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mmedit.models.editors.dcgan.dcgan_generator

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DCGANGenerator

Generator for DCGAN.

class mmedit.models.editors.dcgan.dcgan_generator.DCGANGenerator(output_scale, out_channels=3, base_channels=1024, input_scale=4, noise_size=100, default_norm_cfg=dict(type='BN'), default_act_cfg=dict(type='ReLU'), out_act_cfg=dict(type='Tanh'), pretrained=None)[source]

Bases: torch.nn.Module

Generator for DCGAN.

Implementation Details for DCGAN architecture:

  1. Adopt transposed convolution in the generator;

  2. Use batchnorm in the generator except for the final output layer;

  3. Use ReLU in the generator in addition to the final output layer.

More details can be found in the original paper: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks http://arxiv.org/abs/1511.06434

Parameters
  • output_scale (int | tuple[int]) – Output scale for the generated image. If only a integer is provided, the output image will be a square shape. The tuple of two integers will set the height and width for the output image, respectively.

  • out_channels (int, optional) – The channel number of the output feature. Default to 3.

  • base_channels (int, optional) – The basic channel number of the generator. The other layers contains channels based on this number. Default to 1024.

  • input_scale (int | tuple[int], optional) – Output scale for the generated image. If only a integer is provided, the input feature ahead of the convolutional generator will be a square shape. The tuple of two integers will set the height and width for the input convolutional feature, respectively. Defaults to 4.

  • noise_size (int, optional) – Size of the input noise vector. Defaults to 100.

  • default_norm_cfg (dict, optional) – Norm config for all of layers except for the final output layer. Defaults to dict(type='BN').

  • default_act_cfg (dict, optional) – Activation config for all of layers except for the final output layer. Defaults to dict(type='ReLU').

  • out_act_cfg (dict, optional) – Activation config for the final output layer. Defaults to dict(type='Tanh').

  • pretrained (str, optional) – Path for the pretrained model. Default to None.

forward(noise, num_batches=0, return_noise=False)[source]

Forward function.

Parameters
  • noise (torch.Tensor | callable | None) – You can directly give a batch of noise through a torch.Tensor or offer a callable function to sample a batch of noise data. Otherwise, the None indicates to use the default noise sampler.

  • num_batches (int, optional) – The number of batch size. Defaults to 0.

  • return_noise (bool, optional) – If True, noise_batch will be returned in a dict with fake_img. Defaults to False.

Returns

If not return_noise, only the output image

will be returned. Otherwise, a dict contains fake_img and noise_batch will be returned.

Return type

torch.Tensor | dict

init_weights(pretrained=None)[source]

Init weights for models.

We just use the initialization method proposed in the original paper.

Parameters

pretrained (str, optional) – Path for pretrained weights. If given None, pretrained weights will not be loaded. Defaults to None.

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