Upload script.py

script.py

@@ -0,0 +1,91 @@
+import os
+import json
+import pandas as pd
+import torch
+from PIL import Image
+from torchvision import transforms
+from model import resnet101
+
+
+def predict(test_metadata, root_path='/tmp/data/private_testset', output_csv_path='./submission.csv'):
+
+    data_transform = transforms.Compose(
+        [transforms.Resize(256),
+         transforms.CenterCrop(224),
+         transforms.ToTensor(),
+         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
+
+    # load image
+    # img_name_list = ["1163.jpg", "1164.jpg"]
+    # id_list = [1, 2]
+    id_list = test_metadata['observation_id'].tolist()
+    img_name_list = test_metadata['filename'].tolist()
+    print(os.path.abspath(os.path.dirname(__file__)))
+
+    id2classId = dict()
+    id2prob = dict()
+    prob_list = list()
+    classId_list = list()
+    for img_name in img_name_list:
+        img_path = os.path.join(root_path, img_name)
+        assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
+        img = Image.open(img_path).convert('RGB')
+        img = data_transform(img)
+        img = torch.unsqueeze(img, dim=0)
+
+        with torch.no_grad():
+            # predict class
+            output = model(img.to(device)).cpu()
+            predict = torch.softmax(output, dim=1)
+            probs, classesId = torch.max(predict, dim=1)
+            prob = probs.data.numpy().tolist()[0]
+            classesId = classesId.data.numpy().tolist()[0]
+            prob_list.append(prob)
+            classId_list.append(classesId)
+
+    for i, id in enumerate(id_list):
+        if id not in id2classId.keys():
+            id2classId[id] = classId_list[i]
+            id2prob[id] = prob_list[i]
+        else:
+            if prob_list[i] > id2prob[id]:
+                id2classId[id] = classId_list[i]
+                id2prob[id] = prob_list[i]
+    classes = list()
+    for id in id_list:
+        classes.append(str(id2classId[id]))
+    test_metadata["class_id"] = classes
+
+    user_pred_df = test_metadata.drop_duplicates("observation_id", keep="first")
+    user_pred_df[["observation_id", "class_id"]].to_csv(output_csv_path, index=None)
+
+
+if __name__ == '__main__':
+
+    import zipfile
+
+    with zipfile.ZipFile("/tmp/data/private_testset.zip", 'r') as zip_ref:
+        zip_ref.extractall("/tmp/data")
+    root_path = '/tmp/data/private_testset'
+
+    # root_path = "../../data_set/flower_data/val/n1"
+
+    # json_file = open(json_path, "r")
+    # index2class = json.load(json_file)
+
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    # create model
+    model = resnet101(num_classes=1784).to(device)
+
+    # load model weights
+    weights_path = "./resNet101.pth"
+    assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)
+    model.load_state_dict(torch.load(weights_path, map_location=device))
+
+    # prediction
+    model.eval()
+
+    metadata_file_path = "./SnakeCLEF2024_TestMetadata.csv"
+    # metadata_file_path = "./test1.csv"
+    test_metadata = pd.read_csv(metadata_file_path)
+    predict(test_metadata, root_path)