604 lines
28 KiB
Python
604 lines
28 KiB
Python
import torch
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import torch.nn as nn
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from torch.cuda.amp import autocast
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from trainer.loss import GANLoss
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import random
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import functools
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import torch.nn.functional as F
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from utils.util import opt_get
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def create_loss(opt_loss, env):
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type = opt_loss['type']
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if 'teco_' in type:
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from trainer.custom_training_components import create_teco_loss
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return create_teco_loss(opt_loss, env)
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elif 'stylegan2_' in type:
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from models.stylegan import create_stylegan2_loss
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return create_stylegan2_loss(opt_loss, env)
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elif 'style_sr_' in type:
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from models.styled_sr import create_stylesr_loss
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return create_stylesr_loss(opt_loss, env)
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elif 'lightweight_gan_divergence' == type:
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from models.lightweight_gan import LightweightGanDivergenceLoss
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return LightweightGanDivergenceLoss(opt_loss, env)
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elif type == 'crossentropy':
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return CrossEntropy(opt_loss, env)
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elif type == 'pix':
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return PixLoss(opt_loss, env)
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elif type == 'sr_pix':
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return SrPixLoss(opt_loss, env)
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elif type == 'direct':
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return DirectLoss(opt_loss, env)
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elif type == 'feature':
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return FeatureLoss(opt_loss, env)
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elif type == 'interpreted_feature':
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return InterpretedFeatureLoss(opt_loss, env)
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elif type == 'generator_gan':
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return GeneratorGanLoss(opt_loss, env)
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elif type == 'discriminator_gan':
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return DiscriminatorGanLoss(opt_loss, env)
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elif type == 'geometric':
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return GeometricSimilarityGeneratorLoss(opt_loss, env)
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elif type == 'translational':
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return TranslationInvarianceLoss(opt_loss, env)
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elif type == 'recursive':
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return RecursiveInvarianceLoss(opt_loss, env)
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elif type == 'recurrent':
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return RecurrentLoss(opt_loss, env)
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elif type == 'for_element':
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return ForElementLoss(opt_loss, env)
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elif type == 'mixture_of_experts':
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from models.switched_conv.mixture_of_experts import MixtureOfExpertsLoss
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return MixtureOfExpertsLoss(opt_loss, env)
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elif type == 'switch_transformer_balance':
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from models.switched_conv.mixture_of_experts import SwitchTransformersLoadBalancingLoss
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return SwitchTransformersLoadBalancingLoss(opt_loss, env)
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elif type == 'nv_tacotron2_loss':
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from models.tacotron2.loss import Tacotron2Loss
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return Tacotron2Loss(opt_loss, env)
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else:
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raise NotImplementedError
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# Converts params to a list of tensors extracted from state. Works with list/tuple params as well as scalars.
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def extract_params_from_state(params: object, state: object, root: object = True) -> object:
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if isinstance(params, list) or isinstance(params, tuple):
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p = [extract_params_from_state(r, state, False) for r in params]
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elif isinstance(params, str):
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if params == 'None':
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p = None
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else:
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p = state[params]
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else:
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p = params
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# The root return must always be a list.
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if root and not isinstance(p, list):
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p = [p]
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return p
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class ConfigurableLoss(nn.Module):
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def __init__(self, opt, env):
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super(ConfigurableLoss, self).__init__()
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self.opt = opt
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self.env = env
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self.metrics = []
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# net is either a scalar network being trained or a list of networks being trained, depending on the configuration.
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def forward(self, net, state):
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raise NotImplementedError
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def is_stateful(self) -> bool:
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"""
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Losses can inject into the state too. useful for when a loss computation can be used by another loss.
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if this is true, the forward pass must return (loss, new_state). If false (the default), forward() only returns
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the loss value.
