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  4. lpips.networks_basic.NetLinLayer
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How to use the lpips.networks_basic.NetLinLayer function in lpips

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github revbucket / mister_ed / lpips / networks_basic.py View on Github external
net_type = pn.alexnet
            self.chns = [64,192,384,256,256]
        elif(self.pnet_type=='squeeze'):
            net_type = pn.squeezenet
            self.chns = [64,128,256,384,384,512,512]

        if(self.pnet_tune):
            self.net = net_type(requires_grad=True)
        else:
            self.net = [net_type(requires_grad=False),]

        self.lin0 = NetLinLayer(self.chns[0],use_dropout=use_dropout)
        self.lin1 = NetLinLayer(self.chns[1],use_dropout=use_dropout)
        self.lin2 = NetLinLayer(self.chns[2],use_dropout=use_dropout)
        self.lin3 = NetLinLayer(self.chns[3],use_dropout=use_dropout)
        self.lin4 = NetLinLayer(self.chns[4],use_dropout=use_dropout)
        self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
        if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
            self.lin5 = NetLinLayer(self.chns[5],use_dropout=use_dropout)
            self.lin6 = NetLinLayer(self.chns[6],use_dropout=use_dropout)
            self.lins+=[self.lin5,self.lin6]

        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))

        if(use_gpu):
            if(self.pnet_tune):
                self.net.cuda()
            else:
                self.net[0].cuda()
            self.shift = self.shift.cuda()
            self.scale = self.scale.cuda()
github revbucket / mister_ed / lpips / networks_basic.py View on Github external
if(self.pnet_type in ['vgg','vgg16']):
            net_type = pn.vgg16
            self.chns = [64,128,256,512,512]
        elif(self.pnet_type=='alex'):
            net_type = pn.alexnet
            self.chns = [64,192,384,256,256]
        elif(self.pnet_type=='squeeze'):
            net_type = pn.squeezenet
            self.chns = [64,128,256,384,384,512,512]

        if(self.pnet_tune):
            self.net = net_type(requires_grad=True)
        else:
            self.net = [net_type(requires_grad=False),]

        self.lin0 = NetLinLayer(self.chns[0],use_dropout=use_dropout)
        self.lin1 = NetLinLayer(self.chns[1],use_dropout=use_dropout)
        self.lin2 = NetLinLayer(self.chns[2],use_dropout=use_dropout)
        self.lin3 = NetLinLayer(self.chns[3],use_dropout=use_dropout)
        self.lin4 = NetLinLayer(self.chns[4],use_dropout=use_dropout)
        self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
        if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
            self.lin5 = NetLinLayer(self.chns[5],use_dropout=use_dropout)
            self.lin6 = NetLinLayer(self.chns[6],use_dropout=use_dropout)
            self.lins+=[self.lin5,self.lin6]

        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))

        if(use_gpu):
            if(self.pnet_tune):
                self.net.cuda()
github revbucket / mister_ed / lpips / networks_basic.py View on Github external
self.chns = [64,128,256,384,384,512,512]

        if(self.pnet_tune):
            self.net = net_type(requires_grad=True)
        else:
            self.net = [net_type(requires_grad=False),]

        self.lin0 = NetLinLayer(self.chns[0],use_dropout=use_dropout)
        self.lin1 = NetLinLayer(self.chns[1],use_dropout=use_dropout)
        self.lin2 = NetLinLayer(self.chns[2],use_dropout=use_dropout)
        self.lin3 = NetLinLayer(self.chns[3],use_dropout=use_dropout)
        self.lin4 = NetLinLayer(self.chns[4],use_dropout=use_dropout)
        self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
        if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
            self.lin5 = NetLinLayer(self.chns[5],use_dropout=use_dropout)
            self.lin6 = NetLinLayer(self.chns[6],use_dropout=use_dropout)
            self.lins+=[self.lin5,self.lin6]

        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))

        if(use_gpu):
            if(self.pnet_tune):
                self.net.cuda()
            else:
                self.net[0].cuda()
            self.shift = self.shift.cuda()
            self.scale = self.scale.cuda()
            self.lin0.cuda()
            self.lin1.cuda()
            self.lin2.cuda()
            self.lin3.cuda()
github revbucket / mister_ed / lpips / networks_basic.py View on Github external
self.chns = [64,128,256,512,512]
        elif(self.pnet_type=='alex'):
            net_type = pn.alexnet
            self.chns = [64,192,384,256,256]
        elif(self.pnet_type=='squeeze'):
            net_type = pn.squeezenet
            self.chns = [64,128,256,384,384,512,512]

        if(self.pnet_tune):
            self.net = net_type(requires_grad=True)
        else:
            self.net = [net_type(requires_grad=False),]

        self.lin0 = NetLinLayer(self.chns[0],use_dropout=use_dropout)
        self.lin1 = NetLinLayer(self.chns[1],use_dropout=use_dropout)
        self.lin2 = NetLinLayer(self.chns[2],use_dropout=use_dropout)
        self.lin3 = NetLinLayer(self.chns[3],use_dropout=use_dropout)
        self.lin4 = NetLinLayer(self.chns[4],use_dropout=use_dropout)
        self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
        if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
            self.lin5 = NetLinLayer(self.chns[5],use_dropout=use_dropout)
            self.lin6 = NetLinLayer(self.chns[6],use_dropout=use_dropout)
            self.lins+=[self.lin5,self.lin6]

