from __future__ import absolute_import import sys import torch import torch.nn as nn import torch.nn.init as init from torch.autograd import Variable import numpy as np from pdb import set_trace as st from skimage import color from IPython import embed from models.stylegan2.lpips import pretrained_networks as pn import models.stylegan2.lpips as util def spatial_average(in_tens, keepdim=True): return in_tens.mean([2,3],keepdim=keepdim) def upsample(in_tens, out_H=64): # assumes scale factor is same for H and W in_H = in_tens.shape[2] scale_factor = 1.*out_H/in_H return nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False)(in_tens) # Learned perceptual metric class PNetLin(nn.Module): def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False, version='0.1', lpips=True): super(PNetLin, self).__init__() self.pnet_type = pnet_type self.pnet_tune = pnet_tune self.pnet_rand = pnet_rand self.spatial = spatial self.lpips = lpips self.version = version self.scaling_layer = ScalingLayer() 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] self.L = len(self.chns) self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune) if(lpips): 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] def forward(self, in0, in1, retPerLayer=False): # v0.0 - original release had a bug, where input was not scaled in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version=='0.1' else (in0, in1) outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input) feats0, feats1, diffs = {}, {}, {} for kk in range(self.L): feats0[kk], feats1[kk] = util.normalize_tensor(outs0[kk]), util.normalize_tensor(outs1[kk]) diffs[kk] = (feats0[kk]-feats1[kk])**2 if(self.lpips): if(self.spatial): res = [upsample(self.lins[kk].model(diffs[kk]), out_H=in0.shape[2]) for kk in range(self.L)] else: res = [spatial_average(self.lins[kk].model(diffs[kk]), keepdim=True) for kk in range(self.L)] else: if(self.spatial): res = [upsample(diffs[kk].sum(dim=1,keepdim=True), out_H=in0.shape[2]) for kk in range(self.L)] else: res = [spatial_average(diffs[kk].sum(dim=1,keepdim=True), keepdim=True) for kk in range(self.L)] val = res[0] for l in range(1,self.L): val += res[l] if(retPerLayer): return (val, res) else: return val class ScalingLayer(nn.Module): def __init__(self): super(ScalingLayer, self).__init__() self.register_buffer('shift', torch.Tensor([-.030,-.088,-.188])[None,:,None,None]) self.register_buffer('scale', torch.Tensor([.458,.448,.450])[None,:,None,None]) def forward(self, inp): return (inp - self.shift) / self.scale class NetLinLayer(nn.Module): ''' A single linear layer which does a 1x1 conv ''' 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) class Dist2LogitLayer(nn.Module): ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) ''' def __init__(self, chn_mid=32, use_sigmoid=True): super(Dist2LogitLayer, self).__init__() layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True),] layers += [nn.LeakyReLU(0.2,True),] layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True),] layers += [nn.LeakyReLU(0.2,True),] layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True),] if(use_sigmoid): layers += [nn.Sigmoid(),] self.model = nn.Sequential(*layers) def forward(self,d0,d1,eps=0.1): return self.model.forward(torch.cat((d0,d1,d0-d1,d0/(d1+eps),d1/(d0+eps)),dim=1)) class BCERankingLoss(nn.Module): def __init__(self, chn_mid=32): super(BCERankingLoss, self).__init__() self.net = Dist2LogitLayer(chn_mid=chn_mid) # self.parameters = list(self.net.parameters()) self.loss = torch.nn.BCELoss() def forward(self, d0, d1, judge): per = (judge+1.)/2. self.logit = self.net.forward(d0,d1) return self.loss(self.logit, per) # L2, DSSIM metrics class FakeNet(nn.Module): def __init__(self, use_gpu=True, colorspace='Lab'): super(FakeNet, self).__init__() self.use_gpu = use_gpu self.colorspace=colorspace class L2(FakeNet): def forward(self, in0, in1, retPerLayer=None): assert(in0.size()[0]==1) # currently only supports batchSize 1 if(self.colorspace=='RGB'): (N,C,X,Y) = in0.size() value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N) return value elif(self.colorspace=='Lab'): value = util.l2(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)), util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float') ret_var = Variable( torch.Tensor((value,) ) ) if(self.use_gpu): ret_var = ret_var.cuda() return ret_var class DSSIM(FakeNet): def forward(self, in0, in1, retPerLayer=None): assert(in0.size()[0]==1) # currently only supports batchSize 1 if(self.colorspace=='RGB'): value = util.dssim(1.*util.tensor2im(in0.data), 1.*util.tensor2im(in1.data), range=255.).astype('float') elif(self.colorspace=='Lab'): value = util.dssim(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)), util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float') ret_var = Variable( torch.Tensor((value,) ) ) if(self.use_gpu): ret_var = ret_var.cuda() return ret_var def print_network(net): num_params = 0 for param in net.parameters(): num_params += param.numel() print('Network',net) print('Total number of parameters: %d' % num_params)