import logging from collections import OrderedDict import torch import torch.nn as nn from torch.nn.parallel import DataParallel, DistributedDataParallel import models.networks as networks from models.steps.steps import create_step import models.lr_scheduler as lr_scheduler from models.base_model import BaseModel from models.loss import GANLoss, FDPLLoss from apex import amp from data.weight_scheduler import get_scheduler_for_opt from .archs.SPSR_arch import ImageGradient, ImageGradientNoPadding import torch.nn.functional as F import glob import random import torchvision.utils as utils import os logger = logging.getLogger('base') class ExtensibleTrainer(BaseModel): def __init__(self, opt): super(ExtensibleTrainer, self).__init__(opt) if opt['dist']: self.rank = torch.distributed.get_rank() else: self.rank = -1 # non dist training train_opt = opt['train'] self.mega_batch_factor = 1 self.netsG = {} self.netsD = {} self.networks = [] for name, net in opt['networks'].items(): if net['type'] == 'generator': new_net = networks.define_G(net) self.netsG[name] = new_net elif net['type'] == 'discriminator': new_net = networks.define_D(net) self.netsD[name] = new_net else: raise NotImplementedError("Can only handle generators and discriminators") self.networks.append(new_net) if self.is_train: self.mega_batch_factor = train_opt['mega_batch_factor'] if self.mega_batch_factor is None: self.mega_batch_factor = 1 # Initialize amp. amp_nets, amp_opts = amp.initialize(self.networks, self.optimizers, opt_level=opt['amp_level'], num_losses=len(self.optimizers)) # self.networks is stored unwrapped. It should never be used for forward() or backward() passes, instead use # self.netG and self.netD for that. self.networks = amp_nets # DataParallel dnets = [] for anet in amp_nets: if opt['dist']: dnet = DistributedDataParallel(anet, device_ids=[torch.cuda.current_device()], find_unused_parameters=True) else: dnet = DataParallel(anet) if self.is_train: dnet.train() else: dnet.eval() dnets.append(dnet) # Backpush the wrapped networks into the network dicts.. found = 0 for dnet in dnets: for net_dict in [self.netsD, self.netsG]: for k, v in net_dict.items(): if v == dnet: net_dict[k] = dnet found += 1 assert found == len(self.networks) # Initialize the training steps self.steps = [] for step in opt['steps']: step = create_step(step, self.netsG, self.netsD) self.steps.append(step) self.optimizers.extend(step.get_optimizers()) # Find the optimizers that are using the default scheduler, then build them. def_opt = [] for s in self.steps: def_opt.extend(s.get_optimizers_with_default_scheduler()) lr_scheduler.get_scheduler_for_name(train_opt['default_lr_scheme'], def_opt, train_opt) self.print_network() # print network self.load() # load G and D if needed # Setting this to false triggers SRGAN to call the models update_model() function on the first iteration. self.updated = True def feed_data(self, data): self.var_L = torch.chunk(corrupted_L, chunks=self.mega_batch_factor, dim=0) self.var_H = [t.to(self.device) for t in torch.chunk(data['GT'], chunks=self.mega_batch_factor, dim=0)] input_ref = data['ref'] if 'ref' in data else data['GT'] self.var_ref = [t.to(self.device) for t in torch.chunk(input_ref, chunks=self.mega_batch_factor, dim=0)] def optimize_parameters(self, step): # Some models need to make parametric adjustments per-step. Do that here. for net in self.networks.values(): if hasattr(net, "update_for_step"): net.update_for_step(step, os.path.join(self.opt['path']['models'], "..")) # Iterate through the steps, performing them one at a time. state = {'lr': self.var_L, 'hr': self.var_H, 'ref': self.var_ref} for s in self.steps: # Only set requires_grad=True for the network being trained. nets_to_train = s.get_networks_trained() for name, net in self.networks.items(): net_enabled = name in nets_to_train for p in self.netsG.parameters(): if p.dtype != torch.int64 and p.dtype != torch.bool: p.requires_grad = net_enabled else: p.requires_grad = False # Now do a forward and backward pass for each gradient accumulation step. for m in range(self.mega_batch_factor): state = s.do_forward_backward(state, m) # And finally perform optimization. s.do_step() # G for p in self.netsD.parameters(): p.requires_grad = False if self.spsr_enabled: for p in self.netD_grad.parameters(): p.requires_grad = False self.swapout_D(step) self.swapout_G(step) # Turning off G-grad is required to enable mega-batching and D_update_ratio to work together for some reason. if step % self.D_update_ratio == 0 and step >= self.D_init_iters: if self.spsr_enabled and self.branch_pretrain and step < self.branch_init_iters: for k, v in self.netsG.named_parameters(): if v.dtype != torch.int64 and v.dtype != torch.bool: v.requires_grad = '_branch_pretrain' in k else: for p in self.netsG.parameters(): if p.dtype != torch.int64 and p.dtype != torch.bool: p.requires_grad = True else: for p in self.netsG.parameters(): p.requires_grad = False # Calculate a standard deviation for the gaussian noise to be applied to the discriminator, termed noise-theta. if self.D_noise_final == 0: noise_theta = 0 else: 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 if _profile: print("Misc setup %f" % (time() - _t,)) _t = time() if step >= self.D_init_iters: self.optimizer_G.zero_grad() self.fake_GenOut = [] self.fea_GenOut = [] self.fake_H = [] self.spsr_grad_GenOut = [] var_ref_skips = [] 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): if self.spsr_enabled: using_gan_img = False # SPSR models have outputs from three different branches. fake_H_branch, fake_GenOut, grad_LR = self.netsG(var_L) fea_GenOut = fake_GenOut self.spsr_grad_GenOut.append(fake_H_branch) # Get image gradients for later use. fake_H_grad = self.get_grad_nopadding(fake_GenOut) else: if random.random() > self.gan_lq_img_use_prob: fea_GenOut, fake_GenOut = self.netsG(var_L) using_gan_img = False else: fea_GenOut, fake_GenOut = self.netsG(var_LGAN) using_gan_img = True if _profile: print("Gen forward %f" % (time() - _t,)) _t = time() self.fake_GenOut.append(fake_GenOut.detach()) self.fea_GenOut.append(fea_GenOut.detach()) l_g_total = 0 if step % self.D_update_ratio == 0 and step >= self.D_init_iters: fea_w = self.l_fea_sched.get_weight_for_step(step) l_g_pix_log = None l_g_fea_log = None l_g_fdpl = None l_g_fea_log = None if self.cri_pix and not using_gan_img: # pixel loss l_g_pix = self.l_pix_w * self.cri_pix(fea_GenOut, pix) l_g_pix_log = l_g_pix / self.l_pix_w l_g_total += l_g_pix if self.spsr_enabled and self.cri_pix_grad: # gradient pixel loss if self.disjoint_data: grad_truth = self.get_grad_nopadding(var_L) grad_pred = F.interpolate(fake_H_grad, size=grad_truth.shape[2:], mode="nearest") else: grad_truth = self.get_grad_nopadding(var_H) grad_pred = fake_H_grad l_g_pix_grad = self.l_pix_grad_w * self.cri_pix_grad(grad_pred, grad_truth) l_g_total += l_g_pix_grad if self.spsr_enabled and self.cri_pix_branch: # branch pixel loss if self.disjoint_data: grad_truth = self.get_grad_nopadding(var_L) grad_pred = F.interpolate(fake_H_branch, size=grad_truth.shape[2:], mode="nearest") else: grad_truth = self.get_grad_nopadding(var_H) grad_pred = fake_H_branch l_g_pix_grad_branch = self.l_pix_branch_w * self.cri_pix_branch(grad_pred, grad_truth) l_g_total += l_g_pix_grad_branch if self.fdpl_enabled and not using_gan_img: l_g_fdpl = self.cri_fdpl(fea_GenOut, pix) l_g_total += l_g_fdpl * self.fdpl_weight if self.cri_fea and not using_gan_img and fea_w > 0: # feature loss if self.lr_netF is not None: real_fea = self.lr_netF(var_L, interpolate_factor=self.opt['scale']) else: real_fea = self.netF(pix).detach() fake_fea = self.netF(fea_GenOut) l_g_fea = fea_w * self.cri_fea(fake_fea, real_fea) l_g_fea_log = l_g_fea / fea_w l_g_total += l_g_fea if _profile: print("Fea forward %f" % (time() - _t,)) _t = time() # Note to future self: The BCELoss(0, 1) and BCELoss(0, 0) = .6931 # Effectively this means that the generator has only completely "won" when l_d_real and l_d_fake is # equal to this value. If I ever come up with an algorithm that tunes fea/gan weights automatically, # it should target this l_g_fix_disc = torch.zeros(1, requires_grad=False, device=self.device).squeeze() for fixed_disc in self.fixed_disc_nets: weight = fixed_disc.module.fdisc_weight real_fea = fixed_disc(pix).detach() fake_fea = fixed_disc(fea_GenOut) l_g_fix_disc = l_g_fix_disc + weight * self.cri_fea(fake_fea, real_fea) l_g_total += l_g_fix_disc if self.l_gan_w > 0: if self.opt['train']['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']: if self.opt['train']['gan_type'] == 'crossgan': pred_g_fake = self.netsD(fake_GenOut, var_L) else: pred_g_fake = self.netsD(fake_GenOut) l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True) elif self.opt['train']['gan_type'] == 'ragan': pred_d_real = self.netsD(var_ref).detach() pred_g_fake = self.netsD(fake_GenOut) l_g_gan = self.l_gan_w * ( self.cri_gan(pred_d_real - torch.mean(pred_g_fake), False) + self.cri_gan(pred_g_fake - torch.mean(pred_d_real), True)) / 2 l_g_gan_log = l_g_gan / self.l_gan_w l_g_total += l_g_gan if self.spsr_enabled and self.cri_grad_gan: if self.opt['train']['gan_type'] == 'crossgan': pred_g_fake_grad = self.netsD(fake_H_grad, var_L) else: pred_g_fake_grad = self.netsD(fake_H_grad) pred_g_fake_grad_branch = self.netD_grad(fake_H_branch) if self.opt['train']['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']: l_g_gan_grad = self.l_gan_grad_w * self.cri_grad_gan(pred_g_fake_grad, True) l_g_gan_grad_branch = self.l_gan_grad_w * self.cri_grad_gan(pred_g_fake_grad_branch, True) elif self.opt['train']['gan_type'] == 'ragan': pred_g_real_grad = self.netD_grad(self.get_grad_nopadding(var_ref)).detach() l_g_gan_grad = self.l_gan_w * ( self.cri_gan(pred_g_real_grad - torch.mean(pred_g_fake_grad), False) + self.cri_gan(pred_g_fake_grad - torch.mean(pred_g_real_grad), True)) / 2 l_g_gan_grad_branch = self.l_gan_w * ( self.cri_gan(pred_g_real_grad - torch.mean(pred_g_fake_grad_branch), False) + self.cri_gan(pred_g_fake_grad_branch - torch.mean(pred_g_real_grad), True)) / 2 l_g_total += l_g_gan_grad + l_g_gan_grad_branch # Scale the loss down by the batch factor. l_g_total_log = l_g_total l_g_total = l_g_total / self.mega_batch_factor with amp.scale_loss(l_g_total, self.optimizer_G, loss_id=0) as l_g_total_scaled: l_g_total_scaled.backward() if _profile: print("Gen backward %f" % (time() - _t,)) _t = time() self.optimizer_G.step() if _profile: print("Gen step %f" % (time() - _t,)) _t = time() # D if self.l_gan_w > 0 and step >= self.G_warmup: for p in self.netsD.parameters(): if p.dtype != torch.int64 and p.dtype != torch.bool: p.requires_grad = True noise = torch.randn_like(var_ref) * noise_theta noise.to(self.device) real_disc_images = [] fake_disc_images = [] 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): if random.random() > self.gan_lq_img_use_prob: fake_H = fake_GenOut.clone().detach().requires_grad_(False) else: # Re-compute generator outputs with the GAN inputs. with torch.no_grad(): if self.spsr_enabled: _, fake_H, _ = self.netsG(var_LGAN) else: _, fake_H = self.netsG(var_LGAN) fake_H = fake_H.detach() if _profile: print("Gen forward for disc %f" % (time() - _t,)) _t = time() # Apply noise to the inputs to slow discriminator convergence. var_ref = var_ref + noise fake_H = fake_H + noise l_d_fea_real = 0 l_d_fea_fake = 0 self.optimizer_D.zero_grad() if self.opt['train']['gan_type'] == 'pixgan_fea': # Compute a feature loss which is added to the GAN loss computed later to guide the discriminator better. disc_fea_scale = .1 _, fea_real = self.netsD(var_ref, output_feature_vector=True) actual_fea = self.netF(var_ref) l_d_fea_real = self.cri_fea(fea_real, actual_fea) * disc_fea_scale / self.mega_batch_factor _, fea_fake = self.netsD(fake_H, output_feature_vector=True) actual_fea = self.netF(fake_H) l_d_fea_fake = self.cri_fea(fea_fake, actual_fea) * disc_fea_scale / self.mega_batch_factor if self.opt['train']['gan_type'] == 'crossgan': # need to forward and backward separately, since batch norm statistics differ # real pred_d_real = self.netsD(var_ref, var_L) l_d_real = self.cri_gan(pred_d_real, True) l_d_real_log = l_d_real # fake pred_d_fake = self.netsD(fake_H, var_L) l_d_fake = self.cri_gan(pred_d_fake, False) l_d_fake_log = l_d_fake # mismatched mismatched_L = torch.roll(var_L, shifts=1, dims=0) pred_d_real_mismatched = self.netsD(var_ref, mismatched_L) pred_d_fake_mismatched = self.netsD(fake_H, mismatched_L) l_d_mismatched = (self.cri_gan(pred_d_real_mismatched, False) + self.cri_gan(pred_d_fake_mismatched, False)) / 2 l_d_total = (l_d_real + l_d_fake + l_d_mismatched) / 3 l_d_total = 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() elif self.opt['train']['gan_type'] == 'gan': # real pred_d_real = self.netsD(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.netsD(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.netsD.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.netsD(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.netsD(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.netsD(fake_H) pred_d_real = self.netsD(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.netsG.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.netsG(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.netsG(self.var_L[0])] self.netsG.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.netsG.module, "get_debug_values"): return_log.update(self.netsG.module.get_debug_values(step)) if hasattr(self.netsD.module, "get_debug_values"): return_log.update(self.netsD.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)