Dear Yuhang,

I have noticed that you are using a ResNet20 for CIFAR10 with 11.3 Million parameters. In the original ResNet publication of He et al [1] the definition of ResNet20 on CIFAR10 is given and results in 0.27 Million parameters. I know that it is somewhat "conventional" to use the implementation of ResNet20 you are using, the problem is that I am really interested in the one with the smaller number of parameters : P

I have defined the "original" ResNet20 for CIFAR10 with 0.27 M parameters as shown below. I have added the file under models in your repository and run first the ANN training and then SNN calibration on it:

python -m SNN_Calibration.CIFAR.main_train --dataset CIFAR10 --arch orgres20 --dpath 'datasets/CIFAR10/' --usebn
python -m SNN_Calibration.CIFAR.main_calibration --dataset CIFAR10 --arch orgres20 --T 16 --usebn --calib advanced --dpath 'datasets/CIFAR10/'

The ANN training is working well and results in 93.5% accuracy. But for some reason the SNN_Calibration doesn't work on the network below and results in 20% accuracy. Please help to get the SNN Calibration working on this : ) It would be much appreciated to understand the issue here.

[1] He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 770–778. https://doi.org/10.1109/CVPR.2016.90

'''
ResNet20 on CIFAR10 with the correct number of parameter (0.27M) as in the original publication [1].

References:
[1] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
[2] K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. In ECCV, 2016.
'''
import torch
import torch.nn as nn
import math
# @anna: I fixed the following relative imports
from ...CIFAR.models.utils import AvgPoolConv, StraightThrough
from ...CIFAR.models.spiking_layer import SpikeModel, SpikeModule, Union
import torch.nn.functional as F
from .resnet import SpikeBasicBlock

def conv3x3(in_planes, out_planes, stride=1):
    " 3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = BN(planes)
        self.relu1 = ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = BN(planes)
        self.downsample = downsample
        self.stride = stride
        self.relu2 = ReLU(inplace=True)

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu2(out)

        return out


class Org_ResNet_Cifar_Modified(nn.Module):

    def __init__(self, block, layers, num_classes=10, use_bn=True):
        super(Org_ResNet_Cifar_Modified, self).__init__()

        global BN
        BN = nn.BatchNorm2d if use_bn else StraightThrough
        global ReLU
        ReLU = nn.ReLU

        self.inplanes = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            BN(64),
            ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            BN(64),
            ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False),
            BN(64),
            ReLU(inplace=True),
        )
        self.layer1 = self._make_layer(block, 16, layers[0], stride=1)
        self.layer2 = self._make_layer(block, 32, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 64, layers[2], stride=2)
        #self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = AvgPoolConv(kernel_size=4, stride=1, input_channel=64)
        self.fc_save = nn.Linear(64, num_classes)
        #self.fc = nn.Linear(64, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d) and not isinstance(m, AvgPoolConv):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                n = m.weight.size(1)
                m.weight.data.normal_(0, 1.0 / float(n))
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                BN(planes * block.expansion)
            )
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        #x = F.avg_pool2d(x, x.size()[3])
        #x = self.layer4(x)
        #print(x.size())
        x = self.avgpool(x)
        print(x.shape)
        x = x.view(x.size(0), -1)
        x = self.fc_save(x)

        return x

def org_resnet20(**kwargs):
    model = Org_ResNet_Cifar_Modified(BasicBlock, [3, 3, 3], **kwargs)
    return model


res_specials = {BasicBlock: SpikeBasicBlock}

Hello, I have tested the ANN model of VGG16 without usebn provided by you on ImageNet dataset, but the accuracy is only 55.074%. Could you please update the pre-training ANN model?

Dear @yhhhli

This is an awesome repository and thank you so much for publishing it.

My question/ issue is regarding the parameter --T for the SNN calibration: In the code of the CIFAR SNN calibration it seems that the SNN simulation length is hardcoded to sim_length = 32 (line 71) and the args.T parameter is unused.

Can you comment on this? Because in your table of results you mention T=16. How do you achieve this to be used during testing (not necessarily calibration itself)?

Hello, when I run the program, an error occurred "AttributeError: Can 't pickle local object' SubPolicy. Just set the < locals >. < lambda > '", don't know if you Can help me to solve it? Thank you very much!