From c58c2b09ca822fdbf1ea02e32052e3d1e3664a5c Mon Sep 17 00:00:00 2001 From: James Betker Date: Sat, 4 Jul 2020 22:41:02 -0600 Subject: [PATCH] Back to remove all biases (looks like a ConvBnRelu made its way in..) --- codes/models/archs/SRG1_arch.py | 29 ++++++++++------------------- 1 file changed, 10 insertions(+), 19 deletions(-) diff --git a/codes/models/archs/SRG1_arch.py b/codes/models/archs/SRG1_arch.py index 99fa4095..d91eb09a 100644 --- a/codes/models/archs/SRG1_arch.py +++ b/codes/models/archs/SRG1_arch.py @@ -8,11 +8,11 @@ from switched_conv_util import save_attention_to_image class ConvBnLelu(nn.Module): - def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True, bn=True): + def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True, bn=True, bias=True): super(ConvBnLelu, self).__init__() padding_map = {1: 0, 3: 1, 5: 2, 7: 3} assert kernel_size in padding_map.keys() - self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size]) + self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size], bias=bias) if bn: self.bn = nn.BatchNorm2d(filters_out) else: @@ -22,15 +22,6 @@ class ConvBnLelu(nn.Module): else: self.lelu = None - # Init params. - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, a=.1, mode='fan_out', - nonlinearity='leaky_relu' if self.lelu else 'linear') - elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - def forward(self, x): x = self.conv(x) if self.bn: @@ -42,13 +33,14 @@ class ConvBnLelu(nn.Module): class ResidualBranch(nn.Module): - def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth): + def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, bn=False): assert depth >= 2 super(ResidualBranch, self).__init__() self.noise_scale = nn.Parameter(torch.full((1,), fill_value=.01)) - self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, bn=False)] + - [ConvBnLelu(filters_mid, filters_mid, kernel_size, bn=False) for i in range(depth-2)] + - [ConvBnLelu(filters_mid, filters_out, kernel_size, lelu=False, bn=False)]) + self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, bn=bn, bias=False)] + + [ConvBnLelu(filters_mid, filters_mid, kernel_size, bn=bn, bias=False) for i in range(depth-2)] + + [ConvBnLelu(filters_mid, filters_out, kernel_size, lelu=False, bn=False, bias=False)]) + self.scale = nn.Parameter(torch.ones(1)) self.bias = nn.Parameter(torch.zeros(1)) @@ -66,9 +58,8 @@ class ResidualBranch(nn.Module): class HalvingProcessingBlock(nn.Module): def __init__(self, filters): super(HalvingProcessingBlock, self).__init__() - self.bnconv1 = ConvBnLelu(filters, filters * 2, stride=2, bn=False) - self.bnconv2 = ConvBnLelu(filters * 2, filters * 2, bn=True) - + self.bnconv1 = ConvBnLelu(filters, filters * 2, stride=2, bn=False, bias=False) + self.bnconv2 = ConvBnLelu(filters * 2, filters * 2, bn=True, bias=False) def forward(self, x): x = self.bnconv1(x) return self.bnconv2(x) @@ -80,7 +71,7 @@ def create_sequential_growing_processing_block(filters_init, filter_growth, num_ convs = [] current_filters = filters_init for i in range(num_convs): - convs.append(ConvBnLelu(current_filters, current_filters + filter_growth, bn=True)) + convs.append(ConvBnLelu(current_filters, current_filters + filter_growth, bn=True, bias=False)) current_filters += filter_growth return nn.Sequential(*convs), current_filters