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predict.py

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+import os
+
+# 指定使用1号显卡
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+import jittor as jt
+from PIL import Image
+import numpy as np
+import jclip as clip
+from tqdm import tqdm
+from test_clip import zeroshot_classifier
+from path_config import TESTSET_ROOT_DIR
+
+jt.flags.use_cuda = 1
+
+class TestSet(jt.dataset.Dataset):
+    def __init__(self, 
+                 image_list, 
+                 transform,
+                 batch_size=256, 
+                 num_workers=8):
+        super(TestSet, self).__init__()
+        self.image_list = image_list
+        self.batch_size = batch_size 
+        self.num_workers = num_workers
+        self.transform = transform
+        
+        self.set_attrs(
+            batch_size=self.batch_size,
+            total_len=len(self.image_list),
+            num_workers=self.num_workers
+        )
+
+
+    def __getitem__(self, idx):
+        image_path = self.image_list[idx]
+        label = image_path.split('/')[-1]
+        image = Image.open(f'{TESTSET_ROOT_DIR}/{image_path}').convert('RGB')
+        if self.transform is not None:
+            image = self.transform(image) 
+        img = np.asarray(image)
+        return img, label
+    
+def load_model(model_path):
+    model, preprocess = clip.load("/jittor-competiiton/pretrained/ViT-B-32.pkl")
+    model.eval()
+    model.load_state_dict(jt.load(model_path))
+    return model, preprocess
+
+def predict(model, preprocess, weights_version=1):
+    # image_paths = [os.path.join(TESTSET_ROOT_DIR, img_path) for img_path in ]
+    image_paths = os.listdir(TESTSET_ROOT_DIR)
+    dataloader = TestSet(image_paths, preprocess )
+    zeroshot_weights = zeroshot_classifier(model, weights_version=weights_version)
+    result = []
+    img_paths = []
+    
+    with jt.no_grad():
+        for batch in tqdm(dataloader, desc='Preprocessing'):
+            images, paths = batch
+            image_features = model.encode_image(images)
+            image_features = image_features / image_features.norm(dim=1, keepdim=True)
+            logits = (100 * image_features @ zeroshot_weights).softmax(dim=-1)
+            _, predict_indices = jt.topk(logits, k=5, dim=1)
+            img_paths += paths
+            result += predict_indices.tolist()
+    return result, img_paths
+
+def main(model_path, weights_version=2):
+    model, preprocess = load_model(model_path)
+    result, img_paths = predict(model, preprocess, weights_version)
+    out_txt(result, img_paths)
+
+def out_txt(result, img_paths, file_name='result.txt'):
+    with open(file_name, 'w') as f:
+        for img, preds in zip(img_paths, result):
+            f.write(f'{img} '+ ' '.join([str(i) for i in preds]) + '\n')
+    print(f'Result file generated!\nFile Path: {os.path.abspath(file_name)}')
+    
+if __name__ == "__main__":
+    # 完整的模型路径
+    model_path = '/jittor-competiiton/ckptFE/07-30/version_1/min_loss.pth'
+    main(model_path, weights_version=2)
+# 提交系统测试0.6788
+