"""
MindSpore implementation of `MobileNetV3`.
Refer to Searching for MobileNetV3.
"""
import math
from mindspore import nn, Tensor
import mindspore.common.initializer as init
from .layers.squeeze_excite import SqueezeExcite
from .layers.pooling import GlobalAvgPooling
from .utils import load_pretrained, make_divisible
from .registry import register_model
__all__ = [
"MobileNetV3",
"mobilenet_v3_large_075",
"mobilenet_v3_large_100",
"mobilenet_v3_small_075",
"mobilenet_v3_small_100"
]
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'first_conv': 'features.0', 'classifier': 'classifier.3',
**kwargs
}
default_cfgs = {
'mobilenet_v3_small_1.0': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-Ascend.ckpt'),
'mobilenet_v3_large_1.0': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-Ascend.ckpt'),
'mobilenet_v3_small_0.75': _cfg(url=''),
'mobilenet_v3_large_0.75': _cfg(url='')
}
class Bottleneck(nn.Cell):
"""Bottleneck Block of MobilenetV3. depth-wise separable convolutions + inverted residual + squeeze excitation"""
def __init__(self,
in_channels: int,
mid_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
activation: str = 'relu',
use_se: bool = False) -> None:
super().__init__()
self.use_se = use_se
self.use_res_connect = stride == 1 and in_channels == out_channels
assert activation in ['relu', 'hswish']
self.activation = nn.HSwish if activation == 'hswish' else nn.ReLU
layers = []
# Expand.
if in_channels != mid_channels:
layers.extend([
nn.Conv2d(in_channels, mid_channels, 1, 1, pad_mode="pad", padding=0, has_bias=False),
nn.BatchNorm2d(mid_channels),
self.activation()
])
# DepthWise.
layers.extend([
nn.Conv2d(mid_channels, mid_channels, kernel_size, stride,
pad_mode="same", group=mid_channels, has_bias=False),
nn.BatchNorm2d(mid_channels),
self.activation(),
])
# SqueezeExcitation.
if use_se:
layers.append(
SqueezeExcite(mid_channels, 1.0 / 4, act_layer=nn.ReLU, gate_layer=nn.HSigmoid)
)
# Project.
layers.extend([
nn.Conv2d(mid_channels, out_channels, 1, 1, pad_mode="pad", padding=0, has_bias=False),
nn.BatchNorm2d(out_channels),
])
self.layers = nn.SequentialCell(layers)
def construct(self, x: Tensor) -> Tensor:
if self.use_res_connect:
return x + self.layers(x)
return self.layers(x)
[文档]class MobileNetV3(nn.Cell):
r"""MobileNetV3 model class, based on
`"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_
Args:
arch: size of the architecture. 'small' or 'large'.
alpha: scale factor of model width. Default: 1.
round_nearest: divisor of make divisible function. Default: 8.
in_channels: number the channels of the input. Default: 3.
num_classes: number of classification classes. Default: 1000.
"""
def __init__(self,
arch: str,
alpha: float = 1.0,
round_nearest: int = 8,
in_channels: int = 3,
num_classes: int = 1000
) -> None:
super().__init__()
input_channels = make_divisible(16 * alpha, round_nearest)
# Setting of bottleneck blocks. ex: [k, e, c, se, nl, s]
# k: kernel size of depth-wise conv
# e: expansion size
# c: number of output channel
# se: whether there is a Squeeze-And-Excite in that block
# nl: type of non-linearity used
# s: stride of depth-wise conv
if arch == "large":
bottleneck_setting = [
[3, 16, 16, False, 'relu', 1],
[3, 64, 24, False, 'relu', 2],
[3, 72, 24, False, 'relu', 1],
[5, 72, 40, True, 'relu', 2],
[5, 120, 40, True, 'relu', 1],
[5, 120, 40, True, 'relu', 1],
[3, 240, 80, False, 'hswish', 2],
[3, 200, 80, False, 'hswish', 1],
[3, 184, 80, False, 'hswish', 1],
[3, 184, 80, False, 'hswish', 1],
[3, 480, 112, True, 'hswish', 1],
[3, 672, 112, True, 'hswish', 1],
[5, 672, 160, True, 'hswish', 2],
[5, 960, 160, True, 'hswish', 1],
[5, 960, 160, True, 'hswish', 1]
]
last_channels = make_divisible(alpha * 1280, round_nearest)
elif arch == "small":
bottleneck_setting = [
[3, 16, 16, True, 'relu', 2],
[3, 72, 24, False, 'relu', 2],
[3, 88, 24, False, 'relu', 1],
[5, 96, 40, True, 'hswish', 2],
[5, 240, 40, True, 'hswish', 1],
[5, 240, 40, True, 'hswish', 1],
[5, 120, 48, True, 'hswish', 1],
[5, 144, 48, True, 'hswish', 1],
[5, 288, 96, True, 'hswish', 2],
[5, 576, 96, True, 'hswish', 1],
[5, 576, 96, True, 'hswish', 1]
]
last_channels = make_divisible(alpha * 1024, round_nearest)
else:
raise ValueError(f"Unsupported model type {arch}")
