def get_basic_generators(self):
        self.load_dataset()
        self.do_split()

        if self.threeD:
            dl_tr = DataLoader3D(self.dataset_tr,
                                 self.basic_generator_patch_size,
                                 self.patch_size,
                                 self.batch_size,
                                 False,
                                 oversample_foreground_percent=self.
                                 oversample_foreground_percent,
                                 pad_mode="constant",
                                 pad_sides=self.pad_all_sides)
            dl_trRAW = DataLoader3D(self.dataset_tr,
                                    self.patch_size,
                                    self.patch_size,
                                    self.batch_size,
                                    False,
                                    oversample_foreground_percent=self.
                                    oversample_foreground_percent,
                                    pad_mode="constant",
                                    pad_sides=self.pad_all_sides)
            dl_val = DataLoader3D(self.dataset_val,
                                  self.patch_size,
                                  self.patch_size,
                                  self.batch_size,
                                  False,
                                  oversample_foreground_percent=self.
                                  oversample_foreground_percent,
                                  pad_mode="constant",
                                  pad_sides=self.pad_all_sides)
        else:
            dl_tr = DataLoader2D(
                self.dataset_tr,
                self.basic_generator_patch_size,
                self.patch_size,
                self.batch_size,
                transpose=None,  # self.plans.get('transpose_forward'),
                oversample_foreground_percent=self.
                oversample_foreground_percent,
                pad_mode="constant",
                pad_sides=self.pad_all_sides)
            dl_val = DataLoader2D(
                self.dataset_val,
                self.patch_size,
                self.patch_size,
                self.batch_size,
                transpose=None,  # self.plans.get('transpose_forward'),
                oversample_foreground_percent=self.
                oversample_foreground_percent,
                pad_mode="constant",
                pad_sides=self.pad_all_sides)
        return dl_tr, dl_trRAW, dl_val
Пример #2
0
    def get_basic_generators(self):
        self.load_dataset()
        self.do_split()

        print('self.basic_generator_patch_size',
              self.basic_generator_patch_size)
        if self.threeD:
            dl_tr = DataLoader3D(self.dataset_tr,
                                 self.basic_generator_patch_size,
                                 self.patch_size,
                                 self.batch_size,
                                 False,
                                 oversample_foreground_percent=self.
                                 oversample_foreground_percent,
                                 pad_mode="constant",
                                 pad_sides=self.pad_all_sides,
                                 memmap_mode='r')
            dl_val = DataLoader3D(self.dataset_val,
                                  self.patch_size,
                                  self.patch_size,
                                  self.batch_size,
                                  False,
                                  oversample_foreground_percent=self.
                                  oversample_foreground_percent,
                                  pad_mode="constant",
                                  pad_sides=self.pad_all_sides,
                                  memmap_mode='r')
        else:
            dl_tr = DataLoader2D(self.dataset_tr,
                                 self.basic_generator_patch_size,
                                 self.patch_size,
                                 self.batch_size,
                                 oversample_foreground_percent=self.
                                 oversample_foreground_percent,
                                 pad_mode="constant",
                                 pad_sides=self.pad_all_sides,
                                 memmap_mode='r')
            dl_val = DataLoader2D(self.dataset_val,
                                  self.patch_size,
                                  self.patch_size,
                                  self.batch_size,
                                  oversample_foreground_percent=self.
                                  oversample_foreground_percent,
                                  pad_mode="constant",
                                  pad_sides=self.pad_all_sides,
                                  memmap_mode='r')
        return dl_tr, dl_val
Пример #3
0
    def get_basic_generators(self):
        self.load_dataset()
        """
        def load_dataset(folder):
        # we don't load the actual data but instead return the filename to the np file. the properties are loaded though
        case_identifiers = get_case_identifiers(folder)
        case_identifiers.sort()
        dataset = OrderedDict()
        for c in case_identifiers:
            dataset[c] = OrderedDict()
            dataset[c]['data_file'] = join(folder, "%s.npz"%c)
            with open(join(folder, "%s.pkl"%c), 'rb') as f:
                dataset[c]['properties'] = pickle.load(f)
            if dataset[c].get('seg_from_prev_stage_file') is not None:
                dataset[c]['seg_from_prev_stage_file'] = join(folder, "%s_segs.npz"%c)
        return dataset
        """
        self.do_split()

        if self.threeD:
            dl_tr = DataLoader3D(self.dataset_tr, self.basic_generator_patch_size, self.patch_size, self.batch_size,
                                 False, oversample_foreground_percent=self.oversample_foreground_percent,
                                 pad_mode="constant", pad_sides=self.pad_all_sides)
            dl_val = DataLoader3D(self.dataset_val, self.patch_size, self.patch_size, self.batch_size, False,
                                  oversample_foreground_percent=self.oversample_foreground_percent,
                                  pad_mode="constant", pad_sides=self.pad_all_sides)
        else:
            dl_tr = DataLoader2D(self.dataset_tr, self.basic_generator_patch_size, self.patch_size, self.batch_size,
                                 transpose=self.plans.get('transpose_forward'),
                                 oversample_foreground_percent=self.oversample_foreground_percent,
                                 pad_mode="constant", pad_sides=self.pad_all_sides)
            dl_val = DataLoader2D(self.dataset_val, self.patch_size, self.patch_size, self.batch_size,
                                  transpose=self.plans.get('transpose_forward'),
                                  oversample_foreground_percent=self.oversample_foreground_percent,
                                  pad_mode="constant", pad_sides=self.pad_all_sides)
        return dl_tr, dl_val