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PMSNet stereo disparity 3 pixel error at test phase
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""" | |
KITTI stereo disparity 3 pixel error | |
REFERENCES: | |
* [PMSNet code](https://github.com/JiaRenChang/PSMNet/blob/b6520ff9b77168e3dfe3485f8bf6a2a798361d85/finetune.py) | |
* [type converter](https://github.com/traveller59/torchplus/blob/master/tools.py) | |
MATLAB code: | |
``` | |
% disp_error.m | |
function d_err = disp_error (D_gt,D_est,tau) | |
E = abs(D_gt-D_est); | |
n_err = length(find(D_gt>0 & E>tau(1) & E./abs(D_gt)>tau(2))); | |
n_total = length(find(D_gt>0)); | |
d_err = n_err/n_total; | |
%main.m | |
% error threshold | |
tau = [3 0.05]; | |
% stereo demo | |
disp('Load and show disparity map ... '); | |
D_est = disp_read('data/disp_est.png'); | |
D_gt = disp_read('data/disp_gt.png'); | |
d_err = disp_error(D_gt,D_est,tau); | |
``` | |
So, we should also include the `equal` of both comparison | |
FrameWork: PyTorch | |
""" | |
import torch | |
import numpy as np | |
# [type converter](https://github.com/traveller59/torchplus/blob/master/tools.py) | |
def np_dtype_to_torch(dtype): | |
type_map = { | |
np.dtype(np.float16): torch.HalfTensor, | |
np.dtype(np.float32): torch.FloatTensor, | |
np.dtype(np.float64): torch.DoubleTensor, | |
np.dtype(np.int32): torch.IntTensor, | |
np.dtype(np.int64): torch.LongTensor, | |
np.dtype(np.uint8): torch.ByteTensor, | |
} | |
return type_map[dtype] | |
def to_tensor(arg): | |
if isinstance(arg, np.ndarray): | |
return torch.from_numpy(arg).type(np_dtype_to_torch(arg.dtype)) | |
elif isinstance(arg, (list, tuple)): | |
arg = np.array(arg) | |
return torch.from_numpy(arg).type(np_dtype_to_torch(arg.dtype)) | |
else: | |
raise ValueError("unsupported arg type.") | |
def calc_3pe(disp_pred, disp_true, original=False): | |
assert disp_pred.shape == disp_true.shape | |
assert disp_pred.dim() == 3 | |
disp_pred = disp_pred.clone().type(torch.FloatTensor) | |
disp_true = disp_true.clone().type(torch.FloatTensor) | |
if original: | |
true_disp = disp_true | |
else: | |
true_disp = disp_true.clone() | |
index = np.argwhere(true_disp > 0) | |
disp_true[index[0][:], index[1][:], index[2][:]] = np.abs( | |
true_disp[index[0][:], index[1][:], index[2][:]] - | |
disp_pred[index[0][:], index[1][:], index[2][:]]) | |
correct = (disp_true[index[0][:], index[1][:], index[2][:]] <= | |
3) | (disp_true[index[0][:], index[1][:], index[2][:]] <= | |
true_disp[index[0][:], index[1][:], index[2][:]] * 0.05) | |
return 1 - (float(torch.sum(correct)) / float(len(index[0]))) | |
def calc_3pe_standalone(disp_src, disp_dst): | |
assert disp_src.shape == disp_dst.shape, "{}, {}".format( | |
disp_src.shape, disp_dst.shape) | |
assert len(disp_src.shape) == 2 # (N*M) | |
not_empty = (disp_src > 0) & (~np.isnan(disp_src)) & (disp_dst > 0) & ( | |
~np.isnan(disp_dst)) | |
disp_src_flatten = disp_src[not_empty].flatten().astype(np.float32) | |
disp_dst_flatten = disp_dst[not_empty].flatten().astype(np.float32) | |
disp_diff_l = abs(disp_src_flatten - disp_dst_flatten) | |
accept_3p = (disp_diff_l <= 3) | (disp_diff_l <= disp_dst_flatten * 0.05) | |
err_3p = 1 - np.count_nonzero(accept_3p) / len(disp_diff_l) | |
return err_3p | |
if __name__ == '__main__': | |
USE_ORIGINAL = False | |
np.random.seed(42) | |
# bz, h, w = (1, 2, 5) | |
bz, h, w = (20, 20, 50) | |
# est_disp = np.random.randint(low=10, high=200, size= (bz, h, w), dtype=np.uint8) | |
# gt_disp = np.random.randint(low=10, high=200, size= (bz, h, w), dtype=np.uint8) | |
est_disp = np.random.randint(low=100, high=2000, size=(bz, h, w)) / 10.0 | |
gt_disp = np.random.randint(low=100, high=2000, size=(bz, h, w)) / 10.0 | |
def err_two_ndarr(arr1, arr2): | |
tensor1 = to_tensor(arr1) | |
tensor2 = to_tensor(arr2) | |
# print(tensor1.shape) | |
e = calc_3pe(tensor1, tensor2, original=USE_ORIGINAL) | |
return e | |
def split_arr(arr, step): | |
l = [] | |
for i in range(0, arr.shape[0], step): | |
l.append(arr[i:i + step]) | |
return l | |
def clc_split_step(step=1): | |
est_l = split_arr(est_disp, step=step) | |
gt_l = split_arr(gt_disp, step=step) | |
err = 0 | |
for est, gt in zip(est_l, gt_l): | |
err_tmp = err_two_ndarr(est, gt) | |
# print('{:.4f}'.format(err_tmp)) | |
err += err_tmp | |
return err / len(est_l) | |
step_l = [1, 2, 3, 4, 6] | |
print("ORIGINAL:{}".format(USE_ORIGINAL)) | |
for step in step_l: | |
e = clc_split_step(step=step) | |
print("step:{}, err:{:.6f}".format(step, e)) | |
err = 0 | |
for i in range(len(est_disp)): | |
est = est_disp[i] | |
gt = gt_disp[i] | |
err_3p = calc_3pe_standalone(disp_src=est, disp_dst=gt) # gt as dst | |
# print('{:.4f}'.format(err_3p)) | |
err += err_3p | |
print("STANDALONE: {:.6f}".format(err / len(est_disp))) |
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WHY?
It is somewhat strange at first glance, it should be the same, right? Let's show with one small example:
o
meansTrue
andx
forFalse
.And, another one:
SO, that is it, the last batch are not treated equally with previous ones (there is only two batch for bz=3, but easily extended), it is not so proper to calculate mean of these batches.
Then the problem becomes, how can we deal with it?