mmagic.models.editors.flavr

Package Contents

Classes

FLAVR	FLAVR model for video interpolation.
FLAVRNet	PyTorch implementation of FLAVR for video frame interpolation.

class mmagic.models.editors.flavr.FLAVR(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)

Bases: mmagic.models.base_models.BasicInterpolator

FLAVR model for video interpolation.

Paper:

FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation

Ref repo: https://github.com/tarun005/FLAVR

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

static merge_frames(input_tensors, output_tensors)

merge input frames and output frames.

Interpolate a frame between the given two frames.

Merged from

[[in1, in2, in3, in4], [in2, in3, in4, in5], …] [[out1], [out2], [out3], …]

to

[in1, in2, out1, in3, out2, …, in(-3), out(-1), in(-2), in(-1)]

Parameters

• input_tensors (Tensor) – The input frames with shape [n, 4, c, h, w]

• output_tensors (Tensor) – The output frames with shape [n, 1, c, h, w].

Returns

The final frames.

Return type

list[np.array]

class mmagic.models.editors.flavr.FLAVRNet(num_input_frames, num_output_frames, mid_channels_list=[512, 256, 128, 64], encoder_layers_list=[2, 2, 2, 2], bias=False, norm_cfg=None, join_type='concat', up_mode='transpose', init_cfg=None)

Bases: mmengine.model.BaseModule

PyTorch implementation of FLAVR for video frame interpolation.

Paper:

FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation

Ref repo: https://github.com/tarun005/FLAVR

Parameters

• num_input_frames (int) – Number of input frames.

• num_output_frames (int) – Number of output frames.

• mid_channels_list (list[int]) – List of number of mid channels. Default: [512, 256, 128, 64]

• encoder_layers_list (list[int]) – List of number of layers in encoder. Default: [2, 2, 2, 2]

• bias (bool) – If True, adds a learnable bias to the conv layers. Default: True

• norm_cfg (dict | None) – Config dict for normalization layer. Default: None

• join_type (str) – Join type of tensors from decoder and encoder. Candidates are concat and add. Default: concat

• up_mode (str) – Up-mode UpConv3d, candidates are transpose and trilinear. Default: transpose

• init_cfg (dict, optional) – Initialization config dict. Default: None.

forward(images: torch.Tensor)

Forward function.

Parameters

images (Tensor) – Input frames tensor with shape (N, T, C, H, W).

Returns

Output tensor.

Return type

out (Tensor)