Spaces:
Build error
Build error
| import torch | |
| import torch.nn as nn | |
| import torchvision.models as models | |
| def requires_grad(model, flag=True): | |
| for p in model.parameters(): | |
| p.requires_grad = flag | |
| class VGG16_Activations(nn.Module): | |
| def __init__(self, feature_idx): | |
| super(VGG16_Activations, self).__init__() | |
| vgg16 = models.vgg16(pretrained=True) | |
| features = list(vgg16.features) | |
| self.features = nn.ModuleList(features).eval() | |
| self.layer_id_list = feature_idx | |
| def forward(self, x): | |
| activations = [] | |
| for i, model in enumerate(self.features): | |
| x = model(x) | |
| if i in self.layer_id_list: | |
| activations.append(x) | |
| return activations | |
| class VGG19_Activations(nn.Module): | |
| def __init__(self, feature_idx, requires_grad=False): | |
| super(VGG19_Activations, self).__init__() | |
| vgg19 = models.vgg19(pretrained=True) | |
| requires_grad(vgg19, flag=False) | |
| features = list(vgg19.features) | |
| self.features = nn.ModuleList(features).eval() | |
| self.layer_id_list = feature_idx | |
| def forward(self, x): | |
| activations = [] | |
| for i, model in enumerate(self.features): | |
| x = model(x) | |
| if i in self.layer_id_list: | |
| activations.append(x) | |
| return activations | |
| # http://www.robots.ox.ac.uk/~albanie/models/pytorch-mcn/vgg_face_dag.py | |
| 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, x): | |
| activations = [] | |
| x1 = self.conv1_1(x) | |
| activations.append(x1) | |
| x2 = self.relu1_1(x1) | |
| x3 = self.conv1_2(x2) | |
| x4 = self.relu1_2(x3) | |
| x5 = self.pool1(x4) | |
| x6 = self.conv2_1(x5) | |
| activations.append(x6) | |
| x7 = self.relu2_1(x6) | |
| x8 = self.conv2_2(x7) | |
| x9 = self.relu2_2(x8) | |
| x10 = self.pool2(x9) | |
| x11 = self.conv3_1(x10) | |
| activations.append(x11) | |
| 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) | |
| activations.append(x18) | |
| 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) | |
| activations.append(x25) | |
| """ | |
| 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 activations | |