resnet_xvector.py

# -*- coding:utf-8 -*-

# Copyright xmuspeech (Author: Snowdar 2020-02-28)

import sys
import torch
import torch.nn.functional as F
import math
sys.path.insert(0, "subtools/pytorch")

import libs.support.utils as utils
from libs.nnet import *


class ResNetXvector(TopVirtualNnet):
    """ A resnet x-vector framework """
    
    def init(self, inputs_dim, num_targets, aug_dropout=0., tail_dropout=0., training=True, extracted_embedding="near", cmvn=False,cmvn_params={},
             resnet_params={}, pooling="statistics", pooling_params={}, fc1=False, fc1_params={}, fc2_params={}, margin_loss=False, margin_loss_params={},
             use_step=False, step_params={}, transfer_from="softmax_loss", jit_compile=False):

        ## Params.
        default_cmvn_params = {
            "mean_norm" : True,
            "std_norm" : False,
        }

        default_resnet_params = {
            "head_conv":True, "head_conv_params":{"kernel_size":3, "stride":1, "padding":1},
            "head_maxpool":False, "head_maxpool_params":{"kernel_size":3, "stride":1, "padding":1},
            "block":"BasicBlock",
            "layers": [3, 4, 6, 3],
            "planes": [32, 64, 128, 256], # a.k.a channels.
            "use_se": False,
            "se_ratio": 4,
            "convXd":2,
            "norm_layer_params":{"momentum":0.5, "affine":True},
            "full_pre_activation":True,
            "zero_init_residual":False
            }

        default_pooling_params = {
            "num_head":1,
            "hidden_size":64,
            "share":True,
            "affine_layers":1,
            "context":[0],
            "stddev":True,
            "temperature":False, 
            "fixed":True
        }
        
        default_fc_params = {
            "nonlinearity":'relu', "nonlinearity_params":{"inplace":True},
            "bn-relu":False, 
            "bn":True, 
            "bn_params":{"momentum":0.5, "affine":True, "track_running_stats":True}
            }

        default_margin_loss_params = {
            "method":"am", "m":0.2, "feature_normalize":True, 
            "s":30, "mhe_loss":False, "mhe_w":0.01
            }
        
        default_step_params = {
            "margin_warm":False,
            "margin_warm_conf":{"start_epoch":5.,"end_epoch":10.,"offset_margin":-0.2,"init_lambda":0.0},
            "T":None,
            "m":False, "lambda_0":0, "lambda_b":1000, "alpha":5, "gamma":1e-4,
            "s":False, "s_tuple":(30, 12), "s_list":None,
            "t":False, "t_tuple":(0.5, 1.2), 
            "p":False, "p_tuple":(0.5, 0.1)
            }
        cmvn_params = utils.assign_params_dict(default_cmvn_params, cmvn_params)
        resnet_params = utils.assign_params_dict(default_resnet_params, resnet_params)
        pooling_params = utils.assign_params_dict(default_pooling_params, pooling_params)
        fc1_params = utils.assign_params_dict(default_fc_params, fc1_params)
        fc2_params = utils.assign_params_dict(default_fc_params, fc2_params)
        margin_loss_params = utils.assign_params_dict(default_margin_loss_params, margin_loss_params)
        step_params = utils.assign_params_dict(default_step_params, step_params)

        ## Var.
        self.extracted_embedding = extracted_embedding # only near here.
        self.use_step = use_step
        self.step_params = step_params
        self.convXd = resnet_params["convXd"]
        
        ## Nnet.
        self.aug_dropout = torch.nn.Dropout2d(p=aug_dropout) if aug_dropout > 0 else None
        self.cmvn_=InputSequenceNormalization(**cmvn_params) if cmvn else torch.nn.Identity()

        # [batch, 1, feats-dim, frames] for 2d and  [batch, feats-dim, frames] for 1d.
        # Should keep the channel/plane is always in 1-dim of tensor (index-0 based).
        inplanes = 1 if self.convXd == 2 else inputs_dim

        self.resnet = ResNet(inplanes, **resnet_params)

