes) {
+            SPDLOG_DEBUG("Input string format conversion failed");
+            return StatusCode::INVALID_STRING_INPUT;
+        }
         stringSizes.push_back(inputSize);
-        totalStringsLength += inputSize;
+        totalStringsLength += static_cast<size_t>(inputSize);
     }
     size_t batchSize = stringSizes.size();
     if ((totalStringsLength + batchSize * sizeof(uint32_t)) != buffer->size()) {
@@ -343,7 +361,12 @@ Status convertBinaryExtensionStringFromBufferToNativeOVTensor(const ::KFSRequest
     std::string* data = tensor.data<std::string>();
     size_t tensorStringsOffset = 0;
     for (size_t i = 0; i < stringSizes.size(); i++) {
-        data[i].assign(reinterpret_cast<const char*>(buffer->data() + (i + 1) * sizeof(uint32_t) + tensorStringsOffset), stringSizes[i]);
+        const size_t sourceOffset = (i + 1) * sizeof(uint32_t) + tensorStringsOffset;
+        if (sourceOffset > buffer->size() || static_cast<size_t>(stringSizes[i]) > buffer->size() - sourceOffset) {
+            SPDLOG_DEBUG("Input string format conversion failed");
+            return StatusCode::INVALID_STRING_INPUT;
+        }
+        data[i].assign(reinterpret_cast<const char*>(buffer->data() + sourceOffset), stringSizes[i]);
         tensorStringsOffset += stringSizes[i];
     }
     return StatusCode::OK;

src/tensor_conversion.cpp

@@ -72,13 +72,24 @@ shape_t getShapeFromImages(const std::vector<cv::Mat>& images, const TensorInfo&
 
 ov::Tensor createTensorFromMats(const std::vector<cv::Mat>& images, const TensorInfo& tensorInfo) {
     OVMS_PROFILE_FUNCTION();
+    if (images.empty()) {
+        return ov::Tensor();
+    }
     ov::Shape shape = getShapeFromImages(images, tensorInfo);
     ov::element::Type precision = tensorInfo.getOvPrecision();
     ov::Tensor tensor(precision, shape);
     char* ptr = (char*)tensor.data();
-    for (cv::Mat image : images) {
-        memcpy(ptr, (char*)image.data, image.total() * image.elemSize());
-        ptr += (image.total() * image.elemSize());
+    const size_t firstImageSizeBytes = images[0].total() * images[0].elemSize();
+    for (const auto& image : images) {
+        const size_t imageSizeBytes = image.total() * image.elemSize();
+        if (imageSizeBytes != firstImageSizeBytes) {
+            SPDLOG_DEBUG("Image conversion failed due to inconsistent image size in one batch. Expected bytes: {} current bytes: {}",
+                firstImageSizeBytes,
+                imageSizeBytes);
+            return ov::Tensor();
+        }
+        memcpy(ptr, (char*)image.data, imageSizeBytes);
+        ptr += imageSizeBytes;
     }
     return tensor;
 }

src/tensor_conversion.hpp

@@ -121,6 +121,14 @@ static Status convertTensorToMatsMatchingTensorInfo(const TensorType& src, std::
             image = std::move(imageResized);
         }
 
+        if (firstImage != nullptr && image.channels() != firstImage->channels()) {
+            SPDLOG_DEBUG("Binary data sent to input: {} has mixed channel count within one batch. First image channels: {} current image channels: {}",
+                tensorInfo.getMappedName(),
+                firstImage->channels(),
+                image.channels());
+            return StatusCode::INVALID_NO_OF_CHANNELS;
+        }
+
         // if (i == 0 && src.contents().bytes_contents_size() > 1) {
         //     // Multiply src.string_val_size() * image resolution * precision size
         // }

src/tensor_conversion_common.cpp

@@ -236,13 +236,24 @@ shape_t getShapeFromImages(const std::vector<cv::Mat>& images, const TensorInfo&
 }
 ov::Tensor createTensorFromMats(const std::vector<cv::Mat>& images, const TensorInfo& tensorInfo) {
     OVMS_PROFILE_FUNCTION();
+    if (images.empty()) {
+        return ov::Tensor();
+    }
     ov::Shape shape = getShapeFromImages(images, tensorInfo);
     ov::element::Type precision = tensorInfo.getOvPrecision();
     ov::Tensor tensor(precision, shape);
     char* ptr = (char*)tensor.data();
-    for (cv::Mat image : images) {
-        memcpy(ptr, (char*)image.data, image.total() * image.elemSize());
-        ptr += (image.total() * image.elemSize());
+    const size_t firstImageSizeBytes = images[0].total() * images[0].elemSize();
+    for (const auto& image : images) {
+        const size_t imageSizeBytes = image.total() * image.elemSize();
+        if (imageSizeBytes != firstImageSizeBytes) {
+            SPDLOG_DEBUG("Image conversion failed due to inconsistent image size in one batch. Expected bytes: {} current bytes: {}",
+                firstImageSizeBytes,
+                imageSizeBytes);
+            return ov::Tensor();
+        }
+        memcpy(ptr, (char*)image.data, imageSizeBytes);
+        ptr += imageSizeBytes;
     }
     return tensor;
 }

src/test/localfilesystem_test.cpp

@@ -27,6 +27,9 @@
 
 #include "src/filesystem/filesystem.hpp"
 #include "src/filesystem/localfilesystem.hpp"
+#if CLOUD_DISABLE == 0
+#include "src/filesystem/s3filesystem.hpp"
+#endif
 
 using namespace testing;
 using ::testing::UnorderedElementsAre;
@@ -54,6 +57,18 @@ static void createTmpFiles() {
     std::filesystem::create_directories(TMP_PATH / TMP_DIR2);
 }
 
