Extensible trainer (in progress)
This commit is contained in:
James Betker
parent
cb316fabc7
commit
ab04ca1778
661
codes/models/ExtensibleTrainer.py
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661
codes/models/ExtensibleTrainer.py
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import logging
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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from torch.nn.parallel import DataParallel, DistributedDataParallel
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import models.networks as networks
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from models.steps.steps import create_step
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import models.lr_scheduler as lr_scheduler
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from models.base_model import BaseModel
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from models.loss import GANLoss, FDPLLoss
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from apex import amp
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from data.weight_scheduler import get_scheduler_for_opt
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from .archs.SPSR_arch import ImageGradient, ImageGradientNoPadding
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import torch.nn.functional as F
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import glob
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import random
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import torchvision.utils as utils
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import os
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logger = logging.getLogger('base')
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class ExtensibleTrainer(BaseModel):
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def __init__(self, opt):
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super(ExtensibleTrainer, self).__init__(opt)
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if opt['dist']:
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self.rank = torch.distributed.get_rank()
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else:
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self.rank = -1 # non dist training
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train_opt = opt['train']
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self.mega_batch_factor = 1
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self.netG = {}
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self.netD = {}
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self.networks = []
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for name, net in opt['networks'].items():
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if net['type'] == 'generator':
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new_net = networks.define_G(net)
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self.netG[name] = new_net
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elif net['type'] == 'discriminator':
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new_net = networks.define_D(net)
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self.netD[name] = new_net
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else:
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raise NotImplementedError("Can only handle generators and discriminators")
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self.networks.append(new_net)
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if self.is_train:
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self.mega_batch_factor = train_opt['mega_batch_factor']
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if self.mega_batch_factor is None:
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self.mega_batch_factor = 1
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# Initialize amp.
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amp_nets, amp_opts = amp.initialize(self.networks, self.optimizers, opt_level=opt['amp_level'], num_losses=len(self.optimizers))
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# self.networks is stored unwrapped. It should never be used for forward() or backward() passes, instead use
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# self.netG and self.netD for that.
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self.networks = amp_nets
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# DataParallel
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dnets = []
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for anet in amp_nets:
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if opt['dist']:
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dnet = DistributedDataParallel(anet,
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device_ids=[torch.cuda.current_device()],
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find_unused_parameters=True)
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else:
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dnet = DataParallel(anet)
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if self.is_train:
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dnet.train()
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else:
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dnet.eval()
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dnets.append(dnet)
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# Backpush the wrapped networks into the network dicts..
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found = 0
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for dnet in dnets:
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for net_dict in [self.netD, self.netG]:
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for k, v in net_dict.items():
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if v == dnet:
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net_dict[k] = dnet
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found += 1
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assert found == len(self.networks)
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# Initialize the training steps
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self.steps = []
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for step in opt['steps']:
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step = create_step(step, self.netG, self.netD)
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self.steps.append(step)
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self.optimizers.extend(step.get_optimizers())
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# Find the optimizers that are using the default scheduler, then build them.
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def_opt = []
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for s in self.steps:
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def_opt.extend(s.get_optimizers_with_default_scheduler())
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lr_scheduler.get_scheduler_for_name(train_opt['default_lr_scheme'], def_opt, train_opt)
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self.print_network() # print network
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self.load() # load G and D if needed
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# Setting this to false triggers SRGAN to call the models update_model() function on the first iteration.
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self.updated = True
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def feed_data(self, data):
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self.var_L = torch.chunk(corrupted_L, chunks=self.mega_batch_factor, dim=0)
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self.var_H = [t.to(self.device) for t in torch.chunk(data['GT'], chunks=self.mega_batch_factor, dim=0)]
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input_ref = data['ref'] if 'ref' in data else data['GT']
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self.var_ref = [t.to(self.device) for t in torch.chunk(input_ref, chunks=self.mega_batch_factor, dim=0)]
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def optimize_parameters(self, step):
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# Some models need to make parametric adjustments per-step. Do that here.
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for net in self.networks.values():
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if hasattr(net, "update_for_step"):
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net.update_for_step(step, os.path.join(self.opt['path']['models'], ".."))
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# Iterate through the steps, performing them one at a time.
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state = {'lr': self.var_L, 'hr': self.var_H, 'ref': self.var_ref}
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for s in self.steps:
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# Only set requires_grad=True for the network being trained.
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nets_to_train = s.get_networks_trained()
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for name, net in self.networks.items():
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net_enabled = name in nets_to_train
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for p in self.netG.parameters():
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if p.dtype != torch.int64 and p.dtype != torch.bool:
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p.requires_grad = net_enabled
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else:
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p.requires_grad = False
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# Now do a forward and backward pass for each gradient accumulation step.
