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wrapperNYU.py
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wrapperNYU.py
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import torch
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
import models
# Return triplet of predictions, ground-truth and error
def wrapperNYU(dataBatch, opt,
encoder, albedoDecoder, normalDecoder, roughDecoder, depthDecoder ):
# Load data from cpu to gpu
normal_cpu = dataBatch['normal']
normalBatch = Variable(normal_cpu ).cuda()
depth_cpu = dataBatch['depth']
depthBatch = Variable(depth_cpu ).cuda()
seg_cpu = dataBatch['segNormal']
segNormalBatch = Variable( seg_cpu ).cuda()
seg_cpu = dataBatch['segDepth']
segDepthBatch = Variable(seg_cpu ).cuda()
# Load the image from cpu to gpu
im_cpu = (dataBatch['im'] )
imBatch = Variable(im_cpu ).cuda()
if opt.cascadeLevel > 0:
albedoPre_cpu = dataBatch['albedoPre']
albedoPreBatch = Variable(albedoPre_cpu ).cuda()
normalPre_cpu = dataBatch['normalPre']
normalPreBatch = Variable(normalPre_cpu ).cuda()
roughPre_cpu = dataBatch['roughPre']
roughPreBatch = Variable(roughPre_cpu ).cuda()
depthPre_cpu = dataBatch['depthPre']
depthPreBatch = Variable(depthPre_cpu ).cuda()
diffusePre_cpu = dataBatch['diffusePre']
diffusePreBatch = Variable(diffusePre_cpu ).cuda()
specularPre_cpu = dataBatch['specularPre']
specularPreBatch = Variable(specularPre_cpu ).cuda()
if albedoPreBatch.size(2) < opt.imHeight or albedoPreBatch.size(3) < opt.imWidth:
albedoPreBatch = F.interpolate(albedoPreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
if normalPreBatch.size(2) < opt.imHeight or normalPreBatch.size(3) < opt.imWidth :
normalPreBatch = F.interpolate(normalPreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
if roughPreBatch.size(2) < opt.imHeight or roughPreBatch.size(3) < opt.imWidth :
roughPreBatch = F.interpolate(roughPreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
if depthPreBatch.size(2) < opt.imHeight or depthPreBatch.size(3) < opt.imWidth :
depthPreBatch = F.interpolate(depthPreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
# Regress the diffusePred and specular Pred
envRow, envCol = diffusePreBatch.size(2), diffusePreBatch.size(3)
imBatchSmall = F.adaptive_avg_pool2d(imBatch, (envRow, envCol) )
diffusePreBatch, specularPreBatch = models.LSregressDiffSpec(
diffusePreBatch.detach(),
specularPreBatch.detach(),
imBatchSmall,
diffusePreBatch, specularPreBatch )
if diffusePreBatch.size(2) < opt.imHeight or diffusePreBatch.size(3) < opt.imWidth:
diffusePreBatch = F.interpolate(diffusePreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
if specularPreBatch.size(2) < opt.imHeight or specularPreBatch.size(3) < opt.imWidth:
specularPreBatch = F.interpolate(specularPreBatch, [opt.imHeight, opt.imWidth ], mode='bilinear')
# Normalize Albedo and depth
bn, ch, nrow, ncol = albedoPreBatch.size()
albedoPreBatch = albedoPreBatch.view(bn, -1)
albedoPreBatch = albedoPreBatch / torch.clamp(torch.mean(albedoPreBatch, dim=1), min=1e-10).unsqueeze(1) / 3.0
albedoPreBatch = albedoPreBatch.view(bn, ch, nrow, ncol)
bn, ch, nrow, ncol = depthPreBatch.size()
depthPreBatch = depthPreBatch.view(bn, -1)
depthPreBatch = depthPreBatch / torch.clamp(torch.mean(depthPreBatch, dim=1), min=1e-10).unsqueeze(1) / 3.0
depthPreBatch = depthPreBatch.view(bn, ch, nrow, ncol)
########################################################
# Build the cascade network architecture #
if opt.cascadeLevel == 0:
inputBatch = imBatch
elif opt.cascadeLevel > 0:
inputBatch = torch.cat([imBatch, albedoPreBatch,
normalPreBatch, roughPreBatch, depthPreBatch,
diffusePreBatch, specularPreBatch ], dim=1)
# Initial Prediction
x1, x2, x3, x4, x5, x6 = encoder(inputBatch )
albedoPred = 0.5 * (albedoDecoder(imBatch, x1, x2, x3, x4, x5, x6) + 1)
normalPred = normalDecoder(imBatch, x1, x2, x3, x4, x5, x6)
roughPred = roughDecoder(imBatch, x1, x2, x3, x4, x5, x6)
depthPred = 0.5 * (depthDecoder(imBatch, x1, x2, x3, x4, x5, x6) + 1)
normalPred = F.interpolate(normalPred, [normalBatch.size(2), normalBatch.size(3)], mode='bilinear')
depthPred = F.interpolate(depthPred, [depthBatch.size(2), depthBatch.size(3)], mode='bilinear')
depthPred = models.LSregress(depthPred.detach() * segDepthBatch.expand_as(depthPred),
depthBatch * segDepthBatch.expand_as(depthBatch), depthPred)
## Compute Errors
pixelAllNumNormal = (torch.sum(segNormalBatch ).cpu().data).item()
normalErr = torch.sum( (normalPred - normalBatch)
* (normalPred - normalBatch) * segNormalBatch.expand_as(normalBatch) ) / pixelAllNumNormal / 3.0
pixelAllNumDepth = (torch.sum(segDepthBatch ).cpu().data).item()
depthErr = torch.sum( (torch.log(depthPred + 0.1) - torch.log(depthBatch + 0.1 ) )
* ( torch.log(depthPred + 0.1) - torch.log(depthBatch + 0.1) ) * segDepthBatch.expand_as(depthBatch ) ) / pixelAllNumDepth
angleMean = torch.sum(torch.acos( torch.clamp(torch.sum(normalPred * normalBatch, dim=1).unsqueeze(1), -1, 1) ) / np.pi * 180 * segNormalBatch) / pixelAllNumNormal
normalPred_np = normalPred.data.cpu().numpy()
normalBatch_np = normalBatch.data.cpu().numpy()
segNormalBatch_np = segNormalBatch.cpu().numpy()
theta = np.arccos( np.clip(np.sum(normalPred_np * normalBatch_np, axis=1)[:, np.newaxis, :, :], -1, 1) ) / np.pi * 180
angleMean_np = (theta * segNormalBatch_np ) / pixelAllNumNormal
return [albedoPred, None], [normalPred, normalErr, angleMean], \
[roughPred, None ], [depthPred, depthErr], \