% i)
disp_gt[disp_gt == np.inf] = .0
#disp = readPFM('/media/ccjData2/research-projects/GCNet/results/gcnet-F8-RMSp-sfF3k-epo31-4dsConv-k5-testKT15/disp-epo-030/%06d_10.pfm' %i)
disp = readPFM(
'/home/ccj/atten-stereo/results/ganet-sfepo10-kt15epo100/val-30/%06d_10.pfm'
% i)
disp[disp == np.inf] = .0
rst_disp = KT15FalseClr.writeKT15FalseColor(disp).astype(np.uint8)
rst_disp_gt = KT15FalseClr.writeKT15FalseColor(disp_gt).astype(
np.uint8)
show_uint8(rst_disp, title='disp_kt15_false_color')
show_uint8(rst_disp_gt, title='disp_gt_kt15_false_color')
err = np.abs(disp_gt - disp)
rst_err = KT15LogClr.writeKT15ErrorDispLogColor(disp, disp_gt)
show_uint8(rst_err, title='err_kt15_log_color')
show(err, title='err gray color')
time_elapsed = time.time() - since
print('Training complete in {:.0f}s'.format(time_elapsed))
# see the GPU runtime counting code from https://github.com/sacmehta/ESPNet/issues/57;
def computeTime(model, device='cuda'):
inputs = torch.randn(1, 3, 512, 1024)
if device == 'cuda':
model = model.cuda()
inputs = inputs.cuda()
model.eval()
i = 0
time_spent = []
def __postprocessing_mem(self, imgl, imgr, cost, direction, display=False):
post.InitCUDA()
start = time.time()
cross_array_init = np.zeros(
(4, imgl.shape[0], imgl.shape[1])).astype(np.float32)
imgl_d = post.CUDA_float_array(imgl)
imgr_d = post.CUDA_float_array(imgr)
leftc_d = post.CUDA_float_array(cross_array_init)
rightc_d = post.CUDA_float_array(cross_array_init)
post.cross(imgl_d, leftc_d, self.__L1, self.__tau1)
post.cross(imgr_d, rightc_d, self.__L1, self.__tau1)
cost_d = post.CUDA_double_array(cost)
temp_cost_d = post.CUDA_double_array(cost)
post.swap_axis_back(cost_d, temp_cost_d)
post.CUDA_copy_double(temp_cost_d, cost_d)
for i in range(0, self.__cbca_i1):
post.cbca(leftc_d, rightc_d, cost_d, temp_cost_d, direction)
post.CUDA_copy_double(temp_cost_d, cost_d)
if display:
print "CBCA 1"
cc = cost_d.get_array()
ddisp = np.argmin(cc, axis=0)
pfm.show(ddisp)
post.swap_axis(cost_d, temp_cost_d)
post.CUDA_copy_double(temp_cost_d, cost_d)
tmp = np.zeros((cost.shape[1], cost.shape[2]))
tmp_d = post.CUDA_double_array(tmp)
for i in range(0, self.__sgm_i):
temp_cost_d.set_to_zero()
tmp_d.set_to_zero()
post.sgm(imgl_d, imgr_d, cost_d, temp_cost_d, tmp_d, self.__pi1,
self.__pi2, self.__tau_so, self.__alpha1, self.__sgm_q1,
self.__sgm_q2, direction)
post.CUDA_divide_double(temp_cost_d, 4)
post.CUDA_copy_double(temp_cost_d, cost_d)
post.swap_axis_back(cost_d, temp_cost_d)
post.CUDA_copy_double(temp_cost_d, cost_d)
if display:
print "SGM"
cc = cost_d.get_array()
ddisp = np.argmin(cc, axis=0)
pfm.show(ddisp)
for i in range(0, self.__cbca_i2):
post.cbca(leftc_d, rightc_d, cost_d, temp_cost_d, direction)
post.CUDA_copy_double(temp_cost_d, cost_d)
if display:
print "CBCA2"
cc = cost_d.get_array()
ddisp = np.argmin(cc, axis=0)
pfm.show(ddisp)
cc = cost_d.get_array()
disp_l = np.argmin(cc, axis=0).astype(np.float32)
out_s = cc.shape[0]
del cc
disp_d = post.CUDA_float_array(disp_l)
disp_temp = post.CUDA_float_array(disp_l)
post.subpixel_enchancement(disp_d, cost_d, disp_temp)
post.CUDA_copy_float(disp_temp, disp_d)
if display:
disp = disp_d.get_array()
print "Subpixel"
pfm.show(disp)
for i in range(0, self.__median_i):
disp = post.median2d(disp_d, disp_temp, self.__median_w)
post.CUDA_copy_float(disp_temp, disp_d)
if display:
disp = disp_d.get_array()
print "Median"
pfm.show(disp)
post.mean2d(disp_d, disp_temp, self.__gaussian(self.__blur_sigma),
self.__blur_t)
post.CUDA_copy_float(disp_temp, disp_d)
if display:
disp = disp_d.get_array()
print "Bilateral"
pfm.show(disp)
disp = disp_d.get_array()
end = time.time()
print(end - start)
return disp