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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 matplotlib.pyplot as plt
import datasets
import models
import utility
def plot_preds(model, loader, epoch,
save_dir="/content",
target_shape=(128,128),
batch_size=16,
return_data=False):
batch = next(iter(loader))
for k, v in batch.items():
batch[k] = v.cuda()
data_norm = {
'inp': {'sub': [0], 'div': [1]},
'gt': {'sub': [0], 'div': [0.5]}
}
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()
inp = (batch['inp'] / inp_div) - inp_sub
#inp = inp.clamp_(0, 1)
model.eval()
coord, cell = make_coord_cell(target_shape=target_shape, batch_size=batch_size)
with torch.no_grad():
pred = model(inp, coord.cuda(), cell.cuda())
pred = (pred * gt_div) + gt_sub
pred = pred.clamp_(0, 1)
pred = reshape(pred, target_shape)
inp = batch['inp'].clamp_(0, 1)
hr_gt = reshape(batch["hr"], batch["inp"].shape[-2:])
plt.figure(figsize=(4, batch_size * 3))
for i, (p, g) in enumerate(zip(pred, hr_gt)):
plt.subplot(batch_size,3,(i * 3) + 1)
plt.imshow(p.cpu().numpy().transpose(1,2,0))
plt.subplot(batch_size,3,(i * 3) + 2)
plt.imshow(g.cpu().numpy().transpose(1,2,0))
plt.subplot(batch_size,3,(i * 3) + 3)
plt.imshow(batch['inp'][i].cpu().numpy().transpose(1,2,0))
plt.savefig(f"{save_dir}/testfig_{epoch}.png")
if return_data:
return pred, batch
def make_coord_cell(target_shape=(32, 32), batch_size=8):
coord = make_coord(target_shape).repeat(batch_size, 1, 1)
cell = torch.ones_like(coord)
cell[..., 0] *= 2 / target_shape[1]
cell[..., 1] *= 2 / target_shape[0]
return coord, cell
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 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 reshape(pred, t_shape):
ih, iw = t_shape
s = math.sqrt(pred.shape[1] / (ih * iw))
shape = [pred.shape[0], round(ih * s), round(iw * s), 3]
pred = pred.view(*shape) \
.permute(0, 3, 1, 2).contiguous()
return pred
def make_coord(shape, ranges=None, flatten=True):
""" Make coordinates at grid centers.
"""
coord_seqs = []
for i, n in enumerate(shape):
if ranges is None:
v0, v1 = -1, 1
else:
v0, v1 = ranges[i]
r = (v1 - v0) / (2 * n)
seq = v0 + r + (2 * r) * torch.arange(n)
coord_seqs.append(seq)
ret = torch.stack(torch.meshgrid(*coord_seqs), dim=-1)
if flatten:
ret = ret.view(-1, ret.shape[-1])
return ret
def eval_psnr(loader, model, epoch, data_norm=None, eval_type=None, eval_bsize=None,
verbose=False, savedir="/content"):
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 = utility.calc_psnr
elif eval_type.startswith('div2k'):
scale = int(eval_type.split('-')[1])
metric_fn = partial(utility.calc_psnr, dataset='div2k', scale=scale)
elif eval_type.startswith('benchmark'):
scale = int(eval_type.split('-')[1])
metric_fn = partial(utility.calc_psnr, dataset='benchmark', scale=scale)
else:
raise NotImplementedError
val_res = utility.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
with torch.no_grad():
pred = model(inp, batch['coord'], batch['cell'])
pred = pred * gt_div + gt_sub
pred.clamp_(0, 1)
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))
shape = [batch['inp'].shape[0], round(ih * s), round(iw * s), 3]
pred = pred.view(*shape) \
.permute(0, 3, 1, 2).contiguous()
batch['gt'] = batch['gt'].view(*shape) \
.permute(0, 3, 1, 2).contiguous()
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()))
if eval_bsize:
try:
plot_preds(model, loader, epoch, save_dir=savedir,
batch_size=pred.shape[0])
except Exception as e:
print(f"Failed to save validation preview\n{e}")
return val_res.item()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config')
parser.add_argument('--model')
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'),
verbose=True)
print('result: {:.4f}'.format(res))