inference.py

# -*- coding: utf-8 -*-
"""
Created on Sun Sep 15 18:27:17 2024

@author: salikha4
"""

import os
import csv
import json
import shutil
import random
import argparse
from datetime import datetime
import pandas as pd
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader, TensorDataset
from torch.optim import Adam
import numpy as np
#from lwm_model import LWM, load_model
import warnings
warnings.filterwarnings('ignore')
from input_preprocess import *

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    torch.cuda.empty_cache()

# Folders
# MODELS_FOLDER = 'models/'

def dataset_gen(preprocessed_chs, input_type, lwm_model):
    
    if input_type in ['cls_emb', 'channel_emb']:
        dataset = prepare_for_LWM(preprocessed_chs, device)
    elif input_type == 'raw':
        dataset = create_raw_dataset(preprocessed_chs, device)
    
    if input_type in ['cls_emb','channel_emb']:

        # Process data through LWM
        lwm_loss, embedding_data = evaluate(lwm_model, dataset)
        
        print(f'LWM loss: {lwm_loss:.4f}')
        
        if input_type == 'cls_emb':
            embedding_data = embedding_data[:, 0]
        elif input_type == 'channel_emb':  
            embedding_data = embedding_data[:, 1:]
        
        dataset = embedding_data.float()
        
    return dataset


def prepare_for_LWM(data, device, batch_size=64, shuffle=False):

    input_ids, masked_tokens, masked_pos = zip(*data)
    
    input_ids_tensor = torch.tensor(input_ids, device=device).float()
    masked_tokens_tensor = torch.tensor(masked_tokens, device=device).float()
    masked_pos_tensor = torch.tensor(masked_pos, device=device).long()

    dataset = TensorDataset(input_ids_tensor, masked_tokens_tensor, masked_pos_tensor)
    
    return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)


def create_raw_dataset(data, device):
    """Create a dataset for raw channel data."""
    input_ids, _, _ = zip(*data)
    input_data = torch.tensor(input_ids, device=device)[:, 1:]
    return input_data.float()


def label_gen(task, data, scenario, n_beams=64):
    
    idxs = np.where(data['user']['LoS'] != -1)[0]
            
    if task == 'LoS/NLoS Classification':
        label = data['user']['LoS'][idxs]
    elif task == 'Beam Prediction':
        parameters, row_column_users, n_ant_bs, n_ant_ue, n_subcarriers = get_parameters(scenario)
        n_users = len(data['user']['channel'])
        n_subbands = 1
        fov = 120

        # Setup Beamformers
        beam_angles = np.around(np.arange(-fov/2, fov/2+.1, fov/(n_beams-1)), 2)

        F1 = np.array([steering_vec(parameters['bs_antenna']['shape'], 
                                    phi=azi*np.pi/180, 
                                    kd=2*np.pi*parameters['bs_antenna']['spacing']).squeeze()
                       for azi in beam_angles])

        full_dbm = np.zeros((n_beams, n_subbands, n_users), dtype=float)
        for ue_idx in tqdm(range(n_users), desc='Computing the channel for each user'):
            if data['user']['LoS'][ue_idx] == -1:
                full_dbm[:,:,ue_idx] = np.nan
            else:
                chs = F1 @ data['user']['channel'][ue_idx]
                full_linear = np.abs(np.mean(chs.squeeze().reshape((n_beams, n_subbands, -1)), axis=-1))
                full_dbm[:,:,ue_idx] = np.around(20*np.log10(full_linear) + 30, 1)

        best_beams = np.argmax(np.mean(full_dbm,axis=1), axis=0)
        best_beams = best_beams.astype(float)
        best_beams[np.isnan(full_dbm[0,0,:])] = np.nan
        max_bf_pwr = np.max(np.mean(full_dbm,axis=1), axis=0) 
    
        label = best_beams[idxs]
        
    return label.astype(int)


def steering_vec(array, phi=0, theta=0, kd=np.pi):
    # phi = azimuth
    # theta = elevation
    idxs = DeepMIMOv3.ant_indices(array)
    resp = DeepMIMOv3.array_response(idxs, phi, theta+np.pi/2, kd)
    return resp / np.linalg.norm(resp)


def evaluate(model, dataloader):

    model.eval()
    running_loss = 0.0
    outputs = []
    criterionMCM = nn.MSELoss()
    
    with torch.no_grad():
        for batch in dataloader:
            input_ids = batch[0]
            masked_tokens = batch[1]
            masked_pos = batch[2]
            
            logits_lm, output = model(input_ids, masked_pos)
            
            output_batch_preproc = output 
            outputs.append(output_batch_preproc)

            loss_lm = criterionMCM(logits_lm, masked_tokens)
            loss = loss_lm/torch.var(masked_tokens)
            running_loss += loss.item()
            
    average_loss = running_loss / len(dataloader)
    output_total = torch.cat(outputs, dim=0)
    
    return average_loss, output_total


def label_prepend(deepmimo_data, preprocessed_chs, task, scenario_idxs, n_beams=64):
    labels = []
    for scenario_idx in scenario_idxs:
        scenario_name = scenarios_list()[scenario_idx]
        # data = DeepMIMO_data_gen(scenario_name)
        data = deepmimo_data[scenario_idx]
        labels.extend(label_gen(task, data, scenario_name, n_beams=n_beams))
    
    preprocessed_chs = [preprocessed_chs[i] + [labels[i]] for i in range(len(preprocessed_chs))]
    
    return preprocessed_chs