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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)
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