Shortcuts

Note

You are reading the documentation for MMEditing 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMEditing 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMEditing 1.0 for more details.

Source code for mmedit.models.extractors.lte

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

from mmedit.models.common import ImgNormalize
from mmedit.models.registry import COMPONENTS
from mmedit.utils import get_root_logger


[docs]@COMPONENTS.register_module() class LTE(nn.Module): """Learnable Texture Extractor. Based on pretrained VGG19. Generate features in 3 levels. Args: requires_grad (bool): Require grad or not. Default: True. pixel_range (float): Pixel range of geature. Default: 1. pretrained (str): Path for pretrained model. Default: None. load_pretrained_vgg (bool): Load pretrained VGG from torchvision. Default: True. Train: must load pretrained VGG Eval: needn't load pretrained VGG, because we will load pretrained LTE. """ def __init__(self, requires_grad=True, pixel_range=1., pretrained=None, load_pretrained_vgg=True): super().__init__() vgg_mean = (0.485, 0.456, 0.406) vgg_std = (0.229 * pixel_range, 0.224 * pixel_range, 0.225 * pixel_range) self.img_normalize = ImgNormalize( pixel_range=pixel_range, img_mean=vgg_mean, img_std=vgg_std) # use vgg19 weights to initialize vgg_pretrained_features = models.vgg19( pretrained=load_pretrained_vgg).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() for x in range(2): self.slice1.add_module(str(x), vgg_pretrained_features[x]) for x in range(2, 7): self.slice2.add_module(str(x), vgg_pretrained_features[x]) for x in range(7, 12): self.slice3.add_module(str(x), vgg_pretrained_features[x]) if not requires_grad: for param in self.slice1.parameters(): param.requires_grad = requires_grad for param in self.slice2.parameters(): param.requires_grad = requires_grad for param in self.slice3.parameters(): param.requires_grad = requires_grad # pretrained if pretrained: self.init_weights(pretrained)
[docs] def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, 3, h, w). Returns: Tuple[Tensor]: Forward results in 3 levels. x_level3: Forward results in level 3 (n, 256, h/4, w/4). x_level2: Forward results in level 2 (n, 128, h/2, w/2). x_level1: Forward results in level 1 (n, 64, h, w). """ x = self.img_normalize(x) x_level1 = x = self.slice1(x) x_level2 = x = self.slice2(x) x_level3 = x = self.slice3(x) return [x_level3, x_level2, x_level1]
[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 None: pass # use default initialization else: raise TypeError('"pretrained" must be a str or None. ' f'But received {type(pretrained)}.')
Read the Docs v: latest
Versions
latest
stable
1.x
v0.16.0
v0.15.2
v0.15.1
v0.15.0
v0.14.0
v0.13.0
v0.12.0
dev-1.x
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.