Loading ImageNet weights of MobileNetV3

[CauchyComplete]

Hi,
When I try to load pretrained weights of MobileNetV3 (either -S or -L) to BiSeNetV1, this error appears.

	size mismatch for features.0.0.weight: copying a param with shape torch.Size([16, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3]).
	size mismatch for features.0.1.weight: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
	size mismatch for features.0.1.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
	size mismatch for features.0.1.running_mean: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
	size mismatch for features.0.1.running_var: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).```

MobileNetV2-1.0 works fine.
Thanks.

[StuLiu]

This is my inplemetation of mobilenetv3 modified from this repository.. And the pytorch pretrained model can be download here

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init

class hswish(nn.Module):
def forward(self, x):
out = x * F.relu6(x + 3, inplace=True) / 6
return out

class hsigmoid(nn.Module):
def forward(self, x):
out = F.relu6(x + 3, inplace=True) / 6
return out

class SeModule(nn.Module):
def init(self, in_size, reduction=4):
super(SeModule, self).init()
self.avg_pool = nn.AdaptiveAvgPool2d(1)

    self.se = nn.Sequential(
        nn.Conv2d(in_size, in_size // reduction, kernel_size=1, stride=1, padding=0, bias=False),
        nn.BatchNorm2d(in_size // reduction),
        nn.ReLU(inplace=True),
        nn.Conv2d(in_size // reduction, in_size, kernel_size=1, stride=1, padding=0, bias=False),
        nn.BatchNorm2d(in_size),
        hsigmoid()
    )

def forward(self, x):
    return x * self.se(x)

class Block(nn.Module):
"""expand + depthwise + pointwise"""

def __init__(self, kernel_size, in_size, expand_size, out_size, nolinear, semodule, stride):
    super(Block, self).__init__()
    self.stride = stride
    self.se = semodule

    self.conv1 = nn.Conv2d(in_size, expand_size, kernel_size=1, stride=1, padding=0, bias=False)
    self.bn1 = nn.BatchNorm2d(expand_size)
    self.nolinear1 = nolinear
    self.conv2 = nn.Conv2d(expand_size, expand_size, kernel_size=kernel_size, stride=stride,
                           padding=kernel_size // 2, groups=expand_size, bias=False)
    self.bn2 = nn.BatchNorm2d(expand_size)
    self.nolinear2 = nolinear
    self.conv3 = nn.Conv2d(expand_size, out_size, kernel_size=1, stride=1, padding=0, bias=False)
    self.bn3 = nn.BatchNorm2d(out_size)

    self.shortcut = nn.Sequential()
    if stride == 1 and in_size != out_size:
        self.shortcut = nn.Sequential(
            nn.Conv2d(in_size, out_size, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(out_size),
        )

def forward(self, x):
    out = self.nolinear1(self.bn1(self.conv1(x)))
    out = self.nolinear2(self.bn2(self.conv2(out)))
    out = self.bn3(self.conv3(out))
    if self.se is not None:
        out = self.se(out)
    out = out + self.shortcut(x) if self.stride == 1 else out
    return out

mobilenetv3_settings = {
'small': [[0, 1, 2, 4, 11], [12, 24, 40, 96]],
'large': [[0, 2, 4, 7, 15], [24, 40, 80, 160]],
}

class MobileNetV3(nn.Module):
def init(self, model_name='large'):
super(MobileNetV3, self).init()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.hs1 = hswish()

