def test_slowonly_backbone():
"""Test SlowOnly backbone."""
with pytest.raises(AssertionError):
# SlowOnly should contain no lateral connection
ResNet3dSlowOnly(50, None, lateral=True)
# test SlowOnly for PoseC3D
so_50 = ResNet3dSlowOnly(
depth=50,
pretrained=None,
in_channels=17,
base_channels=32,
num_stages=3,
out_indices=(2, ),
stage_blocks=(4, 6, 3),
conv1_stride_s=1,
pool1_stride_s=1,
inflate=(0, 1, 1),
spatial_strides=(2, 2, 2),
temporal_strides=(1, 1, 2),
dilations=(1, 1, 1))
so_50.init_weights()
so_50.train()
# test SlowOnly with normal config
so_50 = ResNet3dSlowOnly(50, None)
so_50.init_weights()
so_50.train()
# SlowOnly inference test
input_shape = (1, 3, 8, 64, 64)
imgs = generate_backbone_demo_inputs(input_shape)
# parrots 3dconv is only implemented on gpu
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
so_50 = so_50.cuda()
imgs_gpu = imgs.cuda()
feat = so_50(imgs_gpu)
else:
feat = so_50(imgs)
assert feat.shape == torch.Size([1, 2048, 8, 2, 2])
def test_slowonly_backbone():
"""Test SlowOnly backbone."""
with pytest.raises(AssertionError):
# SlowOnly should contain no lateral connection
ResNet3dSlowOnly(50, None, lateral=True)
# test SlowOnly with normal config
so_50 = ResNet3dSlowOnly(50, None)
so_50.init_weights()
so_50.train()
# SlowOnly inference test
input_shape = (1, 3, 8, 64, 64)
imgs = _demo_inputs(input_shape)
# parrots 3dconv is only implemented on gpu
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
so_50 = so_50.cuda()
imgs_gpu = imgs.cuda()
feat = so_50(imgs_gpu)
else:
feat = so_50(imgs)
assert feat.shape == torch.Size([1, 2048, 8, 2, 2])