        # It is just equal to Ceil function.
        resnet_output_dim = (inputs_dim + self.resnet.get_downsample_multiple() - 1) // self.resnet.get_downsample_multiple() \
                            * self.resnet.get_output_planes() if self.convXd == 2 else self.resnet.get_output_planes()

        # Pooling
        stddev = pooling_params.pop("stddev")
        if pooling == "lde":
            self.stats = LDEPooling(resnet_output_dim, c_num=pooling_params["num_head"])
        elif pooling == "attentive":
            self.stats = AttentiveStatisticsPooling(resnet_output_dim, hidden_size=pooling_params["hidden_size"], 
                                                    context=pooling_params["context"], stddev=stddev)
        elif pooling == "multi-head":
            self.stats = MultiHeadAttentionPooling(resnet_output_dim, stddev=stddev, **pooling_params)
        elif pooling == "multi-resolution":
            self.stats = MultiResolutionMultiHeadAttentionPooling(resnet_output_dim, **pooling_params)
        else:
            self.stats = StatisticsPooling(resnet_output_dim, stddev=stddev)

        self.fc1 = ReluBatchNormTdnnLayer(self.stats.get_output_dim(), resnet_params["planes"][3], **fc1_params) if fc1 else None

        if fc1:
            fc2_in_dim = resnet_params["planes"][3]
        else:
            fc2_in_dim = self.stats.get_output_dim()

        self.fc2 = ReluBatchNormTdnnLayer(fc2_in_dim, resnet_params["planes"][3], **fc2_params)

        self.tail_dropout = torch.nn.Dropout2d(p=tail_dropout) if tail_dropout > 0 else None
        self.embd_dim=resnet_params["planes"][3]
        ## Do not need when extracting embedding.
        if training :
            if margin_loss:
                self.loss = MarginSoftmaxLoss(resnet_params["planes"][3], num_targets, **margin_loss_params)
                if self.use_step and self.step_params["margin_warm"]:
                    self.margin_warm = MarginWarm(**step_params["margin_warm_conf"])
            else:
                self.loss = SoftmaxLoss(resnet_params["planes"][3], num_targets)

            # An example to using transform-learning without initializing loss.affine parameters
            # self.transform_keys = ["resnet", "stats", "fc1", "fc2"]

            self.transform_keys = ["resnet", "stats", "fc1", "fc2","loss.weight"]

            if margin_loss and transfer_from == "softmax_loss":
                # For softmax_loss to am_softmax_loss
                self.rename_transform_keys = {"loss.affine.weight":"loss.weight"} 

    @torch.jit.unused
    @utils.for_device_free
    def forward(self, x, x_len: torch.Tensor=torch.empty(0)):
        """
        @inputs: a 3-dimensional tensor (a batch), including [samples-index, frames-dim-index, frames-index]
        """
        
        x = self.self.cmvn_(x)

        x = self.auto(self.aug_dropout, x) # This auto function is equal to "x = layer(x) if layer is not None else x" for convenience.
        # [samples-index, frames-dim-index, frames-index] -> [samples-index, 1, frames-dim-index, frames-index]
        x = x.unsqueeze(1) if self.convXd == 2 else x
        x = self.resnet(x)
        # [samples-index, channel, frames-dim-index, frames-index] -> [samples-index, channel*frames-dim-index, frames-index]
        x = x.reshape(x.shape[0], x.shape[1]*x.shape[2], x.shape[3]) if self.convXd == 2 else x
        x = self.stats(x) 
        x = self.auto(self.fc1, x)
        x = self.fc2(x)
        x = self.auto(self.tail_dropout, x)
        
        return x


    @utils.for_device_free
    def get_loss(self, inputs, targets):
        """Should call get_loss() after forward() with using Xvector model function.
        e.g.:
            m=Xvector(20,10)
            loss=m.get_loss(m(inputs),targets)
        """
        return self.loss(inputs, targets)

    def get_posterior(self):
        """Should call get_posterior after get_loss. This function is to get outputs from loss component.
        @return: return posterior
        """
        return self.loss.get_posterior()

