Add causal person fusion and MuVAP stage-3 baselines
This commit is contained in:
Xin Wang
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from criterions.loss_builder import CRITERIONS
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@CRITERIONS.register('NONUNIFORM')
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class OadLoss(nn.Module):
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def __init__(self, cfg, reduction='mean'):
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super(OadLoss, self).__init__()
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self.reduction = reduction
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self.num_classes = cfg['num_classes']
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self.loss_offset = cfg['loss_offset'] if 'loss_offset' in cfg else 0
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assert self.loss_offset==0, f'loss_offset is not 0: {self.loss_offset}'
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self.loss = self.end_loss
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def end_loss(self, out_dict, target):
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# logits: (B, seq, K) target: (B, seq, K)
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assert self.loss_offset < out_dict['logits'].shape[1], f'loss_offset is longer than the sequence loss_offset: {self.loss_offset}, logits.shape: {out_dict["logits"].shape}'
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logits = out_dict['logits']
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logits = logits[:,-1 - self.loss_offset,:].contiguous()
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target = target[:,-1 - self.loss_offset,:].contiguous()
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ce_loss = self.mlce_loss(logits, target)
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return ce_loss
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def mlce_loss(self, logits, target):
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'''
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multi label cross entropy loss.
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logits: (B, K) target: (B, K)
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'''
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logsoftmax = nn.LogSoftmax(dim=-1).to(logits.device)
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output = torch.sum(-F.normalize(target) * logsoftmax(logits), dim=1) # B
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if self.reduction == 'mean':
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loss = torch.mean(output)
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elif self.reduction == 'sum':
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loss = torch.sum(output)
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return loss
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def forward(self, out_dict, target):
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return self.loss(out_dict, target)
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@CRITERIONS.register('UNIFORM')
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class OadUniformLoss(nn.Module):
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def __init__(self, cfg, reduction='mean'):
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super(OadUniformLoss, self).__init__()
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self.reduction = reduction
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self.num_classes = cfg['num_classes']
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# self.loss_offset = cfg['loss_offset'] if 'loss_offset' in cfg else 0
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self.loss = self.end_loss
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def end_loss(self, out_dict, target):
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# logits: (B, seq, K) target: (B, seq, K)
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logits = out_dict['logits']
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# logits = logits[:,-1 - self.loss_offset,:].contiguous()
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# target = target[:,-1 - self.loss_offset,:].contiguous()
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ce_loss = self.mlce_loss(logits, target)
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return ce_loss
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def mlce_loss(self, logits, target):
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'''
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multi label cross entropy loss.
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logits: (B, K) target: (B, K)
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'''
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logsoftmax = nn.LogSoftmax(dim=-1).to(logits.device)
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output = torch.sum(-target * logsoftmax(logits), dim=1) # B
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# output = torch.sum(-F.normalize(target) * logsoftmax(logits), dim=1) # B
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if self.reduction == 'mean':
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loss = torch.mean(output)
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elif self.reduction == 'sum':
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loss = torch.sum(output)
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return loss
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def forward(self, out_dict, target):
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return self.loss(out_dict, target)
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@CRITERIONS.register('OADTR')
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class OadTRLoss(nn.Module):
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def __init__(self, cfg, reduction='mean'):
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super(OadTRLoss, self).__init__()
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self.reduction = reduction
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self.num_classes = cfg['num_classes']
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# self.loss_offset = cfg['loss_offset'] if 'loss_offset' in cfg else 0
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self.loss = self.end_loss
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def end_loss(self, out_dict, target):
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# logits: (B, seq, K) target: (B, seq, K)
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assert self.loss_offset < out_dict['logits'].shape[1], f'loss_offset is longer than the sequence loss_offset: {self.loss_offset}, logits.shape: {out_dict["logits"].shape}'
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logits = out_dict['logits']
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# logits = logits[:,-1 - self.loss_offset,:].contiguous()
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# target = target[:,-1 - self.loss_offset,:].contiguous()
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ce_loss = self.mlce_loss(logits, target)
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return ce_loss
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def mlce_loss(self, logits, target):
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'''
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multi label cross entropy loss.
