Function create_sintel_submission

evaluate_FlowFormer_tile.py:89–130

Create submission for the Sintel leaderboard

(model, output_path='sintel_submission_multi8_768', sigma=0.05)

@torch.no_grad()
def create_sintel_submission(model, output_path='sintel_submission_multi8_768', sigma=0.05):
    """ Create submission for the Sintel leaderboard """
    print("no warm start")
    #print(f"output path: {output_path}")
    IMAGE_SIZE = [436, 1024]

    hws = compute_grid_indices(IMAGE_SIZE)
    weights = compute_weight(hws, IMAGE_SIZE, TRAIN_SIZE, sigma)

    model.eval()
    for dstype in ['final', "clean"]:
        test_dataset = datasets.MpiSintel_submission(split='test', aug_params=None, dstype=dstype, root="./dataset/Sintel/test")
        epe_list = []
        for test_id in range(len(test_dataset)):
            if (test_id+1) % 100 == 0:
                print(f"{test_id} / {len(test_dataset)}")
                # break
            image1, image2, (sequence, frame) = test_dataset[test_id]
            image1, image2 = image1[None].cuda(), image2[None].cuda()

            flows = 0
            flow_count = 0

            for idx, (h, w) in enumerate(hws):
                image1_tile = image1[:, :, h:h+TRAIN_SIZE[0], w:w+TRAIN_SIZE[1]]
                image2_tile = image2[:, :, h:h+TRAIN_SIZE[0], w:w+TRAIN_SIZE[1]]
                flow_pre, flow_low = model(image1_tile, image2_tile)

                padding = (w, IMAGE_SIZE[1]-w-TRAIN_SIZE[1], h, IMAGE_SIZE[0]-h-TRAIN_SIZE[0], 0, 0)
                flows += F.pad(flow_pre * weights[idx], padding)
                flow_count += F.pad(weights[idx], padding)

            flow_pre = flows / flow_count
            flow = flow_pre[0].permute(1, 2, 0).cpu().numpy()

            output_dir = os.path.join(output_path, dstype, sequence)
            output_file = os.path.join(output_dir, 'frame%04d.flo' % (frame+1))

            if not os.path.exists(output_dir):
                os.makedirs(output_dir)

            frame_utils.writeFlow(output_file, flow)

@torch.no_grad()
def create_kitti_submission(model, output_path='kitti_submission', sigma=0.05):