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- '''
- Deep Residual Learning for Image Recognition
- https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
- '''
- import os
- import sys
- from functools import partial
- from timm.models import register_model
- from timm.models.layers import trunc_normal_, DropPath
- from braincog.model_zoo.base_module import *
- from braincog.base.node.node import *
-
- __all__ = [
- 'ResNet',
- 'resnet18',
- 'resnet34_half',
- 'resnet34',
- 'resnet50_half',
- 'resnet50',
- 'resnet101',
- 'resnet152',
- 'resnext50_32x4d',
- 'resnext101_32x8d',
- 'wide_resnet50_2',
- 'wide_resnet101_2',
- ]
-
-
- def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
- '''3x3 convolution with padding'''
- return nn.Conv2d(in_planes,
- out_planes,
- kernel_size=3,
- stride=stride,
- padding=dilation,
- groups=groups,
- bias=False,
- dilation=dilation)
-
-
- def conv1x1(in_planes, out_planes, stride=1):
- '''1x1 convolution'''
- return nn.Conv2d(in_planes,
- out_planes,
- kernel_size=1,
- stride=stride,
- bias=False)
-
-
- class BasicBlock(nn.Module):
- """
- ResNet的基础模块, 采用identity-connection的方式.
- :param inplanes: 输出通道数
- :param planes: 内部通道数量
- :param stride: stride
- :param downsample: 是否降采样
- :param groups: 分组卷积
- :param base_width: 基础通道数量
- :param dilation: 空洞卷积
- :param norm_layer: Norm的方式
- :param node: 神经元类型, 默认为 ``LIFNode``
- """
-
- expansion = 1
- __constants__ = ['downsample']
- def __init__(self,
- inplanes,
- planes,
- stride=1,
- downsample=None,
- groups=1,
- base_width=64,
- dilation=1,
- norm_layer=None,
- node=LIFNode):
- super(BasicBlock, self).__init__()
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- if groups != 1 or base_width != 64:
- raise ValueError(
- 'BasicBlock only supports groups=1 and base_width=64')
- if dilation > 1:
- raise NotImplementedError(
- 'Dilation > 1 not supported in BasicBlock')
- # Both self.conv1 and self.downsample layers downsample the input when stride != 1
- self.bn1 = norm_layer(inplanes)
- self.node1 = node()
- self.conv1 = conv3x3(inplanes, planes, stride)
- # self.relu = nn.ReLU(inplace=False)
- self.node2 = node()
- self.bn2 = norm_layer(planes)
- self.conv2 = conv3x3(planes, planes)
-
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- identity = x
-
- out = self.bn1(x)
- out = self.node1(out)
- out = self.conv1(out)
-
- out = self.bn2(out)
- out = self.node2(out)
- out = self.conv2(out)
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
-
- return out
-
-
- class Bottleneck(nn.Module):
- """
- ResNet的Botteneck模块, 采用identity-connection的方式.
- :param inplanes: 输出通道数
- :param planes: 内部通道数量
- :param stride: stride
- :param downsample: 是否降采样
- :param groups: 分组卷积
- :param base_width: 基础通道数量
- :param dilation: 空洞卷积
- :param norm_layer: Norm的方式
- :param node: 神经元类型, 默认为 ``LIFNode``
- """
- expansion = 4
- __constants__ = ['downsample']
-
- def __init__(self,
- inplanes,
- planes,
- stride=1,
- downsample=None,
- groups=1,
- base_width=64,
- dilation=1,
- norm_layer=None,
- node=torch.nn.Identity):
- super(Bottleneck, self).__init__()
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- width = int(planes * (base_width / 64.)) * groups
- # Both self.conv2 and self.downsample layers downsample the input when stride != 1
- self.bn1 = norm_layer(inplanes)
- self.conv1 = conv1x1(inplanes, width)
-
- self.bn2 = norm_layer(width)
- self.conv2 = conv3x3(width, width, stride, groups, dilation)
-
- self.bn3 = norm_layer(width)
- self.conv3 = conv1x1(width, planes * self.expansion)
-
- # self.relu = nn.ReLU(inplace=False)
- self.downsample = downsample
- self.stride = stride
- self.node1 = node()
- self.node2 = node()
- self.node3 = node()
-
- def forward(self, x):
- identity = x
-
- out = self.bn1(x)
- out = self.node1(out)
- out = self.conv1(out)
-
- out = self.bn2(out)
- out = self.node2(out)
- out = self.conv2(out)
-
- out = self.bn3(out)
- out = self.node3(out)
- out = self.conv3(out)
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- return out
-
-
- class ResNet(BaseModule):
- """
- ResNet-SNN
- :param block: Block类型
- :param layers: block 层数
- :param inplanes: 输入通道数量
- :param num_classes: 输出类别数
