wehub-resource-sync
295 lines
11 KiB
Python
295 lines
11 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import is_custom_device
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import paddle
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from paddle import base
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class TestGraphKhopSampler(unittest.TestCase):
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def setUp(self):
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num_nodes = 20
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edges = np.random.randint(num_nodes, size=(100, 2))
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edges = np.unique(edges, axis=0)
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edges_id = np.arange(0, len(edges))
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sorted_edges = edges[np.argsort(edges[:, 1])]
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sorted_eid = edges_id[np.argsort(edges[:, 1])]
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# Calculate dst index cumsum counts.
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dst_count = np.zeros(num_nodes)
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dst_src_dict = {}
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for dst in range(0, num_nodes):
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true_index = sorted_edges[:, 1] == dst
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dst_count[dst] = np.sum(true_index)
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dst_src_dict[dst] = sorted_edges[:, 0][true_index]
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dst_count = dst_count.astype("int64")
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colptr = np.cumsum(dst_count)
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colptr = np.insert(colptr, 0, 0)
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self.row = sorted_edges[:, 0].astype("int64")
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self.colptr = colptr.astype("int64")
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self.sorted_eid = sorted_eid.astype("int64")
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self.nodes = np.unique(np.random.randint(num_nodes, size=5)).astype(
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"int64"
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)
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self.sample_sizes = [5, 5]
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self.dst_src_dict = dst_src_dict
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def test_sample_result(self):
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paddle.disable_static()
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row = paddle.to_tensor(self.row)
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colptr = paddle.to_tensor(self.colptr)
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nodes = paddle.to_tensor(self.nodes)
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(
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edge_src,
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edge_dst,
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sample_index,
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reindex_nodes,
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) = paddle.incubate.graph_khop_sampler(
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row, colptr, nodes, self.sample_sizes, return_eids=False
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)
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# Reindex edge_src and edge_dst to original index.
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edge_src = edge_src.reshape([-1])
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edge_dst = edge_dst.reshape([-1])
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sample_index = sample_index.reshape([-1])
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for i in range(len(edge_src)):
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edge_src[i] = sample_index[edge_src[i]]
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edge_dst[i] = sample_index[edge_dst[i]]
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for n in self.nodes:
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edge_src_n = edge_src[edge_dst == n]
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if edge_src_n.shape[0] == 0:
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continue
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# Ensure no repetitive sample neighbors.
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self.assertTrue(
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edge_src_n.shape[0] == np.unique(edge_src_n).shape[0]
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)
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# Ensure the correct sample size.
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self.assertTrue(
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edge_src_n.shape[0] == self.sample_sizes[0]
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or edge_src_n.shape[0] == len(self.dst_src_dict[n])
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)
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in_neighbors = np.isin(edge_src_n.numpy(), self.dst_src_dict[n])
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# Ensure the correct sample neighbors.
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self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
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def test_uva_sample_result(self):
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paddle.disable_static()
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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row = None
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if base.framework.in_dygraph_mode():
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row = paddle.base.core.eager.to_uva_tensor(
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self.row.astype(self.row.dtype), 0
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)
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sorted_eid = paddle.base.core.eager.to_uva_tensor(
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self.sorted_eid.astype(self.sorted_eid.dtype), 0
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)
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else:
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row = paddle.base.core.to_uva_tensor(
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self.row.astype(self.row.dtype)
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)
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sorted_eid = paddle.base.core.to_uva_tensor(
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self.sorted_eid.astype(self.sorted_eid.dtype)
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)
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colptr = paddle.to_tensor(self.colptr)
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nodes = paddle.to_tensor(self.nodes)
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(
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edge_src,
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edge_dst,
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sample_index,
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reindex_nodes,
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edge_eids,
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) = paddle.incubate.graph_khop_sampler(
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row,
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colptr,
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nodes,
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self.sample_sizes,
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sorted_eids=sorted_eid,
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return_eids=True,
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)
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edge_src = edge_src.reshape([-1])
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edge_dst = edge_dst.reshape([-1])
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sample_index = sample_index.reshape([-1])
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for i in range(len(edge_src)):
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edge_src[i] = sample_index[edge_src[i]]
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edge_dst[i] = sample_index[edge_dst[i]]
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for n in self.nodes:
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edge_src_n = edge_src[edge_dst == n]
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if edge_src_n.shape[0] == 0:
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continue
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self.assertTrue(
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edge_src_n.shape[0] == np.unique(edge_src_n).shape[0]
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)
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self.assertTrue(
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edge_src_n.shape[0] == self.sample_sizes[0]
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or edge_src_n.shape[0] == len(self.dst_src_dict[n])
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)
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in_neighbors = np.isin(edge_src_n.numpy(), self.dst_src_dict[n])
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self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
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def test_sample_result_static_with_eids(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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row = paddle.static.data(
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name="row", shape=self.row.shape, dtype=self.row.dtype
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)
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sorted_eids = paddle.static.data(
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name="eids",
