mmagic.models.editors.tof.tof_vsr_net
¶
Module Contents¶
Classes¶
PyTorch implementation of TOFlow. |
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Basic module of SPyNet. |
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SPyNet architecture. |
- class mmagic.models.editors.tof.tof_vsr_net.TOFlowVSRNet(adapt_official_weights=False, init_cfg=None)[source]¶
Bases:
mmengine.model.BaseModule
PyTorch implementation of TOFlow.
In TOFlow, the LR frames are pre-upsampled and have the same size with the GT frames.
Paper: Xue et al., Video Enhancement with Task-Oriented Flow, IJCV 2018 Code reference:
- Parameters
adapt_official_weights (bool) – Whether to adapt the weights translated from the official implementation. Set to false if you want to train from scratch. Default: False
- class mmagic.models.editors.tof.tof_vsr_net.BasicModule[source]¶
Bases:
torch.nn.Module
Basic module of SPyNet.
Note that unlike the common spynet architecture, the basic module here contains batch normalization.
- class mmagic.models.editors.tof.tof_vsr_net.SPyNet[source]¶
Bases:
torch.nn.Module
SPyNet architecture.
Note that this implementation is specifically for TOFlow. It differs from the common SPyNet in the following aspects:
The basic modules here contain BatchNorm.
- Normalization and denormalization are not done here, as
they are done in TOFlow.
- Paper:
Optical Flow Estimation using a Spatial Pyramid Network
- Code reference: