"""
MindSpore implementation of `MobileNetV2`.
Refer to MobileNetV2: Inverted Residuals and Linear Bottlenecks.
"""
import math
from mindspore import nn, Tensor
import mindspore.common.initializer as init
from .layers.pooling import GlobalAvgPooling
from .utils import make_divisible, load_pretrained
from .registry import register_model
__all__ = [
'MobileNetV2',
'mobilenet_v2_140_224',
'mobilenet_v2_130_224',
'mobilenet_v2_100_224',
'mobilenet_v2_100_192',
'mobilenet_v2_100_160',
'mobilenet_v2_100_128',
'mobilenet_v2_100_96',
'mobilenet_v2_075_224',
'mobilenet_v2_075_192',
'mobilenet_v2_075_160',
'mobilenet_v2_075_128',
'mobilenet_v2_075_96',
'mobilenet_v2_050_224',
'mobilenet_v2_050_192',
'mobilenet_v2_050_160',
'mobilenet_v2_050_128',
'mobilenet_v2_050_96',
'mobilenet_v2_035_224',
'mobilenet_v2_035_192',
'mobilenet_v2_035_160',
'mobilenet_v2_035_128',
'mobilenet_v2_035_96'
]
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'first_conv': 'features.0', 'classifier': 'classifier.1',
**kwargs
}
default_cfgs = {
'mobilenet_v2_1.4_224': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_1.4_224.ckpt'),
'mobilenet_v2_1.3_224': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_1.3_224.ckpt'),
'mobilenet_v2_1.0_224': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_1.0_224.ckpt'),
'mobilenet_v2_1.0_192': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_1.0_192.ckpt'),
'mobilenet_v2_1.0_160': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_1.0_160.ckpt'),
'mobilenet_v2_1.0_128': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_1.0_128.ckpt'),
'mobilenet_v2_1.0_96': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_1.0_96.ckpt'),
'mobilenet_v2_0.75_224': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.75_224.ckpt'),
'mobilenet_v2_0.75_192': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.75_192.ckpt'),
'mobilenet_v2_0.75_160': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.75_160.ckpt'),
'mobilenet_v2_0.75_128': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.75_128.ckpt'),
'mobilenet_v2_0.75_96': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.75_96.ckpt'),
'mobilenet_v2_0.5_224': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.5_224.ckpt'),
'mobilenet_v2_0.5_192': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.5_192.ckpt'),
'mobilenet_v2_0.5_160': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.5_160.ckpt'),
'mobilenet_v2_0.5_128': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.5_128.ckpt'),
'mobilenet_v2_0.5_96': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.5_96.ckpt'),
'mobilenet_v2_0.35_224': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.35_224.ckpt'),
'mobilenet_v2_0.35_192': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.35_192.ckpt'),
'mobilenet_v2_0.35_160': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.35_160.ckpt'),
'mobilenet_v2_0.35_128': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.35_128.ckpt'),
'mobilenet_v2_0.35_96': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenet_v2_0.35_96.ckpt'),
}
class InvertedResidual(nn.Cell):
"""Inverted Residual Block of MobileNetV2"""
def __init__(self,
in_channels: int,
out_channels: int,
stride: int,
expand_ratio: int,
) -> None:
super().__init__()
assert stride in [1, 2]
hidden_dim = round(in_channels * expand_ratio)
self.use_res_connect = stride == 1 and in_channels == out_channels
layers = []
if expand_ratio != 1:
# pw
layers.extend([
nn.Conv2d(in_channels, hidden_dim, 1, 1, pad_mode="pad", padding=0, has_bias=False),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6()
])
layers.extend([
# dw
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, pad_mode="pad", padding=1, group=hidden_dim, has_bias=False),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(),
# pw-linear
nn.Conv2d(hidden_dim, 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 MobileNetV2(nn.Cell):
r"""MobileNetV2 model class, based on
`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_
Args:
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,
alpha: float = 1.0,
round_nearest: int = 8,
in_channels: int = 3,
num_classes: int = 1000
) -> None:
super().__init__()
input_channels = make_divisible(32 * alpha, round_nearest)
