def val_kitti(val_dataloader, model):
"""
Validate the model.
"""
smoothed_absRel = SmoothedValue(len(val_dataloader))
smoothed_silog = SmoothedValue(len(val_dataloader))
smoothed_silog2 = SmoothedValue(len(val_dataloader))
smoothed_criteria = {
'err_absRel': smoothed_absRel,
'err_silog': smoothed_silog,
'err_silog2': smoothed_silog2
}
for i, data in enumerate(val_dataloader):
pred_depth = model.module.inference_kitti(data)
smoothed_criteria = validate_err_kitti(pred_depth['b_fake'],
data['B_raw'],
smoothed_criteria)
print(
np.sqrt(smoothed_criteria['err_silog2'].GetGlobalAverageValue() - (
smoothed_criteria['err_silog'].GetGlobalAverageValue())**2))
val_metrics = {
'abs_rel':
smoothed_criteria['err_absRel'].GetGlobalAverageValue(),
'silog':
np.sqrt(smoothed_criteria['err_silog2'].GetGlobalAverageValue() -
(smoothed_criteria['err_silog'].GetGlobalAverageValue())**2)
}
print(val_metrics)
return val_metrics
def val(val_dataloader, model):
"""
Validate the model.
"""
smoothed_absRel = SmoothedValue(len(val_dataloader))
smoothed_whdr = SmoothedValue(len(val_dataloader))
smoothed_criteria = {
'err_absRel': smoothed_absRel,
'err_whdr': smoothed_whdr
}
for i, data in enumerate(val_dataloader):
out = model.module.inference(data)
pred_depth = torch.squeeze(out['pred'])
pred_depth_resize = cv2.resize(pred_depth.cpu().numpy(),
(torch.squeeze(data['B_raw']).shape[1],
torch.squeeze(data['B_raw']).shape[0]))
pred_depth_metric = recover_metric_depth(pred_depth_resize,
data['B_raw'])
smoothed_criteria = validate_rel_depth_err(pred_depth_metric,
data['B_raw'],
smoothed_criteria,
scale=1.0)
return {
'abs_rel': smoothed_criteria['err_absRel'].GetGlobalAverageValue(),
'whdr': smoothed_criteria['err_whdr'].GetGlobalAverageValue()
}
def val(val_dataloader, model):
"""
Validate the model.
"""
smoothed_absRel = SmoothedValue(len(val_dataloader))
smoothed_whdr = SmoothedValue(len(val_dataloader))
smoothed_criteria = {
'err_absRel': smoothed_absRel,
'err_whdr': smoothed_whdr
}
for i, data in enumerate(val_dataloader):
invalid_side = data['invalid_side'][0]
out = model.module.inference(data)
pred_depth = torch.squeeze(out['b_fake'])
pred_depth = pred_depth[invalid_side[0]:pred_depth.size(0) -
invalid_side[1],
invalid_side[2]:pred_depth.size(1) -
invalid_side[3]]
pred_depth_resize = resize_image(pred_depth,
torch.squeeze(data['B_raw']).shape)
pred_depth_metric = recover_metric_depth(pred_depth_resize,
data['B_raw'])
smoothed_criteria = validate_rel_depth_err(pred_depth_metric,
data['B_raw'],
smoothed_criteria,
scale=1.0)
return {
'abs_rel': smoothed_criteria['err_absRel'].GetGlobalAverageValue(),
'whdr': smoothed_criteria['err_whdr'].GetGlobalAverageValue()
}
def __init__(self, args, log_period=20, tensorboard_logger=None):
# Output logging period in SGD iterations
self.args = args
self.log_period = log_period
self.tblogger = tensorboard_logger
self.tb_ignored_keys = ['iter', 'eta', 'epoch', 'time']
self.iter_timer = Timer()
# Window size for smoothing tracked values (with median filtering)
self.filter_size = log_period
def create_smoothed_value():
return SmoothedValue(self.filter_size)
self.smoothed_losses = defaultdict(create_smoothed_value)
self.smoothed_metrics = defaultdict(create_smoothed_value)
self.smoothed_total_loss = SmoothedValue(self.filter_size)
def val(val_dataloader, model):
"""
Validate the model.
