init

test_t_sne.py

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+import numpy as np
+from sklearn.manifold import TSNE
+import matplotlib.pyplot as plt
+from opts import opts
+from clip import clip
+import torch
+from utils.utils import prepare_directories, set_seed, get_dataset_loader, configure_clip_encoders, save_model, \
+    set_adapter_weights, get_text_feature, AverageMeter, accuracy, calculate_zeroshot_weights, gpt_clip_classifier,calculate_zeroshot_weights_GPT
+import glob
+import json
+import os
+import numpy as np
+from PIL import Image
+
+# 设置随机种子以确保可重复性
+np.random.seed(0)
+
+args = opts()
+model, preprocess = clip.load(args.name)
+model = model.cuda()
+model.float()
+CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder = configure_clip_encoders(args, model, 0, 1)
+prepare_directories(args, CLIP_Text, CLIP_Image)
+json_files = glob.glob('gpt_file/caltech_prompt.json')
+for file_path in json_files:
+    # 打开并读取每个YAML文件
+    with open(file_path, 'r') as f:
+        gpt3_prompt = json.load(f)
+
+
+dir = './weights/'
+CLIP_Text=torch.load(dir+'CLIP_Text.pth')
+CLIP_Image=torch.load(dir+'CLIP_Image.pth')
+Image_Encoder=torch.load(dir+'Image_Encoder.pth')
+Text_Encoder=torch.load(dir+'Text_Encoder.pth')
+adapter=torch.load(dir+'adapter.pth')
+
+image_folder = "./images"
+image_folder2 = "./images_2"
+image_folder3 = "./images_3"
+# 读取图片并提取特征
+features = []
+for image_name in os.listdir(image_folder):
+    if image_name.lower().endswith(('.png', '.jpg', '.jpeg')):
+        # 读取图片
+        image_path = os.path.join(image_folder, image_name)
+        image = preprocess(Image.open(image_path)).unsqueeze(0).cuda()
+        with torch.no_grad():
+            input_target_temp = CLIP_Image(image)
+            input_target_add = Image_Encoder(input_target_temp)
+
+
+        # 存储提取的特征
+        # features.append(input_target_add.detach().numpy())
+        features.append(input_target_add.detach().cpu().numpy())
+
+features2 = []
+for image_name in os.listdir(image_folder2):
+    if image_name.lower().endswith(('.png', '.jpg', '.jpeg')):
+        # 读取图片
+        image_path = os.path.join(image_folder2, image_name)
+        image = preprocess(Image.open(image_path)).unsqueeze(0).cuda()
+        with torch.no_grad():
+            input_target_temp = CLIP_Image(image)
+            input_target_add = Image_Encoder(input_target_temp)
+
+
+        # 存储提取的特征
+        # features.append(input_target_add.detach().numpy())
+        features2.append(input_target_add.detach().cpu().numpy())
+
+# 将特征列表转换为NumPy数组
+features = np.vstack(features)
+features2 = np.vstack(features2)
+
+features3 = []
+for image_name in os.listdir(image_folder3):
+    if image_name.lower().endswith(('.png', '.jpg', '.jpeg')):
+        # 读取图片
+        image_path = os.path.join(image_folder3, image_name)
+        image = preprocess(Image.open(image_path)).unsqueeze(0).cuda()
+        with torch.no_grad():
+            input_target_temp = CLIP_Image(image)
+            input_target_add = Image_Encoder(input_target_temp)
+
+
+        # 存储提取的特征
+        # features.append(input_target_add.detach().numpy())
+        features3.append(input_target_add.detach().cpu().numpy())
+
+# 将特征列表转换为NumPy数组
+features = np.vstack(features)
+features2 = np.vstack(features2)
+features3 = np.vstack(features3)
+
+
+# 源域样本：一个二维特征
+# source_domain_sample = np.array([[0.5, 0.5]])  # 示例特征
+
+# 生成随机数据：目标域，20个样本，每个样本1024维
+# target_domain_samples = np.random.rand(20, 1024)
+target_domain_samples = features
+source_domain_samples=features2
+
+
+# 由于源域只有一个二维样本，我们不需要对它使用t-SNE
+tsne = TSNE(n_components=2, random_state=0,perplexity=19)
+source_domain_tsne_results = tsne.fit_transform(source_domain_samples)
+
+# 直接对目标域样本应用t-SNE降维到2维
+# tsne = TSNE(n_components=2, random_state=0,perplexity=7)
+target_domain_tsne_results = tsne.fit_transform(target_domain_samples)
+
+results3=tsne.fit_transform(features3)
+
+# 可视化结果
+plt.figure(figsize=(10, 6))
+# plt.scatter(source_domain_tsne_results[:, 0], source_domain_tsne_results[:, 1], c='blue', label='Source Domain (Single Sample)', edgecolors='w', s=200)
+plt.scatter(source_domain_tsne_results[:, 0], source_domain_tsne_results[:, 1], c='blue', label='Source Domain (Single Sample)')#, edgecolors='w')
+plt.scatter(target_domain_tsne_results[:, 0], target_domain_tsne_results[:, 1], c='red', label='Target Domain')
+plt.scatter(results3[:, 0], results3[:, 1], c='yellow', label='3')
+plt.title('t-SNE visualization of feature representations')
+plt.xlabel('Component 1')
+plt.ylabel('Component 2')
+plt.legend()
+plt.show()