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"""
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return False
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def extra_metrics(self):
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return self.metrics
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def clear_metrics(self):
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self.metrics = []
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def get_basic_criterion_for_name(name, device):
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if name == 'l1':
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return nn.L1Loss().to(device)
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elif name == 'l2':
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return nn.MSELoss().to(device)
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elif name == 'cosine':
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return nn.CosineEmbeddingLoss().to(device)
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else:
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raise NotImplementedError
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class CrossEntropy(ConfigurableLoss):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.opt = opt
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self.subtype = opt_get(opt, ['subtype'], 'ce')
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if self.subtype == 'ce':
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self.ce = nn.CrossEntropyLoss()
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elif self.subtype == 'bce':
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self.ce = nn.BCEWithLogitsLoss()
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else:
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assert False
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def forward(self, _, state):
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logits = state[self.opt['logits']]
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labels = state[self.opt['labels']]
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if self.opt['rescale']:
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labels = F.interpolate(labels.type(torch.float), size=logits.shape[2:], mode="nearest").type(torch.long)
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if 'mask' in self.opt.keys():
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mask = state[self.opt['mask']]
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if self.opt['rescale']:
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mask = F.interpolate(mask, size=logits.shape[2:], mode="nearest")
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logits = logits * mask
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if self.opt['swap_channels']:
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logits = logits.permute(0,2,3,1).contiguous()
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if self.subtype == 'bce':
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logits = logits.reshape(-1, 1)
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labels = labels.reshape(-1, 1)
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else:
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logits = logits.view(-1, logits.size(-1))
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labels = labels.view(-1)
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assert labels.max()+1 <= logits.shape[-1]
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return self.ce(logits, labels)
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class PixLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(PixLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
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self.real_scale = opt['real_scale'] if 'real_scale' in opt.keys() else 1
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self.real_offset = opt['real_offset'] if 'real_offset' in opt.keys() else 0
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self.report_metrics = opt['report_metrics'] if 'report_metrics' in opt.keys() else False
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def forward(self, _, state):
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real = state[self.opt['real']] * self.real_scale + float(self.real_offset)
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fake = state[self.opt['fake']]
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if self.report_metrics:
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self.metrics.append(("real_pix_mean_histogram", torch.mean(real, dim=[1,2,3]).detach()))
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self.metrics.append(("fake_pix_mean_histogram", torch.mean(fake, dim=[1,2,3]).detach()))
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self.metrics.append(("real_pix_std", torch.std(real).detach()))
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self.metrics.append(("fake_pix_std", torch.std(fake).detach()))
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return self.criterion(fake.float(), real.float())
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class SrPixLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.opt = opt
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self.base_loss = opt_get(opt, ['base_loss'], .2)
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self.exp = opt_get(opt, ['exp'], 2)
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self.scale = opt['scale']
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def forward(self, _, state):
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real = state[self.opt['real']]
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fake = state[self.opt['fake']]
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l2 = (fake - real) ** 2
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self.metrics.append(("l2_loss", l2.mean()))
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# Adjust loss by prioritizing reconstruction of HF details.
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no_hf = F.interpolate(F.interpolate(real, scale_factor=1/self.scale, mode="area"), scale_factor=self.scale, mode="nearest")
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weights = (torch.abs(real - no_hf) + self.base_loss) ** self.exp
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weights = weights / weights.mean()
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loss = l2*weights
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# Preserve the intensity of the loss, just adjust the weighting.
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loss = loss*l2.mean()/loss.mean()
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return loss.mean()
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# Loss defined by averaging the input tensor across all dimensions and optionally inverting it.
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class DirectLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(DirectLoss, self).__init__(opt, env)
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self.opt = opt
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self.inverted = opt['inverted'] if 'inverted' in opt.keys() else False
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self.key = opt['key']
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self.anneal = opt_get(opt, ['annealing_termination_step'], 0)
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def forward(self, _, state):
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if self.inverted:
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loss = -torch.mean(state[self.key])
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else:
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loss = torch.mean(state[self.key])
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if self.anneal > 0:
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loss = loss * (1 - (self.anneal - min(self.env['step'], self.anneal)) / self.anneal)
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return loss
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class FeatureLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(FeatureLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
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import trainer.networks
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self.netF = trainer.networks.define_F(which_model=opt['which_model_F'],
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load_path=opt['load_path'] if 'load_path' in opt.keys() else None).to(self.env['device'])
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if not env['opt']['dist']:
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self.netF = torch.nn.parallel.DataParallel(self.netF, device_ids=env['opt']['gpu_ids'])
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def forward(self, _, state):
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with autocast(enabled=self.env['opt']['fp16']):
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with torch.no_grad():
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logits_real = self.netF(state[self.opt['real']])
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logits_fake = self.netF(state[self.opt['fake']])
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if self.opt['criterion'] == 'cosine':
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return self.criterion(logits_fake.float(), logits_real.float(), torch.ones(1, device=logits_fake.device))
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else:
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return self.criterion(logits_fake.float(), logits_real.float())
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# Special form of feature loss which first computes the feature embedding for the truth space, then uses a second
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# network which was trained to replicate that embedding on an altered input space (for example, LR or greyscale) to
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# compute the embedding in the generated space. Useful for weakening the influence of the feature network in controlled
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# ways.
