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test.py

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