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')
'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.module.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())
