def __call__(self, x: Array) -> Array:
"""Applies the equivariant transform to the inputs along the last two
dimensions (-2: features, -1: group elements)
"""
in_features = x.shape[-2]
x = x.reshape(*x.shape[:-1], self.n_cells, self.n_point)
x = x.transpose(0, 1, 3, 2)
x = x.reshape(*x.shape[:-1], *self.shape)
if self.use_bias:
bias = self.param(
"bias", self.bias_init, (self.features,), self.param_dtype
)
else:
bias = None
kernel = self.param(
"kernel",
self.kernel_init,
(self.features, in_features, self.n_point * self.n_cells),
self.param_dtype,
)
if self.mask is not None:
kernel = kernel * jnp.expand_dims(self.scaled_mask, (0, 1))
x, kernel, bias = promote_dtype(x, kernel, bias, dtype=None)
dtype = x.dtype
# Convert the convolutional kernel of shape (features, in_features, n_symm)
# to the expanded kernel of shape (features, in_features, n_point(in),
# n_point(out), *shape) used in FFT-based group convolutions
kernel = kernel[..., self.mapping]
x = jnp.fft.fftn(x, s=self.shape).reshape(*x.shape[:3], self.n_cells)
kernel = jnp.fft.fftn(kernel, s=self.shape).reshape(
*kernel.shape[:4], self.n_cells
)
x = lax.dot_general(
x, kernel, (((1, 2), (1, 2)), ((3,), (4,))), precision=self.precision
)
x = x.transpose(1, 2, 3, 0)
x = x.reshape(*x.shape[:3], *self.shape)
x = jnp.fft.ifftn(x, s=self.shape).reshape(*x.shape[:3], self.n_cells)
x = x.transpose(0, 1, 3, 2)
x = x.reshape(*x.shape[:2], -1)
if self.use_bias:
x += jnp.expand_dims(bias, (0, 2))
if jnp.can_cast(x, dtype):
return x
else:
return x.real
def __call__(self, x: Array) -> Array:
"""Applies the equivariant transform to the inputs along the last two
dimensions (-2: features, -1: group elements)
"""
dtype = jnp.promote_types(x.dtype, self.dtype)
x = jnp.asarray(x, dtype)
x = x.reshape(*x.shape[:-1], self.n_cells, self.n_point)
x = x.transpose(0, 1, 3, 2)
x = x.reshape(*x.shape[:-1], *self.shape)
kernel = self.param(
"kernel",
self.kernel_init,
(
self.out_features,
self.in_features,
self.n_point * self.n_cells,
),
self.dtype,
)
kernel = jnp.asarray(kernel, dtype)
if self.mask is not None:
kernel = kernel * jnp.expand_dims(self.mask, (0, 1))
kernel = self.make_kernel(kernel)
x = jnp.fft.fftn(x, s=self.shape).reshape(*x.shape[:3], self.n_cells)
kernel = jnp.fft.fftn(kernel,
s=self.shape).reshape(*kernel.shape[:4],
self.n_cells)
x = lax.dot_general(x,
kernel, (((1, 2), (1, 2)), ((3, ), (4, ))),
precision=self.precision)
x = x.transpose(1, 2, 3, 0)
x = x.reshape(*x.shape[:3], *self.shape)
x = jnp.fft.ifftn(x, s=self.shape).reshape(*x.shape[:3], self.n_cells)
x = x.transpose(0, 1, 3, 2)
x = x.reshape(*x.shape[:2], -1)
if self.use_bias:
bias = self.param("bias", self.bias_init, (self.out_features, ),
self.dtype)
bias = jnp.asarray(bias, dtype)
x += jnp.expand_dims(bias, (0, 2))
if jnp.can_cast(x, dtype):
return x
else:
return x.real
def __call__(self, x: Array) -> Array:
"""Applies the equivariant transform to the inputs along the last two
dimensions (-2: features, -1: group elements)
"""
in_features = x.shape[-2]
if self.use_bias:
bias = self.param(
"bias", self.bias_init, (self.features,), self.param_dtype
)
else:
bias = None
kernel = self.param(
"kernel",
self.kernel_init,
(self.features, in_features, self.n_symm),
self.param_dtype,
)
if self.mask is not None:
kernel = kernel * jnp.expand_dims(self.scaled_mask, (0, 1))
x, kernel, bias = promote_dtype(x, kernel, bias, dtype=None)
dtype = x.dtype
x = self.forward_ft(x)
kernel = self.forward_ft(kernel)
x = tuple(
lax.dot_general(
x[i], kernel[i], (((1, 4), (1, 3)), ((2,), (2,)))
).transpose(1, 3, 0, 2, 4)
for i in range(len(x))
)
x = self.inverse_ft(x)
if self.use_bias:
x += jnp.expand_dims(bias, (0, 2))
if jnp.can_cast(x, dtype):
return x
else:
return x.real
def __call__(self, x: Array) -> Array:
"""Applies the equivariant transform to the inputs along the last two
dimensions (-2: features, -1: group elements)
"""
dtype = jnp.promote_types(x.dtype, self.dtype)
x = jnp.asarray(x, dtype)
# TODO: Deprecated: Eventually remove and error if less than 3 dimensions
# infer in_features and ensure input dimensions (batch, in_features,n_sites)
if x.ndim < 3:
old_shape = x.shape
if x.ndim == 1:
x = jnp.expand_dims(x, (0, 1))
elif x.ndim == 2:
x = jnp.expand_dims(x, 1)
symm_input_warning(old_shape, x.shape, "DenseSymm")
in_features = x.shape[1]
x = x.reshape(*x.shape[:-1], self.n_cells, self.sites_per_cell)
x = x.transpose(0, 1, 3, 2)
x = x.reshape(*x.shape[:-1], *self.shape)
kernel = self.param(
"kernel",
self.kernel_init,
(self.features, in_features, self.n_cells * self.sites_per_cell),
self.dtype,
)
kernel = jnp.asarray(kernel, dtype)
if self.mask is not None:
kernel = kernel * jnp.expand_dims(self.scaled_mask, (0, 1))
# Converts the convolutional kernel of shape (features, in_features, n_sites)
# to the expanded kernel of shape (features, in_features, sites_per_cell,
# n_point, *shape) used in FFT-based group convolutions.
kernel = kernel[..., self.mapping]
x = jnp.fft.fftn(x, s=self.shape).reshape(*x.shape[:3], self.n_cells)
kernel = jnp.fft.fftn(kernel,
s=self.shape).reshape(*kernel.shape[:4],
self.n_cells)
# TODO: the batch ordering should be revised: batch dimensions should
# be leading
x = lax.dot_general(x,
kernel, (((1, 2), (1, 2)), ((3, ), (4, ))),
precision=self.precision)
x = x.transpose(1, 2, 3, 0)
x = x.reshape(*x.shape[:3], *self.shape)
x = jnp.fft.ifftn(x, s=self.shape).reshape(*x.shape[:3], self.n_cells)
x = x.transpose(0, 1, 3, 2).reshape(*x.shape[:2], -1)
if self.use_bias:
bias = self.param("bias", self.bias_init, (self.features, ),
self.dtype)
bias = jnp.asarray(bias, dtype)
x += jnp.expand_dims(bias, (0, 2))
if jnp.can_cast(x, dtype):
return x
else:
return x.real