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# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmdet3d.models.decode_heads import DGCNNHead
from mmdet3d.structures import Det3DDataSample, PointData
class TestDGCNNHead(TestCase):
def test_dgcnn_head_loss(self):
"""Tests DGCNN head loss."""
dgcnn_head = DGCNNHead(
fp_channels=(1024, 512),
channels=256,
num_classes=13,
dropout_ratio=0.5,
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
act_cfg=dict(type='LeakyReLU', negative_slope=0.2),
loss_decode=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=False,
class_weight=None,
loss_weight=1.0),
ignore_index=13)
# DGCNN head expects dict format features
fa_points = torch.rand(1, 4096, 1024).float()
feat_dict = dict(fa_points=fa_points)
# Test forward
seg_logits = dgcnn_head.forward(feat_dict)
self.assertEqual(seg_logits.shape, torch.Size([1, 13, 4096]))
# When truth is non-empty then losses
# should be nonzero for random inputs
pts_semantic_mask = torch.randint(0, 13, (4096, )).long()
gt_pts_seg = PointData(pts_semantic_mask=pts_semantic_mask)
datasample = Det3DDataSample()
datasample.gt_pts_seg = gt_pts_seg
gt_losses = dgcnn_head.loss_by_feat(seg_logits, [datasample])
gt_sem_seg_loss = gt_losses['loss_sem_seg'].item()
self.assertGreater(gt_sem_seg_loss, 0,
'semantic seg loss should be positive')
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