"""
MindSpore implementation of `InceptionV4`.
Refer to Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.
"""
from typing import Union, Tuple
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__ = [
'InceptionV4',
'inception_v4'
]
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'first_conv': 'features.0.conv2d_1a_3x3.conv', 'classifier': 'classifier',
**kwargs
}
default_cfgs = {
'inception_v4': _cfg(url='')
}
class BasicConv2d(nn.Cell):
"""A block for conv bn and relu"""
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple] = 1,
stride: int = 1,
padding: int = 0,
pad_mode: str = 'same'
) -> None:
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride,
padding=padding, pad_mode=pad_mode)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.9997)
self.relu = nn.ReLU()
def construct(self, x: Tensor) -> Tensor:
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class Stem(nn.Cell):
"""Inception V4 model blocks."""
def __init__(self, in_channels: int) -> None:
super().__init__()
self.conv2d_1a_3x3 = BasicConv2d(in_channels, 32, kernel_size=3, stride=2, pad_mode='valid')
self.conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3, stride=1, pad_mode='valid')
self.conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, stride=1, pad_mode='pad', padding=1)
self.mixed_3a_branch_0 = nn.MaxPool2d(3, stride=2)
self.mixed_3a_branch_1 = BasicConv2d(64, 96, kernel_size=3, stride=2, pad_mode='valid')
self.mixed_4a_branch_0 = nn.SequentialCell([
BasicConv2d(160, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, pad_mode='valid')
])
self.mixed_4a_branch_1 = nn.SequentialCell([
BasicConv2d(160, 64, kernel_size=1, stride=1),
BasicConv2d(64, 64, kernel_size=(1, 7), stride=1),
BasicConv2d(64, 64, kernel_size=(7, 1), stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, pad_mode='valid')
])
self.mixed_5a_branch_0 = BasicConv2d(192, 192, kernel_size=3, stride=2, pad_mode='valid')
self.mixed_5a_branch_1 = nn.MaxPool2d(3, stride=2)
def construct(self, x: Tensor) -> Tensor:
x = self.conv2d_1a_3x3(x) # 149 x 149 x 32
x = self.conv2d_2a_3x3(x) # 147 x 147 x 32
x = self.conv2d_2b_3x3(x) # 147 x 147 x 64
x0 = self.mixed_3a_branch_0(x)
x1 = self.mixed_3a_branch_1(x)
x = ops.concat((x0, x1), axis=1) # 73 x 73 x 160
x0 = self.mixed_4a_branch_0(x)
x1 = self.mixed_4a_branch_1(x)
x = ops.concat((x0, x1), axis=1) # 71 x 71 x 192
x0 = self.mixed_5a_branch_0(x)
x1 = self.mixed_5a_branch_1(x)
x = ops.concat((x0, x1), axis=1) # 35 x 35 x 384
return x
class InceptionA(nn.Cell):
"""Inception V4 model basic architecture"""
def __init__(self) -> None:
super().__init__()
self.branch_0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
self.branch_1 = nn.SequentialCell([
BasicConv2d(384, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, pad_mode='pad', padding=1)
])
self.branch_2 = nn.SequentialCell([
BasicConv2d(384, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, pad_mode='pad', padding=1),
BasicConv2d(96, 96, kernel_size=3, stride=1, pad_mode='pad', padding=1)
])
self.branch_3 = nn.SequentialCell([
nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same'),
BasicConv2d(384, 96, kernel_size=1, stride=1)
])
def construct(self, x: Tensor) -> Tensor:
x0 = self.branch_0(x)
x1 = self.branch_1(x)
x2 = self.branch_2(x)
x3 = self.branch_3(x)
x4 = ops.concat((x0, x1, x2, x3), axis=1)
return x4
class InceptionB(nn.Cell):
"""Inception V4 model basic architecture"""
def __init__(self) -> None:
super().__init__()
self.branch_0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
self.branch_1 = nn.SequentialCell([
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 224, kernel_size=(1, 7), stride=1),
BasicConv2d(224, 256, kernel_size=(7, 1), stride=1),
])
self.branch_2 = nn.SequentialCell([
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=(7, 1), stride=1),
BasicConv2d(192, 224, kernel_size=(1, 7), stride=1),
BasicConv2d(224, 224, kernel_size=(7, 1), stride=1),
BasicConv2d(224, 256, kernel_size=(1, 7), stride=1)
])
self.branch_3 = nn.SequentialCell([
nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same'),
BasicConv2d(1024, 128, kernel_size=1, stride=1)
])
def construct(self, x: Tensor) -> Tensor:
