"""
MindSpore implementation of `ShuffleNetV1`.
Refer to ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
"""
from mindspore import nn, ops, Tensor
import mindspore.common.initializer as init
from .utils import load_pretrained
from .registry import register_model
from .layers.pooling import GlobalAvgPooling
__all__ = [
"ShuffleNetV1",
"shufflenet_v1_g3_x0_5",
"shufflenet_v1_g3_x1_0",
"shufflenet_v1_g3_x1_5",
"shufflenet_v1_g3_x2_0",
"shufflenet_v1_g8_x0_5",
"shufflenet_v1_g8_x1_0",
"shufflenet_v1_g8_x1_5",
"shufflenet_v1_g8_x2_0"
]
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'first_conv': 'first_conv.0', 'classifier': 'classifier',
**kwargs
}
default_cfgs = {
'shufflenet_v1_g3_0.5': _cfg(url=''),
'shufflenet_v1_g3_1.0': _cfg(url=''),
'shufflenet_v1_g3_1.5': _cfg(url=''),
'shufflenet_v1_g3_2.0': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenet_v1_g3_2.0_224.ckpt'),
'shufflenet_v1_g8_0.5': _cfg(url=''),
'shufflenet_v1_g8_1.0': _cfg(url=''),
'shufflenet_v1_g8_1.5': _cfg(url=''),
'shufflenet_v1_g8_2.0': _cfg(url=''),
}
class ShuffleV1Block(nn.Cell):
"""Basic block of ShuffleNetV1. 1x1 GC -> CS -> 3x3 DWC -> 1x1 GC"""
def __init__(self,
in_channels: int,
out_channels: int,
mid_channels: int,
stride: int,
group: int,
first_group: bool,
) -> None:
super().__init__()
assert stride in [1, 2]
self.stride = stride
self.group = group
if stride == 2:
out_channels = out_channels - in_channels
branch_main_1 = [
# pw
nn.Conv2d(in_channels, mid_channels, kernel_size=1, stride=1,
group=1 if first_group else group),
nn.BatchNorm2d(mid_channels),
nn.ReLU(),
]
branch_main_2 = [
# dw
nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=stride, pad_mode='pad', padding=1,
group=mid_channels),
nn.BatchNorm2d(mid_channels),
# pw-linear
nn.Conv2d(mid_channels, out_channels, kernel_size=1, stride=1, group=group),
nn.BatchNorm2d(out_channels),
]
self.branch_main_1 = nn.SequentialCell(branch_main_1)
self.branch_main_2 = nn.SequentialCell(branch_main_2)
if stride == 2:
self.branch_proj = nn.AvgPool2d(kernel_size=3, stride=2, pad_mode='same')
self.relu = nn.ReLU()
def construct(self, x: Tensor) -> Tensor:
identify = x
x = self.branch_main_1(x)
if self.group > 1:
x = self.channel_shuffle(x)
x = self.branch_main_2(x)
if self.stride == 1:
out = self.relu(identify + x)
else:
out = self.relu(ops.concat((self.branch_proj(identify), x), axis=1))
return out
def channel_shuffle(self, x: Tensor) -> Tensor:
batch_size, num_channels, height, width = x.shape
group_channels = num_channels // self.group
x = ops.reshape(x, (batch_size, group_channels, self.group, height, width))
x = ops.transpose(x, (0, 2, 1, 3, 4))
x = ops.reshape(x, (batch_size, num_channels, height, width))
return x
[文档]class ShuffleNetV1(nn.Cell):
r"""ShuffleNetV1 model class, based on
`"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" <https://arxiv.org/abs/1707.01083>`_
Args:
num_classes: number of classification classes. Default: 1000.
in_channels: number of input channels. Default: 3.
model_size: scale factor which controls the number of channels. Default: '2.0x'.
group: number of group for group convolution. Default: 3.