"""
smoothed_absRel = SmoothedValue(len(val_dataloader))
smoothed_criteria = {'err_absRel': smoothed_absRel}
for i, data in enumerate(val_dataloader):
invalid_side = data['invalid_side'][0]
out = model.module.inference(data)
pred_depth = torch.squeeze(out['b_fake'])
pred_depth = pred_depth[invalid_side[0]:pred_depth.size(0) - invalid_side[1], :]
pred_depth = pred_depth / data['ratio'].cuda()
pred_depth = resize_image(pred_depth, torch.squeeze(data['B_raw']).shape)
smoothed_criteria = validate_err(pred_depth, data['B_raw'], smoothed_criteria, (45, 471, 41, 601))
return {'abs_rel': smoothed_criteria['err_absRel'].GetGlobalAverageValue()}
def test(model_path):
test_args = TestOptions().parse()
test_args.thread = 0
test_args.batchsize = 1
merge_cfg_from_file(test_args)
data_loader = CustomerDataLoader(test_args)
test_datasize = len(data_loader)
logger.info('{:>15}: {:<30}'.format('test_data_size', test_datasize))
# load model
model = MetricDepthModel()
model.eval()
test_args.load_ckpt = model_path
# load checkpoint
if test_args.load_ckpt:
load_ckpt(test_args, model)
model.cuda()
# model = torch.nn.DataParallel(model)
# test
smoothed_absRel = SmoothedValue(test_datasize)
smoothed_rms = SmoothedValue(test_datasize)
smoothed_logRms = SmoothedValue(test_datasize)
smoothed_squaRel = SmoothedValue(test_datasize)
smoothed_silog = SmoothedValue(test_datasize)
smoothed_silog2 = SmoothedValue(test_datasize)
smoothed_log10 = SmoothedValue(test_datasize)
smoothed_delta1 = SmoothedValue(test_datasize)
smoothed_delta2 = SmoothedValue(test_datasize)
smoothed_delta3 = SmoothedValue(test_datasize)
smoothed_whdr = SmoothedValue(test_datasize)
smoothed_criteria = {
'err_absRel': smoothed_absRel,
'err_squaRel': smoothed_squaRel,
'err_rms': smoothed_rms,
'err_silog': smoothed_silog,
'err_logRms': smoothed_logRms,
'err_silog2': smoothed_silog2,
'err_delta1': smoothed_delta1,
'err_delta2': smoothed_delta2,
'err_delta3': smoothed_delta3,
'err_log10': smoothed_log10,
'err_whdr': smoothed_whdr
}
for i, data in enumerate(data_loader):
out = model.inference(data)
pred_depth = torch.squeeze(out['b_fake'])
img_path = data['A_paths']
invalid_side = data['invalid_side'][0]
pred_depth = pred_depth[invalid_side[0]:pred_depth.size(0) -
invalid_side[1], :]
pred_depth = pred_depth / data['ratio'].cuda() # scale the depth
pred_depth = resize_image(pred_depth,
torch.squeeze(data['B_raw']).shape)
smoothed_criteria = evaluate_err(pred_depth,
data['B_raw'],
smoothed_criteria,
mask=(45, 471, 41, 601),
scale=10.)
# save images
model_name = test_args.load_ckpt.split('/')[-1].split('.')[0]
image_dir = os.path.join(cfg.ROOT_DIR, './evaluation',
cfg.MODEL.ENCODER, model_name)
if not os.path.exists(image_dir):
os.makedirs(image_dir)
img_name = img_path[0].split('/')[-1]
#plt.imsave(os.path.join(image_dir, 'd_' + img_name), pred_depth, cmap='rainbow')
#cv2.imwrite(os.path.join(image_dir, 'rgb_' + img_name), data['A_raw'].numpy().squeeze())
# print('processing (%04d)-th image... %s' % (i, img_path))
# print("###############absREL ERROR: %f", smoothed_criteria['err_absRel'].GetGlobalAverageValue())
# print("###############silog ERROR: %f", np.sqrt(smoothed_criteria['err_silog2'].GetGlobalAverageValue() - (
# smoothed_criteria['err_silog'].GetGlobalAverageValue()) ** 2))
# print("###############log10 ERROR: %f", smoothed_criteria['err_log10'].GetGlobalAverageValue())
# print("###############RMS ERROR: %f", np.sqrt(smoothed_criteria['err_rms'].GetGlobalAverageValue()))
# print("###############delta_1 ERROR: %f", smoothed_criteria['err_delta1'].GetGlobalAverageValue())
# print("###############delta_2 ERROR: %f", smoothed_criteria['err_delta2'].GetGlobalAverageValue())
# print("###############delta_3 ERROR: %f", smoothed_criteria['err_delta3'].GetGlobalAverageValue())
# print("###############squaRel ERROR: %f", smoothed_criteria['err_squaRel'].GetGlobalAverageValue())
# print("###############logRms ERROR: %f", np.sqrt(smoothed_criteria['err_logRms'].GetGlobalAverageValue()))
f.write("tested model:" + model_path)