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logits: (B, K) target: (B, K)
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'''
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logsoftmax = nn.LogSoftmax(dim=-1).to(logits.device)
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output = torch.sum(-F.normalize(target) * logsoftmax(logits), dim=1) # B
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if self.reduction == 'mean':
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loss = torch.mean(output)
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elif self.reduction == 'sum':
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loss = torch.sum(output)
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return loss
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def forward(self, out_dict, enc_target, dec_target):
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return self.loss(out_dict, enc_target, dec_target)
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@CRITERIONS.register('ANTICIPATION')
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class OadAntLoss(nn.Module):
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# reduction was sum, not mean, what if we change to sum?
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def __init__(self, cfg, reduction='mean'):
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super(OadAntLoss, self).__init__()
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self.reduction = reduction
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self.loss = self.anticipation_loss
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self.num_classes = cfg['num_classes']
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def anticipation_loss(self, out_dict, target, ant_target):
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# anticipation_logits: (B, seq, ANTICIPATION_LENGTH, num_class)
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# ant_target: (B, ANTICIPATION_LENGTH, num_class)
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anticipation_logits = out_dict['anticipation_logits']
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# [B, window_size, anticipation_length, num_class]
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pred_anticipation_logits = anticipation_logits[:,-1,:,:].contiguous().view(-1, self.num_classes)
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anticipation_logit_targets = ant_target.view(-1, self.num_classes)
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ant_loss = self.mlce_loss(pred_anticipation_logits, anticipation_logit_targets)
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return ant_loss
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def ce_loss(self, out_dict, target):
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# logits: (B, seq, K) target: (B, seq, K)
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logits = out_dict['logits']
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logits = logits[:,-1,:].contiguous()
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target = target[:,-1,:].contiguous()
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ce_loss = self.mlce_loss(logits, target)
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return ce_loss
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def mlce_loss(self, logits, target):
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'''
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multi label cross entropy loss.
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logits: (B, K) target: (B, K)
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'''
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logsoftmax = nn.LogSoftmax(dim=-1).to(logits.device)
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output = torch.sum(-F.normalize(target) * logsoftmax(logits), dim=1) # B
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if self.reduction == 'mean':
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loss = torch.mean(output)
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elif self.reduction == 'sum':
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loss = torch.sum(output)
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return loss
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def forward(self, out_dict, target, ant_target):
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return self.loss(out_dict, target, ant_target)
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@CRITERIONS.register('UNIFORM_ANTICIPATION')
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class OadUniformAntLoss(nn.Module):
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# reduction was sum, not mean, what if we change to sum?
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def __init__(self, cfg, reduction='mean'):
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super(OadUniformAntLoss, self).__init__()
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self.reduction = reduction
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self.loss = self.anticipation_loss
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self.num_classes = cfg['num_classes']
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def anticipation_loss(self, out_dict, target, ant_target):
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anticipation_logits = out_dict['anticipation_logits']
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pred_anticipation_logits = anticipation_logits[:,-1,:,:].contiguous().view(-1, self.num_classes)
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anticipation_logit_targets = ant_target.view(-1, self.num_classes)
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ant_loss = self.mlce_loss(pred_anticipation_logits, anticipation_logit_targets)
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return ant_loss
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def ce_loss(self, out_dict, target):
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# logits: (B, seq, K) target: (B, seq, K)
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logits = out_dict['logits']
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logits = logits[:,-1,:].contiguous()
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target = target[:,-1,:].contiguous()
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ce_loss = self.mlce_loss(logits, target)
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return ce_loss
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def mlce_loss(self, logits, target):
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'''
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multi label cross entropy loss.
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logits: (B, K) target: (B, K)
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'''
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logsoftmax = nn.LogSoftmax(dim=-1).to(logits.device)
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output = torch.sum(-target * logsoftmax(logits), dim=1) # B
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# output = torch.sum(-F.normalize(target) * logsoftmax(logits), dim=1) # B
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if self.reduction == 'mean':
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loss = torch.mean(output)
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elif self.reduction == 'sum':
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loss = torch.sum(output)
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return loss
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def forward(self, out_dict, target, ant_target):
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return self.loss(out_dict, target, ant_target)
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