forked from mrq/DL-Art-School
Huge set of mods to support progressive generator growth
This commit is contained in:
James Betker
parent
47a525241f
commit
8a9f215653
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@ -103,12 +103,15 @@ class SRGANModel(BaseModel):
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# G
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wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
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optim_params = []
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for k, v in self.netG.named_parameters(): # can optimize for a part of the model
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if v.requires_grad:
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optim_params.append(v)
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else:
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if self.rank <= 0:
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logger.warning('Params [{:s}] will not optimize.'.format(k))
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if train_opt['lr_scheme'] == 'ProgressiveMultiStepLR':
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optim_params = self.netG.get_param_groups()
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else:
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for k, v in self.netG.named_parameters(): # can optimize for a part of the model
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if v.requires_grad:
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optim_params.append(v)
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else:
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if self.rank <= 0:
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logger.warning('Params [{:s}] will not optimize.'.format(k))
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self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'],
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weight_decay=wd_G,
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betas=(train_opt['beta1_G'], train_opt['beta2_G']))
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@ -148,6 +151,15 @@ class SRGANModel(BaseModel):
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gamma=train_opt['lr_gamma'],
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clear_state=train_opt['clear_state'],
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force_lr=train_opt['force_lr']))
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elif train_opt['lr_scheme'] == 'ProgressiveMultiStepLR':
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# Only supported when there are two optimizers: G and D.
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assert len(self.optimizers) == 2
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self.schedulers.append(lr_scheduler.ProgressiveMultiStepLR(self.optimizer_G, train_opt['gen_lr_steps'],
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self.netG.module.get_progressive_starts(),
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train_opt['lr_gamma']))
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self.schedulers.append(lr_scheduler.ProgressiveMultiStepLR(self.optimizer_D, train_opt['disc_lr_steps'],
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[0],
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train_opt['lr_gamma']))
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elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
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for optimizer in self.optimizers:
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self.schedulers.append(
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@ -492,6 +504,12 @@ class SRGANModel(BaseModel):
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self.add_log_entry('l_d_fake_total', l_d_fake.item() * self.mega_batch_factor)
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self.add_log_entry('l_d_real_total', l_d_real.item() * self.mega_batch_factor)
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# Log learning rates.
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for i, pg in enumerate(self.optimizer_G.param_groups):
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self.add_log_entry('gen_lr_%i' % (i,), pg['lr'])
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for i, pg in enumerate(self.optimizer_D.param_groups):
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self.add_log_entry('disc_lr_%i' % (i,), pg['lr'])
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if step % self.corruptor_swapout_steps == 0 and step > 0:
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self.load_random_corruptor()
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@ -14,7 +14,7 @@ import torch.nn.functional as F
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# 4) start_step will need to get set properly when constructing these models, even when resuming - OR another method needs to be added to resume properly.
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class GrowingSRGBase(nn.Module):
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def __init__(self, progressive_schedule, growth_fade_in_steps, switch_filters, switch_processing_layers, trans_counts,
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def __init__(self, progressive_step_schedule, switch_reductions, growth_fade_in_steps, switch_filters, switch_processing_layers, trans_counts,
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trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, upsample_factor=1,
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add_scalable_noise_to_transforms=False, start_step=0):
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super(GrowingSRGBase, self).__init__()
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@ -29,9 +29,8 @@ class GrowingSRGBase(nn.Module):
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self.switch_processing_layers = switch_processing_layers
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self.trans_layers = trans_layers
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self.transformation_filters = transformation_filters
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self.switches = nn.ModuleList([])
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self.progressive_schedule = progressive_schedule
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self.progressive_schedule = progressive_step_schedule
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self.switch_reductions = switch_reductions # This lists the reductions for all switches (even ones not activated yet).
