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PaddleGAN

PaddlePaddle / PaddleGAN
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P
PaddleGAN
提交 a0a56e75 编写于 作者: L LielinJiang
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ppgan/models/srgan_model.py
浏览文件 @ a0a56e75
......@@ -12,126 +12,24 @@ from .losses import GANLoss
from .builder import MODELS
# logger = logging.getLogger('base')
@MODELS.register()
class SRGANModel(BaseModel):
def __init__(self, cfg):
super(SRGANModel, self).__init__(cfg)
# if opt['dist']:
# self.rank = torch.distributed.get_rank()
# else:
# self.rank = -1 # non dist training
# train_opt = opt['train']
# define networks and load pretrained models
# define networks
self.model_names = ['G']
self.netG = build_generator(cfg.model.generator)
self.visual_names = ['LQ', 'GT', 'fake_H']
# self.netG = networks.define_G(opt).to(self.device)
# if opt['dist']:
# self.netG = DistributedDataParallel(self.netG, device_ids=[torch.cuda.current_device()])
# else:
# self.netG = DataParallel(self.netG)
if False:#self.is_train:
self.netD = build_discriminator(cfg.model.discriminator)
# if opt['dist']:
# self.netD = DistributedDataParallel(self.netD,
# device_ids=[torch.cuda.current_device()])
# else:
# self.netD = DataParallel(self.netD)
# TODO: support srgan train.
if False:
# self.netD = build_discriminator(cfg.model.discriminator)
self.netG.train()
self.netD.train()
# define losses, optimizer and scheduler
# if self.is_train:
# pass
# G pixel loss
# if train_opt['pixel_weight'] > 0:
# l_pix_type = train_opt['pixel_criterion']
# if l_pix_type == 'l1':
# self.cri_pix = nn.L1Loss().to(self.device)
# elif l_pix_type == 'l2':
# self.cri_pix = nn.MSELoss().to(self.device)
# else:
# raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
# self.l_pix_w = train_opt['pixel_weight']
# else:
# # logger.info('Remove pixel loss.')
# self.cri_pix = None
# # G feature loss
# if train_opt['feature_weight'] > 0:
# l_fea_type = train_opt['feature_criterion']
# if l_fea_type == 'l1':
# self.cri_fea = nn.L1Loss().to(self.device)
# elif l_fea_type == 'l2':
# self.cri_fea = nn.MSELoss().to(self.device)
# else:
# raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
# self.l_fea_w = train_opt['feature_weight']
# else:
# logger.info('Remove feature loss.')
# self.cri_fea = None
# if self.cri_fea: # load VGG perceptual loss
# self.netF = networks.define_F(opt, use_bn=False).to(self.device)
# if opt['dist']:
# self.netF = DistributedDataParallel(self.netF,
# device_ids=[torch.cuda.current_device()])
# else:
# self.netF = DataParallel(self.netF)
# # GD gan loss
# self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
# self.l_gan_w = train_opt['gan_weight']
# # D_update_ratio and D_init_iters
# self.D_update_ratio = train_opt['D_update_ratio'] if train_opt['D_update_ratio'] else 1
# self.D_init_iters = train_opt['D_init_iters'] if train_opt['D_init_iters'] else 0
# # optimizers
# # G
# wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
# optim_params = []
# for k, v in self.netG.named_parameters(): # can optimize for a part of the model
# if v.requires_grad:
# optim_params.append(v)
# else:
# if self.rank <= 0:
# logger.warning('Params [{:s}] will not optimize.'.format(k))
# self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'],
# weight_decay=wd_G,
# betas=(train_opt['beta1_G'], train_opt['beta2_G']))
# self.optimizers.append(self.optimizer_G)
# # D
# wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
# self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'],
# weight_decay=wd_D,
# betas=(train_opt['beta1_D'], train_opt['beta2_D']))
# self.optimizers.append(self.optimizer_D)
# # schedulers
# if train_opt['lr_scheme'] == 'MultiStepLR':
# for optimizer in self.optimizers:
# self.schedulers.append(
# lr_scheduler.MultiStepLR_Restart(optimizer, train_opt['lr_steps'],
# restarts=train_opt['restarts'],
# weights=train_opt['restart_weights'],
# gamma=train_opt['lr_gamma'],
# clear_state=train_opt['clear_state']))
# elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
# for optimizer in self.optimizers:
# self.schedulers.append(
# lr_scheduler.CosineAnnealingLR_Restart(
# optimizer, train_opt['T_period'], eta_min=train_opt['eta_min'],
# restarts=train_opt['restarts'], weights=train_opt['restart_weights']))
# else:
# raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
# self.log_dict = OrderedDict()
# self.print_network() # print network
# self.load() # load G and D if needed
# self.netD.train()
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