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for m in range(self.mega_batch_factor):
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state = s.do_forward_backward(state, m)
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# And finally perform optimization.
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s.do_step()
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# G
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for p in self.netD.parameters():
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p.requires_grad = False
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if self.spsr_enabled:
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for p in self.netD_grad.parameters():
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p.requires_grad = False
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self.swapout_D(step)
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self.swapout_G(step)
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# Turning off G-grad is required to enable mega-batching and D_update_ratio to work together for some reason.
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if step % self.D_update_ratio == 0 and step >= self.D_init_iters:
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if self.spsr_enabled and self.branch_pretrain and step < self.branch_init_iters:
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for k, v in self.netG.named_parameters():
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if v.dtype != torch.int64 and v.dtype != torch.bool:
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v.requires_grad = '_branch_pretrain' in k
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else:
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for p in self.netG.parameters():
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if p.dtype != torch.int64 and p.dtype != torch.bool:
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p.requires_grad = True
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else:
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for p in self.netG.parameters():
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p.requires_grad = False
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# Calculate a standard deviation for the gaussian noise to be applied to the discriminator, termed noise-theta.
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if self.D_noise_final == 0:
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noise_theta = 0
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else:
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noise_theta = (self.D_noise_theta - self.D_noise_theta_floor) * (self.D_noise_final - min(step, self.D_noise_final)) / self.D_noise_final + self.D_noise_theta_floor
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if _profile:
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print("Misc setup %f" % (time() - _t,))
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_t = time()
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if step >= self.D_init_iters:
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self.optimizer_G.zero_grad()
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self.fake_GenOut = []
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self.fea_GenOut = []
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self.fake_H = []
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self.spsr_grad_GenOut = []
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var_ref_skips = []
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for var_L, var_LGAN, var_H, var_ref, pix in zip(self.var_L, self.gan_img, self.var_H, self.var_ref, self.pix):
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if self.spsr_enabled:
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using_gan_img = False
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# SPSR models have outputs from three different branches.
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fake_H_branch, fake_GenOut, grad_LR = self.netG(var_L)
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fea_GenOut = fake_GenOut
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self.spsr_grad_GenOut.append(fake_H_branch)
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# Get image gradients for later use.
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fake_H_grad = self.get_grad_nopadding(fake_GenOut)
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else:
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if random.random() > self.gan_lq_img_use_prob:
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fea_GenOut, fake_GenOut = self.netG(var_L)
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using_gan_img = False
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else:
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fea_GenOut, fake_GenOut = self.netG(var_LGAN)
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using_gan_img = True
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if _profile:
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print("Gen forward %f" % (time() - _t,))
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_t = time()
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self.fake_GenOut.append(fake_GenOut.detach())
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self.fea_GenOut.append(fea_GenOut.detach())
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l_g_total = 0
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if step % self.D_update_ratio == 0 and step >= self.D_init_iters:
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fea_w = self.l_fea_sched.get_weight_for_step(step)
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l_g_pix_log = None
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l_g_fea_log = None
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l_g_fdpl = None
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l_g_fea_log = None
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if self.cri_pix and not using_gan_img: # pixel loss
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l_g_pix = self.l_pix_w * self.cri_pix(fea_GenOut, pix)
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l_g_pix_log = l_g_pix / self.l_pix_w
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l_g_total += l_g_pix
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if self.spsr_enabled and self.cri_pix_grad: # gradient pixel loss
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if self.disjoint_data:
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grad_truth = self.get_grad_nopadding(var_L)
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grad_pred = F.interpolate(fake_H_grad, size=grad_truth.shape[2:], mode="nearest")
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else:
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grad_truth = self.get_grad_nopadding(var_H)
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grad_pred = fake_H_grad
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l_g_pix_grad = self.l_pix_grad_w * self.cri_pix_grad(grad_pred, grad_truth)
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l_g_total += l_g_pix_grad
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if self.spsr_enabled and self.cri_pix_branch: # branch pixel loss
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if self.disjoint_data:
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grad_truth = self.get_grad_nopadding(var_L)
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grad_pred = F.interpolate(fake_H_branch, size=grad_truth.shape[2:], mode="nearest")
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else:
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grad_truth = self.get_grad_nopadding(var_H)
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grad_pred = fake_H_branch
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l_g_pix_grad_branch = self.l_pix_branch_w * self.cri_pix_branch(grad_pred, grad_truth)
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l_g_total += l_g_pix_grad_branch
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if self.fdpl_enabled and not using_gan_img:
