def trainImageFilter(scene, benchmark=False):
expr_dir = os.path.join(opt.output, opt.name)
if not os.path.isdir(expr_dir):
os.makedirs(expr_dir)
trainer = Trainer(opt, scene)
trainer.setup(opt, scene.cloud)
logInterval = math.floor(1+sum(opt.steps)//20)
renderForwardTime = 0.0
lossTime = 0.0
optimizerStep = 0.0
with torch.autograd.detect_anomaly():
with open(os.path.join(expr_dir, "loss.csv"), 'w') as loss_log:
for c in range(opt.cycles):
# creat new reference
tb = c*sum(opt.steps)+opt.startingStep
te = (c+1)*sum(opt.steps)+opt.startingStep
t = tb
with torch.no_grad():
trainer.create_reference(scene)
trainer.initiate_cycle()
for i, pair in enumerate(zip(trainer.groundtruths, trainer.predictions)):
post, pre = pair
diff = post - pre
saveAsPng(pre.cpu(), os.path.join(expr_dir, 't%03d_cam%d_init.png' % (t, i)))
saveAsPng(post.cpu(), os.path.join(expr_dir, 't%03d_cam%d_gt.png' % (t, i)))
saveAsPng(diff.cpu(), os.path.join(expr_dir, 't%03d_cam%d_diff.png' % (t, i)))
for t in range(tb, te):
if t % logInterval == 0 and not benchmark:
writeScene(scene, os.path.join(expr_dir, 't%03d' % t +
'_values.json'), os.path.join(expr_dir, 't%03d' % t + '.ply'))
trainer.optimize_parameters()
if t % logInterval == 0 and not benchmark:
for i, prediction in enumerate(trainer.predictions):
saveAsPng(prediction.detach().cpu()[0], os.path.join(expr_dir, 't%03d_cam%d' % (t, i) + ".png"))
if not benchmark:
loss_str = ",".join(["%.3f" % (100*v) for v in trainer.loss_image])
reg_str = ",".join(["%.3f" % (100*v) for v in trainer.loss_reg])
entries = [trainer.modifier] + [loss_str] + [reg_str]
loss_log.write(",".join(entries)+"\n")
print("{:03d} {}: lr {} loss ({}) \n : reg ({})".format(
t, trainer.modifier, trainer.lr, loss_str, reg_str))
trainer.finish_cycle()
writeScene(scene, os.path.join(expr_dir, 'final_scene.json'),
os.path.join(expr_dir, 'final_cloud.ply'))
def trainShapeOnImage(scene, refScene, opt, baseline=False):
expr_dir = os.path.join(opt.output, opt.name)
if not os.path.isdir(expr_dir):
os.makedirs(expr_dir)
trainer = Trainer(opt, scene)
trainer.setup(opt, scene.cloud)
logInterval = math.floor(1+sum(opt.steps)//20)
renderForwardTime = 0.0
lossTime = 0.0
optimizerStep = 0.0
log_variables = {}
writeScene(refScene, os.path.join(expr_dir, 't000_scene_gt.json'), os.path.join(expr_dir, "gt.ply"))
with open(expr_dir + "/loss.csv", 'w') as loss_log:
for c in range(opt.cycles):
# creat new reference
tb = c*sum(opt.steps)+opt.startingStep
te = (c+1)*sum(opt.steps)+opt.startingStep
t = tb
with torch.no_grad():
trainer.create_reference(refScene)
writeScene(refScene, os.path.join(
expr_dir, 't%03d_scene_gt.json' % t))
writeCameras(refScene, os.path.join(
expr_dir, 't%03d_cameras.ply' % t))
for i, gt in enumerate(trainer.groundtruths):
saveAsPng(gt.cpu()[0], os.path.join(
expr_dir, 't%03d_cam%d_gt.png' % (t, i)))
trainer.initiate_cycle()
for t in range(tb, te):
if t % logInterval == 0:
writeScene(scene, os.path.join(
expr_dir, 't%03d_scene.json' % t), os.path.join(expr_dir, "t%03d.ply" % t))
trainer.optimize_parameters()
for k in tmp_saved_v:
if k == "renderable_idx":
continue
if k not in log_variables:
log_variables[k] = tmp_saved_v[k].detach().numpy()
else:
log_variables[k] = np.concatenate(
[log_variables[k], tmp_saved_v[k].detach().numpy()], axis=0)
if t % logInterval == 0:
for i, prediction in enumerate(trainer.predictions):
saveAsPng(prediction.detach().cpu()[0], os.path.join(
expr_dir, 't%03d_cam%d' % (t, i) + ".png"))
loss_str = ",".join(
["%.3f" % v for v in trainer.loss_image])
reg_str = ",".join(["%.3f" % v for v in trainer.loss_reg])
entries = [trainer.modifier] + [loss_str] + [reg_str]
loss_log.write(",".join(entries)+"\n")
print("{:03d} {}: lr {} loss ({}) \n : reg ({})".format(
t, trainer.modifier, trainer.lr, loss_str, reg_str))
trainer.finish_cycle()
writeScene(scene, os.path.join(expr_dir, 'final_scene.json'),
