File size: 8,205 Bytes
02c5426 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 |
import argparse
import json
import os
import math
from functools import partial
import cv2
import numpy as np
import yaml
import torch
from einops import rearrange
from torch.utils.data import DataLoader
from tqdm import tqdm
import datasets
import models
import utils
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
def batched_predict(model, inp, coord, bsize):
with torch.no_grad():
pred = model(inp, coord)
return pred
def eval_psnr(loader, class_names, model,
data_norm=None, eval_type=None, save_fig=False,
scale_ratio=1, save_path=None, verbose=False, crop_border=4,
cal_metrics=True,
):
crop_border = int(crop_border) if crop_border else crop_border
print('crop border: ', crop_border)
model.eval()
if data_norm is None:
data_norm = {
'inp': {'sub': [0], 'div': [1]},
'gt': {'sub': [0], 'div': [1]}
}
t = data_norm['inp']
inp_sub = torch.FloatTensor(t['sub']).view(1, -1, 1, 1).to(device)
inp_div = torch.FloatTensor(t['div']).view(1, -1, 1, 1).to(device)
t = data_norm['gt']
gt_sub = torch.FloatTensor(t['sub']).view(1, 1, -1).to(device)
gt_div = torch.FloatTensor(t['div']).view(1, 1, -1).to(device)
if eval_type is None:
metric_fn = [utils.calculate_psnr_pt, utils.calculate_ssim_pt]
elif eval_type == 'psnr+ssim':
metric_fn = [utils.calculate_psnr_pt, utils.calculate_ssim_pt]
elif eval_type.startswith('div2k'):
scale = int(eval_type.split('-')[1])
metric_fn = partial(utils.calc_psnr, dataset='div2k', scale=scale)
elif eval_type.startswith('benchmark'):
scale = int(eval_type.split('-')[1])
metric_fn = partial(utils.calc_psnr, dataset='benchmark', scale=scale)
else:
raise NotImplementedError
val_res_psnr = utils.Averager(class_names)
val_res_ssim = utils.Averager(class_names)
pbar = tqdm(loader, leave=False, desc='val')
for batch in pbar:
for k, v in batch.items():
if torch.is_tensor(v):
batch[k] = v.to(device)
inp = (batch['inp'] - inp_sub) / inp_div
with torch.no_grad():
pred = model(inp, batch['coord'], batch['cell'])
pred = pred * gt_div + gt_sub
if eval_type is not None: # reshape for shaving-eval
ih, iw = batch['inp'].shape[-2:]
s = math.sqrt(batch['coord'].shape[1] / (ih * iw))
if s > 1:
shape = [batch['inp'].shape[0], round(ih * s), round(iw * s), 3]
else:
shape = [batch['inp'].shape[0], 32, batch['coord'].shape[1]//32, 3]
pred = pred.view(*shape) \
.permute(0, 3, 1, 2).contiguous()
batch['gt'] = batch['gt'].view(*shape) \
.permute(0, 3, 1, 2).contiguous()
if cal_metrics:
res_psnr = metric_fn[0](
pred,
batch['gt'],
crop_border=crop_border
)
res_ssim = metric_fn[1](
pred,
batch['gt'],
crop_border=crop_border
)
else:
res_psnr = torch.ones(len(pred))
res_ssim = torch.ones(len(pred))
file_names = batch.get('filename', None)
if file_names is not None and save_fig:
for idx in range(len(batch['inp'])):
ori_img = batch['inp'][idx].cpu().numpy() * 255
ori_img = np.clip(ori_img, a_min=0, a_max=255)
ori_img = ori_img.astype(np.uint8)
ori_img = rearrange(ori_img, 'C H W -> H W C')
pred_img = pred[idx].cpu().numpy() * 255
pred_img = np.clip(pred_img, a_min=0, a_max=255)
pred_img = pred_img.astype(np.uint8)
pred_img = rearrange(pred_img, 'C H W -> H W C')
gt_img = batch['gt'][idx].cpu().numpy() * 255
gt_img = np.clip(gt_img, a_min=0, a_max=255)
gt_img = gt_img.astype(np.uint8)
gt_img = rearrange(gt_img, 'C H W -> H W C')
