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test.py
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test.py
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import argparse
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
def batched_predict(model, inp, coord, cell, bsize):
with torch.no_grad():
model.gen_feat(inp)
n = coord.shape[1]
ql = 0
preds = []
while ql < n:
qr = min(ql + bsize, n)
pred = model.query_rgb(coord[:, ql: qr, :], cell[:, ql: qr, :])
preds.append(pred)
ql = qr
pred = torch.cat(preds, dim=1)
return pred
def eval_psnr(loader, model, data_norm=None, eval_type=None, eval_bsize=None, window_size=0, scale_max=4, fast=False,
verbose=False):
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).cuda()
inp_div = torch.FloatTensor(t['div']).view(1, -1, 1, 1).cuda()
t = data_norm['gt']
gt_sub = torch.FloatTensor(t['sub']).view(1, 1, -1).cuda()
gt_div = torch.FloatTensor(t['div']).view(1, 1, -1).cuda()
if eval_type is None:
metric_fn = utils.calc_psnr
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 = utils.Averager()
pbar = tqdm(loader, leave=False, desc='val')
for batch in pbar:
for k, v in batch.items():
batch[k] = v.cuda()
inp = (batch['inp'] - inp_sub) / inp_div
# SwinIR Evaluation - reflection padding
if window_size != 0:
_, _, h_old, w_old = inp.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
inp = torch.cat([inp, torch.flip(inp, [2])], 2)[:, :, :h_old + h_pad, :]
inp = torch.cat([inp, torch.flip(inp, [3])], 3)[:, :, :, :w_old + w_pad]
coord = utils.make_coord((scale*(h_old+h_pad), scale*(w_old+w_pad))).unsqueeze(0).cuda()
cell = torch.ones_like(coord)
cell[:, :, 0] *= 2 / inp.shape[-2] / scale
cell[:, :, 1] *= 2 / inp.shape[-1] / scale
else:
h_pad = 0
w_pad = 0
coord = batch['coord']
cell = batch['cell']
if eval_bsize is None:
with torch.no_grad():
pred = model(inp, coord, cell)
else:
if fast:
pred = model(inp, coord, cell*max(scale/scale_max, 1))
else:
pred = batched_predict(model, inp, coord, cell*max(scale/scale_max, 1), eval_bsize) # cell clip for extrapolation
pred = pred * gt_div + gt_sub
pred.clamp_(0, 1)
if eval_type is not None and fast == False: # reshape for shaving-eval
# gt reshape
ih, iw = batch['inp'].shape[-2:]
s = math.sqrt(batch['coord'].shape[1] / (ih * iw))
shape = [batch['inp'].shape[0], round(ih * s), round(iw * s), 3]
batch['gt'] = batch['gt'].view(*shape) \
.permute(0, 3, 1, 2).contiguous()
# prediction reshape
ih += h_pad
iw += w_pad
s = math.sqrt(coord.shape[1] / (ih * iw))
shape = [batch['inp'].shape[0], round(ih * s), round(iw * s), 3]
pred = pred.view(*shape) \
.permute(0, 3, 1, 2).contiguous()
pred = pred[..., :batch['gt'].shape[-2], :batch['gt'].shape[-1]]
res = metric_fn(pred, batch['gt'])
val_res.add(res.item(), inp.shape[0])
if verbose:
pbar.set_description('val {:.4f}'.format(val_res.item()))
return val_res.item()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config')
parser.add_argument('--model')
parser.add_argument('--window', default='0')
parser.add_argument('--scale_max', default='4')
parser.add_argument('--fast', default=False)
parser.add_argument('--gpu', default='0')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
with open(args.config, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
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=8, pin_memory=True)
model_spec = torch.load(args.model)['model']
model = models.make(model_spec, load_sd=True).cuda()
res = eval_psnr(loader, model,
data_norm=config.get('data_norm'),
eval_type=config.get('eval_type'),
eval_bsize=config.get('eval_bsize'),
window_size=int(args.window),
scale_max = int(args.scale_max),
fast = args.fast,
verbose=True)
print('result: {:.4f}'.format(res))