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class InterpretedFeatureLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(InterpretedFeatureLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
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import trainer.networks
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self.netF_real = trainer.networks.define_F(which_model=opt['which_model_F']).to(self.env['device'])
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self.netF_gen = trainer.networks.define_F(which_model=opt['which_model_F'], load_path=opt['load_path']).to(self.env['device'])
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if not env['opt']['dist']:
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self.netF_real = torch.nn.parallel.DataParallel(self.netF_real)
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self.netF_gen = torch.nn.parallel.DataParallel(self.netF_gen)
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def forward(self, _, state):
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logits_real = self.netF_real(state[self.opt['real']])
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logits_fake = self.netF_gen(state[self.opt['fake']])
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return self.criterion(logits_fake.float(), logits_real.float())
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class GeneratorGanLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(GeneratorGanLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
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self.noise = None if 'noise' not in opt.keys() else opt['noise']
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self.detach_real = opt['detach_real'] if 'detach_real' in opt.keys() else True
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# This is a mechanism to prevent backpropagation for a GAN loss if it goes too low. This can be used to balance
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# generators and discriminators by essentially having them skip steps while their counterparts "catch up".
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self.min_loss = opt['min_loss'] if 'min_loss' in opt.keys() else 0
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if self.min_loss != 0:
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self.loss_rotating_buffer = torch.zeros(10, requires_grad=False)
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self.rb_ptr = 0
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self.losses_computed = 0
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def forward(self, _, state):
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netD = self.env['discriminators'][self.opt['discriminator']]
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real = extract_params_from_state(self.opt['real'], state)
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fake = extract_params_from_state(self.opt['fake'], state)
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if self.noise:
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nreal = []
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nfake = []
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for i, t in enumerate(real):
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if isinstance(t, torch.Tensor):
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nreal.append(t + torch.rand_like(t) * self.noise)
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nfake.append(fake[i] + torch.rand_like(t) * self.noise)
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else:
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nreal.append(t)
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nfake.append(fake[i])
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real = nreal
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fake = nfake
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with autocast(enabled=self.env['opt']['fp16']):
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if self.opt['gan_type'] in ['gan', 'pixgan', 'pixgan_fea']:
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pred_g_fake = netD(*fake)
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loss = self.criterion(pred_g_fake, True)
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elif self.opt['gan_type'] == 'ragan':
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pred_d_real = netD(*real)
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if self.detach_real:
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pred_d_real = pred_d_real.detach()
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pred_g_fake = netD(*fake)
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d_fake_diff = pred_g_fake - torch.mean(pred_d_real)
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self.metrics.append(("d_fake", torch.mean(pred_g_fake)))
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self.metrics.append(("d_fake_diff", torch.mean(d_fake_diff)))
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loss = (self.criterion(pred_d_real - torch.mean(pred_g_fake), False) +
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self.criterion(d_fake_diff, True)) / 2
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else:
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raise NotImplementedError
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if self.min_loss != 0:
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self.loss_rotating_buffer[self.rb_ptr] = loss.item()
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self.rb_ptr = (self.rb_ptr + 1) % self.loss_rotating_buffer.shape[0]
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if torch.mean(self.loss_rotating_buffer) < self.min_loss:
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return 0
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self.losses_computed += 1
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self.metrics.append(("loss_counter", self.losses_computed))
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return loss
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class DiscriminatorGanLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(DiscriminatorGanLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
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self.noise = None if 'noise' not in opt.keys() else opt['noise']
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# This is a mechanism to prevent backpropagation for a GAN loss if it goes too low. This can be used to balance
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# generators and discriminators by essentially having them skip steps while their counterparts "catch up".
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self.min_loss = opt['min_loss'] if 'min_loss' in opt.keys() else 0
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self.gradient_penalty = opt['gradient_penalty'] if 'gradient_penalty' in opt.keys() else False
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if self.min_loss != 0:
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assert not self.env['dist'] # distributed training does not support 'min_loss' - it can result in backward() desync by design.