f.write('\n')
f.write("###############absREL ERROR:" +
str(smoothed_criteria['err_absRel'].GetGlobalAverageValue()))
f.write('\n')
f.write("###############silog ERROR:" + str(
np.sqrt(smoothed_criteria['err_silog2'].GetGlobalAverageValue() -
(smoothed_criteria['err_silog'].GetGlobalAverageValue())**2)))
f.write('\n')
f.write("###############log10 ERROR:" +
str(smoothed_criteria['err_log10'].GetGlobalAverageValue()))
f.write('\n')
f.write("###############RMS ERROR:" +
str(np.sqrt(smoothed_criteria['err_rms'].GetGlobalAverageValue())))
f.write('\n')
f.write("###############delta_1 ERROR:" +
str(smoothed_criteria['err_delta1'].GetGlobalAverageValue()))
f.write('\n')
f.write("###############delta_2 ERROR:" +
str(smoothed_criteria['err_delta2'].GetGlobalAverageValue()))
f.write('\n')
f.write("###############delta_3 ERROR:" +
str(smoothed_criteria['err_delta3'].GetGlobalAverageValue()))
f.write('\n')
f.write("###############squaRel ERROR:" +
str(smoothed_criteria['err_squaRel'].GetGlobalAverageValue()))
f.write('\n')
f.write(
"###############logRms ERROR:" +
str(np.sqrt(smoothed_criteria['err_logRms'].GetGlobalAverageValue())))
f.write('\n')
f.write(
'-----------------------------------------------------------------------------'
)
f.write('\n')
data_loader = CustomerDataLoader(test_args)
test_datasize = len(data_loader)
logger.info('{:>15}: {:<30}'.format('test_data_size', test_datasize))
# load model
model = MetricDepthModel()
model.eval()
# load checkpoint
if test_args.load_ckpt:
load_ckpt(test_args, model)
model.cuda()
model = torch.nn.DataParallel(model)
# test
smoothed_absRel = SmoothedValue(test_datasize)
smoothed_rms = SmoothedValue(test_datasize)
smoothed_logRms = SmoothedValue(test_datasize)
smoothed_squaRel = SmoothedValue(test_datasize)
smoothed_silog = SmoothedValue(test_datasize)
smoothed_silog2 = SmoothedValue(test_datasize)
smoothed_log10 = SmoothedValue(test_datasize)
smoothed_delta1 = SmoothedValue(test_datasize)
smoothed_delta2 = SmoothedValue(test_datasize)
smoothed_delta3 = SmoothedValue(test_datasize)
smoothed_whdr = SmoothedValue(test_datasize)
smoothed_criteria = {
'err_absRel': smoothed_absRel,
'err_squaRel': smoothed_squaRel,
'err_rms': smoothed_rms,
def create_smoothed_value():
return SmoothedValue(self.filter_size)
class TrainingStats(object):
"""Track vital training statistics."""
def __init__(self, args, log_period=20, tensorboard_logger=None):
# Output logging period in SGD iterations
self.args = args
self.log_period = log_period
self.tblogger = tensorboard_logger
self.tb_ignored_keys = ['iter', 'eta', 'epoch', 'time']
self.iter_timer = Timer()
# Window size for smoothing tracked values (with median filtering)
self.filter_size = 20
def create_smoothed_value():
return SmoothedValue(self.filter_size)
self.smoothed_losses = defaultdict(create_smoothed_value)
# self.smoothed_metrics = defaultdict(create_smoothed_value)
self.smoothed_total_loss = SmoothedValue(self.filter_size)
def IterTic(self):
self.iter_timer.tic()
def IterToc(self):
return self.iter_timer.toc(average=False)
def ResetIterTimer(self):
self.iter_timer.reset()
def UpdateIterStats(self, loss):
"""Update tracked iteration statistics."""
total_loss = 0
for k in loss:
# all losses except the total loss: loss['all']
if k != 'total_loss':
self.smoothed_losses[k].AddValue(float(loss[k]))
total_loss += loss['total_loss']
self.smoothed_total_loss.AddValue(float(total_loss))
def LogIterStats(self, cur_iter, cur_epoch, optimizer, val_err={}):
"""Log the tracked statistics."""
