build(tvm_utility): remove download logic from CMake and update documentation

common/tvm_utility/.gitignore

@@ -1,2 +1 @@
-artifacts/**/*.jpg
-data/
+data/models

common/tvm_utility/README.md

@@ -50,35 +50,17 @@ error description.
 
 ### Neural Networks Provider
 
-This package also provides a utility to get pre-compiled neural networks to packages using them for their inference.
-
 The neural networks are compiled as part of the
 [Model Zoo](https://github.com/autowarefoundation/modelzoo/) CI pipeline and saved to an S3 bucket.
-This package exports cmake variables and functions for ease of access to those neural networks.
 
 The `get_neural_network` function creates an abstraction for the artifact management.
-The artifacts are saved under the source directory of the package making use of the function; under "data/".
-Priority is given to user-provided files, under "data/user/${MODEL_NAME}/".
-If there are no user-provided files, the function tries to reuse previously-downloaded artifacts.
-If there are no previously-downloaded artifacts, and if the `DOWNLOAD_ARTIFACTS` cmake variable is set, they will be downloaded from the bucket.
-Otherwise, nothing happens.
+Users should provide model files under "data/user/${MODEL_NAME}/". Otherwise, nothing happens and compilation of the package will be skipped.
 
 The structure inside of the source directory of the package making use of the function is as follow:
 
 ```{text}
 .
 ├── data
-│   ├── downloads
-│   │   ├── ${MODEL 1}-${ARCH 1}-{BACKEND 1}-{VERSION 1}.tar.gz
-│   │   ├── ...
-│   │   └── ${MODEL ...}-${ARCH ...}-{BACKEND ...}-{VERSION ...}.tar.gz
-│   ├── models
-│   │   ├── ${MODEL 1}
-│   │   │   ├── ...
-│   │   │   └── inference_engine_tvm_config.hpp
-│   │   ├── ...
-│   │   └── ${MODEL ...}
-│   │       └── ...
 │   └── user
 │       ├── ${MODEL 1}
 │       │   ├── deploy_graph.json
@@ -90,36 +72,21 @@ The structure inside of the source directory of the package making use of the fu
 │           └── ...
 ```
 
-The `inference_engine_tvm_config.hpp` file needed for compilation by dependent packages is made available under "data/models/${MODEL_NAME}/inference_engine_tvm_config.hpp".
+The `inference_engine_tvm_config.hpp` file needed for compilation by dependent packages should be available under "data/models/${MODEL_NAME}/inference_engine_tvm_config.hpp".
 Dependent packages can use the cmake `add_dependencies` function with the name provided in the `DEPENDENCY` output parameter of `get_neural_network` to ensure this file is created before it gets used.
 
 The other `deploy_*` files are installed to "models/${MODEL_NAME}/" under the `share` directory of the package.
 
-The target version to be downloaded can be overwritten by setting the `MODELZOO_VERSION` cmake variable.
-
-#### Assumptions / Known limits
-
-If several packages make use of the same neural network, it will be downloaded once per package.
-
-In case a requested artifact doesn't exist in the S3 bucket, the error message from ExternalProject is not explicit enough for the user to understand what went wrong.
-
-In case the user manually sets `MODELZOO_VERSION` to "latest", the archive will not be re-downloaded when it gets updated in the S3 bucket (it is not a problem for tagged versions as they are not expected to be updated).
-
 #### Inputs / Outputs
 
-Inputs:
-
-- `DOWNLOAD_ARTIFACTS` cmake variable; needs to be set to enable downloading the artifacts
-- `MODELZOO_VERSION` cmake variable; can be used to overwrite the default target version of downloads
-
 Outputs:
 
-- `get_neural_network` cmake function; can be used to get a neural network compiled for a specific backend
+- `get_neural_network` cmake function; create proper external dependency for a package with use of the model provided by the user.
 
 In/Out:
 
 - The `DEPENDENCY` argument of `get_neural_network` can be checked for the outcome of the function.
-  It is an empty string when the neural network couldn't be made available.
+  It is an empty string when the neural network wasn't provided by the user.
 
