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| from fastapi import APIRouter | |
| from datetime import datetime | |
| from datasets import load_dataset | |
| from sklearn.metrics import accuracy_score | |
| import numpy as np | |
| import os | |
| import torch | |
| from torch.utils.data import DataLoader | |
| from .utils.evaluation import AudioEvaluationRequest | |
| from .utils.emissions import tracker, clean_emissions_data, get_space_info | |
| from .utils.data import FFTDataset | |
| from .utils.models import DualEncoder | |
| from .utils.train import Trainer | |
| from .utils.data_utils import collate_fn, Container | |
| import yaml | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| router = APIRouter() | |
| DESCRIPTION = "Random Baseline" | |
| ROUTE = "/audio" | |
| async def evaluate_audio(request: AudioEvaluationRequest): | |
| """ | |
| Evaluate audio classification for rainforest sound detection. | |
| Current Model: Random Baseline | |
| - Makes random predictions from the label space (0-1) | |
| - Used as a baseline for comparison | |
| """ | |
| # Get space info | |
| username, space_url = get_space_info() | |
| # Define the label mapping | |
| LABEL_MAPPING = { | |
| "chainsaw": 0, | |
| "environment": 1 | |
| } | |
| # Load and prepare the dataset | |
| # Because the dataset is gated, we need to use the HF_TOKEN environment variable to authenticate | |
| dataset = load_dataset(request.dataset_name,token=os.getenv("HF_TOKEN")) | |
| # Split dataset | |
| train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed) | |
| test_dataset = train_test["test"] | |
| # Start tracking emissions | |
| tracker.start() | |
| tracker.start_task("inference") | |
| #-------------------------------------------------------------------------------------------- | |
| # YOUR MODEL INFERENCE CODE HERE | |
| # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked. | |
| #-------------------------------------------------------------------------------------------- | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| args_path = 'utils/config.yaml' | |
| data_args = Container(**yaml.safe_load(open(args_path, 'r'))['Data']) | |
| model_args = Container(**yaml.safe_load(open(args_path, 'r'))['CNNEncoder']) | |
| model_args_f = Container(**yaml.safe_load(open(args_path, 'r'))['CNNEncoder_f']) | |
| conformer_args = Container(**yaml.safe_load(open(args_path, 'r'))['Conformer']) | |
| test_dataset = FFTDataset(test_dataset) | |
| test_dl = DataLoader(test_dataset, batch_size=data_args.batch_size, collate_fn=collate_fn) | |
| model = DualEncoder(model_args, model_args_f, conformer_args) | |
| model = model.to(device) | |
| missing, unexpected = model.load_state_dict(torch.load(model_args.checkpoint_path)) | |
| loss_fn = torch.nn.BCEWithLogitsLoss() | |
| optimizer = torch.optim.Adam(model.parameters(), lr=5e-4) | |
| trainer = Trainer(model=model, optimizer=optimizer, | |
| criterion=loss_fn, output_dim=model_args.output_dim, scaler=None, | |
| scheduler=None, train_dataloader=None, | |
| val_dataloader=None, device=device, | |
| exp_num='test', log_path=None, | |
| range_update=None, | |
| accumulation_step=1, max_iter=np.inf, | |
| exp_name=f"frugal_cnnencoder_inference") | |
| predictions, acc = trainer.predict(test_dl, device=device) | |
| # Make random predictions (placeholder for actual model inference) | |
| true_labels = test_dataset["label"] | |
| #-------------------------------------------------------------------------------------------- | |
| # YOUR MODEL INFERENCE STOPS HERE | |
| #-------------------------------------------------------------------------------------------- | |
| # Stop tracking emissions | |
| emissions_data = tracker.stop_task() | |
| # Calculate accuracy | |
| accuracy = accuracy_score(true_labels, predictions) | |
| # Prepare results dictionary | |
| results = { | |
| "username": username, | |
| "space_url": space_url, | |
| "submission_timestamp": datetime.now().isoformat(), | |
| "model_description": DESCRIPTION, | |
| "accuracy": float(accuracy), | |
| "energy_consumed_wh": emissions_data.energy_consumed * 1000, | |
| "emissions_gco2eq": emissions_data.emissions * 1000, | |
| "emissions_data": clean_emissions_data(emissions_data), | |
| "api_route": ROUTE, | |
| "dataset_config": { | |
| "dataset_name": request.dataset_name, | |
| "test_size": request.test_size, | |
| "test_seed": request.test_seed | |
| } | |
| } | |
| return results |