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self.growth_fade_in_per_step = 1 / growth_fade_in_steps
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self.transformation_counts = trans_counts
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self.init_temperature = initial_temp
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@ -39,39 +38,64 @@ class GrowingSRGBase(nn.Module):
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self.attentions = None
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self.upsample_factor = upsample_factor
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self.add_noise_to_transform = add_scalable_noise_to_transforms
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self.latest_step = 0
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self.start_step = start_step
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self.latest_step = start_step
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self.fades = []
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assert self.upsample_factor == 2 or self.upsample_factor == 4
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for i, step in enumerate(progressive_schedule):
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if step >= start_step:
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self.add_layer(i + 1)
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def add_layer(self, reductions):
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multiplx_fn = functools.partial(srg.ConvBasisMultiplexer, self.transformation_filters, self.switch_filters,
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switches = []
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for i, (step, reductions) in enumerate(zip(progressive_step_schedule, switch_reductions)):
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multiplx_fn = functools.partial(srg.ConvBasisMultiplexer, self.transformation_filters, self.switch_filters,
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reductions, self.switch_processing_layers, self.transformation_counts)
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pretransform_fn = functools.partial(ConvBnLelu, self.transformation_filters, self.transformation_filters, norm=False,
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bias=False, weight_init_factor=.1)
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transform_fn = functools.partial(srg.MultiConvBlock, self.transformation_filters, int(self.transformation_filters * 1.5),
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self.transformation_filters, kernel_size=3, depth=self.trans_layers,
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weight_init_factor=.1)
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self.switches.append(srg.ConfigurableSwitchComputer(self.transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn,
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transform_block=transform_fn,
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transform_count=self.transformation_counts, init_temp=self.init_temperature,
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add_scalable_noise_to_transforms=self.add_noise_to_transform))
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pretransform_fn = functools.partial(ConvGnLelu, self.transformation_filters, self.transformation_filters, norm=False,
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bias=False, weight_init_factor=.1)
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transform_fn = functools.partial(srg.MultiConvBlock, self.transformation_filters, int(self.transformation_filters * 1.5),
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self.transformation_filters, kernel_size=3, depth=self.trans_layers,
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weight_init_factor=.1)
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switches.append(srg.ConfigurableSwitchComputer(self.transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn,
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transform_block=transform_fn,
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transform_count=self.transformation_counts, init_temp=self.init_temperature,
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add_scalable_noise_to_transforms=self.add_noise_to_transform,
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attention_norm=False))
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self.progressive_switches = nn.ModuleList(switches)
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def get_param_groups(self):
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param_groups = []
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base_param_group = []
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for k, v in self.named_parameters():
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if "progressive_switches" not in k and v.requires_grad:
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base_param_group.append(v)
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param_groups.append({'params': base_param_group})
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for i, sw in enumerate(self.progressive_switches):
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sw_param_group = []
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for k, v in sw.named_parameters():
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if v.requires_grad:
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sw_param_group.append(v)
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param_groups.append({'params': sw_param_group})
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return param_groups
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def get_progressive_starts(self):
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# The base param group starts at step 0, the rest are defined via progressive_switches.
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return [0] + self.progressive_schedule
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def forward(self, x):
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x = self.initial_conv(x)
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self.attentions = []
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for i, sw in enumerate(self.switches):
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fade_in = 1
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self.fades = []
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self.enabled_switches = 0
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for i, sw in enumerate(self.progressive_switches):
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fade_in = 1 if self.progressive_schedule[i] == 0 else 0
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if self.latest_step > 0 and self.progressive_schedule[i] != 0:
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switch_age = self.latest_step - self.progressive_schedule[i]
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fade_in = min(1, switch_age * self.growth_fade_in_per_step)
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x, att = sw.forward(x, True, fixed_scale=fade_in)
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self.attentions.append(att)
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if fade_in > 0:
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self.enabled_switches += 1
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x, att = sw.forward(x, True, fixed_scale=fade_in)
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self.attentions.append(att)
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self.fades.append(fade_in)
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x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
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if self.upsample_factor > 2:
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@ -81,18 +105,13 @@ class GrowingSRGBase(nn.Module):
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return x, x
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def update_for_step(self, step, experiments_path='.'):
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self.latest_step = step
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# Add any new layers as spelled out by the schedule.
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if step != 0:
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for i, s in enumerate(self.progressive_schedule):
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if s == step:
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self.add_layer(i + 1)
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self.latest_step = step + self.start_step
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# Set the temperature of the switches, per-layer.
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for i, (first_step, sw) in enumerate(zip(self.progressive_schedule, self.switches)):
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for i, (first_step, sw) in enumerate(zip(self.progressive_schedule, self.progressive_switches)):
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temp_loss_per_step = (self.init_temperature - 1) / self.final_temperature_step
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sw.set_temperature(self.init_temperature - temp_loss_per_step * (step - first_step))
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sw.set_temperature(min(self.init_temperature,
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max(self.init_temperature - temp_loss_per_step * (step - first_step), 1)))
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# Save attention images.