    if model_name == 'small':
        self.bneck = nn.Sequential(
            Block(3, 16, 16, 16, nn.ReLU(inplace=True), SeModule(16), 2),
            Block(3, 16, 72, 24, nn.ReLU(inplace=True), None, 2),
            Block(3, 24, 88, 24, nn.ReLU(inplace=True), None, 1),
            Block(5, 24, 96, 40, hswish(), SeModule(40), 2),
            Block(5, 40, 240, 40, hswish(), SeModule(40), 1),
            Block(5, 40, 240, 40, hswish(), SeModule(40), 1),
            Block(5, 40, 120, 48, hswish(), SeModule(48), 1),
            Block(5, 48, 144, 48, hswish(), SeModule(48), 1),
            Block(5, 48, 288, 96, hswish(), SeModule(96), 2),
            Block(5, 96, 576, 96, hswish(), SeModule(96), 1),
            Block(5, 96, 576, 96, hswish(), SeModule(96), 1),
        )
    elif model_name == 'large':
        self.bneck = nn.Sequential(
            Block(3, 16, 16, 16, nn.ReLU(inplace=True), None, 1),
            Block(3, 16, 64, 24, nn.ReLU(inplace=True), None, 2),
            Block(3, 24, 72, 24, nn.ReLU(inplace=True), None, 1),
            Block(5, 24, 72, 40, nn.ReLU(inplace=True), SeModule(40), 2),
            Block(5, 40, 120, 40, nn.ReLU(inplace=True), SeModule(40), 1),
            Block(5, 40, 120, 40, nn.ReLU(inplace=True), SeModule(40), 1),
            Block(3, 40, 240, 80, hswish(), None, 2),
            Block(3, 80, 200, 80, hswish(), None, 1),
            Block(3, 80, 184, 80, hswish(), None, 1),
            Block(3, 80, 184, 80, hswish(), None, 1),
            Block(3, 80, 480, 112, hswish(), SeModule(112), 1),
            Block(3, 112, 672, 112, hswish(), SeModule(112), 1),
            Block(5, 112, 672, 160, hswish(), SeModule(160), 1),
            Block(5, 160, 672, 160, hswish(), SeModule(160), 2),
            Block(5, 160, 960, 160, hswish(), SeModule(160), 1),
        )
    else:
        raise Exception('not small or large')

    self.divs, self.channels = mobilenetv3_settings[model_name]
    self.init_params()

def init_params(self):
    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            init.kaiming_normal_(m.weight, mode='fan_out')
            if m.bias is not None:
                init.constant_(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            init.constant_(m.weight, 1)
            init.constant_(m.bias, 0)
        elif isinstance(m, nn.Linear):
            init.normal_(m.weight, std=0.001)
            if m.bias is not None:
                init.constant_(m.bias, 0)

def forward(self, x):
    outs = []
    out = self.hs1(self.bn1(self.conv1(x)))
    for i in range(0, 4):
        out = self.bneck[self.divs[i]:self.divs[i + 1]](out)
        outs.append(out)
    return outs

if name == 'main':
model = MobileNetV3('small')
model.load_state_dict(torch.load('../../../checkpoints/backbones/mobilenet/mobilenetv3_small.pth',
map_location='cpu'), strict=False)
model.train()
_x = torch.randn(2, 3, 512, 512)
_outs = model(_x)
for y in _outs:
print(y.shape)

[StuLiu]

This is my inplemetation of mobilenetv3 modified from this repository.. And the pytorch pretrained model can be download here

import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import init

class hswish(nn.Module): def forward(self, x): out = x * F.relu6(x + 3, inplace=True) / 6 return out

class hsigmoid(nn.Module): def forward(self, x): out = F.relu6(x + 3, inplace=True) / 6 return out

class SeModule(nn.Module): def init(self, in_size, reduction=4): super(SeModule, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1)

    self.se = nn.Sequential(
        nn.Conv2d(in_size, in_size // reduction, kernel_size=1, stride=1, padding=0, bias=False),
        nn.BatchNorm2d(in_size // reduction),
        nn.ReLU(inplace=True),
        nn.Conv2d(in_size // reduction, in_size, kernel_size=1, stride=1, padding=0, bias=False),
        nn.BatchNorm2d(in_size),
        hsigmoid()
    )

def forward(self, x):
    return x * self.se(x)

class Block(nn.Module): """expand + depthwise + pointwise"""

def __init__(self, kernel_size, in_size, expand_size, out_size, nolinear, semodule, stride):
    super(Block, self).__init__()
    self.stride = stride
    self.se = semodule

    self.conv1 = nn.Conv2d(in_size, expand_size, kernel_size=1, stride=1, padding=0, bias=False)
    self.bn1 = nn.BatchNorm2d(expand_size)
    self.nolinear1 = nolinear
    self.conv2 = nn.Conv2d(expand_size, expand_size, kernel_size=kernel_size, stride=stride,
                           padding=kernel_size // 2, groups=expand_size, bias=False)
    self.bn2 = nn.BatchNorm2d(expand_size)
    self.nolinear2 = nolinear
    self.conv3 = nn.Conv2d(expand_size, out_size, kernel_size=1, stride=1, padding=0, bias=False)
    self.bn3 = nn.BatchNorm2d(out_size)

