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