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Change GT_size to target_size

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This commit is contained in:
James Betker 2020-04-22 00:37:41 -06:00
parent cc834bd5a3
commit af5dfaa90d
11 changed files with 158 additions and 27 deletions
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127
codes/data/GTLQ_dataset.py Normal file
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@ -0,0 +1,127 @@
import random
import numpy as np
import cv2
import lmdb
import torch
import torch.utils.data as data
import data.util as util
class LQGTDataset(data.Dataset):
"""
Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, etc) and GT image pairs.
If only GT images are provided, generate LQ images on-the-fly.
"""
def __init__(self, opt):
super(LQGTDataset, self).__init__()
self.opt = opt
self.data_type = self.opt['data_type']
self.paths_LQ, self.paths_GT = None, None
self.sizes_LQ, self.sizes_GT = None, None
self.LQ_env, self.GT_env = None, None # environments for lmdb
self.paths_GT, self.sizes_GT = util.get_image_paths(self.data_type, opt['dataroot_GT'])
self.paths_LQ, self.sizes_LQ = util.get_image_paths(self.data_type, opt['dataroot_LQ'])
assert self.paths_GT, 'Error: GT path is empty.'
if self.paths_LQ and self.paths_GT:
assert len(self.paths_LQ) == len(
self.paths_GT
), 'GT and LQ datasets have different number of images - {}, {}.'.format(
len(self.paths_LQ), len(self.paths_GT))
self.random_scale_list = [1]
def _init_lmdb(self):
# https://github.com/chainer/chainermn/issues/129
self.GT_env = lmdb.open(self.opt['dataroot_GT'], readonly=True, lock=False, readahead=False,
meminit=False)
self.LQ_env = lmdb.open(self.opt['dataroot_LQ'], readonly=True, lock=False, readahead=False,
meminit=False)
def __getitem__(self, index):
if self.data_type == 'lmdb' and (self.GT_env is None or self.LQ_env is None):
self._init_lmdb()
GT_path, LQ_path = None, None
scale = self.opt['scale']
GT_size = self.opt['GT_size']
# get GT image
GT_path = self.paths_GT[index]
resolution = [int(s) for s in self.sizes_GT[index].split('_')
] if self.data_type == 'lmdb' else None
img_GT = util.read_img(self.GT_env, GT_path, resolution)
if self.opt['phase'] != 'train': # modcrop in the validation / test phase
img_GT = util.modcrop(img_GT, scale)
if self.opt['color']: # change color space if necessary
img_GT = util.channel_convert(img_GT.shape[2], self.opt['color'], [img_GT])[0]
# get LQ image
if self.paths_LQ:
LQ_path = self.paths_LQ[index]
resolution = [int(s) for s in self.sizes_LQ[index].split('_')
] if self.data_type == 'lmdb' else None
img_LQ = util.read_img(self.LQ_env, LQ_path, resolution)
else: # down-sampling on-the-fly
# randomly scale during training
if self.opt['phase'] == 'train':
random_scale = random.choice(self.random_scale_list)
H_s, W_s, _ = img_GT.shape
def _mod(n, random_scale, scale, thres):
rlt = int(n * random_scale)
rlt = (rlt // scale) * scale
return thres if rlt < thres else rlt
H_s = _mod(H_s, random_scale, scale, GT_size)
W_s = _mod(W_s, random_scale, scale, GT_size)
img_GT = cv2.resize(img_GT, (W_s, H_s), interpolation=cv2.INTER_LINEAR)
if img_GT.ndim == 2:
img_GT = cv2.cvtColor(img_GT, cv2.COLOR_GRAY2BGR)
H, W, _ = img_GT.shape
# using matlab imresize
img_LQ = util.imresize_np(img_GT, 1 / scale, True)
if img_LQ.ndim == 2:
img_LQ = np.expand_dims(img_LQ, axis=2)
if self.opt['phase'] == 'train':
# if the image size is too small
H, W, _ = img_GT.shape
if H < GT_size or W < GT_size:
img_GT = cv2.resize(img_GT, (GT_size, GT_size), interpolation=cv2.INTER_LINEAR)
# using matlab imresize
img_LQ = util.imresize_np(img_GT, 1 / scale, True)
if img_LQ.ndim == 2:
img_LQ = np.expand_dims(img_LQ, axis=2)
H, W, C = img_LQ.shape
LQ_size = GT_size // scale
# randomly crop
rnd_h = random.randint(0, max(0, H - LQ_size))
rnd_w = random.randint(0, max(0, W - LQ_size))
img_LQ = img_LQ[rnd_h:rnd_h + LQ_size, rnd_w:rnd_w + LQ_size, :]
rnd_h_GT, rnd_w_GT = int(rnd_h * scale), int(rnd_w * scale)
img_GT = img_GT[rnd_h_GT:rnd_h_GT + GT_size, rnd_w_GT:rnd_w_GT + GT_size, :]
# augmentation - flip, rotate
img_LQ, img_GT = util.augment([img_LQ, img_GT], self.opt['use_flip'],
self.opt['use_rot'])
if self.opt['color']: # change color space if necessary
img_LQ = util.channel_convert(C, self.opt['color'],
[img_LQ])[0] # TODO during val no definition
# BGR to RGB, HWC to CHW, numpy to tensor
if img_GT.shape[2] == 3:
img_GT = img_GT[:, :, [2, 1, 0]]
img_LQ = img_LQ[:, :, [2, 1, 0]]
img_GT = torch.from_numpy(np.ascontiguousarray(np.transpose(img_GT, (2, 0, 1)))).float()
img_LQ = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LQ, (2, 0, 1)))).float()
if LQ_path is None:
LQ_path = GT_path
return {'LQ': img_LQ, 'GT': img_GT, 'LQ_path': LQ_path, 'GT_path': GT_path}
def __len__(self):
return len(self.paths_GT)

