def test_fastscnn_backbone():
with pytest.raises(AssertionError):
# Fast-SCNN channel constraints.
FastSCNN(
3, (32, 48),
64, (64, 96, 128), (2, 2, 1),
global_out_channels=127,
higher_in_channels=64,
lower_in_channels=128)
# Test FastSCNN Standard Forward
model = FastSCNN()
model.init_weights()
model.train()
batch_size = 4
imgs = torch.randn(batch_size, 3, 512, 1024)
feat = model(imgs)
assert len(feat) == 3
# higher-res
assert feat[0].shape == torch.Size([batch_size, 64, 64, 128])
# lower-res
assert feat[1].shape == torch.Size([batch_size, 128, 16, 32])
# FFM output
assert feat[2].shape == torch.Size([batch_size, 128, 64, 128])
def test_fastscnn_backbone():
with pytest.raises(AssertionError):
# Fast-SCNN channel constraints.
FastSCNN(3, (32, 48),
64, (64, 96, 128), (2, 2, 1),
global_out_channels=127,
higher_in_channels=64,
lower_in_channels=128)
# Test FastSCNN Standard Forward
model = FastSCNN(
in_channels=3,
downsample_dw_channels=(4, 6),
global_in_channels=8,
global_block_channels=(8, 12, 16),
global_block_strides=(2, 2, 1),
global_out_channels=16,
higher_in_channels=8,
lower_in_channels=16,
fusion_out_channels=16,
)
model.init_weights()
model.train()
batch_size = 4
imgs = torch.randn(batch_size, 3, 64, 128)
feat = model(imgs)
assert len(feat) == 3
# higher-res
assert feat[0].shape == torch.Size([batch_size, 8, 8, 16])
# lower-res
assert feat[1].shape == torch.Size([batch_size, 16, 2, 4])
# FFM output
assert feat[2].shape == torch.Size([batch_size, 16, 8, 16])