-
Notifications
You must be signed in to change notification settings - Fork 0
/
dataloader_cloudsen12.py
148 lines (126 loc) · 4.58 KB
/
dataloader_cloudsen12.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
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
import pathlib
import numpy as np
import pandas as pd
import torch
def load_data(path_root, shape):
"""
Load data from memory-mapped files.
Args:
path_root (pathlib.Path): The root directory path.
shape (tuple): The shape of the data.
Returns:
dict: A dictionary containing the loaded data.
"""
total = {
"B1": np.memmap(
path_root / "L1C_B1.dat", dtype=np.int16, mode="r", shape=shape
),
"B2": np.memmap(
path_root / "L1C_B2.dat", dtype=np.int16, mode="r", shape=shape
),
"B3": np.memmap(
path_root / "L1C_B3.dat", dtype=np.int16, mode="r", shape=shape
),
"B4": np.memmap(
path_root / "L1C_B4.dat", dtype=np.int16, mode="r", shape=shape
),
"B5": np.memmap(
path_root / "L1C_B5.dat", dtype=np.int16, mode="r", shape=shape
),
"B6": np.memmap(
path_root / "L1C_B6.dat", dtype=np.int16, mode="r", shape=shape
),
"B7": np.memmap(
path_root / "L1C_B7.dat", dtype=np.int16, mode="r", shape=shape
),
"B8": np.memmap(
path_root / "L1C_B8.dat", dtype=np.int16, mode="r", shape=shape
),
"B8A": np.memmap(
path_root / "L1C_B8A.dat", dtype=np.int16, mode="r", shape=shape
),
"B9": np.memmap(
path_root / "L1C_B9.dat", dtype=np.int16, mode="r", shape=shape
),
"B10": np.memmap(
path_root / "L1C_B10.dat", dtype=np.int16, mode="r", shape=shape
),
"B11": np.memmap(
path_root / "L1C_B11.dat", dtype=np.int16, mode="r", shape=shape
),
"B12": np.memmap(
path_root / "L1C_B12.dat", dtype=np.int16, mode="r", shape=shape
),
"LABEL": np.memmap(
path_root / "LABEL_manual_hq.dat", dtype=np.int8, mode="r", shape=shape
),
}
return total
class CloudDataset(torch.utils.data.Dataset):
def __init__(self, root, type, model="reg"):
"""
Cloud dataset class for loading and preprocessing data.
Args:
root (str): The root directory path.
type (str): The type of dataset ("train", "val", or "test").
model (str, optional): The model type ("reg" for regression or other for classification). Defaults to "reg".
"""
self.root = pathlib.Path(root)
self.type = type
self.model = model
if type == "train":
self.X = load_data(self.root / "high_train", (8490, 512, 512))
self.y = pd.read_csv(self.root / "high_train" / "metadata.csv")
if type == "val":
self.X = load_data(self.root / "high_val", (535, 512, 512))
self.y = pd.read_csv(self.root / "high_val" / "metadata.csv")
if type == "test":
self.X = load_data(self.root / "high_test", (975, 512, 512))
self.y = pd.read_csv(self.root / "high_test" / "metadata.csv")
if self.model == "reg":
self.y = np.array(self.y.difficulty)
else:
self.y = self.X["LABEL"]
def __len__(self):
"""
Get the length of the dataset.
Returns:
int: The number of samples in the dataset.
"""
return self.X["B2"].shape[0]
def __getitem__(self, index):
"""
Get a sample from the dataset.
Args:
index (int): The index of the sample.
Returns:
tuple: A tuple containing the input and target data.
"""
X = (
self.X["B1"][index],
self.X["B2"][index],
self.X["B3"][index],
self.X["B4"][index],
self.X["B5"][index],
self.X["B6"][index],
self.X["B7"][index],
self.X["B8"][index],
self.X["B8A"][index],
self.X["B9"][index],
self.X["B10"][index],
self.X["B11"][index],
self.X["B12"][index],
)
# Concatenate the bands
X = np.stack(X, axis=0) / 10000
X = torch.from_numpy(X).type(torch.float)
if self.model == "reg":
# Convert to binary classification, 0 for easy and 1 for hard
y = float(float(self.y[index]) >= 3)
else:
y = torch.from_numpy(self.y[index]).type(torch.long)
return X, y
# Create dataloader
training_data = CloudDataset(root="/data3/cloudsen12_high/", type="train")
validation_data = CloudDataset(root="/data3/cloudsen12_high/", type="val")
testing_data = CloudDataset(root="/data3/cloudsen12_high/", type="test")