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# An example of how to run inference using a model trained with cvair.
# We want to use a pretrained model to make predictions on a dataset of new examples.
import numpy as np
import pandas as pd
import torch
from pytorch_lightning import Trainer
from torch.utils.data import DataLoader
from utils.datasets import ECGSingleLeadDataset
from utils.models import EffNet
from utils.training_models import BinaryClassificationModel
def append_noise_predictions_to_manifest(data_path, manifest_path, weights_path):
# Initialize a dataset
test_ds = ECGSingleLeadDataset(
data_path=data_path,
manifest_path=manifest_path,
update_manifest_func=None,
)
# Wrap the dataset in a dataloader
test_dl = DataLoader(
test_ds,
num_workers=16,
batch_size=512,
drop_last=False,
shuffle=False
)
# Initialize the backbone model
backbone = EffNet(input_channels=1, output_neurons=1)
# Pass the backbone to a wrapper
model = BinaryClassificationModel(backbone)
# Load the pretrained weights
weights = torch.load(weights_path)
model.load_state_dict(weights)
# Initialize a Trainer object
trainer = Trainer(accelerator="gpu", devices=1)
# Run inference
trainer.predict(model, dataloaders=test_dl)
# Read the predictions CSV file
df = pd.read_csv('dataloader_0_predictions.csv')
# Normalize predictions
max_preds = df['preds'].max()
min_preds = df['preds'].min()
df['noise_preds_normal'] = (df['preds'] - min_preds) / (max_preds - min_preds)
# Drop the original predictions column
df.drop(columns='preds', inplace=True)
# Save the modified dataframe to the original manifest path
df.to_csv(manifest_path)
if __name__ == "__main__":
data_path="/your/wearable/ecg/data path/"
manifest_path="manifest"
weights_path = "model_noise_classifier.pt"
append_noise_predictions_to_manifest(data_path, manifest_path, weights_path)
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