+#if CLOUD_DISABLE == 0
+TEST(S3FileSystem, ParseEndpointUsesHttpByDefault) {
+    auto http_default = ovms::S3FileSystem::parseEndpoint("localhost:9000");
+    EXPECT_EQ(http_default.first, "localhost:9000");
+    EXPECT_EQ(http_default.second, Aws::Http::Scheme::HTTP);
+
+    auto https_explicit = ovms::S3FileSystem::parseEndpoint("https://localhost:9000");
+    EXPECT_EQ(https_explicit.first, "localhost:9000");
+    EXPECT_EQ(https_explicit.second, Aws::Http::Scheme::HTTPS);
+}
+#endif
+
 TEST(LocalFileSystem, FileExists) {
     ovms::LocalFileSystem lfs;
     bool exists = false;

src/test/node_library_manager_test.cpp

@@ -16,6 +16,9 @@
 #include <gmock/gmock.h>
 #include <gtest/gtest.h>
 
+#include <array>
+#include <vector>
+
 #include "../dags/custom_node_library_manager.hpp"
 #include "constructor_enabled_model_manager.hpp"
 #include "platform_utils.hpp"
@@ -109,6 +112,82 @@ TEST(NodeLibraryManagerTest, ErrorWhenLibraryPathNotEscaped) {
     EXPECT_EQ(status, StatusCode::PATH_INVALID);
 }
 
+TEST(NodeLibraryManagerTest, ModelZooObjectDetectionCapsAllOutputsToMaxOutputBatch) {
+    CustomNodeLibraryManager manager;
+    NodeLibrary library;
+    auto status = manager.loadLibrary("model_zoo_object_detection", getGenericFullPathForBazelOut("/ovms/bazel-bin/src/libcustom_node_model_zoo_intel_object_detection.so"));
+    ASSERT_EQ(status, StatusCode::OK);
+    ASSERT_EQ(manager.getLibrary("model_zoo_object_detection", library), StatusCode::OK);
+
+    std::array<CustomNodeParam, 7> params = {{{"original_image_height", "4"},
+        {"original_image_width", "4"},
+        {"target_image_height", "2"},
+        {"target_image_width", "2"},
+        {"confidence_threshold", "0.5"},
+        {"max_output_batch", "2"},
+        {"buffer_queue_size", "2"}}};
+
+    void* customNodeLibraryInternalManager = nullptr;
+    ASSERT_EQ(library.initialize(&customNodeLibraryInternalManager, params.data(), params.size()), 0);
+
+    std::vector<float> imageData(1 * 3 * 4 * 4, 1.0f);
+    std::vector<uint64_t> imageDims{1, 3, 4, 4};
+    CustomNodeTensor imageTensor{
+        "image",
+        reinterpret_cast<uint8_t*>(imageData.data()),
+        static_cast<uint64_t>(imageData.size() * sizeof(float)),
+        imageDims.data(),
+        imageDims.size(),
+        FP32};
+
+    const uint64_t detectionsCount = 5;
+    const uint64_t featuresCount = 7;
+    std::vector<float> detectionData(detectionsCount * featuresCount, 0.0f);
+    for (size_t i = 0; i < detectionsCount; ++i) {
+        auto* detection = detectionData.data() + i * featuresCount;
+        detection[0] = 0.0f;   // image_id
+        detection[1] = 1.0f;   // label_id
+        detection[2] = 0.99f;  // confidence
+        detection[3] = 0.1f;
+        detection[4] = 0.1f;
+        detection[5] = 0.9f;
+        detection[6] = 0.9f;
+    }
+    std::vector<uint64_t> detectionDims{1, 1, detectionsCount, featuresCount};
+    CustomNodeTensor detectionTensor{
+        "detection",
+        reinterpret_cast<uint8_t*>(detectionData.data()),
+        static_cast<uint64_t>(detectionData.size() * sizeof(float)),
+        detectionDims.data(),
+        detectionDims.size(),
+        FP32};
+
+    std::array<CustomNodeTensor, 2> inputs{imageTensor, detectionTensor};
+    CustomNodeTensor* outputs = nullptr;
+    int outputsCount = 0;
+
+    ASSERT_EQ(library.execute(inputs.data(), inputs.size(), &outputs, &outputsCount, params.data(), params.size(), customNodeLibraryInternalManager), 0);
+    ASSERT_NE(outputs, nullptr);
+    ASSERT_EQ(outputsCount, 4);
+
+    constexpr uint64_t maxOutputBatch = 2;
+    for (int i = 0; i < outputsCount; ++i) {
+        ASSERT_NE(outputs[i].dims, nullptr);
+        ASSERT_GT(outputs[i].dimsCount, 0);
+        EXPECT_EQ(outputs[i].dims[0], maxOutputBatch);
+    }
+    EXPECT_EQ(outputs[1].dataBytes, sizeof(int32_t) * 4 * maxOutputBatch);  // coordinates
+    EXPECT_EQ(outputs[2].dataBytes, sizeof(float) * maxOutputBatch);        // confidences
+    EXPECT_EQ(outputs[3].dataBytes, sizeof(int32_t) * maxOutputBatch);      // label_ids
+
+    for (int i = 0; i < outputsCount; ++i) {
+        library.release(outputs[i].data, customNodeLibraryInternalManager);
+        library.release(outputs[i].dims, customNodeLibraryInternalManager);
+    }
+    library.release(outputs, customNodeLibraryInternalManager);
+    EXPECT_EQ(library.deinitialize(customNodeLibraryInternalManager), 0);
+}
+
 class ModelManagerNodeLibraryTest : public TestWithTempDir {};
 
 TEST_F(ModelManagerNodeLibraryTest, LoadCustomNodeLibrary) {