mmagic.models.editors.pix2pix.pix2pix_generator
¶
Module Contents¶
Classes¶
Construct the Unet-based generator from the innermost layer to the |
- class mmagic.models.editors.pix2pix.pix2pix_generator.UnetGenerator(in_channels, out_channels, num_down=8, base_channels=64, norm_cfg=dict(type='BN'), use_dropout=False, init_cfg=dict(type='normal', gain=0.02))[source]¶
Bases:
mmengine.model.BaseModule
Construct the Unet-based generator from the innermost layer to the outermost layer, which is a recursive process.
- Parameters
in_channels (int) – Number of channels in input images.
out_channels (int) – Number of channels in output images.
num_down (int) – Number of downsamplings in Unet. If num_down is 8, the image with size 256x256 will become 1x1 at the bottleneck. Default: 8.
base_channels (int) – Number of channels at the last conv layer. Default: 64.
norm_cfg (dict) – Config dict to build norm layer. Default: dict(type=’BN’).
use_dropout (bool) – Whether to use dropout layers. Default: False.
init_cfg (dict) – Config dict for initialization. type: The name of our initialization method. Default: ‘normal’. gain: Scaling factor for normal, xavier and orthogonal. Default: 0.02.
- forward(x)[source]¶
Forward function.
- Parameters
x (Tensor) – Input tensor with shape (n, c, h, w).
- Returns
Forward results.
- Return type
Tensor
- init_weights()[source]¶
Initialize weights for the model.
- Parameters
pretrained (str, optional) – Path for pretrained weights. If given None, pretrained weights will not be loaded. Default: None.
strict (bool, optional) – Whether to allow different params for the model and checkpoint. Default: True.