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back2future.py
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back2future.py
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# Author: Anurag Ranjan
# Copyright (c) 2018, Max Planck Society
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.serialization import load_lua
from torch.autograd import Variable
from correlation_package.modules.correlation import Correlation
def conv_feat_block(nIn, nOut):
return nn.Sequential(
nn.Conv2d(nIn, nOut, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(nOut, nOut, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2)
)
def conv_dec_block(nIn):
return nn.Sequential(
nn.Conv2d(nIn, 128, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(128, 96, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(96, 64, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(32, 2, kernel_size=3, stride=1, padding=1)
)
class Model(nn.Module):
def __init__(self, pretrained=None):
super(Model, self).__init__()
idx = [list(range(n, -1, -9)) for n in range(80,71,-1)]
idx = list(np.array(idx).flatten())
self.idx_fwd = Variable(torch.LongTensor(np.array(idx)).cuda(), requires_grad=False)
self.idx_bwd = Variable(torch.LongTensor(np.array(list(reversed(idx)))).cuda(), requires_grad=False)
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear')
self.softmax2d = nn.Softmax2d()
self.conv1a = conv_feat_block(3,16)
self.conv1b = conv_feat_block(3,16)
self.conv1c = conv_feat_block(3,16)
self.conv2a = conv_feat_block(16,32)
self.conv2b = conv_feat_block(16,32)
self.conv2c = conv_feat_block(16,32)
self.conv3a = conv_feat_block(32,64)
self.conv3b = conv_feat_block(32,64)
self.conv3c = conv_feat_block(32,64)
self.conv4a = conv_feat_block(64,96)
self.conv4b = conv_feat_block(64,96)
self.conv4c = conv_feat_block(64,96)
self.conv5a = conv_feat_block(96,128)
self.conv5b = conv_feat_block(96,128)
self.conv5c = conv_feat_block(96,128)
self.conv6a = conv_feat_block(128,192)
self.conv6b = conv_feat_block(128,192)
self.conv6c = conv_feat_block(128,192)
self.corr = Correlation(pad_size=4, kernel_size=1, max_displacement=4, stride1=1, stride2=1, corr_multiply=1)
self.decoder_fwd6 = conv_dec_block(162)
self.decoder_bwd6 = conv_dec_block(162)
self.decoder_fwd5 = conv_dec_block(292)
self.decoder_bwd5 = conv_dec_block(292)
self.decoder_fwd4 = conv_dec_block(260)
self.decoder_bwd4 = conv_dec_block(260)
self.decoder_fwd3 = conv_dec_block(228)
self.decoder_bwd3 = conv_dec_block(228)
self.decoder_fwd2 = conv_dec_block(196)
self.decoder_bwd2 = conv_dec_block(196)
self.decoder_occ6 = conv_dec_block(354)
self.decoder_occ5 = conv_dec_block(292)
self.decoder_occ4 = conv_dec_block(260)
self.decoder_occ3 = conv_dec_block(228)
self.decoder_occ2 = conv_dec_block(196)
if pretrained is not None:
self.load_state_dict(torch.load(pretrained))
print('Model loaded from ', pretrained)
def load(self, key, load_id):
module = getattr(self, key)
loadpath = os.path.join(LOAD_DIR, load_id)
for i,m in enumerate(module):
if type(m)==nn.Conv2d:
weight_path = loadpath + '_' + str(i+1) + 'weight.t7'
bias_path = loadpath + '_' + str(i+1) + 'bias.t7'
m.weight.data.copy_(load_lua(weight_path))
m.bias.data.copy_(load_lua(bias_path))
def initialize(self):
#This is used for the first load after saving weight files from lua
map = {'conv1a': '3', 'conv1b': '6', 'conv1c': '22',
'conv2a': '4', 'conv2b': '7', 'conv2c': '23',
