2023-12-11 night

main_signal_text.py

@@ -27,14 +27,15 @@ def main(args):
     epochs = args.epochs
     device = "cuda" if torch.cuda.is_available() else "cpu"
 
-    filename = 'dataset/window_400_300.h5'
+    filename = 'dataset/window.h5'
     weight_path = 'res/best.pt'
     best_precision, current_precision = 0, 0
 
     model_dim = 2 # 数据维数 1为(B,8,400,1)，2为(B,1,400,8)
-    classification = True # 是否是分类任务
+    classification = False # 是否是分类任务
     # 'RN50', 'ViT-B/32'
-    model = clip.EMGload("ViT-B/32", device=device, classification=classification, vis_pretrain=False, model_dim=model_dim)
+    model_types = ['RN50', 'ViT-B/32']
+    model = clip.EMGload(model_types[0], device=device, classification=classification, vis_pretrain=False, model_dim=model_dim)
 
     # optimizer = Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.98), weight_decay=0.2)
     optimizer = Adam(model.parameters(), lr=args.lr, eps=1e-3)
@@ -53,23 +54,23 @@ def main(args):
           (train_len, val_len, data_len - train_len - val_len))
 
     # 数据按照8:1:1分为训练集、验证集和测试集
-    # train_emg = emg[: train_len]
-    # train_label = label[: train_len]
-    # val_emg = emg[train_len : train_len + val_len]
-    # val_label = label[train_len : train_len + val_len]
-    # eval_emg = emg[train_len + val_len :]
-    # eval_label = label[train_len + val_len :]
-
-    data_filename = 'D:/Download/Datasets/Ninapro/DB2/S1/S1_E1_A1.mat'
-    emg, label = load_emg_label_from_file(data_filename)
-    # [(4,1,1), 200] [(2,1,1), 300]
-    train_emg, train_label, val_emg, val_label, eval_emg, eval_label = split_window_ration(emg, label, (2,1,1), window_overlap=300)
-    train_index = np.random.permutation(len(train_emg))
-    val_index = np.random.permutation(len(val_emg))
-    eval_index = np.random.permutation(len(eval_emg))
-    train_emg, train_label = train_emg[train_index] * 20000, train_label[train_index]
-    val_emg, val_label = val_emg[val_index] * 20000, val_label[val_index]
-    eval_emg, eval_label = eval_emg[eval_index] * 20000, eval_label[eval_index]
+    train_emg = emg[: train_len]
+    train_label = label[: train_len]
+    val_emg = emg[train_len : train_len + val_len]
+    val_label = label[train_len : train_len + val_len]
+    eval_emg = emg[train_len + val_len :]
+    eval_label = label[train_len + val_len :]
+
+    # data_filename = 'D:/Download/Datasets/Ninapro/DB2/S1/S1_E1_A1.mat'
+    # emg, label = load_emg_label_from_file(data_filename)
+    # # [(4,1,1), 200] [(2,1,1), 300]
+    # train_emg, train_label, val_emg, val_label, eval_emg, eval_label = split_window_ration(emg, label, (2,1,1), window_overlap=300)
+    # train_index = np.random.permutation(len(train_emg))
+    # val_index = np.random.permutation(len(val_emg))
+    # eval_index = np.random.permutation(len(eval_emg))
+    # train_emg, train_label = train_emg[train_index] * 20000, train_label[train_index]
+    # val_emg, val_label = val_emg[val_index] * 20000, val_label[val_index]
+    # eval_emg, eval_label = eval_emg[eval_index] * 20000, eval_label[eval_index]
 
     train_loader = DataLoader(
                     SignalWindow(train_emg, train_label),