114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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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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"""
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This code is refer from:
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https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textdet/modules/gcn.py
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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class BatchNorm1D(nn.BatchNorm1D):
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def __init__(self,
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num_features,
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eps=1e-05,
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momentum=0.1,
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affine=True,
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track_running_stats=True):
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momentum = 1 - momentum
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weight_attr = None
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bias_attr = None
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if not affine:
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weight_attr = paddle.ParamAttr(learning_rate=0.0)
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bias_attr = paddle.ParamAttr(learning_rate=0.0)
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super().__init__(
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num_features,
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momentum=momentum,
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epsilon=eps,
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weight_attr=weight_attr,
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bias_attr=bias_attr,
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use_global_stats=track_running_stats)
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class MeanAggregator(nn.Layer):
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def forward(self, features, A):
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x = paddle.bmm(A, features)
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return x
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class GraphConv(nn.Layer):
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def __init__(self, in_dim, out_dim):
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super().__init__()
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self.in_dim = in_dim
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self.out_dim = out_dim
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self.weight = self.create_parameter(
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[in_dim * 2, out_dim],
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default_initializer=nn.initializer.XavierUniform())
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self.bias = self.create_parameter(
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[out_dim],
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is_bias=True,
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default_initializer=nn.initializer.Assign([0] * out_dim))
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self.aggregator = MeanAggregator()
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def forward(self, features, A):
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b, n, d = features.shape
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assert d == self.in_dim
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agg_feats = self.aggregator(features, A)
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cat_feats = paddle.concat([features, agg_feats], axis=2)
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out = paddle.einsum('bnd,df->bnf', cat_feats, self.weight)
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out = F.relu(out + self.bias)
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return out
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class GCN(nn.Layer):
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def __init__(self, feat_len):
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super(GCN, self).__init__()
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self.bn0 = BatchNorm1D(feat_len, affine=False)
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self.conv1 = GraphConv(feat_len, 512)
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self.conv2 = GraphConv(512, 256)
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self.conv3 = GraphConv(256, 128)
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self.conv4 = GraphConv(128, 64)
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self.classifier = nn.Sequential(
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nn.Linear(64, 32), nn.PReLU(32), nn.Linear(32, 2))
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def forward(self, x, A, knn_inds):
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num_local_graphs, num_max_nodes, feat_len = x.shape
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x = x.reshape([-1, feat_len])
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x = self.bn0(x)
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x = x.reshape([num_local_graphs, num_max_nodes, feat_len])
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x = self.conv1(x, A)
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x = self.conv2(x, A)
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x = self.conv3(x, A)
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x = self.conv4(x, A)
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k = knn_inds.shape[-1]
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mid_feat_len = x.shape[-1]
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edge_feat = paddle.zeros([num_local_graphs, k, mid_feat_len])
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for graph_ind in range(num_local_graphs):
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edge_feat[graph_ind, :, :] = x[graph_ind][paddle.to_tensor(knn_inds[
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graph_ind])]
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edge_feat = edge_feat.reshape([-1, mid_feat_len])
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pred = self.classifier(edge_feat)
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return pred
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