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dataset_multi.py
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import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
import os
from torchvision import transforms
class Multi_Dataset_Pair(Dataset):
def __init__(self, type="train", pair=True):
super(Multi_Dataset_Pair, self).__init__()
self.patient = np.array("./all_patient_pair_{}.npy".format(type))
self.p_id = self.patient[0]
self.p_label = self.patient[1]
self.type = type
self.ct = []
self.ct_t = []
self.y = []
for i, id in self.p_id:
ct_seq = np.load("./data/ts/{}.npy".format(id))
for i in range(len(ct_seq)):
self.ct.append(ct_seq[i][0])
self.ct_t.append(ct_seq[i][1])
self.y.append(self.p_label[id])
ts_data = np.load("./data/ts/all_ts.npy")
self.ts = ts_data[0]
self.mask = ts_data[1]
self.ts_t = ts_data[2]
def __len__(self):
return len(self.p_id)
def __getitem__(self, idx):
id = self.p_id[idx]
img = self.ct[idx]
ct_t = self.ct_t[idx]
y = self.y[idx]
ts = self.ts[id]
mask = self.mask[id]
ts_t = self.ts_t[id]
data = {
"ct": img,
"ct_t": ct_t,
"ts": ts,
"ts_t": ts_t,
"mask": mask,
"label": y
}
return data
class Multi_Dataset_Miss(Dataset):
def __init__(self, type="train", pair=True):
super(Multi_Dataset_Miss, self).__init__()
self.patient = np.array("./all_patient_miss_{}.npy".format(type))
self.p_id = self.patient[0]
self.p_label = self.patient[1]
self.type = type
ts_data = np.load("./data/ts/all_ts.npy")
self.ts = ts_data[0]
self.mask = ts_data[1]
self.ts_t = ts_data[2]
def __len__(self):
return len(self.p_id)
def __getitem__(self, idx):
id = self.p_id[idx]
y = self.p_label[id]
ts = self.ts[id]
mask = self.mask[id]
ts_t = self.ts_t[id]
data = {
"ts": ts,
"ts_t": ts_t,
"mask": mask,
"label": y
}
return data
class collater():
def __init__(self):
self.test = None
self.keys = ["ct"]
def __call__(self, batch):
all_ct = []
all_ts = []
all_mask = []
all_ts_t = []
ct_idx = []
all_ct_t = []
all_y = []
for i, data in enumerate(batch):
all_ct.append(data["ct"])
all_ct_t.append(data["ct_t"])
for j in range(len(data["ct"])):
ct_idx.append(i + 1)
all_ts.append(data["ts"])
all_ts_t.append(data["ts_t"])
all_mask.append(data["mask"])
all_y.append(data["label"])
dicts = {
"ct": torch.cat(all_ct, dim=0),
"ct_t": torch.cat(all_ct_t, dim=0),
"ct_idx": torch.LongTensor(ct_idx),
"ts": torch.cat(all_ts, dim=0),
"ts_t": torch.cat(all_ts_t, dim=0),
"mask": torch.cat(all_mask, dim=0),
"y": torch.tensor(all_y, dtype=torch.float32)
}
return dicts
def get_multi_dataloader(batch_size):
collate_fn = collater()
train_set_pair = Multi_Dataset_Pair(type="train")
val_set_pair = Multi_Dataset_Pair(type="val")
test_set_pair = Multi_Dataset_Pair(type="test")
train_set_miss = Multi_Dataset_Miss(type="train")
val_set_miss = Multi_Dataset_Miss(type="val")
test_set_miss = Multi_Dataset_Miss(type="test")
train_loader_pair = DataLoader(
train_set_pair, batch_size=batch_size, shuffle=False, collate_fn=collate_fn
)
val_loader_pair = DataLoader(
val_set_pair, batch_size=batch_size, shuffle=False, collate_fn=collate_fn
)
test_loader_pair = DataLoader(
test_set_pair, batch_size=batch_size, shuffle=False, collate_fn=collate_fn
)
train_loader_miss = DataLoader(train_set_miss, batch_size=batch_size, shuffle=True)
val_loader_miss = DataLoader(val_set_miss, batch_size=batch_size, shuffle=False)
test_loader_miss = DataLoader(test_set_miss, batch_size=batch_size, shuffle=False)
return train_loader_pair, val_loader_pair, test_loader_pair, train_loader_miss, val_loader_miss, test_loader_miss