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import torch
import torch.nn as nn
class Vgg_face_dag(nn.Module):
def __init__(self):
super(Vgg_face_dag, self).__init__()
self.meta = {'mean': [129.186279296875, 104.76238250732422, 93.59396362304688],
'std': [1, 1, 1],
'imageSize': [224, 224, 3]}
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu1_2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=0, dilation=1, ceil_mode=False)
self.conv2_1 = nn.Conv2d(64, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu2_1 = nn.ReLU(inplace=True)
self.conv2_2 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu2_2 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=0, dilation=1, ceil_mode=False)
self.conv3_1 = nn.Conv2d(128, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu3_1 = nn.ReLU(inplace=True)
self.conv3_2 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu3_2 = nn.ReLU(inplace=True)
self.conv3_3 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu3_3 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=0, dilation=1, ceil_mode=False)
self.conv4_1 = nn.Conv2d(256, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu4_1 = nn.ReLU(inplace=True)
self.conv4_2 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu4_2 = nn.ReLU(inplace=True)
self.conv4_3 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu4_3 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=0, dilation=1, ceil_mode=False)
self.conv5_1 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu5_1 = nn.ReLU(inplace=True)
self.conv5_2 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu5_2 = nn.ReLU(inplace=True)
self.conv5_3 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
self.relu5_3 = nn.ReLU(inplace=True)
self.pool5 = nn.MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=0, dilation=1, ceil_mode=False)
self.fc6 = nn.Linear(in_features=25088, out_features=4096, bias=True)
self.relu6 = nn.ReLU(inplace=True)
self.dropout6 = nn.Dropout(p=0.5)
self.fc7 = nn.Linear(in_features=4096, out_features=4096, bias=True)
self.relu7 = nn.ReLU(inplace=True)
self.dropout7 = nn.Dropout(p=0.5)
self.fc8 = nn.Linear(in_features=4096, out_features=2622, bias=True)
def forward(self, x0):
x1 = self.conv1_1(x0)
x2 = self.relu1_1(x1)
x3 = self.conv1_2(x2)
x4 = self.relu1_2(x3)
x5 = self.pool1(x4)
x6 = self.conv2_1(x5)
x7 = self.relu2_1(x6)
x8 = self.conv2_2(x7)
x9 = self.relu2_2(x8)
x10 = self.pool2(x9)
x11 = self.conv3_1(x10)
x12 = self.relu3_1(x11)
x13 = self.conv3_2(x12)
x14 = self.relu3_2(x13)
x15 = self.conv3_3(x14)
x16 = self.relu3_3(x15)
x17 = self.pool3(x16)
x18 = self.conv4_1(x17)
x19 = self.relu4_1(x18)
x20 = self.conv4_2(x19)
x21 = self.relu4_2(x20)
x22 = self.conv4_3(x21)
x23 = self.relu4_3(x22)
x24 = self.pool4(x23)
x25 = self.conv5_1(x24)
x26 = self.relu5_1(x25)
x27 = self.conv5_2(x26)
x28 = self.relu5_2(x27)
x29 = self.conv5_3(x28)
x30 = self.relu5_3(x29)
x31_preflatten = self.pool5(x30)
x31 = x31_preflatten.view(x31_preflatten.size(0), -1)
x32 = self.fc6(x31)
x33 = self.relu6(x32)
x34 = self.dropout6(x33)
x35 = self.fc7(x34)
x36 = self.relu7(x35)
x37 = self.dropout7(x36)
x38 = self.fc8(x37)
return x38
def vgg_face_dag(weights_path=None, **kwargs):
"""
load imported model instance
Args:
weights_path (str): If set, loads model weights from the given path
"""
model = Vgg_face_dag()
if weights_path:
state_dict = torch.load(weights_path)
model.load_state_dict(state_dict)
return model
class VGGFaceFeats(Vgg_face_dag):
def forward(self, x0):
x1 = self.conv1_1(x0)
x2 = self.relu1_1(x1)
x3 = self.conv1_2(x2)
x4 = self.relu1_2(x3)
x5 = self.pool1(x4)
x6 = self.conv2_1(x5)
x7 = self.relu2_1(x6)
x8 = self.conv2_2(x7)
x9 = self.relu2_2(x8)
x10 = self.pool2(x9)
x11 = self.conv3_1(x10)
x12 = self.relu3_1(x11)
x13 = self.conv3_2(x12)
x14 = self.relu3_2(x13)
x15 = self.conv3_3(x14)
x16 = self.relu3_3(x15)
x17 = self.pool3(x16)
x18 = self.conv4_1(x17)
x19 = self.relu4_1(x18)
x20 = self.conv4_2(x19)
x21 = self.relu4_2(x20)
x22 = self.conv4_3(x21)
x23 = self.relu4_3(x22)
x24 = self.pool4(x23)
x25 = self.conv5_1(x24)
# x26 = self.relu5_1(x25)
# x27 = self.conv5_2(x26)
# x28 = self.relu5_2(x27)
# x29 = self.conv5_3(x28)
# x30 = self.relu5_3(x29)
# x31_preflatten = self.pool5(x30)
# x31 = x31_preflatten.view(x31_preflatten.size(0), -1)
# x32 = self.fc6(x31)
# x33 = self.relu6(x32)
# x34 = self.dropout6(x33)
# x35 = self.fc7(x34)
# x36 = self.relu7(x35)
# x37 = self.dropout7(x36)
# x38 = self.fc8(x37)
return x1, x6, x11, x18, x25
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