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import torch
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import torch.nn.functional as F
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import config
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import json
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import numpy as np
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from date_build import Chem_Dataset
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model=torch.load('models\model3.1(150).pt')
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def padding( chemic, lens=config.seq_len):
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while len(chemic) < lens:
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chemic.append(0)
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return chemic
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def target_padding( chemical, lens=config.seq_len):
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chemical.append(1)
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while len(chemical)<lens:
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chemical.append(0)
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return chemical
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class Chemical_Trans:
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def __init__(self,len=config.seq_len):
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with open('vocab/vocabulary.json', 'r', encoding='utf-8') as f:
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f = json.load(f)
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self.vocab = f
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self.len = len
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def CCO(self,adj_matrix):
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source_nodes = []
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target_nodes = []
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# 遍历邻接矩阵（只记录i < j的边）
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for i in range(len(adj_matrix)):
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for j in range(i + 1, len(adj_matrix[i])): # 注意这里从i + 1开始
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if adj_matrix[i][j] != 0: # 存在边
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source_nodes.append(i)
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target_nodes.append(j)
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return [source_nodes, target_nodes]
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def chemical_trans(self, chemical):
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chemical = chemical.split('-')
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sequence = []
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for i in chemical:
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sequence.append(self.vocab[i])
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return sequence
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def generate_adjacency_matrix(self,num_vertices, edges, directed=False):
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# 初始化邻接矩阵为0
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adjacency_matrix = [[0] * num_vertices for _ in range(num_vertices)]
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print(edges)
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# 遍历边的列表，更新邻接矩阵
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for u, v in edges:
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# 确保顶点索引在有效范围内
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if 0 <= u-1 < num_vertices and 0 <= v-1 < num_vertices:
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adjacency_matrix[u-1][v-1] = 1
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if not directed:
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adjacency_matrix[v-1][u-1] = 1
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return adjacency_matrix
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def adjacency_to_degree_matrix(self,adjacency_matrix):
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# 确保输入是NumPy数组
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adjacency_matrix = np.array(adjacency_matrix)
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# 计算每个顶点的度数（即每行的和）
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degrees = np.sum(adjacency_matrix, axis=1)
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# 构造度矩阵（对角矩阵，对角线元素为度数）
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degree_matrix = np.zeros_like(adjacency_matrix)
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np.fill_diagonal(degree_matrix, degrees)
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return degree_matrix
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def __len__(self):
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return len(self.vocab)
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def __getitem__(self, key):
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return self.vocab.keys()
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Chemical_Trans=Chemical_Trans()
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model.eval()
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model.to(config.device)
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with torch.no_grad():
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losses=[]
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for batch_idx, (input_,edge_index,target,enc) in enumerate(Chem_Dataset):
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model = model.to(config.device)
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input_ = input_.to(config.device)
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output = model(input_,edge_index.to(config.device),enc.to(config.device))
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losses.append(F.l1_loss(output.to('cpu'),target.to('cpu')).item())
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print(output)
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print(target)
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print(sum(losses)/len(losses)) |