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l_g_fdpl = self.cri_fdpl(fea_GenOut, pix)
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l_g_total += l_g_fdpl * self.fdpl_weight
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if self.cri_fea and not using_gan_img and fea_w > 0: # feature loss
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if self.lr_netF is not None:
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real_fea = self.lr_netF(var_L, interpolate_factor=self.opt['scale'])
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else:
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real_fea = self.netF(pix).detach()
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fake_fea = self.netF(fea_GenOut)
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l_g_fea = fea_w * self.cri_fea(fake_fea, real_fea)
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l_g_fea_log = l_g_fea / fea_w
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l_g_total += l_g_fea
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if _profile:
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print("Fea forward %f" % (time() - _t,))
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_t = time()
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# Note to future self: The BCELoss(0, 1) and BCELoss(0, 0) = .6931
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# Effectively this means that the generator has only completely "won" when l_d_real and l_d_fake is
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# equal to this value. If I ever come up with an algorithm that tunes fea/gan weights automatically,
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# it should target this
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l_g_fix_disc = torch.zeros(1, requires_grad=False, device=self.device).squeeze()
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for fixed_disc in self.fixed_disc_nets:
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weight = fixed_disc.module.fdisc_weight
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real_fea = fixed_disc(pix).detach()
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fake_fea = fixed_disc(fea_GenOut)
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l_g_fix_disc = l_g_fix_disc + weight * self.cri_fea(fake_fea, real_fea)
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l_g_total += l_g_fix_disc
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if self.l_gan_w > 0:
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if self.opt['train']['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
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if self.opt['train']['gan_type'] == 'crossgan':
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pred_g_fake = self.netD(fake_GenOut, var_L)
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else:
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pred_g_fake = self.netD(fake_GenOut)
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l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True)
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elif self.opt['train']['gan_type'] == 'ragan':
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pred_d_real = self.netD(var_ref).detach()
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pred_g_fake = self.netD(fake_GenOut)
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l_g_gan = self.l_gan_w * (
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self.cri_gan(pred_d_real - torch.mean(pred_g_fake), False) +
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self.cri_gan(pred_g_fake - torch.mean(pred_d_real), True)) / 2
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l_g_gan_log = l_g_gan / self.l_gan_w
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l_g_total += l_g_gan
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if self.spsr_enabled and self.cri_grad_gan:
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if self.opt['train']['gan_type'] == 'crossgan':
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pred_g_fake_grad = self.netD(fake_H_grad, var_L)
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else:
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pred_g_fake_grad = self.netD(fake_H_grad)
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pred_g_fake_grad_branch = self.netD_grad(fake_H_branch)
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if self.opt['train']['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
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l_g_gan_grad = self.l_gan_grad_w * self.cri_grad_gan(pred_g_fake_grad, True)
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l_g_gan_grad_branch = self.l_gan_grad_w * self.cri_grad_gan(pred_g_fake_grad_branch, True)
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elif self.opt['train']['gan_type'] == 'ragan':
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pred_g_real_grad = self.netD_grad(self.get_grad_nopadding(var_ref)).detach()
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l_g_gan_grad = self.l_gan_w * (
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self.cri_gan(pred_g_real_grad - torch.mean(pred_g_fake_grad), False) +
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self.cri_gan(pred_g_fake_grad - torch.mean(pred_g_real_grad), True)) / 2
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l_g_gan_grad_branch = self.l_gan_w * (
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self.cri_gan(pred_g_real_grad - torch.mean(pred_g_fake_grad_branch), False) +
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self.cri_gan(pred_g_fake_grad_branch - torch.mean(pred_g_real_grad), True)) / 2
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l_g_total += l_g_gan_grad + l_g_gan_grad_branch
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# Scale the loss down by the batch factor.
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l_g_total_log = l_g_total
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l_g_total = l_g_total / self.mega_batch_factor
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with amp.scale_loss(l_g_total, self.optimizer_G, loss_id=0) as l_g_total_scaled:
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l_g_total_scaled.backward()
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if _profile:
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print("Gen backward %f" % (time() - _t,))
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_t = time()
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self.optimizer_G.step()
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if _profile:
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print("Gen step %f" % (time() - _t,))
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_t = time()
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# D
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if self.l_gan_w > 0 and step >= self.G_warmup:
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for p in self.netD.parameters():
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if p.dtype != torch.int64 and p.dtype != torch.bool:
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p.requires_grad = True
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noise = torch.randn_like(var_ref) * noise_theta
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noise.to(self.device)
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real_disc_images = []
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fake_disc_images = []
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for fake_GenOut, var_LGAN, var_L, var_H, var_ref, pix in zip(self.fake_GenOut, self.gan_img, self.var_L, self.var_H, self.var_ref, self.pix):
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if random.random() > self.gan_lq_img_use_prob:
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fake_H = fake_GenOut.clone().detach().requires_grad_(False)
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else:
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# Re-compute generator outputs with the GAN inputs.
