def test_point_head():
inputs = [torch.randn(1, 32, 45, 45)]
point_head = PointHead(
in_channels=[32], in_index=[0], channels=16, num_classes=19)
assert len(point_head.fcs) == 3
fcn_head = FCNHead(in_channels=32, channels=16, num_classes=19)
if torch.cuda.is_available():
head, inputs = to_cuda(point_head, inputs)
head, inputs = to_cuda(fcn_head, inputs)
prev_output = fcn_head(inputs)
test_cfg = ConfigDict(
subdivision_steps=2, subdivision_num_points=8196, scale_factor=2)
output = point_head.forward_test(inputs, prev_output, None, test_cfg)
assert output.shape == (1, point_head.num_classes, 180, 180)
def test_point_head():
inputs = [torch.randn(1, 32, 45, 45)]
point_head = PointHead(
in_channels=[32], in_index=[0], channels=16, num_classes=19)
assert len(point_head.fcs) == 3
fcn_head = FCNHead(in_channels=32, channels=16, num_classes=19)
if torch.cuda.is_available():
head, inputs = to_cuda(point_head, inputs)
head, inputs = to_cuda(fcn_head, inputs)
prev_output = fcn_head(inputs)
test_cfg = ConfigDict(
subdivision_steps=2, subdivision_num_points=8196, scale_factor=2)
output = point_head.forward_test(inputs, prev_output, None, test_cfg)
assert output.shape == (1, point_head.num_classes, 180, 180)
# test multiple losses case
inputs = [torch.randn(1, 32, 45, 45)]
point_head_multiple_losses = PointHead(
in_channels=[32],
in_index=[0],
channels=16,
num_classes=19,
loss_decode=[
dict(type='CrossEntropyLoss', loss_name='loss_1'),
dict(type='CrossEntropyLoss', loss_name='loss_2')
])
assert len(point_head_multiple_losses.fcs) == 3
fcn_head_multiple_losses = FCNHead(
in_channels=32,
channels=16,
num_classes=19,
loss_decode=[
dict(type='CrossEntropyLoss', loss_name='loss_1'),
dict(type='CrossEntropyLoss', loss_name='loss_2')
])
if torch.cuda.is_available():
head, inputs = to_cuda(point_head_multiple_losses, inputs)
head, inputs = to_cuda(fcn_head_multiple_losses, inputs)
prev_output = fcn_head_multiple_losses(inputs)
test_cfg = ConfigDict(
subdivision_steps=2, subdivision_num_points=8196, scale_factor=2)
output = point_head_multiple_losses.forward_test(inputs, prev_output, None,
test_cfg)
assert output.shape == (1, point_head.num_classes, 180, 180)
fake_label = torch.ones([1, 180, 180], dtype=torch.long)
if torch.cuda.is_available():
fake_label = fake_label.cuda()
loss = point_head_multiple_losses.losses(output, fake_label)
assert 'pointloss_1' in loss
assert 'pointloss_2' in loss