运行环境适配

base_optimizer/optimizer_common.py

@@ -1,7 +1,7 @@
 from functools import wraps
 from collections import defaultdict
 from tqdm import tqdm
-from gurobipy import *
+
 from sklearn.linear_model import LinearRegression
 from sklearn.svm import SVR
 
@@ -23,6 +23,7 @@ import matplotlib.pyplot as plt
 import matplotlib
 import traceback
 import openpyxl
+import itertools
 
 matplotlib.use('TkAgg')
 

base_optimizer/smopt_feederpriority.py

@@ -168,7 +168,7 @@ def feeder_allocate(component_data, pcb_data, feeder_data, nozzle_pattern, figur
     assert len(nozzle_pattern) == max_head_index
     while True:
         best_assign, best_assign_points = [], []
-        best_assign_slot, best_assign_value = -1, -np.Inf
+        best_assign_slot, best_assign_value = -1, -np.inf
         best_nozzle_component, best_nozzle_component_points = None, None
         for slot in range(1, max_slot_index // 2 - (max_head_index - 1) * interval_ratio + 1):
             feeder_assign, feeder_assign_points = [], []

estimator.py

@@ -105,13 +105,12 @@ class Estimator:
 class NeuralEstimator(Estimator):
     def __init__(self):
         super().__init__()
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-        self.net = Net(input_size=self.data_mgr.get_feature(), output_size=1).to(device)
+        self.net = Net(input_size=self.data_mgr.get_feature(), output_size=1).to(self.device)
         self.net_file = 'model/net_model.pth'
-        if os.path.exists(self.net_file):
         try:
-                self.net.load_state_dict(torch.load(self.net_file))
+            self.net.load_state_dict(torch.load(self.net_file, map_location=self.device))
         except:
             warnings.warn('the parameters of neural net model load failed', UserWarning)
 
@@ -128,8 +127,8 @@ class NeuralEstimator(Estimator):
         x_train = np.array(data_mgr.neural_encode(data[0][::data_mgr.get_update_round()]))
         y_train = np.array(data[1][::data_mgr.get_update_round()])
 
-        x_train = torch.from_numpy(x_train.reshape((-1, np.shape(x_train)[1]))).float().to(device)
-        y_train = torch.from_numpy(y_train.reshape((-1, 1))).float().to(device)
+        x_train = torch.from_numpy(x_train.reshape((-1, np.shape(x_train)[1]))).float().to(self.device)
+        y_train = torch.from_numpy(y_train.reshape((-1, 1))).float().to(self.device)
 
         optimizer = torch.optim.Adam(self.net.parameters(), lr=params.lr)
         # scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5000, gamma=0.1)
@@ -161,7 +160,7 @@ class NeuralEstimator(Estimator):
         data = data_mgr.loader('opt/' + params.test_file)
 
         x_test, y_test = np.array(data_mgr.neural_encode(data[0])), np.array(data[1])
-        x_test = torch.from_numpy(x_test.reshape((-1, np.shape(x_test)[1]))).float().to(device)
+        x_test = torch.from_numpy(x_test.reshape((-1, np.shape(x_test)[1]))).float().to(self.device)
 
         self.net.eval()
         with torch.no_grad():
@@ -171,7 +170,7 @@ class NeuralEstimator(Estimator):
     def predict(self, cp_points, cp_nozzle, board_width=None, board_height=None):
         assert board_width is not None and board_height is not None
         encoding = np.array(self.data_mgr.encode(cp_points, cp_nozzle, board_width, board_height))
-        encoding = torch.from_numpy(encoding.reshape((-1, np.shape(encoding)[0]))).float().to("cuda")
+        encoding = torch.from_numpy(encoding.reshape((-1, np.shape(encoding)[0]))).float().to(self.device)
         return self.net(encoding)[0, 0].item()
 
 
@@ -184,6 +183,8 @@ class HeuristicEstimator(Estimator):
         if os.path.exists(self.pickle_file):
             with open(self.pickle_file, 'rb') as f:
                 self.lr = pickle.load(f)
+        else:
+            warnings.warn('the parameters of heuristic lr model load failed', UserWarning)
 
     def training(self, params):
         data = data_mgr.loader('opt/' + params.train_file)
@@ -304,6 +305,8 @@ class ReconfigEstimator(Estimator):
         if os.path.exists(self.pickle_file):
             with open(self.pickle_file, 'rb') as f:
                 self.lr = pickle.load(f)
+        else:
+            warnings.warn('the parameters of reconfig model load failed', UserWarning)
 
     def training(self, params):
         data = data_mgr.loader('opt/' + params.train_file)

lineopt_hyperheuristic.py

@@ -411,7 +411,7 @@ def line_optimizer_hyperheuristic(component_data, pcb_data, machine_number):
                     best_component_list = component_list.copy()
 
     machine_cp_points = convert_assignment_result(heuristic_map, cp_index, cp_points, cp_nozzle, cp_feeders,
-                                                  best_component_list, best_heuristic_list, machine_number, is_opt=True)
+                                                  best_component_list, best_heuristic_list, machine_number)
 
     assignment_result = [[0 for _ in range(len(component_data))] for _ in range(machine_number)]
     for machine_idx in range(machine_number):

optimizer.py

@@ -44,7 +44,7 @@ def main():
     parser = argparse.ArgumentParser(description='assembly line optimizer implementation')
     parser.add_argument('--mode', default=1, type=int, help='mode: 0 -directly load pcb data without optimization '
                                                             'for data analysis, 1 -optimize pcb data, 2 -batch test')
-    parser.add_argument('--filename', default='PCB.txt', type=str, help='load pcb data')
+    parser.add_argument('--filename', default='data-1.txt', type=str, help='load pcb data')
 
     parser.add_argument('--comp_register', default=1, type=int, help='register the component according the pcb data')
     parser.add_argument('--machine_number', default=3, type=int, help='the number of machine in the assembly line')