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with torch.no_grad():
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if self.spsr_enabled:
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_, fake_H, _ = self.netG(var_LGAN)
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else:
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_, fake_H = self.netG(var_LGAN)
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fake_H = fake_H.detach()
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if _profile:
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print("Gen forward for disc %f" % (time() - _t,))
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_t = time()
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# Apply noise to the inputs to slow discriminator convergence.
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var_ref = var_ref + noise
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fake_H = fake_H + noise
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l_d_fea_real = 0
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l_d_fea_fake = 0
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self.optimizer_D.zero_grad()
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if self.opt['train']['gan_type'] == 'pixgan_fea':
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# Compute a feature loss which is added to the GAN loss computed later to guide the discriminator better.
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disc_fea_scale = .1
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_, fea_real = self.netD(var_ref, output_feature_vector=True)
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actual_fea = self.netF(var_ref)
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l_d_fea_real = self.cri_fea(fea_real, actual_fea) * disc_fea_scale / self.mega_batch_factor
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_, fea_fake = self.netD(fake_H, output_feature_vector=True)
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actual_fea = self.netF(fake_H)
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l_d_fea_fake = self.cri_fea(fea_fake, actual_fea) * disc_fea_scale / self.mega_batch_factor
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if self.opt['train']['gan_type'] == 'crossgan':
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# need to forward and backward separately, since batch norm statistics differ
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# real
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pred_d_real = self.netD(var_ref, var_L)
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l_d_real = self.cri_gan(pred_d_real, True)
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l_d_real_log = l_d_real
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# fake
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pred_d_fake = self.netD(fake_H, var_L)
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l_d_fake = self.cri_gan(pred_d_fake, False)
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l_d_fake_log = l_d_fake
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# mismatched
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mismatched_L = torch.roll(var_L, shifts=1, dims=0)
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pred_d_real_mismatched = self.netD(var_ref, mismatched_L)
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pred_d_fake_mismatched = self.netD(fake_H, mismatched_L)
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l_d_mismatched = (self.cri_gan(pred_d_real_mismatched, False) + self.cri_gan(pred_d_fake_mismatched, False)) / 2
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l_d_total = (l_d_real + l_d_fake + l_d_mismatched) / 3
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l_d_total = l_d_total / self.mega_batch_factor
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with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled:
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l_d_total_scaled.backward()
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elif self.opt['train']['gan_type'] == 'gan':
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# real
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pred_d_real = self.netD(var_ref)
|
||||
l_d_real = self.cri_gan(pred_d_real, True) / self.mega_batch_factor
|
||||
l_d_real_log = l_d_real * self.mega_batch_factor
|
||||
# fake
|
||||
pred_d_fake = self.netD(fake_H)
|
||||
l_d_fake = self.cri_gan(pred_d_fake, False) / self.mega_batch_factor
|
||||
l_d_fake_log = l_d_fake * self.mega_batch_factor
|
||||
|
||||
l_d_total = (l_d_real + l_d_fake) / 2
|
||||
with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled:
|
||||
l_d_total_scaled.backward()
|
||||
elif 'pixgan' in self.opt['train']['gan_type']:
|
||||
pixdisc_channels, pixdisc_output_reduction = self.netD.module.pixgan_parameters()
|
||||
disc_output_shape = (var_ref.shape[0], pixdisc_channels, var_ref.shape[2] // pixdisc_output_reduction, var_ref.shape[3] // pixdisc_output_reduction)
|
||||
b, _, w, h = var_ref.shape
|
||||
real = torch.ones((b, pixdisc_channels, w, h), device=var_ref.device)
|
||||
fake = torch.zeros((b, pixdisc_channels, w, h), device=var_ref.device)
|
||||
if not self.disjoint_data:
|
||||
# randomly determine portions of the image to swap to keep the discriminator honest.
|
||||
SWAP_MAX_DIM = w // 4
|
||||
SWAP_MIN_DIM = 16
|
||||
assert SWAP_MAX_DIM > 0
|
||||
if random.random() > .5: # Make this only happen half the time. Earlier experiments had it happen
|
||||
# more often and the model was "cheating" by using the presence of
|
||||
# easily discriminated fake swaps to count the entire generated image
|
||||
# as fake.
|
||||
random_swap_count = random.randint(0, 4)
|
||||
for i in range(random_swap_count):
|
||||
# Make the swap across fake_H and var_ref
|
||||
swap_x, swap_y = random.randint(0, w - SWAP_MIN_DIM), random.randint(0, h - SWAP_MIN_DIM)
|
||||
swap_w, swap_h = random.randint(SWAP_MIN_DIM, SWAP_MAX_DIM), random.randint(SWAP_MIN_DIM, SWAP_MAX_DIM)
|
||||
if swap_x + swap_w > w:
|
||||
swap_w = w - swap_x
|
||||
if swap_y + swap_h > h:
|
||||
swap_h = h - swap_y
|
||||
t = fake_H[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)].clone()
|
||||
fake_H[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = var_ref[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)]
