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misc.py
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misc.py
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import os
import numpy as np
import random
import torch
from torch import nn
from torchvision import transforms
def seed_torch(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
inv_normalize = transforms.Normalize(
mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
std=[1/0.229, 1/0.224, 1/0.225]
)
class Silog_loss(nn.Module):
def __init__(self, variance_focus):
super(Silog_loss, self).__init__()
self.variance_focus = variance_focus
def forward(self, depth_est, depth_gt, mask):
d = torch.log(depth_est[mask] + 1e-7) - torch.log(depth_gt[mask] + 1e-7)
return torch.sqrt((d ** 2).mean() - self.variance_focus * (d.mean() ** 2)) * 10.0
def _compute_depth_errors(gt, pred):
thresh = torch.maximum((gt / pred), (pred / gt))
d1 = (thresh < 1.25).float().mean()
d2 = (thresh < 1.25 ** 2).float().mean()
d3 = (thresh < 1.25 ** 3).float().mean()
rms = (gt - pred) ** 2
rms = torch.sqrt(rms.mean())
log_rms = (torch.log(gt) - torch.log(pred)) ** 2
log_rms = torch.sqrt(log_rms.mean())
abs_rel = torch.mean(torch.abs(gt - pred) / gt)
sq_rel = torch.mean(((gt - pred) ** 2) / gt)
err = torch.log(pred) - torch.log(gt)
silog = torch.sqrt(torch.mean(err ** 2) - torch.mean(err) ** 2) * 100
err = torch.abs(torch.log10(pred) - torch.log10(gt))
log10 = torch.mean(err)
return [silog, abs_rel, log10, rms, sq_rel, log_rms, d1, d2, d3]
@torch.no_grad()
def compute_depth_metrics(gt, pred, garg_crop=False, eigen_crop=True, dataset='nyu', min_depth_eval=0.1, max_depth_eval=10):
pred = pred.squeeze()
pred[pred < min_depth_eval] = min_depth_eval
pred[pred > max_depth_eval] = max_depth_eval
pred[torch.isinf(pred)] = max_depth_eval
pred[torch.isnan(pred)] = min_depth_eval
gt_depth = gt.squeeze()
valid_mask = torch.logical_and(gt_depth > min_depth_eval, gt_depth < max_depth_eval)
if garg_crop or eigen_crop:
gt_height, gt_width = gt_depth.shape
eval_mask = torch.zeros(valid_mask.shape, device=valid_mask.device)
if garg_crop:
eval_mask[int(0.40810811 * gt_height):int(0.99189189 * gt_height),
int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1
elif eigen_crop:
# print("-"*10, " EIGEN CROP ", "-"*10)
if dataset == 'kitti':
eval_mask[int(0.3324324 * gt_height):int(0.91351351 * gt_height),
int(0.0359477 * gt_width):int(0.96405229 * gt_width)] = 1
else:
# assert gt_depth.shape == (480, 640), "Error: Eigen crop is currently only valid for (480, 640) images"
eval_mask[45:471, 41:601] = 1
else:
eval_mask = torch.ones(valid_mask.shape)
valid_mask = torch.logical_and(valid_mask, eval_mask)
return _compute_depth_errors(gt_depth[valid_mask], pred[valid_mask])
@torch.no_grad()
def normalize_depth_result(value, vmin=None, vmax=None):
value = value[0, :, :]
vmin = value.min() if vmin is None else vmin
vmax = value.max() if vmax is None else vmax
if vmin != vmax:
value = (value - vmin) / (vmax - vmin)
else:
value = value * 0.
