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
def _context_for_ohem_multiple_loss():
return 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')
])
def test_ocr_head():
inputs = [torch.randn(1, 8, 23, 23)]
ocr_head = OCRHead(
in_channels=8, channels=4, num_classes=19, ocr_channels=8)
fcn_head = FCNHead(in_channels=8, channels=4, num_classes=19)
if torch.cuda.is_available():
head, inputs = to_cuda(ocr_head, inputs)
head, inputs = to_cuda(fcn_head, inputs)
prev_output = fcn_head(inputs)
output = ocr_head(inputs, prev_output)
assert output.shape == (1, ocr_head.num_classes, 23, 23)
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 _context_for_ohem():
return FCNHead(in_channels=32, channels=16, num_classes=19)
def test_fcn_head():
with pytest.raises(AssertionError):
# num_convs must be not less than 0
FCNHead(num_classes=19, num_convs=-1)
# test no norm_cfg
head = FCNHead(in_channels=32, channels=16, num_classes=19)
for m in head.modules():
if isinstance(m, ConvModule):
assert not m.with_norm
# test with norm_cfg
head = FCNHead(in_channels=32,
channels=16,
num_classes=19,
norm_cfg=dict(type='SyncBN'))
for m in head.modules():
if isinstance(m, ConvModule):
assert m.with_norm and isinstance(m.bn, SyncBatchNorm)
# test concat_input=False
inputs = [torch.randn(1, 32, 45, 45)]
head = FCNHead(in_channels=32,
channels=16,
num_classes=19,
concat_input=False)
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert len(head.convs) == 2
assert not head.concat_input and not hasattr(head, 'conv_cat')
outputs = head(inputs)
assert outputs.shape == (1, head.num_classes, 45, 45)
# test concat_input=True
inputs = [torch.randn(1, 32, 45, 45)]
head = FCNHead(in_channels=32,
channels=16,
num_classes=19,
concat_input=True)
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert len(head.convs) == 2
assert head.concat_input
assert head.conv_cat.in_channels == 48
outputs = head(inputs)
assert outputs.shape == (1, head.num_classes, 45, 45)
# test kernel_size=3
inputs = [torch.randn(1, 32, 45, 45)]
head = FCNHead(in_channels=32, channels=16, num_classes=19)
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
for i in range(len(head.convs)):
assert head.convs[i].kernel_size == (3, 3)
assert head.convs[i].padding == 1
outputs = head(inputs)
assert outputs.shape == (1, head.num_classes, 45, 45)
# test kernel_size=1
inputs = [torch.randn(1, 32, 45, 45)]
head = FCNHead(in_channels=32, channels=16, num_classes=19, kernel_size=1)
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
for i in range(len(head.convs)):
assert head.convs[i].kernel_size == (1, 1)
assert head.convs[i].padding == 0
outputs = head(inputs)
assert outputs.shape == (1, head.num_classes, 45, 45)
# test num_conv
inputs = [torch.randn(1, 32, 45, 45)]
head = FCNHead(in_channels=32, channels=16, num_classes=19, num_convs=1)
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert len(head.convs) == 1
outputs = head(inputs)
assert outputs.shape == (1, head.num_classes, 45, 45)
# test num_conv = 0
inputs = [torch.randn(1, 32, 45, 45)]
head = FCNHead(in_channels=32,
channels=32,
num_classes=19,
num_convs=0,
concat_input=False)
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert isinstance(head.convs, torch.nn.Identity)
outputs = head(inputs)
assert outputs.shape == (1, head.num_classes, 45, 45)