if (cur_iter % self.log_period == 0):
stats = self.GetStats(cur_iter, cur_epoch, optimizer, val_err)
log_stats(stats, self.args)
if self.tblogger:
self.tb_log_stats(stats, cur_iter)
def tb_log_stats(self, stats, cur_iter):
"""Log the tracked statistics to tensorboard"""
for k in stats:
if k not in self.tb_ignored_keys:
v = stats[k]
if isinstance(v, dict):
self.tb_log_stats(v, cur_iter)
else:
self.tblogger.add_scalar(k, v, cur_iter)
def GetStats(self, cur_iter, cur_epoch, optimizer, val_err={}):
eta_seconds = self.iter_timer.average_time * (cfg.TRAIN.MAX_ITER -
cur_iter)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
stats = OrderedDict(
iter=cur_iter, # 1-indexed
time=self.iter_timer.average_time,
eta=eta,
total_loss=self.smoothed_total_loss.GetMedianValue(),
epoch=cur_epoch,
)
optimizer_state_dict = optimizer.state_dict()
lr = {}
for i in range(len(optimizer_state_dict['param_groups'])):
lr_name = 'group%d_lr' % i
lr[lr_name] = optimizer_state_dict['param_groups'][i]['lr']
stats['lr'] = OrderedDict(lr)
for k, v in self.smoothed_losses.items():
stats[k] = OrderedDict([(k, v.GetMedianValue())])
stats['val_err'] = OrderedDict(val_err)
return stats
def val_kitti(val_dataloader, model):
"""
Validate the model.
"""
smoothed_absRel = SmoothedValue(len(val_dataloader))
smoothed_silog = SmoothedValue(len(val_dataloader))
smoothed_silog2 = SmoothedValue(len(val_dataloader))
smoothed_criteria = {
'err_absRel': smoothed_absRel,
'err_silog': smoothed_silog,
'err_silog2': smoothed_silog2
}
# rois-level
rois_smoothed_absRel = SmoothedValue(len(val_dataloader))
rois_smoothed_silog = SmoothedValue(len(val_dataloader))
rois_smoothed_silog2 = SmoothedValue(len(val_dataloader))
rois_smoothed_criteria = {
'err_absRel': rois_smoothed_absRel,
'err_silog': rois_smoothed_silog,
'err_silog2': rois_smoothed_silog2
}
# bg-level
bg_smoothed_absRel = SmoothedValue(len(val_dataloader))
bg_smoothed_silog = SmoothedValue(len(val_dataloader))
bg_smoothed_silog2 = SmoothedValue(len(val_dataloader))
bg_smoothed_criteria = {
'err_absRel': bg_smoothed_absRel,
'err_silog': bg_smoothed_silog,
'err_silog2': bg_smoothed_silog2
}
for i, data in enumerate(val_dataloader):
pred_depth = model.module.inference_kitti(data)
smoothed_criteria = validate_err_kitti(pred_depth['b_fake'],
data['B_raw'],
smoothed_criteria)
rois_smoothed_criteria = validate_err_kitti(pred_depth['b_fake'],
data['B_raw_rois'],
rois_smoothed_criteria)
bg_smoothed_criteria = validate_err_kitti(pred_depth['b_fake'],
data['B_raw_bg'],
bg_smoothed_criteria)
#print(np.sqrt(smoothed_criteria['err_silog2'].GetGlobalAverageValue() - (smoothed_criteria['err_silog'].GetGlobalAverageValue())**2))
val_metrics = {
'abs_rel':
smoothed_criteria['err_absRel'].GetGlobalAverageValue(),
'silog':
np.sqrt(smoothed_criteria['err_silog2'].GetGlobalAverageValue() -
(smoothed_criteria['err_silog'].GetGlobalAverageValue())**2)
}
rois_val_metrics = {
'abs_rel':
rois_smoothed_criteria['err_absRel'].GetGlobalAverageValue(),
'silog':
np.sqrt(
rois_smoothed_criteria['err_silog2'].GetGlobalAverageValue() -
(rois_smoothed_criteria['err_silog'].GetGlobalAverageValue())**2)
}
bg_val_metrics = {
'abs_rel':
bg_smoothed_criteria['err_absRel'].GetGlobalAverageValue(),
'silog':
np.sqrt(bg_smoothed_criteria['err_silog2'].GetGlobalAverageValue() -
(bg_smoothed_criteria['err_silog'].GetGlobalAverageValue())**2)
}
print("global: ", val_metrics)
print("roi: ", rois_val_metrics)
print("bg: ", bg_val_metrics)
return val_metrics
