def test_vq_shape():
inputs = torch.rand((2, 3, 5, 7), dtype=torch.float32, requires_grad=True)
codebook = torch.rand((11, 7), dtype=torch.float32, requires_grad=True)
indices = vq(inputs, codebook)
assert indices.size() == (2, 3, 5)
assert not indices.requires_grad
assert indices.dtype == torch.int64
def test_vq():
inputs = torch.rand((2, 3, 5, 7), dtype=torch.float32, requires_grad=True)
codebook = torch.rand((11, 7), dtype=torch.float32, requires_grad=True)
indices = vq(inputs, codebook)
differences = inputs.unsqueeze(3) - codebook
distances = torch.norm(differences, p=2, dim=4)
_, indices_torch = torch.min(distances, dim=3)
assert np.allclose(indices.numpy(), indices_torch.numpy())
def test_vq_st_gradient2():
inputs = torch.rand((2, 3, 5, 7), dtype=torch.float32, requires_grad=True)
codebook = torch.rand((11, 7), dtype=torch.float32, requires_grad=True)
codes, _ = vq_st(inputs, codebook)
indices = vq(inputs, codebook)
codes_torch = torch.embedding(codebook, indices, padding_idx=-1,
scale_grad_by_freq=False, sparse=False)
grad_output = torch.rand((2, 3, 5, 7), dtype=torch.float32)
grad_codebook, = torch.autograd.grad(codes, codebook,
grad_outputs=[grad_output])
grad_codebook_torch, = torch.autograd.grad(codes_torch, codebook,
grad_outputs=[grad_output])
# Gradient is the same as torch.embedding function
assert grad_codebook.size() == (11, 7)
assert np.allclose(grad_codebook.numpy(), grad_codebook_torch.numpy())
def forward(self, z_e_x):
z_e_x_ = z_e_x.permute(0, 2, 3, 1).contiguous()
latents = vq(z_e_x_, self.embedding.weight)
return latents
