SpeakerNet.py

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

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy, math, pdb, sys, random
import time, os, itertools, shutil, importlib

from tuneThreshold import tuneThresholdfromScore
from DatasetLoader import test_dataset_loader
from torch.cuda.amp import autocast, GradScaler

class WrappedModel(nn.Module):

    ## The purpose of this wrapper is to make the model structure consistent between single and multi-GPU

    def __init__(self, model):
        super(WrappedModel, self).__init__()
        self.module = model

    def forward(self, x, label=None):
        return self.module(x, label)


class SpeakerNet(nn.Module):

    def __init__(self, model, optimizer, trainfunc, nPerSpeaker, **kwargs):
        super(SpeakerNet, self).__init__();

        # SpeakerEncoder模型
        SpeakerNetModel = importlib.import_module('models.'+model).__getattribute__('MainModel')
        self.__S__ = SpeakerNetModel(**kwargs);

        # Loss函数
        LossFunction = importlib.import_module('loss.'+trainfunc).__getattribute__('LossFunction')
        self.__L__ = LossFunction(**kwargs);

        self.nPerSpeaker = nPerSpeaker

    def forward(self, data, label=None):

        # SpeakerEncoder子模型前向传播 得到SpeakerEmbedding
        data    = data.reshape(-1,data.size()[-1]).cuda() 
        outp    = self.__S__.forward(data)

        if label == None:
            return outp

        else:

            outp    = outp.reshape(self.nPerSpeaker,-1,outp.size()[-1]).transpose(1,0).squeeze(1)
            
            # SpeakerEmbedding送给Loss子模型计算loss和precision
            nloss, prec1 = self.__L__.forward(outp,label)

            return nloss, prec1


class ModelTrainer(object):

    def __init__(self, speaker_model, optimizer, scheduler, gpu, mixedprec, **kwargs):

        self.__model__  = speaker_model

        Optimizer = importlib.import_module('optimizer.'+optimizer).__getattribute__('Optimizer')
        self.__optimizer__ = Optimizer(self.__model__.parameters(), **kwargs)

        Scheduler = importlib.import_module('scheduler.'+scheduler).__getattribute__('Scheduler')
        self.__scheduler__, self.lr_step = Scheduler(self.__optimizer__, **kwargs)

        self.scaler = GradScaler() 

        self.gpu = gpu

        self.mixedprec = mixedprec

        assert self.lr_step in ['epoch', 'iteration']

    # ## ===== ===== ===== ===== ===== ===== ===== =====
    # ## Train network 训练网络
    # ## ===== ===== ===== ===== ===== ===== ===== =====

    def train_network(self, loader, verbose):

        self.__model__.train();

        stepsize = loader.batch_size;

        counter = 0;
        index   = 0;
        loss    = 0;
        top1    = 0;    # EER or accuracy

        tstart = time.time()
        
        for data, data_label in loader:

            data    = data.transpose(1,0)

            self.__model__.zero_grad();

            label   = torch.LongTensor(data_label).cuda()

            if self.mixedprec:
                with autocast():
                    nloss, prec1 = self.__model__(data, label)
                self.scaler.scale(nloss).backward();
                self.scaler.step(self.__optimizer__);
                self.scaler.update();       
            else:
                nloss, prec1 = self.__model__(data, label)
                nloss.backward();
                self.__optimizer__.step();


            loss    += nloss.detach().cpu().item();
            top1    += prec1.detach().cpu().item();
            counter += 1;
            index   += stepsize;

            telapsed = time.time() - tstart
            tstart = time.time()

            if verbose:
                sys.stdout.write("\rProcessing {:d} of {:d}:".format(index, loader.__len__()*loader.batch_size));
                sys.stdout.write("Loss {:f} TrainEER/TrainAcc {:2.3f}% - {:.2f} Hz ".format(loss/counter, top1/counter, stepsize/telapsed));
                sys.stdout.flush();

            if self.lr_step == 'iteration': self.__scheduler__.step()

        if self.lr_step == 'epoch': self.__scheduler__.step()
        
        return (loss/counter, top1/counter);


