mmagic.models.editors.deepfillv1.deepfill_decoder

Module Contents

Classes

DeepFillDecoder

Decoder used in DeepFill model.

class mmagic.models.editors.deepfillv1.deepfill_decoder.DeepFillDecoder(in_channels, conv_type='conv', norm_cfg=None, act_cfg=dict(type='ELU'), out_act_cfg=dict(type='clip', min=- 1.0, max=1.0), channel_factor=1.0, **kwargs)

Bases: mmengine.model.BaseModule

Decoder used in DeepFill model.

This implementation follows: Generative Image Inpainting with Contextual Attention

Parameters

• in_channels (int) – The number of input channels.

• conv_type (str) – The type of conv module. In DeepFillv1 model, the conv_type should be ‘conv’. In DeepFillv2 model, the conv_type should be ‘gated_conv’.

• norm_cfg (dict) – Config dict to build norm layer. Default: None.

• act_cfg (dict) – Config dict for activation layer, “elu” by default.

• out_act_cfg (dict) – Config dict for output activation layer. Here, we provide commonly used clamp or clip operation.

• channel_factor (float) – The scale factor for channel size. Default: 1.

• kwargs (keyword arguments) –

_conv_type

forward(input_dict)

Forward Function.

Parameters

input_dict (dict | torch.Tensor) – Input dict with middle features or torch.Tensor.

Returns

Output tensor with shape of (n, c, h, w).

Return type

torch.Tensor