-
Notifications
You must be signed in to change notification settings - Fork 1
/
dataloader.py
69 lines (50 loc) · 1.6 KB
/
dataloader.py
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
from os.path import join
import os
import torch
from torch.utils.data import Dataset
from skimage import io as skimage
def matRead(data):
data=data.transpose(2,0,1)/2047.
data=torch.from_numpy(data)
return data
class Dataset(Dataset):
def __init__(self, path):
super(Dataset, self).__init__()
self.panpath = path + 'SimulatedRawPAN/'
self.mspath = path + 'SimulatedRawMS/'
self.refpath = path + 'ReferenceRawMS/'
files = os.listdir(self.panpath)
img_list = []
for index in files:
num = index.split('p')[0]
img_list.append(num)
self.img_list = img_list
def __getitem__(self, index):
fn = self.img_list[index]
panBatch = skimage.imread(self.panpath + str(fn) + 'p.tif')
panBatch = panBatch[:, :, None]
panBatch = matRead(panBatch)
msBatch = skimage.imread(self.mspath + str(fn) + 'ms.tif')
msBatch = matRead(msBatch)
gtBatch = skimage.imread(self.refpath + str(fn) + 'MSref.tif')
gtBatch = matRead(gtBatch)
return gtBatch, msBatch, panBatch
def __len__(self):
return len(self.img_list)
def get_training_set(train_dir):
return Dataset(train_dir)
def get_val_set(val_dir):
return Dataset(val_dir)
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count