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datasets.py
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datasets.py
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# --------------------------------------------------------
# Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks (https://arxiv.org/abs/2208.10442)
# Github source: https:/microsoft/unilm/tree/master/beit3
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------'
import os
import json
import random
import torch
import glob
from collections import defaultdict, Counter
from torchvision import transforms
from torchvision.datasets.folder import default_loader
from torchvision.transforms import InterpolationMode
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.data.transforms import RandomResizedCropAndInterpolation
from timm.data import create_transform
import utils
import openslide
import pandas as pd
import numpy as np
import PIL
PIL.Image.MAX_IMAGE_PIXELS = None
class BaseDataset(torch.utils.data.Dataset):
def __init__(
self, data_path, split, transform,
task=None, k_fold=0,
):
index_files = self.get_index_files(split, k_fold=k_fold, task=task)
self.data_path = data_path
items = []
self.index_files = index_files
offset = 0
for _index_file in index_files:
index_file = os.path.join(data_path, _index_file)
with open(index_file, mode="r", encoding="utf-8") as reader:
for line in reader:
data = json.loads(line)
items.append(data)
print("Load %d image-text pairs from %s. " % (len(items) - offset, index_file))
offset = len(items)
self.items = items
self.loader = default_loader
self.transform = transform
self.split = split
@staticmethod
def get_index_files(split):
raise NotImplementedError()
def _get_image(self, image_path: str):
image_path = os.path.join(self.data_path, image_path)
image = self.loader(image_path)
return self.transform(image)
def __getitem__(self, index: int):
raise NotImplementedError()
def __len__(self) -> int:
return len(self.items)
def __repr__(self) -> str:
head = "Dataset " + self.__class__.__name__
body = '{' + "\n Number of items: %s," % self.__len__()
body += "\n data root = %s," % self.data_path
body += "\n split = %s," % self.split
body += "\n dataset index files = %s" % str(self.index_files)
body += "\n transforms = ["
for t in self.transform.transforms:
body += "\n %s" % str(t)
body += "\n ]"
body += "\n}"
return head + body
def _write_data_into_jsonl(items, jsonl_file):
with open(jsonl_file, mode="w", encoding="utf-8") as writer:
for data in items:
writer.write(json.dumps(data, indent=None))
writer.write('\n')
print("Write %s with %d items !" % (jsonl_file, len(items)))
def df_prep(data, label_dict, ignore, label_col):
if label_col != 'label':
data['label'] = data[label_col].copy()
mask = data['label'].isin(ignore)
data = data[~mask]
data.reset_index(drop=True, inplace=True)
for i in data.index:
key = data.loc[i, 'label']
data.at[i, 'label'] = label_dict[key]
return data
def get_split_from_df(slide_data, all_splits, prop=1.0, seed=1, split_key='train'):
split = all_splits[split_key].str.rstrip('.svs')
split = split.dropna().reset_index(drop=True)
if len(split) > 0:
mask = slide_data['slide_id'].isin(split.tolist())
df_slice = slide_data[mask].reset_index(drop=True)
if split_key == 'train' and prop != 1.0:
df_slice = df_slice.sample(frac=prop, random_state=seed).reset_index(drop=True)
if split_key == 'train':
print(df_slice.head())
print("Traing Data Size ({%0.2f}): %d" % (prop, df_slice.shape[0]))
else:
df_slice = None
return df_slice
class TCGASubtypingDataset(BaseDataset):
