[Docs] Fix pdf build (#1266)

* [Fix] Fix pdf error when building docs

* fix docs

* fix pdf

* fix pdf

* fix pdf

docs/get_started/introduction.md

@@ -4,8 +4,6 @@
     <img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/mmcv-logo.png" width="300"/>
 </div>
 
-[![PyPI](https://img.shields.io/pypi/v/mmcv)](https://pypi.org/project/mmcv) [![badge](https://github.com/open-mmlab/mmcv/workflows/build/badge.svg)](https://github.com/open-mmlab/mmcv/actions) [![codecov](https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmcv) [![license](https://img.shields.io/github/license/open-mmlab/mmcv.svg)](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
-
 MMCV is a foundational library for computer vision research and supports many
 research projects as below:
 

docs/index.rst

@@ -7,12 +7,12 @@ You can switch between Chinese and English documents in the lower-left corner of
    :maxdepth: 2
 
    get_started.rst
-   deployment.rst
    understand_mmcv.rst
-   api.rst
+   deployment.rst
    compatibility.md
    faq.md
    community.rst
+   api.rst
 
 
 Indices and tables

docs/understand_mmcv/runner.md

@@ -121,13 +121,13 @@ runner = build_runner(
 ```python
 # register defalt hooks neccesary for traning
 runner.register_training_hooks(
-    # configs of learning rate，it is typically set as:
+    # configs of learning rate, it is typically set as:
     # lr_config = dict(policy='step', step=[100, 150])
     cfg.lr_config,
     # configuration of optimizer, e.g. grad_clip
     optimizer_config,
     # configuration of saving checkpoints, it is typically set as:
-    # checkpoint_config = dict(interval=1)，saving checkpoints every epochs
+    # checkpoint_config = dict(interval=1), saving checkpoints every epochs
     cfg.checkpoint_config,
     # configuration of logs
     cfg.log_config,

docs/understand_mmcv/utils.md

@@ -18,7 +18,8 @@ mmcv.track_progress(func, tasks)
 ```
 
 The output is like the following.
-![progress](../_static/progress.gif)
+
+![progress](../_static/progress.*)
 
 There is another method `track_parallel_progress`, which wraps multiprocessing and
 progress visualization.
@@ -27,7 +28,7 @@ progress visualization.
 mmcv.track_parallel_progress(func, tasks, 8)  # 8 workers
 ```
 
-![progress](../_static/parallel_progress.gif)
+![progress](../_static/parallel_progress.*)
 
 If you want to iterate or enumerate a list of items and track the progress, `track_iter_progress`
 is a good choice. It will display a progress bar to tell the progress and ETA.

docs_zh_CN/get_started/introduction.md

@@ -4,8 +4,6 @@
     <img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/mmcv-logo.png" width="300"/>
 </div>
 
-[![PyPI](https://img.shields.io/pypi/v/mmcv)](https://pypi.org/project/mmcv) [![badge](https://github.com/open-mmlab/mmcv/workflows/build/badge.svg)](https://github.com/open-mmlab/mmcv/actions) [![codecov](https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmcv) [![license](https://img.shields.io/github/license/open-mmlab/mmcv.svg)](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
-
 ## 简介
 
 MMCV 是一个面向计算机视觉的基础库，它支持了很多开源项目，例如：

docs_zh_CN/index.rst

@@ -7,12 +7,12 @@
    :maxdepth: 2
 
    get_started.rst
-   deployment.rst
    understand_mmcv.rst
-   api.rst
+   deployment.rst
    compatibility.md
    faq.md
    community.rst
+   api.rst
 
 
 Indices and tables

mmcv/ops/deform_conv.py

@@ -268,7 +268,7 @@ class DeformConv2d(nn.Module):
         Args:
             x (Tensor): Input feature, shape (B, C_in, H_in, W_in)
             offset (Tensor): Offset for deformable convolution, shape
-                (B, deform_groups*kernel_size[0]*kernel_size[1]*2，
+                (B, deform_groups*kernel_size[0]*kernel_size[1]*2,
                 H_out, W_out), H_out, W_out are equal to the output's.
 
                 An offset is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`.

mmcv/ops/fused_bias_leakyrelu.py

@@ -196,7 +196,7 @@ class FusedBiasLeakyReLU(nn.Module):
     The bias term comes from the convolution operation. In addition, to keep
     the variance of the feature map or gradients unchanged, they also adopt a
     scale similarly with Kaiming initialization. However, since the
-    :math:`1 + \alpha^2` : is too small, we can just ignore it. Therefore, the
+    :math:`1+{alpha}^2` : is too small, we can just ignore it. Therefore, the
     final scale is just :math:`\sqrt{2}`:. Of course, you may change it with # noqa: W605, E501
     your own scale.
 
@@ -231,7 +231,7 @@ def fused_bias_leakyrelu(input, bias, negative_slope=0.2, scale=2**0.5):
     The bias term comes from the convolution operation. In addition, to keep
     the variance of the feature map or gradients unchanged, they also adopt a
     scale similarly with Kaiming initialization. However, since the
-    :math:`1 + \alpha^2` : is too small, we can just ignore it. Therefore, the
+    :math:`1+{alpha}^2` : is too small, we can just ignore it. Therefore, the
     final scale is just :math:`\sqrt{2}`:. Of course, you may change it with # noqa: W605, E501
     your own scale.
 

mmcv/runner/hooks/ema.py

@@ -13,8 +13,8 @@ class EMAHook(Hook):
 
         .. math::
 
-            \text{Xema_{t+1}} = (1 - \text{momentum}) \times
-            \text{Xema_{t}} +  \text{momentum} \times X_t
+            \text{Xema\_{t+1}} = (1 - \text{momentum}) \times
+            \text{Xema\_{t}} +  \text{momentum} \times X_t
 
     Args:
         momentum (float): The momentum used for updating ema parameter.