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# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is refer from:
https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textdet/modules/gcn.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class BatchNorm1D(nn.BatchNorm1D):
def __init__(self,
num_features,
eps=1e-05,
momentum=0.1,
affine=True,
track_running_stats=True):
momentum = 1 - momentum
weight_attr = None
bias_attr = None
if not affine:
weight_attr = paddle.ParamAttr(learning_rate=0.0)
bias_attr = paddle.ParamAttr(learning_rate=0.0)
super().__init__(
num_features,
momentum=momentum,
epsilon=eps,
weight_attr=weight_attr,
bias_attr=bias_attr,
use_global_stats=track_running_stats)
class MeanAggregator(nn.Layer):
def forward(self, features, A):
x = paddle.bmm(A, features)
return x
class GraphConv(nn.Layer):
def __init__(self, in_dim, out_dim):
super().__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.weight = self.create_parameter(
[in_dim * 2, out_dim],
default_initializer=nn.initializer.XavierUniform())
self.bias = self.create_parameter(
[out_dim],
is_bias=True,
default_initializer=nn.initializer.Assign([0] * out_dim))
self.aggregator = MeanAggregator()
def forward(self, features, A):
b, n, d = features.shape
assert d == self.in_dim
agg_feats = self.aggregator(features, A)
cat_feats = paddle.concat([features, agg_feats], axis=2)
out = paddle.einsum('bnd,df->bnf', cat_feats, self.weight)
out = F.relu(out + self.bias)
return out
class GCN(nn.Layer):
def __init__(self, feat_len):
super(GCN, self).__init__()
self.bn0 = BatchNorm1D(feat_len, affine=False)
self.conv1 = GraphConv(feat_len, 512)
self.conv2 = GraphConv(512, 256)
self.conv3 = GraphConv(256, 128)
self.conv4 = GraphConv(128, 64)
self.classifier = nn.Sequential(
nn.Linear(64, 32), nn.PReLU(32), nn.Linear(32, 2))
def forward(self, x, A, knn_inds):
num_local_graphs, num_max_nodes, feat_len = x.shape
x = x.reshape([-1, feat_len])
x = self.bn0(x)
x = x.reshape([num_local_graphs, num_max_nodes, feat_len])
x = self.conv1(x, A)
x = self.conv2(x, A)
x = self.conv3(x, A)
x = self.conv4(x, A)
k = knn_inds.shape[-1]
mid_feat_len = x.shape[-1]
edge_feat = paddle.zeros([num_local_graphs, k, mid_feat_len])
for graph_ind in range(num_local_graphs):
edge_feat[graph_ind, :, :] = x[graph_ind][paddle.to_tensor(knn_inds[
graph_ind])]
edge_feat = edge_feat.reshape([-1, mid_feat_len])
pred = self.classifier(edge_feat)
return pred
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