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DGCNNModel.py
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DGCNNModel.py
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import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, MaxPool1d, Linear, Dropout
from torch_geometric.nn import GCNConv, global_sort_pool
from torch_geometric.utils import remove_self_loops
class DGCNN(nn.Module):
def __init__(self, num_features, num_classes):
super(DGCNN, self).__init__()
print('\033[1;32m===> DGCNN Model......\033[0m')
self.conv1 = GCNConv(num_features, 32)
self.conv2 = GCNConv(32, 32)
self.conv3 = GCNConv(32, 32)
self.conv4 = GCNConv(32, 1)
self.conv5 = Conv1d(1, 16, 97, 97)
self.conv6 = Conv1d(16, 32, 5, 1)
self.pool = MaxPool1d(2, 2)
self.classifier_1 = Linear(352, 128)
# self.classifier_1 = Linear(512, 128) # k = 40 -> (40 - 30) / 10 * 160 + 352
# self.classifier_1 = Linear(576, 128) # k = 45
# self.classifier_1 = Linear(672, 128) # k = 50
# self.classifier_1 = Linear(832, 128) # k = 60
self.classifier_1 = Linear(992, 128) # k = 70
# self.classifier_1 = Linear(1152, 128) # k = 80
# self.classifier_1 = Linear(1312, 128) # k = 90
# self.classifier_1 = Linear(1472, 128) # k = 100
self.drop_out = Dropout(0.5)
self.classifier_2 = Linear(128, num_classes)
self.relu = nn.ReLU(inplace=True)
def forward(self, x, edges, batch):
# x, edge_index, batch = data.x, data.edge_index, data.batch
edges, _ = remove_self_loops(edges)
x_1 = torch.tanh(self.conv1(x, edges))
x_2 = torch.tanh(self.conv2(x_1, edges))
x_3 = torch.tanh(self.conv3(x_2, edges))
x_4 = torch.tanh(self.conv4(x_3, edges))
x = torch.cat([x_1, x_2, x_3, x_4], dim=-1)
x = global_sort_pool(x, batch, k=70)
x = x.view(x.size(0), 1, x.size(-1))
x = self.relu(self.conv5(x))
x = self.pool(x)
x = self.relu(self.conv6(x))
x = x.view(x.size(0), -1)
x = self.relu(self.classifier_1(x))
out = self.relu(self.classifier_2(x))
# out = self.drop_out(out)
# classes = F.log_softmax(self.classifier_2(out), dim=-1)
return out
# class DGCNN(nn.Module):
# def __init__(self, num_features, num_classes):
# super(DGCNN, self).__init__()
# self.conv1 = GCNConv(num_features, 32)
# self.conv2 = GCNConv(32, 32)
# self.conv3 = GCNConv(32, 32)
# self.conv4 = GCNConv(32, 1)
# self.conv5 = Conv1d(1, 16, 97, 97)
# self.conv6 = Conv1d(16, 32, 5, 1)
# self.pool = MaxPool1d(2, 2)
# self.classifier_1 = Linear(352, 128)
# self.drop_out = Dropout(0.5)
# self.classifier_2 = Linear(128, num_classes)
# self.relu = nn.ReLU(inplace=True)
# def forward(self, data):
# x, edge_index, batch = data.x, data.edge_index, data.batch
# edge_index, _ = remove_self_loops(edge_index)
# x_1 = torch.tanh(self.conv1(x, edge_index))
# x_2 = torch.tanh(self.conv2(x_1, edge_index))
# x_3 = torch.tanh(self.conv3(x_2, edge_index))
# x_4 = torch.tanh(self.conv4(x_3, edge_index))
# x = torch.cat([x_1, x_2, x_3, x_4], dim=-1)
# x = global_sort_pool(x, batch, k=30)
# x = x.view(x.size(0), 1, x.size(-1))
# x = self.relu(self.conv5(x))
# x = self.pool(x)
# x = self.relu(self.conv6(x))
# x = x.view(x.size(0), -1)
# out = self.relu(self.classifier_1(x))
# out = self.drop_out(out)
# classes = F.log_softmax(self.classifier_2(out), dim=-1)
# return classes