|
||||
var_ref[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = t
|
||||
real[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 0.0
|
||||
fake[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 1.0
|
||||
|
||||
# Interpolate down to the dimensionality that the discriminator uses.
|
||||
real = F.interpolate(real, size=disc_output_shape[2:], mode="bilinear", align_corners=False)
|
||||
fake = F.interpolate(fake, size=disc_output_shape[2:], mode="bilinear", align_corners=False)
|
||||
|
||||
# We're also assuming that this is exactly how the flattened discriminator output is generated.
|
||||
real = real.view(-1, 1)
|
||||
fake = fake.view(-1, 1)
|
||||
|
||||
# real
|
||||
pred_d_real = self.netD(var_ref)
|
||||
l_d_real = self.cri_gan(pred_d_real, real) / self.mega_batch_factor
|
||||
l_d_real_log = l_d_real * self.mega_batch_factor
|
||||
l_d_real += l_d_fea_real
|
||||
# fake
|
||||
pred_d_fake = self.netD(fake_H)
|
||||
l_d_fake = self.cri_gan(pred_d_fake, fake) / self.mega_batch_factor
|
||||
l_d_fake_log = l_d_fake * self.mega_batch_factor
|
||||
l_d_fake += l_d_fea_fake
|
||||
|
||||
l_d_total = (l_d_real + l_d_fake) / 2
|
||||
with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled:
|
||||
l_d_total_scaled.backward()
|
||||
|
||||
pdr = pred_d_real.detach() + torch.abs(torch.min(pred_d_real))
|
||||
pdr = pdr / torch.max(pdr)
|
||||
real_disc_images.append(pdr.view(disc_output_shape))
|
||||
pdf = pred_d_fake.detach() + torch.abs(torch.min(pred_d_fake))
|
||||
pdf = pdf / torch.max(pdf)
|
||||
fake_disc_images.append(pdf.view(disc_output_shape))
|
||||
elif self.opt['train']['gan_type'] == 'ragan':
|
||||
pred_d_fake = self.netD(fake_H)
|
||||
pred_d_real = self.netD(var_ref)
|
||||
l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True)
|
||||
l_d_real_log = l_d_real
|
||||
l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real), False)
|
||||
l_d_fake_log = l_d_fake
|
||||
l_d_total = (l_d_real + l_d_fake) / 2
|
||||
l_d_total /= self.mega_batch_factor
|
||||
with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled:
|
||||
l_d_total_scaled.backward()
|
||||
var_ref_skips.append(var_ref.detach())
|
||||
self.fake_H.append(fake_H.detach())
|
||||
self.optimizer_D.step()
|
||||
|
||||
if _profile:
|
||||
print("Disc step %f" % (time() - _t,))
|
||||
_t = time()
|
||||
|
||||
# D_grad.
|
||||
if self.spsr_enabled and self.cri_grad_gan and step >= self.G_warmup:
|
||||
for p in self.netD_grad.parameters():
|
||||
p.requires_grad = True
|
||||
self.optimizer_D_grad.zero_grad()
|
||||
for var_ref, fake_H, fake_H_grad_branch in zip(var_ref_skips, self.fake_H, self.spsr_grad_GenOut):
|
||||
fake_H_grad = self.get_grad_nopadding(fake_H).detach()
|
||||
var_ref_grad = self.get_grad_nopadding(var_ref)
|
||||
pred_d_real_grad = self.netD_grad(var_ref_grad)
|
||||
pred_d_fake_grad = self.netD_grad(fake_H_grad) # Tensor already detached above.
