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import os
from typing import Literal
import numpy as np
import torch
from monai.data import PersistentDataset, load_decathlon_datalist
from monai.transforms import (
Compose,
ConcatItemsd,
DeleteItemsd,
EnsureTyped,
LoadImaged,
RandCropByPosNegLabeld,
RandRotate90d,
ToTensord,
Transform,
Transposed,
)
from torch.utils.data.dataloader import default_collate
from .custom_transforms import (
ClipMaskIntensityPercentilesd,
ElementwiseProductd,
NormalizeIntensity_customd,
NormalizePSAd,
)
def list_data_collate(batch: list):
"""
Combine instances from a list of dicts into a single dict, by stacking them along first dim
[{'image' : 3xHxW}, {'image' : 3xHxW}, {'image' : 3xHxW}...] - > {'image' : Nx3xHxW}
followed by the default collate which will form a batch BxNx3xHxW
"""
for i, item in enumerate(batch):
data = item[0]
data["image"] = torch.stack([ix["image"] for ix in item], dim=0)
if all("final_heatmap" in ix for ix in item):
data["final_heatmap"] = torch.stack([ix["final_heatmap"] for ix in item], dim=0)
data["smooth_mask"] = torch.stack([ix["smooth_mask"] for ix in item], dim=0)
batch[i] = data
return default_collate(batch)
def data_transform(args: argparse.Namespace, split) -> Transform:
if split == "train":
transform = Compose(
[
LoadImaged(
keys=["image", "mask", "dwi", "adc", "heatmap", "smooth_mask"],
reader="ITKReader",
ensure_channel_first=True,
dtype=np.float32,
),
# LabelEncodeIntegerGraded(keys=["label"], num_classes=args.num_classes),
ClipMaskIntensityPercentilesd(keys=["image"], lower=0, upper=99.5, mask_key="mask"),
ClipMaskIntensityPercentilesd(keys=["dwi"], lower=0, upper=99.5, mask_key="mask"),
NormalizeIntensity_customd(keys=["image"], mask_key="mask"),
NormalizeIntensity_customd(keys=["dwi"], mask_key="mask"),
ConcatItemsd(
keys=["image", "dwi", "adc"], name="image", dim=0
), # stacks to (3, H, W)
ElementwiseProductd(keys=["heatmap", "smooth_mask"], output_key="final_heatmap"),
RandCropByPosNegLabeld(
keys=["image", "final_heatmap", "smooth_mask"],
label_key="smooth_mask",
spatial_size=(args.tile_size, args.tile_size, args.depth),
pos=1,
neg=0,
num_samples=args.tile_count,
),
RandRotate90d(
keys=["image", "final_heatmap", "smooth_mask"],
prob=0.6,
spatial_axes=(0, 1),
max_k=3,
),
NormalizePSAd(keys=["psa"], mean=args.psa_mean, std=args.psa_std),
EnsureTyped(keys=["label", "psa"], dtype=torch.float32),
Transposed(keys=["image"], indices=(0, 3, 1, 2)),
DeleteItemsd(keys=["dwi", "adc", "heatmap", "mask"]),
ToTensord(keys=["image", "label", "final_heatmap", "smooth_mask", "psa"]),
]
)
else:
transform = Compose(
[
LoadImaged(
keys=["image", "mask", "dwi", "adc", "heatmap", "smooth_mask"],
reader="ITKReader",
ensure_channel_first=True,
dtype=np.float32,
),
# LabelEncodeIntegerGraded(keys=["label"], num_classes=args.num_classes),
ClipMaskIntensityPercentilesd(keys=["image"], lower=0, upper=99.5, mask_key="mask"),
ClipMaskIntensityPercentilesd(keys=["dwi"], lower=0, upper=99.5, mask_key="mask"),