    ## ===== ===== ===== ===== ===== ===== ===== =====
    ## Evaluate from list 根据给定的测试列表进行评估, 可用于训练过程中的过拟合监测或最终的测试评估
    ## ===== ===== ===== ===== ===== ===== ===== =====

    def evaluateFromList(self, test_list, test_path, nDataLoaderThread, distributed, print_interval=100, num_eval=10, **kwargs):

        if distributed:
            rank = torch.distributed.get_rank()
        else:
            rank = 0
        
        self.__model__.eval();
        
        lines       = []
        files       = []
        feats       = {}
        tstart      = time.time()

        ## Read all lines 读取测试列表
        with open(test_list) as f:
            lines = f.readlines()

        ## Get a list of unique file names
        files = list(itertools.chain(*[x.strip().split()[-2:] for x in lines]))
        setfiles = list(set(files))
        setfiles.sort()

        ## Define test data loader
        test_dataset = test_dataset_loader(setfiles, test_path, num_eval=num_eval, **kwargs)

        if distributed:
            sampler = torch.utils.data.distributed.DistributedSampler(test_dataset, shuffle=False)
        else:
            sampler = None

        test_loader = torch.utils.data.DataLoader(
            test_dataset,
            batch_size=1,
            shuffle=False,
            num_workers=nDataLoaderThread,
            drop_last=False,
            sampler=sampler
        )

        ## Extract features for every image
        for idx, data in enumerate(test_loader):
            inp1                = data[0][0].cuda()
            with torch.no_grad():
                ref_feat            = self.__model__(inp1).detach().cpu()
            feats[data[1][0]]   = ref_feat
            telapsed            = time.time() - tstart

            if idx % print_interval == 0 and rank == 0:
                sys.stdout.write("\rReading {:d} of {:d}: {:.2f} Hz, embedding size {:d}".format(idx,test_loader.__len__(),idx/telapsed,ref_feat.size()[1]));

        all_scores = [];
        all_labels = [];
        all_trials = [];

        if distributed:
            ## Gather features from all GPUs
            feats_all = [None for _ in range(0,torch.distributed.get_world_size())]
            torch.distributed.all_gather_object(feats_all, feats)

        if rank == 0:

            tstart = time.time()
            print('')

            ## Combine gathered features
            if distributed:
                feats = feats_all[0]
                for feats_batch in feats_all[1:]:
                    feats.update(feats_batch)

            ## Read files and compute all scores 打分
            for idx, line in enumerate(lines):

                data = line.split();

                ## Append random label if missing
                if len(data) == 2: data = [random.randint(0,1)] + data

                ref_feat = feats[data[1]].cuda()
                com_feat = feats[data[2]].cuda()

                # 对embdding进行L2 normalization
                if self.__model__.module.__L__.test_normalize:
                    ref_feat = F.normalize(ref_feat, p=2, dim=1)
                    com_feat = F.normalize(com_feat, p=2, dim=1)

                # L2距离打分
                dist = F.pairwise_distance(ref_feat.unsqueeze(-1), com_feat.unsqueeze(-1).transpose(0,2)).detach().cpu().numpy();

                score = -1 * numpy.mean(dist);

                all_scores.append(score);  
                all_labels.append(int(data[0]));
                all_trials.append(data[1]+" "+data[2])

                if idx % print_interval == 0:
                    telapsed = time.time() - tstart
                    sys.stdout.write("\rComputing {:d} of {:d}: {:.2f} Hz".format(idx,len(lines),idx/telapsed));
                    sys.stdout.flush();

        return (all_scores, all_labels, all_trials);


    ## ===== ===== ===== ===== ===== ===== ===== =====
    ## Save parameters
    ## ===== ===== ===== ===== ===== ===== ===== =====

    def saveParameters(self, path):
        
        torch.save(self.__model__.module.state_dict(), path);


    ## ===== ===== ===== ===== ===== ===== ===== =====
    ## Load parameters 从模型文件中加载模型参数
    ## ===== ===== ===== ===== ===== ===== ===== =====

    def loadParameters(self, path):

        self_state = self.__model__.module.state_dict();
        loaded_state = torch.load(path, map_location="cuda:%d"%self.gpu);
        for name, param in loaded_state.items():
            origname = name;
            if name not in self_state:
                name = name.replace("module.", "");

                if name not in self_state:
                    print("{} is not in the model.".format(origname));
                    continue;

            if self_state[name].size() != loaded_state[origname].size():
                print("Wrong parameter length: {}, model: {}, loaded: {}".format(origname, self_state[name].size(), loaded_state[origname].size()));
                continue;

            self_state[name].copy_(param);