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Add parameterized noise injection into resgen

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This commit is contained in:
James Betker 2020-06-23 10:16:02 -06:00
parent 0584c3b587
commit 83c3b8b982
2 changed files with 16 additions and 6 deletions
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20
codes/models/archs/SwitchedResidualGenerator_arch.py
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@ -37,13 +37,17 @@ class ResidualBranch(nn.Module):
def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth):
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.scale = nn.Parameter(torch.ones(1))
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, x):
def forward(self, x, noise=None):
if noise is not None:
noise = noise * self.noise_scale
x = x + noise
for m in self.bnconvs:
x = m.forward(x)
return x * self.scale + self.bias
@ -75,7 +79,7 @@ def create_sequential_growing_processing_block(filters_init, filter_growth, num_
class SwitchComputer(nn.Module):
def __init__(self, channels_in, filters, growth, transform_block, transform_count, reduction_blocks, processing_blocks=0,
init_temp=20, enable_negative_transforms=False):
init_temp=20, enable_negative_transforms=False, add_scalable_noise_to_transforms=False):
super(SwitchComputer, self).__init__()
self.enable_negative_transforms = enable_negative_transforms
@ -91,6 +95,7 @@ class SwitchComputer(nn.Module):
self.final_switch_conv = nn.Conv2d(proc_block_filters, tc, 1, 1, 0)
self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
self.add_noise = add_scalable_noise_to_transforms
# And the switch itself, including learned scalars
self.switch = BareConvSwitch(initial_temperature=init_temp)
@ -98,7 +103,11 @@ class SwitchComputer(nn.Module):
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, x, output_attention_weights=False):
xformed = [t.forward(x) for t in self.transforms]
if self.add_noise:
rand_feature = torch.randn_like(x)
xformed = [t.forward(x, rand_feature) for t in self.transforms]
else:
xformed = [t.forward(x) for t in self.transforms]
if self.enable_negative_transforms:
xformed.extend([-t for t in xformed])
@ -126,11 +135,12 @@ class SwitchComputer(nn.Module):
class ConfigurableSwitchedResidualGenerator(nn.Module):
def __init__(self, switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
trans_layers, trans_filters_mid, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
heightened_final_step=50000, upsample_factor=1):
heightened_final_step=50000, upsample_factor=1, enable_negative_transforms=False,
add_scalable_noise_to_transforms=False):
super(ConfigurableSwitchedResidualGenerator, self).__init__()
switches = []
for filters, growth, sw_reduce, sw_proc, trans_count, kernel, layers, mid_filters in zip(switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers, trans_filters_mid):
switches.append(SwitchComputer(3, filters, growth, functools.partial(ResidualBranch, 3, mid_filters, 3, kernel_size=kernel, depth=layers), trans_count, sw_reduce, sw_proc, initial_temp))
switches.append(SwitchComputer(3, filters, growth, functools.partial(ResidualBranch, 3, mid_filters, 3, kernel_size=kernel, depth=layers), trans_count, sw_reduce, sw_proc, initial_temp, enable_negative_transforms=enable_negative_transforms, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
initialize_weights(switches, 1)
# Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
initialize_weights([s.transforms for s in switches], .2 / len(switches))

2
codes/models/networks.py
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@ -70,7 +70,7 @@ def define_G(opt, net_key='network_G'):
trans_filters_mid=opt_net['trans_filters_mid'],
initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
upsample_factor=scale)
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
# image corruption
elif which_model == 'HighToLowResNet':