diff --git a/test.py b/test.py
deleted file mode 100644
index 0cf703a..0000000
--- a/test.py
+++ /dev/null
@@ -1,97 +0,0 @@
-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))
\ No newline at end of file