......@@ -150,239 +48,8 @@ class SRGANModel(BaseModel):
if 'A_paths' in input:
self.image_paths = input['A_paths']
# def feed_data(self, data, need_GT=True):
# self.var_L = data['LQ'].to(self.device) # LQ
# if need_GT:
# self.var_H = data['GT'].to(self.device) # GT
# input_ref = data['ref'] if 'ref' in data else data['GT']
# self.var_ref = input_ref.to(self.device)
def forward(self):
self.fake_H = self.netG(self.LQ)
def optimize_parameters(self, step):
pass
# # G
# for p in self.netD.parameters():
# p.requires_grad = False
# self.optimizer_G.zero_grad()
# self.fake_H = self.netG(self.var_L.detach())
# l_g_total = 0
# if step % self.D_update_ratio == 0 and step > self.D_init_iters:
# if self.cri_pix: # pixel loss
# l_g_pix = self.l_pix_w * self.cri_pix(self.fake_H, self.var_H)
# l_g_total += l_g_pix
# if self.cri_fea: # feature loss
# real_fea = self.netF(self.var_H).detach()
# fake_fea = self.netF(self.fake_H)
# l_g_fea = self.l_fea_w * self.cri_fea(fake_fea, real_fea)
# l_g_total += l_g_fea
# pred_g_fake = self.netD(self.fake_H)
# if self.opt['train']['gan_type'] == 'gan':
# 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.netD(self.var_ref).detach()
# 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_total += l_g_gan
# l_g_total.backward()
# self.optimizer_G.step()
# # D
# for p in self.netD.parameters():
# p.requires_grad = True
# self.optimizer_D.zero_grad()
# l_d_total = 0
# pred_d_real = self.netD(self.var_ref)
# pred_d_fake = self.netD(self.fake_H.detach()) # detach to avoid BP to G
# if self.opt['train']['gan_type'] == 'gan':
# l_d_real = self.cri_gan(pred_d_real, True)
# l_d_fake = self.cri_gan(pred_d_fake, False)
# l_d_total = l_d_real + l_d_fake
# elif self.opt['train']['gan_type'] == 'ragan':
# l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True)
# l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real), False)
# l_d_total = (l_d_real + l_d_fake) / 2
# l_d_total.backward()
# self.optimizer_D.step()
# # set log
# if step % self.D_update_ratio == 0 and step > self.D_init_iters:
# if self.cri_pix:
# self.log_dict['l_g_pix'] = l_g_pix.item()
# # self.log_dict['l_g_mean_color'] = l_g_mean_color.item()
# if self.cri_fea:
# self.log_dict['l_g_fea'] = l_g_fea.item()
# self.log_dict['l_g_gan'] = l_g_gan.item()
# self.log_dict['l_d_real'] = l_d_real.item()
# self.log_dict['l_d_fake'] = l_d_fake.item()
# self.log_dict['D_real'] = torch.mean(pred_d_real.detach())
# self.log_dict['D_fake'] = torch.mean(pred_d_fake.detach())
# def test(self):
# self.netG.eval()
# with torch.no_grad():
# self.fake_H = self.netG(self.var_L)
# self.netG.train()
# def back_projection(self):
# lr_error = self.var_L - torch.nn.functional.interpolate(self.fake_H,
# scale_factor=1/self.opt['scale'],
# mode='bicubic',
# align_corners=False)
# us_error = torch.nn.functional.interpolate(lr_error,
# scale_factor=self.opt['scale'],
# mode='bicubic',
# align_corners=False)
# self.fake_H += self.opt['back_projection_lamda'] * us_error
# torch.clamp(self.fake_H, 0, 1)
# def test_chop(self):
# self.netG.eval()
# with torch.no_grad():
# self.fake_H = self.forward_chop(self.var_L)
# self.netG.train()
# def forward_chop(self, *args, shave=10, min_size=160000):
# # scale = 1 if self.input_large else self.scale[self.idx_scale]
# scale = self.opt['scale']
# n_GPUs = min(torch.cuda.device_count(), 4)
# args = [a.squeeze().unsqueeze(0) for a in args]
# # height, width
# h, w = args[0].size()[-2:]
# # print('len(args)', len(args))
# # print('args[0].size()', args[0].size())
# top = slice(0, h//2 + shave)
# bottom = slice(h - h//2 - shave, h)
# left = slice(0, w//2 + shave)
# right = slice(w - w//2 - shave, w)
# x_chops = [torch.cat([
# a[..., top, left],
# a[..., top, right],
# a[..., bottom, left],
# a[..., bottom, right]
# ]) for a in args]
# # print('len(x_chops)', len(x_chops))
# # print('x_chops[0].size()', x_chops[0].size())
# y_chops = []
# if h * w < 4 * min_size:
# for i in range(0, 4, n_GPUs):
# x = [x_chop[i:(i + n_GPUs)] for x_chop in x_chops]
# # print(len(x))
# # print(x[0].size())
# y = P.data_parallel(self.netG, *x, range(n_GPUs))
# if not isinstance(y, list): y = [y]
# if not y_chops:
# y_chops = [[c for c in _y.chunk(n_GPUs, dim=0)] for _y in y]
# else:
# for y_chop, _y in zip(y_chops, y):
# y_chop.extend(_y.chunk(n_GPUs, dim=0))
# else:
# # print(x_chops[0].size())
# for p in zip(*x_chops):
# # print('len(p)', len(p))
# # print('p[0].size()', p[0].size())
# y = self.forward_chop(*p, shave=shave, min_size=min_size)
# if not isinstance(y, list): y = [y]
# if not y_chops:
# y_chops = [[_y] for _y in y]
# else:
# for y_chop, _y in zip(y_chops, y): y_chop.append(_y)
# h *= scale
# w *= scale
# top = slice(0, h//2)
# bottom = slice(h - h//2, h)
# bottom_r = slice(h//2 - h, None)
# left = slice(0, w//2)
# right = slice(w - w//2, w)
# right_r = slice(w//2 - w, None)
# # batch size, number of color channels
# b, c = y_chops[0][0].size()[:-2]
# y = [y_chop[0].new(b, c, h, w) for y_chop in y_chops]
# for y_chop, _y in zip(y_chops, y):
# _y[..., top, left] = y_chop[0][..., top, left]
# _y[..., top, right] = y_chop[1][..., top, right_r]
# _y[..., bottom, left] = y_chop[2][..., bottom_r, left]
# _y[..., bottom, right] = y_chop[3][..., bottom_r, right_r]
# if len(y) == 1:
# y = y[0]
# return y
# def get_current_log(self):
# return self.log_dict
# def get_current_visuals(self, need_GT=True):
# out_dict = OrderedDict()
# out_dict['LQ'] = self.var_L.detach()[0].float().cpu()
# out_dict['SR'] = self.fake_H.detach()[0].float().cpu()
# if need_GT:
# out_dict['GT'] = self.var_H.detach()[0].float().cpu()
# return out_dict
# def print_network(self):
# # Generator
# s, n = self.get_network_description(self.netG)
# if isinstance(self.netG, nn.DataParallel) or isinstance(self.netG, DistributedDataParallel):
# net_struc_str = '{} - {}'.format(self.netG.__class__.__name__,
# self.netG.module.__class__.__name__)
# else:
# net_struc_str = '{}'.format(self.netG.__class__.__name__)
# if self.rank <= 0:
# logger.info('Network G structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
# logger.info(s)
# if self.is_train:
# # Discriminator
# s, n = self.get_network_description(self.netD)
# if isinstance(self.netD, nn.DataParallel) or isinstance(self.netD,
# DistributedDataParallel):
# net_struc_str = '{} - {}'.format(self.netD.__class__.__name__,
# self.netD.module.__class__.__name__)
# else:
# net_struc_str = '{}'.format(self.netD.__class__.__name__)
# if self.rank <= 0:
# logger.info('Network D structure: {}, with parameters: {:,d}'.format(
# net_struc_str, n))
# logger.info(s)
# if self.cri_fea: # F, Perceptual Network
# s, n = self.get_network_description(self.netF)
# if isinstance(self.netF, nn.DataParallel) or isinstance(
# self.netF, DistributedDataParallel):
# net_struc_str = '{} - {}'.format(self.netF.__class__.__name__,
# self.netF.module.__class__.__name__)
# else:
# net_struc_str = '{}'.format(self.netF.__class__.__name__)
# if self.rank <= 0:
# logger.info('Network F structure: {}, with parameters: {:,d}'.format(
# net_struc_str, n))
# logger.info(s)
# def load(self):
# load_path_G = self.opt['path']['pretrain_model_G']
# if load_path_G is not None:
# logger.info('Loading model for G [{:s}] ...'.format(load_path_G))
# self.load_network(load_path_G, self.netG, self.opt['path']['strict_load'])
# load_path_D = self.opt['path']['pretrain_model_D']
# if self.opt['is_train'] and load_path_D is not None:
# logger.info('Loading model for D [{:s}] ...'.format(load_path_D))
# self.load_network(load_path_D, self.netD, self.opt['path']['strict_load'])
# def save(self, iter_step):
# self.save_network(self.netG, 'G', iter_step)
# self.save_network(self.netD, 'D', iter_step)
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