## CNN

We provide some building bricks for CNNs, includeing layer building, module bundles and weight initialization.

### Layer building

We may need to try different layers of the same type when running experiments,
but do not want to modify the code from time to time.
Here we provide some layer building methods to construct layers from a dict,
which can be written in configs or specified via command line arguments.

#### Usage

A simplest example is
```python
cfg = dict(type='Conv3d')
layer = build_norm_layer(cfg, in_channels=3, out_channels=8, kernel_size=3)
```

- `build_conv_layer`: Supported types are Conv1d, Conv2d, Conv3d, Conv (alias for Conv2d).
- `build_norm_layer`: Supported types are BN1d, BN2d, BN3d, BN (alias for BN2d), SyncBN, GN, LN, IN1d, IN2d, IN3d, IN (alias for IN2d).
- `build_activation_layer`: Supported types are ReLU, LeakyReLU, PReLU, RReLU, ReLU6, ELU, Sigmoid, Tanh.
- `build_upsample_layer`: Supported types are nearest, bilinear, deconv, pixel_shuffle.
- `build_padding_layer`: Supported types are zero, reflect, replicate.

#### Extension

We also allow extending the building methods with custom layers and operators.

1. Write and register your own module.

    ```python
    from mmcv.cnn import UPSAMPLE_LAYERS

    @UPSAMPLE_LAYERS.register_module()
    class MyUpsample:

        def __init__(self, scale_factor):
            pass

        def forward(self, x):
            pass
    ```

2. Import `MyUpsample` somewhere (e.g., in `__init__.py`) and then use it.

    ```python
    cfg = dict(type='MyUpsample', scale_factor=2)
    layer = build_upsample_layer(cfg)
    ```

### Module bundles

We also provide common module bundles to facilitate the network construction.
`ConvModule` is a bundle of convolution, normalization and activation layers,
please refer to the [api](api.html#mmcv.cnn.ConvModule) for details.

```python
# conv + bn + relu
conv = ConvModule(3, 8, 2, norm_cfg=dict(type='BN'))
# conv + gn + relu
conv = ConvModule(3, 8, 2, norm_cfg=dict(type='GN', num_groups=2))
# conv + relu
conv = ConvModule(3, 8, 2)
# conv
conv = ConvModule(3, 8, 2, act_cfg=None)
# conv + leaky relu
conv = ConvModule(3, 8, 3, padding=1, act_cfg=dict(type='LeakyReLU'))
# bn + conv + relu
conv = ConvModule(
    3, 8, 2, norm_cfg=dict(type='BN'), order=('norm', 'conv', 'act'))
```

### Weight initialization

We wrap some initialization methods which accept a module as argument.

- `constant_init`
- `xavier_init`
- `normal_init`
- `uniform_init`
- `kaiming_init`
- `caffe2_xavier_init`
- `bias_init_with_prob`

Examples:

```python
conv1 = nn.Conv2d(3, 3, 1)
normal_init(conv1, std=0.01, bias=0)
xavier_init(conv1, distribution='uniform')
```