def test(test_data_loader, model):
srocc = SROCC()
plcc = PLCC()
rmse = RMSE()
len_test = len(test_data_loader)
pb = ProgressBar(len_test, show_step=True)
print("Testing")
model.eval()
with torch.no_grad():
for i, ((img, ref), score) in enumerate(test_data_loader):
img, ref = img.cuda(), ref.cuda()
output = model(img, ref).cpu().data.numpy()
score = score.data.numpy()
srocc.update(score, output)
plcc.update(score, output)
rmse.update(score, output)
pb.show(
i, "Test: [{0:5d}/{1:5d}]\t"
"Score: {2:.4f}\t"
"Label: {3:.4f}".format(i + 1, len_test, float(output),
float(score)))
print("\n\nSROCC: {0:.4f}\n"
"PLCC: {1:.4f}\n"
"RMSE: {2:.4f}".format(srocc.compute(), plcc.compute(),
rmse.compute()))
def test(test_data_loader, model):
scores = []
srocc = SROCC()
plcc = PLCC()
rmse = RMSE()
len_test = len(test_data_loader)
pb = ProgressBar(len_test-1, show_step=True)
print("Testing")
model.eval()
with torch.no_grad():
for i, ((img, ref), score) in enumerate(test_data_loader):
img, ref = img.cuda(), ref.cuda()
output = model(img, ref).cpu().data.numpy()
score = score.data.numpy()
srocc.update(score, output)
plcc.update(score, output)
rmse.update(score, output)
pb.show(i, 'Test: [{0:5d}/{1:5d}]\t'
'Score: {2:.4f}\t'
'Label: {3:.4f}'
.format(i, len_test, float(output), float(score)))
scores.append(output)
# Write scores to file
with open('../test/scores.txt', 'w') as f:
stat = list(map(lambda s: f.write(str(s)+'\n'), scores))
print('\n\nSROCC: {0:.4f}\n'
'PLCC: {1:.4f}\n'
'RMSE: {2:.4f}'
.format(srocc.compute(), plcc.compute(), rmse.compute())
)