I am trying to do post quantization.
added QuantStub() and DeQuantStub() in the model
   super(ConvTasNet, self).__init__()
    # Hyper-parameter
    self.N, self.L, self.B, self.Sk, self.H, self.P, self.X, self.R, self.C = N, L, B, Sk, H, P, X, R, C
    self.norm_type = norm_type
    self.causal = causal
    self.mask_nonlinear = mask_nonlinear
    self.skip_conn = []
    #quantization
    self.quant = QuantStub()
    self.dequant = DeQuantStub()
    # Components
    self.encoder = Encoder(L, N)
    self.separator = TemporalConvNet(N, B, Sk, H, P, X, R, C, norm_type, causal, mask_nonlinear)
    self.decoder = Decoder(N, L)
    # init
    for p in self.parameters():
        if p.dim() > 1:
            nn.init.xavier_normal_(p)

def forward(self, mixture):
    """
    Args:
        mixture: [M, T], M is batch size, T is #samples
    Returns:
        est_source: [M, C, T]
    """
    mixture = self.quant(mixture)
    mixture_w = self.encoder(mixture)
    est_mask = self.separator(mixture_w)
    est_source = self.decoder(mixture_w, est_mask)
    est_source = self.dequant(est_source)

    return est_source

Now,  I want to evaluate model without and with Quantize converted model
 if __name__ == '__main__':
args = parser.parse_args()
print(args)
#evaluate(args)


num_calibration_batches = 10
model = ConvTasNet.load_model('E:\\Project\\MVNS_EC\\quantization\\Simple-Neural-Nets-with-PyTorch-master\\Eager_Post_quant\\MVNS_model\\Quantization\\model\\final.pth.tar')
model.eval()
model.qconfig = torch.quantization.default_qconfig
torch.quantization.prepare(model, inplace=True)
evaluate(model,args)
torch.quantization.convert(model, inplace=True)
evaluate(model,args)

Evaluation without quantization is done, and also the model is converted to Quantized model.
I am recieving error in the last line, when I am trying to evaluate with the quantized model.
Please guide me, what is that I am doing wrong?