Spaces:
Running
Running
File size: 10,417 Bytes
684943d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 |
#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact [email protected]
#
from math import exp
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
def l1_loss(network_output, gt):
return torch.abs((network_output - gt)).mean()
def l2_loss(network_output, gt):
return ((network_output - gt) ** 2).mean()
def cos_loss(network_output, gt):
return 1 - F.cosine_similarity(network_output, gt, dim=0).mean()
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
return gauss / gauss.sum()
def create_window(window_size, channel):
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
return window
def ssim(img1, img2, window_size=11, size_average=True):
channel = img1.size(-3)
window = create_window(window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
return _ssim(img1, img2, window, window_size, channel, size_average)
def _ssim(img1, img2, window, window_size, channel, size_average=True):
mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1 * mu2
sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
C1 = 0.01 ** 2
C2 = 0.03 ** 2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
if size_average:
return ssim_map.mean()
else:
return ssim_map.mean(1).mean(1).mean(1)
def ssim2(img1, img2, window_size=11):
channel = img1.size(-3)
window = create_window(window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1 * mu2
sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
C1 = 0.01 ** 2
C2 = 0.03 ** 2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
return ssim_map.mean(0)
def get_img_grad_weight(img, beta=2.0):
_, hd, wd = img.shape
bottom_point = img[..., 2:hd, 1:wd - 1]
top_point = img[..., 0:hd - 2, 1:wd - 1]
right_point = img[..., 1:hd - 1, 2:wd]
left_point = img[..., 1:hd - 1, 0:wd - 2]
grad_img_x = torch.mean(torch.abs(right_point - left_point), 0, keepdim=True)
grad_img_y = torch.mean(torch.abs(top_point - bottom_point), 0, keepdim=True)
grad_img = torch.cat((grad_img_x, grad_img_y), dim=0)
grad_img, _ = torch.max(grad_img, dim=0)
grad_img = (grad_img - grad_img.min()) / (grad_img.max() - grad_img.min())
grad_img = torch.nn.functional.pad(grad_img[None, None], (1, 1, 1, 1), mode='constant', value=1.0).squeeze()
return grad_img
def lncc(ref, nea):
# ref_gray: [batch_size, total_patch_size]
# nea_grays: [batch_size, total_patch_size]
bs, tps = nea.shape
patch_size = int(np.sqrt(tps))
ref_nea = ref * nea
ref_nea = ref_nea.view(bs, 1, patch_size, patch_size)
ref = ref.view(bs, 1, patch_size, patch_size)
nea = nea.view(bs, 1, patch_size, patch_size)
ref2 = ref.pow(2)
nea2 = nea.pow(2)
# sum over kernel
filters = torch.ones(1, 1, patch_size, patch_size, device=ref.device)
padding = patch_size // 2
ref_sum = F.conv2d(ref, filters, stride=1, padding=padding)[:, :, padding, padding]
nea_sum = F.conv2d(nea, filters, stride=1, padding=padding)[:, :, padding, padding]
ref2_sum = F.conv2d(ref2, filters, stride=1, padding=padding)[:, :, padding, padding]
nea2_sum = F.conv2d(nea2, filters, stride=1, padding=padding)[:, :, padding, padding]
ref_nea_sum = F.conv2d(ref_nea, filters, stride=1, padding=padding)[:, :, padding, padding]
# average over kernel
ref_avg = ref_sum / tps
nea_avg = nea_sum / tps
cross = ref_nea_sum - nea_avg * ref_sum
ref_var = ref2_sum - ref_avg * ref_sum
nea_var = nea2_sum - nea_avg * nea_sum
cc = cross * cross / (ref_var * nea_var + 1e-8)
ncc = 1 - cc
ncc = torch.clamp(ncc, 0.0, 2.0)
ncc = torch.mean(ncc, dim=1, keepdim=True)
mask = (ncc < 0.9)
return ncc, mask
def loss_cls_3d(features, predictions, k=5, lambda_val=2.0, max_points=200000, sample_size=800):
# Randomly downsample
if features.size(0) > max_points:
indices = torch.randperm(features.size(0))[:max_points]
features = features[indices]
predictions = predictions[indices]
# Normalize predictions to [0, 1] range
min_value = predictions.min()
max_value = predictions.max()
if max_value > min_value:
predictions = (predictions - min_value) / (max_value - min_value)
# Randomly sample points for which we'll compute the loss
indices = torch.randperm(features.size(0))[:sample_size]
sample_features = features[indices]
sample_preds = predictions[indices]
# Compute top-k nearest neighbors directly in PyTorch
dists = torch.cdist(sample_features, features) # Compute pairwise distances
_, neighbor_indices_tensor = dists.topk(k, largest=False) # Get top-k smallest distances
# Fetch neighbor predictions using indexing
neighbor_preds = predictions[neighbor_indices_tensor]
# Compute KL divergence
kl = sample_preds.unsqueeze(1) * (torch.log(sample_preds.unsqueeze(1) + 1e-10) - torch.log(neighbor_preds + 1e-10))
loss = torch.abs(kl).mean()
return lambda_val * loss
def get_loss_semantic_group(gt_seg, language_feature, num=10000):
# Randomly select num indices from gt_seg
if gt_seg.size(0) < num:
indices = torch.randperm(gt_seg.size(0))
num = gt_seg.size(0)
else:
indices = torch.randperm(gt_seg.size(0))[:num]
input_id1 = input_id2 = gt_seg[indices]
language_feature = language_feature[indices]
