|
| | TMVA_SOFIE_GNN.c0 = ROOT.TCanvas() |
| |
| | TMVA_SOFIE_GNN.c1 = c0.cd(1) |
| |
| | TMVA_SOFIE_GNN.c2 = c0.cd(2) |
| |
| | TMVA_SOFIE_GNN.core = ROOT.TMVA.Experimental.SOFIE.RModel_GNN.ParseFromMemory(ep_model._core._network, CoreGraphData, filename = "core") |
| |
| | TMVA_SOFIE_GNN.CoreGraphData = get_graph_data_dict(num_nodes, num_edges, 2*LATENT_SIZE, 2*LATENT_SIZE, 2*LATENT_SIZE) |
| |
| | TMVA_SOFIE_GNN.data = GenerateData() |
| |
| list | TMVA_SOFIE_GNN.dataSet = [] |
| |
| | TMVA_SOFIE_GNN.DecodeGraphData = get_graph_data_dict(num_nodes,num_edges, LATENT_SIZE, LATENT_SIZE, LATENT_SIZE) |
| |
| | TMVA_SOFIE_GNN.decoder = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._decoder._network, DecodeGraphData, filename = "decoder") |
| |
| | TMVA_SOFIE_GNN.edge_data |
| |
| int | TMVA_SOFIE_GNN.edge_size = 4 |
| |
| | TMVA_SOFIE_GNN.edgesG = outGnet[1].edges.numpy() |
| |
| | TMVA_SOFIE_GNN.edgesS = np.asarray(outSofie[1].edge_data) |
| |
| | TMVA_SOFIE_GNN.encoder = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._encoder._network, GraphData, filename = "encoder") |
| |
| | TMVA_SOFIE_GNN.end = time.time() |
| |
| | TMVA_SOFIE_GNN.endSC = time.time() |
| |
| | TMVA_SOFIE_GNN.ep_model = EncodeProcessDecode() |
| |
| | TMVA_SOFIE_GNN.g = out[1].globals.numpy() |
| |
| | TMVA_SOFIE_GNN.global_data |
| |
| int | TMVA_SOFIE_GNN.global_size = 1 |
| |
| | TMVA_SOFIE_GNN.globG = outGnet[1].globals.numpy() |
| |
| | TMVA_SOFIE_GNN.globS = np.asarray(outSofie[1].global_data) |
| |
| | TMVA_SOFIE_GNN.gnet_data_i = utils_tf.data_dicts_to_graphs_tuple([graphData]) |
| |
| list | TMVA_SOFIE_GNN.gnetData = [] |
| |
| | TMVA_SOFIE_GNN.gnn = SofieGNN() |
| |
| | TMVA_SOFIE_GNN.GraphData = get_graph_data_dict(num_nodes,num_edges, node_size, edge_size, global_size) |
| |
| list | TMVA_SOFIE_GNN.graphData = dataSet[i] |
| |
| | TMVA_SOFIE_GNN.hDe = ROOT.TH1D("hDe","Difference for edge data",100,1,0) |
| |
| | TMVA_SOFIE_GNN.hDg = ROOT.TH1D("hDg","Difference for global data",100,1,0) |
| |
| | TMVA_SOFIE_GNN.hDn = ROOT.TH1D("hDn","Difference for node data",100,1,0) |
| |
| | TMVA_SOFIE_GNN.hG = ROOT.TH1D("hG","Result from graphnet",100,1,0) |
| |
| | TMVA_SOFIE_GNN.hS = ROOT.TH1D("hS","Result from SOFIE",100,1,0) |
| |
| | TMVA_SOFIE_GNN.input_core_graph_data = utils_tf.data_dicts_to_graphs_tuple([CoreGraphData]) |
| |
| | TMVA_SOFIE_GNN.input_data = ROOT.TMVA.Experimental.SOFIE.GNN_Data() |
| |
| | TMVA_SOFIE_GNN.input_graph_data = utils_tf.data_dicts_to_graphs_tuple([GraphData]) |
| |
| int | TMVA_SOFIE_GNN.LATENT_SIZE = 100 |
| |
| | TMVA_SOFIE_GNN.node_data |
| |
| int | TMVA_SOFIE_GNN.node_size = 4 |
| |
| | TMVA_SOFIE_GNN.nodesG = outGnet[1].nodes.numpy() |
| |
| | TMVA_SOFIE_GNN.nodesS = np.asarray(outSofie[1].node_data) |
| |
| int | TMVA_SOFIE_GNN.num_edges = 20 |
| |
| int | TMVA_SOFIE_GNN.NUM_LAYERS = 4 |
| |
| int | TMVA_SOFIE_GNN.num_nodes = 5 |
| |
| int | TMVA_SOFIE_GNN.numevts = 100 |
| |
| | TMVA_SOFIE_GNN.out = RunGNet(gnetData[i]) |
| |
| | TMVA_SOFIE_GNN.outGnet = RunGNet(gnetData[i]) |
| |
| | TMVA_SOFIE_GNN.output_gn = ep_model(input_graph_data, processing_steps) |
| |
| | TMVA_SOFIE_GNN.output_transform = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._output_transform._network, DecodeGraphData, filename = "output_transform") |
| |
| | TMVA_SOFIE_GNN.outSofie = gnn.infer(sofieData[i]) |
| |
| int | TMVA_SOFIE_GNN.processing_steps = 5 |
| |
| | TMVA_SOFIE_GNN.rec = np.array([0,0,0,0,1,1,1,2,2,3,1,2,3,4,2,3,4,3,4,4], dtype='int32') |
| |
| | TMVA_SOFIE_GNN.receivers |
| |
| | TMVA_SOFIE_GNN.senders |
| |
| | TMVA_SOFIE_GNN.snd = np.array([1,2,3,4,2,3,4,3,4,4,0,0,0,0,1,1,1,2,2,3], dtype='int32') |
| |
| list | TMVA_SOFIE_GNN.sofieData = [] |
| |
| | TMVA_SOFIE_GNN.start = time.time() |
| |
| | TMVA_SOFIE_GNN.start0 = time.time() |
| |
