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import torch
from torch import nn
import numpy as np
import torch.nn.functional as F
from .depth_to_normal import Depth2Normal
"""
Sampling strategies: RS (Random Sampling), EGS (Edge-Guided Sampling), and IGS (Instance-Guided Sampling)
"""
###########
# RANDOM SAMPLING
# input:
# inputs[i,:], targets[i, :], masks[i, :], self.mask_value, self.point_pairs
# return:
# inputs_A, inputs_B, targets_A, targets_B, consistent_masks_A, consistent_masks_B
###########
def randomSamplingNormal(inputs, targets, masks, sample_num):
# find A-B point pairs from prediction
num_effect_pixels = torch.sum(masks)
shuffle_effect_pixels = torch.randperm(num_effect_pixels, device="cuda")
valid_inputs = inputs[:, masks]
valid_targes = targets[:, masks]
inputs_A = valid_inputs[:, shuffle_effect_pixels[0 : sample_num * 2 : 2]]
inputs_B = valid_inputs[:, shuffle_effect_pixels[1 : sample_num * 2 : 2]]
# find corresponding pairs from GT
targets_A = valid_targes[:, shuffle_effect_pixels[0 : sample_num * 2 : 2]]
targets_B = valid_targes[:, shuffle_effect_pixels[1 : sample_num * 2 : 2]]
if inputs_A.shape[1] != inputs_B.shape[1]:
num_min = min(targets_A.shape[1], targets_B.shape[1])
inputs_A = inputs_A[:, :num_min]
inputs_B = inputs_B[:, :num_min]
targets_A = targets_A[:, :num_min]
targets_B = targets_B[:, :num_min]
return inputs_A, inputs_B, targets_A, targets_B
###########
# EDGE-GUIDED SAMPLING
# input:
# inputs[i,:], targets[i, :], masks[i, :], edges_img[i], thetas_img[i], masks[i, :], h, w
# return:
# inputs_A, inputs_B, targets_A, targets_B, masks_A, masks_B
###########
def ind2sub(idx, cols):
r = torch.div(idx, cols, rounding_mode='floor')
c = idx - r * cols
return r, c
def sub2ind(r, c, cols):
idx = r * cols + c
return idx
def edgeGuidedSampling(inputs, targets, edges_img, thetas_img, masks, h, w):
# find edges
edges_max = edges_img.max()
edges_min = edges_img.min()
edges_mask = edges_img.ge(edges_max * 0.1)
edges_loc = edges_mask.nonzero(as_tuple=False)
thetas_edge = torch.masked_select(thetas_img, edges_mask)
minlen = thetas_edge.size()[0]
# find anchor points (i.e, edge points)
sample_num = minlen
index_anchors = torch.randint(0, minlen, (sample_num,), dtype=torch.long, device="cuda")
theta_anchors = torch.gather(thetas_edge, 0, index_anchors)
row_anchors, col_anchors = ind2sub(edges_loc[index_anchors].squeeze(1), w)
## compute the coordinates of 4-points, distances are from [2, 30]
distance_matrix = torch.randint(3, 20, (4, sample_num), device="cuda")
pos_or_neg = torch.ones(4, sample_num, device="cuda")
pos_or_neg[:2, :] = -pos_or_neg[:2, :]
distance_matrix = distance_matrix.float() * pos_or_neg
col = (
col_anchors.unsqueeze(0).expand(4, sample_num).long()
+ torch.round(
distance_matrix.float() * torch.abs(torch.cos(theta_anchors)).unsqueeze(0)
).long()
)
row = (
row_anchors.unsqueeze(0).expand(4, sample_num).long()
+ torch.round(
distance_matrix.float() * torch.abs(torch.sin(theta_anchors)).unsqueeze(0)
).long()
)
# constrain 0=<c<=w, 0<=r<=h
# Note: index should minus 1
col[col < 0] = 0
col[col > w - 1] = w - 1
row[row < 0] = 0
row[row > h - 1] = h - 1
# a-b, b-c, c-d
a = sub2ind(row[0, :], col[0, :], w)
b = sub2ind(row[1, :], col[1, :], w)
