def setup_layers(self):
"""
Creating the layers.
"""
self.calculate_bottleneck_features()
# 三层GCN
'''
GCN init: def __init__(self, in_channels, out_channels)
def forward(self, x, edge_index):
x has shape [N, in_channels]
edge_index has shape [2, E]
'''
self.convolution_1 = GCNConv(self.number_labels, self.args.filters_1)
self.convolution_2 = GCNConv(self.args.filters_1, self.args.filters_2)
self.convolution_3 = GCNConv(self.args.filters_2, self.args.filters_3)
# 得到 [n, out_channels]
# att
self.attention = AttentionModule(self.args)
# 用来计算 embedding_graph_1 和 embedding_graph_2 的合并向量
self.tensor_network = TenorNetworkModule(self.args)
# bottle-neck-neurons , 16
# feature_count , 16
# [16, 16]
self.fully_connected_first = torch.nn.Linear(
self.feature_count, self.args.bottle_neck_neurons)
# [16, 1]
self.scoring_layer = torch.nn.Linear(self.args.bottle_neck_neurons, 1)
def setup_layers(self):
"""
Creating the layers.
"""
self.calculate_bottleneck_features()
self.convolution_1 = GCNConv(self.number_labels, self.args.filters_1)
self.convolution_2 = GCNConv(self.args.filters_1, self.args.filters_2)
self.convolution_3 = GCNConv(self.args.filters_2, self.args.filters_3)
self.attention = AttentionModule(self.args)
self.tensor_network = TenorNetworkModule(self.args)
self.fully_connected_first = torch.nn.Linear(
self.feature_count, self.args.bottle_neck_neurons)
self.scoring_layer = torch.nn.Linear(self.args.bottle_neck_neurons, 1)
def setup_layers(self):
"""
Creating the layers.
"""
self.calculate_bottleneck_features()
self.convolution_1 = GCNConv(100352, 128)
self.convolution_2 = GCNConv(self.args.filters_1, self.args.filters_2)
self.convolution_3 = GCNConv(self.args.filters_2, self.args.filters_3)
self.attention = AttentionModule(self.args)
self.tensor_network = TenorNetworkModule(self.args)
self.fully_connected_first = torch.nn.Linear(self.feature_count,
self.args.bottle_neck_neurons)
self.scoring_layer = torch.nn.Linear(self.args.bottle_neck_neurons, 1)
self.vggg = list(torchvision.models.vgg16(pretrained=True).cuda().children())[0][:24]
self.backbone = torch.nn.Sequential(*self.vggg)