    @for_extract_embedding(maxChunk=10000, isMatrix=True)
    def extract_embedding(self, x):
        """
        x: a 3-dimensional tensor with batch-dim = 1 or normal features matrix
        return: an 1-dimensional vector after processed by decorator
        """
        # Tensor shape is not modified in libs.nnet.resnet.py for calling free, such as using this framework in cv.
        x = self.cmvn_(x)
        x = x.unsqueeze(1) if self.convXd == 2 else x
        x = self.resnet(x)
        x = x.reshape(x.shape[0], x.shape[1]*x.shape[2], x.shape[3]) if self.convXd == 2 else x
        x = self.stats(x)

        if self.extracted_embedding == "far":
            assert self.fc1 is not None
            xvector = self.fc1.affine(x)
        elif self.extracted_embedding == "near_affine":
            x = self.auto(self.fc1, x)
            xvector = self.fc2.affine(x)
        elif self.extracted_embedding == "near":
            x = self.auto(self.fc1, x)
            xvector = self.fc2(x)
        else:
            raise TypeError("Expected far or near position, but got {}".format(self.extracted_embedding))

        return xvector

    def extract_embedding_jit(self, x: torch.Tensor, position: str = 'near') -> torch.Tensor:
        """
        x: a 3-dimensional tensor with batch-dim = 1 or normal features matrix
        return: an 1-dimensional vector after processed by decorator 
        """

        x = self.cmvn_(x)
        # Tensor shape is not modified in libs.nnet.resnet.py for calling free, such as using this framework in cv.
        x = x.unsqueeze(1) if self.convXd == 2 else x
        x = self.resnet(x)
        x = x.reshape(x.shape[0], x.shape[1]*x.shape[2], x.shape[3]) if self.convXd == 2 else x
        x = self.stats(x)

        if position == "far" and self.fc1 is not None:
            xvector = self.fc1.affine(x)
        elif position == "near_affine":
            if self.fc1 is not None:
                x=self.fc1(x)
            xvector = self.fc2.affine(x)
        elif position == "near":
            if self.fc1 is not None:
                x=self.fc1(x)
            xvector = self.fc2(x)

        else:
            raise TypeError("Expected far or near position, but got {}".format(position))

        return xvector
    @torch.jit.export
    def extract_embedding_whole(self, input: torch.Tensor, position: str = 'near', maxChunk: int = 4000, isMatrix: bool = True):
        with torch.no_grad():
            if isMatrix:
                input = torch.unsqueeze(input, dim=0)
                input = input.transpose(1, 2)
            num_frames = input.shape[2]
            num_split = (num_frames + maxChunk - 1) // maxChunk
            split_size = num_frames // num_split
            offset = 0
            embedding_stats = torch.zeros(1, self.embd_dim, 1).to(input.device)
            for _ in range(0, num_split-1):
                this_embedding = self.extract_embedding_jit(
                    input[:, :, offset:offset+split_size], position)
                offset += split_size
                embedding_stats += split_size*this_embedding

            last_embedding = self.extract_embedding_jit(
                input[:, :, offset:], position)

            embedding = (embedding_stats + (num_frames-offset)
                        * last_embedding) / num_frames
            return torch.squeeze(embedding.transpose(1, 2)).cpu()

