import torch.nn as nn
from mmcv.runner import load_checkpoint
from mmedit.models.registry import COMPONENTS
from mmedit.utils import get_root_logger
[docs]@COMPONENTS.register_module()
class ModifiedVGG(nn.Module):
"""A modified VGG discriminator with input size 128 x 128.
It is used to train SRGAN and ESRGAN.
Args:
in_channels (int): Channel number of inputs. Default: 3.
mid_channels (int): Channel number of base intermediate features.
Default: 64.
"""
def __init__(self, in_channels, mid_channels):
super(ModifiedVGG, self).__init__()
self.conv0_0 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1, bias=True)
self.conv0_1 = nn.Conv2d(
mid_channels, mid_channels, 4, 2, 1, bias=False)
self.bn0_1 = nn.BatchNorm2d(mid_channels, affine=True)
self.conv1_0 = nn.Conv2d(
mid_channels, mid_channels * 2, 3, 1, 1, bias=False)
self.bn1_0 = nn.BatchNorm2d(mid_channels * 2, affine=True)
self.conv1_1 = nn.Conv2d(
mid_channels * 2, mid_channels * 2, 4, 2, 1, bias=False)
self.bn1_1 = nn.BatchNorm2d(mid_channels * 2, affine=True)
self.conv2_0 = nn.Conv2d(
mid_channels * 2, mid_channels * 4, 3, 1, 1, bias=False)
self.bn2_0 = nn.BatchNorm2d(mid_channels * 4, affine=True)
self.conv2_1 = nn.Conv2d(
mid_channels * 4, mid_channels * 4, 4, 2, 1, bias=False)
self.bn2_1 = nn.BatchNorm2d(mid_channels * 4, affine=True)
self.conv3_0 = nn.Conv2d(
mid_channels * 4, mid_channels * 8, 3, 1, 1, bias=False)
self.bn3_0 = nn.BatchNorm2d(mid_channels * 8, affine=True)
self.conv3_1 = nn.Conv2d(
mid_channels * 8, mid_channels * 8, 4, 2, 1, bias=False)
self.bn3_1 = nn.BatchNorm2d(mid_channels * 8, affine=True)
self.conv4_0 = nn.Conv2d(
mid_channels * 8, mid_channels * 8, 3, 1, 1, bias=False)
self.bn4_0 = nn.BatchNorm2d(mid_channels * 8, affine=True)
self.conv4_1 = nn.Conv2d(
mid_channels * 8, mid_channels * 8, 4, 2, 1, bias=False)
self.bn4_1 = nn.BatchNorm2d(mid_channels * 8, affine=True)
self.linear1 = nn.Linear(mid_channels * 8 * 4 * 4, 100)
self.linear2 = nn.Linear(100, 1)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
[docs] def forward(self, x):
"""Forward function.
Args:
x (Tensor): Input tensor with shape (n, c, h, w).
Returns:
Tensor: Forward results.
"""
assert x.size(2) == 128 and x.size(3) == 128, (
f'Input spatial size must be 128x128, '
f'but received {x.size()}.')
feat = self.lrelu(self.conv0_0(x))
feat = self.lrelu(self.bn0_1(
self.conv0_1(feat))) # output spatial size: (64, 64)
feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))
feat = self.lrelu(self.bn1_1(
self.conv1_1(feat))) # output spatial size: (32, 32)
feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))
feat = self.lrelu(self.bn2_1(
self.conv2_1(feat))) # output spatial size: (16, 16)
feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))
feat = self.lrelu(self.bn3_1(
self.conv3_1(feat))) # output spatial size: (8, 8)
feat = self.lrelu(self.bn4_0(self.conv4_0(feat)))
feat = self.lrelu(self.bn4_1(
self.conv4_1(feat))) # output spatial size: (4, 4)
feat = feat.view(feat.size(0), -1)
feat = self.lrelu(self.linear1(feat))
out = self.linear2(feat)
return out
[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 PyTorch default initialization.
else:
raise TypeError(f'"pretrained" must be a str or None. '
f'But received {type(pretrained)}.')