vista3d_ohifv3 support #1771
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| Original file line number | Diff line number | Diff line change |
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| from typing import Any, Dict | ||
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There was a problem hiding this comment. when vista3d bundle is used.. why we need to have this related code in monailabel? |
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| import numpy as np | ||
| import torch | ||
| from monai.data import decollate_batch | ||
| from monai.utils import optional_import | ||
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| rearrange, _ = optional_import("einops", name="rearrange") | ||
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| def transform_points(point, affine): | ||
| bs, n = point.shape[:2] | ||
| point = np.concatenate((point, np.ones((bs, n, 1))), axis=-1) | ||
| point = rearrange(point, "b n d -> d (b n)") | ||
| point = affine @ point | ||
| point = rearrange(point, "d (b n)-> b n d", b=bs)[:, :, :3] | ||
| return point | ||
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| def check_prompts_format(label_prompt, points, point_labels): | ||
| """check the format of user prompts | ||
| label_prompt: [1,2,3,4,...,B] List of tensors | ||
| points: [[[x,y,z], [x,y,z], ...]] List of coordinates of a single object | ||
| point_labels: [[1,1,0,...]] List of scalar that matches number of points | ||
| """ | ||
| # check prompt is given | ||
| if label_prompt is None and points is None: | ||
| everything_labels = list( | ||
| {i + 1 for i in range(132)} - {2, 16, 18, 20, 21, 23, 24, 25, 26, 27, 128, 129, 130, 131, 132} | ||
| ) | ||
| if everything_labels is not None: | ||
| label_prompt = [torch.tensor(_) for _ in everything_labels] | ||
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| return label_prompt, points, point_labels | ||
| else: | ||
| raise ValueError("Prompt must be given for inference.") | ||
| # check label_prompt | ||
| if label_prompt is not None: | ||
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| if isinstance(label_prompt, list): | ||
| # if not np.all([len(_) == 1 for _ in label_prompt]): | ||
| # raise ValueError("Label prompt must be a list of single scalar, [1,2,3,4,...,].") | ||
| if not np.all([(x < 255).item() for x in label_prompt]): | ||
| raise ValueError("Current bundle only supports label prompt smaller than 255.") | ||
| if points is None: | ||
| supported_list = list({i + 1 for i in range(132)} - {16, 18, 129, 130, 131}) | ||
| if not np.all([x in supported_list for x in label_prompt]): | ||
| raise ValueError("Undefined label prompt detected. Provide point prompts for zero-shot.") | ||
| else: | ||
| raise ValueError("Label prompt must be a list, [1,2,3,4,...,].") | ||
| # check points | ||
| if points is not None: | ||
| if point_labels is None: | ||
| raise ValueError("Point labels must be given if points are given.") | ||
| if not np.all([len(_) == 3 for _ in points]): | ||
| raise ValueError("Points must be three dimensional (x,y,z) in the shape of [[x,y,z],...,[x,y,z]].") | ||
| if len(points) != len(point_labels): | ||
| raise ValueError("Points must match point labels.") | ||
| if not np.all([_ in [-1, 0, 1, 2, 3] for _ in point_labels]): | ||
| raise ValueError("Point labels can only be -1,0,1 and 2,3 for special flags.") | ||
| if label_prompt is not None and points is not None: | ||
| if len(label_prompt) != 1: | ||
| raise ValueError("Label prompt can only be a single object if provided with point prompts.") | ||
| # check point_labels | ||
| if point_labels is not None: | ||
| if points is None: | ||
| raise ValueError("Points must be given if point labels are given.") | ||
| return label_prompt, points, point_labels | ||
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| def prompt_run_inferer( | ||
| data: Dict[str, Any], | ||
| inferer, | ||
| network, | ||
| input_key="image", | ||
| output_label_key="pred", | ||
| device="cuda", | ||
| convert_to_batch=True, | ||
| ): | ||
| # Retrieve label_prompt, points, and point_labels | ||
| label_prompt, points, point_labels = ( | ||
| data.get("label_prompt", None), | ||
| data.get("points", None), | ||
| data.get("point_labels", None), | ||
| ) | ||
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| if label_prompt is not None: | ||
| label_prompt = [torch.tensor(_) for _ in label_prompt] | ||
| if isinstance(label_prompt, torch.Tensor): | ||
| if label_prompt.numel() == 0: | ||
| label_prompt = None | ||
| elif isinstance(label_prompt, list): | ||
| if len(label_prompt) == 0: | ||
| label_prompt = None | ||
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| label_prompt, points, point_labels = check_prompts_format(label_prompt, points, point_labels) | ||
| label_prompt = ( | ||
| torch.as_tensor([label_prompt]).to(torch.device(device))[0].unsqueeze(-1) if label_prompt is not None else None | ||
| ) | ||
| data["label_prompt"] = label_prompt | ||
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| # Transform points based on spatial scaling factors | ||
| if points is not None: | ||
| points = torch.as_tensor([points]) | ||
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| original_spatial_shape = np.array(data["image_meta_dict"]["spatial_shape"]) | ||
| resized_spatial_shape = np.array(data[input_key].shape[1:]) | ||
| scaling_factors = resized_spatial_shape / original_spatial_shape | ||
| transformed_point = points * scaling_factors | ||
| transformed_point_rounded = np.round(transformed_point) | ||
| points = transformed_point_rounded.to(torch.device(device)) | ||
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| point_labels = torch.as_tensor([point_labels]).to(torch.device(device)) if point_labels is not None else None | ||
| data["points"] = points | ||
| data["point_labels"] = point_labels | ||
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| inputs = data[input_key] | ||
| inputs = inputs if torch.is_tensor(inputs) else torch.from_numpy(inputs) | ||
| inputs = inputs[None].to(torch.device(device)) | ||
| inputs = inputs.to(torch.device(device)) | ||
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| with torch.no_grad(): | ||
| outputs = inferer(inputs, network, point_coords=points, point_labels=point_labels, class_vector=label_prompt) | ||
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| if device.startswith("cuda"): | ||
| torch.cuda.empty_cache() | ||
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| if convert_to_batch: | ||
| if isinstance(outputs, dict): | ||
| outputs_d = decollate_batch(outputs) | ||
| outputs = outputs_d[0] | ||
| else: | ||
| outputs = outputs[0] | ||
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| data[output_label_key] = outputs[0] if isinstance(outputs, list) else outputs | ||
| return data | ||
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any reasons why we needed these changes? which is applicable to everyone