def __init__(self, data_path, split, transform, task, k_fold, image_dir, seq_parallel=False, cached_randaug=False):
super().__init__(
data_path=data_path, split=split,
transform=transform, task=task, k_fold=k_fold,
)
self.k_fold = k_fold
self.image_dir = image_dir
self.seq_parallel = seq_parallel
self.cached_randaug = cached_randaug
@staticmethod
def get_index_files(split, k_fold=0, task=None):
if split == "train":
return ("{}.train.index.{}.jsonl".format(task.replace("_subtyping", ""), k_fold), )
elif split == "val":
return ("{}.val.index.{}.jsonl".format(task.replace("_subtyping", ""), k_fold), )
elif split == "test":
return ("{}.test.index.{}.jsonl".format(task.replace("_subtyping", ""), k_fold), )
else:
raise RuntimeError("split %s is not found!" % split)
def __getitem__(self, index: int):
data = dict()
item = self.items[index]
if self.seq_parallel:
img_path = item["image_path"] + "_{}.jpg".format(utils.get_rank())
else:
img_path = item["image_path"] + ".jpg"
img = self._get_image(img_path)
data["image"] = img
data["label"] = item["label"]
return data
def _get_image(self, image_path: str):
if self.cached_randaug:
if self.split == "train":
cur_epoch = int(os.environ.get('cur_epoch'))
image_path = os.path.join(self.image_dir, "epoch_{}".format(cur_epoch), image_path)
else:
image_path = os.path.join(self.image_dir, "wo_augmentation", image_path)
else:
image_path = os.path.join(self.image_dir, image_path)
image = self.loader(image_path)
return self.transform(image)
@staticmethod
def _make_tcga_index(task, csv_path, csv_split_path, k_fold, index_path, ignore, label_dict, split):
items = []
index_file = os.path.join(index_path, f"{task}.{split}.index.{k_fold}.jsonl")
slide_data = pd.read_csv(csv_path)
slide_data = df_prep(slide_data, label_dict, ignore, label_col="oncotree_code")
slide_data['slide_id'] = slide_data['slide_id'].apply(lambda x: x.replace(".svs", ""))
all_splits = pd.read_csv(csv_split_path)
slide_data_split = get_split_from_df(slide_data, all_splits, split_key=split)
for index, row in slide_data_split.iterrows():
items.append({
"image_path": row["slide_id"],
"label": row["label"],
})
file_path = os.path.join(index_path.replace("tcga_", "") + "_svs", "{}.svs".format(row["slide_id"]))
if not os.path.exists(file_path):
print("file {} do not exists".format(row["slide_id"]))
_write_data_into_jsonl(items, index_file)
@classmethod
def make_dataset_index(cls, task, csv_path, csv_split_path, k_fold, index_path, ignore, label_dict):
cls._make_tcga_index(
task=task, csv_path=csv_path, csv_split_path=csv_split_path, k_fold=k_fold, index_path=index_path,
ignore=ignore, label_dict=label_dict, split="train",
)
cls._make_tcga_index(
task=task, csv_path=csv_path, csv_split_path=csv_split_path, k_fold=k_fold, index_path=index_path,
ignore=ignore, label_dict=label_dict, split="val",
)
cls._make_tcga_index(
task=task, csv_path=csv_path, csv_split_path=csv_split_path, k_fold=k_fold, index_path=index_path,
ignore=ignore, label_dict=label_dict, split="test",
)
def get_survival_split_from_df(slide_data, all_splits, split_key='train'):
split = all_splits[split_key]
split = split.dropna().reset_index(drop=True)
if len(split) > 0:
mask = slide_data['slide_id'].isin(split.tolist())
df_slice = slide_data[mask].reset_index(drop=True)
else:
df_slice = None
return df_slice
class TCGASurvivalDataset(BaseDataset):
def __init__(self, data_path, split, transform, task, k_fold, image_dir, seq_parallel=False, cached_randaug=False):
super().__init__(
data_path=data_path, split=split,
transform=transform, task=task, k_fold=k_fold,
)
self.k_fold = k_fold
self.image_dir = image_dir
self.seq_parallel = seq_parallel
