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test.py
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test.py
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import argparse
import os
import yaml
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
import datasets
import models
import utils
from torchvision import transforms
from mmcv.runner import load_checkpoint
def batched_predict(model, inp, coord, 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, :])
preds.append(pred)
ql = qr
pred = torch.cat(preds, dim=1)
return pred, preds
def tensor2PIL(tensor):
toPIL = transforms.ToPILImage()
return toPIL(tensor)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def eval_psnr(loader, model, data_norm=None, eval_type=None, eval_bsize=None,
verbose=False):
model.eval()
if data_norm is None:
data_norm = {
'inp': {'sub': [0], 'div': [1]},
'gt': {'sub': [0], 'div': [1]}
}
if eval_type == 'f1':
metric_fn = utils.calc_f1
metric1, metric2, metric3, metric4 = 'f1', 'auc', 'none', 'none'
elif eval_type == 'fmeasure':
metric_fn = utils.calc_fmeasure
metric1, metric2, metric3, metric4 = 'f_mea', 'mae', 'none', 'none'
elif eval_type == 'ber':
metric_fn = utils.calc_ber
metric1, metric2, metric3, metric4 = 'shadow', 'non_shadow', 'ber', 'none'
elif eval_type == 'cod':
metric_fn = utils.calc_cod
metric1, metric2, metric3, metric4 = 'sm', 'em', 'wfm', 'mae'
val_metric1 = utils.Averager()
val_metric2 = utils.Averager()
val_metric3 = utils.Averager()
val_metric4 = 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']
pred = torch.sigmoid(model.infer(inp))
result1, result2, result3, result4 = metric_fn(pred, batch['gt'])
val_metric1.add(result1.item(), inp.shape[0])
val_metric2.add(result2.item(), inp.shape[0])
val_metric3.add(result3.item(), inp.shape[0])
val_metric4.add(result4.item(), inp.shape[0])
if verbose:
pbar.set_description('val {} {:.4f}'.format(metric1, val_metric1.item()))
pbar.set_description('val {} {:.4f}'.format(metric2, val_metric2.item()))
pbar.set_description('val {} {:.4f}'.format(metric3, val_metric3.item()))
pbar.set_description('val {} {:.4f}'.format(metric4, val_metric4.item()))
return val_metric1.item(), val_metric2.item(), val_metric3.item(), val_metric4.item()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config')
parser.add_argument('--model')
parser.add_argument('--prompt', default='none')
args = parser.parse_args()
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)
model = models.make(config['model']).cuda()
sam_checkpoint = torch.load(args.model, map_location='cuda:0')
model.load_state_dict(sam_checkpoint, strict=True)
metric1, metric2, metric3, metric4 = eval_psnr(loader, model,
data_norm=config.get('data_norm'),
eval_type=config.get('eval_type'),
eval_bsize=config.get('eval_bsize'),
verbose=True)
print('metric1: {:.4f}'.format(metric1))
print('metric2: {:.4f}'.format(metric2))
print('metric3: {:.4f}'.format(metric3))
print('metric4: {:.4f}'.format(metric4))