"""
MindSpore implementation of `NasNet`.
Refer to: Learning Transferable Architectures for Scalable Image Recognition
"""
import math
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__ = [
'NASNetAMobile',
'nasnet'
]
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'first_conv': 'conv0.0', 'classifier': 'classifier',
**kwargs
}
default_cfgs = {
'nasnet': _cfg(url='https://download.mindspore.cn/toolkits/mindcv/nasnet/nasnet_224.ckpt'),
}
class SeparableConv2d(nn.Cell):
"""depth-wise convolutions + point-wise convolutions"""
def __init__(self,
in_channels: int,
out_channels: int,
dw_kernel: int,
dw_stride: int,
dw_padding: int,
bias: bool = False) -> None:
super().__init__()
self.depthwise_conv2d = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=dw_kernel,
stride=dw_stride, pad_mode='pad', padding=dw_padding, group=in_channels,
has_bias=bias)
self.pointwise_conv2d = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1,
pad_mode='pad', has_bias=bias)
def construct(self, x: Tensor) -> Tensor:
x = self.depthwise_conv2d(x)
x = self.pointwise_conv2d(x)
return x
class BranchSeparables(nn.Cell):
"""NasNet model basic architecture"""
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
padding: int,
bias: bool = False) -> None:
super().__init__()
self.relu = nn.ReLU()
self.separable_1 = SeparableConv2d(
in_channels, in_channels, kernel_size, stride, padding, bias=bias
)
self.bn_sep_1 = nn.BatchNorm2d(num_features=in_channels, eps=0.001, momentum=0.9, affine=True)
self.relu1 = nn.ReLU()
self.separable_2 = SeparableConv2d(
in_channels, out_channels, kernel_size, 1, padding, bias=bias
)
self.bn_sep_2 = nn.BatchNorm2d(num_features=out_channels, eps=0.001, momentum=0.9, affine=True)
def construct(self, x: Tensor) -> Tensor:
x = self.relu(x)
x = self.separable_1(x)
x = self.bn_sep_1(x)
x = self.relu1(x)
x = self.separable_2(x)
x = self.bn_sep_2(x)
return x
class BranchSeparablesStem(nn.Cell):
"""NasNet model basic architecture"""
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
padding: int,
bias: bool = False) -> None:
super().__init__()
self.relu = nn.ReLU()
self.separable_1 = SeparableConv2d(
in_channels, out_channels, kernel_size, stride, padding, bias=bias
)
self.bn_sep_1 = nn.BatchNorm2d(num_features=out_channels, eps=0.001, momentum=0.9, affine=True)
self.relu1 = nn.ReLU()
self.separable_2 = SeparableConv2d(
out_channels, out_channels, kernel_size, 1, padding, bias=bias
)
self.bn_sep_2 = nn.BatchNorm2d(num_features=out_channels, eps=0.001, momentum=0.9, affine=True)
def construct(self, x: Tensor) -> Tensor:
x = self.relu(x)
x = self.separable_1(x)
x = self.bn_sep_1(x)
x = self.relu1(x)
x = self.separable_2(x)
x = self.bn_sep_2(x)
return x
class BranchSeparablesReduction(BranchSeparables):
"""NasNet model Residual Connections"""
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
padding: int,
z_padding: int = 1,
bias: bool = False) -> None:
BranchSeparables.__init__(
self, in_channels, out_channels, kernel_size, stride, padding, bias
)
self.padding = nn.Pad(paddings=((0, 0), (0, 0), (z_padding, 0), (z_padding, 0)), mode="CONSTANT")
def construct(self, x: Tensor) -> Tensor:
x = self.relu(x)
x = self.padding(x)
x = self.separable_1(x)
x = x[:, :, 1:, 1:]
x = self.bn_sep_1(x)
x = self.relu1(x)
x = self.separable_2(x)
x = self.bn_sep_2(x)
return x
class CellStem0(nn.Cell):
"""NasNet model basic architecture"""
def __init__(self,
stem_filters: int,
num_filters: int = 42) -> None:
super().__init__()
self.num_filters = num_filters
self.stem_filters = stem_filters
self.conv_1x1 = nn.SequentialCell([
nn.ReLU(),
nn.Conv2d(in_channels=self.stem_filters, out_channels=self.num_filters, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False),
nn.BatchNorm2d(num_features=self.num_filters, eps=0.001, momentum=0.9, affine=True)
])
self.comb_iter_0_left = BranchSeparables(
self.num_filters, self.num_filters, 5, 2, 2
)
self.comb_iter_0_right = BranchSeparablesStem(
self.stem_filters, self.num_filters, 7, 2, 3, bias=False
)
self.comb_iter_1_left = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
self.comb_iter_1_right = BranchSeparablesStem(
self.stem_filters, self.num_filters, 7, 2, 3, bias=False
)
self.comb_iter_2_left = nn.AvgPool2d(kernel_size=3, stride=2, pad_mode='same')
self.comb_iter_2_right = BranchSeparablesStem(
self.stem_filters, self.num_filters, 5, 2, 2, bias=False
)
