fix:修改艺术字体美化和距离度量的BUG

BFDS_train.py

@@ -67,14 +67,13 @@ class Argument:
         self.middle_epoch = 0  # 引入目标域数据的起始轮次
 
         # 基于映射
-        # FIXME 基于映射有bug
-        self.distance_option = False  # 是否采用基于映射的损失
-        self.distance_loss = "JMMD"  # 损失模型 MK-MMD/JMMD/CORAL
+        self.distance_option = True  # 是否采用基于映射的损失
+        self.distance_loss = "MK-MMD"  # 损失模型 MK-MMD/JMMD/CORAL
         self.distance_tradeoff = "Step"  # 损失的trade_off参数 Cons/Step
         self.distance_lambda = 1  # 若调整模式为Cons，指定其具体值
 
         # 基于领域对抗
-        self.adversarial_option = True  # 是否采用领域对抗
+        self.adversarial_option = False  # 是否采用领域对抗
         self.adversarial_loss = "CDA"  # 领域对抗损失
         self.hidden_size = 1024  # 对抗网络的隐藏层维数
         self.grl_option = "Step"  # 梯度反转层权重选择静态or动态更新
@@ -96,7 +95,6 @@ class Argument:
                 print(f"警告: Parameter '{param_name}' does not exist.")
 
     def set_recommended_params(self):
-        # TODO cyq来写一个
         # 给用户设定的推荐参数
         recommended_params = {
             "data_set": "BFDS-Project/Bearing-Fault-Diagnosis-System",

BFDS_web.py

@@ -239,22 +239,16 @@ def change_adversarial_tradeoff(adversarial_option, adversarial_tradeoff):
     return (gr.update(visible=(adversarial_option and adversarial_tradeoff == "Cons")),)
 
 
+with open("docs/BFDS_font.html", "r", encoding="utf-8") as f:
+    BFDS_font_html = f.read()
+
 # gradio BFDS_web.py --demo-name app
-# FIXME 做一个网页logo
 with gr.Blocks(title="BFDS WebUI") as app:
-    # FIXME 怎么显示有问题呢？？？？
-    gr.HTML(r"""
-<pre style="text-align: center;">
- ________  ________ ________  ________                 ________  ________  ________        ___  _______   ________ _________   
-|\   __  \|\  _____\\   ___ \|\   ____\               |\   __  \|\   __  \|\   __  \      |\  \|\  ___ \ |\   ____\\___   ___\ 
-\ \  \|\ /\ \  \__/\ \  \_|\ \ \  \___|_  ____________\ \  \|\  \ \  \|\  \ \  \|\  \     \ \  \ \   __/|\ \  \___\|___ \  \_| 
- \ \   __  \ \   __\\ \  \ \\ \ \_____  \|\____________\ \   ____\ \   _  _\ \  \\\  \  __ \ \  \ \  \_|/_\ \  \       \ \  \  
-  \ \  \|\  \ \  \_| \ \  \_\\ \|____|\  \|____________|\ \  \___|\ \  \\  \\ \  \\\  \|\  \\_\  \ \  \_|\ \ \  \____   \ \  \ 
-   \ \_______\ \__\   \ \_______\____\_\  \              \ \__\    \ \__\\ _\\ \_______\ \________\ \_______\ \_______\  \ \__\
-    \|_______|\|__|    \|_______|\_________\              \|__|     \|__|\|__|\|_______|\|________|\|_______|\|_______|   \|__|
-                                \|_________|                                                                                                                                                                                     
-</pre>
-""")
+    gr.HTML(BFDS_font_html)
+    gr.Markdown("""
+    # 轴承故障诊断系统
+    基于深度迁移学习的智能轴承故障诊断系统。支持多种迁移学习算法、信号处理方法和故障诊断模型。
+    """)
     with gr.Tab("模型训练"):
         gr.Markdown("在此模块中，您可以选择不同的迁移学习方法进行模型训练。")
         with gr.Row():