self.cached_randaug = cached_randaug
@staticmethod
def get_index_files(split, k_fold=0, task=None):
if split == "train":
return ("{}.train.index.{}.jsonl".format(task.replace("_survival", ""), k_fold), )
elif split == "val":
return ("{}.val.index.{}.jsonl".format(task.replace("_survival", ""), k_fold), )
elif split == "test":
return ("{}.val.index.{}.jsonl".format(task.replace("_survival", ""), k_fold), )
else:
raise RuntimeError("split %s is not found!" % split)
def __getitem__(self, index: int):
data = dict()
item = self.items[index]
if self.seq_parallel:
img_path = item["image_paths"][0].replace(".svs", "") + "_{}.jpg".format(utils.get_rank())
else:
img_path = item["image_paths"][0].replace(".svs", "") + ".jpg"
img = self._get_image(img_path)
case_id = item["case_id"]
data["image"] = img
data["label"] = item["label"]
data["event_time"] = item["event_time"]
data["censorship"] = item["censorship"]
return data
def _get_image(self, image_path: str):
if self.cached_randaug:
if self.split == "train":
cur_epoch = int(os.environ.get('cur_epoch'))
image_path = os.path.join(self.image_dir, "epoch_{}".format(cur_epoch), image_path)
else:
image_path = os.path.join(self.image_dir, "wo_augmentation", image_path)
else:
image_path = os.path.join(self.image_dir, image_path)
image = self.loader(image_path)
return self.transform(image)
@staticmethod
def _make_tcga_index(task, csv_path, csv_split_path, k_fold, index_path, split):
items = []
os.makedirs(index_path, exist_ok=True)
index_file = os.path.join(index_path, f"{task}.{split}.index.{k_fold}.jsonl")
slide_data = pd.read_csv(csv_path, low_memory=False)
if 'case_id' not in slide_data:
slide_data.index = slide_data.index.str[:12]
slide_data['case_id'] = slide_data.index
slide_data = slide_data.reset_index(drop=True)
label_col = "survival_months"
assert label_col in slide_data.columns
# if "IDC" in slide_data['oncotree_code'].values: # must be BRCA (and if so, use only IDCs)
# slide_data = slide_data[slide_data['oncotree_code'] == 'IDC']
patients_df = slide_data.drop_duplicates(['case_id']).copy()
uncensored_df = patients_df[patients_df['censorship'] < 1]
n_bins = 4
eps = 1e-6
disc_labels, q_bins = pd.qcut(uncensored_df[label_col], q=n_bins, retbins=True, labels=False)
q_bins[-1] = slide_data[label_col].max() + eps
q_bins[0] = slide_data[label_col].min() - eps
disc_labels, q_bins = pd.cut(patients_df[label_col], bins=q_bins, retbins=True, labels=False, right=False, include_lowest=True)
patients_df.insert(2, 'label', disc_labels.values.astype(int))
patient_dict = {}
slide_data = slide_data.set_index('case_id')
for patient in patients_df['case_id']:
slide_ids = slide_data.loc[patient, 'slide_id']
if isinstance(slide_ids, str):
slide_ids = np.array(slide_ids).reshape(-1).tolist()
else:
slide_ids = slide_ids.values.tolist()
patient_dict.update({patient:slide_ids})
slide_data = patients_df
slide_data.reset_index(drop=True, inplace=True)
slide_data = slide_data.assign(slide_id=slide_data['case_id'])
label_dict = {}
key_count = 0
for i in range(len(q_bins)-1):
for c in [0, 1]:
print('{} : {}'.format((i, c), key_count))
label_dict.update({(i, c):key_count})
key_count+=1
for i in slide_data.index:
key = slide_data.loc[i, 'label']
slide_data.at[i, 'disc_label'] = key
censorship = slide_data.loc[i, 'censorship']
key = (key, int(censorship))
slide_data.at[i, 'label'] = label_dict[key]
patients_df = slide_data.drop_duplicates(['case_id'])
patient_data = {'case_id':patients_df['case_id'].values, 'label':patients_df['label'].values}
new_cols = list(slide_data.columns[-2:]) + list(slide_data.columns[:-2])
slide_data = slide_data[new_cols]
all_splits = pd.read_csv(csv_split_path)
slide_data_split = get_survival_split_from_df(slide_data, all_splits, split_key=split)
for index, row in slide_data_split.iterrows():
case_id = row["case_id"]
items.append({
"case_id" : row["case_id"],
"label": row["disc_label"],
"event_time": row["survival_months"],
"censorship": row["censorship"],
"image_paths": patient_dict[case_id],
})
for slide_id in patient_dict[case_id]:
file_path = os.path.join(f"/tmp/tcga/{task}_svs".replace("tcga_", ""), slide_id)
if not os.path.exists(file_path):
print("file {} do not exists".format(row["slide_id"]))
_write_data_into_jsonl(items, index_file)
@classmethod
def make_dataset_index(cls, task, csv_path, csv_split_path, k_fold, index_path):
cls._make_tcga_index(
task=task, csv_path=csv_path, csv_split_path=csv_split_path, k_fold=k_fold, index_path=index_path,
split="train",
)
cls._make_tcga_index(
task=task, csv_path=csv_path, csv_split_path=csv_split_path, k_fold=k_fold, index_path=index_path,
split="val",
)
task2dataset = {
"tcga_lung_subtyping": TCGASubtypingDataset,
"tcga_kidney_subtyping": TCGASubtypingDataset,
"tcga_brca_subtyping": TCGASubtypingDataset,
"tcga_ucec_survival": TCGASurvivalDataset,
"tcga_luad_survival": TCGASurvivalDataset,
"tcga_brca_survival": TCGASurvivalDataset,
}
def create_dataloader(dataset, is_train, batch_size, num_workers, pin_mem, seq_parallel=False, seed=None):
if is_train:
if seq_parallel:
generator = torch.Generator()
generator.manual_seed(seed)
sampler = torch.utils.data.RandomSampler(dataset, generator=generator)
else:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler = torch.utils.data.DistributedSampler(
dataset, num_replicas=num_tasks, rank=global_rank, shuffle=is_train
)
else:
sampler = torch.utils.data.SequentialSampler(dataset)
return torch.utils.data.DataLoader(
dataset, sampler=sampler,
batch_size=batch_size,
num_workers=num_workers,
pin_memory=pin_mem,
drop_last=is_train,
collate_fn=utils.merge_batch_tensors_by_dict_key,
)
def build_transform(is_train, args):
if is_train:
t = []
if args.randaug:
t += [
RandomResizedCropAndInterpolation(args.input_size, scale=(0.5, 1.0), interpolation=args.train_interpolation),
transforms.RandomHorizontalFlip(),
]
t += [
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
]
t = transforms.Compose(t)
else:
t = transforms.Compose([
# transforms.Resize((args.input_size, args.input_size), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
return t
def create_dataset_by_split(args, split, is_train=True):
transform = build_transform(is_train=is_train, args=args)
print(transform)
dataset_class = task2dataset[args.task]
opt_kwargs = {}
if args.task.startswith("tcga"):
opt_kwargs["k_fold"] = args.k_fold
opt_kwargs["image_dir"] = args.image_dir
opt_kwargs["seq_parallel"] = args.seq_parallel
opt_kwargs["cached_randaug"] = args.cached_randaug
dataset = dataset_class(
data_path=args.data_path, split=split,
transform=transform, task=args.task, **opt_kwargs,
)
if is_train:
batch_size = args.batch_size
elif hasattr(args, "eval_batch_size") and args.eval_batch_size is not None:
batch_size = args.eval_batch_size
else:
batch_size = int(args.batch_size * 1.0)
return create_dataloader(
dataset, is_train=is_train, batch_size=batch_size,
num_workers=args.num_workers, pin_mem=args.pin_mem,
seq_parallel=args.seq_parallel, seed=args.seed,
)
def create_downstream_dataset(args, is_eval=False):
if is_eval:
return create_dataset_by_split(args, split="test", is_train=False)
else:
return \
create_dataset_by_split(args, split="train", is_train=True), \
create_dataset_by_split(args, split="val", is_train=False),