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losses.py
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import torch
import numpy as np
# Maximum sparse log likelihood loss
class SparseLogProbLoss(torch.nn.Module):
def __init__(self, epsilon=1.0e-8, gpu_id=0):
super(SparseLogProbLoss, self).__init__()
self.epsilon = epsilon
self.gpu_id = gpu_id
self.zero = torch.tensor(0.0).float().cuda(self.gpu_id)
self.one = torch.tensor(1.0).float().cuda(self.gpu_id)
self.offset = torch.tensor(0.5 * np.log(2.0 * np.pi)).float()
def forward(self, x):
mean_depth_maps, std_depth_maps, sparse_depth_maps, binary_sparse_masks = x
# mean_sparse_depth = torch.sum(binary_sparse_masks * sparse_depth_maps, dim=(1, 2, 3)) / torch.sum(
# binary_sparse_masks, dim=(1, 2, 3))
# std_depth_maps = torch.clamp(std_depth_maps, min=torch.min(mean_sparse_depth * 1.0e-3).item())
std_depth_maps = torch.clamp(std_depth_maps, min=self.epsilon)
temp = sparse_depth_maps - mean_depth_maps
temp_2 = (0.5 * temp ** 2) / std_depth_maps ** 2
temp_3 = torch.sum(binary_sparse_masks * (
self.offset.to(mean_depth_maps.device) + torch.log(std_depth_maps) + temp_2), dim=(1, 2, 3))
loss = temp_3 / (
self.epsilon + torch.sum(binary_sparse_masks, dim=(1, 2, 3)))
return torch.mean(loss)
# Maximum dense log likelihood loss
class DenseLogProbLoss(torch.nn.Module):
def __init__(self, epsilon=1.0e-8):
super(DenseLogProbLoss, self).__init__()
self.epsilon = epsilon
self.offset = torch.tensor(0.5 * np.log(2.0 * np.pi)).float()
def forward(self, x):
mean_depth_maps, std_depth_maps, warped_mean_depth_maps, intersect_masks = x
# mean_depth = torch.sum(intersect_masks * (mean_depth_maps + warped_mean_depth_maps), dim=(1, 2, 3)) / torch.sum(
# intersect_masks, dim=(1, 2, 3))
# std_depth_maps = torch.clamp(std_depth_maps, min=torch.min(mean_depth * 1.0e-3).item())
std_depth_maps = torch.clamp(std_depth_maps, min=self.epsilon)
temp = warped_mean_depth_maps - mean_depth_maps
loss = torch.sum(intersect_masks * (self.offset.to(mean_depth_maps.device) + torch.log(
std_depth_maps) + (0.5 * temp * temp) / (std_depth_maps * std_depth_maps)), dim=(1, 2, 3)) / (
self.epsilon + torch.sum(intersect_masks, dim=(1, 2, 3)))
return torch.mean(loss)
class NormalizedSparseMaskedL1Loss(torch.nn.Module):
def __init__(self, epsilon=1.0e-8):
super(NormalizedSparseMaskedL1Loss, self).__init__()
self.epsilon = epsilon
def forward(self, x):
flows, flows_from_depth, sparse_masks = x
mean_flow_magnitude = torch.sum(sparse_masks * torch.abs(flows), dim=(1, 2, 3)) / torch.sum(
sparse_masks, dim=(1, 2, 3))
loss = torch.sum(sparse_masks * torch.abs(flows - flows_from_depth),
dim=(1, 2, 3)) / (self.epsilon + mean_flow_magnitude * torch.sum(sparse_masks, dim=(1, 2, 3)))
return torch.mean(loss)
class RelativeResponseLoss(torch.nn.Module):
def __init__(self, eps=1.0e-10):
super(RelativeResponseLoss, self).__init__()
self.eps = eps
def forward(self, x):
response_map, source_feature_1d_locations, boundaries = x
batch_size, sampling_size, height, width = response_map.shape
response_map = response_map / torch.sum(response_map, dim=(2, 3), keepdim=True)
# B x Sampling_size x 1
sampled_cosine_distance = torch.gather(response_map.view(batch_size, sampling_size, height * width), 2,
source_feature_1d_locations.view(batch_size, sampling_size,
1).long())
sampled_boundaries = torch.gather(
boundaries.view(batch_size, 1, height * width).expand(-1, sampling_size, -1), 2,
source_feature_1d_locations.view(batch_size, sampling_size,
1).long())