dataset/dataset.py

@@ -56,36 +56,35 @@ class SignalDatasetCreator:
     def data_split(self, batch_size, num_workers, device):
         # 这里源域和目标域都是我们提供用来验证迁移学习的正确性
         # get source train and val
-        data_frame = get_dataset(self.data_set, self.source[0], self.source[1])
-        data_set = SignalDataset(data_frame)
-        lengths = [round(0.8 * len(data_set)), len(data_set) - round(0.8 * len(data_set))]
-        train_data, eval_data = random_split(data_set, lengths)
-        source_train = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=(device == "cuda"))
-        source_val = DataLoader(dataset=eval_data, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=(device == "cuda"))
+        data_frame_source = get_dataset(self.data_set, self.source[0], self.source[1])
+        data_set_source = SignalDataset(data_frame_source)
+        lengths_source = [round(0.8 * len(data_set_source)), len(data_set_source) - round(0.8 * len(data_set_source))]
+        train_data_source, eval_data_source = random_split(data_set_source, lengths_source)
+        source_train = DataLoader(dataset=train_data_source, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=(device == "cuda"), drop_last=True)
+        source_val = DataLoader(dataset=eval_data_source, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=(device == "cuda"), drop_last=True)
         # get target train and val
-        data_frame = get_dataset(self.data_set, self.target[0], self.target[1])
-        data_set = SignalDataset(data_frame)
-        lengths = [round(0.8 * len(data_set)), len(data_set) - round(0.8 * len(data_set))]
-        train_data, eval_data = random_split(data_set, lengths)
-        target_train = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=(device == "cuda"))
-        target_val = DataLoader(dataset=eval_data, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=(device == "cuda"))
+        data_frame_target = get_dataset(self.data_set, self.target[0], self.target[1])
+        data_set_target = SignalDataset(data_frame_target)
+        lengths_target = [round(0.8 * len(data_set_target)), len(data_set_target) - round(0.8 * len(data_set_target))]
+        train_data_target, eval_data_target = random_split(data_set_target, lengths_target)
+        target_train = DataLoader(dataset=train_data_target, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=(device == "cuda"), drop_last=True)
+        target_val = DataLoader(dataset=eval_data_target, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=(device == "cuda"), drop_last=True)
         return source_train, source_val, target_train, target_val
 
     def owned_data_split(self, data_path, batch_size, num_workers, device):
-        # FIXME 这里不给标签影响acc吗？
         # 这里目标域是用户自己提供的数据集
         # get source train and val
-        data_frame = get_dataset(self.data_set, self.source[0], self.source[1])
-        data_set = SignalDataset(data_frame)
-        lengths = [round(0.8 * len(data_set)), len(data_set) - round(0.8 * len(data_set))]
-        train_data, eval_data = random_split(data_set, lengths)
-        source_train = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=(device == "cuda"))
-        source_val = DataLoader(dataset=eval_data, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=(device == "cuda"))
+        data_frame_source = get_dataset(self.data_set, self.source[0], self.source[1])
+        data_set_source = SignalDataset(data_frame_source)
+        lengths_source = [round(0.8 * len(data_set_source)), len(data_set_source) - round(0.8 * len(data_set_source))]
+        train_data_source, eval_data_source = random_split(data_set_source, lengths_source)
+        source_train = DataLoader(dataset=train_data_source, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=(device == "cuda"), drop_last=True)
+        source_val = DataLoader(dataset=eval_data_source, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=(device == "cuda"), drop_last=True)
         # get target train and val
-        data_frame = get_owned_dataset(data_path)
-        data_set = SignalDataset(data_frame)
-        lengths = [round(0.8 * len(data_set)), len(data_set) - round(0.8 * len(data_set))]
-        train_data, eval_data = random_split(data_set, lengths)
-        target_train = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=(device == "cuda"))
-        target_val = DataLoader(dataset=eval_data, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=(device == "cuda"))
+        data_frame_target = get_owned_dataset(data_path)
+        data_set_target = SignalDataset(data_frame_target)
+        lengths_target = [round(0.8 * len(data_set_target)), len(data_set_target) - round(0.8 * len(data_set_target))]
+        train_data_target, eval_data_target = random_split(data_set_target, lengths_target)
+        target_train = DataLoader(dataset=train_data_target, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=(device == "cuda"), drop_last=True)
+        target_val = DataLoader(dataset=eval_data_target, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=(device == "cuda"), drop_last=True)
         return source_train, source_val, target_train, target_val

