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More SRG2 adjustments..

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This commit is contained in:
James Betker 2020-07-06 22:40:40 -06:00
parent 086b2f0570
commit 0acad81035
1 changed files with 42 additions and 44 deletions
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86
codes/models/archs/SwitchedResidualGenerator_arch.py
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@ -54,6 +54,46 @@ def create_sequential_growing_processing_block(filters_init, filter_growth, num_
return nn.Sequential(*convs), current_filters
class ConvBasisMultiplexer(nn.Module):
def __init__(self, input_channels, base_filters, growth, reductions, processing_depth, multiplexer_channels, use_bn=True):
super(ConvBasisMultiplexer, self).__init__()
self.filter_conv = ConvBnSilu(input_channels, base_filters, bias=True)
self.reduction_blocks = nn.Sequential(OrderedDict([('block%i:' % (i,), HalvingProcessingBlock(base_filters * 2 ** i)) for i in range(reductions)]))
reduction_filters = base_filters * 2 ** reductions
self.processing_blocks, self.output_filter_count = create_sequential_growing_processing_block(reduction_filters, growth, processing_depth)
gap = self.output_filter_count - multiplexer_channels
# Hey silly - if you're going to interpolate later, do it here instead. Then add some processing layers to let the model adjust it properly.
self.cbl1 = ConvBnSilu(self.output_filter_count, self.output_filter_count - (gap // 2), bn=use_bn, bias=False)
self.cbl2 = ConvBnSilu(self.output_filter_count - (gap // 2), self.output_filter_count - (3 * gap // 4), bn=use_bn, bias=False)
self.cbl3 = ConvBnSilu(self.output_filter_count - (3 * gap // 4), multiplexer_channels, bias=True)
def forward(self, x):
x = self.filter_conv(x)
x = self.reduction_blocks(x)
x = self.processing_blocks(x)
x = self.cbl1(x)
x = self.cbl2(x)
x = self.cbl3(x)
return x
class SpineNetMultiplexer(nn.Module):
def __init__(self, input_channels, transform_count):
super(SpineNetMultiplexer, self).__init__()
self.backbone = SpineNet('49', in_channels=input_channels)
self.rdc1 = ConvBnSilu(256, 128, kernel_size=3, bias=False)
self.rdc2 = ConvBnSilu(128, 64, kernel_size=3, bias=False)
self.rdc3 = ConvBnSilu(64, transform_count, bias=False, bn=False, relu=False)
def forward(self, x):
spine = self.backbone(x)
feat = self.rdc1(spine[0])
feat = self.rdc2(feat)
feat = self.rdc3(feat)
return feat
class ConfigurableSwitchComputer(nn.Module):
def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, init_temp=20,
enable_negative_transforms=False, add_scalable_noise_to_transforms=False, init_scalar=1):
@ -106,46 +146,6 @@ class ConfigurableSwitchComputer(nn.Module):
self.switch.set_attention_temperature(temp)
class ConvBasisMultiplexer(nn.Module):
def __init__(self, input_channels, base_filters, growth, reductions, processing_depth, multiplexer_channels, use_bn=True):
super(ConvBasisMultiplexer, self).__init__()
self.filter_conv = ConvBnSilu(input_channels, base_filters, bias=True)
self.reduction_blocks = nn.Sequential(OrderedDict([('block%i:' % (i,), HalvingProcessingBlock(base_filters * 2 ** i)) for i in range(reductions)]))
reduction_filters = base_filters * 2 ** reductions
self.processing_blocks, self.output_filter_count = create_sequential_growing_processing_block(reduction_filters, growth, processing_depth)
gap = self.output_filter_count - multiplexer_channels
# Hey silly - if you're going to interpolate later, do it here instead. Then add some processing layers to let the model adjust it properly.
self.cbl1 = ConvBnSilu(self.output_filter_count, self.output_filter_count - (gap // 2), bn=use_bn, bias=False)
self.cbl2 = ConvBnSilu(self.output_filter_count - (gap // 2), self.output_filter_count - (3 * gap // 4), bn=use_bn, bias=False)
self.cbl3 = ConvBnSilu(self.output_filter_count - (3 * gap // 4), multiplexer_channels, bias=True)
def forward(self, x):
x = self.filter_conv(x)
x = self.reduction_blocks(x)
x = self.processing_blocks(x)
x = self.cbl1(x)
x = self.cbl2(x)
x = self.cbl3(x)
return x
class SpineNetMultiplexer(nn.Module):
def __init__(self, input_channels, transform_count):
super(SpineNetMultiplexer, self).__init__()
self.backbone = SpineNet('49', in_channels=input_channels)
self.rdc1 = ConvBnSilu(256, 128, kernel_size=3, bias=False)
self.rdc2 = ConvBnSilu(128, 64, kernel_size=3, bias=False)
self.rdc3 = ConvBnSilu(64, transform_count, bias=False, bn=False, relu=False)
def forward(self, x):
spine = self.backbone(x)
feat = self.rdc1(spine[0])
feat = self.rdc2(feat)
feat = self.rdc3(feat)
return feat
class ConfigurableSwitchedResidualGenerator2(nn.Module):
def __init__(self, switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
@ -165,7 +165,7 @@ class ConfigurableSwitchedResidualGenerator2(nn.Module):
pre_transform_block=functools.partial(ConvBnLelu, transformation_filters, transformation_filters, bn=False, bias=False),
transform_block=functools.partial(MultiConvBlock, transformation_filters, transformation_filters + growth, transformation_filters, kernel_size=kernel, depth=layers),
transform_count=trans_count, init_temp=initial_temp, enable_negative_transforms=enable_negative_transforms,
add_scalable_noise_to_transforms=add_scalable_noise_to_transforms, init_scalar=.2))
add_scalable_noise_to_transforms=add_scalable_noise_to_transforms, init_scalar=.1))
self.switches = nn.ModuleList(switches)
self.transformation_counts = trans_counts
@ -181,11 +181,9 @@ class ConfigurableSwitchedResidualGenerator2(nn.Module):
x = self.initial_conv(x)
self.attentions = []
swx = x
for i, sw in enumerate(self.switches):
swx, att = sw.forward(swx, True)
x, att = sw.forward(x, True)
self.attentions.append(att)
x = swx + self.sw_conv(x)
x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
if self.upsample_factor > 2: