File size: 6,916 Bytes
2a5693e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 |
# Copyright 2020 - 2022 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import math
import os
import numpy as np
import torch
from monai import data, transforms
from monai.data import NibabelReader
from monai.transforms import MapTransform
#Load biobert features
class LoadNumpyd(MapTransform):
def __init__(self, keys):
super().__init__(keys)
def __call__(self, data):
d = dict(data)
for key in self.keys:
d[key] = np.load(d[key])
d[key] = np.squeeze(d[key],axis=0)
return d
class Sampler(torch.utils.data.Sampler):
def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True, make_even=True):
if num_replicas is None:
if not torch.distributed.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = torch.distributed.get_world_size()
if rank is None:
if not torch.distributed.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = torch.distributed.get_rank()
self.shuffle = shuffle
self.make_even = make_even
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
indices = list(range(len(self.dataset)))
self.valid_length = len(indices[self.rank : self.total_size : self.num_replicas])
def __iter__(self):
if self.shuffle:
g = torch.Generator()
g.manual_seed(self.epoch)
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = list(range(len(self.dataset)))
if self.make_even:
if len(indices) < self.total_size:
if self.total_size - len(indices) < len(indices):
indices += indices[: (self.total_size - len(indices))]
else:
extra_ids = np.random.randint(low=0, high=len(indices), size=self.total_size - len(indices))
indices += [indices[ids] for ids in extra_ids]
assert len(indices) == self.total_size
indices = indices[self.rank : self.total_size : self.num_replicas]
self.num_samples = len(indices)
return iter(indices)
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
def datafold_read(datalist, basedir, fold=0, key="training"):
with open(datalist) as f:
json_data = json.load(f)
json_data = json_data[key]
for d in json_data:
for k, v in d.items():
if isinstance(d[k], list):
d[k] = [os.path.join(basedir, iv) for iv in d[k]]
elif isinstance(d[k], str):
d[k] = os.path.join(basedir, d[k]) if len(d[k]) > 0 else d[k]
tr = []
val = []
for d in json_data:
if "fold" in d and d["fold"] == fold:
val.append(d)
else:
tr.append(d)
return tr, val
def get_loader(args):
data_dir = args.data_dir
datalist_json = args.json_list
train_files, validation_files = datafold_read(datalist=datalist_json, basedir=data_dir, fold=args.fold)
train_transform = transforms.Compose(
[
transforms.LoadImaged(keys=["image", "label"],reader=NibabelReader()),
LoadNumpyd(keys=["text_feature"]),
transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys="label"),
transforms.Resized(keys=["image","label"],spatial_size=[args.roi_x,args.roi_y,args.roi_z]),
transforms.NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True),
transforms.RandScaleIntensityd(keys="image", factors=0.1, prob=1.0),
transforms.RandShiftIntensityd(keys="image", offsets=0.1, prob=1.0),
transforms.ToTensord(keys=["image", "label", "text_feature"]),
]
)
val_transform = transforms.Compose(
[
transforms.LoadImaged(keys=["image", "label"],reader=NibabelReader()),
LoadNumpyd(keys=["text_feature"]),
transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys="label"),
transforms.Resized(keys=["image", "label"], spatial_size=[args.roi_x, args.roi_y, args.roi_z]),
transforms.NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True),
transforms.ToTensord(keys=["image", "label", "text_feature"]),
]
)
test_transform = transforms.Compose(
[
transforms.LoadImaged(keys=["image", "label"],reader=NibabelReader()),
LoadNumpyd(keys=["text_feature"]),
transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys="label"),
transforms.Resized(keys=["image", "label"], spatial_size=[args.roi_x, args.roi_y, args.roi_z]),
transforms.NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True),
transforms.ToTensord(keys=["image", "label", "text_feature"]),
]
)
if args.test_mode:
val_ds = data.Dataset(data=validation_files, transform=test_transform)
val_sampler = Sampler(val_ds, shuffle=False) if args.distributed else None
test_loader = data.DataLoader(
val_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=val_sampler, pin_memory=True
)
loader = test_loader
else:
train_ds = data.Dataset(data=train_files, transform=train_transform)
train_sampler = Sampler(train_ds) if args.distributed else None
train_loader = data.DataLoader(
train_ds,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
sampler=train_sampler,
pin_memory=True,
)
val_ds = data.Dataset(data=validation_files, transform=val_transform)
val_sampler = Sampler(val_ds, shuffle=False) if args.distributed else None
val_loader = data.DataLoader(
val_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=val_sampler, pin_memory=True
)
loader = [train_loader, val_loader]
return loader
|