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shape=self.sorted_eid.shape,
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dtype=self.sorted_eid.dtype,
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)
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colptr = paddle.static.data(
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name="colptr", shape=self.colptr.shape, dtype=self.colptr.dtype
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)
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nodes = paddle.static.data(
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name="nodes", shape=self.nodes.shape, dtype=self.nodes.dtype
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)
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(
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edge_src,
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edge_dst,
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sample_index,
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reindex_nodes,
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edge_eids,
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) = paddle.incubate.graph_khop_sampler(
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row, colptr, nodes, self.sample_sizes, sorted_eids, True
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)
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exe = paddle.static.Executor(paddle.CPUPlace())
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ret = exe.run(
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feed={
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'row': self.row,
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'eids': self.sorted_eid,
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'colptr': self.colptr,
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'nodes': self.nodes,
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},
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fetch_list=[edge_src, edge_dst, sample_index],
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)
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edge_src, edge_dst, sample_index = ret
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edge_src = edge_src.reshape([-1])
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edge_dst = edge_dst.reshape([-1])
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sample_index = sample_index.reshape([-1])
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for i in range(len(edge_src)):
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edge_src[i] = sample_index[edge_src[i]]
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edge_dst[i] = sample_index[edge_dst[i]]
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for n in self.nodes:
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edge_src_n = edge_src[edge_dst == n]
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if edge_src_n.shape[0] == 0:
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continue
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self.assertTrue(
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edge_src_n.shape[0] == np.unique(edge_src_n).shape[0]
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)
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self.assertTrue(
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edge_src_n.shape[0] == self.sample_sizes[0]
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or edge_src_n.shape[0] == len(self.dst_src_dict[n])
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)
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in_neighbors = np.isin(edge_src_n, self.dst_src_dict[n])
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self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
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def test_sample_result_static_without_eids(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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row = paddle.static.data(
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name="row", shape=self.row.shape, dtype=self.row.dtype
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)
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colptr = paddle.static.data(
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name="colptr", shape=self.colptr.shape, dtype=self.colptr.dtype
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)
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nodes = paddle.static.data(
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name="nodes", shape=self.nodes.shape, dtype=self.nodes.dtype
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)
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(
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edge_src,
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edge_dst,
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sample_index,
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reindex_nodes,
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) = paddle.incubate.graph_khop_sampler(
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row, colptr, nodes, self.sample_sizes
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)
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exe = paddle.static.Executor(paddle.CPUPlace())
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ret = exe.run(
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feed={
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'row': self.row,
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'colptr': self.colptr,
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'nodes': self.nodes,
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},
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fetch_list=[edge_src, edge_dst, sample_index],
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)
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edge_src, edge_dst, sample_index = ret
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edge_src = edge_src.reshape([-1])
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edge_dst = edge_dst.reshape([-1])
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sample_index = sample_index.reshape([-1])
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for i in range(len(edge_src)):
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edge_src[i] = sample_index[edge_src[i]]
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edge_dst[i] = sample_index[edge_dst[i]]
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for n in self.nodes:
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edge_src_n = edge_src[edge_dst == n]
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if edge_src_n.shape[0] == 0:
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continue
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self.assertTrue(
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edge_src_n.shape[0] == np.unique(edge_src_n).shape[0]
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)
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self.assertTrue(
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edge_src_n.shape[0] == self.sample_sizes[0]
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or edge_src_n.shape[0] == len(self.dst_src_dict[n])
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)
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in_neighbors = np.isin(edge_src_n, self.dst_src_dict[n])
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self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
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def test_for_null_pointer_error(self):
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def test_in_row():
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array = np.array([], dtype=np.float32)
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x = paddle.to_tensor(np.reshape(array, [0]), dtype='int32')
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y = paddle.to_tensor([10], dtype='int32')
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layer = paddle.incubate.graph_khop_sampler(
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row=x, colptr=x, input_nodes=y, sample_sizes=[0]
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)
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def test_in_col():
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array = np.array([], dtype=np.float32)
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x = paddle.to_tensor([10], dtype='int32')
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col = paddle.to_tensor(np.reshape(array, [0]), dtype='int32')
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y = paddle.to_tensor([10], dtype='int32')
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layer = paddle.incubate.graph_khop_sampler(
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row=x, colptr=col, input_nodes=y, sample_sizes=[0]
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)
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def test_in_input_nodes():
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array = np.array([], dtype=np.float32)
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x = paddle.to_tensor(np.reshape(array, [0]), dtype='int32')
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y = paddle.to_tensor([10], dtype='int32')
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layer = paddle.incubate.graph_khop_sampler(
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row=y, colptr=y, input_nodes=x, sample_sizes=[0]
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)
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self.assertRaises(ValueError, test_in_row)
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self.assertRaises(ValueError, test_in_col)
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self.assertRaises(ValueError, test_in_input_nodes)
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if __name__ == "__main__":
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unittest.main()
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