Error when create loss function custom

1

RuntimeError: smooth_l1_loss_forward is not implemented for type torch.cuda.IntTensor
-> 1687 return torch._C._nn.smooth_l1_loss(input, target, reduction)

code:

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

class AGLoss(nn.Module):
	def __init__(self):
		super(AGLoss, self).__init__()

	# def forward(self, gender_preds, gender_targets):
	def forward(self, age_preds, age_targets, gender_preds, gender_targets):
		"""Compute loss between (age_preds, age_targets) and (gender_preds, gender_targets)"""
		age_prob = F.softmax(age_preds, dim=1).cuda()
		
		age_expect = torch.from_numpy(np.array([torch.argmax(age_prob[i]) + 1 for i in range(0, 128)])).int().cuda()

		print(age_prob.shape)
		print(age_expect.shape)
		print(gender_preds.shape)
		print(gender_preds.shape)
		age_loss = F.smooth_l1_loss(age_expect, age_targets)
		gender_loss = F.binary_cross_entropy_with_logits(gender_preds.float().cuda(), gender_targets.float().cuda())
		print("age_loss: %.3f | gender_loss: %.3f" & (age_loss.data[0], gender_loss.data[0]), end='|')
		# print("gender_loss: {}".format(gender_loss.data[0]))
		return age_loss + gender_loss
		# return gender_loss```
2

That is because you need floatTensors to pass into SmoothL1. L1 loss is for regression, it can only be done for continuous tensors like floatTensors. IntTensors or LongTensors are not continuous (they are discrete).

So your code should be

age_loss = F.smooth_l1_loss(age_expect.float(), age_targets.float())
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