def eval_one(net,
loss_fn,
config,
loader,
image_id,
device,
plot=False,
verbose=False):
input, label_map, image_id = loader.dataset[image_id]
input = input.to(device)
label_map, label_list = loader.dataset.get_label(image_id)
loader.dataset.reg_target_transform(label_map)
label_map = torch.from_numpy(label_map).permute(2, 0,
1).unsqueeze_(0).to(device)
# Forward Pass
t_start = time.time()
pred = net(input.unsqueeze(0))
t_forward = time.time() - t_start
loss, cls_loss, loc_loss = loss_fn(pred, label_map)
pred.squeeze_(0)
cls_pred = pred[0, ...]
if verbose:
print("Forward pass time", t_forward)
# Filter Predictions
t_start = time.time()
corners, scores = filter_pred(config, pred)
t_post = time.time() - t_start
if verbose:
print("Non max suppression time:", t_post)
gt_boxes = np.array(label_list)
gt_match, pred_match, overlaps = compute_matches(gt_boxes,
corners,
scores,
iou_threshold=0.5)
num_gt = len(label_list)
num_pred = len(scores)
input_np = input.cpu().permute(1, 2, 0).numpy()
pred_image = get_bev(input_np, corners)
if plot == True:
# Visualization
plot_bev(input_np, label_list, window_name='GT')
plot_bev(input_np, corners, window_name='Prediction')
plot_label_map(cls_pred.numpy())
return num_gt, num_pred, scores, pred_image, pred_match, loss.item(
), t_forward, t_post
def __call__(self, velo, path):
t_start = time.time()
bev = self.preprocess(velo, path)
t_pre = time.time()
with torch.no_grad():
pred = self.net(bev.unsqueeze(0)).squeeze_(0)
t_m = time.time()
corners, scores = filter_pred(self.config, pred)
input_np = bev.permute(1, 2, 0).cpu().numpy()
t_post = time.time()
pred_bev = get_bev(input_np, corners)
t_s = [t_pre - t_start, t_m - t_pre, t_post - t_m]
return t_s, corners, scores, pred_bev