| ### v2.0.0 |
|
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| The OpenMMLab team released a new generation of training engine [MMEngine](https://github.com/open-mmlab/mmengine) at the World Artificial Intelligence Conference on September 1, 2022. It is a foundational library for training deep learning models. Compared with MMCV, it provides a universal and powerful runner, an open architecture with a more unified interface, and a more customizable training process. |
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| The OpenMMLab team released MMCV v2.0.0 on April 6, 2023. In the 2.x version, it has the following significant changes: |
|
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| (1) It removed the following components: |
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| - `mmcv.fileio` module, removed in PR [#2179](https://github.com/open-mmlab/mmcv/pull/2179). FileIO module from mmengine will be used wherever required. |
| - `mmcv.runner`, `mmcv.parallel`, `mmcv. engine` and `mmcv.device`, removed in PR [#2216](https://github.com/open-mmlab/mmcv/pull/2216). |
| - All classes in `mmcv.utils` (eg `Config` and `Registry`) and many functions, removed in PR [#2217](https://github.com/open-mmlab/mmcv/pull/2217). Only a few functions related to mmcv are reserved. |
| - `mmcv.onnx`, `mmcv.tensorrt` modules and related functions, removed in PR [#2225](https://github.com/open-mmlab/mmcv/pull/2225). |
| - Removed all root registrars in MMCV and registered classes or functions to the [root registrar](https://github.com/open-mmlab/mmengine/blob/main/mmengine/registry/root.py) in MMEngine. |
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| (2) It added the [`mmcv.transforms`](https://github.com/open-mmlab/mmcv/tree/main/mmcv/transforms) data transformation module. |
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| (3) It renamed the package name **mmcv** to **mmcv-lite** and **mmcv-full** to **mmcv** in PR [#2235](https://github.com/open-mmlab/mmcv/pull/2235). Also, change the default value of the environment variable `MMCV_WITH_OPS` from 0 to 1. |
|
|
| <table class="docutils"> |
| <thead> |
| <tr> |
| <th align="center">MMCV < 2.0</th> |
| <th align="center">MMCV >= 2.0 </th> |
| <tbody> |
| <tr> |
| <td valign="top"> |
| |
| ```bash |
| # Contains ops, because the highest version of mmcv-full is less than 2.0.0, so there is no need to add version restrictions |
| pip install openmim |
| mim install mmcv-full |
| |
| # do not contain ops |
| pip install openmim |
| mim install "mmcv < 2.0.0" |
| ``` |
|
|
| </td> |
| <td valign="top"> |
|
|
| ```bash |
| # Contains ops |
| pip install openmim |
| mim install mmcv |
| |
| # Ops are not included, because the starting version of mmcv-lite is 2.0.0rc1, so there is no need to add version restrictions |
| pip install openmim |
| mim install mmcv-lite |
| ``` |
|
|
| </td> |
| </tr> |
| </thead> |
| </table> |
|
|
| ### v1.3.18 |
|
|
| Some ops have different implementations on different devices. Lots of macros and type checks are scattered in several files, which makes the code hard to maintain. For example: |
|
|
| ```c++ |
| if (input.device().is_cuda()) { |
| #ifdef MMCV_WITH_CUDA |
| CHECK_CUDA_INPUT(input); |
| CHECK_CUDA_INPUT(rois); |
| CHECK_CUDA_INPUT(output); |
| CHECK_CUDA_INPUT(argmax_y); |
| CHECK_CUDA_INPUT(argmax_x); |
| |
| roi_align_forward_cuda(input, rois, output, argmax_y, argmax_x, |
| aligned_height, aligned_width, spatial_scale, |
| sampling_ratio, pool_mode, aligned); |
| #else |
| AT_ERROR("RoIAlign is not compiled with GPU support"); |
| #endif |
| } else { |
| CHECK_CPU_INPUT(input); |
| CHECK_CPU_INPUT(rois); |
| CHECK_CPU_INPUT(output); |
| CHECK_CPU_INPUT(argmax_y); |
| CHECK_CPU_INPUT(argmax_x); |
| roi_align_forward_cpu(input, rois, output, argmax_y, argmax_x, |
| aligned_height, aligned_width, spatial_scale, |
| sampling_ratio, pool_mode, aligned); |
| } |
| ``` |
|
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| Registry and dispatcher are added to manage these implementations. |
|
|
| ```c++ |
| |
| void ROIAlignForwardCUDAKernelLauncher(Tensor input, Tensor rois, Tensor output, |
