File size: 1,871 Bytes
9040d50 820b49d 9040d50 62c391f 820b49d | 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 | import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
def ensure_gray(image: Image.Image) -> Image.Image:
if image.mode != "L":
image = image.convert("L")
return image
trnscm = transforms.Compose([
transforms.Lambda(ensure_gray),
transforms.Resize((100, 100)),
transforms.ToTensor(),
])
class Siamese(nn.Module):
def __init__(self):
super().__init__()
self.cnn1 = nn.Sequential(
nn.ReflectionPad2d(1),
nn.Conv2d(1, 4, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(4),
nn.ReflectionPad2d(1),
nn.Conv2d(4, 8, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(8),
nn.ReflectionPad2d(1),
nn.Conv2d(8, 8, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(8),
)
self.fc1 = nn.Sequential(
nn.Linear(8 * 100 * 100, 500),
nn.ReLU(inplace=True),
nn.Linear(500, 500),
nn.ReLU(inplace=True),
nn.Linear(500, 5),
)
def forward_once(self, x):
out = self.cnn1(x)
out = out.view(out.size(0), -1)
out = self.fc1(out)
return out
def forward(self, x1, x2):
return self.forward_once(x1), self.forward_once(x2)
class FaceClassifier(nn.Module):
def __init__(self, input_dim=5, num_classes=7):
super().__init__()
self.fc = nn.Sequential(
nn.Linear(input_dim, 128),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(128, num_classes),
)
def forward(self, x):
return self.fc(x)
# Update to match your captured_face_images ImageFolder order
classes = ["person1","person2","person3","person4","person5","person6","person7"] |