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self.loss_rotating_buffer = torch.zeros(10, requires_grad=False)
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self.rb_ptr = 0
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self.losses_computed = 0
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def forward(self, net, state):
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real = extract_params_from_state(self.opt['real'], state)
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real = [r.detach() for r in real]
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if self.gradient_penalty:
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[r.requires_grad_() for r in real]
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fake = extract_params_from_state(self.opt['fake'], state)
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new_state = {}
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fake = [f.detach() for f in fake]
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new_state = {}
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if self.noise:
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nreal = []
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nfake = []
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for i, t in enumerate(real):
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if isinstance(t, torch.Tensor):
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nreal.append(t + torch.rand_like(t) * self.noise)
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nfake.append(fake[i] + torch.rand_like(t) * self.noise)
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else:
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nreal.append(t)
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nfake.append(fake[i])
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real = nreal
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fake = nfake
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with autocast(enabled=self.env['opt']['fp16']):
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d_real = net(*real)
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d_fake = net(*fake)
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if self.opt['gan_type'] in ['gan', 'pixgan']:
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self.metrics.append(("d_fake", torch.mean(d_fake)))
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self.metrics.append(("d_real", torch.mean(d_real)))
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l_real = self.criterion(d_real, True)
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l_fake = self.criterion(d_fake, False)
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l_total = l_real + l_fake
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loss = l_total
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elif self.opt['gan_type'] == 'ragan' or self.opt['gan_type'] == 'max_spread':
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d_fake_diff = d_fake - torch.mean(d_real)
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self.metrics.append(("d_fake_diff", torch.mean(d_fake_diff)))
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loss = (self.criterion(d_real - torch.mean(d_fake), True) +
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self.criterion(d_fake_diff, False))
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else:
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raise NotImplementedError
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if self.min_loss != 0:
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self.loss_rotating_buffer[self.rb_ptr] = loss.item()
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self.rb_ptr = (self.rb_ptr + 1) % self.loss_rotating_buffer.shape[0]
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self.metrics.append(("loss_counter", self.losses_computed))
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if torch.mean(self.loss_rotating_buffer) < self.min_loss:
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return 0
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self.losses_computed += 1
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if self.gradient_penalty:
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# Apply gradient penalty. TODO: migrate this elsewhere.
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from models.stylegan.stylegan2_lucidrains import gradient_penalty
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assert len(real) == 1 # Grad penalty doesn't currently support multi-input discriminators.
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gp, gp_structure = gradient_penalty(real[0], d_real, return_structured_grads=True)
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self.metrics.append(("gradient_penalty", gp.clone().detach()))
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loss = loss + gp
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self.metrics.append(("gradient_penalty", gp))
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# The gp_structure is a useful visual debugging tool to see what areas of the generated image the disc is paying attention to.
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gpimg = (gp_structure / (torch.std(gp_structure, dim=(-1, -2), keepdim=True) * 2)) \
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- torch.mean(gp_structure, dim=(-1, -2), keepdim=True) + .5
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new_state['%s_%s_gp_structure_img' % (self.opt['fake'], self.opt['real'])] = gpimg
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return loss, new_state
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# This loss is stateful because it injects a debugging result from the GP term when enabled.
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def is_stateful(self) -> bool:
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return True
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# Computes a loss created by comparing the output of a generator to the output from the same generator when fed an
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# input that has been altered randomly by rotation or flip.
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# The "real" parameter to this loss is the actual output of the generator (from an injection point)
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# The "fake" parameter is the LR input that produced the "real" parameter when fed through the generator.