sampled_boundaries_sum = 1.0 + torch.sum(sampled_boundaries)
rr_loss = torch.sum(
sampled_boundaries * -torch.log(self.eps + sampled_cosine_distance)) / sampled_boundaries_sum
return rr_loss
class ContrastiveRelativeResponseLoss(torch.nn.Module):
def __init__(self, eps=1.0e-10):
super(ContrastiveRelativeResponseLoss, self).__init__()
self.eps = eps
def forward(self, x):
response_map, source_feature_1d_locations, boundaries = x
batch_size, sampling_size, height, width = response_map.shape
response_map = response_map / torch.sum(response_map, dim=(2, 3), keepdim=True)
# B x Sampling_size x 1
sampled_cosine_distance = torch.gather(response_map.view(batch_size, sampling_size, height * width), 2,
source_feature_1d_locations.view(batch_size, sampling_size,
1).long())
sampled_boundaries = torch.gather(
boundaries.view(batch_size, 1, height * width).expand(-1, sampling_size, -1), 2,
source_feature_1d_locations.view(batch_size, sampling_size,
1).long())
sampled_boundaries_sum = 1.0 + torch.sum(sampled_boundaries)
pos_rr_loss = torch.sum(
sampled_boundaries * -torch.log(self.eps + sampled_cosine_distance)) / sampled_boundaries_sum
neg_rr_loss = torch.sum(
sampled_boundaries * -torch.log(self.eps + (1 - sampled_cosine_distance))) / sampled_boundaries_sum
return pos_rr_loss, neg_rr_loss
class ContrastiveLoss(torch.nn.Module):
def __init__(self, margin=0.5):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, x):
response_map, source_feature_1d_locations, boundaries = x
batch_size, sampling_size, height, width = response_map.shape
response_map = response_map / torch.sum(response_map, dim=(2, 3), keepdim=True)
# B x Sampling_size x 1
sampled_cosine_distance = torch.gather(response_map.view(batch_size, sampling_size, height * width), 2,
source_feature_1d_locations.view(batch_size, sampling_size,
1).long())
sampled_boundaries = torch.gather(
boundaries.view(batch_size, 1, height * width).expand(-1, sampling_size, -1), 2,
source_feature_1d_locations.view(batch_size, sampling_size,
1).long())
sampled_boundaries_sum = 1.0 + torch.sum(sampled_boundaries, dim=1)
pos_cont_loss = torch.sum(
sampled_boundaries * torch.pow(1 - sampled_cosine_distance, 2)) / sampled_boundaries_sum
neg_cont_loss = torch.sum(
sampled_boundaries * torch.pow(torch.max(torch.tensor(0).cuda(), self.margin - 1 + sampled_cosine_distance), 2)) / sampled_boundaries_sum
return pos_cont_loss, neg_cont_loss
class MatchingAccuracyMetric(torch.nn.Module):
def __init__(self, threshold):
super(MatchingAccuracyMetric, self).__init__()
self.threshold = threshold
def forward(self, x):
response_map, source_feature_2d_locations, boundaries = x
batch_size, sampling_size, height, width = response_map.shape
_, detected_target_1d_locations = \
torch.max(response_map.view(batch_size, sampling_size, height * width), dim=2, keepdim=True)
detected_target_1d_locations = detected_target_1d_locations.float()
detected_target_2d_locations = torch.cat(
[torch.fmod(detected_target_1d_locations, width),
torch.floor(detected_target_1d_locations / width)],
dim=2).view(batch_size, sampling_size, 2).float()
distance = torch.norm(detected_target_2d_locations - source_feature_2d_locations,
dim=2, keepdim=False)
ratio_1 = torch.sum((distance < self.threshold).float()) / (batch_size * sampling_size)
ratio_2 = torch.sum((distance < 2.0 * self.threshold).float()) / (batch_size * sampling_size)
ratio_3 = torch.sum((distance < 4.0 * self.threshold).float()) / (batch_size * sampling_size)
return ratio_1, ratio_2, ratio_3