c = sub2ind(row[2, :], col[2, :], w)
d = sub2ind(row[3, :], col[3, :], w)
A = torch.cat((a, b, c), 0)
B = torch.cat((b, c, d), 0)
inputs_A = inputs[:, A]
inputs_B = inputs[:, B]
targets_A = targets[:, A]
targets_B = targets[:, B]
masks_A = torch.gather(masks, 0, A.long())
masks_B = torch.gather(masks, 0, B.long())
# create A, B, C, D mask for visualization
# vis_mask = masks.reshape(h, w).cpu().numpy()
# vis_row = row.cpu()
# vis_col = col.cpu()
# visual_A = np.zeros((h, w)).astype(np.bool)
# visual_B = np.zeros_like(visual_A)
# visual_C = np.zeros_like(visual_A)
# visual_D = np.zeros_like(visual_A)
# visual_A[vis_row[0, :], vis_col[0, :]] = True
# visual_B[vis_row[1, :], vis_col[1, :]] = True
# visual_C[vis_row[2, :], vis_col[2, :]] = True
# visual_D[vis_row[3, :], vis_col[3, :]] = True
# visual_ABCD = [visual_A & vis_mask, visual_B & vis_mask,
# visual_C& vis_mask, visual_D& vis_mask]
return (
inputs_A,
inputs_B,
targets_A,
targets_B,
masks_A,
masks_B,
sample_num,
row,
col,
)
######################################################
# EdgeguidedNormalRankingLoss
#####################################################
class EdgeguidedNormalLoss(nn.Module):
def __init__(
self,
point_pairs=10000,
cos_theta1=0.25,
cos_theta2=0.98,
cos_theta3=0.5,
cos_theta4=0.86,
mask_value=1e-8,
loss_weight=1.0,
data_type=['stereo', 'denselidar', 'denselidar_nometric','denselidar_syn'],
**kwargs
):
super(EdgeguidedNormalLoss, self).__init__()
self.point_pairs = point_pairs # number of point pairs
self.mask_value = mask_value
self.cos_theta1 = cos_theta1 # 75 degree
self.cos_theta2 = cos_theta2 # 10 degree
self.cos_theta3 = cos_theta3 # 60 degree
self.cos_theta4 = cos_theta4 # 30 degree
# self.kernel = torch.tensor(
# np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.float32),
# requires_grad=False,
# )[None, None, :, :].cuda()
self.depth2normal = Depth2Normal()
self.loss_weight = loss_weight
self.data_type = data_type
self.eps = 1e-6
def getEdge(self, images):
n, c, h, w = images.size()
a = (
torch.tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32, device="cuda")
.contiguous()
.view((1, 1, 3, 3))
.repeat(1, 1, 1, 1)
)
b = (
torch.tensor([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], dtype=torch.float32, device="cuda")
.contiguous()
.view((1, 1, 3, 3))
.repeat(1, 1, 1, 1)
)
if c == 3:
gradient_x = F.conv2d(images[:, 0, :, :].unsqueeze(1), a)
gradient_y = F.conv2d(images[:, 0, :, :].unsqueeze(1), b)
else:
gradient_x = F.conv2d(images, a)
gradient_y = F.conv2d(images, b)
edges = torch.sqrt(torch.pow(gradient_x, 2) + torch.pow(gradient_y, 2))
edges = F.pad(edges, (1, 1, 1, 1), "constant", 0)
thetas = torch.atan2(gradient_y, gradient_x)
thetas = F.pad(thetas, (1, 1, 1, 1), "constant", 0)
return edges, thetas
def getNormalEdge(self, normals):
n, c, h, w = normals.size()
a = (
torch.Tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32, device="cuda")
.contiguous()
.view((1, 1, 3, 3))
.repeat(3, 1, 1, 1)
)
b = (
torch.Tensor([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], dtype=torch.float32, device="cuda")
.contiguous()
.view((1, 1, 3, 3))
.repeat(3, 1, 1, 1)
)
gradient_x = torch.abs(F.conv2d(normals, a, groups=c))
gradient_y = torch.abs(F.conv2d(normals, b, groups=c))
