Delete script.py

script.py

@@ -1,94 +0,0 @@
-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()
-    img_list = []
-    print(os.path.abspath(os.path.dirname(__file__)))
-    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_list.append(img)
-
-    # batch img
-    batch_img = torch.stack(img_list, dim=0)
-
-    with torch.no_grad():
-        # predict class
-        output = model(batch_img.to(device)).cpu()
-        predict = torch.softmax(output, dim=1)
-        probs, classesId = torch.max(predict, dim=1)
-        probs = probs.data.numpy().tolist()
-        classesId = classesId.data.numpy().tolist()
-        id2classId = dict()
-        id2prob = dict()
-        for i, id in enumerate(id_list):
-            if id not in id2classId.keys():
-                id2classId[id] = classesId[i]
-                id2prob[id] = probs[i]
-            else:
-                if probs[i] > id2prob[id]:
-                    id2classId[id] = classesId[i]
-                    id2prob[id] = probs[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"
-
-    # read class_indict
-    json_path = './class_indices.json'
-    assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)
-
-    # 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 = "./test.csv"
-    test_metadata = pd.read_csv(metadata_file_path)
-    predict(test_metadata, root_path)