 ## Security considerations
 

common/tvm_utility/data/user/yolo_v2_tiny/inference_engine_tvm_config.hpp

@@ -0,0 +1,55 @@
+// Copyright 2021 Arm Limited and Contributors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "tvm_utility/pipeline.hpp"
+
+#ifndef COMMON__TVM_UTILITY__DATA__USER__YOLO_V2_TINY__INFERENCE_ENGINE_TVM_CONFIG_HPP_  // NOLINT
+#define COMMON__TVM_UTILITY__DATA__USER__YOLO_V2_TINY__INFERENCE_ENGINE_TVM_CONFIG_HPP_
+
+namespace model_zoo
+{
+namespace perception
+{
+namespace camera_obstacle_detection
+{
+namespace yolo_v2_tiny
+{
+namespace tensorflow_fp32_coco
+{
+
+static const tvm_utility::pipeline::InferenceEngineTVMConfig config{
+  {3, 0, 0},  // modelzoo_version
+
+  "yolo_v2_tiny",  // network_name
+  "llvm",          // network_backend
+
+  "deploy_lib.so",        // network_module_path
+  "deploy_graph.json",    // network_graph_path
+  "deploy_param.params",  // network_params_path
+
+  kDLCPU,  // tvm_device_type
+  0,       // tvm_device_id
+
+  {{"input", kDLFloat, 32, 1, {-1, 416, 416, 3}}},  // network_inputs
+
+  {{"output", kDLFloat, 32, 1, {1, 13, 13, 425}}}  // network_outputs
+};
+
+}  // namespace tensorflow_fp32_coco
+}  // namespace yolo_v2_tiny
+}  // namespace camera_obstacle_detection
+}  // namespace perception
+}  // namespace model_zoo
+#endif  // COMMON__TVM_UTILITY__DATA__USER__YOLO_V2_TINY__INFERENCE_ENGINE_TVM_CONFIG_HPP_
+        // NOLINT

common/tvm_utility/include/tvm_utility/pipeline.hpp

@@ -224,6 +224,76 @@ typedef struct
 class InferenceEngineTVM : public InferenceEngine
 {
 public:
+  explicit InferenceEngineTVM(
+    const InferenceEngineTVMConfig & config, const std::string & pkg_name,
+    const std::string & autoware_data_path)
+  : config_(config)
+  {
+    // Get full network path
+    std::string network_prefix =
+      autoware_data_path + pkg_name + "/models/" + config.network_name + "/";
+    std::string network_module_path = network_prefix + config.network_module_path;
+    std::string network_graph_path = network_prefix + config.network_graph_path;
+    std::string network_params_path = network_prefix + config.network_params_path;
+
+    // Load compiled functions
+    std::ifstream module(network_module_path);
+    if (!module.good()) {
+      throw std::runtime_error(
+        "File " + network_module_path + " specified in inference_engine_tvm_config.hpp not found");
+    }
+    module.close();
+    tvm::runtime::Module mod = tvm::runtime::Module::LoadFromFile(network_module_path);
+
+    // Load json graph
+    std::ifstream json_in(network_graph_path, std::ios::in);
+    if (!json_in.good()) {
+      throw std::runtime_error(
+        "File " + network_graph_path + " specified in inference_engine_tvm_config.hpp not found");
+    }
+    std::string json_data(
+      (std::istreambuf_iterator<char>(json_in)), std::istreambuf_iterator<char>());
+    json_in.close();
+
+    // Load parameters from binary file
+    std::ifstream params_in(network_params_path, std::ios::binary);
+    if (!params_in.good()) {
+      throw std::runtime_error(
+        "File " + network_params_path + " specified in inference_engine_tvm_config.hpp not found");
+    }
+    std::string params_data(
+      (std::istreambuf_iterator<char>(params_in)), std::istreambuf_iterator<char>());
+    params_in.close();
+
+    // Parameters need to be in TVMByteArray format
+    TVMByteArray params_arr;
+    params_arr.data = params_data.c_str();
+    params_arr.size = params_data.length();
+
+    // Create tvm runtime module
+    tvm::runtime::Module runtime_mod = (*tvm::runtime::Registry::Get("tvm.graph_executor.create"))(
+      json_data, mod, static_cast<uint32_t>(config.tvm_device_type), config.tvm_device_id);
+
+    // Load parameters
+    auto load_params = runtime_mod.GetFunction("load_params");
+    load_params(params_arr);
+
+    // Get set_input function
+    set_input = runtime_mod.GetFunction("set_input");
+
+    // Get the function which executes the network
+    execute = runtime_mod.GetFunction("run");
+
+    // Get the function to get output data
+    get_output = runtime_mod.GetFunction("get_output");
+
+    for (auto & output_config : config.network_outputs) {
+      output_.push_back(TVMArrayContainer(
+        output_config.node_shape, output_config.tvm_dtype_code, output_config.tvm_dtype_bits,
+        output_config.tvm_dtype_lanes, config.tvm_device_type, config.tvm_device_id));
+    }
+  }
+
   explicit InferenceEngineTVM(const InferenceEngineTVMConfig & config, const std::string & pkg_name)
   : config_(config)
   {

common/tvm_utility/test/yolo_v2_tiny/main.cpp

@@ -247,7 +247,9 @@ TEST(PipelineExamples, SimplePipeline)
   using PostPT = PostProcessorYoloV2Tiny;
 
   PrePT PreP{config};
-  IET IE{config, "tvm_utility"};
+  std::string home_dir = getenv("HOME");
+  std::string autoware_data = "/autoware_data/";
+  IET IE{config, "tvm_utility", home_dir + autoware_data};
   PostPT PostP{config};
 
   tvm_utility::pipeline::Pipeline<PrePT, IET, PostPT> pipeline(PreP, IE, PostP);

common/tvm_utility/tvm-utility-yolo-v2-tiny-tests.md

@@ -15,10 +15,10 @@ curl https://github.com/pjreddie/darknet/master/data/dog.jpg \
   > artifacts/yolo_v2_tiny/test_image_0.jpg
 ```
 
-1. Build and test with the `DOWNLOAD_ARTIFACTS` flag set.
+1. Build and test.
 
