Spaces:
Sleeping
Sleeping
| 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 | |
| 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 | |