NormalizeIntensity_customd(keys=["image"], mask_key="mask"),
NormalizeIntensity_customd(keys=["dwi"], mask_key="mask"),
ConcatItemsd(
keys=["image", "dwi", "adc"], name="image", dim=0
), # stacks to (3, H, W)
ElementwiseProductd(keys=["heatmap", "smooth_mask"], output_key="final_heatmap"),
RandCropByPosNegLabeld(
keys=["image", "final_heatmap", "smooth_mask"],
label_key="smooth_mask",
spatial_size=(args.tile_size, args.tile_size, args.depth),
pos=1,
neg=0,
num_samples=args.tile_count,
),
NormalizePSAd(keys=["psa"], mean=args.psa_mean, std=args.psa_std),
EnsureTyped(keys=["label", "psa"], dtype=torch.float32),
Transposed(keys=["image"], indices=(0, 3, 1, 2)),
DeleteItemsd(keys=["dwi", "adc", "heatmap", "mask"]),
ToTensord(keys=["image", "label", "final_heatmap", "smooth_mask", "psa"]),
]
)
return transform
"""
def data_transform(args: argparse.Namespace) -> Transform:
if args.use_heatmap:
if args.use_psa:
transform = Compose(
[
LoadImaged(
keys=["image", "mask", "dwi", "adc", "heatmap","smooth_mask"],
reader="ITKReader",
ensure_channel_first=True,
dtype=np.float32,
),
ClipMaskIntensityPercentilesd(keys=["image"], lower=0, upper=99.5, mask_key="mask"),
ClipMaskIntensityPercentilesd(keys=["dwi"], lower=0, upper=99.5, mask_key="mask"),
NormalizeIntensity_customd(keys=["image"], mask_key="mask"),
NormalizeIntensity_customd(keys=["dwi"], mask_key="mask"),
ConcatItemsd(
keys=["image", "dwi", "adc"], name="image", dim=0
), # stacks to (3, H, W)
ElementwiseProductd(keys=["heatmap", "smooth_mask"], output_key="final_heatmap"),
RandCropByPosNegLabeld(
keys=["image", "final_heatmap", "smooth_mask"],
label_key="smooth_mask",
spatial_size=(args.tile_size, args.tile_size, args.depth),
pos=1,
neg=0,
num_samples=args.tile_count,
),
NormalizePSAd(keys=["psa"], mean=args.psa_mean, std=args.psa_std),
EnsureTyped(keys=["label", "psa"], dtype=torch.float32),
Transposed(keys=["image"], indices=(0, 3, 1, 2)),
DeleteItemsd(keys=[ "dwi", "adc", "heatmap", "mask"]),
ToTensord(keys=["image", "label", "final_heatmap", "smooth_mask", "psa"]),
]
)
else:
transform = Compose(
[
LoadImaged(
keys=["image", "mask", "dwi", "adc", "heatmap","smooth_mask"],
reader="ITKReader",
ensure_channel_first=True,
dtype=np.float32,
),
ClipMaskIntensityPercentilesd(keys=["image"], lower=0, upper=99.5, mask_key="mask"),
ClipMaskIntensityPercentilesd(keys=["dwi"], lower=0, upper=99.5, mask_key="mask"),
NormalizeIntensity_customd(keys=["image"], mask_key="mask"),
NormalizeIntensity_customd(keys=["dwi"], mask_key="mask"),
ConcatItemsd(
keys=["image", "dwi", "adc"], name="image", dim=0
), # stacks to (3, H, W)
ElementwiseProductd(keys=["heatmap", "smooth_mask"], output_key="final_heatmap"),
#RandRotate90d(keys=["image", "final_heatmap", "smooth_mask"], prob=0.5, spatial_axes=(0, 1)),
RandCropByPosNegLabeld(
keys=["image", "final_heatmap", "smooth_mask"],
label_key="smooth_mask",
spatial_size=(args.tile_size, args.tile_size, args.depth),
pos=1,
neg=0,
num_samples=args.tile_count,
),
EnsureTyped(keys=["label"], dtype=torch.float32),