docs/BFDS_font.html

@@ -0,0 +1,31 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Bearing Fault Diagnosis ASCII Art</title>
+    <style>
+        #taag_output_text {
+            font-family: "Courier New", ui-monospace, monospace;
+            font-size: 10pt;
+            white-space: pre;
+            overflow-wrap: break-word;
+            margin-top: 15px;
+            margin-bottom: 15px;
+            float: left;
+        }
+    </style>
+</head>
+<body>
+    <pre id="taag_output_text">
+ ________  ________ ________  ________                 ________  ________  ________        ___  _______   ________ _________   
+|\   __  \|\  _____\\   ___ \|\   ____\               |\   __  \|\   __  \|\   __  \      |\  \|\  ___ \ |\   ____\\___   ___\ 
+\ \  \|\ /\ \  \__/\ \  \_|\ \ \  \___|_  ____________\ \  \|\  \ \  \|\  \ \  \|\  \     \ \  \ \   __/|\ \  \___\|___ \  \_| 
+ \ \   __  \ \   __\\ \  \ \\ \ \_____  \|\____________\ \   ____\ \   _  _\ \  \\\  \  __ \ \  \ \  \_|/_\ \  \       \ \  \  
+  \ \  \|\  \ \  \_| \ \  \_\\ \|____|\  \|____________|\ \  \___|\ \  \\  \\ \  \\\  \|\  \\_\  \ \  \_|\ \ \  \____   \ \  \ 
+   \ \_______\ \__\   \ \_______\____\_\  \              \ \__\    \ \__\\ _\\ \_______\ \________\ \_______\ \_______\  \ \__\
+    \|_______|\|__|    \|_______|\_________\              \|__|     \|__|\|__|\|_______|\|________|\|_______|\|_______|   \|__|
+                                 \|_________|                                                                                   
+    </pre>
+</body>
+</html>

utils/predict.py

@@ -6,7 +6,6 @@ from pathlib import Path
 import models
 
 
-# TODO 处理更多文件
 def audio_to_signal(audio_file, sr=None):
     signal, _ = librosa.load(audio_file, sr=sr)
     return signal
@@ -36,7 +35,6 @@ def predict(model_state_dict, signal_file, args):
         file_extension = Path(signal_file).suffix
         if file_extension == ".csv":
             signal = csv_to_signal(signal_file).reshape(-1, 1, 224)
-        # FIXME 这里能搞多少后缀就多少后缀
         elif file_extension in [".wav", ".mp3"]:
             signal = audio_to_signal(signal_file).reshape(-1, 1, 224)
         else:
@@ -44,5 +42,4 @@ def predict(model_state_dict, signal_file, args):
         signal = torch.tensor(signal, dtype=torch.float32).to(device)
         output = model_all(signal)
         predictions = output.mean(dim=0)
-        # FIXME 这里没有过softmax
     return predictions

utils/train.py

@@ -14,7 +14,7 @@ import matplotlib.pyplot as plt
 import models
 from models.AdversarialNet import AdversarialNet, calc_coeff, grl_hook, Entropy
 from dataset.dataset import SignalDatasetCreator
-from .loss import DAN, JAN, CORAL
+from utils.loss import DAN, JAN, CORAL
 
 
 class train_utils:
@@ -26,7 +26,7 @@ class train_utils:
     def setup(self):
         args = self.args
         self.save_dir = os.path.join(args.checkpoint_dir, args.model_name + "_" + datetime.strftime(datetime.now(), "%m%d-%H%M%S"))
-        
+
         # 判断训练设备
         if torch.cuda.is_available():
             self.device = torch.device("cuda")