From 13043965b4cbb1b4484bc5b6742864de8aa1197d Mon Sep 17 00:00:00 2001
From: yifei cheng <chengyifei@iie.ac.cn>
Date: Mon, 26 Jun 2023 12:35:33 +0000
Subject: Upload New File

---
 attack/excute.py | 93 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 93 insertions(+)
 create mode 100644 attack/excute.py

diff --git a/attack/excute.py b/attack/excute.py
new file mode 100644
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+++ b/attack/excute.py
@@ -0,0 +1,93 @@
+"""
+Date: 2022-03-08
+Author: sunhanwu@iie.ac.cn
+Desc: execute the adversarial attack
+"""
+
+from tqdm import tqdm
+import torch
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import recall_score
+from sklearn.metrics import f1_score
+from sklearn.metrics import confusion_matrix
+from TargetModel.FSNet.FSNet import FSNet
+from attack.adversarialDataset import AdversarialC2Data
+from torch.utils.data import DataLoader
+
+def executeAttack(dataloader, model, device, file):
+    y_true = []
+    y_pred = []
+    for batch_x, batch_y in tqdm(dataloader):
+        batch_x = batch_x.to(device)
+        batch_y = batch_y.to(device)
+        y_true += batch_y.detach().cpu().numpy().reshape(-1).tolist()
+        output = model(batch_x)
+        y_pred += torch.argmax(output, dim=1).detach().cpu().numpy().tolist()
+    acc = accuracy_score(y_true, y_pred)
+    recall = recall_score(y_true, y_pred, average='macro')
+    f1 = f1_score(y_true, y_pred, average='macro')
+    con_matrix = confusion_matrix(y_true, y_pred)
+    print("acc: {:.2%}, recall: {:.2%}, f1: {:.2%}".format(acc, recall, f1))
+    print(con_matrix)
+    # print("acc: {:.2%}, recall: {:.2%}, f1: {:.2%}".format(acc, recall, f1), file=file)
+    # print(con_matrix, file=file)
+    return acc, recall, f1, con_matrix
+
+
+
+
+def attackFsnet(theta, gamma, file, advdataFile, modelfile, device):
+    state = torch.load(modelfile)
+    fsnet = FSNet(state['param'])
+    fsnet.load_state_dict(state['model_dict'])
+    fsnet.to(device)
+
+    # adversarial data
+    batch_size = 128
+    adversarialC2Data = AdversarialC2Data(advdataFile, keep_target=True)
+    adversarialDataloader = DataLoader(adversarialC2Data, batch_size, shuffle=True, drop_last=False)
+    print("Attack FSNet")
+    print(fsnet)
+
+    fsnet.eval()
+    y_true = []
+    y_pred = []
+    for batch_x, batch_y in tqdm(adversarialDataloader):
+        batch_x = batch_x.to(device)
+        batch_y = batch_y.to(device)
+        # batch_x.shape: (batch_size, sequenceLen)
+        # batch_y.shape: (batch_size, 1)
+        # reconstruction
+        z_e = fsnet.encode(batch_x)
+        # z_e.shape: (batch_size, num_layers * num_direction, hidden_size)
+        z_d, D = fsnet.decode(z_e)
+        # z_d.shape: (batch_size, num_layers * num_direction, hidden_size)
+        # D.shape: (batch_size, sequence_len, hidden_size * num_direction)
+        z_reconstruction = fsnet.reconstruction(D)
+        # z_reconstruction.shape=(batch_size, sequence_len, vocab_size)
+
+        # classification layer
+        z_dense = fsnet.dense(z_e, z_d)
+        # z_dense.shape=(batch_size, num_class)
+
+        # compute reconstruction loss
+        # z_reconstruction.shape = (-1, vocab_size)
+        # batch_x.shape=(-1)
+
+        # compute classification loss
+        # batch_y.shape=(batch, )
+
+        # compute FPR
+        y_true += batch_y.detach().cpu().data.numpy().reshape(-1).tolist()
+        y_pred += torch.argmax(z_dense, dim=1).detach().cpu().data.numpy().tolist()
+    acc = accuracy_score(y_true, y_pred)
+    recall = recall_score(y_true, y_pred, average='macro')
+    f1 = f1_score(y_true, y_pred, average='macro')
+    con_matrix = confusion_matrix(y_true, y_pred)
+    print("acc: {:.2%}, recall: {:.2%}, f1: {:.2%}".format(acc, recall, f1))
+    print(con_matrix)
+    print("acc: {:.2%}, recall: {:.2%}, f1: {:.2%}".format(acc, recall, f1), file=file)
+    print(con_matrix, file=file)
+    return con_matrix
+
+
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