"""
def __init__(self,
num_classes: int = 1000,
in_channels: int = 3,
model_size: str = '2.0x',
group: int = 3):
super().__init__()
self.stage_repeats = [4, 8, 4]
self.model_size = model_size
if group == 3:
if model_size == '0.5x':
self.stage_out_channels = [-1, 12, 120, 240, 480]
elif model_size == '1.0x':
self.stage_out_channels = [-1, 24, 240, 480, 960]
elif model_size == '1.5x':
self.stage_out_channels = [-1, 24, 360, 720, 1440]
elif model_size == '2.0x':
self.stage_out_channels = [-1, 48, 480, 960, 1920]
else:
raise NotImplementedError
elif group == 8:
if model_size == '0.5x':
self.stage_out_channels = [-1, 16, 192, 384, 768]
elif model_size == '1.0x':
self.stage_out_channels = [-1, 24, 384, 768, 1536]
elif model_size == '1.5x':
self.stage_out_channels = [-1, 24, 576, 1152, 2304]
elif model_size == '2.0x':
self.stage_out_channels = [-1, 48, 768, 1536, 3072]
else:
raise NotImplementedError
# building first layer
input_channel = self.stage_out_channels[1]
self.first_conv = nn.SequentialCell(
nn.Conv2d(in_channels, input_channel, kernel_size=3, stride=2, pad_mode='pad', padding=1),
nn.BatchNorm2d(input_channel),
nn.ReLU(),
)
self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
features = []
for idxstage, numrepeat in enumerate(self.stage_repeats):
output_channel = self.stage_out_channels[idxstage + 2]
for i in range(numrepeat):
stride = 2 if i == 0 else 1
first_group = idxstage == 0 and i == 0
features.append(ShuffleV1Block(input_channel, output_channel,
group=group, first_group=first_group,
mid_channels=output_channel // 4, stride=stride))
input_channel = output_channel
self.features = nn.SequentialCell(features)
self.global_pool = GlobalAvgPooling()
self.classifier = nn.Dense(self.stage_out_channels[-1], num_classes, has_bias=False)
self._initialize_weights()
def _initialize_weights(self):
"""Initialize weights for cells."""
for name, cell in self.cells_and_names():
if isinstance(cell, nn.Conv2d):
if 'first' in name:
cell.weight.set_data(
init.initializer(init.Normal(0.01, 0), cell.weight.shape, cell.weight.dtype))
else:
cell.weight.set_data(
init.initializer(init.Normal(1.0 / cell.weight.shape[1], 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.Dense):
cell.weight.set_data(
init.initializer(init.Normal(0.01, 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.first_conv(x)
x = self.max_pool(x)
x = self.features(x)
return x
[文档] def forward_head(self, x: Tensor) -> Tensor:
x = self.global_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 shufflenet_v1_g3_x0_5(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> ShuffleNetV1:
"""Get ShuffleNetV1 model with width scaled by 0.5 and 3 groups of GPConv.
Refer to the base class `models.ShuffleNetV1` for more details.
"""
default_cfg = default_cfgs['shufflenet_v1_g3_0.5']
model = ShuffleNetV1(group=3, model_size='0.5x', 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 shufflenet_v1_g3_x1_0(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> ShuffleNetV1:
"""Get ShuffleNetV1 model with width scaled by 1.0 and 3 groups of GPConv.
Refer to the base class `models.ShuffleNetV1` for more details.
"""
default_cfg = default_cfgs['shufflenet_v1_g3_1.0']
model = ShuffleNetV1(group=3, model_size='1.0x', 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 shufflenet_v1_g3_x1_5(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> ShuffleNetV1:
"""Get ShuffleNetV1 model with width scaled by 1.5 and 3 groups of GPConv.
Refer to the base class `models.ShuffleNetV1` for more details.
"""
default_cfg = default_cfgs['shufflenet_v1_g3_1.5']
model = ShuffleNetV1(group=3, model_size='1.5x', 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 shufflenet_v1_g3_x2_0(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> ShuffleNetV1:
"""Get ShuffleNetV1 model with width scaled by 2.0 and 3 groups of GPConv.
Refer to the base class `models.ShuffleNetV1` for more details.
"""
default_cfg = default_cfgs['shufflenet_v1_g3_2.0']
model = ShuffleNetV1(group=3, model_size='2.0x', 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 shufflenet_v1_g8_x0_5(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> ShuffleNetV1:
"""Get ShuffleNetV1 model with width scaled by 0.5 and 8 groups of GPConv.
Refer to the base class `models.ShuffleNetV1` for more details.
"""
default_cfg = default_cfgs['shufflenet_v1_g8_0.5']
model = ShuffleNetV1(group=8, model_size='0.5x', 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 shufflenet_v1_g8_x1_0(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> ShuffleNetV1:
"""Get ShuffleNetV1 model with width scaled by 1.0 and 8 groups of GPConv.
Refer to the base class `models.ShuffleNetV1` for more details.
"""
default_cfg = default_cfgs['shufflenet_v1_g8_1.0']
model = ShuffleNetV1(group=8, model_size='1.0x', 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 shufflenet_v1_g8_x1_5(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> ShuffleNetV1:
"""Get ShuffleNetV1 model with width scaled by 1.5 and 8 groups of GPConv.
Refer to the base class `models.ShuffleNetV1` for more details.
"""
default_cfg = default_cfgs['shufflenet_v1_g8_1.5']
model = ShuffleNetV1(group=8, model_size='1.5x', 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 shufflenet_v1_g8_x2_0(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> ShuffleNetV1:
"""Get ShuffleNetV1 model with width scaled by 2.0 and 8 groups of GPConv.
Refer to the base class `models.ShuffleNetV1` for more details.
"""
default_cfg = default_cfgs['shufflenet_v1_g8_2.0']
model = ShuffleNetV1(group=8, model_size='2.0x', 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