Transposed(keys=["image"], indices=(0, 3, 1, 2)),
DeleteItemsd(keys=[ "dwi", "adc", "heatmap", "mask"]),
ToTensord(keys=["image", "label", "final_heatmap", "smooth_mask"]),
]
)
else:
if args.use_psa:
transform = Compose(
[
LoadImaged(
keys=["image", "mask", "dwi", "adc","smooth_mask"],
reader="ITKReader",
ensure_channel_first=True,
dtype=np.float32,
),
ClipMaskIntensityPercentilesd(keys=["image"], lower=0, upper=99.5, mask_key="mask"),
ClipMaskIntensityPercentilesd(keys=["dwi"], lower=0, upper=99.5, mask_key="mask"),
NormalizeIntensity_customd(keys=["image"], mask_key="mask"),
NormalizeIntensity_customd(keys=["dwi"], mask_key="mask"),
ConcatItemsd(
keys=["image", "dwi", "adc"], name="image", dim=0
), # stacks to (3, H, W)
RandCropByPosNegLabeld(
keys=["image", "smooth_mask"],
label_key="smooth_mask",
spatial_size=(args.tile_size, args.tile_size, args.depth),
pos=1,
neg=0,
num_samples=args.tile_count,
),
NormalizePSAd(keys=["psa"], mean=args.psa_mean, std=args.psa_std),
EnsureTyped(keys=["label", "psa"], dtype=torch.float32),
Transposed(keys=["image"], indices=(0, 3, 1, 2)),
DeleteItemsd(keys=[ "dwi", "adc", "mask"]),
ToTensord(keys=["image", "label", "smooth_mask", "psa"]),
]
)
else:
transform = Compose(
[
LoadImaged(
keys=["image", "mask", "dwi", "adc","smooth_mask"],
reader="ITKReader",
ensure_channel_first=True,
dtype=np.float32,
),
ClipMaskIntensityPercentilesd(keys=["image"], lower=0, upper=99.5, mask_key="mask"),
ClipMaskIntensityPercentilesd(keys=["dwi"], lower=0, upper=99.5, mask_key="mask"),
NormalizeIntensity_customd(keys=["image"], mask_key="mask"),
NormalizeIntensity_customd(keys=["dwi"], mask_key="mask"),
ConcatItemsd(
keys=["image", "dwi", "adc"], name="image", dim=0
), # stacks to (3, H, W)
RandCropByPosNegLabeld(
keys=["image", "smooth_mask"],
label_key="smooth_mask",
spatial_size=(args.tile_size, args.tile_size, args.depth),
pos=1,
neg=0,
num_samples=args.tile_count,
),
EnsureTyped(keys=["label"], dtype=torch.float32),
Transposed(keys=["image"], indices=(0, 3, 1, 2)),
DeleteItemsd(keys=[ "dwi", "adc", "mask"]),
ToTensord(keys=["image", "label", "smooth_mask"]),
]
)
return transform
"""
def get_dataloader(
args: argparse.Namespace, split: Literal["train", "test"]
) -> torch.utils.data.DataLoader:
data_list = load_decathlon_datalist(
data_list_file_path=args.dataset_json,
data_list_key=split,
base_dir=args.data_root,
)
data_list_updated = [{**i, "psa": i.get("psa", [0, 0])} for i in data_list]
cache_dir_ = os.path.join(args.logdir, "cache")
os.makedirs(os.path.join(cache_dir_, split), exist_ok=True)
transform = data_transform(args, split)
dataset = PersistentDataset(
data=data_list_updated, transform=transform, cache_dir=os.path.join(cache_dir_, split)
)
loader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=(split == "train"),
num_workers=args.workers if split == "train" else 2,
pin_memory=True,
multiprocessing_context="fork" if args.workers > 0 else None,
sampler=None,
collate_fn=list_data_collate,
)
return loader
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