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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F
from mmaction.models import HVULoss
def test_hvu_loss():
pred = torch.tensor([[-1.0525, -0.7085, 0.1819, -0.8011],
[0.1555, -1.5550, 0.5586, 1.9746]])
gt = torch.tensor([[1., 0., 0., 0.], [0., 0., 1., 1.]])
mask = torch.tensor([[1., 1., 0., 0.], [0., 0., 1., 1.]])
category_mask = torch.tensor([[1., 0.], [0., 1.]])
categories = ['action', 'scene']
category_nums = (2, 2)
category_loss_weights = (1, 1)
loss_all_nomask_sum = HVULoss(
categories=categories,
category_nums=category_nums,
category_loss_weights=category_loss_weights,
loss_type='all',
with_mask=False,
reduction='sum')
loss = loss_all_nomask_sum(pred, gt, mask, category_mask)
loss1 = F.binary_cross_entropy_with_logits(pred, gt, reduction='none')
loss1 = torch.sum(loss1, dim=1)
assert torch.eq(loss['loss_cls'], torch.mean(loss1))
loss_all_mask = HVULoss(
categories=categories,
category_nums=category_nums,
category_loss_weights=category_loss_weights,
loss_type='all',
with_mask=True)
loss = loss_all_mask(pred, gt, mask, category_mask)
loss1 = F.binary_cross_entropy_with_logits(pred, gt, reduction='none')
loss1 = torch.sum(loss1 * mask, dim=1) / torch.sum(mask, dim=1)
loss1 = torch.mean(loss1)
assert torch.eq(loss['loss_cls'], loss1)
loss_ind_mask = HVULoss(
categories=categories,
category_nums=category_nums,
category_loss_weights=category_loss_weights,
loss_type='individual',
with_mask=True)
loss = loss_ind_mask(pred, gt, mask, category_mask)
action_loss = F.binary_cross_entropy_with_logits(pred[:1, :2], gt[:1, :2])
scene_loss = F.binary_cross_entropy_with_logits(pred[1:, 2:], gt[1:, 2:])
loss1 = (action_loss + scene_loss) / 2
assert torch.eq(loss['loss_cls'], loss1)
loss_ind_nomask_sum = HVULoss(
categories=categories,
category_nums=category_nums,
category_loss_weights=category_loss_weights,
loss_type='individual',
with_mask=False,
reduction='sum')
loss = loss_ind_nomask_sum(pred, gt, mask, category_mask)
action_loss = F.binary_cross_entropy_with_logits(
pred[:, :2], gt[:, :2], reduction='none')
action_loss = torch.sum(action_loss, dim=1)
action_loss = torch.mean(action_loss)
scene_loss = F.binary_cross_entropy_with_logits(
pred[:, 2:], gt[:, 2:], reduction='none')
scene_loss = torch.sum(scene_loss, dim=1)
scene_loss = torch.mean(scene_loss)
loss1 = (action_loss + scene_loss) / 2
assert torch.eq(loss['loss_cls'], loss1)
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