# Building stem conv layer.
features = [
nn.Conv2d(in_channels, input_channels, 3, 2, pad_mode="pad", padding=1, has_bias=False),
nn.BatchNorm2d(input_channels),
nn.HSwish()
]
# Building bottleneck blocks.
for k, e, c, se, nl, s in bottleneck_setting:
exp_channels = make_divisible(alpha * e, round_nearest)
output_channels = make_divisible(alpha * c, round_nearest)
features.append(Bottleneck(input_channels, exp_channels, output_channels,
kernel_size=k, stride=s, activation=nl, use_se=se))
input_channels = output_channels
# Building last point-wise conv layers.
output_channels = input_channels * 6
features.extend([
nn.Conv2d(input_channels, output_channels, 1, 1, pad_mode="pad", padding=0, has_bias=False),
nn.BatchNorm2d(output_channels),
nn.HSwish()
])
self.features = nn.SequentialCell(features)
self.pool = GlobalAvgPooling()
self.classifier = nn.SequentialCell([
nn.Dense(output_channels, last_channels),
nn.HSwish(),
nn.Dropout(keep_prob=0.8),
nn.Dense(last_channels, num_classes)
])
self._initialize_weights()
def _initialize_weights(self) -> None:
"""Initialize weights for cells."""
for _, cell in self.cells_and_names():
if isinstance(cell, nn.Conv2d):
n = cell.kernel_size[0] * cell.kernel_size[1] * cell.out_channels
cell.weight.set_data(
init.initializer(init.Normal(sigma=math.sqrt(2. / n), mean=0.0),
cell.weight.shape, cell.weight.dtype))
if cell.bias is not None:
cell.bias.set_data(init.initializer('zeros', cell.bias.shape, cell.bias.dtype))
elif isinstance(cell, nn.BatchNorm2d):
cell.gamma.set_data(init.initializer('ones', cell.gamma.shape, cell.gamma.dtype))
cell.beta.set_data(init.initializer('zeros', cell.beta.shape, cell.beta.dtype))
elif isinstance(cell, nn.Dense):
cell.weight.set_data(
init.initializer(init.Normal(sigma=0.01, mean=0.0), cell.weight.shape, cell.weight.dtype))
if cell.bias is not None:
cell.bias.set_data(init.initializer('zeros', cell.bias.shape, cell.bias.dtype))
def forward_features(self, x: Tensor) -> Tensor:
x = self.features(x)
return x
def forward_head(self, x: Tensor) -> Tensor:
x = self.pool(x)
x = self.classifier(x)
return x
def construct(self, x: Tensor) -> Tensor:
x = self.forward_features(x)
x = self.forward_head(x)
return x
@register_model
def mobilenet_v3_small_100(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV3:
"""Get small MobileNetV3 model without width scaling.
Refer to the base class `models.MobileNetV3` for more details.
"""
default_cfg = default_cfgs['mobilenet_v3_small_1.0']
model = MobileNetV3(arch='small', alpha=1.0, in_channels=in_channels, num_classes=num_classes, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
@register_model
def mobilenet_v3_large_100(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV3:
"""Get large MobileNetV3 model without width scaling.
Refer to the base class `models.MobileNetV3` for more details.
"""
default_cfg = default_cfgs['mobilenet_v3_large_1.0']
model = MobileNetV3(arch='large', alpha=1.0, in_channels=in_channels, num_classes=num_classes, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
@register_model
def mobilenet_v3_small_075(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV3:
"""Get small MobileNetV3 model with width scaled by 0.75.
Refer to the base class `models.MobileNetV3` for more details.
"""
default_cfg = default_cfgs['mobilenet_v3_small_0.75']
model = MobileNetV3(arch='small', alpha=0.75, in_channels=in_channels, num_classes=num_classes, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
@register_model
def mobilenet_v3_large_075(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV3:
"""Get large MobileNetV3 model with width scaled by 0.75.
Refer to the base class `models.MobileNetV3` for more details.
"""
default_cfg = default_cfgs['mobilenet_v3_large_0.75']
model = MobileNetV3(arch='large', alpha=0.75, in_channels=in_channels, num_classes=num_classes, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model