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import os
import sys
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from core.utils.utils import coords_grid, disparity_computation
from core.utils.utils import LoggerCommon
logger = LoggerCommon("LOSS")
try:
autocast = torch.cuda.amp.autocast
except:
# dummy autocast for PyTorch < 1.6
class autocast:
def __init__(self, enabled):
pass
def __enter__(self):
pass
def __exit__(self, *args):
pass
def sequence_loss(flow_preds, flow_gt, valid, loss_gamma=0.9, max_flow=700):
""" Loss function defined over sequence of flow predictions """
n_predictions = len(flow_preds)
assert n_predictions >= 1
flow_loss = 0.0
# exlude invalid pixels and extremely large diplacements
mag = torch.sum(flow_gt**2, dim=1).sqrt()
# exclude extremly large displacements
valid = ((valid >= 0.5) & (mag < max_flow)).unsqueeze(1)
assert valid.shape == flow_gt.shape, [valid.shape, flow_gt.shape]
assert not torch.isinf(flow_gt[valid.bool()]).any()
for i in range(n_predictions):
if not torch.isnan(flow_preds[i]).any() and not torch.isinf(flow_preds[i]).any():
# We adjust the loss_gamma so it is consistent for any number of RAFT-Stereo iterations
adjusted_loss_gamma = loss_gamma**(15/(n_predictions - 1))
i_weight = adjusted_loss_gamma**(n_predictions - i - 1)
i_loss = (flow_preds[i] - flow_gt).abs()
assert i_loss.shape == valid.shape, [i_loss.shape, valid.shape, flow_gt.shape, flow_preds[i].shape]
flow_loss += i_weight * i_loss[valid.bool()].mean()
epe = torch.sum((flow_preds[-1] - flow_gt)**2, dim=1).sqrt()
epe = epe.view(-1)[valid.view(-1)]
metrics = {
'epe': epe.mean().item(),
'1px': (epe < 1).float().mean().item(),
'3px': (epe < 3).float().mean().item(),
'5px': (epe < 5).float().mean().item(),
}
return flow_loss, metrics
def my_loss(res, flow_gt, valid, loss_gamma=0.9, max_flow=700):
pass
class Loss(nn.Module):
def __init__(self, loss_gamma=0.9, max_flow=700, loss_zeta=0.3,
smoothness=None, slant=None, slant_norm=False,
ner_kernel_size=3, ner_weight_reduce=False,
local_rank=None, mixed_precision=True,
args=None):
super(Loss, self).__init__()
self.loss_gamma = loss_gamma
self.loss_zeta = loss_zeta
self.max_flow = max_flow
self.smoothness = smoothness
self.mixed_precision = mixed_precision
self.conf_disp = args.conf_disp
self.args = args
if self.smoothness is not None and len(self.smoothness)>0:
self.smooth_loss_computer = SmoothLoss(self.smoothness,
slant=slant,
slant_norm=slant_norm,
kernel_size=ner_kernel_size,
ner_weight_reduce=ner_weight_reduce)
logger.info(f"smoothness: {smoothness}, " +\
f"slant: {slant}, slant_norm: {slant_norm}, " +\
f"ner_kernel_size: {ner_kernel_size}, " +\
f"ner_weight_reduce: {ner_weight_reduce}, " +\
f"conf_disp: {self.conf_disp}. " )
def forward(self, flow_preds, flow_gt, valid,
disp_preds=None, disp_preds_refine=None,
confidence_list=None,
params_list=None, params_list_refine=None,
plane_abc=None,
imgL=None, imgR=None,
global_batch_num=None,):
""" Loss function defined over sequence of flow predictions """
n_predictions = len(flow_preds)
assert n_predictions >= 1
flow_loss = 0.0
disp_loss = 0.0
disp_refine_loss = 0.0
smooth_loss = 0.0
confidence_loss = 0.0
params_loss = 0.0
params_refine_loss = 0.0
# exlude invalid pixels and extremely large diplacements
mag = torch.sum(flow_gt**2, dim=1).sqrt()
# exclude extremly large displacements
valid = ((valid >= 0.5) & (mag < self.max_flow)).unsqueeze(1)
assert valid.shape == flow_gt.shape, [valid.shape, flow_gt.shape]
assert not torch.isinf(flow_gt[valid.bool()]).any()
for i in range(n_predictions):
assert not torch.isnan(flow_preds[i]).any() and not torch.isinf(flow_preds[i]).any()
# We adjust the loss_gamma so it is consistent for any number of RAFT-Stereo iterations
adjusted_loss_gamma = self.loss_gamma**(15/(n_predictions - 1))
i_weight = adjusted_loss_gamma**(n_predictions - i - 1)
