Python sliding_window_inference Examples

Example #1

def _sliding_window_processor(engine, batch):
    net.eval()
    img, seg, meta_data = batch
    with torch.no_grad():
        seg_probs = sliding_window_inference(img, roi_size, sw_batch_size, net,
                                             device)
        return seg_probs, seg.to(device)

Example #2

def _sliding_window_processor(_engine, batch):
    net.eval()
    img, seg, meta_data = batch
    with torch.no_grad():
        seg_probs = sliding_window_inference(img, roi_size, sw_batch_size,
                                             lambda x: net(x)[0], device)
        return predict_segmentation(seg_probs)

Example #3

File: test_sliding_window_inference.py Project: pieper/MONAI

    def test_sliding_window_default(self, image_shape, roi_shape, sw_batch_size):
        inputs = np.ones(image_shape)
        device = torch.device("cpu:0")

        def compute(data):
            return data.to(device) + 1

        result = sliding_window_inference(inputs, roi_shape, sw_batch_size, compute, device)
        expected_val = np.ones(image_shape, dtype=np.float32) + 1
        self.assertTrue(np.allclose(result.numpy(), expected_val))

Example #4

File: unet_evaluation_dict.py Project: kuan-li/MONAI

def _sliding_window_processor(engine, batch):
    net.eval()
    with torch.no_grad():
        seg_probs = sliding_window_inference(batch['img'], roi_size,
                                             sw_batch_size, net, device)
        return seg_probs, batch['seg'].to(device)