def __init__(self, device):
self.dtype = torch.FloatTensor if device == 'cpu' else torch.cuda.FloatTensor
VGG_FACE = vgg_face(pretrained=True)
self.VGG_FACE_AC = VGG_Activations(VGG_FACE,
[1, 6, 11, 18, 25]).type(self.dtype)
VGG19 = vgg19(pretrained=True)
self.VGG19_AC = VGG_Activations(VGG19,
[1, 6, 11, 20, 29]).type(self.dtype)
def __init__(self, feed_forward=True):
super(LossEG, self).__init__()
self.VGG_FACE_AC = VGG_Activations(vgg_face(pretrained=True),
[1, 6, 11, 18, 25])
self.VGG19_AC = VGG_Activations(vgg19(pretrained=True),
[1, 6, 11, 20, 29])
self.match_loss = not feed_forward
def __init__(self, device, feed_forward=True):
super(LossEG, self).__init__()
self.dtype = torch.FloatTensor if device == 'cpu' else torch.cuda.FloatTensor
self.VGG_FACE_AC = VGG_Activations(vgg_face(pretrained=True),
[1, 6, 11, 18, 25]).type(self.dtype)
self.VGG19_AC = VGG_Activations(vgg19(pretrained=True),
[1, 6, 11, 20, 29]).type(self.dtype)
self.match_loss = not feed_forward
def __init__(self, feed_forward=True):
super(LossEG, self).__init__()
self.VGG_FACE_AC = VGG_Activations(vgg_face(pretrained=True), [1, 6, 11, 18, 25])
self.VGG19_AC = VGG_Activations(vgg19(pretrained=True), [1, 6, 11, 20, 29])
self.IMG_NET_MEAN = torch.Tensor([0.485, 0.456, 0.406]).reshape([1, 3, 1, 1])
self.IMG_NET_STD = torch.Tensor([0.229, 0.224, 0.225]).reshape([1, 3, 1, 1])
self.match_loss = not feed_forward
def __init__(self, feed_forward=True, gpu=None):
super(LossEG, self).__init__()
self.VGG19_AC = VGG_Activations(vgg19(pretrained=True),
[1, 6, 11, 20, 29])
self.match_loss = not feed_forward
self.gpu = gpu
if gpu is not None:
self.cuda(gpu)
def __init__(self, feed_forward=True, gpu=None):
super(LossEG, self).__init__()
self.VGG_FACE_AC = VGG_Activations(vgg_face(pretrained=True),
[1, 6, 11, 18, 25])
# self.match_loss = not feed_forward
self.gpu = gpu
if gpu is not None:
self.cuda(gpu)