mmagic.models.editors.mspie.pe_singan_generator 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from functools import partial

import mmengine
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from mmagic.registry import MODELS
from ..singan.singan_generator import SinGANMultiScaleGenerator
from ..singan.singan_modules import GeneratorBlock

[文档]class SinGANMSGeneratorPE(SinGANMultiScaleGenerator): """Multi-Scale Generator used in SinGAN with positional encoding. More details can be found in: Positional Encoding as Spatial Inductive Bias in GANs, CVPR'2021. Notes: - In this version, we adopt the interpolation function from the official PyTorch APIs, which is different from the original implementation by the authors. However, in our experiments, this influence can be ignored. Args: in_channels (int): Input channels. out_channels (int): Output channels. num_scales (int): The number of scales/stages in generator. Note that this number is counted from zero, which is the same as the original paper. kernel_size (int, optional): Kernel size, same as :obj:`nn.Conv2d`. Defaults to 3. padding (int, optional): Padding for the convolutional layer, same as :obj:`nn.Conv2d`. Defaults to 0. num_layers (int, optional): The number of convolutional layers in each generator block. Defaults to 5. base_channels (int, optional): The basic channels for convolutional layers in the generator block. Defaults to 32. min_feat_channels (int, optional): Minimum channels for the feature maps in the generator block. Defaults to 32. out_act_cfg (dict | None, optional): Configs for output activation layer. Defaults to dict(type='Tanh'). padding_mode (str, optional): The mode of convolutional padding, same as :obj:`nn.Conv2d`. Defaults to 'zero'. pad_at_head (bool, optional): Whether to add padding at head. Defaults to True. interp_pad (bool, optional): The padding value of interpolating feature maps. Defaults to False. noise_with_pad (bool, optional): Whether the input fixed noises are with explicit padding. Defaults to False. positional_encoding (dict | None, optional): Configs for the positional encoding. Defaults to None. first_stage_in_channels (int | None, optional): The input channel of the first generator block. If None, the first stage will adopt the same input channels as other stages. Defaults to None. """ def __init__(self, in_channels, out_channels, num_scales, kernel_size=3, padding=0, num_layers=5, base_channels=32, min_feat_channels=32, out_act_cfg=dict(type='Tanh'), padding_mode='zero', pad_at_head=True, interp_pad=False, noise_with_pad=False, positional_encoding=None, first_stage_in_channels=None, **kwargs): super(SinGANMultiScaleGenerator, self).__init__() self.pad_at_head = pad_at_head self.interp_pad = interp_pad self.noise_with_pad = noise_with_pad self.with_positional_encode = positional_encoding is not None if self.with_positional_encode: self.head_position_encode = self.pad_head = int((kernel_size - 1) / 2 * num_layers) self.blocks = nn.ModuleList() self.upsample = partial( F.interpolate, mode='bicubic', align_corners=True) for scale in range(num_scales + 1): base_ch = min(base_channels * pow(2, int(np.floor(scale / 4))), 128) min_feat_ch = min( min_feat_channels * pow(2, int(np.floor(scale / 4))), 128) if scale == 0: in_ch = ( first_stage_in_channels if first_stage_in_channels else in_channels) else: in_ch = in_channels self.blocks.append( GeneratorBlock( in_channels=in_ch, out_channels=out_channels, kernel_size=kernel_size, padding=padding, num_layers=num_layers, base_channels=base_ch, min_feat_channels=min_feat_ch, out_act_cfg=out_act_cfg, padding_mode=padding_mode, **kwargs)) if padding_mode == 'zero': self.noise_padding_layer = nn.ZeroPad2d(self.pad_head) self.img_padding_layer = nn.ZeroPad2d(self.pad_head) self.mask_padding_layer = nn.ReflectionPad2d(self.pad_head) elif padding_mode == 'reflect': self.noise_padding_layer = nn.ReflectionPad2d(self.pad_head) self.img_padding_layer = nn.ReflectionPad2d(self.pad_head) self.mask_padding_layer = nn.ReflectionPad2d(self.pad_head) mmengine.print_log('Using Reflection padding', 'current') else: raise NotImplementedError( f'Padding mode {padding_mode} is not supported')
[文档] def forward(self, input_sample, fixed_noises, noise_weights, rand_mode, curr_scale, num_batches=1, get_prev_res=False, return_noise=False): """Forward function. Args: input_sample (Tensor | None): The input for generator. In the original implementation, a tensor filled with zeros is adopted. If None is given, we will construct it from the first fixed noises. fixed_noises (list[Tensor]): List of the fixed noises in SinGAN. noise_weights (list[float]): List of the weights for random noises. rand_mode (str): Choices from ['rand', 'recon']. In ``rand`` mode, it will sample from random noises. Otherwise, the reconstruction for the single image will be returned. curr_scale (int): The scale for the current inference or training. num_batches (int, optional): The number of batches. Defaults to 1. get_prev_res (bool, optional): Whether to return results from previous stages. Defaults to False. return_noise (bool, optional): Whether to return noises tensor. Defaults to False. Returns: Tensor | dict: Generated image tensor or dictionary containing \ more data. """ if get_prev_res or return_noise: prev_res_list = [] noise_list = [] if input_sample is None: h, w = fixed_noises[0].shape[-2:] if self.noise_with_pad: h -= 2 * self.pad_head w -= 2 * self.pad_head input_sample = torch.zeros( (num_batches, 3, h, w)).to(fixed_noises[0]) g_res = input_sample for stage in range(curr_scale + 1): if rand_mode == 'recon': noise_ = fixed_noises[stage] else: noise_ = torch.randn(num_batches, *fixed_noises[stage].shape[1:]).to(g_res) if return_noise: noise_list.append(noise_) if self.with_positional_encode and stage == 0: head_grid = self.head_position_encode(fixed_noises[0]) noise_ = noise_ + head_grid # add padding at head if self.pad_at_head: if self.interp_pad: if self.noise_with_pad: size = noise_.shape[-2:] else: size = (noise_.size(2) + 2 * self.pad_head, noise_.size(3) + 2 * self.pad_head) noise_ = self.upsample(noise_, size) g_res_pad = self.upsample(g_res, size) else: if not self.noise_with_pad: noise_ = self.noise_padding_layer(noise_) g_res_pad = self.img_padding_layer(g_res) else: g_res_pad = g_res if stage == 0 and self.with_positional_encode: noise = noise_ * noise_weights[stage] else: noise = noise_ * noise_weights[stage] + g_res_pad g_res = self.blocks[stage](noise.detach(), g_res) if get_prev_res and stage != curr_scale: prev_res_list.append(g_res) # upsample, here we use interpolation from PyTorch if stage != curr_scale: h_next, w_next = fixed_noises[stage + 1].shape[-2:] if self.noise_with_pad: # remove the additional padding if noise with pad h_next -= 2 * self.pad_head w_next -= 2 * self.pad_head g_res = self.upsample(g_res, (h_next, w_next)) if get_prev_res or return_noise: output_dict = dict( fake_img=g_res, prev_res_list=prev_res_list, noise_batch=noise_list) return output_dict return g_res
Read the Docs v: latest
On Read the Docs
Project Home

Free document hosting provided by Read the Docs.