""" implementation of the PWC-DC network for optical flow estimation by Sun et al., 2018 Jinwei Gu and Zhile Ren """ import torch import torch.nn as nn from torch.autograd import Variable import os #from spatial_correlation_sampler import spatial_correlation_sample import numpy as np __all__ = [ 'pwc_dc_net', 'pwc_dc_net_old' ] from models.flownet2.networks.correlation_package.correlation import CorrelationFunction, Correlation from models.flownet2.networks.resample2d_package.resample2d import Resample2d from trainer.networks import register_model def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), nn.LeakyReLU(0.1)) def predict_flow(in_planes): return nn.Conv2d(in_planes,2,kernel_size=3,stride=1,padding=1,bias=True) def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1): return nn.ConvTranspose2d(in_planes, out_planes, kernel_size, stride, padding, bias=True) class PWCDCNet(nn.Module): """ PWC-DC net. add dilation convolution and densenet connections """ def __init__(self, md=4, pretrained=False): """ input: md --- maximum displacement (for correlation. default: 4), after warpping """ super(PWCDCNet,self).__init__() self.upsample = nn.Upsample(scale_factor=4, mode='bilinear') self.conv1a = conv(3, 16, kernel_size=3, stride=2) self.conv1aa = conv(16, 16, kernel_size=3, stride=1) self.conv1b = conv(16, 16, kernel_size=3, stride=1) self.conv2a = conv(16, 32, kernel_size=3, stride=2) self.conv2aa = conv(32, 32, kernel_size=3, stride=1) self.conv2b = conv(32, 32, kernel_size=3, stride=1) self.conv3a = conv(32, 64, kernel_size=3, stride=2) self.conv3aa = conv(64, 64, kernel_size=3, stride=1) self.conv3b = conv(64, 64, kernel_size=3, stride=1) self.conv4a = conv(64, 96, kernel_size=3, stride=2) self.conv4aa = conv(96, 96, kernel_size=3, stride=1) self.conv4b = conv(96, 96, kernel_size=3, stride=1) self.conv5a = conv(96, 128, kernel_size=3, stride=2) self.conv5aa = conv(128,128, kernel_size=3, stride=1) self.conv5b = conv(128,128, kernel_size=3, stride=1) self.conv6aa = conv(128,196, kernel_size=3, stride=2) self.conv6a = conv(196,196, kernel_size=3, stride=1) self.conv6b = conv(196,196, kernel_size=3, stride=1) #self.corr = Correlation(padding=md, kernel_size=1, patch_size=md, stride=1) self.corr = Correlation(pad_size=md, kernel_size=1, max_displacement=md, stride1=1, stride2=1, corr_multiply=1) self.leakyRELU = nn.LeakyReLU(0.1) nd = (2*md+1)**2 dd = np.cumsum([128,128,96,64,32]) od = nd self.conv6_0 = conv(od, 128, kernel_size=3, stride=1) self.conv6_1 = conv(od+dd[0],128, kernel_size=3, stride=1) self.conv6_2 = conv(od+dd[1],96, kernel_size=3, stride=1) self.conv6_3 = conv(od+dd[2],64, kernel_size=3, stride=1) self.conv6_4 = conv(od+dd[3],32, kernel_size=3, stride=1) self.predict_flow6 = predict_flow(od+dd[4]) self.deconv6 = deconv(2, 2, kernel_size=4, stride=2, padding=1) self.upfeat6 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) od = nd+128+4 self.conv5_0 = conv(od, 128, kernel_size=3, stride=1) self.conv5_1 = conv(od+dd[0],128, kernel_size=3, stride=1) self.conv5_2 = conv(od+dd[1],96, kernel_size=3, stride=1) self.conv5_3 = conv(od+dd[2],64, kernel_size=3, stride=1) self.conv5_4 = conv(od+dd[3],32, kernel_size=3, stride=1) self.predict_flow5 = predict_flow(od+dd[4]) self.deconv5 = deconv(2, 2, kernel_size=4, stride=2, padding=1) self.upfeat5 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) od = nd+96+4 self.conv4_0 = conv(od, 128, kernel_size=3, stride=1) self.conv4_1 = conv(od+dd[0],128, kernel_size=3, stride=1) self.conv4_2 = conv(od+dd[1],96, kernel_size=3, stride=1) self.conv4_3 = conv(od+dd[2],64, kernel_size=3, stride=1) self.conv4_4 = conv(od+dd[3],32, kernel_size=3, stride=1) self.predict_flow4 = predict_flow(od+dd[4]) self.deconv4 = deconv(2, 2, kernel_size=4, stride=2, padding=1) self.upfeat4 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) od = nd+64+4 self.conv3_0 = conv(od, 128, kernel_size=3, stride=1) self.conv3_1 = conv(od+dd[0],128, kernel_size=3, stride=1) self.conv3_2 = conv(od+dd[1],96, kernel_size=3, stride=1) self.conv3_3 = conv(od+dd[2],64, kernel_size=3, stride=1) self.conv3_4 = conv(od+dd[3],32, kernel_size=3, stride=1) self.predict_flow3 = predict_flow(od+dd[4]) self.deconv3 = deconv(2, 2, kernel_size=4, stride=2, padding=1) self.upfeat3 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) od = nd+32+4 self.conv2_0 = conv(od, 128, kernel_size=3, stride=1) self.conv2_1 = conv(od+dd[0],128, kernel_size=3, stride=1) self.conv2_2 = conv(od+dd[1],96, kernel_size=3, stride=1) self.conv2_3 = conv(od+dd[2],64, kernel_size=3, stride=1) self.conv2_4 = conv(od+dd[3],32, kernel_size=3, stride=1) self.predict_flow2 = predict_flow(od+dd[4]) self.deconv2 = deconv(2, 2, kernel_size=4, stride=2, padding=1) self.dc_conv1 = conv(od+dd[4], 128, kernel_size=3, stride=1, padding=1, dilation=1) self.dc_conv2 = conv(128, 128, kernel_size=3, stride=1, padding=2, dilation=2) self.dc_conv3 = conv(128, 128, kernel_size=3, stride=1, padding=4, dilation=4) self.dc_conv4 = conv(128, 96, kernel_size=3, stride=1, padding=8, dilation=8) self.dc_conv5 = conv(96, 64, kernel_size=3, stride=1, padding=16, dilation=16) self.dc_conv6 = conv(64, 32, kernel_size=3, stride=1, padding=1, dilation=1) self.dc_conv7 = predict_flow(32) for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d): nn.init.kaiming_normal(m.weight.data, mode='fan_in') if m.bias is not None: m.bias.data.zero_() if pretrained: self.load_state_dict('models/pwc_net_chairs.pth.tar') 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 """ return Resample2d()(x, flo) ''' 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) mask = torch.autograd.Variable(torch.ones(x.size())).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<0.9999] = 0 mask[mask>0] = 1 return output*mask''' def weight_parameters(self): return [param for name, param in self.named_parameters() if 'weight' in name] def bias_parameters(self): return [param for name, param in self.named_parameters() if 'bias' in name] def forward(self, x): im1 = x[:,:3,:,:] im2 = x[:,3:,:,:] c11 = self.conv1b(self.conv1aa(self.conv1a(im1))) c21 = self.conv1b(self.conv1aa(self.conv1a(im2))) c12 = self.conv2b(self.conv2aa(self.conv2a(c11))) c22 = self.conv2b(self.conv2aa(self.conv2a(c21))) c13 = self.conv3b(self.conv3aa(self.conv3a(c12))) c23 = self.conv3b(self.conv3aa(self.conv3a(c22))) c14 = self.conv4b(self.conv4aa(self.conv4a(c13))) c24 = self.conv4b(self.conv4aa(self.conv4a(c23))) c15 = self.conv5b(self.conv5aa(self.conv5a(c14))) c25 = self.conv5b(self.conv5aa(self.conv5a(c24))) c16 = self.conv6b(self.conv6a(self.conv6aa(c15))) c26 = self.conv6b(self.conv6a(self.conv6aa(c25))) corr6 = self.corr(c16, c26) corr6 = self.leakyRELU(corr6) x = torch.cat((self.conv6_0(corr6), corr6),1) x = torch.cat((self.conv6_1(x), x),1) x = torch.cat((self.conv6_2(x), x),1) x = torch.cat((self.conv6_3(x), x),1) x = torch.cat((self.conv6_4(x), x),1) flow6 = self.predict_flow6(x) up_flow6 = self.deconv6(flow6) up_feat6 = self.upfeat6(x) warp5 = self.warp(c25, up_flow6*0.625) corr5 = self.corr(c15, warp5) corr5 = self.leakyRELU(corr5) x = torch.cat((corr5, c15, up_flow6, up_feat6), 1) x = torch.cat((self.conv5_0(x), x),1) x = torch.cat((self.conv5_1(x), x),1) x = torch.cat((self.conv5_2(x), x),1) x = torch.cat((self.conv5_3(x), x),1) x = torch.cat((self.conv5_4(x), x),1) flow5 = self.predict_flow5(x) up_flow5 = self.deconv5(flow5) up_feat5 = self.upfeat5(x) warp4 = self.warp(c24, up_flow5*1.25) corr4 = self.corr(c14, warp4) corr4 = self.leakyRELU(corr4) x = torch.cat((corr4, c14, up_flow5, up_feat5), 1) x = torch.cat((self.conv4_0(x), x),1) x = torch.cat((self.conv4_1(x), x),1) x = torch.cat((self.conv4_2(x), x),1) x = torch.cat((self.conv4_3(x), x),1) x = torch.cat((self.conv4_4(x), x),1) flow4 = self.predict_flow4(x) up_flow4 = self.deconv4(flow4) up_feat4 = self.upfeat4(x) warp3 = self.warp(c23, up_flow4*2.5) corr3 = self.corr(c13, warp3) corr3 = self.leakyRELU(corr3) x = torch.cat((corr3, c13, up_flow4, up_feat4), 1) x = torch.cat((self.conv3_0(x), x),1) x = torch.cat((self.conv3_1(x), x),1) x = torch.cat((self.conv3_2(x), x),1) x = torch.cat((self.conv3_3(x), x),1) x = torch.cat((self.conv3_4(x), x),1) flow3 = self.predict_flow3(x) up_flow3 = self.deconv3(flow3) up_feat3 = self.upfeat3(x) warp2 = self.warp(c22, up_flow3*5.0) corr2 = self.corr(c12, warp2) corr2 = self.leakyRELU(corr2) x = torch.cat((corr2, c12, up_flow3, up_feat3), 1) x = torch.cat((self.conv2_0(x), x),1) x = torch.cat((self.conv2_1(x), x),1) x = torch.cat((self.conv2_2(x), x),1) x = torch.cat((self.conv2_3(x), x),1) x = torch.cat((self.conv2_4(x), x),1) flow2 = self.predict_flow2(x) x = self.dc_conv4(self.dc_conv3(self.dc_conv2(self.dc_conv1(x)))) flow2 += self.dc_conv7(self.dc_conv6(self.dc_conv5(x))) # flow2 = 20*4*self.upsample(flow2) # flow3 = 20*4*self.upsample(flow3) # flow4 = 20*4*self.upsample(flow4) # flow5 = 20*4*self.upsample(flow5) # flow6 = 20*4*self.upsample(flow6) if self.training: return flow2,flow3,flow4,flow5,flow6 else: return flow2 def pwc(data=None): model = PWCDCNet() if data is not None: if 'state_dict' in data.keys(): model.load_state_dict(data['state_dict']) else: model.load_state_dict(data) return model def pwc_dc_net(path=None): model = PWCDCNet() if path is not None: data = torch.load(path) if 'state_dict' in data.keys(): model.load_state_dict(data['state_dict']) else: model.load_state_dict(data) return model @register_model def register_pwc_humanflow(opt_net, opt): return pwc_dc_net(opt_net['pretrained_path']) if __name__ == '__main__': pwc = pwc_dc_net('../../../experiments/pwc_humanflow.pth') t = torch.randn(1,6,64,64) out = pwc(t) print(out.shape)