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Create vanillanet.py
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@HantingChen
HantingChen authored May 23, 2023
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#Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.

#This program is free software; you can redistribute it and/or modify it under the terms of the MIT License.

#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License for more details.

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
from timm.models.registry import register_model


class activation(nn.ReLU):
def __init__(self, dim, act_num=3, deploy=False):
super(activation, self).__init__()
self.deploy = deploy
self.weight = torch.nn.Parameter(torch.randn(dim, 1, act_num*2 + 1, act_num*2 + 1))
self.bias = None
self.bn = nn.BatchNorm2d(dim, eps=1e-6)
self.dim = dim
self.act_num = act_num
weight_init.trunc_normal_(self.weight, std=.02)

def forward(self, x):
if self.deploy:
return torch.nn.functional.conv2d(
super(activation, self).forward(x),
self.weight, self.bias, padding=(self.act_num*2 + 1)//2, groups=self.dim)
else:
return self.bn(torch.nn.functional.conv2d(
super(activation, self).forward(x),
self.weight, padding=(self.act_num*2 + 1)//2, groups=self.dim))

def _fuse_bn_tensor(self, weight, bn):
kernel = weight
running_mean = bn.running_mean
running_var = bn.running_var
gamma = bn.weight
beta = bn.bias
eps = bn.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta + (0 - running_mean) * gamma / std

def switch_to_deploy(self):
kernel, bias = self._fuse_bn_tensor(self.weight, self.bn)
self.weight.data = kernel
self.bias = torch.nn.Parameter(torch.zeros(self.dim))
self.bias.data = bias
self.__delattr__('bn')
self.deploy = True


class Block(nn.Module):
def __init__(self, dim, dim_out, act_num=3, stride=2, deploy=False, ada_pool=None):
super().__init__()
self.act_learn = 1
self.deploy = deploy
if self.deploy:
self.conv = nn.Conv2d(dim, dim_out, kernel_size=1)
else:
self.conv1 = nn.Sequential(
nn.Conv2d(dim, dim, kernel_size=1),
nn.BatchNorm2d(dim, eps=1e-6),
)
self.conv2 = nn.Sequential(
nn.Conv2d(dim, dim_out, kernel_size=1),
nn.BatchNorm2d(dim_out, eps=1e-6)
)

if not ada_pool:
self.pool = nn.Identity() if stride == 1 else nn.MaxPool2d(stride)
else:
self.pool = nn.Identity() if stride == 1 else nn.AdaptiveMaxPool2d((ada_pool, ada_pool))

self.act = activation(dim_out, act_num)

def forward(self, x):
if self.deploy:
x = self.conv(x)
else:
x = self.conv1(x)
x = torch.nn.functional.leaky_relu(x,self.act_learn)
x = self.conv2(x)

x = self.pool(x)
x = self.act(x)
return x

def _fuse_bn_tensor(self, conv, bn):
kernel = conv.weight
bias = conv.bias
running_mean = bn.running_mean
running_var = bn.running_var
gamma = bn.weight
beta = bn.bias
eps = bn.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta + (bias - running_mean) * gamma / std

def switch_to_deploy(self):
kernel, bias = self._fuse_bn_tensor(self.conv1[0], self.conv1[1])
self.conv1[0].weight.data = kernel
self.conv1[0].bias.data = bias
# kernel, bias = self.conv2[0].weight.data, self.conv2[0].bias.data
kernel, bias = self._fuse_bn_tensor(self.conv2[0], self.conv2[1])
self.conv = self.conv2[0]
self.conv.weight.data = torch.matmul(kernel.transpose(1,3), self.conv1[0].weight.data.squeeze(3).squeeze(2)).transpose(1,3)
self.conv.bias.data = bias + (self.conv1[0].bias.data.view(1,-1,1,1)*kernel).sum(3).sum(2).sum(1)
self.__delattr__('conv1')
self.__delattr__('conv2')
self.act.switch_to_deploy()
self.deploy = True


class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False, ada_pool=None, **kwargs):
super().__init__()
self.deploy = deploy
if self.deploy:
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
nn.Conv2d(dims[0], dims[0], kernel_size=1, stride=1),
activation(dims[0], act_num)
)
else:
self.stem1 = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
nn.BatchNorm2d(dims[0], eps=1e-6),
)
self.stem2 = nn.Sequential(
nn.Conv2d(dims[0], dims[0], kernel_size=1, stride=1),
nn.BatchNorm2d(dims[0], eps=1e-6),
activation(dims[0], act_num)
)

self.act_learn = 1

self.stages = nn.ModuleList()
for i in range(len(strides)):
if not ada_pool:
stage = Block(dim=dims[i], dim_out=dims[i+1], act_num=act_num, stride=strides[i], deploy=deploy)
else:
stage = Block(dim=dims[i], dim_out=dims[i+1], act_num=act_num, stride=strides[i], deploy=deploy, ada_pool=ada_pool[i])
self.stages.append(stage)
self.depth = len(strides)

if self.deploy:
self.cls = nn.Sequential(
nn.AdaptiveAvgPool2d((1,1)),
nn.Dropout(drop_rate),
nn.Conv2d(dims[-1], num_classes, 1),
)
else:
self.cls1 = nn.Sequential(
nn.AdaptiveAvgPool2d((1,1)),
nn.Dropout(drop_rate),
nn.Conv2d(dims[-1], num_classes, 1),
nn.BatchNorm2d(num_classes, eps=1e-6),
)
self.cls2 = nn.Sequential(
nn.Conv2d(num_classes, num_classes, 1)
)

