[Fix] update code in docs and fix broken urls (open-mmlab#3947)

docs/getting_started.md

@@ -43,7 +43,7 @@ python tools/convert_datasets/cityscapes.py ./data/cityscapes --nproc 8 --out-di
 Currently the config files in `cityscapes` use COCO pre-trained weights to initialize.
 You could download the pre-trained models in advance if network is unavailable or slow, otherwise it would cause errors at the beginning of training.
 
-For using custom datasets, please refer to [Tutorials 2: Adding New Dataset](tutorials/new_dataset.md).
+For using custom datasets, please refer to [Tutorials 2: Customize Datasets](tutorials/customize_dataset.md).
 
 ## Inference with pretrained models
 
@@ -461,5 +461,5 @@ If you need a lightweight GUI for visualizing the detection results, you can ref
 
 ## Tutorials
 
-Currently, we provide four tutorials for users to [finetune models](tutorials/finetune.md), [add new dataset](tutorials/new_dataset.md), [design data pipeline](tutorials/data_pipeline.md) and [add new modules](tutorials/new_modules.md).
+Currently, we provide four tutorials for users to [finetune models](tutorials/finetune.md), [customize datasets](tutorials/customize_dataset.md), [design data pipeline](tutorials/data_pipeline.md), [customize modules](tutorials/customize_models.md), [customize runtime settings](tutorials/customize_runtime.md), and [customize losses](tutorials/customize_losses.md).
 We also provide a full description about the [config system](config.md).

docs/tutorials/customize_models.md

@@ -21,7 +21,7 @@ Create a new file `mmdet/models/backbones/mobilenet.py`.
 ```python
 import torch.nn as nn
 
-from ..registry import BACKBONES
+from ..builder import BACKBONES
 
 
 @BACKBONES.register_module()
@@ -74,7 +74,7 @@ model = dict(
 Create a new file `mmdet/models/necks/pafpn.py`.
 
 ```python
-from ..registry import NECKS
+from ..builder import NECKS
 
 @NECKS.register
 class PAFPN(nn.Module):
@@ -125,11 +125,14 @@ neck=dict(
 
 Here we show how to develop a new head with the example of [Double Head R-CNN](https://arxiv.org/abs/1904.06493) as the following.
 
-First, add a new bbox head in `mmdet/models/bbox_heads/double_bbox_head.py`.
+First, add a new bbox head in `mmdet/models/roi_heads/bbox_heads/double_bbox_head.py`.
 Double Head R-CNN implements a new bbox head for object detection.
 To implement a bbox head, basically we need to implement three functions of the new module as the following.
 
 ```python
+from mmdet.models.builder import HEADS
+from .bbox_head import BBoxHead
+
 @HEADS.register_module()
 class DoubleConvFCBBoxHead(BBoxHead):
     r"""Bbox head used in Double-Head R-CNN

docs/tutorials/finetune.md

@@ -4,7 +4,7 @@ Detectors pre-trained on the COCO dataset can serve as a good pre-trained model
 This tutorial provides instruction for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obtain better performance.
 
 There are two steps to finetune a model on a new dataset.
-- Add support for the new dataset following [Tutorial 2: Adding New Dataset](new_dataset.md).
+- Add support for the new dataset following [Tutorial 2: Customize Datasets](customize_dataset.md).
 - Modify the configs as will be discussed in this tutorial.