'conv3a': '9', 'conv3b': '10', 'conv3c': '24',
'conv4a': '12', 'conv4b': '13', 'conv4c': '25',
'conv5a': '15', 'conv5b': '16', 'conv5c': '26',
'conv6a': '18', 'conv6b': '19', 'conv6c': '27',
'decoder_fwd6': '30', 'decoder_bwd6': '93', 'decoder_occ6': '183',
'decoder_fwd5': '45', 'decoder_bwd5': '96', 'decoder_occ5': '164',
'decoder_fwd4': '60', 'decoder_bwd4': '99', 'decoder_occ4': '145',
'decoder_fwd3': '75', 'decoder_bwd3': '102', 'decoder_occ3': '126',
'decoder_fwd2': '90', 'decoder_bwd2': '105', 'decoder_occ2': '109'
}
for key in map.keys():
print("Loading ", key)
self.load(key, map[key])
def normalize(self, ims):
imt = []
for im in ims:
im[:,0,:,:] = im[:,0,:,:] - 0.485 # Red
im[:,1,:,:] = im[:,1,:,:] - 0.456 # Green
im[:,2,:,:] = im[:,2,:,:] - 0.406 # Blue
im[:,0,:,:] = im[:,0,:,:] / 0.229 # Red
im[:,1,:,:] = im[:,1,:,:] / 0.224 # Green
im[:,2,:,:] = im[:,2,:,:] / 0.225 # Blue
imt.append(im)
return imt
def forward(self, im_tar, im_refs):
'''
Arguments:
im_tar : Centre Frame
im_refs : List constaining [Past_Frame, Future_Frame]
'''
im_norm = self.normalize([im_tar] + im_refs)
feat1a = self.conv1a(im_norm[0])
feat2a = self.conv2a(feat1a)
feat3a = self.conv3a(feat2a)
feat4a = self.conv4a(feat3a)
feat5a = self.conv5a(feat4a)
feat6a = self.conv6a(feat5a)
feat1b = self.conv1b(im_norm[2])
feat2b = self.conv2b(feat1b)
feat3b = self.conv3b(feat2b)
feat4b = self.conv4b(feat3b)
feat5b = self.conv5b(feat4b)
feat6b = self.conv6b(feat5b)
feat1c = self.conv1c(im_norm[1])
feat2c = self.conv2c(feat1c)
feat3c = self.conv3c(feat2c)
feat4c = self.conv4c(feat3c)
feat5c = self.conv5c(feat4c)
feat6c = self.conv6c(feat5c)
corr6_fwd = self.corr(feat6a, feat6b)
corr6_fwd = corr6_fwd.index_select(1,self.idx_fwd)
corr6_bwd = self.corr(feat6a, feat6c)
corr6_bwd = corr6_bwd.index_select(1,self.idx_bwd)
corr6 = torch.cat((corr6_fwd, corr6_bwd), 1)
flow6_fwd = self.decoder_fwd6(corr6)
flow6_fwd_up = self.upsample(flow6_fwd)
flow6_bwd = self.decoder_bwd6(corr6)
flow6_bwd_up = self.upsample(flow6_bwd)
feat5b_warped = self.warp(feat5b, 0.625*flow6_fwd_up)
feat5c_warped = self.warp(feat5c, -0.625*flow6_bwd_up)
occ6_feat = torch.cat((corr6, feat6a), 1)
occ6 = self.softmax2d(self.decoder_occ6(occ6_feat))
corr5_fwd = self.corr(feat5a, feat5b_warped)
corr5_fwd = corr5_fwd.index_select(1,self.idx_fwd)
corr5_bwd = self.corr(feat5a, feat5c_warped)
corr5_bwd = corr5_bwd.index_select(1,self.idx_bwd)
corr5 = torch.cat((corr5_fwd, corr5_bwd), 1)
upfeat5_fwd = torch.cat((corr5, feat5a, flow6_fwd_up), 1)
flow5_fwd = self.decoder_fwd5(upfeat5_fwd)
flow5_fwd_up = self.upsample(flow5_fwd)
upfeat5_bwd = torch.cat((corr5, feat5a, flow6_bwd_up),1)
flow5_bwd = self.decoder_bwd5(upfeat5_bwd)
flow5_bwd_up = self.upsample(flow5_bwd)
feat4b_warped = self.warp(feat4b, 1.25*flow5_fwd_up)
feat4c_warped = self.warp(feat4c, -1.25*flow5_bwd_up)
occ5 = self.softmax2d(self.decoder_occ5(upfeat5_fwd))
corr4_fwd = self.corr(feat4a, feat4b_warped)
corr4_fwd = corr4_fwd.index_select(1,self.idx_fwd)
corr4_bwd = self.corr(feat4a, feat4c_warped)
corr4_bwd = corr4_bwd.index_select(1,self.idx_bwd)
corr4 = torch.cat((corr4_fwd, corr4_bwd), 1)
upfeat4_fwd = torch.cat((corr4, feat4a, flow5_fwd_up), 1)
flow4_fwd = self.decoder_fwd4(upfeat4_fwd)
flow4_fwd_up = self.upsample(flow4_fwd)
upfeat4_bwd = torch.cat((corr4, feat4a, flow5_bwd_up),1)
flow4_bwd = self.decoder_bwd4(upfeat4_bwd)
flow4_bwd_up = self.upsample(flow4_bwd)
feat3b_warped = self.warp(feat3b, 2.5*flow4_fwd_up)
