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Remove dualoutputsrg

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Good idea, didn't pan out.
This commit is contained in:
James Betker 2020-07-16 10:09:24 -06:00
parent 4bcc409fc7
commit 0c4c388e15
1 changed files with 0 additions and 92 deletions
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92
codes/models/archs/SwitchedResidualGenerator_arch.py
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@ -335,95 +335,3 @@ class ConfigurableSwitchedResidualGenerator3(nn.Module):
val["switch_%i_histogram" % (i,)] = hists[i]
return val
class DualOutputSRG(nn.Module):
def __init__(self, switch_depth, switch_filters, 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,
heightened_final_step=50000, upsample_factor=1,
add_scalable_noise_to_transforms=False):
super(DualOutputSRG, self).__init__()
switches = []
self.initial_conv = ConvBnLelu(3, transformation_filters, norm=False, activation=False, bias=True)
self.fea_upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
self.fea_upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
self.fea_hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
self.fea_final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
for _ in range(switch_depth):
multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts)
pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5), transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers, weight_init_factor=.1)
switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
transform_count=trans_counts, init_temp=initial_temp,
add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
self.switches = nn.ModuleList(switches)
self.transformation_counts = trans_counts
self.init_temperature = initial_temp
self.final_temperature_step = final_temperature_step
self.heightened_temp_min = heightened_temp_min
self.heightened_final_step = heightened_final_step
self.attentions = None
self.upsample_factor = upsample_factor
assert self.upsample_factor == 2 or self.upsample_factor == 4
def forward(self, x):
x = self.initial_conv(x)
self.attentions = []
for i, sw in enumerate(self.switches):
x, att = sw.forward(x, True)
self.attentions.append(att)
if i == len(self.switches)-2:
fea = self.fea_upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
if self.upsample_factor > 2:
fea = F.interpolate(fea, scale_factor=2, mode="nearest")
fea = self.fea_upconv2(fea)
fea = self.fea_final_conv(self.hr_conv(fea))
x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
if self.upsample_factor > 2:
x = F.interpolate(x, scale_factor=2, mode="nearest")
x = self.upconv2(x)
return fea, self.final_conv(self.hr_conv(x))
def set_temperature(self, temp):
[sw.set_temperature(temp) for sw in self.switches]
def update_for_step(self, step, experiments_path='.'):
if self.attentions:
temp = max(1,
1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)
if temp == 1 and self.heightened_final_step and step > self.final_temperature_step and \
self.heightened_final_step != 1:
# Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
# without this, the attention specificity "spikes" incredibly fast in the last few iterations.
h_steps_total = self.heightened_final_step - self.final_temperature_step
h_steps_current = min(step - self.final_temperature_step, h_steps_total)
# The "gap" will represent the steps that need to be traveled as a linear function.
h_gap = 1 / self.heightened_temp_min
temp = h_gap * h_steps_current / h_steps_total
# Invert temperature to represent reality on this side of the curve
temp = 1 / temp
self.set_temperature(temp)
if step % 50 == 0:
save_attention_to_image(experiments_path, self.attentions[0], self.transformation_counts, step, "a%i" % (1,), l_mult=10)
def get_debug_values(self, step):
temp = self.switches[0].switch.temperature
mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
means = [i[0] for i in mean_hists]
hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
val = {"switch_temperature": temp}
for i in range(len(means)):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val