model/loss.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

class JointsMSELoss(nn.Module):
    def __init__(self, use_target_weight):
        super(JointsMSELoss, self).__init__()
        self.criterion = nn.MSELoss(reduction='mean')
        self.use_target_weight = use_target_weight

    def forward(self, output, target, target_weight):
        batch_size = output.size(0)
        num_joints = output.size(1)
        heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, 1)
        heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1)
        loss = 0

        for idx in range(num_joints):
            heatmap_pred = heatmaps_pred[idx].squeeze()
            heatmap_gt = heatmaps_gt[idx].squeeze()
            if self.use_target_weight:
                loss += 0.5 * self.criterion(
                    heatmap_pred.mul(target_weight[:, idx]),
                    heatmap_gt.mul(target_weight[:, idx])
                )
            else:
                loss += 0.5 * self.criterion(heatmap_pred, heatmap_gt)

        return loss / num_joints
    

class NMTCritierion(nn.Module):
    def __init__(self, label_smoothing=0.0):
        super(NMTCritierion, self).__init__()
        self.label_smoothing = label_smoothing
        self.LogSoftmax = nn.LogSoftmax(dim=1) #[B,LOGITS]
 
        if label_smoothing > 0:
            self.criterion_ = nn.KLDivLoss(reduction='none')
        else:
            self.criterion_ = nn.NLLLoss(reduction='none', ignore_index=100000)
        self.confidence = 1.0 - label_smoothing
 
    def _smooth_label(self, num_tokens):
        one_hot = torch.randn(1, num_tokens)
        one_hot.fill_(self.label_smoothing / (num_tokens - 1))
        return one_hot
 
    def _bottle(self, v):
        return v.view(-1, v.size(2))
 
    def criterion(self, dec_outs, labels):
        scores = self.LogSoftmax(dec_outs)
        num_tokens = scores.size(-1)
 
        # conduct label_smoothing module
        gtruth = labels.view(-1)
        if self.confidence < 1:
            tdata = gtruth.detach()
            one_hot = self._smooth_label(num_tokens)  # Do label smoothing, shape is [M]
            if labels.is_cuda:
                one_hot = one_hot.cuda()
            tmp_ = one_hot.repeat(gtruth.size(0), 1)  # [N, M]
#             print(tmp_)
#             print(tdata)
            tmp_.scatter_(1, tdata.unsqueeze(1), self.confidence)  # after tdata.unsqueeze(1) , tdata shape is [N,1]
            gtruth = tmp_.detach()
#         print(scores.shape,gtruth.shape)
        loss = torch.sum(self.criterion_(scores, gtruth), dim=1)
        return loss

    def forward(self, output_x, output_y, target, target_weight):
        batch_size = output_x.size(0)
        num_joints = output_x.size(1)
        loss = 0
#         print(output_x.shape,output_y.shape,target.shape)
        for idx in range(num_joints):
            coord_x_pred = output_x[:,idx]
            coord_y_pred = output_y[:,idx]
#             print(target_weight.shape)
            coord_gt = target[:,idx]
            weight = target_weight[:,idx]

            loss += self.criterion(coord_x_pred,coord_gt[:,0]).mul(weight).sum()
            loss += self.criterion(coord_y_pred,coord_gt[:,1]).mul(weight).sum()
        return loss / batch_size