Source code for mmedit.models.backbones.sr_backbones.glean_styleganv2

# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.nn as nn
from mmcv.runner import load_checkpoint

from mmedit.models.backbones.sr_backbones.rrdb_net import RRDB
from mmedit.models.builder import build_component
from mmedit.models.common import PixelShufflePack, make_layer
from mmedit.models.registry import BACKBONES
from mmedit.utils import get_root_logger

[docs]@BACKBONES.register_module() class GLEANStyleGANv2(nn.Module): r"""GLEAN (using StyleGANv2) architecture for super-resolution. Paper: GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution, CVPR, 2021 This method makes use of StyleGAN2 and hence the arguments mostly follow that in 'StyleGAN2v2Generator'. In StyleGAN2, we use a static architecture composing of a style mapping module and number of covolutional style blocks. More details can be found in: Analyzing and Improving the Image Quality of StyleGAN CVPR2020. You can load pretrained model through passing information into ``pretrained`` argument. We have already offered official weights as follows: - styelgan2-ffhq-config-f: # noqa - stylegan2-horse-config-f: # noqa - stylegan2-car-config-f: # noqa - styelgan2-cat-config-f: # noqa - stylegan2-church-config-f: # noqa If you want to load the ema model, you can just use following codes: .. code-block:: python # ckpt_http is one of the valid path from http source generator = StyleGANv2Generator(1024, 512, pretrained=dict( ckpt_path=ckpt_http, prefix='generator_ema')) Of course, you can also download the checkpoint in advance and set ``ckpt_path`` with local path. If you just want to load the original generator (not the ema model), please set the prefix with 'generator'. Note that our implementation allows to generate BGR image, while the original StyleGAN2 outputs RGB images by default. Thus, we provide ``bgr2rgb`` argument to convert the image space. Args: in_size (int): The size of the input image. out_size (int): The output size of the StyleGAN2 generator. img_channels (int): Number of channels of the input images. 3 for RGB image and 1 for grayscale image. Default: 3. rrdb_channels (int): Number of channels of the RRDB features. Default: 64. num_rrdbs (int): Number of RRDB blocks in the encoder. Default: 23. style_channels (int): The number of channels for style code. Default: 512. num_mlps (int, optional): The number of MLP layers. Defaults to 8. channel_multiplier (int, optional): The mulitiplier factor for the channel number. Defaults to 2. blur_kernel (list, optional): The blurry kernel. Defaults to [1, 3, 3, 1]. lr_mlp (float, optional): The learning rate for the style mapping layer. Defaults to 0.01. default_style_mode (str, optional): The default mode of style mixing. In training, we defaultly adopt mixing style mode. However, in the evaluation, we use 'single' style mode. `['mix', 'single']` are currently supported. Defaults to 'mix'. eval_style_mode (str, optional): The evaluation mode of style mixing. Defaults to 'single'. mix_prob (float, optional): Mixing probability. The value should be in range of [0, 1]. Defaults to 0.9. pretrained (dict | None, optional): Information for pretained models. The necessary key is 'ckpt_path'. Besides, you can also provide 'prefix' to load the generator part from the whole state dict. Defaults to None. bgr2rgb (bool, optional): Whether to flip the image channel dimension. Defaults to False. """ def __init__(self, in_size, out_size, img_channels=3, rrdb_channels=64, num_rrdbs=23, style_channels=512, num_mlps=8, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], lr_mlp=0.01, default_style_mode='mix', eval_style_mode='single', mix_prob=0.9, pretrained=None, bgr2rgb=False): super().__init__() # input size must be strictly smaller than output size if in_size >= out_size: raise ValueError('in_size must be smaller than out_size, but got ' f'{in_size} and {out_size}.') # latent bank (StyleGANv2), with weights being fixed self.generator = build_component( dict( type='StyleGANv2Generator', out_size=out_size, style_channels=style_channels, num_mlps=num_mlps, channel_multiplier=channel_multiplier, blur_kernel=blur_kernel, lr_mlp=lr_mlp, default_style_mode=default_style_mode, eval_style_mode=eval_style_mode, mix_prob=mix_prob, pretrained=pretrained, bgr2rgb=bgr2rgb)) self.generator.requires_grad_(False) self.in_size = in_size self.style_channels = style_channels channels = self.generator.channels # encoder num_styles = int(np.log2(out_size)) * 2 - 2 encoder_res = [2**i for i in range(int(np.log2(in_size)), 1, -1)] self.encoder = nn.ModuleList() self.encoder.append( nn.Sequential( RRDBFeatureExtractor( img_channels, rrdb_channels, num_blocks=num_rrdbs), nn.Conv2d( rrdb_channels, channels[in_size], 