diff --git a/codes/models/archs/SwitchedResidualGenerator_arch.py b/codes/models/archs/SwitchedResidualGenerator_arch.py index 09b5b60d..0998f8bb 100644 --- a/codes/models/archs/SwitchedResidualGenerator_arch.py +++ b/codes/models/archs/SwitchedResidualGenerator_arch.py @@ -86,84 +86,13 @@ class SwitchComputer(nn.Module): multiplexer = self.proc_switch_conv(multiplexer) multiplexer = self.final_switch_conv.forward(multiplexer) # Interpolate the multiplexer across the entire shape of the image. - multiplexer = F.interpolate(multiplexer, size=x.shape[2:], mode='nearest') + multiplexer = F.interpolate(multiplexer, size=x.shape[2:], mode='nearest', recompute_scale_factor=True) return self.switch(xformed, multiplexer, output_attention_weights) def set_temperature(self, temp): self.switch.set_attention_temperature(temp) -class SwitchedResidualGenerator(nn.Module): - def __init__(self, switch_filters, initial_temp=20, final_temperature_step=50000): - super(SwitchedResidualGenerator, self).__init__() - self.switch1 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=7, depth=3), 4, 4, 0, initial_temp) - self.switch2 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=5, depth=3), 8, 3, 0, initial_temp) - self.switch3 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=3, depth=3), 16, 2, 1, initial_temp) - self.switch4 = SwitchComputer(3, switch_filters * 2, functools.partial(ResidualBranch, 3, 3, kernel_size=3, depth=2), 32, 1, 2, initial_temp) - initialize_weights([self.switch1, self.switch2, self.switch3, self.switch4], 1) - # Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image. - initialize_weights([self.switch1.transforms, self.switch2.transforms, self.switch3.transforms, self.switch4.transforms], .05) - - self.init_temperature = initial_temp - self.final_temperature_step = final_temperature_step - self.running_sum = [0, 0, 0, 0] - self.running_hist = [[],[],[],[]] - self.running_count = 0 - - def forward(self, x): - sw1, self.a1 = self.switch1.forward(x, True) - x = x + sw1 - sw2, self.a2 = self.switch2.forward(x, True) - x = x + sw2 - sw3, self.a3 = self.switch3.forward(x, True) - x = x + sw3 - sw4, self.a4 = self.switch4.forward(x, True) - x = x + sw4 - - a1mean, a1i = compute_attention_specificity(self.a1, 2) - a2mean, a2i = compute_attention_specificity(self.a2, 2) - a3mean, a3i = compute_attention_specificity(self.a3, 2) - a4mean, a4i = compute_attention_specificity(self.a4, 2) - running_sum = [ - self.running_sum[0] + a1mean, - self.running_sum[1] + a2mean, - self.running_sum[2] + a3mean, - self.running_sum[3] + a4mean, - ] - self.running_hist[0].append(a1i.detach().cpu().flatten()) - self.running_hist[1].append(a2i.detach().cpu().flatten()) - self.running_hist[2].append(a3i.detach().cpu().flatten()) - self.running_hist[3].append(a4i.detach().cpu().flatten()) - self.running_count += 1 - - return (x,) - - def set_temperature(self, temp): - self.switch1.set_temperature(temp) - self.switch2.set_temperature(temp) - self.switch3.set_temperature(temp) - self.switch4.set_temperature(temp) - - def get_debug_values(self, step): - # Take the chance to update the temperature here. - temp = max(1, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)) - self.set_temperature(temp) - - if step % 250 == 0: - save_attention_to_image(self.a1, 4, step, "a1") - save_attention_to_image(self.a2, 8, step, "a2", 2) - save_attention_to_image(self.a3, 16, step, "a3", 4) - save_attention_to_image(self.a4, 32, step, "a4", 8) - - val = {"switch_temperature": temp} - for i in range(len(self.running_sum)): - val["switch_%i_specificity" % (i,)] = self.running_sum[i] / self.running_count - val["switch_%i_histogram" % (i,)] = torch.cat(self.running_hist[i]) - self.running_sum[i] = 0 - self.running_hist[i] = [] - self.running_count = 0 - return val - class ConfigurableSwitchedResidualGenerator(nn.Module): def __init__(self, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers, initial_temp=20, final_temperature_step=50000): @@ -178,22 +107,13 @@ class ConfigurableSwitchedResidualGenerator(nn.Module): self.transformation_counts = trans_counts self.init_temperature = initial_temp self.final_temperature_step = final_temperature_step - self.running_sum = [0 for i in range(len(switches))] - self.running_hist = [[] for i in range(len(switches))] - self.running_count = 0 def forward(self, x): self.attentions = [] for i, sw in enumerate(self.switches): x, att = sw.forward(x, True) self.attentions.append(att) - spec, hist = compute_attention_specificity(att, 2) - self.running_sum[i] += spec - self.running_hist[i].append(hist.detach().cpu().flatten()) - - self.running_count += 1 - - return (x,) + return x, def set_temperature(self, temp): [sw.set_temperature(temp) for sw in self.switches] @@ -206,11 +126,11 @@ class ConfigurableSwitchedResidualGenerator(nn.Module): if step % 250 == 0: [save_attention_to_image(self.attentions[i], self.transformation_counts[i], step, "a%i" % (i+1,), l_mult=float(self.transformation_counts[i]/4)) for i in range(len(self.switches))] + 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(self.running_sum)): - val["switch_%i_specificity" % (i,)] = self.running_sum[i] / self.running_count - self.running_sum[i] = 0 - val["switch_%i_histogram" % (i,)] = torch.cat(self.running_hist[i]) - self.running_hist[i] = [] - self.running_count = 0 + for i in range(len(means)): + val["switch_%i_specificity" % (i,)] = means[i] + val["switch_%i_histogram" % (i,)] = hists[i] return val \ No newline at end of file diff --git a/codes/models/networks.py b/codes/models/networks.py index 807f1478..255c10cc 100644 --- a/codes/models/networks.py +++ b/codes/models/networks.py @@ -62,16 +62,6 @@ def define_G(opt, net_key='network_G'): init_temperature=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step']) netG = RRDBNet_arch.PixShuffleRRDB(nf=opt_net['nf'], nb=opt_net['nb'], gc=opt_net['gc'], scale=scale, rrdb_block_f=block_f) - elif which_model == 'ResGen': - netG = ResGen_arch.fixup_resnet34(nb_denoiser=opt_net['nb_denoiser'], nb_upsampler=opt_net['nb_upsampler'], - upscale_applications=opt_net['upscale_applications'], num_filters=opt_net['nf']) - elif which_model == 'ResGenV2': - netG = ResGen_arch.fixup_resnet34_v2(nb_denoiser=opt_net['nb_denoiser'], nb_upsampler=opt_net['nb_upsampler'], - upscale_applications=opt_net['upscale_applications'], num_filters=opt_net['nf'], - inject_noise=opt_net['inject_noise']) - elif which_model == "SwitchedResidualGenerator": - netG = SwitchedGen_arch.SwitchedResidualGenerator(switch_filters=opt_net['nf'], initial_temp=opt_net['temperature'], - final_temperature_step=opt_net['temperature_final_step']) elif which_model == "ConfigurableSwitchedResidualGenerator": netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator(switch_filters=opt_net['switch_filters'], switch_reductions=opt_net['switch_reductions'], switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],