def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=16, upscale=4): super(MSRResNet, self).__init__() self.upscale = upscale self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) self.body = arch_util.make_layer( arch_util.ResidualBlockNoBN, num_block, num_feat=num_feat) # upsampling if self.upscale in [2, 3]: self.upconv1 = nn.Conv2d(num_feat, num_feat * self.upscale * self.upscale, 3, 1, 1) self.pixel_shuffle = nn.PixelShuffle(self.upscale) elif self.upscale == 4: self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) self.upconv2 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) self.pixel_shuffle = nn.PixelShuffle(2) self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) # initialization arch_util.default_init_weights( [self.conv_first, self.upconv1, self.conv_hr, self.conv_last], 0.1) if self.upscale == 4: arch_util.default_init_weights(self.upconv2, 0.1)
def __init__(self, num_feat=64, num_grow_ch=32): super(ResidualDenseBlock, self).__init__() self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) # initialization default_init_weights( [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=16, upscale=4): super(MSRResNet_WT_Pixel, self).__init__() self.upscale = upscale self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) self.body = arch_util.make_layer( arch_util.ResidualBlockNoBN, num_block, num_feat=num_feat) # upsampling if self.upscale in [2, 3]: self.upconv1 = nn.Conv2d(num_feat, num_feat * self.upscale * self.upscale, 3, 1, 1) self.pixel_shuffle = nn.PixelShuffle(self.upscale) elif self.upscale == 4: self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) self.upconv2 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) self.pixel_shuffle = nn.PixelShuffle(2) self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) # initialization arch_util.default_init_weights( [self.conv_first, self.upconv1, self.conv_hr, self.conv_last], 0.1) if self.upscale == 4: arch_util.default_init_weights(self.upconv2, 0.1) # WT filter inv_filters = arch_util.create_inv_filters() self.register_buffer('inv_filters', inv_filters) # Normalization buffers self.register_buffer( 'shift', torch.Tensor([3.0])) self.register_buffer( 'scale', torch.Tensor([10.0]))
def __init__( self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=16, ): super().__init__() self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) self.body = arch_util.make_layer(arch_util.ResidualBlockNoBN, num_block, num_feat=num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) # initialization arch_util.default_init_weights([self.conv_first, self.conv_last], 0.1)
def __init__(self, num_feat=64, num_grow_ch=32): super(LowrankResidualDenseBlock, self).__init__() #self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) self.conv1_u = nn.Conv2d(num_feat, int(num_grow_ch / 4), 3, 1, 1) self.conv1_v = nn.Conv2d(int(num_grow_ch / 4), num_grow_ch, 1, 1) #self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) self.conv2_u = nn.Conv2d(num_feat + num_grow_ch, int(num_grow_ch / 4), 3, 1, 1) self.conv2_v = nn.Conv2d(int(num_grow_ch / 4), num_grow_ch, 1, 1) #self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, # 1) self.conv3_u = nn.Conv2d(num_feat + 2 * num_grow_ch, int(num_grow_ch / 4), 3, 1, 1) self.conv3_v = nn.Conv2d(int(num_grow_ch / 4), num_grow_ch, 1, 1) #self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, # 1) self.conv4_u = nn.Conv2d(num_feat + 3 * num_grow_ch, int(num_grow_ch / 4), 3, 1, 1) self.conv4_v = nn.Conv2d(int(num_grow_ch / 4), num_grow_ch, 1, 1) #self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) self.conv5_u = nn.Conv2d(num_feat + 4 * num_grow_ch, int(num_feat / 4), 3, 1, 1) self.conv5_v = nn.Conv2d(int(num_feat / 4), num_feat, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) # initialization #default_init_weights( # [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) default_init_weights([ self.conv1_u, self.conv1_v, self.conv2_u, self.conv2_v, self.conv3_u, self.conv3_v, self.conv4_u, self.conv4_v, self.conv5_u, self.conv5_v ], 0.1)