self.apply(self._init_weights)

def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
weight_init.trunc_normal_(m.weight, std=.02)
nn.init.constant_(m.bias, 0)

def change_act(self, m):
for i in range(self.depth):
self.stages[i].act_learn = m
self.act_learn = m

def forward(self, x):
if self.deploy:
x = self.stem(x)
else:
x = self.stem1(x)
x = torch.nn.functional.leaky_relu(x,self.act_learn)
x = self.stem2(x)

for i in range(self.depth):
x = self.stages[i](x)

if self.deploy:
x = self.cls(x)
else:
x = self.cls1(x)
x = torch.nn.functional.leaky_relu(x,self.act_learn)
x = self.cls2(x)
return x.view(x.size(0),-1)

def _fuse_bn_tensor(self, conv, bn):
kernel = conv.weight
bias = conv.bias
running_mean = bn.running_mean
running_var = bn.running_var
gamma = bn.weight
beta = bn.bias
eps = bn.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta + (bias - running_mean) * gamma / std

def switch_to_deploy(self):
self.stem2[2].switch_to_deploy()
kernel, bias = self._fuse_bn_tensor(self.stem1[0], self.stem1[1])
self.stem1[0].weight.data = kernel
self.stem1[0].bias.data = bias
kernel, bias = self._fuse_bn_tensor(self.stem2[0], self.stem2[1])
self.stem2[0].weight.data = kernel
self.stem2[0].bias.data = bias
self.stem = torch.nn.Sequential(*[self.stem1[0], self.stem2[0], self.stem2[2]])
self.__delattr__('stem1')
self.__delattr__('stem2')

for i in range(self.depth):
self.stages[i].switch_to_deploy()

kernel, bias = self._fuse_bn_tensor(self.cls1[2], self.cls1[3])
self.cls1[2].weight.data = kernel
self.cls1[2].bias.data = bias
kernel, bias = self.cls2[0].weight.data, self.cls2[0].bias.data
self.cls1[2].weight.data = torch.matmul(kernel.transpose(1,3), self.cls1[2].weight.data.squeeze(3).squeeze(2)).transpose(1,3)
self.cls1[2].bias.data = bias + (self.cls1[2].bias.data.view(1,-1,1,1)*kernel).sum(3).sum(2).sum(1)
self.cls = torch.nn.Sequential(*self.cls1[0:3])
self.__delattr__('cls1')
self.__delattr__('cls2')
self.deploy = True


@register_model
def vanillanet_5(pretrained=False,in_22k=False, **kwargs):
model = VanillaNet(dims=[128*4, 256*4, 512*4, 1024*4], strides=[2,2,2], **kwargs)
return model

@register_model
def vanillanet_6(pretrained=False,in_22k=False, **kwargs):
model = VanillaNet(dims=[128*4, 256*4, 512*4, 1024*4, 1024*4], strides=[2,2,2,1], **kwargs)
return model

@register_model
def vanillanet_7(pretrained=False,in_22k=False, **kwargs):
model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 1024*4, 1024*4], strides=[1,2,2,2,1], **kwargs)
return model

@register_model
def vanillanet_8(pretrained=False, in_22k=False, **kwargs):
model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 512*4, 1024*4, 1024*4], strides=[1,2,2,1,2,1], **kwargs)
return model

@register_model
def vanillanet_9(pretrained=False, in_22k=False, **kwargs):
model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 1024*4, 1024*4], strides=[1,2,2,1,1,2,1], **kwargs)
return model

@register_model
def vanillanet_10(pretrained=False, in_22k=False, **kwargs):
model = VanillaNet(
dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],
strides=[1,2,2,1,1,1,2,1],
**kwargs)
return model

@register_model
def vanillanet_11(pretrained=False, in_22k=False, **kwargs):
model = VanillaNet(
dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],
strides=[1,2,2,1,1,1,1,2,1],
**kwargs)
return model

@register_model
def vanillanet_12(pretrained=False, in_22k=False, **kwargs):
model = VanillaNet(
dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],
strides=[1,2,2,1,1,1,1,1,2,1],
**kwargs)
return model

@register_model
def vanillanet_13(pretrained=False, in_22k=False, **kwargs):
model = VanillaNet(
dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],
strides=[1,2,2,1,1,1,1,1,1,2,1],
**kwargs)
return model

@register_model
def vanillanet_13_x1_5(pretrained=False, in_22k=False, **kwargs):
model = VanillaNet(
dims=[128*6, 128*6, 256*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 1024*6, 1024*6],
strides=[1,2,2,1,1,1,1,1,1,2,1],
**kwargs)
return model

@register_model
def vanillanet_13_x1_5_ada_pool(pretrained=False, in_22k=False, **kwargs):
model = VanillaNet(
dims=[128*6, 128*6, 256*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 1024*6, 1024*6],
strides=[1,2,2,1,1,1,1,1,1,2,1],
ada_pool=[0,40,20,0,0,0,0,0,0,10,0],
**kwargs)
return model

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