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class GeometricSimilarityGeneratorLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(GeometricSimilarityGeneratorLoss, self).__init__(opt, env)
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self.opt = opt
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self.generator = opt['generator']
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self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
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self.gen_input_for_alteration = opt['input_alteration_index'] if 'input_alteration_index' in opt.keys() else 0
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self.gen_output_to_use = opt['generator_output_index'] if 'generator_output_index' in opt.keys() else None
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self.detach_fake = opt['detach_fake'] if 'detach_fake' in opt.keys() else False
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# Returns a random alteration and its counterpart (that undoes the alteration)
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def random_alteration(self):
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return random.choice([(functools.partial(torch.flip, dims=(2,)), functools.partial(torch.flip, dims=(2,))),
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(functools.partial(torch.flip, dims=(3,)), functools.partial(torch.flip, dims=(3,))),
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(functools.partial(torch.rot90, k=1, dims=[2,3]), functools.partial(torch.rot90, k=3, dims=[2,3])),
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(functools.partial(torch.rot90, k=2, dims=[2,3]), functools.partial(torch.rot90, k=2, dims=[2,3])),
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(functools.partial(torch.rot90, k=3, dims=[2,3]), functools.partial(torch.rot90, k=1, dims=[2,3]))])
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def forward(self, net, state):
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net = self.env['generators'][self.generator] # Get the network from an explicit parameter.
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# The <net> parameter is not reliable for generator losses since often they are combined with many networks.
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fake = extract_params_from_state(self.opt['fake'], state)
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alteration, undo_fn = self.random_alteration()
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altered = []
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for i, t in enumerate(fake):
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if i == self.gen_input_for_alteration:
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altered.append(alteration(t))
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else:
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altered.append(t)
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with autocast(enabled=self.env['opt']['fp16']):
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if self.detach_fake:
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with torch.no_grad():
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upsampled_altered = net(*altered)
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else:
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upsampled_altered = net(*altered)
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if self.gen_output_to_use is not None:
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upsampled_altered = upsampled_altered[self.gen_output_to_use]
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# Undo alteration on HR image
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upsampled_altered = undo_fn(upsampled_altered)
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if self.opt['criterion'] == 'cosine':
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return self.criterion(state[self.opt['real']], upsampled_altered, torch.ones(1, device=upsampled_altered.device))
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else:
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return self.criterion(state[self.opt['real']].float(), upsampled_altered.float())
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# Computes a loss created by comparing the output of a generator to the output from the same generator when fed an
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# input that has been translated in a random direction.
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# The "real" parameter to this loss is the actual output of the generator on the top left image patch.
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# The "fake" parameter is the output base fed into a ImagePatchInjector.
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class TranslationInvarianceLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(TranslationInvarianceLoss, self).__init__(opt, env)
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self.opt = opt
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self.generator = opt['generator']
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self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
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self.gen_input_for_alteration = opt['input_alteration_index'] if 'input_alteration_index' in opt.keys() else 0
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self.gen_output_to_use = opt['generator_output_index'] if 'generator_output_index' in opt.keys() else None
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self.patch_size = opt['patch_size']
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self.overlap = opt['overlap'] # For maximum overlap, can be calculated as 2*patch_size-image_size
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self.detach_fake = opt['detach_fake']
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assert(self.patch_size > self.overlap)
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def forward(self, net, state):
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net = self.env['generators'][self.generator] # Get the network from an explicit parameter.
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# The <net> parameter is not reliable for generator losses since often they are combined with many networks.
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border_sz = self.patch_size - self.overlap
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translation = random.choice([("top_right", border_sz, border_sz+self.overlap, 0, self.overlap),
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("bottom_left", 0, self.overlap, border_sz, border_sz+self.overlap),
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("bottom_right", 0, self.overlap, 0, self.overlap)])
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trans_name, hl, hh, wl, wh = translation
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# Change the "fake" input name that we are translating to one that specifies the random translation.
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fake = self.opt['fake'].copy()
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fake[self.gen_input_for_alteration] = "%s_%s" % (fake[self.gen_input_for_alteration], trans_name)
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input = extract_params_from_state(fake, state)
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with autocast(enabled=self.env['opt']['fp16']):
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if self.detach_fake:
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with torch.no_grad():
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trans_output = net(*input)
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else:
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trans_output = net(*input)
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if not isinstance(trans_output, list) and not isinstance(trans_output, tuple):
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trans_output = [trans_output]
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if self.gen_output_to_use is not None:
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fake_shared_output = trans_output[self.gen_output_to_use][:, :, hl:hh, wl:wh]
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else:
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fake_shared_output = trans_output[:, :, hl:hh, wl:wh]
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# The "real" input is assumed to always come from the top left tile.
