validate.py

#!/usr/bin/python
# -*- coding: utf-8 -*-

import sys, time, os, argparse, socket
import yaml
import numpy
import pdb
import torch
import glob
import zipfile
import datetime
from tuneThreshold import *
from SpeakerNet import *
from DatasetLoader import *
import torch.distributed as dist
import torch.multiprocessing as mp

## ===== ===== ===== ===== ===== ===== ===== =====
## Parse arguments
## ===== ===== ===== ===== ===== ===== ===== =====
import os

# os.environ["CUDA_VISIBLE_DEVICES"]='0'

parser = argparse.ArgumentParser(description="SpeakerNet");

parser.add_argument('--config', type=str, default=None, help='Config YAML file');

# Image input parameters
parser.add_argument('--num_images', type=int, default=1,
                    help='Number of images to extract from each recording (for both visual and thermal streams)');
parser.add_argument('--image_width', type=int, default=124,
                    help='Width of thermal and rgb images');
parser.add_argument('--image_height', type=int, default=124,
                    help='Height of thermal and rgb images');

## Data loader
parser.add_argument('--max_frames', type=int, default=200,
                    help='Input length to the network for training');
parser.add_argument('--eval_frames', type=int, default=300,
                    help='Input length to the network for testing; 0 uses the whole files');
parser.add_argument('--batch_size', type=int, default=200, help='Batch size, number of speakers per batch');
parser.add_argument('--max_seg_per_spk', type=int, default=500,
                    help='Maximum number of utterances per speaker per epoch');
parser.add_argument('--nDataLoaderThread', type=int, default=0, help='Number of loader threads');
parser.add_argument('--seed', type=int, default=10, help='Seed for the random number generator');

## Training details
parser.add_argument('--test_interval', type=int, default=10, help='Test and save every [test_interval] epochs');
parser.add_argument('--max_epoch', type=int, default=500, help='Maximum number of epochs');
parser.add_argument('--trainfunc', type=str, default="", help='Loss function');

## Optimizer
parser.add_argument('--optimizer', type=str, default="adam", help='sgd or adam');
parser.add_argument('--scheduler', type=str, default="steplr", help='Learning rate scheduler');
parser.add_argument('--lr', type=float, default=0.001, help='Learning rate');
parser.add_argument("--lr_decay", type=float, default=0.95, help='Learning rate decay every [test_interval] epochs');
parser.add_argument('--weight_decay', type=float, default=0, help='Weight decay in the optimizer');

## Loss functions
parser.add_argument("--hard_prob", type=float, default=0.5,
                    help='Hard negative mining probability, otherwise random, only for some loss functions');
parser.add_argument("--hard_rank", type=int, default=10,
                    help='Hard negative mining rank in the batch, only for some loss functions');
parser.add_argument('--margin', type=float, default=0.1, help='Loss margin, only for some loss functions');
parser.add_argument('--scale', type=float, default=30, help='Loss scale, only for some loss functions');
parser.add_argument('--nPerSpeaker', type=int, default=1,
                    help='Number of utterances per speaker per batch, only for metric learning based losses');
parser.add_argument('--nClasses', type=int, default=5994,
                    help='Number of speakers in the softmax layer, only for softmax-based losses');

## Load and save
parser.add_argument('--initial_model', type=str, default="", help='Initial model weights');
parser.add_argument('--save_path', type=str, default="exps/exp1", help='Path for model and logs');
parser.add_argument('--train_lists_save_path', type=str, default="data/metadata/train",
                    help="Path to the list of filenames (train set)");
parser.add_argument('--eval_lists_save_path', type=str, default="data/metadata/",
                    help="Path to the list of filenames (test or valid set");
parser.add_argument('--noisy_eval_lists_save_path', type=str, default="data/metadata/", help="Path to the list of noise applied to every instance of eval list (test or valid set");

