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import pandas as pd |
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import numpy as np |
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import os |
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from tqdm import tqdm |
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import timm |
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import torchvision.transforms as T |
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from PIL import Image |
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import torch |
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from typing import List |
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def is_gpu_available(): |
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"""Check if the python package `onnxruntime-gpu` is installed.""" |
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return torch.cuda.is_available() |
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WIDTH = 224 |
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HEIGHT = 224 |
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MODEL_PATH = "metaformer-s-224.pth" |
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MODEL_NAME = "caformer_s18.sail_in22k" |
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class PytorchWorker: |
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"""Run inference using ONNX runtime.""" |
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def __init__(self, model_path: str, model_name: str, number_of_categories: int = 1605): |
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def _load_model(model_name, model_path): |
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print("Setting up Pytorch Model") |
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self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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print(f"Using devide: {self.device}") |
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model = timm.create_model(model_name, num_classes=number_of_categories, pretrained=False) |
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weights = torch.load(model_path, map_location=self.device) |
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model.load_state_dict({w.replace("model.", ""): v for w, v in weights.items()}) |
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return model.to(self.device).eval() |
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self.model = _load_model(model_name, model_path) |
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self.transforms = T.Compose([T.Resize((HEIGHT, WIDTH)), |
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T.ToTensor(), |
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T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) |
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def predict_image(self, image: np.ndarray) -> List: |
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"""Run inference using ONNX runtime. |
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:param image: Input image as numpy array. |
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:return: A list with logits and confidences. |
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""" |
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logits = self.model(self.transforms(image).unsqueeze(0).to(self.device)) |
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return logits.tolist() |
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def make_submission(test_metadata, model_path, model_name, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"): |
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"""Make submission with given """ |
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model = PytorchWorker(model_path, model_name) |
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predictions = [] |
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for _, row in tqdm(test_metadata.iterrows(), total=len(test_metadata)): |
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image_path = os.path.join(images_root_path, row.image_path.replace("jpg", "JPG")) |
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test_image = Image.open(image_path).convert("RGB") |
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logits = model.predict_image(test_image) |
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predictions.append(np.argmax(logits)) |
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test_metadata["class_id"] = predictions |
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user_pred_df = test_metadata.drop_duplicates("observation_id", keep="first") |
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for ix, row in user_pred_df.iterrows(): |
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if row['class_id'] == 1604: |
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user_pred_df.loc[ix, 'class_id'] = -1 |
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user_pred_df[["observation_id", "class_id"]].to_csv(output_csv_path, index=None) |
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def test_submission(): |
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metadata_file_path = "../trial_test.csv" |
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test_metadata = pd.read_csv(metadata_file_path) |
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make_submission( |
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test_metadata=test_metadata, |
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model_path=MODEL_PATH, |
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model_name=MODEL_NAME, |
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images_root_path="../data/DF_FULL/" |
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) |
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if __name__ == "__main__": |
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import zipfile |
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with zipfile.ZipFile("/tmp/data/private_testset.zip", 'r') as zip_ref: |
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zip_ref.extractall("/tmp/data") |
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metadata_file_path = "./FungiCLEF2024_TestMetadata.csv" |
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test_metadata = pd.read_csv(metadata_file_path) |
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make_submission( |
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test_metadata=test_metadata, |
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model_path=MODEL_PATH, |
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model_name=MODEL_NAME |
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) |
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