- :param zero_init_residual: 是否使用零初始化
- :param groups: 卷积分组
- :param width_per_group: 每一组的宽度
- :param replace_stride_with_dilation: 是否使用stride替换dilation
- :param norm_layer: Norm 方式, 默认为 ``BatchNorm``
- :param step: 仿真步长, 默认为 ``8``
- :param encode_type: 编码方式, 默认为 ``direct``
- :param spike_output: 是否使用脉冲输出, 默认为 ``False``
- :param args:
- :param kwargs:
- """
- def __init__(self,
- block,
- layers,
- inplanes=64,
- num_classes=10,
- zero_init_residual=False,
- groups=1,
- width_per_group=64,
- replace_stride_with_dilation=None,
- norm_layer=None,
- step=8,
- encode_type='direct',
- spike_output=False,
- *args,
- **kwargs):
- super().__init__(
- step,
- encode_type,
- *args,
- **kwargs
- )
- self.spike_output = spike_output
- self.num_classes = num_classes
-
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- self._norm_layer = norm_layer
-
- # print('inplanes %d' % inplanes)
- self.inplanes = inplanes
- self.interplanes = [
- self.inplanes, self.inplanes * 2, self.inplanes * 4,
- self.inplanes * 8
- ]
- self.dilation = 1
-
- self.node = kwargs['node_type']
- if issubclass(self.node, BaseNode):
- self.node = partial(self.node, **kwargs)
-
- if replace_stride_with_dilation is None:
- # each element in the tuple indicates if we should replace
- # the 2x2 stride with a dilated convolution instead
- replace_stride_with_dilation = [False, False, False]
- if len(replace_stride_with_dilation) != 3:
- raise ValueError('replace_stride_with_dilation should be None '
- 'or a 3-element tuple, got {}'.format(
- replace_stride_with_dilation))
- self.groups = groups
- self.base_width = width_per_group
- self.static_data = False
-
- self.dataset = kwargs['dataset']
- if self.dataset == 'dvsg' or self.dataset == 'dvsc10' or self.dataset == 'NCALTECH101' or self.dataset == 'NCARS' or self.dataset == 'DVSG':
- self.conv1 = nn.Conv2d(2 * self.init_channel_mul,
- self.inplanes,
- kernel_size=3,
- padding=1,
- bias=False)
- elif self.dataset == 'imnet':
- self.conv1 = nn.Conv2d(3 * self.init_channel_mul,
- self.inplanes,
- kernel_size=7,
- stride=2,
- padding=3,
- bias=False)
- self.static_data = True
- elif self.dataset == 'esimnet':
- reconstruct = kwargs["reconstruct"] if "reconstruct" in kwargs else False
- print(reconstruct)
- if reconstruct:
- self.conv1 = nn.Conv2d(1 * self.init_channel_mul,
- self.inplanes,
- kernel_size=7,
- stride=2,
- padding=3,
- bias=False)
- self.static_data = True
- else:
- self.conv1 = nn.Conv2d(2 * self.init_channel_mul,
- self.inplanes,
- kernel_size=7,
- stride=2,
- padding=3,
- bias=False)
- self.static_data = True
- elif self.dataset == 'cifar10' or self.dataset == 'cifar100':
- self.conv1 = nn.Conv2d(3 * self.init_channel_mul,
- self.inplanes,
- kernel_size=3,
- padding=1,
- bias=False)
- self.static_data = True
-
- # self.relu = nn.ReLU(inplace=False)
- self.layer1 = self._make_layer(
- block, self.interplanes[0], layers[0], node=self.node)
- self.layer2 = self._make_layer(block,
- self.interplanes[1],
- layers[1],
- stride=2,
- dilate=replace_stride_with_dilation[0], node=self.node)
- self.layer3 = self._make_layer(block,
- self.interplanes[2],
- layers[2],
- stride=2,
- dilate=replace_stride_with_dilation[1], node=self.node)
- self.layer4 = self._make_layer(block,
- self.interplanes[3],
- layers[3],
- stride=2,
- dilate=replace_stride_with_dilation[2], node=self.node)
-
- self.bn1 = norm_layer(self.inplanes)
- self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-
- if self.spike_output:
- self.fc = nn.Linear(
- self.interplanes[3] * block.expansion, num_classes * 10)
- self.node2 = self.node()
- self.vote = VotingLayer(10)
- else:
- self.fc = nn.Linear(
- self.interplanes[3] * block.expansion, num_classes
- )
- self.node2 = nn.Identity()
- self.vote = nn.Identity()
-
- self.warm_up = False
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight,
- mode='fan_out',
- nonlinearity='relu')
- elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
- nn.init.constant_(m.weight, 1)
- nn.init.constant_(m.bias, 0)
-
- # Zero-initialize the last BN in each residual branch,
- # so that the residual branch starts with zeros, and each residual block behaves like an identity.
- # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
- if zero_init_residual:
- for m in self.modules():
- if isinstance(m, Bottleneck):
- nn.init.constant_(m.bn3.weight, 0)
- elif isinstance(m, BasicBlock):
- nn.init.constant_(m.bn2.weight, 0)
-
- def _make_layer(self, block, planes, blocks, stride=1, dilate=False, node=torch.nn.Identity):
- norm_layer = self._norm_layer
- downsample = None
- previous_dilation = self.dilation
- if dilate:
- self.dilation *= stride
- stride = 1
- if stride != 1 or self.inplanes != planes * block.expansion:
- if block == BasicBlock:
- downsample = nn.Sequential(
- norm_layer(self.inplanes),
- self.node(),
- conv1x1(self.inplanes, planes * block.expansion, stride),
- )
- elif block == Bottleneck:
- downsample = nn.Sequential(
- norm_layer(self.inplanes),
- self.node(),
- conv1x1(self.inplanes, planes * block.expansion, stride),
- )
- else:
- raise NotImplementedError
-
- layers = [block(self.inplanes, planes, stride, downsample, self.groups,
- self.base_width, previous_dilation, norm_layer, node=node)]
- self.inplanes = planes * block.expansion
- for _ in range(1, blocks):
- layers.append(
- block(self.inplanes,
- planes,
- groups=self.groups,
- base_width=self.base_width,
- dilation=self.dilation,
- norm_layer=norm_layer, node=node))
-
- return nn.Sequential(*layers)
-
- def forward(self, inputs):
- inputs = self.encoder(inputs)
- self.reset()
-
- if self.layer_by_layer:
-
- x = self.conv1(inputs)
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
-
- x = self.bn1(x)
- # x = self.node1(x)
- x = self.avgpool(x)
-
- x = torch.flatten(x, 1)
- # print(x.shape)
- x = self.fc(x)
- x = rearrange(x, '(t b) c -> t b c', t=self.step).mean(0)
- x = self.node2(x)
- x = self.vote(x)
-
- return x
-
- else:
- outputs = []
-
- if self.warm_up:
- step = 1
- else:
- step = self.step
- for t in range(step):
- x = inputs[t]
-
- x = self.conv1(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
-
- x = self.bn1(x)
- # x = self.node1(x)
- x = self.avgpool(x)
-
- x = torch.flatten(x, 1)
- x = self.fc(x)
-
- x = self.node2(x)
- x = self.vote(x)
-
- outputs.append(x)
-
- return sum(outputs) / len(outputs)
-
-
- def _resnet(arch, block, layers, pretrained=False, **kwargs):
- model = ResNet(block, layers, **kwargs)
- # only load state_dict()
- if pretrained:
- raise NotImplementedError
-
- return model
-
-
- @register_model
- def resnet9(pretrained=False, **kwargs):
- return _resnet('resnet9', BasicBlock, [1, 1, 1, 1], pretrained, **kwargs)
-
-
- @register_model
- def resnet18(pretrained=False, **kwargs):
- # kwargs['inplanes'] = 96
- return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, **kwargs)
-
-
- @register_model
- def resnet34_half(pretrained=False, **kwargs):
- kwargs['inplanes'] = 32
- return _resnet('resnet34_half', BasicBlock, [3, 4, 6, 3], pretrained,
- **kwargs)
-
-
- @register_model
- def resnet34(pretrained=False, **kwargs):
- return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, **kwargs)
-
-
- @register_model
- def resnet50_half(pretrained=False, **kwargs):
- kwargs['inplanes'] = 32
- return _resnet('resnet50_half', Bottleneck, [3, 4, 6, 3], pretrained,
- **kwargs)
-
-
- @register_model
- def resnet50(pretrained=False, **kwargs):
- return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, **kwargs)
-
-
- @register_model
- def resnet101(pretrained=False, **kwargs):
- return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained,
- **kwargs)
-
-
- @register_model
- def resnet152(pretrained=False, **kwargs):
- return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained,
- **kwargs)
-
-
- @register_model
- def resnext50_32x4d(pretrained=False, **kwargs):
- kwargs['groups'] = 32
- kwargs['width_per_group'] = 4
- return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], pretrained,
- **kwargs)
-
-
- @register_model
- def resnext101_32x8d(pretrained=False, **kwargs):
- kwargs['groups'] = 32
- kwargs['width_per_group'] = 8
- return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained,
- **kwargs)
-
-
- @register_model
- def wide_resnet50_2(pretrained=False, **kwargs):
- kwargs['width_per_group'] = 64 * 2
- return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], pretrained,
- **kwargs)
-
-
- @register_model
- def wide_resnet101_2(pretrained=False, **kwargs):
- kwargs['width_per_group'] = 64 * 2
- return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], pretrained,
- **kwargs)
-
-
- if __name__ == '__main__':
- net = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=1000)
- image_h, image_w = 224, 224
- from thop import profile
- from thop import clever_format
-
- flops, params = profile(net,
- inputs=(torch.randn(1, 3, image_h, image_w),),
- verbose=False)
- flops, params = clever_format([flops, params], '%.3f')
- out = net(torch.autograd.Variable(torch.randn(3, 3, image_h, image_w)))
- print(f'1111, flops: {flops}, params: {params},out_shape: {out.shape}')
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