# confidence loss
if confidence_list[i] is not None and \
(self.args.offset_memory_last_iter<0 or \
(self.args.offset_memory_last_iter>0 and i<=self.args.offset_memory_last_iter)):
with autocast(enabled=self.mixed_precision):
gt_error = (flow_preds[i].detach() - flow_gt).abs().detach()
gt_error = F.interpolate(gt_error,scale_factor=1/4,mode='bilinear')
# confidence_loss += i_weight * F.smooth_l1_loss(confidence_list[i], gt_error)
# confidence_loss += i_weight * F.binary_cross_entropy_with_logits(confidence_list[i],
# torch.sigmoid(gt_error-4))
gt_conf = (gt_error>4).float()
weight = torch.pow(F.sigmoid(confidence_list[i])-gt_conf, 2)
tmp_confidence_loss = (1+gt_conf*0.5) * weight *\
F.binary_cross_entropy_with_logits(confidence_list[i],
gt_conf, reduction='none')
confidence_loss += i_weight * tmp_confidence_loss.mean()
# flow loss
i_loss = (flow_preds[i] - flow_gt).abs()
if self.conf_disp and global_batch_num>3 and confidence_list[i] is not None:
weight = F.interpolate(confidence_list[i],scale_factor=4,mode='bilinear')
i_loss = i_loss * (F.sigmoid(weight.detach()/3)*1.5 + 1)
assert i_loss.shape == valid.shape, [i_loss.shape, valid.shape, flow_gt.shape, flow_preds[i].shape]
flow_loss += i_weight * i_loss[valid.bool()].mean()
# disparity loss
if disp_preds is not None and len(disp_preds)>0 and disp_preds[i] is not None:
i_loss = (-disp_preds[i] - flow_gt).abs()
disp_loss += i_weight * i_loss[valid.bool()].mean()
# plane loss
if params_list is not None and len(params_list)>0 and plane_abc is not None and plane_abc.shape[1]==3:
# print("~"*30, params_list[-1].shape, plane_abc.shape)
i_loss = (params_list[i] - plane_abc).abs()
params_loss += i_weight * 0.5 * i_loss.mean()
# refinement loss
if disp_preds_refine is not None and len(disp_preds_refine)>0 and disp_preds_refine[i] is not None:
i_loss = (-disp_preds_refine[i] - flow_gt).abs()
disp_refine_loss += i_weight * i_loss[valid.bool()].mean()
# plane loss
if params_list_refine is not None and len(params_list_refine)>0 and plane_abc is not None and plane_abc.shape[1]==3:
# print("~"*30, params_list_refine[-1].shape, plane_abc.shape)
i_loss = (params_list_refine[i] - plane_abc).abs()
params_refine_loss += i_weight * 0.5 * i_loss.mean()
if i>n_predictions//2:
with autocast(enabled=self.mixed_precision):
if self.smoothness=="gradient":
smooth_loss += i_weight * self.smooth_loss_computer(flow_preds[i], imgL).mean()
elif self.smoothness=="curvature":
smooth_loss += i_weight * self.smooth_loss_computer(params_list[i], imgL).mean()
epe = torch.sum((flow_preds[-1] - flow_gt)**2, dim=1).sqrt()
epe = epe.view(-1)[valid.view(-1)]
metrics = {
'epe': epe.mean().item(),
'1px': (epe < 1).float().mean().item(),
'3px': (epe < 3).float().mean().item(),
'5px': (epe < 5).float().mean().item(),
}
if disp_preds is not None and len(disp_preds)>0 and disp_preds[-1] is not None:
epe = torch.sum((-disp_preds[-1] - flow_gt)**2, dim=1).sqrt()
epe = epe.view(-1)[valid.view(-1)]
metrics.update({'epe_disp': epe.mean().item(),
'3px_disp': (epe < 3).float().mean().item(),})
if disp_preds_refine is not None and len(disp_preds_refine)>0 and disp_preds_refine[-1] is not None:
epe = torch.sum((-disp_preds_refine[-1] - flow_gt)**2, dim=1).sqrt()
epe = epe.view(-1)[valid.view(-1)]
metrics.update({'epe_disp_refine': epe.mean().item(),
'3px_disp_refine': (epe < 3).float().mean().item(),})
if self.smoothness is not None and len(self.smoothness)>0:
loss = flow_loss + disp_loss + params_loss + disp_refine_loss + params_refine_loss + confidence_loss + self.loss_zeta * smooth_loss
else:
loss = flow_loss + disp_loss + params_loss + disp_refine_loss + params_refine_loss + confidence_loss
smooth_loss = torch.Tensor([0.0]).to(flow_loss.device)
return loss, metrics, flow_loss, disp_loss, disp_refine_loss, confidence_loss, smooth_loss, params_loss, params_refine_loss
class SmoothLoss(nn.Module):
"""Smooth constaint for prediction.
