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import pandas as pd
import numpy as np
import os
from tqdm import tqdm
from PIL import Image
import torch
import torch.nn as nn
import torchvision.transforms as T
from torchvision.models import resnet50
def is_gpu_available():
return torch.cuda.is_available()
class ResNetClassifier(nn.Module):
def __init__(self, num_classes, metadata_size):
super(ResNetClassifier, self).__init__()
self.resnet = resnet50(pretrained=True)
self.resnet.fc = nn.Identity() # Remove the fully connected layer
self.metadata_fc = nn.Linear(metadata_size, 128)
self.classifier = nn.Linear(2048 + 128, num_classes) # 2048 is the output size of ResNet50
def forward(self, x, metadata_features):
resnet_features = self.resnet(x)
metadata_features = self.metadata_fc(metadata_features)
combined_features = torch.cat((resnet_features, metadata_features), dim=1)
logits = self.classifier(combined_features)
return logits
class PytorchWorker:
def __init__(self, model_path: str, num_classes: int, metadata_size: int):
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Using device: {self.device}")
self.model = self._load_model(model_path, num_classes, metadata_size)
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 _load_model(self, model_path, num_classes, metadata_size):
model = ResNetClassifier(num_classes, metadata_size)
model.load_state_dict(torch.load(model_path, map_location=self.device))
return model.to(self.device).eval()
def predict_image(self, image: Image.Image, metadata_features: np.ndarray) -> list:
input_tensor = self.transforms(image).unsqueeze(0).to(self.device)
metadata_tensor = torch.tensor(metadata_features).unsqueeze(0).to(self.device)
with torch.no_grad():
logits = self.model(input_tensor, metadata_tensor)
return logits.tolist()
def make_submission(test_metadata, model_path, num_classes, metadata_size, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"):
model = PytorchWorker(model_path, num_classes, metadata_size)
predictions = []
for _, row in tqdm(test_metadata.iterrows(), total=len(test_metadata)):
image_path = os.path.join(images_root_path, row['image_path'])
test_image = Image.open(image_path).convert("RGB")
metadata_features = row.drop(['image_path', 'class_id']).values.astype(np.float32)
logits = model.predict_image(test_image, metadata_features)
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 = "pytorch_model.pth"
metadata_file_path = "./SnakeCLEF2024-TestMetadata.csv"
test_metadata = pd.read_csv(metadata_file_path)
num_classes = 1784
metadata_size = len(test_metadata.columns) - 2 # Excluding 'image_path' and 'class_id'
make_submission(
test_metadata=test_metadata,
model_path=MODEL_PATH,
num_classes=num_classes,
metadata_size=metadata_size
)
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