forked from mrq/DL-Art-School
Modifications that allow developer to explicitly specify a different image set for PIX and feature losses
This commit is contained in:
James Betker
parent
12d92dc443
commit
79aff886b5
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@ -19,12 +19,14 @@ class LQGTDataset(data.Dataset):
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self.data_type = self.opt['data_type']
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self.paths_LQ, self.paths_GT = None, None
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self.sizes_LQ, self.sizes_GT = None, None
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self.LQ_env, self.GT_env = None, None # environments for lmdb
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self.paths_PIX, self.sizes_PIX = None, None
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self.LQ_env, self.GT_env, self.PIX_env = None, None, None # environments for lmdb
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self.paths_GT, self.sizes_GT = util.get_image_paths(self.data_type, opt['dataroot_GT'])
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self.paths_LQ, self.sizes_LQ = util.get_image_paths(self.data_type, opt['dataroot_LQ'])
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if 'dataroot_PIX' in opt:
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if 'dataroot_PIX' in opt.keys():
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self.paths_PIX, self.sizes_PIX = util.get_image_paths(self.data_type, opt['dataroot_PIX'])
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assert self.paths_GT, 'Error: GT path is empty.'
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if self.paths_LQ and self.paths_GT:
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assert len(self.paths_LQ) == len(
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@ -39,7 +41,7 @@ class LQGTDataset(data.Dataset):
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meminit=False)
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self.LQ_env = lmdb.open(self.opt['dataroot_LQ'], readonly=True, lock=False, readahead=False,
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meminit=False)
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if 'dataroot_PIX' in self.opt:
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if 'dataroot_PIX' in self.opt.keys():
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self.PIX_env = lmdb.open(self.opt['dataroot_PIX'], readonly=True, lock=False, readahead=False,
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meminit=False)
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@ -61,12 +63,13 @@ class LQGTDataset(data.Dataset):
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img_GT = util.channel_convert(img_GT.shape[2], self.opt['color'], [img_GT])[0]
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# get the pix image
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if self.PIX_path is not None:
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PIX_path = self.PIX_path[index]
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if self.paths_PIX is not None:
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PIX_path = self.paths_PIX[index]
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img_PIX = util.read_img(self.PIX_env, PIX_path, resolution)
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if self.opt['color']: # change color space if necessary
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img_PIX = util.channel_convert(img_PIX.shape[2], self.opt['color'], [img_PIX])[0]
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else:
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img_PIX = img_GT
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# get LQ image
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if self.paths_LQ:
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@ -98,14 +101,8 @@ class LQGTDataset(data.Dataset):
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img_LQ = np.expand_dims(img_LQ, axis=2)
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if self.opt['phase'] == 'train':
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# if the image size is too small
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H, W, _ = img_GT.shape
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if H < GT_size or W < GT_size:
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img_GT = cv2.resize(img_GT, (GT_size, GT_size), interpolation=cv2.INTER_LINEAR)
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# using matlab imresize
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img_LQ = util.imresize_np(img_GT, 1 / scale, True)
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if img_LQ.ndim == 2:
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img_LQ = np.expand_dims(img_LQ, axis=2)
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assert H >= GT_size and W >= GT_size
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H, W, C = img_LQ.shape
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LQ_size = GT_size // scale
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@ -116,9 +113,10 @@ class LQGTDataset(data.Dataset):
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img_LQ = img_LQ[rnd_h:rnd_h + LQ_size, rnd_w:rnd_w + LQ_size, :]
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rnd_h_GT, rnd_w_GT = int(rnd_h * scale), int(rnd_w * scale)
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img_GT = img_GT[rnd_h_GT:rnd_h_GT + GT_size, rnd_w_GT:rnd_w_GT + GT_size, :]
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img_PIX = img_PIX[rnd_h_GT:rnd_h_GT + GT_size, rnd_w_GT:rnd_w_GT + GT_size, :]
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# augmentation - flip, rotate
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img_LQ, img_GT = util.augment([img_LQ, img_GT], self.opt['use_flip'],
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img_LQ, img_GT, img_PIX = util.augment([img_LQ, img_GT, img_PIX], self.opt['use_flip'],
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self.opt['use_rot'])
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if self.opt['color']: # change color space if necessary
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@ -129,12 +127,14 @@ class LQGTDataset(data.Dataset):
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if img_GT.shape[2] == 3:
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img_GT = img_GT[:, :, [2, 1, 0]]
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img_LQ = img_LQ[:, :, [2, 1, 0]]