 ```sh
-colcon build --packages-up-to tvm_utility --cmake-args -DDOWNLOAD_ARTIFACTS=ON
+colcon build --packages-up-to tvm_utility
 colcon test --packages-select tvm_utility
 ```
 
@@ -28,5 +28,5 @@ Vulkan is supported by default by the tvm_vendor package.
 It can be selected by setting the `tvm_utility_BACKEND` variable:
 
 ```sh
-colcon build --packages-up-to tvm_utility --cmake-args -DDOWNLOAD_ARTIFACTS=ON -Dtvm_utility_BACKEND=vulkan
+colcon build --packages-up-to tvm_utility -Dtvm_utility_BACKEND=vulkan
 ```

common/tvm_utility/tvm_utility-extras.cmake

@@ -12,12 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Get user-provided variables
-set(DOWNLOAD_ARTIFACTS OFF CACHE BOOL "enable artifacts download")
-set(MODELZOO_VERSION "3.0.0-20221221" CACHE STRING "targeted ModelZoo version")
-
 #
-# Download the selected neural network if it is not already present on disk.
 # Make inference_engine_tvm_config.hpp available under "data/models/${MODEL_NAME}/".
 # Install the TVM artifacts to "share/${PROJECT_NAME}/models/".
 # Return the name of the custom target in the DEPENDENCY parameter.
@@ -34,7 +29,7 @@ function(get_neural_network MODEL_NAME MODEL_BACKEND DEPENDENCY)
   set(EXTERNALPROJECT_NAME ${MODEL_NAME}_${MODEL_BACKEND})
   set(PREPROCESSING "")
 
-  # Prioritize user-provided models.
+  # Use user-provided models.
   # cspell: ignore COPYONLY
   if(IS_DIRECTORY "${DATA_PATH}/user/${MODEL_NAME}")
     message(STATUS "Using user-provided model from ${DATA_PATH}/user/${MODEL_NAME}")
@@ -54,27 +49,10 @@ function(get_neural_network MODEL_NAME MODEL_BACKEND DEPENDENCY)
     set(SOURCE_DIR "${DATA_PATH}/user/${MODEL_NAME}")
     set(INSTALL_DIRECTORY "${DATA_PATH}/user/${MODEL_NAME}")
   else()
-    set(ARCHIVE_NAME "${MODEL_NAME}-${CMAKE_SYSTEM_PROCESSOR}-${MODEL_BACKEND}-${MODELZOO_VERSION}.tar.gz")
-
-    # Use previously-downloaded archives if available.
-    set(DOWNLOAD_DIR "${DATA_PATH}/downloads")
-    if(DOWNLOAD_ARTIFACTS)
-      message(STATUS "Downloading ${ARCHIVE_NAME} ...")
-      if(NOT EXISTS "${DATA_PATH}/downloads/${ARCHIVE_NAME}")
-        set(URL "https://autoware-modelzoo.s3.us-east-2.amazonaws.com/models/${MODELZOO_VERSION}/${ARCHIVE_NAME}")
-        file(DOWNLOAD ${URL} "${DOWNLOAD_DIR}/${ARCHIVE_NAME}")
-      endif()
-    else()
-      message(WARNING "Skipped download for ${MODEL_NAME} (enable by setting DOWNLOAD_ARTIFACTS)")
-      set(${DEPENDENCY} "" PARENT_SCOPE)
-      return()
-    endif()
-    set(SOURCE_DIR "${DATA_PATH}/models/${MODEL_NAME}")
-    set(INSTALL_DIRECTORY "${DATA_PATH}/models/${MODEL_NAME}")
-    file(ARCHIVE_EXTRACT INPUT "${DOWNLOAD_DIR}/${ARCHIVE_NAME}" DESTINATION "${SOURCE_DIR}")
-    if(EXISTS "${DATA_PATH}/models/${MODEL_NAME}/preprocessing_inference_engine_tvm_config.hpp")
-      set(PREPROCESSING "${DATA_PATH}/models/${MODEL_NAME}/preprocessing_inference_engine_tvm_config.hpp")
-    endif()
+    message(WARNING " NO ${MODEL_NAME} model provided by user, for more info check"
+    " https://autowarefoundation.github.io/autoware.universe/main/common/tvm_utility/")
+    set(${DEPENDENCY} "" PARENT_SCOPE)
+    return()
 
   endif()