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gen_output = state[self.opt['real']]
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real_shared_output = gen_output[:, :, border_sz:border_sz+self.overlap, border_sz:border_sz+self.overlap]
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if self.opt['criterion'] == 'cosine':
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return self.criterion(fake_shared_output, real_shared_output, torch.ones(1, device=real_shared_output.device))
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else:
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return self.criterion(fake_shared_output.float(), real_shared_output.float())
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# Computes a loss repeatedly feeding the generator downsampled inputs created from its outputs. The expectation is
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# that the generator's outputs do not change on repeated forward passes.
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# The "real" parameter to this loss is the actual output of the generator.
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# The "fake" parameter is the expected inputs that should be fed into the generator. 'input_alteration_index' is changed
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# so that it feeds the recursive input.
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class RecursiveInvarianceLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(RecursiveInvarianceLoss, self).__init__(opt, env)
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self.opt = opt
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self.generator = opt['generator']
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self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
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self.gen_input_for_alteration = opt['input_alteration_index'] if 'input_alteration_index' in opt.keys() else 0
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self.gen_output_to_use = opt['generator_output_index'] if 'generator_output_index' in opt.keys() else None
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self.recursive_depth = opt['recursive_depth'] # How many times to recursively feed the output of the generator back into itself
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self.downsample_factor = opt['downsample_factor'] # Just 1/opt['scale']. Necessary since this loss doesnt have access to opt['scale'].
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assert(self.recursive_depth > 0)
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def forward(self, net, state):
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net = self.env['generators'][self.generator] # Get the network from an explicit parameter.
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# The <net> parameter is not reliable for generator losses since they can be combined with many networks.
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gen_output = state[self.opt['real']]
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recurrent_gen_output = gen_output
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fake = self.opt['fake'].copy()
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input = extract_params_from_state(fake, state)
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for i in range(self.recursive_depth):
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input[self.gen_input_for_alteration] = torch.nn.functional.interpolate(recurrent_gen_output, scale_factor=self.downsample_factor, mode="nearest")
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with autocast(enabled=self.env['opt']['fp16']):
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recurrent_gen_output = net(*input)[self.gen_output_to_use]
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compare_real = gen_output
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compare_fake = recurrent_gen_output
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if self.opt['criterion'] == 'cosine':
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return self.criterion(compare_real, compare_fake, torch.ones(1, device=compare_real.device))
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else:
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return self.criterion(compare_real.float(), compare_fake.float())
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|
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# Loss that pulls tensors from dim 1 of the input and repeatedly feeds them into the
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# 'subtype' loss.
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class RecurrentLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(RecurrentLoss, self).__init__(opt, env)
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o = opt.copy()
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o['type'] = opt['subtype']
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o['fake'] = '_fake'
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o['real'] = '_real'
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self.loss = create_loss(o, self.env)
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# Use this option to specify a differential weighting scheme for losses inside of the recurrent construct. For
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# example, if later recurrent outputs should contribute more to the loss than earlier ones. When specified,
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# must be a list of weights that exactly aligns with the recurrent list fed to forward().
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self.recurrent_weights = opt['recurrent_weights'] if 'recurrent_weights' in opt.keys() else 1
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def forward(self, net, state):
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total_loss = 0
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st = state.copy()
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real = state[self.opt['real']]
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for i in range(real.shape[1]):
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st['_real'] = real[:, i]
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st['_fake'] = state[self.opt['fake']][:, i]
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subloss = self.loss(net, st)
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if isinstance(self.recurrent_weights, list):
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subloss = subloss * self.recurrent_weights[i]
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total_loss += subloss
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return total_loss
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def extra_metrics(self):
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return self.loss.extra_metrics()
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def clear_metrics(self):
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self.loss.clear_metrics()
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# Loss that pulls a tensor from dim 1 of the input and feeds it into a "sub" loss.
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|
class ForElementLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(ForElementLoss, self).__init__(opt, env)
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o = opt.copy()
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o['type'] = opt['subtype']
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self.index = opt['index']
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o['fake'] = '_fake'
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o['real'] = '_real'
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self.loss = create_loss(o, self.env)
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def forward(self, net, state):
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st = state.copy()
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st['_real'] = state[self.opt['real']][:, self.index]
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st['_fake'] = state[self.opt['fake']][:, self.index]
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return self.loss(net, st)
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def extra_metrics(self):
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return self.loss.extra_metrics()
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def clear_metrics(self):
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|
self.loss.clear_metrics()
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