def test_sparseblockouter(self):
o = tensor.ftensor4()
x = tensor.ftensor3()
y = tensor.ftensor3()
xIdx = tensor.imatrix()
yIdx = tensor.imatrix()
out = self.outer_op(o, x, y, xIdx, yIdx)
f = aesara.function(
[o, x, y, xIdx, yIdx], out, on_unused_input="warn", mode=self.mode
)
(
o_val,
x_val,
y_val,
xIdx_val,
yIdx_val,
) = self.outer_data()
th_out = f(o_val, x_val, y_val, xIdx_val, yIdx_val)
ref_out = self.outer_numpy(o_val, x_val, y_val, xIdx_val, yIdx_val)
utt.assert_allclose(ref_out, th_out)
def test_sparseblockgemvF(self):
# Test the fortan order for W (which can happen in the grad for some
# graphs).
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.imatrix()
oIdx = tensor.imatrix()
o = self.gemv_op(
b.take(oIdx, axis=0),
tensor.DimShuffle((False, False, False, False), (0, 1, 3, 2))(
tensor.as_tensor_variable(W)
),
h,
iIdx,
oIdx,
)
f = aesara.function([W, h, iIdx, b, oIdx], o, mode=self.mode)
W_val, h_val, iIdx_val, b_val, oIdx_val = self.gemv_data()
th_out = f(np.swapaxes(W_val, 2, 3), h_val, iIdx_val, b_val, oIdx_val)
ref_out = self.gemv_numpy(
b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val
)
utt.assert_allclose(ref_out, th_out)
def test_outer_infershape(self):
o = tensor.ftensor4()
x = tensor.ftensor3()
y = tensor.ftensor3()
xIdx = tensor.imatrix()
yIdx = tensor.imatrix()
self._compile_and_check(
[o, x, y, xIdx, yIdx],
[self.outer_op(o, x, y, xIdx, yIdx)],
self.outer_data(),
self.outer_class,
)
def test_gemv_infershape(self):
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.imatrix()
oIdx = tensor.imatrix()
self._compile_and_check(
[W, h, iIdx, b, oIdx],
[self.gemv_op(b.take(oIdx, axis=0), W, h, iIdx, oIdx)],
self.gemv_data(),
self.gemv_class,
)
def test_dot_infershape(self):
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.imatrix()
oIdx = tensor.imatrix()
self._compile_and_check(
[W, h, iIdx, b, oIdx],
[sparse_block_dot(W, h, iIdx, b, oIdx)],
self.gemv_data(),
self.gemv_class,
)
def test_sparseblockgemv_grad_shape(self):
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.imatrix()
oIdx = tensor.imatrix()
o = self.gemv_op(b.take(oIdx, axis=0), W, h, iIdx, oIdx)
go = aesara.grad(o.sum(), [b, W, h])
f = aesara.function([W, h, iIdx, b, oIdx], go, mode=self.mode)
W_val, h_val, iIdx_val, b_val, oIdx_val = self.gemv_data()
# just make sure that it runs correcly and all the shapes are ok.
b_g, W_g, h_g = f(W_val, h_val, iIdx_val, b_val, oIdx_val)
assert b_g.shape == b_val.shape
assert h_g.shape == h_val.shape
assert W_g.shape == W_val.shape
def test_sparseblockgemv(self):
# Compares the numpy and aesara versions of sparseblockgemv.
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.imatrix()
oIdx = tensor.imatrix()
o = self.gemv_op(b.take(oIdx, axis=0), W, h, iIdx, oIdx)
f = aesara.function([W, h, iIdx, b, oIdx], o, mode=self.mode)
W_val, h_val, iIdx_val, b_val, oIdx_val = self.gemv_data()
th_out = f(W_val, h_val, iIdx_val, b_val, oIdx_val)
ref_out = self.gemv_numpy(
b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val
)
utt.assert_allclose(ref_out, th_out)
def test_infer_shape(self):
shape = (100, 40, 6, 3)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 1), mode="valid")],
[images],
Images2Neibs,
)
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 3), mode="valid")],
[images],
Images2Neibs,
)
shape = (100, 40, 5, 4)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 1), mode="ignore_borders")],
[images],
Images2Neibs,
)
shape = (100, 40, 5, 3)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 3), mode="ignore_borders")],
[images],
Images2Neibs,
)
shape = (100, 40, 6, 7)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 2), mode="ignore_borders")],
[images],
Images2Neibs,
)
shape = (100, 40, 5, 10)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(3, 3), mode="wrap_centered")],
[images],
Images2Neibs,
)
shape = (100, 40, 6, 4)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 1), mode="half")],
[images],
Images2Neibs,
)
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 3), mode="half")],
[images],
Images2Neibs,
)
shape = (100, 40, 6, 5)
images = np.ones(shape).astype("float32")
x = tt.ftensor4()
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 1), mode="full")],
[images],
Images2Neibs,
)
self._compile_and_check(
[x],
[images2neibs(x, neib_shape=(2, 3), mode="full")],
[images],
Images2Neibs,
)
def test_GpuCumOp4D(self, mode):
op_class = partial(self.op_class, mode=mode)
# Should not use the GPU version.
x = tt.ftensor4("x")
f = aesara.function([x], op_class(axis=1)(x), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort() if isinstance(n.op, CumOp)]