    self.shortcut = nn.Sequential()
    if stride == 1 and in_size != out_size:
        self.shortcut = nn.Sequential(
            nn.Conv2d(in_size, out_size, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(out_size),
        )

def forward(self, x):
    out = self.nolinear1(self.bn1(self.conv1(x)))
    out = self.nolinear2(self.bn2(self.conv2(out)))
    out = self.bn3(self.conv3(out))
    if self.se is not None:
        out = self.se(out)
    out = out + self.shortcut(x) if self.stride == 1 else out
    return out

mobilenetv3_settings = { 'small': [[0, 1, 2, 4, 11], [12, 24, 40, 96]], 'large': [[0, 2, 4, 7, 15], [24, 40, 80, 160]], }

class MobileNetV3(nn.Module): def init(self, model_name='large'): super(MobileNetV3, self).init() self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(16) self.hs1 = hswish()

    if model_name == 'small':
        self.bneck = nn.Sequential(
            Block(3, 16, 16, 16, nn.ReLU(inplace=True), SeModule(16), 2),
            Block(3, 16, 72, 24, nn.ReLU(inplace=True), None, 2),
            Block(3, 24, 88, 24, nn.ReLU(inplace=True), None, 1),
            Block(5, 24, 96, 40, hswish(), SeModule(40), 2),
            Block(5, 40, 240, 40, hswish(), SeModule(40), 1),
            Block(5, 40, 240, 40, hswish(), SeModule(40), 1),
            Block(5, 40, 120, 48, hswish(), SeModule(48), 1),
            Block(5, 48, 144, 48, hswish(), SeModule(48), 1),
            Block(5, 48, 288, 96, hswish(), SeModule(96), 2),
            Block(5, 96, 576, 96, hswish(), SeModule(96), 1),
            Block(5, 96, 576, 96, hswish(), SeModule(96), 1),
        )
    elif model_name == 'large':
        self.bneck = nn.Sequential(
            Block(3, 16, 16, 16, nn.ReLU(inplace=True), None, 1),
            Block(3, 16, 64, 24, nn.ReLU(inplace=True), None, 2),
            Block(3, 24, 72, 24, nn.ReLU(inplace=True), None, 1),
            Block(5, 24, 72, 40, nn.ReLU(inplace=True), SeModule(40), 2),
            Block(5, 40, 120, 40, nn.ReLU(inplace=True), SeModule(40), 1),
            Block(5, 40, 120, 40, nn.ReLU(inplace=True), SeModule(40), 1),
            Block(3, 40, 240, 80, hswish(), None, 2),
            Block(3, 80, 200, 80, hswish(), None, 1),
            Block(3, 80, 184, 80, hswish(), None, 1),
            Block(3, 80, 184, 80, hswish(), None, 1),
            Block(3, 80, 480, 112, hswish(), SeModule(112), 1),
            Block(3, 112, 672, 112, hswish(), SeModule(112), 1),
            Block(5, 112, 672, 160, hswish(), SeModule(160), 1),
            Block(5, 160, 672, 160, hswish(), SeModule(160), 2),
            Block(5, 160, 960, 160, hswish(), SeModule(160), 1),
        )
    else:
        raise Exception('not small or large')

    self.divs, self.channels = mobilenetv3_settings[model_name]
    self.init_params()

def init_params(self):
    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            init.kaiming_normal_(m.weight, mode='fan_out')
            if m.bias is not None:
                init.constant_(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            init.constant_(m.weight, 1)
            init.constant_(m.bias, 0)
        elif isinstance(m, nn.Linear):
            init.normal_(m.weight, std=0.001)
            if m.bias is not None:
                init.constant_(m.bias, 0)

def forward(self, x):
    outs = []
    out = self.hs1(self.bn1(self.conv1(x)))
    for i in range(0, 4):
        out = self.bneck[self.divs[i]:self.divs[i + 1]](out)
        outs.append(out)
    return outs

if name == 'main': model = MobileNetV3('small') model.load_state_dict(torch.load('../../../checkpoints/backbones/mobilenet/mobilenetv3_small.pth', map_location='cpu'), strict=False) model.train() _x = torch.randn(2, 3, 512, 512) _outs = model(_x) for y in _outs: print(y.shape)

SEModule is wrong!