docker_task_2/nnunetv2/training/dataloading/data_loader_3d.py

import numpy as np
from nnunetv2.training.dataloading.base_data_loader import nnUNetDataLoaderBase
from nnunetv2.training.dataloading.nnunet_dataset import nnUNetDataset
import time

class nnUNetDataLoader3D(nnUNetDataLoaderBase):
    def generate_train_batch(self):
        selected_keys = self.get_indices()
        # preallocate memory for data and seg
        data_all = np.zeros(self.data_shape, dtype=np.float32)
        seg_all = np.zeros(self.seg_shape, dtype=np.int16)
        case_properties = []

        for j, i in enumerate(selected_keys):
            # oversampling foreground will improve stability of model training, especially if many patches are empty
            # (Lung for example)
            force_fg = self.get_do_oversample(j)

            data, seg, properties = self._data.load_case(i)
            case_properties.append(properties)

            # If we are doing the cascade then the segmentation from the previous stage will already have been loaded by
            # self._data.load_case(i) (see nnUNetDataset.load_case)
            shape = data.shape[1:]
            dim = len(shape)
            bbox_lbs, bbox_ubs = self.get_bbox(shape, force_fg, properties['class_locations'])

            # whoever wrote this knew what he was doing (hint: it was me). We first crop the data to the region of the
            # bbox that actually lies within the data. This will result in a smaller array which is then faster to pad.
            # valid_bbox is just the coord that lied within the data cube. It will be padded to match the patch size
            # later
            valid_bbox_lbs = [max(0, bbox_lbs[i]) for i in range(dim)]
            valid_bbox_ubs = [min(shape[i], bbox_ubs[i]) for i in range(dim)]

            # At this point you might ask yourself why we would treat seg differently from seg_from_previous_stage.
            # Why not just concatenate them here and forget about the if statements? Well that's because segneeds to
            # be padded with -1 constant whereas seg_from_previous_stage needs to be padded with 0s (we could also
            # remove label -1 in the data augmentation but this way it is less error prone)
            this_slice = tuple([slice(0, data.shape[0])] + [slice(i, j) for i, j in zip(valid_bbox_lbs, valid_bbox_ubs)])
            data = data[this_slice]

            this_slice = tuple([slice(0, seg.shape[0])] + [slice(i, j) for i, j in zip(valid_bbox_lbs, valid_bbox_ubs)])
            seg = seg[this_slice]

            padding = [(-min(0, bbox_lbs[i]), max(bbox_ubs[i] - shape[i], 0)) for i in range(dim)]
            data_all[j] = np.pad(data, ((0, 0), *padding), 'constant', constant_values=0)
            seg_all[j] = np.pad(seg, ((0, 0), *padding), 'constant', constant_values=-1)
        return {'data': data_all, 'seg': seg_all, 'properties': case_properties, 'keys': selected_keys}


class nnUNetDataLoader3D_MRCT(nnUNetDataLoaderBase):
    def generate_train_batch(self):
        selected_keys = self.get_indices()
        # preallocate memory for data and seg
        data_all = np.zeros(self.data_shape, dtype=np.float32)
        seg_all = np.zeros(self.seg_shape, dtype=np.float32)
        case_properties = []

        for j, i in enumerate(selected_keys):
            # oversampling foreground will improve stability of model training, especially if many patches are empty
            # (Lung for example)
            force_fg = self.get_do_oversample(j)

            data, seg, properties = self._data.load_case(i)
            case_properties.append(properties)

            # If we are doing the cascade then the segmentation from the previous stage will already have been loaded by
            # self._data.load_case(i) (see nnUNetDataset.load_case)
            shape = data.shape[1:]
            dim = len(shape)
            bbox_lbs, bbox_ubs = self.get_bbox(shape, force_fg, properties['class_locations'])

            # whoever wrote this knew what he was doing (hint: it was me). We first crop the data to the region of the
            # bbox that actually lies within the data. This will result in a smaller array which is then faster to pad.
            # valid_bbox is just the coord that lied within the data cube. It will be padded to match the patch size
            # later
            valid_bbox_lbs = [max(0, bbox_lbs[i]) for i in range(dim)]
            valid_bbox_ubs = [min(shape[i], bbox_ubs[i]) for i in range(dim)]

