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891e05c | 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 | from functools import partial
import os
import numpy as np
from omegaconf import DictConfig
import pytorch_lightning as pl
import torch
import torchvision.transforms as T
from torch.utils.data import DataLoader
from datasets import load_dataset, concatenate_datasets
from models.loupe import LoupeImageProcessor, LoupeConfig
class DataModule(pl.LightningDataModule):
def __init__(self, cfg: DictConfig, model_config: LoupeConfig) -> None:
super().__init__()
self.cfg = cfg
self.model_config = model_config
self.processor = LoupeImageProcessor(self.model_config)
def setup(self, stage: str) -> None:
dataset = load_dataset("parquet", data_dir=self.cfg.dataset.data_dir)
if stage in [None, "validate", "fit"]:
validset = dataset["validation"]
if isinstance(self.cfg.dataset.valid_size, int):
assert 0 < self.cfg.dataset.valid_size < len(validset)
valid_size = self.cfg.dataset.valid_size
elif isinstance(self.cfg.dataset.valid_size, float):
assert 0 < self.cfg.dataset.valid_size <= 1
valid_size = int(self.cfg.dataset.valid_size * len(validset))
else:
raise ValueError(
f"Invalid valid_size: {self.cfg.dataset.valid_size}. It should be either int or float."
)
# use a small subset to prevent too long validation time
additional_trainset, validset = validset.train_test_split(
test_size=valid_size, seed=self.cfg.seed, shuffle=True
).values()
self.validset = validset
if self.cfg.stage.name in ["cls_seg", "test"] and not getattr(
self.cfg.stage, "train_on_trainset", False
):
self.trainset = additional_trainset
else:
self.trainset = dataset["train"]
elif stage == "test":
self.testset = dataset["validation"]
elif stage == "predict":
self.testset = dataset["test"]
def train_collate_fn(self, batch):
images = [x["image"] for x in batch]
masks = [x["mask"] for x in batch]
labels = [x is not None for x in masks] # mask is None means it is real
return {
**self.processor(
images,
masks if not getattr(self.cfg.stage, "enable_tta", False) else None,
self.model_config.enable_patch_cls,
return_tensors="pt",
),
"labels": torch.tensor(labels, dtype=torch.long), # (N,)
}
def train_dataloader(self):
return DataLoader(
self.trainset,
batch_size=self.cfg.hparams.batch_size,
num_workers=self.cfg.dataset.num_workers,
collate_fn=self.train_collate_fn,
shuffle=True,
)
def val_dataloader(self):
return DataLoader(
self.validset,
batch_size=self.cfg.hparams.batch_size,
num_workers=self.cfg.dataset.num_workers,
collate_fn=self.test_collate_fn,
shuffle=False,
)
def test_collate_fn(self, batch):
"""
Collate function for valid and test dataloaders.
Args:
batch: List of dictionaries containing "image" and "mask" keys.
"""
images = [x["image"] for x in batch]
masks = [x["mask"] for x in batch]
labels = [x is not None for x in masks] # mask is None means it is real
outputs = self.processor(images, masks, return_tensors="pt")
for i, mask in enumerate(masks):
if mask is None:
# note that in PIL image, the size is (W, H)
masks[i] = torch.zeros(
(images[i].size[1], images[i].size[0]),
dtype=torch.uint8,
)
else:
# convert to binary mask with 0 and 1
masks[i] = self.processor.convert_to_binary_masks(mask)
return {
**outputs,
"masks": masks, # a list of (N, H_i, W_i)
"labels": (torch.tensor(labels, dtype=torch.long)), # (N,)
}
def test_dataloader(self):
return DataLoader(
self.testset,
batch_size=self.cfg.hparams.batch_size,
num_workers=self.cfg.dataset.num_workers,
collate_fn=self.test_collate_fn,
)
def predict_collate_fn(self, batch):
"""
Collate function for predict dataloader.
Args:
batch: List of dictionaries containing "image" and "mask" keys.
"""
images = [x["image"] for x in batch]
outputs = self.processor(images, return_tensors="pt")
return {
**outputs,
"target_sizes": [image.size[::-1] for image in images],
"name": [os.path.basename(x["path"]) for x in batch],
}
def predict_dataloader(self):
return DataLoader(
self.testset,
batch_size=self.cfg.hparams.batch_size,
num_workers=self.cfg.dataset.num_workers,
collate_fn=self.predict_collate_fn,
shuffle=False,
)
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