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if self.attentions is not None and step % 50 == 0:
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@ -106,10 +125,12 @@ class GrowingSRGBase(nn.Module):
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for i in range(len(means)):
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val["switch_%i_specificity" % (i,)] = means[i]
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val["switch_%i_histogram" % (i,)] = hists[i]
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val["switch_%i_temperature" % (i,)] = self.switches[i].switch.temperature
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val["switch_%i_temperature" % (i,)] = self.progressive_switches[i].switch.temperature
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for i, f in enumerate(self.fades):
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val["switch_%i_fade" % (i,)] = f
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val["enabled_switches"] = self.enabled_switches
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return val
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class DiscriminatorDownsample(nn.Module):
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def __init__(self, base_filters, end_filters):
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self.conv0 = ConvGnLelu(base_filters, end_filters, kernel_size=3, bias=False)
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@ -296,7 +296,7 @@ class ConvBnLelu(nn.Module):
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''' Convenience class with Conv->GroupNorm->LeakyReLU. Includes weight initialization and auto-padding for standard
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kernel sizes. '''
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class ConvGnLelu(nn.Module):
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def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, activation=True, norm=True, bias=True, num_groups=8):
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def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, activation=True, norm=True, bias=True, num_groups=8, weight_init_factor=1):
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super(ConvGnLelu, self).__init__()
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padding_map = {1: 0, 3: 1, 5: 2, 7: 3}
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assert kernel_size in padding_map.keys()
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@ -315,6 +315,9 @@ class ConvGnLelu(nn.Module):
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, a=.1, mode='fan_out',
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nonlinearity='leaky_relu' if self.lelu else 'linear')
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m.weight.data *= weight_init_factor
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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@ -5,6 +5,26 @@ import torch
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from torch.optim.lr_scheduler import _LRScheduler
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# This scheduler is specifically designed to modulate the learning rate of several different param groups configured
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# by a generator or discriminator that slowly adds new stages one at a time, e.g. like progressive growing of GANs.
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class ProgressiveMultiStepLR(_LRScheduler):
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def __init__(self, optimizer, milestones, group_starts, gamma=0.1):
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self.milestones = Counter(milestones)
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self.gamma = gamma
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self.group_starts = group_starts
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super(ProgressiveMultiStepLR, self).__init__(optimizer)
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def get_lr(self):
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group_lrs = []
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assert len(self.optimizer.param_groups) == len(self.group_starts)
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for group, group_start in zip(self.optimizer.param_groups, self.group_starts):
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if self.last_epoch - group_start not in self.milestones:
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group_lrs.append(group['lr'])
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else:
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group_lrs.append(group['lr'] * self.gamma)
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return group_lrs
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class MultiStepLR_Restart(_LRScheduler):
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def __init__(self, optimizer, milestones, restarts=None, weights=None, gamma=0.1,
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clear_state=False, force_lr=False, last_epoch=-1):
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@ -10,6 +10,7 @@ import models.archs.NestedSwitchGenerator as ng
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import models.archs.feature_arch as feature_arch
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import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
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import models.archs.SRG1_arch as srg1
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import models.archs.ProgressiveSrg_arch as psrg
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import functools
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# Generator
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@ -78,15 +79,14 @@ def define_G(opt, net_key='network_G'):
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initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
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heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
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upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
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elif which_model == "DualOutputSRG":
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netG = SwitchedGen_arch.DualOutputSRG(switch_depth=opt_net['switch_depth'], switch_filters=opt_net['switch_filters'],
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switch_reductions=opt_net['switch_reductions'],
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switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
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trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
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transformation_filters=opt_net['transformation_filters'],
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initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
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heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
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upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
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elif which_model == "ProgressiveSRG2":
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netG = psrg.GrowingSRGBase(progressive_step_schedule=opt_net['schedule'], switch_reductions=opt_net['reductions'],
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growth_fade_in_steps=opt_net['fade_in_steps'], switch_filters=opt_net['switch_filters'],
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switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
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trans_layers=opt_net['trans_layers'], transformation_filters=opt_net['transformation_filters'],
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initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
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upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'],
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start_step=opt_net['start_step'])
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# image corruption
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elif which_model == 'HighToLowResNet':
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@ -32,7 +32,7 @@ def init_dist(backend='nccl', **kwargs):
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def main():
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#### options
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_imgset_pixgan_srg2/train_imgset_pixgan_srg2.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_pixgan_progressive_srg2.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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@ -161,7 +161,7 @@ def main():
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current_step = resume_state['iter']
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model.resume_training(resume_state) # handle optimizers and schedulers
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else:
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current_step = -1
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current_step = 0
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start_epoch = 0
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#### training
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