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import torch
import torch.nn as nn
import torch.nn.functional as F
from basicsr.utils.registry import ARCH_REGISTRY
class SeqConv3x3(nn.Module):
"""The re-parameterizable block used in the ECBSR architecture.
``Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices``
Reference: https://github.com/xindongzhang/ECBSR
Args:
seq_type (str): Sequence type, option: conv1x1-conv3x3 | conv1x1-sobelx | conv1x1-sobely | conv1x1-laplacian.
in_channels (int): Channel number of input.
out_channels (int): Channel number of output.
depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1.
"""
def __init__(self, seq_type, in_channels, out_channels, depth_multiplier=1):
super(SeqConv3x3, self).__init__()
self.seq_type = seq_type
self.in_channels = in_channels
self.out_channels = out_channels
if self.seq_type == 'conv1x1-conv3x3':
self.mid_planes = int(out_channels * depth_multiplier)
conv0 = torch.nn.Conv2d(self.in_channels, self.mid_planes, kernel_size=1, padding=0)
self.k0 = conv0.weight
self.b0 = conv0.bias
conv1 = torch.nn.Conv2d(self.mid_planes, self.out_channels, kernel_size=3)
self.k1 = conv1.weight
self.b1 = conv1.bias
elif self.seq_type == 'conv1x1-sobelx':
conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0)
self.k0 = conv0.weight
self.b0 = conv0.bias
# init scale and bias
scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3
self.scale = nn.Parameter(scale)
bias = torch.randn(self.out_channels) * 1e-3
bias = torch.reshape(bias, (self.out_channels, ))
self.bias = nn.Parameter(bias)
# init mask
self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32)
for i in range(self.out_channels):
self.mask[i, 0, 0, 0] = 1.0
self.mask[i, 0, 1, 0] = 2.0
self.mask[i, 0, 2, 0] = 1.0
self.mask[i, 0, 0, 2] = -1.0
self.mask[i, 0, 1, 2] = -2.0
self.mask[i, 0, 2, 2] = -1.0
self.mask = nn.Parameter(data=self.mask, requires_grad=False)
elif self.seq_type == 'conv1x1-sobely':
conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0)
self.k0 = conv0.weight
self.b0 = conv0.bias
# init scale and bias
scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3
self.scale = nn.Parameter(torch.FloatTensor(scale))
bias = torch.randn(self.out_channels) * 1e-3
bias = torch.reshape(bias, (self.out_channels, ))
self.bias = nn.Parameter(torch.FloatTensor(bias))
# init mask
self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32)
for i in range(self.out_channels):
self.mask[i, 0, 0, 0] = 1.0
self.mask[i, 0, 0, 1] = 2.0
self.mask[i, 0, 0, 2] = 1.0
self.mask[i, 0, 2, 0] = -1.0
self.mask[i, 0, 2, 1] = -2.0
self.mask[i, 0, 2, 2] = -1.0
self.mask = nn.Parameter(data=self.mask, requires_grad=False)
elif self.seq_type == 'conv1x1-laplacian':
conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0)
self.k0 = conv0.weight
self.b0 = conv0.bias
# init scale and bias
scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3
self.scale = nn.Parameter(torch.FloatTensor(scale))
bias = torch.randn(self.out_channels) * 1e-3
bias = torch.reshape(bias, (self.out_channels, ))
self.bias = nn.Parameter(torch.FloatTensor(bias))
# init mask
self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32)
for i in range(self.out_channels):
self.mask[i, 0, 0, 1] = 1.0
self.mask[i, 0, 1, 0] = 1.0
self.mask[i, 0, 1, 2] = 1.0
self.mask[i, 0, 2, 1] = 1.0
self.mask[i, 0, 1, 1] = -4.0
self.mask = nn.Parameter(data=self.mask, requires_grad=False)
else:
raise ValueError('The type of seqconv is not supported!')
def forward(self, x):
if self.seq_type == 'conv1x1-conv3x3':
# conv-1x1
y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1)
# explicitly padding with bias
y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0)
b0_pad = self.b0.view(1, -1, 1, 1)
y0[:, :, 0:1, :] = b0_pad
y0[:, :, -1:, :] = b0_pad
y0[:, :, :, 0:1] = b0_pad
y0[:, :, :, -1:] = b0_pad
# conv-3x3
y1 = F.conv2d(input=y0, weight=self.k1, bias=self.b1, stride=1)
else:
y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1)
# explicitly padding with bias
y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0)
b0_pad = self.b0.view(1, -1, 1, 1)
y0[:, :, 0:1, :] = b0_pad
y0[:, :, -1:, :] = b0_pad
y0[:, :, :, 0:1] = b0_pad
y0[:, :, :, -1:] = b0_pad
# conv-3x3
y1 = F.conv2d(input=y0, weight=self.scale * self.mask, bias=self.bias, stride=1, groups=self.out_channels)
return y1
def rep_params(self):
device = self.k0.get_device()
if device < 0:
device = None
if self.seq_type == 'conv1x1-conv3x3':
# re-param conv kernel
rep_weight = F.conv2d(input=self.k1, weight=self.k0.permute(1, 0, 2, 3))
# re-param conv bias
rep_bias = torch.ones(1, self.mid_planes, 3, 3, device=device) * self.b0.view(1, -1, 1, 1)
rep_bias = F.conv2d(input=rep_bias, weight=self.k1).view(-1, ) + self.b1
else:
tmp = self.scale * self.mask
k1 = torch.zeros((self.out_channels, self.out_channels, 3, 3), device=device)
for i in range(self.out_channels):
k1[i, i, :, :] = tmp[i, 0, :, :]
b1 = self.bias
# re-param conv kernel
rep_weight = F.conv2d(input=k1, weight=self.k0.permute(1, 0, 2, 3))
# re-param conv bias
rep_bias = torch.ones(1, self.out_channels, 3, 3, device=device) * self.b0.view(1, -1, 1, 1)
rep_bias = F.conv2d(input=rep_bias, weight=k1).view(-1, ) + b1
return rep_weight, rep_bias
class ECB(nn.Module):
"""The ECB block used in the ECBSR architecture.
Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
Ref git repo: https://github.com/xindongzhang/ECBSR
Args:
in_channels (int): Channel number of input.
out_channels (int): Channel number of output.
depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1.
act_type (str): Activation type. Option: prelu | relu | rrelu | softplus | linear. Default: prelu.
with_idt (bool): Whether to use identity connection. Default: False.
"""
def __init__(self, in_channels, out_channels, depth_multiplier, act_type='prelu', with_idt=False):
super(ECB, self).__init__()
self.depth_multiplier = depth_multiplier
self.in_channels = in_channels
self.out_channels = out_channels
self.act_type = act_type
if with_idt and (self.in_channels == self.out_channels):
self.with_idt = True
else:
self.with_idt = False
self.conv3x3 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=3, padding=1)
self.conv1x1_3x3 = SeqConv3x3('conv1x1-conv3x3', self.in_channels, self.out_channels, self.depth_multiplier)
self.conv1x1_sbx = SeqConv3x3('conv1x1-sobelx', self.in_channels, self.out_channels)
self.conv1x1_sby = SeqConv3x3('conv1x1-sobely', self.in_channels, self.out_channels)
self.conv1x1_lpl = SeqConv3x3('conv1x1-laplacian', self.in_channels, self.out_channels)
if self.act_type == 'prelu':
self.act = nn.PReLU(num_parameters=self.out_channels)
elif self.act_type == 'relu':
self.act = nn.ReLU(inplace=True)
elif self.act_type == 'rrelu':
self.act = nn.RReLU(lower=-0.05, upper=0.05)
elif self.act_type == 'softplus':
self.act = nn.Softplus()
elif self.act_type == 'linear':
pass
else:
raise ValueError('The type of activation if not support!')
def forward(self, x):
if self.training:
y = self.conv3x3(x) + self.conv1x1_3x3(x) + self.conv1x1_sbx(x) + self.conv1x1_sby(x) + self.conv1x1_lpl(x)
if self.with_idt:
y += x
else:
rep_weight, rep_bias = self.rep_params()
y = F.conv2d(input=x, weight=rep_weight, bias=rep_bias, stride=1, padding=1)
if self.act_type != 'linear':
y = self.act(y)
return y
def rep_params(self):
weight0, bias0 = self.conv3x3.weight, self.conv3x3.bias
weight1, bias1 = self.conv1x1_3x3.rep_params()
weight2, bias2 = self.conv1x1_sbx.rep_params()
weight3, bias3 = self.conv1x1_sby.rep_params()
weight4, bias4 = self.conv1x1_lpl.rep_params()
rep_weight, rep_bias = (weight0 + weight1 + weight2 + weight3 + weight4), (
bias0 + bias1 + bias2 + bias3 + bias4)
if self.with_idt:
device = rep_weight.get_device()
if device < 0:
device = None
weight_idt = torch.zeros(self.out_channels, self.out_channels, 3, 3, device=device)
for i in range(self.out_channels):
weight_idt[i, i, 1, 1] = 1.0
bias_idt = 0.0
rep_weight, rep_bias = rep_weight + weight_idt, rep_bias + bias_idt
return rep_weight, rep_bias
@ARCH_REGISTRY.register()
class ECBSR(nn.Module):
"""ECBSR architecture.
Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
Ref git repo: https://github.com/xindongzhang/ECBSR
Args:
num_in_ch (int): Channel number of inputs.
num_out_ch (int): Channel number of outputs.
num_block (int): Block number in the trunk network.
num_channel (int): Channel number.
with_idt (bool): Whether use identity in convolution layers.
act_type (str): Activation type.
scale (int): Upsampling factor.
"""
def __init__(self, num_in_ch, num_out_ch, num_block, num_channel, with_idt, act_type, scale):
super(ECBSR, self).__init__()
self.num_in_ch = num_in_ch
self.scale = scale
backbone = []
backbone += [ECB(num_in_ch, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)]
for _ in range(num_block):
backbone += [ECB(num_channel, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)]
backbone += [
ECB(num_channel, num_out_ch * scale * scale, depth_multiplier=2.0, act_type='linear', with_idt=with_idt)
]
self.backbone = nn.Sequential(*backbone)
self.upsampler = nn.PixelShuffle(scale)
def forward(self, x):
if self.num_in_ch > 1:
shortcut = torch.repeat_interleave(x, self.scale * self.scale, dim=1)
else:
shortcut = x # will repeat the input in the channel dimension (repeat scale * scale times)
y = self.backbone(x) + shortcut
y = self.upsampler(y)
return y
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