def test_single_block(self):
in_planes = 8
out_planes = 16
blocks = [self.contrust_jasper_block(inplanes=in_planes, planes=out_planes)]
block = jasper.ParallelBlock(blocks)
x = torch.randn(1, in_planes, 140)
xlen = torch.tensor([131])
y, ylen = block(([x], xlen))
assert y[0].shape == torch.Size([1, out_planes, 140])
assert ylen[0] == 131
def test_blocks_with_different_input_output_channels_sum_residual(self):
blocks = []
in_planes = 8
out_planes = 16
for _ in range(2):
blocks.append(self.contrust_jasper_block(inplanes=in_planes, planes=out_planes))
block = jasper.ParallelBlock(blocks, residual_mode='sum')
x = torch.randn(1, in_planes, 140)
xlen = torch.tensor([131])
with pytest.raises(RuntimeError):
block(([x], xlen))
def test_tower_dropout(self):
blocks = []
in_planes = 8
out_planes = 8
for _ in range(2):
blocks.append(self.contrust_jasper_block(inplanes=in_planes, planes=out_planes))
block = jasper.ParallelBlock(blocks, aggregation_mode='dropout', block_dropout_prob=1.0)
x = torch.randn(1, in_planes, 140)
xlen = torch.tensor([131])
y, _ = block(([x], xlen))
# Tower dropout is 1.0, meaning that all towers have to be dropped, so only residual remains.
torch.testing.assert_allclose(y[0], x)
def test_blocks_with_different_input_output_channels_conv_residual(self):
blocks = []
in_planes = 8
out_planes = 16
for _ in range(2):
blocks.append(self.contrust_jasper_block(inplanes=in_planes, planes=out_planes))
block = jasper.ParallelBlock(blocks, residual_mode='conv', in_filters=in_planes, out_filters=out_planes)
x = torch.randn(1, in_planes, 140)
xlen = torch.tensor([131])
y, ylen = block(([x], xlen))
assert y[0].shape == torch.Size([1, out_planes, 140])
assert ylen[0] == 131