gradient_x = gradient_x.mean(dim=1, keepdim=True)
gradient_y = gradient_y.mean(dim=1, keepdim=True)
edges = torch.sqrt(torch.pow(gradient_x, 2) + torch.pow(gradient_y, 2))
edges = F.pad(edges, (1, 1, 1, 1), "constant", 0)
thetas = torch.atan2(gradient_y, gradient_x)
thetas = F.pad(thetas, (1, 1, 1, 1), "constant", 0)
return edges, thetas
def visual_check(self, rgb, samples):
import os
import matplotlib.pyplot as plt
rgb = rgb.cpu().squeeze().numpy()
mean = np.array([123.675, 116.28, 103.53])[:, np.newaxis, np.newaxis]
std= np.array([58.395, 57.12, 57.375])[:, np.newaxis, np.newaxis]
rgb = ((rgb * std) + mean).astype(np.uint8).transpose((1, 2, 0))
mask_A, mask_B, mask_C, mask_D = samples
rgb[mask_A.astype(np.bool)] = [255, 0, 0]
rgb[mask_B.astype(np.bool)] = [0, 255, 0]
rgb[mask_C.astype(np.bool)] = [0, 0, 255]
rgb[mask_D.astype(np.bool)] = [255, 255, 0]
filename = str(np.random.randint(10000))
save_path = os.path.join('test_ranking', filename + '.png')
os.makedirs(os.path.dirname(save_path), exist_ok=True)
plt.imsave(save_path, rgb)
def forward(self, prediction, target, mask, input, intrinsic, **kwargs):
loss = self.get_loss(prediction, target, mask, input, intrinsic, **kwargs)
return loss
def get_loss(self, prediction, target, mask, input, intrinsic, **kwargs):
"""
input and target: surface normal input
input: rgb images
"""
gt_depths = target
if 'predictions_normals' not in kwargs:
predictions_normals, _ = self.depth2normal(prediction, intrinsic, mask)
targets_normals, targets_normals_masks = self.depth2normal(target, intrinsic, mask)
else:
predictions_normals = kwargs['predictions_normals']
targets_normals = kwargs['targets_normals']
targets_normals_masks = kwargs['targets_normals_masks']
masks_normals = mask & targets_normals_masks
# find edges from RGB
edges_img, thetas_img = self.getEdge(input)
# find edges from normals
# edges_normal, thetas_normal = self.getNormalEdge(targets_normals)
#mask_img_border = torch.ones_like(edges_normal) # normals on the borders
#mask_img_border[:, :, 5:-5, 5:-5] = 0
# edges_normal[~targets_normals_masks] = 0
# find edges from depth
edges_depth, thetas_depth = self.getEdge(gt_depths)
# edges_depth_mask = edges_depth.ge(edges_depth.max() * 0.1)
# edges_mask_dilate = torch.clamp(
# torch.nn.functional.conv2d(
# edges_depth_mask.float(), self.kernel, padding=(1, 1)
# ),
# 0,
# 1,
# ).bool()
# edges_normal[edges_mask_dilate] = 0
# edges_img[edges_mask_dilate] = 0
# =============================
n, c, h, w = targets_normals.size()
predictions_normals = predictions_normals.contiguous().view(n, c, -1)
targets_normals = targets_normals.contiguous().view(n, c, -1)
masks_normals = masks_normals.contiguous().view(n, -1)
edges_img = edges_img.contiguous().view(n, -1)
thetas_img = thetas_img.contiguous().view(n, -1)
# edges_normal = edges_normal.view(n, -1)
# thetas_normal = thetas_normal.view(n, -1)
edges_depth = edges_depth.contiguous().view(n, -1)
thetas_depth = thetas_depth.contiguous().view(n, -1)
# # initialization
losses = 0.0
valid_samples = 0.0
for i in range(n):
# Edge-Guided sampling
(
inputs_A,
inputs_B,
targets_A,
targets_B,
masks_A,
masks_B,
sample_num,
row_img,
col_img,
) = edgeGuidedSampling(
predictions_normals[i, :],
targets_normals[i, :],
edges_img[i],