- gradient-based smooth regularization:
\psi_{pq} = max(w_{pq},\epsilon) min(\hat{\psi}_{pq}(f_p,f_q), \tau_{dis}) \\
w_{pq} = e^{-||I_L(p)-I_L(q)||_1 / \eta} \\
\hat{\psi}_{pq} = |d_p(f_p) - d_q(f_q)| \\
d_p(f_p) = a_p p_u + b_p p_v + c_p \\
d_q(f_q) = a_q q_u + b_q q_v + c_q
- curvature-based smooth regularization:
\psi_{pq} = max(w_{pq},\epsilon) min(\hat{\psi}_{pq}(f_p,f_q), \tau_{dis}) \\
w_{pq} = e^{-||I_L(p)-I_L(q)||_1 / \eta} \\
\hat{\psi}_{pq} = |d_p(f_p) - d_p(f_q)| + |d_q(f_q) - d_q(f_p)| \\
d_p(f_p) = a_p p_u + b_p p_v + c_p \\
d_p(f_q) = a_p q_u + b_p q_v + c_p
"""
def __init__(self, smoothness, slant=None, slant_norm=False, kernel_size=3,
ner_weight_reduce=False, epsilon=0.01, tau=3, eta=10):
super(SmoothLoss, self).__init__()
self.smoothness = smoothness
self.slant = slant
self.slant_norm = slant_norm
self.eta = eta
self.tau = tau
self.epsilon = epsilon
self.reduce = ner_weight_reduce
self.img_ner_extractor = NerghborExtractor(3, kernel_size, reduce=self.reduce)
self.coord_ner_extractor = NerghborExtractor(2, kernel_size)
self.params_ner_extractor = NerghborExtractor(3, kernel_size)
def forward(self, params, imgL):
"""Function: compute smoothe loss
args:
params: (B,3,H,W)
imgL: (B,3,H,W)
coordL: (B,2,H,W)
corrdR: (B,2,H,W)
"""
img_ner = self.img_ner_extractor(imgL) # B,3,N,H,W
B, _, H, W = imgL.shape
coord = coords_grid(B, H, W).to(imgL.device) # B,2,H,W
coord_ner = self.coord_ner_extractor(coord) # B,2,N,H,W
coord = coord.unsqueeze(2) # B,2,1,H,W
params_ner = self.params_ner_extractor(params) # B,3,N,H,W
params = params.unsqueeze(2) # B,3,1,H,W
# w_{pq} = e^{-||I_L(p)-I_L(q)||_1 / \eta}
if not self.reduce:
weight = torch.exp(-torch.abs(img_ner-imgL.unsqueeze(2)).mean(dim=1) / self.eta) # B,N,H,W
else:
weight = torch.exp(-torch.abs(img_ner).mean(dim=1) / self.eta) # B,N,H,W
if self.smoothness=="gradient":
# \hat{\psi}_{pq} = |d_p(f_p) - d_q(f_q)|
psi_p = disparity_computation(params, coords0=coord,
slant=self.slant, slant_norm=self.slant_norm) - \
disparity_computation(params_ner, coords0=coord_ner,
slant=self.slant, slant_norm=self.slant_norm)
psi = torch.abs(psi_p) # B,N,H,W
elif self.smoothness=="curvature":
# |d_p(f_p) - d_p(f_q)|
psi_p = disparity_computation(params, coords0=coord,
slant=self.slant, slant_norm=self.slant_norm) - \
disparity_computation(params, coords0=coord_ner,
slant=self.slant, slant_norm=self.slant_norm)
# d_q(f_q) - d_q(f_p)
psi_q = disparity_computation(params_ner, coords0=coord_ner,
slant=self.slant, slant_norm=self.slant_norm) - \
disparity_computation(params_ner, coords0=coord,
slant=self.slant, slant_norm=self.slant_norm)
# \hat{\psi} = |d_p(f_p) - d_p(f_q)| + |d_q(f_q) - d_q(f_p)|
psi = torch.abs(psi_p) + torch.abs(psi_q) # B,N,H,W
