def test_multiply(): dims = [3] sparse_a = SparseTensor(dims) dense_a = DenseTensor(dims) ref_a = np.zeros(dims) sparse_b = SparseTensor(dims) dense_b = DenseTensor(dims) ref_b = np.zeros(dims) sparse_a[0] = 3 dense_a[0] = 3 ref_a[0] = 3 sparse_b[0] = 2 dense_b[0] = 2 ref_b[0] = 2 ref_a_b = ref_a * ref_b assert ((sparse_a * sparse_b).to_nparray() == ref_a_b).all() assert ((dense_a * dense_b).to_nparray() == ref_a_b).all() assert ((dense_a * sparse_b).to_nparray() == ref_a_b).all() assert ((sparse_a * dense_b).to_nparray() == ref_a_b).all() dims = [2, 5] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) sparse[0] = 1 dense[0] = 1 ref[0] = 1 assert ((sparse * 5).to_nparray() == (ref * 5)).all() assert ((dense * 5).to_nparray() == (ref * 5)).all()
def test_eq(): dims = [3] a = SparseTensor(dims) b = SparseTensor(dims) assert (a == b).all() a = DenseTensor(dims) b = DenseTensor(dims) assert (a == b).all() dims = [2, 1] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) sparse[slice(1, None, 1)] = 3 dense[slice(1, None, 1)] = 3 ref[slice(1, None, 1)] = 3 assert (sparse == dense).all() assert (sparse[0].to_nparray() == sparse.to_nparray()[0]).all() assert (dense[0].to_nparray() == dense.to_nparray()[0]).all() dims = [5, 7, 1, 12] a = SparseTensor(dims) b = SparseTensor(dims) c = DenseTensor(dims) d = DenseTensor(dims) a[0, slice(1, -3, 2), :, slice(None, None, 4)] = 2 b[0, slice(1, -3, 2), :, slice(None, None, 4)] = 2 c[0, slice(1, -3, 2), :, slice(None, None, 4)] = 2 d[0, slice(1, -3, 2), :, slice(None, None, 4)] = 2 assert (a == b).all() assert (c == d).all() assert ((a == b) == (c == d)).all() assert (a == c).all()
def test_getitem(): dims = [3] sparse = SparseTensor(dims) ref = np.zeros(dims) sparse_slice = sparse[slice(2, 3, 2)] ref_slice = ref[slice(2, 3, 2)] assert sparse_slice.shape == ref_slice.shape sparse_slice = sparse[slice(1, 3)][slice(1, 2, 2)] ref_slice = ref[slice(1, 3)][slice(1, 2, 2)] assert sparse_slice.shape == ref_slice.shape dims = [2, 3] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) sparse[1, -2] = 1 dense[1, -2] = 1 ref[1, -2] = 1 assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() dims = [5, 3, 1, 10] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) assert sparse[:].shape == ref[:].shape assert dense[:].shape == ref[:].shape assert sparse[:, :].shape == ref[:, :].shape assert sparse[:, :].shape == ref[:, :].shape assert sparse[:, :, 0].shape == ref[:, :, 0].shape assert dense[:, :, 0].shape == ref[:, :, 0].shape assert sparse[:, :, slice(1, None, 2)].shape == ref[:, :, slice(1, None, 2)].shape assert dense[:, :, slice(1, None, 2)].shape == ref[:, :, slice(1, None, 2)].shape sparse_slice = sparse[slice(2), :][slice(1, None, 2)] dense_slice = dense[slice(2), :][slice(1, None, 2)] ref_slice = ref[slice(2), :][slice(1, None, 2)] assert sparse_slice.shape == ref_slice.shape assert (sparse_slice == dense_slice).all() for coord in [(3, 1, 0, 9), (3, -1, 0, 1)]: sparse[coord] = 1 dense[coord] = 1 ref[coord] = 1 assert sparse[coord] == ref[coord] assert dense[coord] == ref[coord] assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() coord = (3, slice(1, 3)) assert (sparse[coord].to_nparray() == ref[coord]).all() assert (dense[coord].to_nparray() == ref[coord]).all()
def __eq__(self, other): if isinstance(other, DenseTensor): return other == self elif isinstance(other, SparseTensor): left = other if other.shape == self.shape else other.broadcast( self.shape) right = self if self.shape == other.shape else self.broadcast( other.shape) eq = other == self.dtype(0) for coord, value in self.filled(): eq[coord] = other[coord] == value return eq else: eq = DenseTensor.ones(self.shape, np.bool) * (other == self.dtype(0)) for coord, value in self.filled(): eq[coord] = other == value return eq
def test_expand_dims(): dims = [3, 1, 5, 2] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) sparse[1, slice(None), slice(1, 5, 2), (0, 1)] = 1 dense[1, slice(None), slice(1, 5, 2), (0, 1)] = 1 ref[1, slice(None), slice(1, 5, 2), (0, 1)] = 1 assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() for axis in [3, 1, 0]: sparse = sparse.expand_dims(axis) dense = dense.expand_dims(axis) ref = np.expand_dims(ref, axis) assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() sparse = sparse.expand_dims(sparse.ndim) dense = dense.expand_dims(dense.ndim) ref = np.expand_dims(ref, ref.ndim) assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all()
