From 63da2da61d3df898f8e43723b6ab63fc91d00440 Mon Sep 17 00:00:00 2001
From: troyyyyy <49091847+troyyyyy@users.noreply.github.com>
Date: Wed, 22 Feb 2023 10:08:32 +0800
Subject: [PATCH] Update example.py

---
 example.py | 30 ++++++------------------------
 1 file changed, 6 insertions(+), 24 deletions(-)

diff --git a/example.py b/example.py
index a81b51d..72c0bca 100644
--- a/example.py
+++ b/example.py
@@ -11,28 +11,25 @@
 # ================================================================#
 
 from utils.plog import logger, INFO
-from utils.utils import copy_state_dict
 import torch.nn as nn
 import torch
 
-from models.nn import LeNet5, SymbolNet, SymbolNetAutoencoder
+from models.nn import LeNet5, SymbolNet
 from models.basic_model import BasicModel, BasicDataset
 from models.wabl_models import DecisionTree, WABLBasicModel
 
 from multiprocessing import Pool
-import os
 from abducer.abducer_base import AbducerBase
 from abducer.kb import add_KB, HWF_KB, HED_prolog_KB
 from datasets.mnist_add.get_mnist_add import get_mnist_add
 from datasets.hwf.get_hwf import get_hwf
-from datasets.hed.get_hed import get_hed, split_equation, get_pretrain_data
-import framework_hed
+from datasets.hed.get_hed import get_hed, split_equation
+import framework
 
 
 def run_test():
 
     # kb = add_KB(True)
-
     # kb = HWF_KB(True)
     # abducer = AbducerBase(kb)
 
@@ -46,25 +43,10 @@ def run_test():
     test_data = get_hed(train=False)
     
     # cls = LeNet5(num_classes=len(kb.pseudo_label_list), image_size=(train_data[0][0][0].shape[1:]))
-    cls_autoencoder = SymbolNetAutoencoder(num_classes=len(kb.pseudo_label_list))
     cls = SymbolNet(num_classes=len(kb.pseudo_label_list))
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     
-    if not os.path.exists("./weights/pretrain_weights.pth"):
-        INFO("Pretrain Start")
-        pretrain_data_X, pretrain_data_Y = get_pretrain_data(['0', '1', '10', '11'])
-        pretrain_data = BasicDataset(pretrain_data_X, pretrain_data_Y)
-        
-        criterion = nn.MSELoss()
-        optimizer = torch.optim.RMSprop(cls_autoencoder.parameters(), lr=0.001, alpha=0.9, weight_decay=1e-6)
-
-        pretrain_model = BasicModel(cls_autoencoder, criterion, optimizer, device, save_interval=1, save_dir=recorder.save_dir, num_epochs=10, recorder=recorder)
-        framework_hed.pretrain(pretrain_model, pretrain_data)
-        torch.save(cls_autoencoder.base_model.state_dict(), "./weights/pretrain_weights.pth")
-        cls.load_state_dict(cls_autoencoder.base_model.state_dict())
-    
-    else:
-        cls.load_state_dict(torch.load("./weights/pretrain_weights.pth"))
+    framework.hed_pretrain(kb, cls, recorder)
     
     criterion = nn.CrossEntropyLoss()
     optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-6)
@@ -80,8 +62,8 @@ def run_test():
     # train_data = get_hwf(train = True, get_pseudo_label = True)
     # test_data = get_hwf(train = False, get_pseudo_label = True)
 
-    framework_hed.train_with_rule(model, abducer, train_data, val_data, select_num=10, verbose=1)
-    # recorder.print(res)
+    model, mapping = framework.train_with_rule(model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8)
+    framework.hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8)
 
     recorder.dump()
     return True