def test_mean_iou():
pred = torch.tensor([0, 0, 1, 1, 0, 1])
target = torch.tensor([0, 1, 0, 1, 0, 0])
out = mean_iou(pred, target, num_classes=2)
assert out == (0.4 + 0.25) / 2
batch = torch.tensor([0, 0, 0, 0, 1, 1])
out = mean_iou(pred, target, num_classes=2, batch=batch)
assert out[0] == (1 / 3 + 1 / 3) / 2
assert out[1] == 0.25
def test(loader):
model.eval()
correct_nodes = total_nodes = 0
ious = []
for data in loader:
data = data.to(device)
with torch.no_grad():
out = model(data)
pred = out.max(dim=1)[1]
correct_nodes += pred.eq(data.y).sum().item()
ious += [mean_iou(pred, data.y, test_dataset.num_classes, data.batch)]
total_nodes += data.num_nodes
return correct_nodes / total_nodes, torch.cat(ious, dim=0).mean().item()
def test(loader):
model.eval()
correct_nodes = total_nodes = 0
ious = []
loss_list = []
for data in loader:
data = data.to(device)
with torch.no_grad():
out = model(data)
loss = F.nll_loss(out, data.y)
loss_list.append(loss.item())
pred = out.max(dim=1)[1]
correct_nodes += pred.eq(data.y).sum().item()
ious += [mean_iou(pred, data.y, params.nClassesTotal, data.batch)]
total_nodes += data.num_nodes
loss_valid.append(sum(loss_list)/len(loss_list))
acc = correct_nodes / total_nodes
save_checkpoint({'state_dict':model.state_dict()}, acc>best, exp_path)
return acc, torch.cat(ious, dim=0).mean().item()
def test(loader):
model.eval()
correct_nodes = total_nodes = 0
ious = []
loss_list = []
for i, data in enumerate(loader):
path = path_list[i]
data = data.to(device)
with torch.no_grad():
out = model(data)
loss = F.nll_loss(out, data.y)
loss_list.append(loss.item())
pred = out.max(dim=1)[1]
path = osp.join(args.out_dir,
path.split(os.sep)[-1].replace('graph', 'face_gnn'))
with open(path, 'wb') as file:
pickle.dump(pred.cpu().numpy(), file)
correct_nodes += pred.eq(data.y).sum().item()
ious += [mean_iou(pred, data.y, params.nClassesTotal, data.batch)]
total_nodes += data.num_nodes
acc = correct_nodes / total_nodes
return acc, torch.cat(ious, dim=0).mean().item()