# Expand labels, create masks for valid positive pairs, excluding self-pairs.
labels1_expanded = input_id1.unsqueeze(1).expand(-1, input_id1.shape[0])
labels2_expanded = input_id2.unsqueeze(0).expand(input_id2.shape[0], -1)
mask_full_positive = labels1_expanded == labels2_expanded
block_mask = torch.ones(num, num, dtype=torch.bool, device=gt_seg.device)
block_mask = torch.triu(block_mask, diagonal=0)
diag_mask = torch.eye(block_mask.shape[0], device=gt_seg.device, dtype=torch.bool)
# Compute semantic loss for positive pairs
total_loss = 0
mask = torch.where(mask_full_positive * block_mask * (~diag_mask))
semantic_loss = torch.norm(
language_feature[mask[0]] - language_feature[mask[1]], p=2, dim=-1
).nansum()
total_loss += semantic_loss
total_loss = total_loss / torch.sum(block_mask).float()
return 2 * total_loss
def get_loss_instance_group(sam_seg, instance_feature, language_feature, num=1000):
# Randomly select num indices from gt_seg
margin = 1.0
if sam_seg.size(0) < num:
indices = torch.randperm(sam_seg.size(0))
num = sam_seg.size(0)
else:
indices = torch.randperm(sam_seg.size(0))[:num]
instance_feature = instance_feature[indices]
input_id1 = input_id2 = sam_seg[indices]
language_feature = language_feature[indices]
# Expand labels, create masks for valid positive pairs, excluding self-pairs.
labels1_expanded = input_id1.unsqueeze(1).expand(-1, input_id1.shape[0])
labels2_expanded = input_id2.unsqueeze(0).expand(input_id2.shape[0], -1)
mask_full_positive = labels1_expanded == labels2_expanded
mask_full_negative = ~mask_full_positive
block_mask = torch.ones(num, num, dtype=torch.bool, device=sam_seg.device)
block_mask = torch.triu(block_mask, diagonal=0)
diag_mask = torch.eye(block_mask.shape[0], device=sam_seg.device, dtype=torch.bool)
# Compute instance loss for positive pairs
total_loss = 0
mask = torch.where(mask_full_positive * block_mask * (~diag_mask))
instance_loss_1 = torch.norm(
instance_feature[mask[0]] - instance_feature[mask[1]], p=2, dim=-1
).nansum()
total_loss += instance_loss_1
# Create mask for negative pairs and compute language similarity using cosine similarity
mask = torch.where(mask_full_negative * block_mask)
language_similarity = torch.nn.functional.cosine_similarity(
language_feature[mask[0]], language_feature[mask[1]], dim=-1
)
# Compute instance loss for negative pairs with margin and language similarity
instance_loss_2 = (
torch.relu(
margin - torch.norm(instance_feature[mask[0]] - instance_feature[mask[1]], p=2, dim=-1)
) * (1 + language_similarity)
).nansum()
total_loss += instance_loss_2
total_loss = total_loss / torch.sum(block_mask).float()
return 2 * total_loss
def ranking_loss(error, penalize_ratio=1.0, type="mean"):
sorted_error, _ = torch.sort(error.flatten(), descending=True)
k = int(penalize_ratio * len(sorted_error))
if k == 0:
return torch.tensor(0.0, device=error.device)
selected_error = sorted_error[:k]
if type == "mean":
return torch.mean(selected_error)
elif type == "sum":
return torch.sum(selected_error)
else:
raise ValueError(f"Unsupported type: {type}")
|