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.2, inplace=True))) for res in encoder_res: in_channels = channels[res] if res > 4: out_channels = channels[res // 2] block = nn.Sequential( nn.Conv2d(in_channels, out_channels, 3, 2, 1, bias=True), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.2, inplace=True)) else: block = nn.Sequential( nn.Conv2d(in_channels, in_channels, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Flatten(), nn.Linear(16 * in_channels, num_styles * style_channels)) self.encoder.append(block) # additional modules for StyleGANv2 self.fusion_out = nn.ModuleList() self.fusion_skip = nn.ModuleList() for res in encoder_res[::-1]: num_channels = channels[res] self.fusion_out.append( nn.Conv2d(num_channels * 2, num_channels, 3, 1, 1, bias=True)) self.fusion_skip.append( nn.Conv2d(num_channels + 3, 3, 3, 1, 1, bias=True)) # decoder decoder_res = [ 2**i for i in range(int(np.log2(in_size)), int(np.log2(out_size) + 1)) ] self.decoder = nn.ModuleList() for res in decoder_res: if res == in_size: in_channels = channels[res] else: in_channels = 2 * channels[res] if res < out_size: out_channels = channels[res * 2] self.decoder.append( PixelShufflePack( in_channels, out_channels, 2, upsample_kernel=3)) else: self.decoder.append( nn.Sequential( nn.Conv2d(in_channels, 64, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(64, img_channels, 3, 1, 1)))
[docs] def forward(self, lq): """Forward function. Args: lq (Tensor): Input LR image with shape (n, c, h, w). Returns: Tensor: Output HR image. """ h, w = lq.shape[2:] if h != self.in_size or w != self.in_size: raise AssertionError( f'Spatial resolution must equal in_size ({self.in_size}).' f' Got ({h}, {w}).') # encoder feat = lq encoder_features = [] for block in self.encoder: feat = block(feat) encoder_features.append(feat) encoder_features = encoder_features[::-1] latent = encoder_features[0].view(lq.size(0), -1, self.style_channels) encoder_features = encoder_features[1:] # generator injected_noise = [ getattr(self.generator, f'injected_noise_{i}') for i in range(self.generator.num_injected_noises) ] # 4x4 stage out = self.generator.constant_input(latent) out = self.generator.conv1(out, latent[:, 0], noise=injected_noise[0]) skip = self.generator.to_rgb1(out, latent[:, 1]) _index = 1 # 8x8 ---> higher res generator_features = [] for up_conv, conv, noise1, noise2, to_rgb in zip( self.generator.convs[::2], self.generator.convs[1::2], injected_noise[1::2], injected_noise[2::2], self.generator.to_rgbs): # feature fusion by channel-wise concatenation if out.size(2) <= self.in_size: fusion_index = (_index - 1) // 2 feat = encoder_features[fusion_index] out =[out, feat], dim=1) out = self.fusion_out[fusion_index](out) skip =[skip, feat], dim=1) skip = self.fusion_skip[fusion_index](skip) # original StyleGAN operations out = up_conv(out, latent[:, _index], noise=noise1) out = conv(out, latent[:, _index + 1], noise=noise2) skip = to_rgb(out, latent[:, _index + 2], skip) # store features for decoder if out.size(2) > self.in_size: generator_features.append(out) _index += 2 # decoder hr = encoder_features[-1] for i, block in enumerate(self.decoder): if i > 0: hr =[hr, generator_features[i - 1]], dim=1) hr = block(hr) return hr
[docs] def init_weights(self, pretrained=None, strict=True): """Init weights for models. Args: pretrained (str, optional): Path for pretrained weights. If given None, pretrained weights will not be loaded. Defaults to None. strict (boo, optional): Whether strictly load the pretrained model. Defaults to True. """ if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, strict=strict, logger=logger) elif pretrained is not None: raise TypeError(f'"pretrained" must be a str or None. ' f'But received {type(pretrained)}.')
class RRDBFeatureExtractor(nn.Module): """Feature extractor composed of Residual-in-Residual Dense Blocks (RRDBs). It is equivalent to ESRGAN with the upsampling module removed. Args: in_channels (int): Channel number of inputs. mid_channels (int): Channel number of intermediate features. Default: 64 num_blocks (int): Block number in the trunk network. Default: 23 growth_channels (int): Channels for each growth. Default: 32. """ def __init__(self, in_channels=3, mid_channels=64, num_blocks=23, growth_channels=32): super().__init__() self.conv_first = nn.Conv2d(in_channels, mid_channels, 3, 1, 1) self.body = make_layer( RRDB, num_blocks, mid_channels=mid_channels, growth_channels=growth_channels) self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ feat = self.conv_first(x) return feat + self.conv_body(self.body(feat))
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