2
codes/data/LQGT_dataset.py
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@ -43,7 +43,7 @@ class LQGTDataset(data.Dataset):
self._init_lmdb()
GT_path, LQ_path = None, None
scale = self.opt['scale']
GT_size = self.opt['GT_size']
GT_size = self.opt['target_size']
# get GT image
GT_path = self.paths_GT[index]

4
codes/data/REDS_dataset.py
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@ -41,7 +41,7 @@ class REDSDataset(data.Dataset):
self.half_N_frames = opt['N_frames'] // 2
self.GT_root, self.LQ_root = opt['dataroot_GT'], opt['dataroot_LQ']
self.data_type = self.opt['data_type']
self.LR_input = False if opt['GT_size'] == opt['LQ_size'] else True # low resolution inputs
self.LR_input = False if opt['target_size'] == opt['LQ_size'] else True # low resolution inputs
#### directly load image keys
if self.data_type == 'lmdb':
self.paths_GT, _ = util.get_image_paths(self.data_type, opt['dataroot_GT'])
@ -107,7 +107,7 @@ class REDSDataset(data.Dataset):
self._init_lmdb()
scale = self.opt['scale']
GT_size = self.opt['GT_size']
GT_size = self.opt['target_size']
key = self.paths_GT[index]
name_a, name_b = key.split('_')
center_frame_idx = int(name_b)

4
codes/data/Vimeo90K_dataset.py
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@ -38,7 +38,7 @@ class Vimeo90KDataset(data.Dataset):
self.GT_root, self.LQ_root = opt['dataroot_GT'], opt['dataroot_LQ']
self.data_type = self.opt['data_type']
self.LR_input = False if opt['GT_size'] == opt['LQ_size'] else True # low resolution inputs
self.LR_input = False if opt['target_size'] == opt['LQ_size'] else True # low resolution inputs
#### determine the LQ frame list
'''
@ -104,7 +104,7 @@ class Vimeo90KDataset(data.Dataset):
self._init_lmdb()
scale = self.opt['scale']
GT_size = self.opt['GT_size']
GT_size = self.opt['target_size']
key = self.paths_GT[index]
name_a, name_b = key.split('_')
#### get the GT image (as the center frame)

6
codes/data_scripts/test_dataloader.py
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@ -23,7 +23,7 @@ def main():
opt['use_shuffle'] = True
opt['n_workers'] = 8
opt['batch_size'] = 16
opt['GT_size'] = 256
opt['target_size'] = 256
opt['LQ_size'] = 64
opt['scale'] = 4
opt['use_flip'] = True
@ -43,7 +43,7 @@ def main():
opt['use_shuffle'] = True
opt['n_workers'] = 8
opt['batch_size'] = 16
opt['GT_size'] = 256
opt['target_size'] = 256
opt['LQ_size'] = 64
opt['scale'] = 4
opt['use_flip'] = True
@ -62,7 +62,7 @@ def main():
opt['use_shuffle'] = True
opt['n_workers'] = 8
opt['batch_size'] = 16
opt['GT_size'] = 128
opt['target_size'] = 128
opt['scale'] = 4
opt['use_flip'] = True
opt['use_rot'] = True