| Tensor argmax_y, Tensor argmax_x, |
| int aligned_height, int aligned_width, |
| float spatial_scale, int sampling_ratio, |
| int pool_mode, bool aligned); |
| |
| void roi_align_forward_cuda(Tensor input, Tensor rois, Tensor output, |
| Tensor argmax_y, Tensor argmax_x, |
| int aligned_height, int aligned_width, |
| float spatial_scale, int sampling_ratio, |
| int pool_mode, bool aligned) { |
| ROIAlignForwardCUDAKernelLauncher( |
| input, rois, output, argmax_y, argmax_x, aligned_height, aligned_width, |
| spatial_scale, sampling_ratio, pool_mode, aligned); |
| } |
| |
| // register cuda implementation |
| void roi_align_forward_impl(Tensor input, Tensor rois, Tensor output, |
| Tensor argmax_y, Tensor argmax_x, |
| int aligned_height, int aligned_width, |
| float spatial_scale, int sampling_ratio, |
| int pool_mode, bool aligned); |
| REGISTER_DEVICE_IMPL(roi_align_forward_impl, CUDA, roi_align_forward_cuda); |
| |
| // roi_align.cpp |
| // use the dispatcher to invoke different implementation depending on device type of input tensors. |
| void roi_align_forward_impl(Tensor input, Tensor rois, Tensor output, |
| Tensor argmax_y, Tensor argmax_x, |
| int aligned_height, int aligned_width, |
| float spatial_scale, int sampling_ratio, |
| int pool_mode, bool aligned) { |
| DISPATCH_DEVICE_IMPL(roi_align_forward_impl, input, rois, output, argmax_y, |
| argmax_x, aligned_height, aligned_width, spatial_scale, |
| sampling_ratio, pool_mode, aligned); |
| } |
| |
| ``` |
|
|
| ### v1.3.11 |
|
|
| In order to flexibly support more backends and hardwares like `NVIDIA GPUs` and `AMD GPUs`, the directory of `mmcv/ops/csrc` is refactored. Note that this refactoring will not affect the usage in API. For related information, please refer to [PR1206](https://github.com/open-mmlab/mmcv/pull/1206). |
|
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| The original directory was organized as follows. |
|
|
| ``` |
| . |
| ├── common_cuda_helper.hpp |
| ├── ops_cuda_kernel.cuh |
| ├── pytorch_cpp_helper.hpp |
| ├── pytorch_cuda_helper.hpp |
| ├── parrots_cpp_helper.hpp |
| ├── parrots_cuda_helper.hpp |
| ├── parrots_cudawarpfunction.cuh |
| ├── onnxruntime |
| │ ├── onnxruntime_register.h |
| │ ├── onnxruntime_session_options_config_keys.h |
| │ ├── ort_mmcv_utils.h |
| │ ├── ... |
| │ ├── onnx_ops.h |
| │ └── cpu |
| │ ├── onnxruntime_register.cpp |
| │ ├── ... |
| │ └── onnx_ops_impl.cpp |
| ├── parrots |
| │ ├── ... |
| │ ├── ops.cpp |
| │ ├── ops_cuda.cu |
| │ ├── ops_parrots.cpp |
| │ └── ops_pytorch.h |
| ├── pytorch |
| │ ├── ... |
| │ ├── ops.cpp |
| │ ├── ops_cuda.cu |
| │ ├── pybind.cpp |
| └── tensorrt |
| ├── trt_cuda_helper.cuh |
| ├── trt_plugin_helper.hpp |
| ├── trt_plugin.hpp |
| ├── trt_serialize.hpp |
| ├── ... |
| ├── trt_ops.hpp |
| └── plugins |
| ├── trt_cuda_helper.cu |
| ├── trt_plugin.cpp |
| ├── ... |
| ├── trt_ops.cpp |
| └── trt_ops_kernel.cu |
| ``` |
|
|
| After refactored, it is organized as follows. |
|
|
| ``` |
| . |
| ├── common |
| │ ├── box_iou_rotated_utils.hpp |
| │ ├── parrots_cpp_helper.hpp |
| │ ├── parrots_cuda_helper.hpp |
| │ ├── pytorch_cpp_helper.hpp |
| │ ├── pytorch_cuda_helper.hpp |
| │ └── cuda |
| │ ├── common_cuda_helper.hpp |
| │ ├── parrots_cudawarpfunction.cuh |
| │ ├── ... |
| │ └── ops_cuda_kernel.cuh |
| ├── onnxruntime |
| │ ├── onnxruntime_register.h |
| │ ├── onnxruntime_session_options_config_keys.h |
| │ ├── ort_mmcv_utils.h |
| │ ├── ... |
| │ ├── onnx_ops.h |
| │ └── cpu |
| │ ├── onnxruntime_register.cpp |
| │ ├── ... |
| │ └── onnx_ops_impl.cpp |
| ├── parrots |
| │ ├── ... |
| │ ├── ops.cpp |
| │ ├── ops_parrots.cpp |
| │ └── ops_pytorch.h |
| ├── pytorch |
| │ ├── info.cpp |
| │ ├── pybind.cpp |
| │ ├── ... |
| │ ├── ops.cpp |
| │ └── cuda |
| │ ├── ... |
| │ └── ops_cuda.cu |
| └── tensorrt |
| ├── trt_cuda_helper.cuh |
| ├── trt_plugin_helper.hpp |
| ├── trt_plugin.hpp |
| ├── trt_serialize.hpp |
| ├── ... |
| ├── trt_ops.hpp |
| └── plugins |
| ├── trt_cuda_helper.cu |
| ├── trt_plugin.cpp |
| ├── ... |
| ├── trt_ops.cpp |
| └── trt_ops_kernel.cu |
| ``` |
|
|