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img_PIX = img_PIX[:, :, [2, 1, 0]]
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img_GT = torch.from_numpy(np.ascontiguousarray(np.transpose(img_GT, (2, 0, 1)))).float()
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img_PIX = torch.from_numpy(np.ascontiguousarray(np.transpose(img_PIX, (2, 0, 1)))).float()
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img_LQ = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LQ, (2, 0, 1)))).float()
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if LQ_path is None:
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LQ_path = GT_path
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return {'LQ': img_LQ, 'GT': img_GT, 'LQ_path': LQ_path, 'GT_path': GT_path}
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return {'LQ': img_LQ, 'GT': img_GT, 'PIX': img_PIX, 'LQ_path': LQ_path, 'GT_path': GT_path}
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def __len__(self):
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return len(self.paths_GT)
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@ -140,10 +140,7 @@ class SRGANModel(BaseModel):
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self.var_H = data['GT'].to(self.device) # GT
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input_ref = data['ref'] if 'ref' in data else data['GT']
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self.var_ref = input_ref.to(self.device)
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if 'PIX' in data:
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self.pix = data['PIX']
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else:
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self.pix = self.var_H
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self.pix = data['PIX'].to(self.device)
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def optimize_parameters(self, step):
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@ -151,6 +148,7 @@ class SRGANModel(BaseModel):
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for i in range(self.var_L.shape[0]):
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utils.save_image(self.var_H[i].cpu().detach(), os.path.join("E:\\4k6k\\temp\hr", "%05i_%02i.png" % (step, i)))
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utils.save_image(self.var_L[i].cpu().detach(), os.path.join("E:\\4k6k\\temp\\lr", "%05i_%02i.png" % (step, i)))
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utils.save_image(self.pix[i].cpu().detach(), os.path.join("E:\\4k6k\\temp\\pix", "%05i_%02i.png" % (step, i)))
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# G
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for p in self.netD.parameters():
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@ -165,7 +163,7 @@ class SRGANModel(BaseModel):
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l_g_pix = self.l_pix_w * self.cri_pix(self.fake_H, self.pix)
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l_g_total += l_g_pix
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if self.cri_fea: # feature loss
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real_fea = self.netF(self.var_H).detach()
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real_fea = self.netF(self.pix).detach()
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fake_fea = self.netF(self.fake_H)
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l_g_fea = self.l_fea_w * self.cri_fea(fake_fea, real_fea)
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l_g_total += l_g_fea
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@ -1,5 +1,5 @@
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#### general settings
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name: ESRGANx4_blacked_ft
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name: ESRGANx4_blacked_lqprn
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use_tb_logger: true
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model: srgan
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distortion: sr
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@ -13,7 +13,8 @@ datasets:
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name: blacked
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mode: LQGT
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dataroot_GT: ../datasets/blacked/train/hr
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dataroot_LQ: ../datasets/blacked/train/lr
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dataroot_LQ: ../datasets/lqprn/train/lr
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dataroot_PIX: ../datasets/lqprn/train/hr
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use_shuffle: true
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n_workers: 4 # per GPU
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@ -42,10 +43,10 @@ network_D:
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#### path
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path:
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pretrain_model_G: ../experiments/ESRGANx4_blacked_ft/models/31500_G.pth
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pretrain_model_D: ../experiments/ESRGANx4_blacked_ft/models/31500_D.pth
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pretrain_model_G: ../experiments/blacked_gen_20000_epochs.pth
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pretrain_model_D: ../experiments/blacked_disc_20000_epochs.pth
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resume_state: ~
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strict_load: true
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resume_state: ../experiments/ESRGANx4_blacked_ft/training_state/31500.state
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#### training settings: learning rate scheme, loss
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train:
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@ -65,7 +66,7 @@ train:
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lr_gamma: 0.5
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pixel_criterion: l1
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pixel_weight: !!float 1e-2
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pixel_weight: !!float 5e-3
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feature_criterion: l1
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feature_weight: 1
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gan_type: ragan # gan | ragan
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