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import pandas as pd
import numpy as np
import onnxruntime as ort
import os
from tqdm import tqdm
import timm
import torchvision.transforms as T
from PIL import Image
import torch
import torch.nn as nn
def is_gpu_available():
"""Check if the python package `onnxruntime-gpu` is installed."""
return torch.cuda.is_available()
class PytorchWorker:
"""Run inference using ONNX runtime."""
def __init__(self, model_path: str, model_name: str, number_of_categories: int = 1605):
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 = timm.create_model(model_name, num_classes=number_of_categories, pretrained=False)
# 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, strict=False)
msg = model.load_state_dict(model_ckpt, strict=False)
print("load_state_dict: ", msg)
# num_features = model.get_classifier().in_features
# model.classifier = nn.Linear(num_features, number_of_categories)
return model.to(self.device).eval()
self.model = _load_model(model_name, model_path)
self.transforms = T.Compose([T.Resize((299, 299)),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
# 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.
"""
self.model.eval()
outputs = self.model(self.transforms(image).unsqueeze(0).to(self.device))
return outputs.cpu() # Convert tensor to list
def make_submission(test_metadata, model_path, model_path2, model_name, model_name2, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"):
"""Make submission with given """
model = PytorchWorker(model_path, model_name, number_of_categories=1604)
model2 = PytorchWorker(model_path2, model_name2)
predictions = []
correct_max_values = []
incorrect_max_values = []
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")
outputs = model.predict_image(test_image)
outputs2 = model2.predict_image(test_image)
# max_value = torch.max(outputs+outputs2)
_, preds = torch.max(outputs, 1) # baseline
_, preds2 = torch.max(outputs2, 1) # 1.4.3
pred_class_id = preds.tolist()
pred_class_id2 = preds2.tolist()
# max_value2 = torch.max(outputs2)
pred_class_id = pred_class_id[0] if pred_class_id2[0] != 1604 else -1
predictions.append(pred_class_id)
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 = './efficientnet_b3_epoch_9_delete_pre.pth' # "./efficientnet_b3_epoch_9.pth"
# MODEL_PATH = './efficientnet_b3_epoch_24_trick1.2.3_0.6067.pth'
# MODEL_PATH = './efficientnet_b3_epoch_10_trick1.2.4_0.6016.pth'
# MODEL_PATH = './efficientnet_b3_epoch_3_trick1.2.3_a0.6067_l5.6311.pth'
# MODEL_PATH = './efficientnet_b3_epoch_21_trick1.2.5_a0.7237_l17.1662.pth'
# MODEL_PATH = './efficientnet_b3_epoch_21_trcik1.5.2.pth'
# MODEL_PATH = './efficientnet_b3_epoch_28_1.4.3.pth'
MODEL_PATH = './efficientnet_b3_epoch_25_trick1.4.4.pth'
MODEL_PATH2 = './efficientnet_b3_epoch_28_1.4.3.pth'
MODEL_NAME = "tf_efficientnet_b3_ns"
MODEL_NAME2 = "tf_efficientnet_b3_ns"
metadata_file_path = "./FungiCLEF2024_TestMetadata.csv"
test_metadata = pd.read_csv(metadata_file_path)
make_submission(
test_metadata=test_metadata,
model_path=MODEL_PATH,
model_path2=MODEL_PATH2,
model_name=MODEL_NAME,
model_name2=MODEL_NAME2
) |