## Training and test data
parser.add_argument('--train_list', type=str, default="data/metadata/train_list.txt", help='Train list');
parser.add_argument('--test_list', type=str, default="data/metadata/valid_list.txt", help='Evaluation list');
parser.add_argument('--train_path', type=str, default="data/train", help='Absolute path to the train set');
parser.add_argument('--test_path', type=str, default="data/valid", help='Absolute path to the test set');
parser.add_argument('--musan_path', type=str, default="data/musan_split", help='Absolute path to the test set');

## Model definition
parser.add_argument('--n_mels', type=int, default=40, help='Number of mel filterbanks');
parser.add_argument('--log_input', type=bool, default=False, help='Log input features')
parser.add_argument('--model', type=str, default="", help='Name of model definition');
parser.add_argument('--encoder_type', type=str, default="SAP", help='Type of encoder');
parser.add_argument('--nOut', type=int, default=512, help='Embedding size in the last FC layer');
parser.add_argument('--filters', nargs=4, type=int, default=[16, 32, 64, 128],
                    help="the list of number of filters for each of the 4 layers in ResNet34")
parser.add_argument('--modality', type=str, default="rgb",
                    help='Data streams to use, e.g. audio: "wav", visual: "rgb", thermal: "thr", all streams: "wavrgbthr');

## For test evaluation only
parser.add_argument('--eval', type=bool, default=False, dest='eval', help='Eval only')
parser.add_argument('--valid_model', type=bool, default=False,
                    help="True if you want to choose evaluate based on the performance on validation set, False otherwise (the model at the last iteration is chosen)")
parser.add_argument('--num_eval', type=int, default=10, dest='num_eval',
                    help='The number of partitions for an audio file at the evalulation mode')

## For noisy data
parser.add_argument('--noisy_eval', type=str, default=False,
                    help='If True then noisy evaluation');
parser.add_argument('--p_noise', type=float, default=0.3,
                    help='The noisy probability');
parser.add_argument('--snr', type=float, default=0,
                    help='The signal to noise ratio');

## Distributed and mixed precision training
parser.add_argument('--port', type=str, default="8888", help='Port for distributed training, input as text');
parser.add_argument('--distributed', dest='distributed', action='store_true', help='Enable distributed training')
parser.add_argument('--mixedprec', dest='mixedprec', action='store_true', help='Enable mixed precision training')
parser.add_argument('--gpu_id', type=int, default=0, help="gpu_id")


## Parse YAML
def find_option_type(key, parser):
    for opt in parser._get_optional_actions():
        if ('--' + key) in opt.option_strings:
            return opt.type
    raise ValueError


## ===== ===== ===== ===== ===== ===== ===== =====
## Trainer script
## ===== ===== ===== ===== ===== ===== ===== =====

def main_worker(gpu, ngpus_per_node, args):
    args.gpu = gpu

    ## Load models
    s = SpeakerNet(**vars(args));

    s = WrappedModel(s).cuda(args.gpu)

    modelfiles = glob.glob('%s/model0*.model' % args.model_save_path)
    modelfiles.sort()

    # The path to save results on the new validation set
    filename = "scores_voxceleb"
    new_result_save_path = args.result_save_path
    if args.noisy_eval:
        new_result_save_path = os.path.join(new_result_save_path, "noise_p_{}".format(args.p_noise))
        filename = filename+"_snr_{}_p_{}".format(args.snr, args.p_noise)

    if "gender" in args.test_list:
        new_result_save_path = os.path.join(new_result_save_path, "gender")
        filename = filename+"_gender"
    filename = filename+".txt"

    if not (os.path.exists(new_result_save_path)):
        os.makedirs(new_result_save_path)
    print("New results save path {}".format(new_result_save_path))

    # Go through all saved models and test on the new validation set
    scorefile = open(os.path.join(new_result_save_path, filename), "a+")
    for i in range(len(modelfiles)):
        trainer = ModelTrainer(s, **vars(args))
        args.model_it = i * args.test_interval + args.test_interval