# \psi_{pq} = max(w_{pq},\epsilon) min(\hat{\psi_{pq}(f_p,f_q)}, \tau_{dis})
smooth_loss = torch.clip(weight, min=self.epsilon,) * \
F.sigmoid(psi/self.tau*8-4) * self.tau
smooth_loss = smooth_loss.mean()
return smooth_loss
def diamond(n):
a = np.arange(n)
b = np.minimum(a,a[::-1])
return (b[:,None]+b)>=(n-1)//2
def diamond_edge(n):
arr = np.diagflat(np.ones(n//2+1), n//2)
arr = np.maximum(arr,np.flip(arr,1))
return np.maximum(arr,np.flip(arr,0))
kernel_dict = {}
kernel_dict["diamond"] = diamond
kernel_dict["diamond_edge"] = diamond_edge
class NerghborExtractor(nn.Module):
"""Extarct the neighbors of each pixel using depthwise convolution.
Input: (B,C,H,W), Output: (B,C,N,H,W).
"""
def __init__(self, input_channel, kernel_size=3, reduce=False):
super(NerghborExtractor, self).__init__()
self.reduce = reduce
self.input_channel = input_channel
# build kernel matrix
if isinstance(kernel_size, int):
H, W = kernel_size, kernel_size
self.neighbors_num = kernel_size*kernel_size
neighbor_kernel = np.zeros((self.neighbors_num, H, W), dtype=np.float16)
for idx in range(self.neighbors_num):
neighbor_kernel[idx, idx//H, idx%W] = 1
elif isinstance(kernel_size, str):
## obatin the compressed kernel
kernel_type, size = kernel_size.split("-")
kernel_size = int(size)
compressed_kernel = kernel_dict[kernel_type](kernel_size)
## decode the compressed kernel into a series of kernels
H, W = compressed_kernel.shape
self.neighbors_num = np.count_nonzero(compressed_kernel)
neighbors_pos = np.nonzero(compressed_kernel)
neighbor_kernel = np.zeros((self.neighbors_num, H, W), dtype=np.float16)
for idx_k, (idx_h, idx_w) in enumerate(zip(neighbors_pos[0],neighbors_pos[1])):
neighbor_kernel[idx_k, idx_h, idx_w] = compressed_kernel[idx_h, idx_w]
else:
raise Exception("kernel_size currently only supports integer")
if self.reduce:
neighbor_kernel[:, H//2, W//2] = -1
if not self.reduce:
neighbor_kernel = np.tile(neighbor_kernel, (input_channel,1,1))
neighbor_kernel = neighbor_kernel[:,np.newaxis] # in*neighbors_num, 1, k, k
output_channel = input_channel*self.neighbors_num
groups = input_channel
else:
neighbor_kernel = np.tile(neighbor_kernel[:, np.newaxis],
(1,input_channel,1,1)) # neighbors_num, in, k, k
output_channel = self.neighbors_num
groups = 1
# extract neighbors through depthwise conv
self.conv = nn.Conv2d(input_channel, output_channel,
kernel_size=kernel_size, padding=kernel_size//2, bias=False,
groups=groups, padding_mode="reflect")
neighbor_kernel = torch.Tensor(neighbor_kernel)
self.conv.weight = nn.Parameter(neighbor_kernel, requires_grad=False)
def forward(self, x):
B,C,H,W = x.shape
neighbors = self.conv(x)
neighbors = neighbors.reshape((B,-1,self.neighbors_num,H,W))
if self.reduce:
neighbors = neighbors / self.input_channel
return neighbors
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