74 lines
3.2 KiB
Python
74 lines
3.2 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pytorch_lightning import LightningDataModule
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from sklearn.model_selection import KFold
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from utils.utils import get_config_file, get_path, get_split, get_test_fnames, is_main_process, load_data
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from data_loading.dali_loader import fetch_dali_loader
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class DataModule(LightningDataModule):
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def __init__(self, args):
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super().__init__()
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self.args = args
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self.train_imgs = []
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self.train_lbls = []
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self.val_imgs = []
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self.val_lbls = []
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self.test_imgs = []
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self.kfold = KFold(n_splits=self.args.nfolds, shuffle=True, random_state=12345)
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self.data_path = get_path(args)
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configs = get_config_file(self.args)
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self.kwargs = {
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"dim": self.args.dim,
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"patch_size": configs["patch_size"],
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"seed": self.args.seed,
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"gpus": self.args.gpus,
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"num_workers": self.args.num_workers,
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"oversampling": self.args.oversampling,
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"benchmark": self.args.benchmark,
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"nvol": self.args.nvol,
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"train_batches": self.args.train_batches,
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"test_batches": self.args.test_batches,
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"meta": load_data(self.data_path, "*_meta.npy"),
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}
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def setup(self, stage=None):
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imgs = load_data(self.data_path, "*_x.npy")
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lbls = load_data(self.data_path, "*_y.npy")
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self.test_imgs, self.kwargs["meta"] = get_test_fnames(self.args, self.data_path, self.kwargs["meta"])
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if self.args.exec_mode != "predict" or self.args.benchmark:
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train_idx, val_idx = list(self.kfold.split(imgs))[self.args.fold]
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self.train_imgs = get_split(imgs, train_idx)
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self.train_lbls = get_split(lbls, train_idx)
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self.val_imgs = get_split(imgs, val_idx)
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self.val_lbls = get_split(lbls, val_idx)
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if is_main_process():
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print(f"Number of examples: Train {len(self.train_imgs)} - Val {len(self.val_imgs)}")
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elif is_main_process():
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print(f"Number of test examples: {len(self.test_imgs)}")
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def train_dataloader(self):
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return fetch_dali_loader(self.train_imgs, self.train_lbls, self.args.batch_size, "train", **self.kwargs)
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def val_dataloader(self):
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return fetch_dali_loader(self.val_imgs, self.val_lbls, 1, "eval", **self.kwargs)
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def test_dataloader(self):
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if self.kwargs["benchmark"]:
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return fetch_dali_loader(self.train_imgs, self.train_lbls, self.args.val_batch_size, "test", **self.kwargs)
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return fetch_dali_loader(self.test_imgs, None, 1, "test", **self.kwargs)
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