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Zero
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# Copyright (c) 2025 Ye Liu. Licensed under the BSD-3-Clause License.
import math
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from nncore.nn import LOSSES, Parameter, build_loss
@LOSSES.register()
class SampledNCELoss(nn.Module):
def __init__(self, temperature=0.07, max_scale=100, learnable=False, direction=('row', 'col'), loss_weight=1.0):
super().__init__()
scale = torch.Tensor([math.log(1 / temperature)])
if learnable:
self.scale = Parameter(scale)
else:
self.register_buffer('scale', scale)
self.temperature = temperature
self.max_scale = max_scale
self.learnable = learnable
self.direction = (direction, ) if isinstance(direction, str) else direction
self.loss_weight = loss_weight
def forward(self, video_emb, query_emb, video_msk, saliency, pos_clip):
batch_inds = torch.arange(video_emb.size(0), device=video_emb.device)
pos_scores = saliency[batch_inds, pos_clip].unsqueeze(-1)
loss_msk = (saliency <= pos_scores) * video_msk
if not loss_msk.any():
warnings.warn(f'loss_msk is all zeros: {loss_msk} {saliency} {video_msk} {pos_clip}')
scale = self.scale.exp().clamp(max=self.max_scale)
i_sim = F.cosine_similarity(video_emb, query_emb, dim=-1) * scale
i_sim = i_sim + torch.where(loss_msk > 0, .0, float('-inf'))
loss = 0
if 'row' in self.direction:
i_met = F.log_softmax(i_sim, dim=1)[batch_inds, pos_clip]
loss = loss - i_met.sum()
if 'col' in self.direction:
j_met = F.log_softmax(i_sim.t(), dim=1)[pos_clip, batch_inds]
loss = loss - j_met.sum() / j_met.size(0)
loss = loss * self.loss_weight
return loss
@LOSSES.register()
class BundleLoss(nn.Module):
def __init__(self, sample_radius=1.5, loss_cls=None, loss_reg=None, loss_sal=None):
super().__init__()
self._loss_cls = build_loss(loss_cls)
self._loss_reg = build_loss(loss_reg)
self._loss_sal = build_loss(loss_sal)
self.sample_radius = sample_radius
def get_target_single(self, point, gt_bnd, gt_cls):
num_pts, num_gts = point.size(0), gt_bnd.size(0)
lens = gt_bnd[:, 1] - gt_bnd[:, 0]
lens = lens[None, :].repeat(num_pts, 1)
gt_seg = gt_bnd[None].expand(num_pts, num_gts, 2)
s = point[:, 0, None] - gt_seg[:, :, 0]
e = gt_seg[:, :, 1] - point[:, 0, None]
r_tgt = torch.stack((s, e), dim=-1)
if self.sample_radius > 0:
center = (gt_seg[:, :, 0] + gt_seg[:, :, 1]) / 2
t_mins = center - point[:, 3, None] * self.sample_radius
t_maxs = center + point[:, 3, None] * self.sample_radius
dist_s = point[:, 0, None] - torch.maximum(t_mins, gt_seg[:, :, 0])
dist_e = torch.minimum(t_maxs, gt_seg[:, :, 1]) - point[:, 0, None]
center = torch.stack((dist_s, dist_e), dim=-1)
cls_msk = center.min(-1)[0] >= 0
else:
cls_msk = r_tgt.min(-1)[0] >= 0
reg_dist = r_tgt.max(-1)[0]
reg_msk = torch.logical_and((reg_dist >= point[:, 1, None]), (reg_dist <= point[:, 2, None]))
lens.masked_fill_(cls_msk == 0, float('inf'))
lens.masked_fill_(reg_msk == 0, float('inf'))
min_len, min_len_inds = lens.min(dim=1)
min_len_mask = torch.logical_and((lens <= (min_len[:, None] + 1e-3)), (lens < float('inf'))).to(r_tgt.dtype)
label = F.one_hot(gt_cls[:, 0], 2).to(r_tgt.dtype)
c_tgt = torch.matmul(min_len_mask, label).clamp(min=0.0, max=1.0)[:, 1]
r_tgt = r_tgt[range(num_pts), min_len_inds] / point[:, 3, None]
return c_tgt, r_tgt
def get_target(self, data):
cls_tgt, reg_tgt = [], []
for i in range(data['boundary'].size(0)):
gt_bnd = data['boundary'][i] * data['video_emb'].size(1)
# gt_bnd = data['boundary'][i]
gt_cls = gt_bnd.new_ones(gt_bnd.size(0), 1).long()
c_tgt, r_tgt = self.get_target_single(data['point'], gt_bnd, gt_cls)
cls_tgt.append(c_tgt)
reg_tgt.append(r_tgt)
cls_tgt = torch.stack(cls_tgt)
reg_tgt = torch.stack(reg_tgt)
return cls_tgt, reg_tgt
def loss_cls(self, data, output, cls_tgt):
src = data['out_class'].squeeze(-1)
msk = torch.cat(data['pymid_msk'], dim=1)
cls_tgt = cls_tgt.repeat(src.size(0) // cls_tgt.size(0), 1)
loss_cls = self._loss_cls(src, cls_tgt, weight=msk)
loss_cls = (loss_cls.sum(dim=1) / msk.sum(dim=1)).sum()
output['loss_cls'] = loss_cls
return output
def loss_reg(self, data, output, cls_tgt, reg_tgt):
src = data['out_coord']
msk = cls_tgt.unsqueeze(2).repeat(1, 1, 2).bool()
assert msk.any(), 'empty mask in reg loss'
reg_tgt = reg_tgt.repeat(src.size(0) // reg_tgt.size(0), 1, 1)
msk = msk.repeat(src.size(0) // msk.size(0), 1, 1)
loss_reg = self._loss_reg(src, reg_tgt, weight=msk)
loss_reg = (loss_reg.sum(dim=[1, 2]) / msk.sum(dim=[1, 2])).sum()
output['loss_reg'] = loss_reg
return output
def loss_sal(self, data, output):
video_emb = data['video_emb']
query_emb = data['query_emb']
video_msk = data['video_msk']
saliency = data['saliency']
pos_clip = data['pos_clip'][:, 0]
saliency = saliency.repeat(video_emb.size(0) // saliency.size(0), 1)
pos_clip = pos_clip.repeat(video_emb.size(0) // pos_clip.size(0))
output['loss_sal'] = self._loss_sal(video_emb, query_emb, video_msk, saliency, pos_clip)
return output
def forward(self, data, output):
if self._loss_reg is not None:
cls_tgt, reg_tgt = self.get_target(data)
output = self.loss_reg(data, output, cls_tgt, reg_tgt)
else:
cls_tgt = data['saliency']
if self._loss_cls is not None:
output = self.loss_cls(data, output, cls_tgt)
if self._loss_sal is not None:
output = self.loss_sal(data, output)
return output
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