|
||||
# var_ref and fake_H already has noise added to it. We **must** add noise to fake_H_grad_branch too.
|
||||
fake_H_grad_branch = fake_H_grad_branch.detach() + noise
|
||||
pred_d_fake_grad_branch = self.netD_grad(fake_H_grad_branch)
|
||||
if self.opt['train']['gan_type'] == 'gan':
|
||||
l_d_real_grad = self.cri_gan(pred_d_real_grad, True)
|
||||
l_d_fake_grad = (self.cri_gan(pred_d_fake_grad, False) + self.cri_gan(pred_d_fake_grad_branch, False)) / 2
|
||||
elif self.opt['train']['gan_type'] == 'crossgan':
|
||||
assert False
|
||||
elif self.opt['train']['gan_type'] == 'pixgan':
|
||||
real = torch.ones_like(pred_d_real_grad)
|
||||
fake = torch.zeros_like(pred_d_fake_grad)
|
||||
l_d_real_grad = self.cri_grad_gan(pred_d_real_grad, real)
|
||||
l_d_fake_grad = (self.cri_grad_gan(pred_d_fake_grad, fake) + \
|
||||
self.cri_grad_gan(pred_d_fake_grad_branch, fake)) / 2
|
||||
elif self.opt['train']['gan_type'] == 'ragan':
|
||||
l_d_real_grad = self.cri_grad_gan(pred_d_real_grad - torch.mean(pred_d_fake_grad), True)
|
||||
l_d_fake_grad = (self.cri_grad_gan(pred_d_fake_grad - torch.mean(pred_d_real_grad), False) + \
|
||||
self.cri_grad_gan(pred_d_fake_grad_branch - torch.mean(pred_d_real_grad), False)) / 2
|
||||
|
||||
l_d_total_grad = (l_d_real_grad + l_d_fake_grad) / 2
|
||||
l_d_total_grad /= self.mega_batch_factor
|
||||
with amp.scale_loss(l_d_total_grad, self.optimizer_D_grad, loss_id=2) as l_d_total_grad_scaled:
|
||||
l_d_total_grad_scaled.backward()
|
||||
self.optimizer_D_grad.step()
|
||||
|
||||
|
||||
# Log sample images from first microbatch.
|
||||
if step % self.img_debug_steps == 0:
|
||||
sample_save_path = os.path.join(self.opt['path']['models'], "..", "temp")
|
||||
os.makedirs(os.path.join(sample_save_path, "hr"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "lr"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "gen_fea"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "gen"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "disc_fake"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "pix"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "disc"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "ref"), exist_ok=True)
|
||||
if self.spsr_enabled:
|
||||
os.makedirs(os.path.join(sample_save_path, "gen_grad"), exist_ok=True)
|
||||
|
||||
for i in range(self.mega_batch_factor):
|
||||
utils.save_image(self.var_H[i].cpu(), os.path.join(sample_save_path, "hr", "%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(self.var_L[i].cpu(), os.path.join(sample_save_path, "lr", "%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(self.pix[i].cpu(), os.path.join(sample_save_path, "pix", "%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(self.fake_GenOut[i].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(self.fea_GenOut[i].cpu(), os.path.join(sample_save_path, "gen_fea", "%05i_%02i.png" % (step, i)))
|
||||
if self.spsr_enabled:
|
||||
utils.save_image(self.spsr_grad_GenOut[i].cpu(), os.path.join(sample_save_path, "gen_grad", "%05i_%02i.png" % (step, i)))
|
||||
if self.l_gan_w > 0 and step >= self.G_warmup and 'pixgan' in self.opt['train']['gan_type']:
|
||||
utils.save_image(var_ref_skips[i].cpu(), os.path.join(sample_save_path, "ref", "%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(self.fake_H[i], os.path.join(sample_save_path, "disc_fake", "fake%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(F.interpolate(fake_disc_images[i], scale_factor=4), os.path.join(sample_save_path, "disc", "fake%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(F.interpolate(real_disc_images[i], scale_factor=4), os.path.join(sample_save_path, "disc", "real%05i_%02i.png" % (step, i)))
|
||||
|
||||
# Log metrics
|
||||
if step % self.D_update_ratio == 0 and step >= self.D_init_iters:
|
||||
if self.cri_pix and l_g_pix_log is not None:
|
||||
self.add_log_entry('l_g_pix', l_g_pix_log.detach().item())
|
||||
if self.fdpl_enabled and l_g_fdpl is not None:
|
||||
self.add_log_entry('l_g_fdpl', l_g_fdpl.detach().item())
|
||||
if self.cri_fea and l_g_fea_log is not None:
|
||||