def test_product(): dims = [3, 5, 2, 4] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) assert (sparse.product(2).to_nparray() == np.product(ref, 2)).all() assert (dense.product(2).to_nparray() == np.product(ref, 2)).all() sparse[:, :, 0, slice(None, None, 3)] = 2 dense[:, :, 0, slice(None, None, 3)] = 2 ref[:, :, 0, slice(None, None, 3)] = 2 for axis in range(4): assert (sparse.product(axis).to_nparray() == np.product(ref, axis)).all() assert (dense.product(axis).to_nparray() == np.product(ref, axis)).all() sparse = sparse.transpose()[:, 0].product(1).expand_dims(1) dense = dense.transpose()[:, 0].product(1).expand_dims(1) ref = np.expand_dims(np.product(np.transpose(ref)[:, 0], 1), 1) assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all()
def test_setitem(): dims = [3, 4] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) sparse[0, 1] = 1 dense[0, 1] = 1 ref[0, 1] = 1 assert (sparse == dense).all() assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() sparse_slice = sparse[slice(None, None, 2)][slice(None, -1, 3)] dense_slice = sparse[slice(None, None, 2)][slice(None, -1, 3)] ref_slice = ref[slice(None, None, 2)][slice(None, -1, 3)] assert sparse_slice.shape == ref_slice.shape assert dense_slice.shape == ref_slice.shape sparse[slice(None, None, 2)][slice(None, None, 3)] = 1 dense[slice(None, None, 2)][slice(None, None, 3)] = 1 ref[slice(None, None, 2)][slice(None, None, 3)] = 1 assert (sparse == dense).all() assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() dims = [5, 7, 3, 10] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) sparse[3, 0, 1, 9] = 1 dense[3, 0, 1, 9] = 1 ref[3, 0, 1, 9] = 1 assert (sparse == dense).all() assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() zero_out = (2, 0, slice(None), slice(1, -3, 3)) sparse[zero_out] = 0 dense[zero_out] = 0 ref[zero_out] = 0 assert (sparse == dense).all() assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() update = SparseTensor([3, 1]) update[2, 0] = 1 sparse[1] = update dense[1] = update ref[1] = update.to_nparray() assert (sparse == dense).all() assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() sparse[:, :, slice(1, None, 2)][slice(1, -3, 3)] = 3 dense[:, :, slice(1, None, 2)][slice(1, -3, 3)] = 3 ref[:, :, slice(1, None, 2)][slice(1, -3, 3)] = 3 assert (sparse == dense).all() assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() sparse[0, slice(None, None, 2)][slice(None, None, 3)] = 4 dense[0, slice(None, None, 2)][slice(None, None, 3)] = 4 ref[0, slice(None, None, 2)][slice(None, None, 3)] = 4 assert (sparse == dense).all() assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() sparse_slice = sparse[0] dense_slice = dense[0] ref_slice = ref[0] assert (sparse == dense).all() assert (sparse_slice.to_nparray() == ref_slice).all() assert (dense_slice.to_nparray() == ref_slice).all() sparse_slice = sparse[0, 0, :, :] dense_slice = dense[0, 0, :, :] ref_slice = ref[0, 0, :, :] assert (sparse_slice == dense_slice).all() assert (sparse_slice.to_nparray() == ref_slice).all() assert (dense_slice.to_nparray() == ref_slice).all() assert (sparse == dense).all() assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all()
def test_broadcast(): a1 = SparseTensor([2, 1, 3]) b1 = SparseTensor([2, 3, 1]) assert (a1.broadcast(b1.shape) == b1.broadcast(a1.shape)).all() a2 = DenseTensor([2, 1, 3]) b2 = DenseTensor([2, 3, 1]) assert (a2.broadcast(b2.shape) == b2.broadcast(a2.shape)).all() assert (a1.broadcast(b2.shape) == b2.broadcast(a1.shape)).all() assert (a2.broadcast(b1.shape) == b2.broadcast(a1.shape)).all() assert (a1.broadcast(b2.shape) == b1.broadcast(a2.shape)).all() ref_a = np.zeros([2, 1, 3]) a1[0] = 2 a2[0] = 2 ref_a[0] = 2 ref_b = np.zeros([2, 3, 1]) b1[0] = 3 b2[0] = 3 ref_b[0] = 3 a1_b1 = a1 * b1 a2_b1 = a2 * b1 a1_b2 = a1 * b2 a2_b2 = a2 * b2 ref_a_b = ref_a * ref_b assert a1_b1.shape == ref_a_b.shape assert a1_b2.shape == ref_a_b.shape assert a2_b1.shape == ref_a_b.shape assert a2_b2.shape == ref_a_b.shape assert (a1_b1.to_nparray() == ref_a_b).all() assert (a1_b2.to_nparray() == ref_a_b).all() assert (a2_b1.to_nparray() == ref_a_b).all() assert (a2_b2.to_nparray() == ref_a_b).all() a = SparseTensor([4, 2]) ref_a = np.zeros([4, 2]) a = a.broadcast([1, 4, 1]) ref_a = np.logical_and(ref_a, np.ones([1, 4, 1])) assert a.shape == ref_a.shape a = SparseTensor([4, 2], np.bool) ref_a = np.zeros([4, 2], np.bool) a[2] = True ref_a[2] = True assert (a.broadcast([1, 4, 1]).to_nparray() == np.logical_and( ref_a, np.ones([1, 4, 1]))).all() a = SparseTensor([4, 1, 1]) b = DenseTensor([4, 1, 1]) ref = np.zeros([4, 1, 1]) a = a.broadcast([4, 4, 1]) b = b.broadcast([4, 4, 1]) ref = np.logical_and(ref, np.ones([4, 4, 1])) assert a[2].shape == b[2].shape assert a[2].shape == ref[2].shape assert b[2].shape == ref[2].shape
def test_transpose(): dims = [3, 2] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) sparse[slice(2), 1] = 1 dense[slice(2), 1] = 1 ref[slice(2), 1] = 1 assert (sparse.transpose().to_nparray() == ref.transpose()).all() assert (sparse.transpose([0, 1]).to_nparray() == ref).all() assert (sparse.transpose([1, 0]).to_nparray() == np.transpose(ref, [1, 0])).all() assert (dense.transpose().to_nparray() == ref.transpose()).all() assert (dense.transpose([0, 1]).to_nparray() == ref).all() assert (dense.transpose([1, 0]).to_nparray() == np.transpose(ref, [1, 0])).all() dims = [5, 1, 8, 3] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) sparse[:, :, slice(None, None, 2)] = 1 dense[:, :, slice(None, None, 2)] = 1 ref[:, :, slice(None, None, 2)] = 1 assert (sparse.transpose().to_nparray() == ref.transpose()).all() assert (sparse.transpose([0, 2, 1, 3]).to_nparray() == np.transpose( ref, [0, 2, 1, 3])).all() assert (sparse.transpose([3, 1, 2, 0]).to_nparray() == np.transpose( ref, [3, 1, 2, 0])).all() assert (dense.transpose().to_nparray() == ref.transpose()).all() assert (dense.transpose([0, 2, 1, 3]).to_nparray() == np.transpose( ref, [0, 2, 1, 3])).all() assert (dense.transpose([3, 1, 2, 0]).to_nparray() == np.transpose( ref, [3, 1, 2, 0])).all() permutation = [3, 1, 0, 2] # shape = (3, 1, 5, 8) sparse_slice = sparse.transpose(permutation)[:, slice(None, 4), 0] dense_slice = dense.transpose(permutation)[:, slice(None, 4), 0] ref_slice = ref.transpose(permutation)[:, slice(None, 4), 0] assert sparse_slice.shape == ref_slice.shape assert dense_slice.shape == ref_slice.shape assert (sparse_slice.to_nparray() == ref_slice).all() assert (dense_slice.to_nparray() == ref_slice).all()
def test_sum(): dims = [2, 3, 4, 5] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) sparse[:, slice(None, None, 2)] = 1 dense[:, slice(None, None, 2)] = 1 ref[:, slice(None, None, 2)] = 1 assert (sparse.sum(0).to_nparray() == ref.sum(0)).all() assert (dense.sum(0).to_nparray() == ref.sum(0)).all() assert (sparse.sum(2).to_nparray() == ref.sum(2)).all() assert (dense.sum(2).to_nparray() == ref.sum(2)).all() sparse_transpose = sparse.transpose()[:, 0].sum(2).expand_dims(1) dense_transpose = dense.transpose()[:, 0].sum(2).expand_dims(1) ref_transpose = np.expand_dims(np.transpose(ref)[:, 0].sum(2), 1) assert (sparse_transpose.to_nparray() == ref_transpose).all() assert (dense_transpose.to_nparray() == ref_transpose).all() for axis in range(2): sparse = sparse.sum(0) dense = dense.sum(0) ref = ref.sum(0) assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all() dims = [3, 5, 2, 4] sparse = SparseTensor(dims) dense = DenseTensor(dims) ref = np.zeros(dims) assert (sparse.sum(2).to_nparray() == np.sum(ref, 2)).all() assert (dense.sum(2).to_nparray() == np.sum(ref, 2)).all() sparse[:, :, 0, slice(None, None, 3)] = 2 dense[:, :, 0, slice(None, None, 3)] = 2 ref[:, :, 0, slice(None, None, 3)] = 2 for axis in range(4): assert (sparse.sum(axis).to_nparray() == np.sum(ref, axis)).all() assert (dense.sum(axis).to_nparray() == np.sum(ref, axis)).all() for axis in range(3): sparse = sparse.sum(0) dense = dense.sum(0) ref = ref.sum(0) assert (sparse.to_nparray() == ref).all() assert (dense.to_nparray() == ref).all()