mmagic.models.editors.cain
¶
Package Contents¶
Classes¶
CAIN model for Video Interpolation. |
|
CAIN network structure. |
- class mmagic.models.editors.cain.CAIN(generator: dict, pixel_loss: dict, train_cfg: Optional[dict] = None, test_cfg: Optional[dict] = None, required_frames: int = 2, step_frames: int = 1, init_cfg: Optional[dict] = None, data_preprocessor: Optional[dict] = None)[source]¶
Bases:
mmagic.models.base_models.BasicInterpolator
CAIN model for Video Interpolation.
Paper: Channel Attention Is All You Need for Video Frame Interpolation Ref repo: https://github.com/myungsub/CAIN
- Parameters
generator (dict) – Config for the generator structure.
pixel_loss (dict) – Config for pixel-wise loss.
train_cfg (dict) – Config for training. Default: None.
test_cfg (dict) – Config for testing. Default: None.
required_frames (int) – Required frames in each process. Default: 2
step_frames (int) – Step size of video frame interpolation. Default: 1
init_cfg (dict, optional) – The weight initialized config for
BaseModule
.data_preprocessor (dict, optional) – The pre-process config of
BaseDataPreprocessor
.
- init_cfg¶
Initialization config dict.
- Type
dict, optional
- data_preprocessor¶
Used for pre-processing data sampled by dataloader to the format accepted by
forward()
.- Type
BaseDataPreprocessor
- forward_inference(inputs, data_samples=None)[source]¶
Forward inference. Returns predictions of validation, testing, and simple inference.
- Parameters
inputs (torch.Tensor) – batch input tensor collated by
data_preprocessor
.data_samples (List[BaseDataElement], optional) – data samples collated by
data_preprocessor
.
- Returns
predictions.
- Return type
List[DataSample]
- class mmagic.models.editors.cain.CAINNet(in_channels=3, kernel_size=3, num_block_groups=5, num_block_layers=12, depth=3, reduction=16, norm=None, padding=7, act=nn.LeakyReLU(0.2, True), init_cfg=None)[source]¶
Bases:
mmengine.model.BaseModule
CAIN network structure.
Paper: Channel Attention Is All You Need for Video Frame Interpolation. Ref repo: https://github.com/myungsub/CAIN
- Parameters
in_channels (int) – Channel number of inputs. Default: 3.
kernel_size (int) – Kernel size of CAINNet. Default: 3.
num_block_groups (int) – Number of block groups. Default: 5.
num_block_layers (int) – Number of blocks in a group. Default: 12.
depth (int) – Down scale depth, scale = 2**depth. Default: 3.
reduction (int) – Channel reduction of CA. Default: 16.
norm (str | None) – Normalization layer. If it is None, no normalization is performed. Default: None.
padding (int) – Padding of CAINNet. Default: 7.
act (function) – activate function. Default: nn.LeakyReLU(0.2, True).
init_cfg (dict, optional) – Initialization config dict. Default: None.