self.add_log_entry('feature_weight', fea_w)
|
||||
self.add_log_entry('l_g_fea', l_g_fea_log.detach().item())
|
||||
self.add_log_entry('l_g_fix_disc', l_g_fix_disc.detach().item())
|
||||
if self.l_gan_w > 0:
|
||||
self.add_log_entry('l_g_gan', l_g_gan_log.detach().item())
|
||||
self.add_log_entry('l_g_total', l_g_total_log.detach().item())
|
||||
if self.opt['train']['gan_type'] == 'pixgan_fea':
|
||||
self.add_log_entry('l_d_fea_fake', l_d_fea_fake.detach().item() * self.mega_batch_factor)
|
||||
self.add_log_entry('l_d_fea_real', l_d_fea_real.detach().item() * self.mega_batch_factor)
|
||||
self.add_log_entry('l_d_fake_total', l_d_fake.detach().item() * self.mega_batch_factor)
|
||||
self.add_log_entry('l_d_real_total', l_d_real.detach().item() * self.mega_batch_factor)
|
||||
if self.opt['train']['gan_type'] == 'crossgan':
|
||||
self.add_log_entry('l_d_mismatched', l_d_mismatched.detach().item())
|
||||
if self.spsr_enabled:
|
||||
if self.cri_pix_grad:
|
||||
self.add_log_entry('l_g_pix_grad_branch', l_g_pix_grad.detach().item())
|
||||
if self.cri_pix_branch:
|
||||
self.add_log_entry('l_g_pix_grad_branch', l_g_pix_grad_branch.detach().item())
|
||||
if self.cri_grad_gan:
|
||||
self.add_log_entry('l_g_gan_grad', l_g_gan_grad.detach().item())
|
||||
self.add_log_entry('l_g_gan_grad_branch', l_g_gan_grad_branch.detach().item())
|
||||
if self.l_gan_w > 0 and step >= self.G_warmup:
|
||||
self.add_log_entry('l_d_real', l_d_real_log.detach().item())
|
||||
self.add_log_entry('l_d_fake', l_d_fake_log.detach().item())
|
||||
self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach()))
|
||||
self.add_log_entry('D_diff', torch.mean(pred_d_fake.detach()) - torch.mean(pred_d_real.detach()))
|
||||
if self.spsr_enabled:
|
||||
self.add_log_entry('l_d_real_grad', l_d_real_grad.detach().item())
|
||||
self.add_log_entry('l_d_fake_grad', l_d_fake_grad.detach().item())
|
||||
self.add_log_entry('D_fake_grad', torch.mean(pred_d_fake_grad.detach()))
|
||||
self.add_log_entry('D_diff_grad', torch.mean(pred_d_fake_grad.detach()) - torch.mean(pred_d_real_grad.detach()))
|
||||
|
||||
# Log learning rates.
|
||||
for i, pg in enumerate(self.optimizer_G.param_groups):
|
||||
self.add_log_entry('gen_lr_%i' % (i,), pg['lr'])
|
||||
for i, pg in enumerate(self.optimizer_D.param_groups):
|
||||
self.add_log_entry('disc_lr_%i' % (i,), pg['lr'])
|
||||
|
||||
if step % self.corruptor_swapout_steps == 0 and step > 0:
|
||||
self.load_random_corruptor()
|
||||
|
||||
# Allows the log to serve as an easy-to-use rotating buffer.
|
||||
def add_log_entry(self, key, value):
|
||||
key_it = "%s_it" % (key,)
|
||||
log_rotating_buffer_size = 50
|
||||
if key not in self.log_dict.keys():
|
||||
self.log_dict[key] = []
|
||||
self.log_dict[key_it] = 0
|
||||
if len(self.log_dict[key]) < log_rotating_buffer_size:
|
||||
self.log_dict[key].append(value)
|
||||
else:
|
||||
self.log_dict[key][self.log_dict[key_it] % log_rotating_buffer_size] = value
|
||||
self.log_dict[key_it] += 1
|
||||
|
||||
def compute_fea_loss(self, real, fake):
|
||||
with torch.no_grad():
|
||||
real = real.unsqueeze(dim=0).to(self.device)
|
||||
fake = fake.unsqueeze(dim=0).to(self.device)
|
||||
real_fea = self.netF(real).detach()
|
||||
fake_fea = self.netF(fake)
|
||||
return self.cri_fea(fake_fea, real_fea).item()
|
||||
|
||||
def test(self):
|
||||
self.netG.eval()
|
||||
with torch.no_grad():
|
||||
if self.spsr_enabled:
|
||||
self.fake_H_branch = []
|
||||
self.fake_GenOut = []
|
||||
self.grad_LR = []
|
||||
fake_H_branch, fake_GenOut, grad_LR = self.netG(self.var_L[0])
|
||||
self.fake_H_branch.append(fake_H_branch)
|
||||
self.fake_GenOut.append(fake_GenOut)
|
||||
self.grad_LR.append(grad_LR)
|
||||
else:
|
||||
self.fake_GenOut = [self.netG(self.var_L[0])]
|
||||
self.netG.train()
|
||||
|
||||
# Fetches a summary of the log.
|
||||
def get_current_log(self, step):
|
||||
return_log = {}
|
||||
for k in self.log_dict.keys():
|
||||
if not isinstance(self.log_dict[k], list):
|
||||
continue
|
||||
return_log[k] = sum(self.log_dict[k]) / len(self.log_dict[k])
