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import pandas as pd
import numpy as np
from torch import nn
import os
from tqdm import tqdm
import timm
import torchvision.transforms as T
from PIL import Image
import torch
def is_gpu_available():
"""Check if the python package `onnxruntime-gpu` is installed."""
return torch.cuda.is_available()
class CustomModel(nn.Module):
def __init__(self, base_model_name, num_classes1, num_classes2):
super(CustomModel, self).__init__()
self.base_model = timm.create_model(base_model_name, pretrained=False)
in_features = self.base_model.get_classifier().in_features
self.base_model.reset_classifier(0) # Remove the original classification layer
self.fc1 = nn.Linear(in_features, num_classes1) # Binary classification output
self.fc2 = nn.Linear(in_features, num_classes2) # Categorical classification output
def forward(self, x):
x = self.base_model(x)
out1 = torch.sigmoid(self.fc1(x)) # Binary output
out2 = self.fc2(x) # Categorical output
return out2
class PytorchWorker:
"""Run inference using ONNX runtime."""
def __init__(self, model_path: str, model_name: str, number_of_categories: int = 1784):
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 devide: {self.device}")
model = CustomModel(model_name, 1, number_of_categories)
# if not torch.cuda.is_available():
# model_ckpt = torch.load(model_path, map_location=torch.device("cpu"))
# else:
# model_ckpt = torch.load(model_path)
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((256, 256)),
T.ToTensor(),
T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
def predict_image(self, image: np.ndarray) -> list():
"""Run inference using ONNX runtime.
:param image: Input image as numpy array.
:return: A list with logits and confidences.
"""
logits = self.model(self.transforms(image).unsqueeze(0).to(self.device))
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 """
model = PytorchWorker(model_path, model_name)
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)
predictions.append(np.argmax(logits))
test_metadata["class_id"] = predictions
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")
MODEL_PATH = "best_accuracy_BCE_SEE.pth"
MODEL_NAME = "swinv2_tiny_window16_256.ms_in1k"
metadata_file_path = "./SnakeCLEF2024-TestMetadata.csv"
test_metadata = pd.read_csv(metadata_file_path)
make_submission(
test_metadata=test_metadata,
model_path=MODEL_PATH,
model_name=MODEL_NAME,
# images_root_path='/home/zeleznyt/mnt/data-ntis/projects/korpusy_cv/SnakeCLEF2024/val/SnakeCLEF2023-medium_size'
)
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