10
codes/models/networks.py
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@ -3,20 +3,23 @@ import models.archs.SRResNet_arch as SRResNet_arch
import models.archs.discriminator_vgg_arch as SRGAN_arch
import models.archs.RRDBNet_arch as RRDBNet_arch
import models.archs.EDVR_arch as EDVR_arch
import math
# Generator
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
scale = opt['scale']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
# RRDB does scaling in two steps, so take the sqrt of the scale we actually want to achieve and feed it to RRDB.
scale_per_step = math.sqrt(scale)
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'])
nf=opt_net['nf'], nb=opt_net['nb'], interpolation_scale_factor=scale_per_step)
# video restoration
elif which_model == 'EDVR':
netG = EDVR_arch.EDVR(nf=opt_net['nf'], nframes=opt_net['nframes'],
@ -24,6 +27,7 @@ def define_G(opt):
back_RBs=opt_net['back_RBs'], center=opt_net['center'],
predeblur=opt_net['predeblur'], HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
else:
raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
@ -32,7 +36,7 @@ def define_G(opt):
# Discriminator
def define_D(opt):
img_sz = opt['datasets']['train']['GT_size']
img_sz = opt['datasets']['train']['target_size']
opt_net = opt['network_D']
which_model = opt_net['which_model_D']

2
codes/options/train/train_EDVR_M.yml
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@ -22,7 +22,7 @@ datasets:
use_shuffle: true
n_workers: 3 # per GPU
batch_size: 32
GT_size: 256
target_size: 256
LQ_size: 64
use_flip: true
use_rot: true

2
codes/options/train/train_EDVR_woTSA_M.yml
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@ -22,7 +22,7 @@ datasets:
use_shuffle: true
n_workers: 3 # per GPU
batch_size: 32
GT_size: 256
target_size: 256
LQ_size: 64
use_flip: true
use_rot: true

24
codes/options/train/train_ESRGAN.yml
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@ -4,28 +4,28 @@ use_tb_logger: true
model: srgan
distortion: sr
scale: 4
gpu_ids: [2]
gpu_ids: [0]
#### datasets
datasets:
train:
name: DIV2K
mode: LQGT
dataroot_GT: ../datasets/DIV2K/DIV2K800_sub.lmdb
dataroot_LQ: ../datasets/DIV2K/DIV2K800_sub_bicLRx4.lmdb
dataroot_GT: ../datasets/div2k/DIV2K800_sub
dataroot_LQ: ../datasets/div2k/DIV2K800_sub_bicLRx4
use_shuffle: true
n_workers: 6 # per GPU
n_workers: 16 # per GPU
batch_size: 16
GT_size: 128
target_size: 128
use_flip: true
use_rot: true
color: RGB
val:
name: val_set14
name: div2kval
mode: LQGT
dataroot_GT: ../datasets/val_set14/Set14
dataroot_LQ: ../datasets/val_set14/Set14_bicLRx4
dataroot_GT: ../datasets/div2k/div2k_valid_hr
dataroot_LQ: ../datasets/div2k/div2k_valid_lr_bicubic
#### network structures
network_G:
@ -41,7 +41,7 @@ network_D:
#### path
path:
pretrain_model_G: ../experiments/pretrained_models/RRDB_PSNR_x4.pth
pretrain_model_G: ../experiments/RRDB_PSNR_x4.pth
strict_load: true
resume_state: ~
@ -73,9 +73,9 @@ train:
D_init_iters: 0
manual_seed: 10
val_freq: !!float 5e3
val_freq: !!float 5e2
#### logger
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3
print_freq: 50
save_checkpoint_freq: !!float 5e2

2
codes/options/train/train_SRGAN.yml
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@ -20,7 +20,7 @@ datasets:
use_shuffle: true
n_workers: 6 # per GPU
batch_size: 16
GT_size: 128
target_size: 128
use_flip: true
use_rot: true
color: RGB

2
codes/options/train/train_SRResNet.yml
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@ -20,7 +20,7 @@ datasets:
use_shuffle: true
n_workers: 6 # per GPU
batch_size: 16
GT_size: 128
target_size: 128
use_flip: true
use_rot: true
color: RGB