|
||||
|
||||
# Some generators can do their own metric logging.
|
||||
if hasattr(self.netG.module, "get_debug_values"):
|
||||
return_log.update(self.netG.module.get_debug_values(step))
|
||||
if hasattr(self.netD.module, "get_debug_values"):
|
||||
return_log.update(self.netD.module.get_debug_values(step))
|
||||
|
||||
return return_log
|
||||
|
||||
def get_current_visuals(self, need_GT=True):
|
||||
out_dict = OrderedDict()
|
||||
out_dict['LQ'] = self.var_L[0].detach().float().cpu()
|
||||
gen_batch = self.fake_GenOut[0]
|
||||
if isinstance(gen_batch, tuple):
|
||||
gen_batch = gen_batch[0]
|
||||
out_dict['rlt'] = gen_batch.detach().float().cpu()
|
||||
if need_GT:
|
||||
out_dict['GT'] = self.var_H[0].detach().float().cpu()
|
||||
if self.spsr_enabled:
|
||||
out_dict['SR_branch'] = self.fake_H_branch[0].float().cpu()
|
||||
out_dict['LR_grad'] = self.grad_LR[0].float().cpu()
|
||||
return out_dict
|
||||
|
||||
def print_network(self):
|
||||
for name, net in self.networks.items():
|
||||
s, n = self.get_network_description(net)
|
||||
net_struc_str = '{}'.format(net.__class__.__name__)
|
||||
if self.rank <= 0:
|
||||
logger.info('Network ' + name + ' structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
|
||||
logger.info(s)
|
||||
|
||||
def load(self):
|
||||
for name, net in self.networks.items():
|
||||
load_path = opt['path'][name]
|
||||
if load_path is not None:
|
||||
logger.info('Loading model for %s: [%s]' % (name, load_path))
|
||||
self.load_network(load_path, net)
|
||||
|
||||
def save(self, iter_step):
|
||||
for name, net in self.networks.items():
|
||||
self.save_network(net, name, iter_step)
|
||||
|
|
@ -5,6 +5,30 @@ import torch
|
|||
from torch.optim.lr_scheduler import _LRScheduler
|
||||
|
||||
|
||||
def get_scheduler_for_name(name, optimizers, scheduler_opt):
|
||||
schedulers = []
|
||||
for o in optimizers:
|
||||
if name == 'MultiStepLR':
|
||||
sched = MultiStepLR_Restart(o, scheduler_opt['gen_lr_steps'],
|
||||
restarts=scheduler_opt['restarts'],
|
||||
weights=scheduler_opt['restart_weights'],
|
||||
gamma=scheduler_opt['lr_gamma'],
|
||||
clear_state=scheduler_opt['clear_state'],
|
||||
force_lr=scheduler_opt['force_lr'])
|
||||
elif name == 'ProgressiveMultiStepLR':
|
||||
sched = ProgressiveMultiStepLR(o, scheduler_opt['gen_lr_steps'],
|
||||
scheduler_opt['progressive_starts'],
|
||||
scheduler_opt['lr_gamma'])
|
||||
elif name == 'CosineAnnealingLR_Restart':
|
||||
sched = CosineAnnealingLR_Restart(
|
||||
o, scheduler_opt['T_period'], eta_min=scheduler_opt['eta_min'],
|
||||
restarts=scheduler_opt['restarts'], weights=scheduler_opt['restart_weights'])
|
||||
else:
|
||||
raise NotImplementedError('Scheduler not available')
|
||||
schedulers.append(sched)
|
||||
return schedulers
|
||||
|
||||
|
||||
# This scheduler is specifically designed to modulate the learning rate of several different param groups configured
|
||||
# by a generator or discriminator that slowly adds new stages one at a time, e.g. like progressive growing of GANs.
|
||||
class ProgressiveMultiStepLR(_LRScheduler):
|
||||
|
|
|
|||
29
codes/models/steps/steps.py
Normal file
29
codes/models/steps/steps.py
Normal file
|
|
@ -0,0 +1,29 @@
|
|||
|
||||
|
||||
def create_step(opt_step):
|
||||
pass
|
||||
|
||||
|
||||
# Defines the expected API for a step
|
||||
class base_step:
|
||||
# Returns all optimizers used in this step.
|
||||
def get_optimizers(self):
|
||||
pass
|
||||
|
||||
# Returns optimizers which are opting in for default LR scheduling.
|
||||
def get_optimizers_with_default_scheduler(self):
|
||||
pass
|
||||
|
||||
# Returns the names of the networks this step will train. Other networks will be frozen.
|
||||
def get_networks_trained(self):
|
||||
pass
|
||||
|
||||
# Performs all forward and backward passes for this step given an input state. All input states are lists or
|
||||
# chunked tensors. Use grad_accum_step to derefernce these steps. Return the state with any variables the step
|
||||
# exports (which may be used by subsequent steps)
|
||||
def do_forward_backward(self, state, grad_accum_step):
|
||||
return state
|
||||
|
||||
# Performs the optimizer step after all gradient accumulation is completed.
|
||||
def do_step(self):
|
||||
pass
|
||||
Loading…
Reference in New Issue
Block a user