mmagic.models.editors.pix2pix
¶
Package Contents¶
Classes¶
Pix2Pix model for paired image-to-image translation. |
|
Construct the Unet-based generator from the innermost layer to the |
- class mmagic.models.editors.pix2pix.Pix2Pix(*args, **kwargs)[source]¶
Bases:
mmagic.models.base_models.BaseTranslationModel
Pix2Pix model for paired image-to-image translation.
- Ref:
Image-to-Image Translation with Conditional Adversarial Networks
- forward_test(img, target_domain, **kwargs)[source]¶
Forward function for testing.
- Parameters
img (tensor) – Input image tensor.
target_domain (str) – Target domain of output image.
kwargs (dict) – Other arguments.
- Returns
Forward results.
- Return type
dict
- _get_disc_loss(outputs)[source]¶
Get the loss of discriminator.
- Parameters
outputs (dict) – A dict of output.
- Returns
Loss and a dict of log of loss terms.
- Return type
Tuple
- _get_gen_loss(outputs)[source]¶
Get the loss of generator.
- Parameters
outputs (dict) – A dict of output.
- Returns
Loss and a dict of log of loss terms.
- Return type
Tuple
- train_step(data, optim_wrapper=None)[source]¶
Training step function.
- Parameters
data_batch (dict) – Dict of the input data batch.
optimizer (dict[torch.optim.Optimizer]) – Dict of optimizers for the generator and discriminator.
ddp_reducer (
Reducer
| None, optional) – Reducer from ddp. It is used to prepare forbackward()
in ddp. Defaults to None.running_status (dict | None, optional) – Contains necessary basic information for training, e.g., iteration number. Defaults to None.
- Returns
Dict of loss, information for logger, the number of samples and results for visualization.
- Return type
dict
- test_step(data: dict) mmagic.utils.typing.SampleList [source]¶
Gets the generated image of given data. Same as
val_step()
.- Parameters
data (dict) – Data sampled from metric specific sampler. More details in Metrics and Evaluator.
- Returns
Generated image or image dict.
- Return type
List[DataSample]
- val_step(data: dict) mmagic.utils.typing.SampleList [source]¶
Gets the generated image of given data. Same as
val_step()
.- Parameters
data (dict) – Data sampled from metric specific sampler. More details in Metrics and Evaluator.
- Returns
Generated image or image dict.
- Return type
List[DataSample]
- class mmagic.models.editors.pix2pix.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.