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import warnings |
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from typing import Tuple |
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import torch |
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import numpy as np |
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from ...torchio import TypeData, CHANNELS_DIMENSION |
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from .grid_sampler import GridSampler |
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class GridAggregator: |
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r"""Aggregate patches for dense inference. |
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This class is typically used to build a volume made of patches after |
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inference of batches extracted by a :py:class:`~torchio.data.GridSampler`. |
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Args: |
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sampler: Instance of :py:class:`~torchio.data.GridSampler` used to |
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extract the patches. |
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overlap_mode: If ``'crop'``, the overlapping predictions will be |
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cropped. If ``'average'``, the predictions in the overlapping areas |
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will be averaged with equal weights. See the |
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`grid aggregator tests`_ for a raw visualization of both modes. |
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.. _grid aggregator tests: https://github.com/fepegar/torchio/blob/master/tests/data/inference/test_aggregator.py |
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.. note:: Adapted from NiftyNet. See `this NiftyNet tutorial |
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<https://niftynet.readthedocs.io/en/dev/window_sizes.html>`_ for more |
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information about patch-based sampling. |
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""" |
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def __init__(self, sampler: GridSampler, overlap_mode: str = 'crop'): |
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sample = sampler.sample |
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self.volume_padded = sampler.padding_mode is not None |
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self.spatial_shape = sample.spatial_shape |
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self._output_tensor = None |
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self.patch_overlap = sampler.patch_overlap |
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self.parse_overlap_mode(overlap_mode) |
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self.overlap_mode = overlap_mode |
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self._avgmask_tensor = None |
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@staticmethod |
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def parse_overlap_mode(overlap_mode): |
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if overlap_mode not in ('crop', 'average'): |
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message = ( |
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f'Overlap mode must be "crop" or "average" but "{overlap_mode}"' |
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' was passed' |
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) |
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raise ValueError(message) |
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def crop_batch( |
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self, |
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batch: torch.Tensor, |
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locations: np.ndarray, |
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overlap: np.ndarray, |
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) -> Tuple[TypeData, np.ndarray]: |
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border = np.array(overlap) // 2 # overlap is even in grid sampler |
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crop_locations = locations.astype(int).copy() |
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indices_ini, indices_fin = crop_locations[:, :3], crop_locations[:, 3:] |
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num_locations = len(crop_locations) |
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border_ini = np.tile(border, (num_locations, 1)) |
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border_fin = border_ini.copy() |
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# Do not crop patches at the border of the volume |
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# Unless we're padding the volume in the grid sampler. In that case, |
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# it doesn't matter if we don't crop patches at the border, because the |
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# output volume will be cropped |
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if not self.volume_padded: |
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mask_border_ini = indices_ini == 0 |
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border_ini[mask_border_ini] = 0 |
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for axis, size in enumerate(self.spatial_shape): |
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mask_border_fin = indices_fin[:, axis] == size |
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border_fin[mask_border_fin, axis] = 0 |
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indices_ini += border_ini |
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indices_fin -= border_fin |
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crop_shapes = indices_fin - indices_ini |
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patch_shape = batch.shape[2:] # ignore batch and channels dim |
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cropped_patches = [] |
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for patch, crop_shape in zip(batch, crop_shapes): |
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diff = patch_shape - crop_shape |
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left = (diff / 2).astype(int) |
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i_ini, j_ini, k_ini = left |
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i_fin, j_fin, k_fin = left + crop_shape |
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cropped_patch = patch[:, i_ini:i_fin, j_ini:j_fin, k_ini:k_fin] |
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cropped_patches.append(cropped_patch) |
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return cropped_patches, crop_locations |
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def initialize_output_tensor(self, batch: torch.Tensor) -> None: |
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if self._output_tensor is not None: |
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return |
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num_channels = batch.shape[CHANNELS_DIMENSION] |
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self._output_tensor = torch.zeros( |
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num_channels, |
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*self.spatial_shape, |
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dtype=batch.dtype, |
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) |
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def initialize_avgmask_tensor(self, batch: torch.Tensor) -> None: |
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if self._avgmask_tensor is not None: |
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return |
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num_channels = batch.shape[CHANNELS_DIMENSION] |
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self._avgmask_tensor = torch.zeros( |
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num_channels, |
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*self.spatial_shape, |
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dtype=batch.dtype, |
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) |
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def add_batch( |
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self, |
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batch_tensor: torch.Tensor, |
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locations: torch.Tensor, |
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) -> None: |
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"""Add batch processed by a CNN to the output prediction volume. |
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Args: |
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batch_tensor: 5D tensor, typically the output of a convolutional |
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neural network, e.g. ``batch['image'][torchio.DATA]``. |
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locations: 2D tensor with shape :math:`(B, 6)` representing the |
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patch indices in the original image. They are typically |
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extracted using ``batch[torchio.LOCATION]``. |
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""" |
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batch = batch_tensor.cpu() |
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locations = locations.cpu().numpy() |
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self.initialize_output_tensor(batch) |
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if self.overlap_mode == 'crop': |
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cropped_patches, crop_locations = self.crop_batch( |
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batch, |
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locations, |
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self.patch_overlap, |
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) |
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for patch, crop_location in zip(cropped_patches, crop_locations): |
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i_ini, j_ini, k_ini, i_fin, j_fin, k_fin = crop_location |
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self._output_tensor[ |
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:, |
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i_ini:i_fin, |
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j_ini:j_fin, |
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k_ini:k_fin] = patch |
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elif self.overlap_mode == 'average': |
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self.initialize_avgmask_tensor(batch) |
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for patch, location in zip(batch, locations): |
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i_ini, j_ini, k_ini, i_fin, j_fin, k_fin = location |
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self._output_tensor[ |
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:, |
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i_ini:i_fin, |
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j_ini:j_fin, |
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k_ini:k_fin] += patch |
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self._avgmask_tensor[ |
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:, |
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i_ini:i_fin, |
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j_ini:j_fin, |
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k_ini:k_fin] += 1 |
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def get_output_tensor(self) -> torch.Tensor: |
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"""Get the aggregated volume after dense inference.""" |
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if self._output_tensor.dtype == torch.int64: |
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message = ( |
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'Medical image frameworks such as ITK do not support int64.' |
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' Casting to int32...' |
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) |
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warnings.warn(message) |
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self._output_tensor = self._output_tensor.type(torch.int32) |
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if self.overlap_mode == 'average': |
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output = self._output_tensor / self._avgmask_tensor |
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else: |
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output = self._output_tensor |
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if self.volume_padded: |
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from ...transforms import Crop |
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border = self.patch_overlap // 2 |
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cropping = border.repeat(2) |
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crop = Crop(cropping) |
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return crop(output) |
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else: |
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return output |
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fepegar /
torchio
