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import pandas as pd
import numpy as np
import os
from tqdm import tqdm
import timm
import torchvision.transforms as T
from PIL import Image
import torch
class PytorchWorker:
"""Run inference using PyTorch."""
def __init__(self, model_path: str, model_name: str, number_of_categories: int):
def _load_model(model_name, model_path):
print("Setting up Pytorch Model")
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Using device: {self.device}")
model = timm.create_model(model_name, num_classes=number_of_categories, pretrained=False)
model_ckpt = torch.load(model_path, map_location=self.device)
model.load_state_dict(model_ckpt)
return model.to(self.device).eval()
self.model = _load_model(model_name, model_path)
self.transforms = T.Compose([
T.Resize((224, 224)),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
def predict_image(self, image: Image.Image) -> list:
"""Run inference using PyTorch.
:param image: Input image as PIL Image.
:return: A list with logits.
"""
image_tensor = self.transforms(image).unsqueeze(0).to(self.device)
logits = self.model(image_tensor)
return logits.tolist()
def make_submission(test_metadata, model_path, model_name, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"):
"""Make submission with given metadata and model."""
model = PytorchWorker(model_path, model_name, number_of_categories=1604) # Adjust number_of_categories as needed
predictions = []
observation_predictions = {}
for _, row in tqdm(test_metadata.iterrows(), total=len(test_metadata)):
image_path = os.path.join(images_root_path, row['filename'])
test_image = Image.open(image_path).convert("RGB")
logits = model.predict_image(test_image)
predicted_class = np.argmax(logits)
obs_id = row['observation_id']
if obs_id not in observation_predictions:
observation_predictions[obs_id] = []
observation_predictions[obs_id].append(predicted_class)
final_predictions = {obs_id: max(set(preds), key=preds.count) for obs_id, preds in observation_predictions.items()}
output_df = pd.DataFrame(list(final_predictions.items()), columns=['observation_id', 'class_id'])
output_df.to_csv(output_csv_path, index=False)
if __name__ == "__main__":
import zipfile
with zipfile.ZipFile("/tmp/data/private_testset.zip", 'r') as zip_ref:
zip_ref.extractall("/tmp/data")
MODEL_PATH = "resnet_classifier.pth" # Ensure this matches the filename of your model
MODEL_NAME = "resnet50" # Adjust this to your specific model
metadata_file_path = "./SnakeCLEF2023_TestMetadata.csv"
test_metadata = pd.read_csv(metadata_file_path)
make_submission(
test_metadata=test_metadata,
model_path=MODEL_PATH,
model_name=MODEL_NAME
)
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