add doc for exist_data_new_model tutorial

docs/tutorials/3_exist_data_new_model.md

+# Tutorial 3: Inference, testing, and training with predefined models and standard datasets
+
+In this tutorial, you will know how to inference, test, and train predefined models with your own settings for standard datasets. We use the cityscapes dataset to train a COCO pretrained Cascade Mask R-CNN model as an example to describe the whole process.
+
+The basic steps are as below:
+
+1. Prepare the standard dataset
+2. Prepare a config
+3. Train, test, inference models on the standard dataset.
+
+### Prepare the standard dataset
+
+In this tutorial, as we use the standard cityscapes dataset as an example, to prepare the dataset please follow the tutorial [getting_started](./getting_started.md).
+
+
+
+### Prepare a config
+
+The second step is to prepare a config for your own training setting. Assume that we want to use Cascade Mask R-CNN with FPN to train the cityscapes dataset, and assume the config is under directory `configs/cityscapes/` and named as `cascade_mask_rcnn_r50_fpn_1x_cityscapes.py`, the config is as below.
+
+```python
+# The new config inherits the base configs to highlight the necessary modification
+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
+]
+
+# We also need to change the num_classes in head from 80 to 8, to match the cityscapes dataset's annotation.
+# This modification involves `bbox_head` and `mask_head`.
+model = dict(
+    pretrained=None,
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=8,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=8,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=8,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=8,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))))
+
+# Set  optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# Set learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[7])
+total_epochs = 8
+
+# We can use the COCO pretrained Cascade Mask R-CNN model for more stable performance initialization
+load_from = 'http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth'
+```
+
+### Train a new model
+
+To train a model with the new config, you can simply run
+
+```shell
+python tools/train.py configs/cityscapes/cascade_mask_rcnn_r50_fpn_1x_cityscapes.py
+```
+
+For more detailed usages, please refer to the [tutorial 1](./tutorials/1_exist_data_model.md).
+
+### Test and inference
+
+To test the trained model, you can simply run
+
+```shell
+python tools/test.py configs/cityscapes/cascade_mask_rcnn_r50_fpn_1x_cityscapes.py work_dirs/cascade_mask_rcnn_r50_fpn_1x_cityscapes/latest.pth --eval bbox segm
+```
+
+For more detailed usages, please refer to the [tutorial 1](./tutorials/1_exist_data_model.md).