mirror of https://github.com/open-mmlab/mmcv.git
add note
parent
1ab29de7eb
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e22740b1d6
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@ -174,6 +174,8 @@ For more details, please refer the the following tables.
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</table>
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</table>
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**Note**: The pre-built packages provided above do not include all versions of mmcv-full, we can click on the corresponding links to see the supported versions. For example, if you click [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html), you can see that `cu102-torch1.8.0` only provides 1.3.0 and above versions of mmcv-full.
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Another way is to compile locally by running
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Another way is to compile locally by running
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```python
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```python
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@ -171,6 +171,8 @@ pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/t
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</tbody>
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</tbody>
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</table>
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</table>
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**注意**:以上提供的预编译包并不囊括所有的 mmcv-full 版本,我们可以点击对应链接查看支持的版本。例如,点击 [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html),可以看到 `cu102-torch1.8.0` 只提供了 1.3.0 及以上的 mmcv-full 版本。
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除了使用预编译包之外,另一种方式是在本地进行编译,直接运行下述命令
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除了使用预编译包之外,另一种方式是在本地进行编译,直接运行下述命令
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```python
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```python
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@ -48,7 +48,7 @@ to install ``mmcv-full==1.3.9`` with ``CUDA 11.1`` and ``PyTorch 1.9.0``, use th
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pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
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pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
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```
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```
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```{tip}
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```{note}
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mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility
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mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility
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usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you
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usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you
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can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well.
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can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well.
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@ -142,6 +142,10 @@ For more details, please refer the the following tables.
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</tbody>
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</tbody>
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</table>
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</table>
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```{note}
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The pre-built packages provided above do not include all versions of mmcv-full, we can click on the corresponding links to see the supported versions. For example, if you click [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html), you can see that `cu102-torch1.8.0` only provides 1.3.0 and above versions of mmcv-full.
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```
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Another way is to compile locally by running
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Another way is to compile locally by running
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```python
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```python
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@ -5,7 +5,9 @@ MMCV 有两个版本:
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- **mmcv-full**: 完整版,包含所有的特性以及丰富的开箱即用的 CUDA 算子。注意完整版本可能需要更长时间来编译。
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- **mmcv-full**: 完整版,包含所有的特性以及丰富的开箱即用的 CUDA 算子。注意完整版本可能需要更长时间来编译。
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- **mmcv**: 精简版,不包含 CUDA 算子但包含其余所有特性和功能,类似 MMCV 1.0 之前的版本。如果你不需要使用 CUDA 算子的话,精简版可以作为一个考虑选项。
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- **mmcv**: 精简版,不包含 CUDA 算子但包含其余所有特性和功能,类似 MMCV 1.0 之前的版本。如果你不需要使用 CUDA 算子的话,精简版可以作为一个考虑选项。
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**注意**: 请不要在同一个环境中安装两个版本,否则可能会遇到类似 `ModuleNotFound` 的错误。在安装一个版本之前,需要先卸载另一个。`如果CUDA可用,强烈推荐安装mmcv-full`。
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```{warning}
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请不要在同一个环境中安装两个版本,否则可能会遇到类似 `ModuleNotFound` 的错误。在安装一个版本之前,需要先卸载另一个。`如果CUDA可用,强烈推荐安装mmcv-full`。
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```
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a. 安装完整版
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a. 安装完整版
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@ -27,7 +29,7 @@ pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{
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pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
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pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
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```
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```
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```{tip}
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```{note}
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PyTorch 在 1.x.0 和 1.x.1 之间通常是兼容的,故 mmcv-full 只提供 1.x.0 的编译包。如果你
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PyTorch 在 1.x.0 和 1.x.1 之间通常是兼容的,故 mmcv-full 只提供 1.x.0 的编译包。如果你
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的 PyTorch 版本是 1.x.1,你可以放心地安装在 1.x.0 版本编译的 mmcv-full。例如,如果你的
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的 PyTorch 版本是 1.x.1,你可以放心地安装在 1.x.0 版本编译的 mmcv-full。例如,如果你的
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PyTorch 版本是 1.8.1、CUDA 版本是 11.1,你可以使用以下命令安装 mmcv-full。
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PyTorch 版本是 1.8.1、CUDA 版本是 11.1,你可以使用以下命令安装 mmcv-full。
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@ -136,6 +138,10 @@ pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/t
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</tbody>
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</tbody>
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</table>
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</table>
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```{note}
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以上提供的预编译包并不囊括所有的 mmcv-full 版本,我们可以点击对应链接查看支持的版本。例如,点击 [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html),可以看到 `cu102-torch1.8.0` 只提供了 1.3.0 及以上的 mmcv-full 版本。
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```
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除了使用预编译包之外,另一种方式是在本地进行编译,直接运行下述命令
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除了使用预编译包之外,另一种方式是在本地进行编译,直接运行下述命令
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```python
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```python
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Reference in New Issue