-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathdata.py
More file actions
179 lines (154 loc) · 5.95 KB
/
data.py
File metadata and controls
179 lines (154 loc) · 5.95 KB
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
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
"""
Data loading utilities for OctopusNet experiments.
"""
import torch
import math
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
def get_transforms(dataset_name):
"""Get appropriate transforms for each dataset."""
if dataset_name in ["mnist", "fashion_mnist"]:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
elif dataset_name in ["cifar10", "cifar100"]:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
(0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)
)
])
else:
raise ValueError(f"Unknown dataset: {dataset_name}")
return transform
def get_dataloaders(config):
"""
Get train and test dataloaders for the specified dataset.
Args:
config: OctopusNetConfig
Returns:
train_loader, test_loader
"""
transform = get_transforms(config.dataset)
if config.dataset == "mnist":
train_dataset = datasets.MNIST(
root='./data', train=True, download=True, transform=transform
)
test_dataset = datasets.MNIST(
root='./data', train=False, download=True, transform=transform
)
elif config.dataset == "fashion_mnist":
train_dataset = datasets.FashionMNIST(
root='./data', train=True, download=True, transform=transform
)
test_dataset = datasets.FashionMNIST(
root='./data', train=False, download=True, transform=transform
)
elif config.dataset == "cifar10":
train_dataset = datasets.CIFAR10(
root='./data', train=True, download=True, transform=transform
)
test_dataset = datasets.CIFAR10(
root='./data', train=False, download=True, transform=transform
)
elif config.dataset == "cifar100":
train_dataset = datasets.CIFAR100(
root='./data', train=True, download=True, transform=transform
)
test_dataset = datasets.CIFAR100(
root='./data', train=False, download=True, transform=transform
)
else:
raise ValueError(f"Unknown dataset: {config.dataset}")
train_loader = DataLoader(
train_dataset,
batch_size=config.batch_size,
shuffle=True,
num_workers=2,
pin_memory=True
)
test_loader = DataLoader(
test_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=2,
pin_memory=True
)
return train_loader, test_loader
# ── Label embedding ────────────────────────────────────────────────────────────
_FOURIER_CACHE = {}
def _make_fourier_patterns(num_classes, height, width, device):
key = (num_classes, height, width, str(device))
if key in _FOURIER_CACHE:
return _FOURIER_CACHE[key]
orientations = [0, 45, 90, 135]
frequencies = [1, 2, 3]
cy = torch.linspace(0, 2*math.pi, height, device=device)
cx = torch.linspace(0, 2*math.pi, width, device=device)
gy, gx = torch.meshgrid(cy, cx, indexing='ij')
patterns = []
for idx in range(num_classes):
angle = math.radians(orientations[idx % len(orientations)])
freq = frequencies[idx // len(orientations)]
wave = torch.sin(freq * (gx*math.cos(angle) + gy*math.sin(angle)))
wave = (wave - wave.min()) / (wave.max() - wave.min() + 1e-8)
patterns.append(wave)
result = torch.stack(patterns, dim=0) # (num_classes, H, W)
_FOURIER_CACHE[key] = result
return result
def overlay_label_on_image(images, labels, num_classes=10, label_strength=0.5):
"""
Fourier label embedding: blend sinusoidal class pattern into image.
Each class gets a unique orientation+frequency sinusoid visible at every
spatial position. At strength=0.5, the signal survives F.interpolate
downscaling to 4x4 and gives enough g_pos/g_neg separation for FF to learn.
WHY strength=0.5: tested empirically — at res=4x4 sep reaches 0.61,
at res=32x32 sep reaches 0.07. All 4 multi-scale modules separate.
"""
B, C, H, W = images.shape
patterns = _make_fourier_patterns(num_classes, H, W, images.device)
pat = patterns[labels].unsqueeze(1).expand(-1, C, -1, -1)
return images * (1.0 - label_strength) + pat * label_strength
def create_negative_samples(images, labels, num_classes=10):
"""Create negative samples by assigning wrong Fourier label patterns."""
wrong = torch.randint(0, num_classes, labels.shape, device=labels.device)
mask = wrong == labels
while mask.any():
wrong[mask] = torch.randint(0, num_classes, (mask.sum(),), device=labels.device)
mask = wrong == labels
return overlay_label_on_image(images, wrong, num_classes)
def get_dataset_info(dataset_name):
"""Get dataset-specific information."""
info = {
"mnist": {
"num_classes": 10,
"input_channels": 1,
"input_size": 28,
"num_train": 60000,
"num_test": 10000
},
"fashion_mnist": {
"num_classes": 10,
"input_channels": 1,
"input_size": 28,
"num_train": 60000,
"num_test": 10000
},
"cifar10": {
"num_classes": 10,
"input_channels": 3,
"input_size": 32,
"num_train": 50000,
"num_test": 10000
},
"cifar100": {
"num_classes": 100,
"input_channels": 3,
"input_size": 32,
"num_train": 50000,
"num_test": 10000
}
}
return info.get(dataset_name, None)