return value.unsqueeze(0)
@torch.no_grad()
def compute_segmentation_metrics(mask_gt, mask_est, n_classes):
mask_est = torch.argmax(mask_est, dim=1)
valid_mask = (mask_gt >= 0) * (mask_gt < n_classes)
mask_gt, mask_est = mask_gt[valid_mask], mask_est[valid_mask]
pixAcc = (mask_gt == mask_est).float().mean()
mask_gt, mask_est = mask_gt + 1, mask_est + 1
inter = mask_est * (mask_est == mask_gt)
area_inter, _ = torch.histogram(inter.float().cpu(), bins=n_classes, range=(1, n_classes+1))
area_est, _ = torch.histogram(mask_est.float().cpu(), bins=n_classes, range=(1, n_classes+1))
area_gt, _ = torch.histogram(mask_gt.float().cpu(), bins=n_classes, range=(1, n_classes+1))
area_union = area_est + area_gt - area_inter
return pixAcc.to(mask_gt.device), area_inter.to(mask_gt.device), area_union.to(mask_gt.device)
ADE20K_PALETTE = [
(120, 120, 120), (180, 120, 120), (6, 230, 230), (80, 50, 50), (4, 200, 3), (120, 120, 80), (140, 140, 140), (204, 5, 255), (230, 230, 230), (4, 250, 7),
(224, 5, 255), (235, 255, 7), (150, 5, 61), (120, 120, 70), (8, 255, 51), (255, 6, 82), (143, 255, 140), (204, 255, 4), (255, 51, 7), (204, 70, 3),
(0, 102, 200), (61, 230, 250), (255, 6, 51), (11, 102, 255), (255, 7, 71), (255, 9, 224), (9, 7, 230), (220, 220, 220), (255, 9, 92), (112, 9, 255),
(8, 255, 214), (7, 255, 224), (255, 184, 6), (10, 255, 71), (255, 41, 10), (7, 255, 255), (224, 255, 8), (102, 8, 255), (255, 61, 6), (255, 194, 7),
(255, 122, 8), (0, 255, 20), (255, 8, 41), (255, 5, 153), (6, 51, 255), (235, 12, 255), (160, 150, 20), (0, 163, 255), (140, 140, 140), (250, 10, 15),
(20, 255, 0), (31, 255, 0), (255, 31, 0), (255, 224, 0), (153, 255, 0), (0, 0, 255), (255, 71, 0), (0, 235, 255), (0, 173, 255), (31, 0, 255),
(11, 200, 200), (255 ,82, 0), (0, 255, 245), (0, 61, 255), (0, 255, 112), (0, 255, 133), (255, 0, 0), (255, 163, 0), (255, 102, 0), (194, 255, 0),
(0, 143, 255), (51, 255, 0), (0, 82, 255), (0, 255, 41), (0, 255, 173), (10, 0, 255), (173, 255, 0), (0, 255, 153), (255, 92, 0), (255, 0, 255),
(255, 0, 245), (255, 0, 102), (255, 173, 0), (255, 0, 20), (255, 184, 184), (0, 31, 255), (0, 255, 61), (0, 71, 255), (255, 0, 204), (0, 255, 194),
(0, 255, 82), (0, 10, 255), (0, 112, 255), (51, 0, 255), (0, 194, 255), (0, 122, 255), (0, 255, 163), (255, 153, 0), (0, 255, 10), (255, 112, 0),
(143, 255, 0), (82, 0, 255), (163, 255, 0), (255, 235, 0), (8, 184, 170), (133, 0, 255), (0, 255, 92), (184, 0, 255), (255, 0, 31), (0, 184, 255),
(0, 214, 255), (255, 0, 112), (92, 255, 0), (0, 224, 255), (112, 224, 255), (70, 184, 160), (163, 0, 255), (153, 0, 255), (71, 255, 0), (255, 0, 163),
(255, 204, 0), (255, 0, 143), (0, 255, 235), (133, 255, 0), (255, 0, 235), (245, 0, 255), (255, 0, 122), (255, 245, 0), (10, 190, 212), (214, 255, 0),
(0, 204, 255), (20, 0, 255), (255, 255, 0), (0, 153, 255), (0, 41, 255), (0, 255, 204), (41, 0, 255), (41, 255, 0), (173, 0, 255), (0, 245, 255),
(71, 0, 255), (122, 0, 255), (0, 255, 184), (0, 92, 255), (184, 255, 0), (0, 133, 255), (255, 214, 0), (25, 194, 194), (102, 255, 0), (92, 0, 255),
(0, 0, 0),
]
PASCAL_VOC_PALETTE = [
(0, 0, 0), (128, 0, 0), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128), (0, 128, 128), (128, 128, 128), (64, 0, 0), (192, 0, 0), (64, 128, 0),
(192, 128, 0), (64, 0, 128), (192, 0, 128), (64, 128, 128), (192, 128, 128), (0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0), (0, 64, 128), (224,224,192)
]
def visualize_segmentation_result(mask, dataset):
# mask: (H, W)
H, W = mask.shape
if dataset == 'ade20k':
palette = ADE20K_PALETTE
elif dataset == 'pascal_voc':
palette = PASCAL_VOC_PALETTE
palette = torch.Tensor(palette)
colorized = torch.index_select(palette, 0, mask.view(-1).cpu()).reshape(H, W, 3).permute(2, 0, 1) / 255.
return colorized.to(mask.device)