kyxt
/
code_test

# 3: Train with customized models and standard datasets

In this note, you will know how to train, test and inference your own customized models under standard datasets. We use the cityscapes dataset to train a customized Cascade Mask R-CNN R50 model as an example to demonstrate the whole process, which using [`AugFPN`](https://github.com/Gus-Guo/AugFPN) to replace the default `FPN` as neck, and add `Rotate` or `Translate` as training-time auto augmentation.

The basic steps are as below:

1. Prepare the standard dataset
2. Prepare your own customized model
3. Prepare a config
4. Train, test, and inference models on the standard dataset.

## Prepare the standard dataset

In this note, as we use the standard cityscapes dataset as an example.

It is recommended to symlink the dataset root to `$MMDETECTION/data`.
If your folder structure is different, you may need to change the corresponding paths in config files.

```none
mmdetection
├── mmdet
├── tools
├── configs
├── data
│   ├── coco
│   │   ├── annotations
│   │   ├── train2017
│   │   ├── val2017
│   │   ├── test2017
│   ├── cityscapes
│   │   ├── annotations
│   │   ├── leftImg8bit
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── gtFine
│   │   │   ├── train
│   │   │   ├── val
│   ├── VOCdevkit
│   │   ├── VOC2007
│   │   ├── VOC2012

```

The cityscapes annotations have to be converted into the coco format using `tools/dataset_converters/cityscapes.py`:

```shell
pip install cityscapesscripts
python tools/dataset_converters/cityscapes.py ./data/cityscapes --nproc 8 --out-dir ./data/cityscapes/annotations
```

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.

## Prepare your own customized model

The second step is to use your own module or training setting. Assume that we want to implement a new neck called `AugFPN` to replace with the default `FPN` under the existing detector Cascade Mask R-CNN R50. The following implements`AugFPN` under MMDetection.

### 1. Define a new neck (e.g. AugFPN)

Firstly create a new file `mmdet/models/necks/augfpn.py`.

```python
from ..builder import NECKS

@NECKS.register_module()
class AugFPN(nn.Module):

    def __init__(self,
                in_channels,
                out_channels,
                num_outs,
                start_level=0,
                end_level=-1,
                add_extra_convs=False):
        pass

    def forward(self, inputs):
        # implementation is ignored
        pass
```

### 2. Import the module

You can either add the following line to `mmdet/models/necks/__init__.py`,

```python
from .augfpn import AugFPN
```

or alternatively add

```python
custom_imports = dict(
    imports=['mmdet.models.necks.augfpn.py'],
    allow_failed_imports=False)
```

to the config file and avoid modifying the original code.

### 3. Modify the config file

```python
neck=dict(
    type='AugFPN',
    in_channels=[256, 512, 1024, 2048],
    out_channels=256,
    num_outs=5)
```

For more detailed usages about customize your own models (e.g. implement a new backbone, head, loss, etc) and runtime training settings (e.g. define a new optimizer, use gradient clip, customize training schedules and hooks, etc), please refer to the guideline [Customize Models](tutorials/customize_models.md) and [Customize Runtime Settings](tutorials/customize_runtime.md) respectively.

## Prepare a config

The third step is to prepare a config for your own training setting. Assume that we want to add `AugFPN` and `Rotate` or `Translate` augmentation to existing Cascade Mask R-CNN R50 to train the cityscapes dataset, and assume the config is under directory `configs/cityscapes/` and named as `cascade_mask_rcnn_r50_augfpn_autoaug_10e_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'
]

model = dict(
    # set None to avoid loading ImageNet pretrained backbone,
    # instead here we set `load_from` to load from COCO pretrained detectors.
    backbone=dict(init_cfg=None),
    # replace neck from defaultly `FPN` to our new implemented module `AugFPN`
    neck=dict(
        type='AugFPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    # 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`.
    roi_head=dict(
        bbox_head=[
            dict(
                type='Shared2FCBBoxHead',
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                # change the number of classes from defaultly COCO to cityscapes
                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,
                # change the number of classes from defaultly COCO to cityscapes
                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,
                # change the number of classes from defaultly COCO to cityscapes
                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,
            # change the number of classes from defaultly COCO to cityscapes
            num_classes=8,
            loss_mask=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))))

# over-write `train_pipeline` for new added `AutoAugment` training setting
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='AutoAugment',
        policies=[
            [dict(
                 type='Rotate',
                 level=5,
                 img_fill_val=(124, 116, 104),
                 prob=0.5,
                 scale=1)
            ],
            [dict(type='Rotate', level=7, img_fill_val=(124, 116, 104)),
             dict(
                 type='Translate',
                 level=5,
                 prob=0.5,
                 img_fill_val=(124, 116, 104))
            ],
        ]),
    dict(
        type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]

# set batch_size per gpu, and set new training pipeline
data = dict(
    samples_per_gpu=1,
    workers_per_gpu=3,
    # over-write `pipeline` with new training pipeline setting
    train=dict(dataset=dict(pipeline=train_pipeline)))

# Set optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# Set customized learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8])
runner = dict(type='EpochBasedRunner', max_epochs=10)

# We can use the COCO pretrained Cascade Mask R-CNN R50 model for more stable performance initialization
load_from = 'https://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_augfpn_autoaug_10e_cityscapes.py
```

For more detailed usages, please refer to the [Case 1](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_augfpn_autoaug_10e_cityscapes.py work_dirs/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py/latest.pth --eval bbox segm
```

For more detailed usages, please refer to the [Case 1](1_exist_data_model.md).