from __future__ import absolute_import import os import numpy as np import torch import torch.nn as nn from torch.autograd import Variable from .pretrained_networks import vgg16, alexnet, squeezenet import torch.nn import torch.nn.functional as F import torchvision.transforms.functional as TF import cv2 from .pwcnet import Network as PWCNet from .utils import * def spatial_average(in_tens, keepdim=True): return in_tens.mean([2,3],keepdim=keepdim) def mw_spatial_average(in_tens, flow, keepdim=True): _,_,h,w = in_tens.shape flow = F.interpolate(flow, (h,w), align_corners=False, mode='bilinear') flow_mag = torch.sqrt(flow[:,0:1]**2 + flow[:,1:2]**2) flow_mag = flow_mag / torch.sum(flow_mag, dim=[1,2,3], keepdim=True) return torch.sum(in_tens*flow_mag, dim=[2,3],keepdim=keepdim) def mtw_spatial_average(in_tens, flow, texture, keepdim=True): _,_,h,w = in_tens.shape flow = F.interpolate(flow, (h,w), align_corners=False, mode='bilinear') texture = F.interpolate(texture, (h,w), align_corners=False, mode='bilinear') flow_mag = torch.sqrt(flow[:,0:1]**2 + flow[:,1:2]**2) flow_mag = (flow_mag - flow_mag.min()) / (flow_mag.max() - flow_mag.min()) + 1e-6 texture = (texture - texture.min()) / (texture.max() - texture.min()) + 1e-6 weight = flow_mag / texture weight /= torch.sum(weight) return torch.sum(in_tens*weight, dim=[2,3],keepdim=keepdim) def m2w_spatial_average(in_tens, flow, keepdim=True): _,_,h,w = in_tens.shape flow = F.interpolate(flow, (h,w), align_corners=False, mode='bilinear') flow_mag = flow[:,0:1]**2 + flow[:,1:2]**2 # B,1,H,W flow_mag = flow_mag / torch.sum(flow_mag) return torch.sum(in_tens*flow_mag, dim=[2,3],keepdim=keepdim) def upsample(in_tens, out_HW=(64,64)): # assumes scale factor is same for H and W in_H, in_W = in_tens.shape[2], in_tens.shape[3] return nn.Upsample(size=out_HW, mode='bilinear', align_corners=False)(in_tens) # Learned perceptual metric class LPIPS(nn.Module): def __init__(self, pretrained=True, net='alex', version='0.1', lpips=True, spatial=False, pnet_rand=False, pnet_tune=False, use_dropout=True, model_path=None, eval_mode=True, verbose=False): # lpips - [True] means with linear calibration on top of base network # pretrained - [True] means load linear weights super(LPIPS, self).__init__() if(verbose): print('Setting up [%s] perceptual loss: trunk [%s], v[%s], spatial [%s]'% ('LPIPS' if lpips else 'baseline', net, version, 'on' if spatial else 'off')) self.pnet_type = net self.pnet_tune = pnet_tune self.pnet_rand = pnet_rand self.spatial = spatial self.lpips = lpips # false means baseline of just averaging all layers self.version = version self.scaling_layer = ScalingLayer() if(self.pnet_type in ['vgg','vgg16']): net_type = vgg16 self.chns = [64,128,256,512,512] elif(self.pnet_type=='alex'): net_type = alexnet self.chns = [64,192,384,256,256] elif(self.pnet_type=='squeeze'): net_type = 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] self.lins = nn.ModuleList(self.lins) if(pretrained): if(model_path is None): import inspect import os model_path = os.path.abspath(os.path.join(inspect.getfile(self.__init__), '..', 'weights/v%s/%s.pth'%(version,net))) if(verbose): print('Loading model from: %s'%model_path) self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) if(eval_mode): self.eval() def forward(self, in0, in1, retPerLayer=False, normalize=False): if normalize: # turn on this flag if input is [0,1] so it can be adjusted to [-1, +1] in0 = 2 * in0 - 1 in1 = 2 * in1 - 1 # 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] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) diffs[kk] = (feats0[kk]-feats1[kk])**2 if(self.lpips): if(self.spatial): res = [upsample(self.lins[kk](diffs[kk]), out_HW=in0.shape[2:]) for kk in range(self.L)] else: res = [spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)] else: if(self.spatial): res = [upsample(diffs[kk].sum(dim=1,keepdim=True), out_HW=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] # print(val) # a = spatial_average(self.lins[kk](diffs[kk]), keepdim=True) # b = torch.max(self.lins[kk](feats0[kk]**2)) # for kk in range(self.L): # a += spatial_average(self.lins[kk](diffs[kk]), keepdim=True) # b = torch.max(b,torch.max(self.lins[kk](feats0[kk]**2))) # a = a/self.L # from IPython import embed # embed() # return 10*torch.log10(b/a) # if(retPerLayer): # return (val, res) # else: return torch.sum(torch.cat(res, 1), dim=(1,2,3), keepdims=False) 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) def forward(self, x): return self.model(x) 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 = l2(tensor2np(tensor2tensorlab(in0.data,to_norm=False)), tensor2np(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 = dssim(1.*tensor2im(in0.data), 1.*tensor2im(in1.data), range=255.).astype('float') elif(self.colorspace=='Lab'): value = dssim(tensor2np(tensor2tensorlab(in0.data,to_norm=False)), tensor2np(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) class FloLPIPS(LPIPS): def __init__(self, pretrained=True, net='alex', version='0.1', lpips=True, spatial=False, pnet_rand=False, pnet_tune=False, use_dropout=True, model_path=None, eval_mode=True, verbose=False): super(FloLPIPS, self).__init__(pretrained, net, version, lpips, spatial, pnet_rand, pnet_tune, use_dropout, model_path, eval_mode, verbose) def forward(self, in0, in1, flow, retPerLayer=False, normalize=False): if normalize: # turn on this flag if input is [0,1] so it can be adjusted to [-1, +1] in0 = 2 * in0 - 1 in1 = 2 * in1 - 1 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] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) diffs[kk] = (feats0[kk]-feats1[kk])**2 res = [mw_spatial_average(self.lins[kk](diffs[kk]), flow, keepdim=True) for kk in range(self.L)] return torch.sum(torch.cat(res, 1), dim=(1,2,3), keepdims=False) class Flolpips(nn.Module): def __init__(self): super(Flolpips, self).__init__() self.loss_fn = FloLPIPS(net='alex',version='0.1') self.flownet = PWCNet() @torch.no_grad() def forward(self, I0, I1, frame_dis, frame_ref): """ args: I0: first frame of the triplet, shape: [B, C, H, W] I1: third frame of the triplet, shape: [B, C, H, W] frame_dis: prediction of the intermediate frame, shape: [B, C, H, W] frame_ref: ground-truth of the intermediate frame, shape: [B, C, H, W] """ assert I0.size() == I1.size() == frame_dis.size() == frame_ref.size(), \ "the 4 input tensors should have same size" flow_ref = self.flownet(frame_ref, I0) flow_dis = self.flownet(frame_dis, I0) flow_diff = flow_ref - flow_dis flolpips_wrt_I0 = self.loss_fn.forward(frame_ref, frame_dis, flow_diff, normalize=True) flow_ref = self.flownet(frame_ref, I1) flow_dis = self.flownet(frame_dis, I1) flow_diff = flow_ref - flow_dis flolpips_wrt_I1 = self.loss_fn.forward(frame_ref, frame_dis, flow_diff, normalize=True) flolpips = (flolpips_wrt_I0 + flolpips_wrt_I1) / 2 return flolpips