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import argparse
import json
import os
import math
from functools import partial
import yaml
import torch
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, eval_bsize=None, verbose=False, crop_border=4):
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
# import pdb
# pdb.set_trace()
if eval_bsize is None:
with torch.no_grad():
scale_ratios = batch.get('scale_ratio', None)
if scale_ratios is None:
pred = model(inp, batch['coord'])[-1]
else:
# scale_ratios = (scale_ratios - gt_sub) / gt_div
pred = model(inp, batch['coord'], scale_ratios)[-1]
else:
pred = batched_predict(model, inp, batch['coord'], eval_bsize)
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 crop_border is not None:
# h = math.sqrt(pred.shape[1])
# shape = [inp.shape[0], round(h), round(h), 3]
# pred = pred.view(*shape).permute(0, 3, 1, 2).contiguous()
# batch['gt'] = batch['gt'].view(*shape).permute(0, 3, 1, 2).contiguous()
# else:
# pred = pred.permute(0, 2, 1).contiguous() # B 3 N
# batch['gt'] = batch['gt'].permute(0, 2, 1).contiguous()
res_psnr = metric_fn[0](
pred,
batch['gt'],
crop_border=crop_border
)
res_ssim = metric_fn[1](
pred,
batch['gt'],
crop_border=crop_border
)
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_UC_INR_mysr.yaml')
parser.add_argument('--model', default='checkpoints/EXP20220610_5/epoch-best.pth')
# parser.add_argument('--model', default='checkpoints/EXP20220610_5/epoch-last.pth')
parser.add_argument('--scale_ratio', default=4, type=float)
parser.add_argument('--gpu', default='0')
args = parser.parse_args()
with open(args.config, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
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)
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)
res = eval_psnr(
loader, class_names, model,
data_norm=config.get('data_norm'),
eval_type=config.get('eval_type'),
eval_bsize=config.get('eval_bsize'),
crop_border=crop_border,
verbose=True)
# print('psnr')
# for k, v in res[0].items():
# print(f'{k}: {v:0.2f}')
# print('ssim')
# for k, v in res[1].items():
# print(f'{k}: {v:0.4f}')
print(f'psnr: {res[0]["all"]:0.2f}')
print(f'ssim: {res[1]["all"]:0.4f}') |