thetas_img[i],
masks_normals[i, :],
h,
w,
)
# Depth-Guided sampling
# (
# depth_inputs_A,
# depth_inputs_B,
# depth_targets_A,
# depth_targets_B,
# depth_masks_A,
# depth_masks_B,
# depth_sample_num,
# row_img,
# col_img,
# ) = edgeGuidedSampling(
# predictions_normals[i, :],
# targets_normals[i, :],
# edges_depth[i],
# thetas_depth[i],
# masks_normals[i, :],
# h,
# w,
# )
# Normal-Guided sampling
# (
# normal_inputs_A,
# normal_inputs_B,
# normal_targets_A,
# normal_targets_B,
# normal_masks_A,
# normal_masks_B,
# normal_sample_num,
# row_normal,
# col_normal,
# ) = edgeGuidedSampling(
# predictions_normals[i, :],
# targets_normals[i, :],
# edges_normal[i],
# thetas_normal[i],
# masks_normals[i, :],
# h,
# w,
# )
# Combine EGS + DEGS
# inputs_A = torch.cat((inputs_A, depth_inputs_A), 1) #normal_inputs_A
# inputs_B = torch.cat((inputs_B, depth_inputs_B), 1) # normal_inputs_B
# targets_A = torch.cat((targets_A, depth_targets_A), 1) #normal_targets_A
# targets_B = torch.cat((targets_B, depth_targets_B), 1) #normal_targets_B
# masks_A = torch.cat((masks_A, depth_masks_A), 0) #normal_masks_A
# masks_B = torch.cat((masks_B, depth_masks_B), 0) #normal_masks_B
# consider forward-backward consistency checking, i.e, only compute losses of point pairs with valid GT
consistency_mask = masks_A & masks_B
# GT ordinal relationship
target_cos = torch.sum(targets_A * targets_B, dim=0)
input_cos = torch.sum(inputs_A * inputs_B, dim=0)
losses += torch.sum(torch.abs(torch.ones_like(target_cos)-input_cos) * consistency_mask.float())
valid_samples += torch.sum(consistency_mask.float())
loss = (losses / (valid_samples + self.eps)) * self.loss_weight
if torch.isnan(loss).item() | torch.isinf(loss).item():
loss = 0 * torch.sum(prediction)
print(f'Pair-wise Normal Regression Loss NAN error, {loss}, valid pix: {valid_samples}')
return loss
def tmp_check_normal(normals, masks, depth):
import matplotlib.pyplot as plt
import os
import cv2
from mono.utils.visualization import vis_surface_normal
vis_normal1 = vis_surface_normal(normals[0, ...].permute(1, 2, 0).detach(), masks[0,...].detach().squeeze())
vis_normal2 = vis_surface_normal(normals[1, ...].permute(1, 2, 0).detach(), masks[1,...].detach().squeeze())
vis_depth1 = depth[0, ...].detach().cpu().squeeze().numpy()
vis_depth2 = depth[1, ...].detach().cpu().squeeze().numpy()
name = np.random.randint(100000)
os.makedirs('test_normal', exist_ok=True)
cv2.imwrite(f'test_normal/{name}.png', vis_normal1)
cv2.imwrite(f'test_normal/{name + 1}.png', vis_normal2)
plt.imsave(f'test_normal/{name}_d.png', vis_depth1)
plt.imsave(f'test_normal/{name + 1}_d.png', vis_depth2)
if __name__ == '__main__':
ENL = EdgeguidedNormalLoss()
depth = np.random.randn(2, 1, 20, 22)
intrin = np.array([[300, 0, 10], [0, 300, 10], [0,0,1]])
prediction = np.random.randn(2, 1, 20, 22)
imgs = np.random.randn(2, 3, 20, 22)
intrinsics = np.stack([intrin, intrin], axis=0)
depth_t = torch.from_numpy(depth).cuda().float()
prediction = torch.from_numpy(prediction).cuda().float()
intrinsics = torch.from_numpy(intrinsics).cuda().float()
imgs = torch.from_numpy(imgs).cuda().float()
depth_t = -1 * torch.abs(depth_t)
loss = ENL(prediction, depth_t, masks=depth_t>0, images=imgs, intrinsic=intrinsics)
print(loss) |