feat3c_warped = self.warp(feat3c, -2.5*flow4_bwd_up)
occ4 = self.softmax2d(self.decoder_occ4(upfeat4_fwd))
corr3_fwd = self.corr(feat3a, feat3b_warped)
corr3_fwd = corr3_fwd.index_select(1,self.idx_fwd)
corr3_bwd = self.corr(feat3a, feat3c_warped)
corr3_bwd = corr3_bwd.index_select(1,self.idx_bwd)
corr3 = torch.cat((corr3_fwd, corr3_bwd), 1)
upfeat3_fwd = torch.cat((corr3, feat3a, flow4_fwd_up), 1)
flow3_fwd = self.decoder_fwd3(upfeat3_fwd)
flow3_fwd_up = self.upsample(flow3_fwd)
upfeat3_bwd = torch.cat((corr3, feat3a, flow4_bwd_up),1)
flow3_bwd = self.decoder_bwd3(upfeat3_bwd)
flow3_bwd_up = self.upsample(flow3_bwd)
feat2b_warped = self.warp(feat2b, 5.0*flow3_fwd_up)
feat2c_warped = self.warp(feat2c, -5.0*flow3_bwd_up)
occ3 = self.softmax2d(self.decoder_occ3(upfeat3_fwd))
corr2_fwd = self.corr(feat2a, feat2b_warped)
corr2_fwd = corr2_fwd.index_select(1,self.idx_fwd)
corr2_bwd = self.corr(feat2a, feat2c_warped)
corr2_bwd = corr2_bwd.index_select(1,self.idx_bwd)
corr2 = torch.cat((corr2_fwd, corr2_bwd), 1)
upfeat2_fwd = torch.cat((corr2, feat2a, flow3_fwd_up), 1)
flow2_fwd = self.decoder_fwd2(upfeat2_fwd)
flow2_fwd_up = self.upsample(flow2_fwd)
upfeat2_bwd = torch.cat((corr2, feat2a, flow3_bwd_up),1)
flow2_bwd = self.decoder_bwd2(upfeat2_bwd)
flow2_bwd_up = self.upsample(flow2_bwd)
occ2 = self.softmax2d(self.decoder_occ2(upfeat2_fwd))
flow2_fwd_fullres = 20*self.upsample(flow2_fwd_up)
flow3_fwd_fullres = 10*self.upsample(flow3_fwd_up)
flow4_fwd_fullres = 5*self.upsample(flow4_fwd_up)
flow5_fwd_fullres = 2.5*self.upsample(flow5_fwd_up)
flow6_fwd_fullres = 1.25*self.upsample(flow6_fwd_up)
flow2_bwd_fullres = -20*self.upsample(flow2_bwd_up)
flow3_bwd_fullres = -10*self.upsample(flow3_bwd_up)
flow4_bwd_fullres = -5*self.upsample(flow4_bwd_up)
flow5_bwd_fullres = -2.5*self.upsample(flow5_bwd_up)
flow6_bwd_fullres = -1.25*self.upsample(flow6_bwd_up)
occ2_fullres = F.upsample(occ2, scale_factor=4)
occ3_fullres = F.upsample(occ3, scale_factor=4)
occ4_fullres = F.upsample(occ4, scale_factor=4)
occ5_fullres = F.upsample(occ5, scale_factor=4)
occ6_fullres = F.upsample(occ6, scale_factor=4)
flow_fwd = [flow2_fwd_fullres, flow3_fwd_fullres, flow4_fwd_fullres, flow5_fwd_fullres, flow6_fwd_fullres]
flow_bwd = [flow2_bwd_fullres, flow3_bwd_fullres, flow4_bwd_fullres, flow5_bwd_fullres, flow6_bwd_fullres]
occ = [occ2_fullres, occ3_fullres, occ4_fullres, occ5_fullres, occ6_fullres]
return flow_fwd, flow_bwd, occ
def warp(self, x, flo):
"""
warp an image/tensor (im2) back to im1, according to the optical flow
x: [B, C, H, W] (im2)
flo: [B, 2, H, W] flow
"""
B, C, H, W = x.size()
# mesh grid
xx = torch.arange(0, W).view(1,-1).repeat(H,1)
yy = torch.arange(0, H).view(-1,1).repeat(1,W)
xx = xx.view(1,1,H,W).repeat(B,1,1,1)
yy = yy.view(1,1,H,W).repeat(B,1,1,1)
grid = torch.cat((xx,yy),1).float()
if x.is_cuda:
grid = grid.cuda()
vgrid = Variable(grid) + flo
# scale grid to [-1,1]
vgrid[:,0,:,:] = 2.0*vgrid[:,0,:,:]/max(W-1,1)-1.0
vgrid[:,1,:,:] = 2.0*vgrid[:,1,:,:]/max(H-1,1)-1.0
vgrid = vgrid.permute(0,2,3,1)
output = nn.functional.grid_sample(x, vgrid, padding_mode='border')
mask = torch.autograd.Variable(torch.ones(x.size()), requires_grad=False).cuda()
mask = nn.functional.grid_sample(mask, vgrid)
# if W==128:
# np.save('mask.npy', mask.cpu().data.numpy())
# np.save('warp.npy', output.cpu().data.numpy())
mask[mask.data<0.9999] = 0
mask[mask.data>0] = 1
return output#*mask