os.path.join(expr_dir, 'final_cloud.ply'))
np.save(os.path.join(expr_dir, "log_variables"), log_variables)
def trainShapeOnImage(scene, refScene, opt, baseline=False, benchmark=False):
expr_dir = os.path.join(opt.output, opt.name)
if not os.path.isdir(expr_dir):
os.makedirs(expr_dir)
trainer = Trainer(opt, scene)
trainer.setup(opt, scene.cloud)
logInterval = (1+sum(opt.steps))//20
renderForwardTime = 0.0
lossTime = 0.0
optimizerStep = 0.0
with open(os.path.join(expr_dir, "loss.csv"), 'w') as loss_log:
learnTick = time.time()
for c in range(opt.cycles):
# creat new reference
tb = c*sum(opt.steps)+opt.startingStep
te = (c+1)*sum(opt.steps)+opt.startingStep
t = tb
with torch.no_grad():
# render reference
trainer.create_reference(refScene)
# render prediction
maxDiffs = []
selectedCameras = []
for i in range(len(refScene.cameras)):
trainer.forward(i)
prediction = trainer.predictions[i]
maxDiff, selectedCamera = viewFromError(opt.genCamera, trainer.groundtruths[i][0], prediction.detach()[0],
trainer.model._localPoints.detach(), trainer.model._projPoints.detach(),
trainer.model,
offset=opt.camOffset*(0.997**c))
maxDiffs.append(maxDiff)
selectedCameras.append(selectedCamera)
maxId = torch.stack(maxDiffs, dim=0).argmax()
selectedCamera = selectedCameras[maxId]
# render again
trainer.create_reference(refScene, selectedCamera)
writeScene(refScene, os.path.join(expr_dir, 't%03d_scene_gt.json' % t))
writeCameras(refScene, os.path.join(expr_dir, 't%03d_cameras.ply' % t))
for i, gt in enumerate(trainer.groundtruths):
if gt is not None:
saveAsPng(gt.cpu()[0], os.path.join(expr_dir, 't%03d_cam%d_gt.png' % (t, i)))
trainer.initiate_cycle()
for t in range(tb, te):
if t % logInterval == 0 and not benchmark:
writeScene(scene, os.path.join(expr_dir, 't%03d_scene.json' % t), os.path.join(expr_dir, "t%03d.ply" % t))
trainer.optimize_parameters()
if t % logInterval == 0 and not benchmark:
for i, prediction in enumerate(trainer.predictions):
saveAsPng(prediction.detach().cpu()[0], os.path.join(expr_dir, 't%03d_cam%d' % (t, i) + ".png"))
if not benchmark:
loss_str = ",".join(["%.3f" % v for v in trainer.loss_image])
reg_str = ",".join(["%.3f" % v for v in trainer.loss_reg])
entries = [trainer.modifier] + [loss_str] + [reg_str]
loss_log.write(",".join(entries)+"\n")
print("{:03d} {}: lr {} loss ({}) \n : reg ({})".format(
t, trainer.modifier, trainer.lr, loss_str, reg_str))
trainer.finish_cycle()
# outlier removal
with torch.no_grad():
# re-project
scores = []
for i, gt in enumerate(trainer.groundtruths):
if gt is None:
continue
trainer.model.setCamera(i)
trainer.model.convertToCameraSpace()
projectedPoints = trainer.model.camera.projectPoints(trainer.model.cameraPoints)
score = removeOutlier(gt, projectedPoints, sigma=100)
saveAsPng(gt.cpu()[0], os.path.join(expr_dir, 'clean_gt_cam%d.png' % i))
save_ply_property(trainer.model.localPoints.cpu()[0].detach(), 1-score.cpu(), os.path.join(expr_dir, 'clean_score_cam % d.ply' % i), property_max=1,
normals=trainer.model.localNormals.detach().cpu()[0], cmap_name="gnuplot2")
rendered = trainer.model.render()[0]
saveAsPng(rendered.cpu(), os.path.join(expr_dir, 'clean_pred_cam%d.png' % i))
scores.append(score)
scores = torch.stack(scores, dim=0)
scores = torch.prod(scores, dim=0)
_, indices = torch.topk(scores, int(trainer.model.localPoints.shape[1]*0.99), dim=0)
# _, indices = torch.topk(score, int(trainer.model.localPoints.shape[1]*0.99), dim=0)
newPoints = torch.index_select(trainer.model.localPoints.data, 1, indices)
newNormals = torch.index_select(trainer.model.localNormals.data, 1, indices)
newColors = torch.index_select(trainer.model.pointColors.data, 1, indices)
scene.cloud.localPoints = newPoints[0]
scene.cloud.localNormals = newNormals[0]
scene.cloud.color = newColors[0]
writeScene(scene, os.path.join(expr_dir, 'final_scene.json'), os.path.join(expr_dir, 'final_cloud.ply'))