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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"
@router.post(ROUTE, tags=["Audio Task"],
description=DESCRIPTION)
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 |