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delete khop_sampler in geometric
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@DesmonDay
DesmonDay committed Aug 17, 2022
1 parent 2f4e475 commit 3e51d85
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208 changes: 0 additions & 208 deletions python/paddle/fluid/tests/unittests/test_graph_khop_sampler.py
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Expand Up @@ -226,213 +226,5 @@ def test_sample_result_static_without_eids(self):
self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])


class TestGeometricGraphKhopSampler(unittest.TestCase):

def setUp(self):
num_nodes = 20
edges = np.random.randint(num_nodes, size=(100, 2))
edges = np.unique(edges, axis=0)
edges_id = np.arange(0, len(edges))
sorted_edges = edges[np.argsort(edges[:, 1])]
sorted_eid = edges_id[np.argsort(edges[:, 1])]

# Calculate dst index cumsum counts.
dst_count = np.zeros(num_nodes)
dst_src_dict = {}
for dst in range(0, num_nodes):
true_index = sorted_edges[:, 1] == dst
dst_count[dst] = np.sum(true_index)
dst_src_dict[dst] = sorted_edges[:, 0][true_index]
dst_count = dst_count.astype("int64")
colptr = np.cumsum(dst_count)
colptr = np.insert(colptr, 0, 0)

self.row = sorted_edges[:, 0].astype("int64")
self.colptr = colptr.astype("int64")
self.sorted_eid = sorted_eid.astype("int64")
self.nodes = np.unique(np.random.randint(num_nodes,
size=5)).astype("int64")
self.sample_sizes = [5, 5]
self.dst_src_dict = dst_src_dict

def func_sample_result(self):
paddle.disable_static()
row = paddle.to_tensor(self.row)
colptr = paddle.to_tensor(self.colptr)
nodes = paddle.to_tensor(self.nodes)

edge_src, edge_dst, sample_index, reindex_nodes = \
paddle.geometric.khop_sampler(row, colptr,
nodes, self.sample_sizes,
return_eids=False)
# Reindex edge_src and edge_dst to original index.
edge_src = edge_src.reshape([-1])
edge_dst = edge_dst.reshape([-1])
sample_index = sample_index.reshape([-1])

for i in range(len(edge_src)):
edge_src[i] = sample_index[edge_src[i]]
edge_dst[i] = sample_index[edge_dst[i]]

for n in self.nodes:
edge_src_n = edge_src[edge_dst == n]
if edge_src_n.shape[0] == 0:
continue
# Ensure no repetitive sample neighbors.
self.assertTrue(
edge_src_n.shape[0] == paddle.unique(edge_src_n).shape[0])
# Ensure the correct sample size.
self.assertTrue(edge_src_n.shape[0] == self.sample_sizes[0]
or edge_src_n.shape[0] == len(self.dst_src_dict[n]))
in_neighbors = np.isin(edge_src_n.numpy(), self.dst_src_dict[n])
# Ensure the correct sample neighbors.
self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])

def test_sample_result(self):
with fluid.framework._test_eager_guard():
self.func_sample_result()
self.func_sample_result()

def func_uva_sample_result(self):
paddle.disable_static()
if paddle.fluid.core.is_compiled_with_cuda():
row = None
if fluid.framework.in_dygraph_mode():
row = paddle.fluid.core.eager.to_uva_tensor(
self.row.astype(self.row.dtype), 0)
sorted_eid = paddle.fluid.core.eager.to_uva_tensor(
self.sorted_eid.astype(self.sorted_eid.dtype), 0)
else:
row = paddle.fluid.core.to_uva_tensor(
self.row.astype(self.row.dtype))
sorted_eid = paddle.fluid.core.to_uva_tensor(
self.sorted_eid.astype(self.sorted_eid.dtype))
colptr = paddle.to_tensor(self.colptr)
nodes = paddle.to_tensor(self.nodes)

edge_src, edge_dst, sample_index, reindex_nodes, edge_eids = \
paddle.geometric.khop_sampler(row, colptr,
nodes, self.sample_sizes,
sorted_eids=sorted_eid,
return_eids=True)
edge_src = edge_src.reshape([-1])
edge_dst = edge_dst.reshape([-1])
sample_index = sample_index.reshape([-1])

for i in range(len(edge_src)):
edge_src[i] = sample_index[edge_src[i]]
edge_dst[i] = sample_index[edge_dst[i]]

for n in self.nodes:
edge_src_n = edge_src[edge_dst == n]
if edge_src_n.shape[0] == 0:
continue
self.assertTrue(
edge_src_n.shape[0] == paddle.unique(edge_src_n).shape[0])
self.assertTrue(
edge_src_n.shape[0] == self.sample_sizes[0]
or edge_src_n.shape[0] == len(self.dst_src_dict[n]))
in_neighbors = np.isin(edge_src_n.numpy(), self.dst_src_dict[n])
self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])