psnr = res_psnr[idx].cpu().numpy()
ssim = res_ssim[idx].cpu().numpy()
ori_file_name = f'{save_path}/{file_names[idx]}_Ori.png'
cv2.imwrite(ori_file_name, ori_img)
pred_file_name = f'{save_path}/{file_names[idx]}_{scale_ratio}X_{psnr:.2f}_{ssim:.4f}.png'
cv2.imwrite(pred_file_name, pred_img)
gt_file_name = f'{save_path}/{file_names[idx]}_GT.png'
cv2.imwrite(gt_file_name, gt_img)
val_res_psnr.add(batch['class_name'], res_psnr)
val_res_ssim.add(batch['class_name'], res_ssim)
if verbose:
pbar.set_description(
'val psnr: {:.4f} ssim: {:.4f}'.format(val_res_psnr.item()['all'], val_res_ssim.item()['all']))
return val_res_psnr.item(), val_res_ssim.item()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='configs/test_INR_mysr.yaml')
parser.add_argument('--model', default='checkpoints/EXP20220610_5/epoch-best.pth')
parser.add_argument('--scale_ratio', default=4, type=float)
parser.add_argument('--save_fig', default=False, type=bool)
parser.add_argument('--save_path', default='tmp', type=str)
parser.add_argument('--cal_metrics', default=True, type=bool)
parser.add_argument('--return_class_metrics', default=False, type=bool)
parser.add_argument('--dataset_name', default='UC', type=str)
args = parser.parse_args()
with open(args.config, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
root_split_file = {'UC':
{
'root_path': '/data/kyanchen/datasets/UC/256',
'split_file': 'data_split/UC_split.json'
},
'AID':
{
'root_path': '/data/kyanchen/datasets/AID',
'split_file': 'data_split/AID_split.json'
}
}
config['test_dataset']['dataset']['args']['root_path'] = root_split_file[args.dataset_name]['root_path']
config['test_dataset']['dataset']['args']['split_file'] = root_split_file[args.dataset_name]['split_file']
config['test_dataset']['wrapper']['args']['scale_ratio'] = args.scale_ratio
spec = config['test_dataset']
dataset = datasets.make(spec['dataset'])
dataset = datasets.make(spec['wrapper'], args={'dataset': dataset})
loader = DataLoader(dataset, batch_size=spec['batch_size'], num_workers=0, pin_memory=True, shuffle=False,
drop_last=False)
if not os.path.exists(args.model):
assert NameError
model_spec = torch.load(args.model)['model']
print(model_spec['args'])
model = models.make(model_spec, load_sd=True).to(device)
file_names = json.load(open(config['test_dataset']['dataset']['args']['split_file']))['test']
class_names = list(set([os.path.basename(os.path.dirname(x)) for x in file_names]))
crop_border = config['test_dataset']['wrapper']['args']['scale_ratio'] + 5
dataset_name = os.path.basename(config['test_dataset']['dataset']['args']['split_file']).split('_')[0]
max_scale = {'UC': 5, 'AID': 12}
if args.scale_ratio > max_scale[dataset_name]:
crop_border = int((args.scale_ratio - max_scale[dataset_name]) / 2 * 48)
if args.save_fig:
os.makedirs(args.save_path, exist_ok=True)
res = eval_psnr(
loader, class_names, model,
data_norm=config.get('data_norm'),
eval_type=config.get('eval_type'),
crop_border=crop_border,
verbose=True,
save_fig=args.save_fig,
scale_ratio=args.scale_ratio,
save_path=args.save_path,
cal_metrics=args.cal_metrics
)
if args.return_class_metrics:
keys = list(res[0].keys())
keys.sort()
print('psnr')
for k in keys:
print(f'{k}: {res[0][k]:0.2f}')
print('ssim')
for k in keys:
print(f'{k}: {res[1][k]:0.4f}')
print(f'psnr: {res[0]["all"]:0.2f}')
print(f'ssim: {res[1]["all"]:0.4f}')
|