imageReal = data.perturbBG(opt,imageRealData)
imageBGfake = data.perturbBG(opt,imageBGfakeData)
pPertFG = opt.pertFG*tf.random_normal([opt.batchSize,opt.warpDim])
# ------ define GP and D ------
geometric = graph.geometric_multires
discriminator = graph.discriminator
# ------ geometric predictor ------
imageFGwarpAll,pAll,_ = geometric(opt,imageBGfake,imageFGfake,pPertFG)
pWarp = pAll[-1]
# ------ composite image ------
summaryImageTrain = []
summaryImageTest = []
summaryImageTrain.append(util.imageSummary(opt,imageReal,"TRAIN_real",opt.H,opt.W))
summaryImageTest.append(util.imageSummary(opt,imageReal,"TEST_real",opt.H,opt.W))
imageFGwarp = imageFGwarpAll[0]
imageComp = graph.composite(opt,imageBGfake,imageFGwarp)
summaryImageTrain.append(util.imageSummary(opt,imageComp,"TRAIN_compST{0}".format(0),opt.H,opt.W))
summaryImageTest.append(util.imageSummary(opt,imageComp,"TEST_compST{0}".format(0),opt.H,opt.W))
alpha = tf.random_uniform(shape=[opt.batchSize,1,1,1])
imageIntp = alpha*imageReal+(1-alpha)*imageComp
# ------ discriminator ------
outComps,outIntps = [],[]
outReal = discriminator(opt,imageReal)
outComp = discriminator(opt,imageComp,reuse=True)
outIntp = discriminator(opt,imageIntp,reuse=True)
# ------ discriminator gradient ------
grad_D_fake = tf.gradients(outIntp,imageIntp)[0]
grad_D_norm = tf.sqrt(tf.reduce_sum(grad_D_fake**2+1e-8,reduction_indices=[1,2,3]))
grad_D_norm_mean = tf.reduce_mean(grad_D_norm)
# ------ define loss (adversarial) ------
loss_D = tf.reduce_mean(outComp)-tf.reduce_mean(outReal)
imageBGreal = tf.placeholder(tf.float32,
shape=[opt.batchSize, opt.H, opt.W, 3])
imageFGreal = tf.placeholder(tf.float32,
shape=[opt.batchSize, opt.H, opt.W, 4])
PH = [imageBGreal, imageFGreal]
# ------ generate perturbation ------
pPert = opt.homoPert * tf.random_normal([opt.batchSize, 8])
# ------ define GP and D ------
geometric = graph.geometric_multires
# ------ geometric predictor ------
imageFGwarpAll, pAll, _ = geometric(opt, imageBGreal, imageFGreal, pPert)
pWarp = pAll[-1]
# ------ composite image ------
summaryImageTrain = []
summaryImageTest = []
imageReal = graph.composite(opt, imageBGreal, imageFGreal)
summaryImageTrain.append(
util.imageSummary(opt, imageReal, "TRAIN_real", opt.H, opt.W))
summaryImageTest.append(
util.imageSummary(opt, imageReal, "TEST_real", opt.H, opt.W))
for l in range(opt.warpN + 1):
imageFGwarp = imageFGwarpAll[l]
imageComp = graph.composite(opt, imageBGreal, imageFGwarp)
summaryImageTrain.append(
util.imageSummary(opt, imageComp, "TRAIN_compST{0}".format(l),
opt.H, opt.W))
summaryImageTest.append(
util.imageSummary(opt, imageComp, "TEST_compST{0}".format(l),
opt.H, opt.W))
# ------ define loss (L2) ------
pGT = tf.zeros_like(pPert)