        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))

        if(use_gpu):
            if(self.pnet_tune):
                self.net.cuda()
            else:
                self.net[0].cuda()
github revbucket / mister_ed / lpips / networks_basic.py View on Github external
net_type = pn.vgg16
            self.chns = [64,128,256,512,512]
        elif(self.pnet_type=='alex'):
            net_type = pn.alexnet
            self.chns = [64,192,384,256,256]
        elif(self.pnet_type=='squeeze'):
            net_type = pn.squeezenet
            self.chns = [64,128,256,384,384,512,512]

        if(self.pnet_tune):
            self.net = net_type(requires_grad=True)
        else:
            self.net = [net_type(requires_grad=False),]

        self.lin0 = NetLinLayer(self.chns[0],use_dropout=use_dropout)
        self.lin1 = NetLinLayer(self.chns[1],use_dropout=use_dropout)
        self.lin2 = NetLinLayer(self.chns[2],use_dropout=use_dropout)
        self.lin3 = NetLinLayer(self.chns[3],use_dropout=use_dropout)
        self.lin4 = NetLinLayer(self.chns[4],use_dropout=use_dropout)
        self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
        if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
            self.lin5 = NetLinLayer(self.chns[5],use_dropout=use_dropout)
            self.lin6 = NetLinLayer(self.chns[6],use_dropout=use_dropout)
            self.lins+=[self.lin5,self.lin6]

        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))

        if(use_gpu):
            if(self.pnet_tune):
                self.net.cuda()
            else:
github revbucket / mister_ed / lpips / networks_basic.py View on Github external
def __init__(self, chn_in, chn_out=1, use_dropout=False):
        super(NetLinLayer, self).__init__()

        layers = [nn.Dropout(),] if(use_dropout) else []
        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),]
        self.model = nn.Sequential(*layers)
github revbucket / mister_ed / lpips / networks_basic.py View on Github external
net_type = pn.squeezenet
            self.chns = [64,128,256,384,384,512,512]

        if(self.pnet_tune):
            self.net = net_type(requires_grad=True)
        else:
            self.net = [net_type(requires_grad=False),]

        self.lin0 = NetLinLayer(self.chns[0],use_dropout=use_dropout)
        self.lin1 = NetLinLayer(self.chns[1],use_dropout=use_dropout)
        self.lin2 = NetLinLayer(self.chns[2],use_dropout=use_dropout)
        self.lin3 = NetLinLayer(self.chns[3],use_dropout=use_dropout)
        self.lin4 = NetLinLayer(self.chns[4],use_dropout=use_dropout)
        self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
        if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
            self.lin5 = NetLinLayer(self.chns[5],use_dropout=use_dropout)
            self.lin6 = NetLinLayer(self.chns[6],use_dropout=use_dropout)
            self.lins+=[self.lin5,self.lin6]

        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))

        if(use_gpu):
            if(self.pnet_tune):
                self.net.cuda()
            else:
                self.net[0].cuda()
            self.shift = self.shift.cuda()
            self.scale = self.scale.cuda()
            self.lin0.cuda()
            self.lin1.cuda()
            self.lin2.cuda()
github revbucket / mister_ed / lpips / networks_basic.py View on Github external
elif(self.pnet_type=='alex'):
            net_type = pn.alexnet
            self.chns = [64,192,384,256,256]
        elif(self.pnet_type=='squeeze'):
            net_type = pn.squeezenet
            self.chns = [64,128,256,384,384,512,512]

        if(self.pnet_tune):
            self.net = net_type(requires_grad=True)
        else:
            self.net = [net_type(requires_grad=False),]

        self.lin0 = NetLinLayer(self.chns[0],use_dropout=use_dropout)
        self.lin1 = NetLinLayer(self.chns[1],use_dropout=use_dropout)
        self.lin2 = NetLinLayer(self.chns[2],use_dropout=use_dropout)
        self.lin3 = NetLinLayer(self.chns[3],use_dropout=use_dropout)
        self.lin4 = NetLinLayer(self.chns[4],use_dropout=use_dropout)
        self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
        if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
            self.lin5 = NetLinLayer(self.chns[5],use_dropout=use_dropout)
            self.lin6 = NetLinLayer(self.chns[6],use_dropout=use_dropout)
            self.lins+=[self.lin5,self.lin6]

        self.shift = torch.autograd.Variable(torch.Tensor([-.030, -.088, -.188]).view(1,3,1,1))
        self.scale = torch.autograd.Variable(torch.Tensor([.458, .448, .450]).view(1,3,1,1))

        if(use_gpu):
            if(self.pnet_tune):
                self.net.cuda()
            else:
                self.net[0].cuda()
            self.shift = self.shift.cuda()

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