def test_uva_sample_result(self):
with fluid.framework._test_eager_guard():
self.func_uva_sample_result()
self.func_uva_sample_result()

def test_sample_result_static_with_eids(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
row = paddle.static.data(name="row",
shape=self.row.shape,
dtype=self.row.dtype)
sorted_eids = paddle.static.data(name="eids",
shape=self.sorted_eid.shape,
dtype=self.sorted_eid.dtype)
colptr = paddle.static.data(name="colptr",
shape=self.colptr.shape,
dtype=self.colptr.dtype)
nodes = paddle.static.data(name="nodes",
shape=self.nodes.shape,
dtype=self.nodes.dtype)

edge_src, edge_dst, sample_index, reindex_nodes, edge_eids = \
paddle.geometric.khop_sampler(row, colptr,
nodes, self.sample_sizes,
sorted_eids, True)
exe = paddle.static.Executor(paddle.CPUPlace())
ret = exe.run(feed={
'row': self.row,
'eids': self.sorted_eid,
'colptr': self.colptr,
'nodes': self.nodes
},
fetch_list=[edge_src, edge_dst, sample_index])

edge_src, edge_dst, sample_index = ret
edge_src = edge_src.reshape([-1])
edge_dst = edge_dst.reshape([-1])
sample_index = sample_index.reshape([-1])

for i in range(len(edge_src)):
edge_src[i] = sample_index[edge_src[i]]
edge_dst[i] = sample_index[edge_dst[i]]

for n in self.nodes:
edge_src_n = edge_src[edge_dst == n]
if edge_src_n.shape[0] == 0:
continue
self.assertTrue(
edge_src_n.shape[0] == np.unique(edge_src_n).shape[0])
self.assertTrue(
edge_src_n.shape[0] == self.sample_sizes[0]
or edge_src_n.shape[0] == len(self.dst_src_dict[n]))
in_neighbors = np.isin(edge_src_n, self.dst_src_dict[n])
self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])

def test_sample_result_static_without_eids(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
row = paddle.static.data(name="row",
shape=self.row.shape,
dtype=self.row.dtype)
colptr = paddle.static.data(name="colptr",
shape=self.colptr.shape,
dtype=self.colptr.dtype)
nodes = paddle.static.data(name="nodes",
shape=self.nodes.shape,
dtype=self.nodes.dtype)
edge_src, edge_dst, sample_index, reindex_nodes = \
paddle.geometric.khop_sampler(row, colptr,
nodes, self.sample_sizes)
exe = paddle.static.Executor(paddle.CPUPlace())
ret = exe.run(feed={
'row': self.row,
'colptr': self.colptr,
'nodes': self.nodes
},
fetch_list=[edge_src, edge_dst, sample_index])
edge_src, edge_dst, sample_index = ret
edge_src = edge_src.reshape([-1])
edge_dst = edge_dst.reshape([-1])
sample_index = sample_index.reshape([-1])

for i in range(len(edge_src)):
edge_src[i] = sample_index[edge_src[i]]
edge_dst[i] = sample_index[edge_dst[i]]

for n in self.nodes:
edge_src_n = edge_src[edge_dst == n]
if edge_src_n.shape[0] == 0:
continue
self.assertTrue(
edge_src_n.shape[0] == np.unique(edge_src_n).shape[0])
self.assertTrue(
edge_src_n.shape[0] == self.sample_sizes[0]
or edge_src_n.shape[0] == len(self.dst_src_dict[n]))
in_neighbors = np.isin(edge_src_n, self.dst_src_dict[n])
self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])


if __name__ == "__main__":
unittest.main()
2 changes: 0 additions & 2 deletions python/paddle/geometric/__init__.py
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Expand Up @@ -21,7 +21,6 @@
from .math import segment_max # noqa: F401
from .sampling import graph_reindex # noqa: F401
from .sampling import heter_graph_reindex # noqa: F401
from .sampling import khop_sampler # noqa: F401
from .sampling import sample_neighbors # noqa: F401

__all__ = [
Expand All @@ -34,6 +33,5 @@
'segment_max',
'graph_reindex',
'heter_graph_reindex',
'khop_sampler',
'sample_neighbors',
]
2 changes: 0 additions & 2 deletions python/paddle/geometric/sampling/__init__.py
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Expand Up @@ -14,12 +14,10 @@

from .graph_reindex import graph_reindex # noqa: F401
from .graph_reindex import heter_graph_reindex # noqa: F401
from .khop_sampler import khop_sampler # noqa: F401
from .neighbors import sample_neighbors # noqa: F401

__all__ = [
'graph_reindex',
'heter_graph_reindex',
'khop_sampler',
'sample_neighbors',
]
151 changes: 0 additions & 151 deletions python/paddle/geometric/sampling/khop_sampler.py
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