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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 |