# Setting of inverted residual blocks.
# t: The expansion factor.
# c: Number of output channel.
# n: Number of block.
# s: First block stride.
inverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1],
]
last_channels = make_divisible(1280 * max(1.0, alpha), round_nearest)
# 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.ReLU6()
]
# Building inverted residual blocks.
for t, c, n, s in inverted_residual_setting:
output_channel = make_divisible(c * alpha, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
features.append(InvertedResidual(input_channels, output_channel, stride, expand_ratio=t))
input_channels = output_channel
# Building last point-wise layers.
features.extend([
nn.Conv2d(input_channels, last_channels, 1, 1, pad_mode="pad", padding=0, has_bias=False),
nn.BatchNorm2d(last_channels),
nn.ReLU6()
])
self.features = nn.SequentialCell(features)
self.pool = GlobalAvgPooling()
self.classifier = nn.SequentialCell([
nn.Dropout(keep_prob=0.8), # confirmed by paper authors
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))
[文档] def forward_features(self, x: Tensor) -> Tensor:
x = self.features(x)
return x
[文档] def forward_head(self, x: Tensor) -> Tensor:
x = self.pool(x)
x = self.classifier(x)
return x
[文档] def construct(self, x: Tensor) -> Tensor:
x = self.forward_features(x)
x = self.forward_head(x)
return x
[文档]@register_model
def mobilenet_v2_140_224(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 1.4 and input image size of 224.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_1.4_224']
model = MobileNetV2(alpha=1.4, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_130_224(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 1.3 and input image size of 224.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_1.3_224']
model = MobileNetV2(alpha=1.3, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_100_224(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model without width scaling and input image size of 224.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_1.0_224']
model = MobileNetV2(alpha=1.0, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_100_192(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model without width scaling and input image size of 192.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_1.0_192']
model = MobileNetV2(alpha=1.0, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_100_160(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model without width scaling and input image size of 160.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_1.0_160']
model = MobileNetV2(alpha=1.0, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_100_128(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model without width scaling and input image size of 128.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_1.0_128']
model = MobileNetV2(alpha=1.0, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_100_96(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model without width scaling and input image size of 96.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_1.0_96']
model = MobileNetV2(alpha=1.0, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_075_224(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.75 and input image size of 224.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.75_224']
model = MobileNetV2(alpha=0.75, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_075_192(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.75 and input image size of 192.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.75_192']
model = MobileNetV2(alpha=0.75, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_075_160(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.75 and input image size of 160.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.75_160']
model = MobileNetV2(alpha=0.75, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_075_128(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.75 and input image size of 128.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.75_128']
model = MobileNetV2(alpha=0.75, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_075_96(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.75 and input image size of 96.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.75_96']
model = MobileNetV2(alpha=0.75, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_050_224(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.5 and input image size of 224.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.5_224']
model = MobileNetV2(alpha=0.5, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_050_192(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.5 and input image size of 192.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.5_192']
model = MobileNetV2(alpha=0.5, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_050_160(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.5 and input image size of 160.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.5_160']
model = MobileNetV2(alpha=0.5, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_050_128(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.5 and input image size of 128.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.5_128']
model = MobileNetV2(alpha=0.5, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_050_96(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.5 and input image size of 96.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.5_96']
model = MobileNetV2(alpha=0.5, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_035_224(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.35 and input image size of 224.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.35_224']
model = MobileNetV2(alpha=0.35, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_035_192(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.35 and input image size of 192.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.35_192']
model = MobileNetV2(alpha=0.35, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_035_160(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.35 and input image size of 160.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.35_160']
model = MobileNetV2(alpha=0.35, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_035_128(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.35 and input image size of 128.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.35_128']
model = MobileNetV2(alpha=0.35, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model
[文档]@register_model
def mobilenet_v2_035_96(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV2:
"""Get MobileNetV2 model with width scaled by 0.35 and input image size of 96.
Refer to the base class `models.MobileNetV2` for more details.
"""
default_cfg = default_cfgs['mobilenet_v2_0.35_96']
model = MobileNetV2(alpha=0.35, num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model