    @torch.jit.export
    def embedding_dim(self) -> int:
        """ Export interface for c++ call, return embedding dim of the model
        """
        return self.embd_dim


    def get_warmR_T(self, T_0, T_mult, epoch):
        n = int(math.log(max(0.05, (epoch / T_0 * (T_mult - 1) + 1)), T_mult))
        T_cur = epoch - T_0 * (T_mult ** n - 1) / (T_mult - 1)
        T_i = T_0 * T_mult ** (n)
        return T_cur, T_i


    def compute_decay_value(self, start, end, T_cur, T_i):
        # Linear decay in every cycle time.
        return start - (start - end)/(T_i-1) * (T_cur%T_i)


    def step(self, epoch, this_iter, epoch_batchs):
        # Heated up for t and s.
        # Decay for margin and dropout p.
        if self.use_step:
            if self.step_params["m"]:
                current_postion = epoch*epoch_batchs + this_iter
                lambda_factor = max(self.step_params["lambda_0"],
                                    self.step_params["lambda_b"]*(1+self.step_params["gamma"]*current_postion)**(-self.step_params["alpha"]))
                lambda_m = 1/(1 + lambda_factor)
                self.loss.step(lambda_m)

            if self.step_params["T"] is not None and (self.step_params["t"] or self.step_params["p"]):
                T_cur, T_i = self.get_warmR_T(*self.step_params["T"], epoch)
                T_cur = T_cur*epoch_batchs + this_iter
                T_i = T_i * epoch_batchs

            if self.step_params["t"]:
                self.loss.t = self.compute_decay_value(
                    *self.step_params["t_tuple"], T_cur, T_i)

            if self.step_params["p"]:
                self.aug_dropout.p = self.compute_decay_value(
                    *self.step_params["p_tuple"], T_cur, T_i)

            if self.step_params["s"]:
                self.loss.s = self.step_params["s_tuple"][self.step_params["s_list"][epoch]]

    def step_iter(self, epoch, cur_step):
        # For iterabledataset
        if self.use_step:
            if self.step_params["margin_warm"]:
                offset_margin, lambda_m = self.margin_warm.step(cur_step)
                lambda_m = max(1e-3,lambda_m)
                self.loss.step(lambda_m,offset_margin)
            if self.step_params["m"]:
                lambda_factor = max(self.step_params["lambda_0"],
                                    self.step_params["lambda_b"]*(1+self.step_params["gamma"]*cur_step)**(-self.step_params["alpha"]))
                lambda_m = 1/(1 + lambda_factor)
                self.loss.step(lambda_m)

            if self.step_params["T"] is not None and (self.step_params["t"] or self.step_params["p"]):
                T_cur, T_i = self.get_warmR_T(*self.step_params["T"], cur_step)

            if self.step_params["t"]:
                self.loss.t = self.compute_decay_value(
                    *self.step_params["t_tuple"], T_cur, T_i)

            if self.step_params["p"]:
                self.aug_dropout.p = self.compute_decay_value(
                    *self.step_params["p_tuple"], T_cur, T_i)

            if self.step_params["s"]:
                self.loss.s = self.step_params["s_tuple"][self.step_params["s_list"][epoch]]


# Test.
if __name__ == "__main__":
    # # Let bach-size:128, fbank:40, frames:200.
    # tensor = torch.randn(128, 40, 200)
    # print("Test resnet2d ...")
    # resnet2d = ResNetXvector(40, 1211, resnet_params={"convXd":2})
    # print(resnet2d)
    # print(resnet2d(tensor).shape)
    # print("\n")
    # print("Test resnet1d ...")
    # resnet1d = ResNetXvector(40, 1211, resnet_params={"convXd":1})
    # print(resnet1d)
    # print(resnet1d(tensor).shape)

    # print("Test done.")




    resnet2d = ResNetXvector(40,1000,training=False)
    print(resnet2d)
    sys.exit()
    # a = torch.randn(1000, 40)
    # m = torch.jit.script(resnet2d)

    # m.save("test.pt")
    # m2 = torch.jit.load("test.pt")
    # m2.eval()
    # resnet2d.eval()

    # res1 = resnet2d.extract_embedding(a)
 
    # with torch.no_grad():
    #     res2 = m2.extract_embedding_whole(a)
    # print(res1-res2)
    # print(res1.shape)

    # print("Test done.")