x0 = self.branch_0(x)
x1 = self.branch_1(x)
x2 = self.branch_2(x)
x3 = self.branch_3(x)
x4 = ops.concat((x0, x1, x2, x3), axis=1)
return x4
class ReductionA(nn.Cell):
"""Inception V4 model Residual Connections"""
def __init__(self) -> None:
super().__init__()
self.branch_0 = BasicConv2d(384, 384, kernel_size=3, stride=2, pad_mode='valid')
self.branch_1 = nn.SequentialCell([
BasicConv2d(384, 192, kernel_size=1, stride=1),
BasicConv2d(192, 224, kernel_size=3, stride=1, pad_mode='pad', padding=1),
BasicConv2d(224, 256, kernel_size=3, stride=2, pad_mode='valid'),
])
self.branch_2 = nn.MaxPool2d(3, stride=2)
def construct(self, x: Tensor) -> Tensor:
x0 = self.branch_0(x)
x1 = self.branch_1(x)
x2 = self.branch_2(x)
x3 = ops.concat((x0, x1, x2), axis=1)
return x3
class ReductionB(nn.Cell):
"""Inception V4 model Residual Connections"""
def __init__(self) -> None:
super().__init__()
self.branch_0 = nn.SequentialCell([
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=3, stride=2, pad_mode='valid'),
])
self.branch_1 = nn.SequentialCell([
BasicConv2d(1024, 256, kernel_size=1, stride=1),
BasicConv2d(256, 256, kernel_size=(1, 7), stride=1),
BasicConv2d(256, 320, kernel_size=(7, 1), stride=1),
BasicConv2d(320, 320, kernel_size=3, stride=2, pad_mode='valid')
])
self.branch_2 = nn.MaxPool2d(3, stride=2)
def construct(self, x: Tensor) -> Tensor:
x0 = self.branch_0(x)
x1 = self.branch_1(x)
x2 = self.branch_2(x)
x3 = ops.concat((x0, x1, x2), axis=1)
return x3 # 8 x 8 x 1536
class InceptionC(nn.Cell):
"""Inception V4 model basic architecture"""
def __init__(self) -> None:
super().__init__()
self.branch_0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
self.branch_1 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
self.branch_1_1 = BasicConv2d(384, 256, kernel_size=(1, 3), stride=1)
self.branch_1_2 = BasicConv2d(384, 256, kernel_size=(3, 1), stride=1)
self.branch_2 = nn.SequentialCell([
BasicConv2d(1536, 384, kernel_size=1, stride=1),
BasicConv2d(384, 448, kernel_size=(3, 1), stride=1),
BasicConv2d(448, 512, kernel_size=(1, 3), stride=1),
])
self.branch_2_1 = BasicConv2d(512, 256, kernel_size=(1, 3), stride=1)
self.branch_2_2 = BasicConv2d(512, 256, kernel_size=(3, 1), stride=1)
self.branch_3 = nn.SequentialCell([
nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same'),
BasicConv2d(1536, 256, kernel_size=1, stride=1)
])
def construct(self, x: Tensor) -> Tensor:
x0 = self.branch_0(x)
x1 = self.branch_1(x)
x1_1 = self.branch_1_1(x1)
x1_2 = self.branch_1_2(x1)
x1 = ops.concat((x1_1, x1_2), axis=1)
x2 = self.branch_2(x)
x2_1 = self.branch_2_1(x2)
x2_2 = self.branch_2_2(x2)
x2 = ops.concat((x2_1, x2_2), axis=1)
x3 = self.branch_3(x)
return ops.concat((x0, x1, x2, x3), axis=1)
[文档]class InceptionV4(nn.Cell):
r"""Inception v4 model architecture from
`"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning" <https://arxiv.org/abs/1602.07261>`_.
Args:
num_classes: number of classification classes. Default: 1000.
in_channels: number the channels of the input. Default: 3.
drop_rate: dropout rate of the layer before main classifier. Default: 0.2.
"""
def __init__(self,
num_classes: int = 1000,
in_channels: int = 3,
drop_rate: float = 0.2
) -> None:
super().__init__()
blocks = [Stem(in_channels)]
for _ in range(4):
blocks.append(InceptionA())
blocks.append(ReductionA())
for _ in range(7):
blocks.append(InceptionB())
blocks.append(ReductionB())
for _ in range(3):
blocks.append(InceptionC())
self.features = nn.SequentialCell(blocks)
self.pool = GlobalAvgPooling()
self.dropout = nn.Dropout(1 - drop_rate)
self.num_features = 1536
self.classifier = nn.Dense(self.num_features, 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):
cell.weight.set_data(
init.initializer(init.XavierUniform(), cell.weight.shape, cell.weight.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.dropout(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 inception_v4(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> InceptionV4:
"""Get InceptionV4 model.
Refer to the base class `models.InceptionV4` for more details."""
default_cfg = default_cfgs['inception_v4']
model = InceptionV4(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