            # At this point you might ask yourself why we would treat seg differently from seg_from_previous_stage.
            # Why not just concatenate them here and forget about the if statements? Well that's because segneeds to
            # be padded with -1 constant whereas seg_from_previous_stage needs to be padded with 0s (we could also
            # remove label -1 in the data augmentation but this way it is less error prone)
            this_slice = tuple([slice(0, data.shape[0])] + [slice(i, j) for i, j in zip(valid_bbox_lbs, valid_bbox_ubs)])
            data = data[this_slice]

            this_slice = tuple([slice(0, seg.shape[0])] + [slice(i, j) for i, j in zip(valid_bbox_lbs, valid_bbox_ubs)])
            seg = seg[this_slice]

            padding = [(-min(0, bbox_lbs[i]), max(bbox_ubs[i] - shape[i], 0)) for i in range(dim)]
            data_all[j] = np.pad(data, ((0, 0), *padding), 'constant', constant_values=0)
            seg_all[j] = np.pad(seg, ((0, 0), *padding), 'constant', constant_values=0)
        return {'data': data_all, 'seg': seg_all, 'properties': case_properties, 'keys': selected_keys}
    

class nnUNetDataLoader3D_MRCT_mask(nnUNetDataLoaderBase):
    def determine_shapes(self):
        # load one case
        data, seg, properties, mask = self._data.load_case(self.indices[0])
        num_color_channels = data.shape[0]

        data_shape = (self.batch_size, num_color_channels, *self.patch_size)
        seg_shape = (self.batch_size, seg.shape[0], *self.patch_size)
        mask_shape = (self.batch_size, mask.shape[0], *self.patch_size)
        return data_shape, seg_shape
    
    def generate_train_batch(self):
        selected_keys = self.get_indices()
        # preallocate memory for data and seg
        data_all = np.zeros(self.data_shape, dtype=np.float32)
        seg_all = np.zeros(self.seg_shape, dtype=np.float32)
        mask_all = np.zeros(self.data_shape, dtype=np.float32) # bx: todo
        case_properties = []

        for j, i in enumerate(selected_keys):
            # oversampling foreground will improve stability of model training, especially if many patches are empty
            # (Lung for example)
            force_fg = self.get_do_oversample(j)

            data, seg, properties, mask = self._data.load_case(i)
            case_properties.append(properties)

            # If we are doing the cascade then the segmentation from the previous stage will already have been loaded by
            # self._data.load_case(i) (see nnUNetDataset.load_case)
            shape = data.shape[1:]
            dim = len(shape)
            bbox_lbs, bbox_ubs = self.get_bbox(shape, force_fg, properties['class_locations'])

            # whoever wrote this knew what he was doing (hint: it was me). We first crop the data to the region of the
            # bbox that actually lies within the data. This will result in a smaller array which is then faster to pad.
            # valid_bbox is just the coord that lied within the data cube. It will be padded to match the patch size
            # later
            valid_bbox_lbs = [max(0, bbox_lbs[i]) for i in range(dim)]
            valid_bbox_ubs = [min(shape[i], bbox_ubs[i]) for i in range(dim)]

            # At this point you might ask yourself why we would treat seg differently from seg_from_previous_stage.
            # Why not just concatenate them here and forget about the if statements? Well that's because segneeds to
            # be padded with -1 constant whereas seg_from_previous_stage needs to be padded with 0s (we could also
            # remove label -1 in the data augmentation but this way it is less error prone)
            this_slice = tuple([slice(0, data.shape[0])] + [slice(i, j) for i, j in zip(valid_bbox_lbs, valid_bbox_ubs)])
            data = data[this_slice]

            this_slice = tuple([slice(0, seg.shape[0])] + [slice(i, j) for i, j in zip(valid_bbox_lbs, valid_bbox_ubs)])
            seg = seg[this_slice]
            mask = mask[this_slice]

            padding = [(-min(0, bbox_lbs[i]), max(bbox_ubs[i] - shape[i], 0)) for i in range(dim)]
            data_all[j] = np.pad(data, ((0, 0), *padding), 'constant', constant_values=0)
            seg_all[j] = np.pad(seg, ((0, 0), *padding), 'constant', constant_values=0)
            mask_all[j] = np.pad(mask, ((0, 0), *padding), 'constant', constant_values=0)
        return {'data': data_all, 'seg': seg_all, 'properties': case_properties, 'keys': selected_keys, 'mask': mask_all}