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import torch
from torch import nn
from warnings import filterwarnings
from torchvision.transforms import ToTensor, Resize, Normalize, Compose
filterwarnings("ignore")
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
KERNEL_SIZE = (3,3)
class VGGFACE(nn.Module):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.conv1_1 = nn.Conv2d(3, 64, KERNEL_SIZE, 1, 1)
self.conv1_2 = nn.Conv2d(64, 64, KERNEL_SIZE, 1, 1)
self.conv2_1 = nn.Conv2d(64, 128, KERNEL_SIZE, 1, 1)
self.conv2_2 = nn.Conv2d(128, 128, KERNEL_SIZE, 1, 1)
self.conv3_1 = nn.Conv2d(128, 256, KERNEL_SIZE, 1, 1)
self.conv3_2 = nn.Conv2d(256, 256, KERNEL_SIZE, 1, 1)
self.conv3_3 = nn.Conv2d(256, 256, KERNEL_SIZE, 1, 1)
self.conv4_1 = nn.Conv2d(256, 512, KERNEL_SIZE, 1, 1)
self.conv4_2 = nn.Conv2d(512, 512, KERNEL_SIZE, 1, 1)
self.conv4_3 = nn.Conv2d(512, 512, KERNEL_SIZE, 1, 1)
self.conv5_1 = nn.Conv2d(512, 512, KERNEL_SIZE, 1, 1)
self.conv5_2 = nn.Conv2d(512, 512, KERNEL_SIZE, 1, 1)
self.conv5_3 = nn.Conv2d(512, 512, KERNEL_SIZE, 1, 1)
self.fc6 = nn.Linear(49*512, 4096)
self.fc7 = nn.Linear(4096, 4096)
self.fc8 = nn.Linear(4096, 2622)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(2)
self._features = [
self.conv1_1, self.relu,
self.conv1_2, self.relu,
self.maxpool,
self.conv2_1, self.relu,
self.conv2_2, self.relu,
self.maxpool,
self.conv3_1, self.relu,
self.conv3_2, self.relu,
self.conv3_3, self.relu,
self.maxpool,
self.conv4_1, self.relu,
self.conv4_2, self.relu,
self.conv4_3, self.relu,
self.maxpool,
self.conv5_1, self.relu,
self.conv5_2, self.relu,
self.conv5_3, self.relu,
self.maxpool,
nn.Flatten(start_dim=0)
]
self._classifier = [
self.fc6, self.relu,
self.fc7, self.relu,
self.fc8
]
self._embedder = [
self.conv1_1, self.relu,
self.conv1_2, self.relu,
self.maxpool,
self.conv2_1, self.relu,
self.conv2_2, self.relu,
self.maxpool,
self.conv3_1, self.relu,
self.conv3_2, self.relu,
self.conv3_3, self.relu,
self.maxpool,
self.conv4_1, self.relu,
self.conv4_2, self.relu,
self.conv4_3, self.relu,
self.maxpool,
self.conv5_1, self.relu,
self.conv5_2, self.relu,
self.conv5_3, self.relu,
self.maxpool,
nn.Flatten(start_dim=0),
self.fc6,
]
self.transform = Compose([ToTensor() ,Resize((224, 224)), Normalize(mean=(93.59396362304688/255, 104.76238250732422/255, 129.186279296875/255), std=(1, 1, 1))])
def features(self, x):
x = self.transform(x)
x = x.to(DEVICE)
for layer in self._features:
x = layer(x)
return x
def classifier(self, x):
for layer in self._classifier:
x = layer(x)
return x
def embedder(self, x):
x = self.transform(x)
x = x.to(DEVICE)
for layer in self._embedder:
x = layer(x)
return x
def forward(self, x:torch.Tensor):
x = self.features(x)
return self.classifier(x)
def embeddings(self, x:torch.Tensor):
return self.embedder(x).cpu().flatten().detach().numpy()
__call__ = embeddings
MODEL_FACE = VGGFACE()
MODEL_FACE.load_state_dict(torch.load("models/vgg_face_dag.pth"), strict=True)
MODEL_FACE.to(DEVICE)
if __name__ == "__main__":
print(MODEL_FACE.state_dict().keys()) |