| |
|
| | import pytest
|
| | import torch
|
| | import torch.nn as nn
|
| |
|
| | from mmaction.models import ResNetTIN
|
| | from mmaction.testing import generate_backbone_demo_inputs
|
| |
|
| |
|
| | @pytest.mark.skipif(
|
| | not torch.cuda.is_available(), reason='requires CUDA support')
|
| | def test_resnet_tin_backbone():
|
| | """Test resnet_tin backbone."""
|
| | with pytest.raises(AssertionError):
|
| |
|
| | resnet_tin = ResNetTIN(50, num_segments=-1)
|
| | resnet_tin.init_weights()
|
| |
|
| | from mmaction.models.backbones.resnet_tin import (CombineNet,
|
| | TemporalInterlace)
|
| |
|
| |
|
| | resnet_tin = ResNetTIN(50)
|
| | resnet_tin.init_weights()
|
| | for layer_name in resnet_tin.res_layers:
|
| | layer = getattr(resnet_tin, layer_name)
|
| | blocks = list(layer.children())
|
| | for block in blocks:
|
| | assert isinstance(block.conv1.conv, CombineNet)
|
| | assert isinstance(block.conv1.conv.net1, TemporalInterlace)
|
| | assert (
|
| | block.conv1.conv.net1.num_segments == resnet_tin.num_segments)
|
| | assert block.conv1.conv.net1.shift_div == resnet_tin.shift_div
|
| |
|
| |
|
| | resnet_tin_pbn = ResNetTIN(50, partial_bn=True)
|
| | resnet_tin_pbn.train()
|
| | count_bn = 0
|
| | for m in resnet_tin_pbn.modules():
|
| | if isinstance(m, nn.BatchNorm2d):
|
| | count_bn += 1
|
| | if count_bn >= 2:
|
| | assert m.training is False
|
| | assert m.weight.requires_grad is False
|
| | assert m.bias.requires_grad is False
|
| | else:
|
| | assert m.training is True
|
| | assert m.weight.requires_grad is True
|
| | assert m.bias.requires_grad is True
|
| |
|
| | input_shape = (8, 3, 64, 64)
|
| | imgs = generate_backbone_demo_inputs(input_shape).cuda()
|
| | resnet_tin = resnet_tin.cuda()
|
| |
|
| |
|
| | feat = resnet_tin(imgs)
|
| | assert feat.shape == torch.Size([8, 2048, 2, 2])
|
| |
|
