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121 lines
4.2 KiB
121 lines
4.2 KiB
# Copyright (c) 2018 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 numpy as np
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from op_test import OpTest
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def collect_node_patch(og, max_depth):
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"""
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The naive method to construct patches
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:param og: original graph
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:param max_depth: the depth of convolution filters
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:return: convolution patches
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"""
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def gen(node, max_depth):
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collected = [(node, 1, 1, 0, max_depth)]
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def recurse_helper(node, depth):
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if depth > max_depth:
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return
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l = len(og[node])
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for idx, c in enumerate(og[node], 1):
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if depth + 1 < max_depth:
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collected.append((c, idx, l, depth + 1, max_depth))
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recurse_helper(c, depth + 1)
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recurse_helper(node, 0)
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return collected
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res = []
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for u in range(1, len(og)):
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lis = gen(u, max_depth)
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if len(lis) > 0:
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res.append(lis)
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return res
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class TestTreeConvOp(OpTest):
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def setUp(self):
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self.n = 17
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self.fea_size = 3
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self.output_size = 1
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self.max_depth = 2
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self.batch_size = 1
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self.num_filters = 1
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adj_array = [
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1, 2, 1, 3, 1, 4, 1, 5, 2, 6, 2, 7, 2, 8, 4, 9, 4, 10, 5, 11, 6, 12,
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6, 13, 9, 14, 9, 15, 9, 16, 9, 17
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]
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adj = np.array(adj_array).reshape((1, self.n - 1, 2)).astype('int32')
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adj = np.tile(adj, (self.batch_size, 1, 1))
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self.op_type = 'tree_conv'
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vectors = np.random.random(
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(self.batch_size, self.n, self.fea_size)).astype('float32')
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self.inputs = {
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'EdgeSet': adj,
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'NodesVector': vectors,
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'Filter': np.random.random((self.fea_size, 3, self.output_size,
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self.num_filters)).astype('float32')
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}
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self.attrs = {'max_depth': self.max_depth}
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vectors = []
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for i in range(self.batch_size):
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vector = self.get_output_naive(i)
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vectors.append(vector)
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self.outputs = {'Out': np.array(vectors).astype('float32'), }
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(
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['NodesVector', 'Filter'], 'Out', max_relative_error=0.5)
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def get_output_naive(self, batch_id):
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og = [[] for i in range(1, self.n + 2)]
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st = np.array(self.inputs['EdgeSet'][batch_id]).tolist()
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for e in st:
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og[e[0]].append(e[1])
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patches = collect_node_patch(og, self.max_depth)
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W = np.array(self.inputs['Filter']).astype('float32')
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W = np.transpose(W, axes=[1, 0, 2, 3])
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vec = []
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for i, patch in enumerate(patches, 1):
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result = np.zeros((1, W.shape[2], W.shape[3]))
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for v in patch:
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eta_t = float(v[4] - v[3]) / float(v[4])
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eta_l = (1.0 - eta_t) * (0.5 if v[2] == 1 else
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float(v[1] - 1.0) / float(v[2] - 1.0))
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eta_r = (1.0 - eta_t) * (1.0 - eta_l)
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x = self.inputs['NodesVector'][batch_id][v[0] - 1]
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eta = np.array([eta_l, eta_r, eta_t]).reshape(
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(3, 1)).astype('float32')
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Wconvi = np.tensordot(eta, W, axes=([0], [0]))
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x = np.array(x).reshape((1, 1, self.fea_size))
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res = np.tensordot(x, Wconvi, axes=2)
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result = result + res
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vec.append(result)
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vec = np.concatenate(vec, axis=0)
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vec = np.concatenate(
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[
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vec, np.zeros(
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(self.n - vec.shape[0], W.shape[2], W.shape[3]),
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dtype='float32')
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],
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axis=0)
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return vec
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