        # Load the model at the specific iteration
        print("Loading the model at iteration id = {}".format(args.model_it))
        trainer.loadParameters(modelfiles[i])
        print("Loaded model {}".format(modelfiles[i]))
        pytorch_total_params = sum(p.numel() for p in s.module.__S__.parameters())
        print('Total parameters: ', pytorch_total_params)

        print('Start evaluation on test list', args.test_list)
        sc, lab, _ = trainer.evaluateFromList(**vars(args))
        result = tuneThresholdfromScore(sc, lab, [1, 0.1]);

        if args.noisy_eval:
            print("\nEpoch {:d} VEER {:2.4f} Noisy evaluation {} snr {}".format(
                args.model_it, result[1], args.noisy_eval, args.snr));
            scorefile.write("Epoch {:d} VEER {:2.4f} Noisy evaluation {} snr {}\n".format(
                args.model_it, result[1], args.noisy_eval, args.snr));
        else:
            print("\nEpoch {:d} VEER {:2.4f} Voxceleb evaluation".format(
                args.model_it, result[1]))
            scorefile.write("Epoch {:d} VEER {:2.4f} Voxceleb evaluation\n".format(
                args.model_it, result[1]))

    scorefile.close()


## ===== ===== ===== ===== ===== ===== ===== =====
## Main function
## ===== ===== ===== ===== ===== ===== ===== =====


def main(args):
    if ("nsml" in sys.modules):
        args.save_path = os.path.join(args.save_path, SESSION_NAME.replace('/', '_'))

    args.model_save_path = args.save_path + "/model"
    args.result_save_path = args.save_path + "/result"
    args.feat_save_path = ""

    if "valid" in args.test_list:
        args.eval_lists_save_path = os.path.join(args.eval_lists_save_path, "valid")
        args.noisy_eval_lists_save_path = os.path.join(args.noisy_eval_lists_save_path, "valid")
    else:
        args.eval_lists_save_path = os.path.join(args.eval_lists_save_path, "test")
        args.noisy_eval_lists_save_path = os.path.join(args.noisy_eval_lists_save_path, "test")
    if "gender" in args.test_list:
        args.eval_lists_save_path = os.path.join(args.eval_lists_save_path, "gender")
        args.noisy_eval_lists_save_path = os.path.join(args.noisy_eval_lists_save_path, "gender")

    if not (os.path.exists(args.model_save_path)):
        os.makedirs(args.model_save_path)

    if not (os.path.exists(args.result_save_path)):
        os.makedirs(args.result_save_path)

    n_gpus = torch.cuda.device_count()

    print('Python Version:', sys.version)
    print('PyTorch Version:', torch.__version__)
    print('Number of GPUs:', torch.cuda.device_count())
    print('Save path:', args.save_path)

    if args.distributed:
        mp.spawn(main_worker, nprocs=n_gpus, args=(n_gpus, args))
    else:
        main_worker(0, None, args)


if __name__ == '__main__':
    args = parser.parse_args();

    if args.config is not None:
        with open(args.config, "r") as f:
            yml_config = yaml.load(f, Loader=yaml.FullLoader)
        for k, v in yml_config.items():
            if k in args.__dict__:
                typ = find_option_type(k, parser)
                args.__dict__[k] = typ(v)
            else:
                sys.stderr.write("Ignored unknown parameter {} in yaml.\n".format(k))

    ## Try to import NSML
    try:
        import nsml
        from nsml import HAS_DATASET, DATASET_PATH, PARALLEL_WORLD, PARALLEL_PORTS, MY_RANK
        from nsml import NSML_NFS_OUTPUT, SESSION_NAME
    except:
        pass;

    ### To select a specific GPU available
    gpu_id = args.gpu_id
    os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)

    ### To select a specific seed for reproducibility
    torch.manual_seed(args.seed)
    random.seed(args.seed)
    numpy.random.seed(args.seed)
    os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
    torch.set_deterministic(True)  # for pytorch version 1.7
    # torch.use_deterministic_algorithms(True)  # for pytorch version 1.8

    main(args)