File size: 1,224 Bytes
832456c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
import torch
import torch.nn as nn
from constants import *
import torch.optim as optim
from edge_generator import EdgeGenerator
from matrix_generator import MatrixGenerator
from combined_generator import CombinedGenerator
edge_generator = EdgeGenerator(INPUT_SIZE_GEN, HIDDEN_SIZE_GEN, OUTPUT_SIZE_EDGE_GEN)
matrix_generator = MatrixGenerator(INPUT_SIZE_GEN, HIDDEN_SIZE_GEN, OUTPUT_SIZE_MAT_GEN)
model = CombinedGenerator(edge_generator, matrix_generator)
model.load_state_dict(torch.load("model.pth"))
model.eval()
def get_fake_data(batch_size, combined):
fake_graphs=[]
for i in range(batch_size):
rand_noise = torch.randn(1, INPUT_SIZE_GEN)
fake_graphs.append(combined(rand_noise))
return fake_graphs
fake_data = get_fake_data(64,model)
def deconstructor(matrix):
mat1 = matrix[:, :3]
mat2 = matrix[:, 3:]
return mat1, mat2
def adj_matrix_to_dict(adj_matrix):
adj_dict = {}
for i, row in enumerate(adj_matrix):
adj_dict[i] = []
for j, edge in enumerate(row):
if edge != 0:
adj_dict[i].append(j)
return adj_dict
dict_list = []
for data in fake_data:
dict_list.append(adj_matrix_to_dict(deconstructor(data)[1])) |