self.comb_iter_3_right = nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same')
self.comb_iter_4_left = BranchSeparables(
self.num_filters, self.num_filters, 3, 1, 1, bias=False
)
self.comb_iter_4_right = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
def construct(self, x: Tensor) -> Tensor:
x1 = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x1)
x_comb_iter_0_right = self.comb_iter_0_right(x)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x1)
x_comb_iter_1_right = self.comb_iter_1_right(x)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x1)
x_comb_iter_2_right = self.comb_iter_2_right(x)
x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
x_comb_iter_4_right = self.comb_iter_4_right(x1)
x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
x_out = ops.concat((x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4), axis=1)
return x_out
class CellStem1(nn.Cell):
"""NasNet model basic architecture"""
def __init__(self,
stem_filters: int,
num_filters: int) -> None:
super().__init__()
self.num_filters = num_filters
self.stem_filters = stem_filters
self.conv_1x1 = nn.SequentialCell([
nn.ReLU(),
nn.Conv2d(in_channels=2 * self.num_filters, out_channels=self.num_filters, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False),
nn.BatchNorm2d(num_features=self.num_filters, eps=0.001, momentum=0.9, affine=True)])
self.relu = nn.ReLU()
self.path_1 = nn.SequentialCell([
nn.AvgPool2d(kernel_size=1, stride=2, pad_mode='valid'),
nn.Conv2d(in_channels=self.stem_filters, out_channels=self.num_filters // 2, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False)])
self.path_2 = nn.CellList([])
self.path_2.append(nn.Pad(paddings=((0, 0), (0, 0), (0, 1), (0, 1)), mode="CONSTANT"))
self.path_2.append(
nn.AvgPool2d(kernel_size=1, stride=2, pad_mode='valid')
)
self.path_2.append(
nn.Conv2d(in_channels=self.stem_filters, out_channels=self.num_filters // 2, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False)
)
self.final_path_bn = nn.BatchNorm2d(num_features=self.num_filters, eps=0.001, momentum=0.9, affine=True)
self.comb_iter_0_left = BranchSeparables(
self.num_filters,
self.num_filters,
5,
2,
2,
bias=False
)
self.comb_iter_0_right = BranchSeparables(
self.num_filters,
self.num_filters,
7,
2,
3,
bias=False
)
self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, pad_mode='same')
self.comb_iter_1_right = BranchSeparables(
self.num_filters,
self.num_filters,
7,
2,
3,
bias=False
)
self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, pad_mode='same')
self.comb_iter_2_right = BranchSeparables(
self.num_filters,
self.num_filters,
5,
2,
2,
bias=False
)
self.comb_iter_3_right = nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same')
self.comb_iter_4_left = BranchSeparables(
self.num_filters,
self.num_filters,
3,
1,
1,
bias=False
)
self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, pad_mode='same')
def construct(self, x_conv0: Tensor, x_stem_0: Tensor) -> Tensor:
x_left = self.conv_1x1(x_stem_0)
x_relu = self.relu(x_conv0)
# path 1
x_path1 = self.path_1(x_relu)
# path 2
x_path2 = self.path_2[0](x_relu)
x_path2 = x_path2[:, :, 1:, 1:]
x_path2 = self.path_2[1](x_path2)
x_path2 = self.path_2[2](x_path2)
# final path
x_right = self.final_path_bn(ops.concat((x_path1, x_path2), axis=1))
x_comb_iter_0_left = self.comb_iter_0_left(x_left)
x_comb_iter_0_right = self.comb_iter_0_right(x_right)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_left)
x_comb_iter_1_right = self.comb_iter_1_right(x_right)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_left)
x_comb_iter_2_right = self.comb_iter_2_right(x_right)
x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
x_comb_iter_4_right = self.comb_iter_4_right(x_left)
x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
x_out = ops.concat((x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4), axis=1)
return x_out
class FirstCell(nn.Cell):
"""NasNet model basic architecture"""
def __init__(self,
in_channels_left: int,
out_channels_left: int,
in_channels_right: int,
out_channels_right: int) -> None:
super().__init__()
self.conv_1x1 = nn.SequentialCell([
nn.ReLU(),
nn.Conv2d(in_channels=in_channels_right, out_channels=out_channels_right, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False),
nn.BatchNorm2d(num_features=out_channels_right, eps=0.001, momentum=0.9, affine=True)])
self.relu = nn.ReLU()
self.path_1 = nn.SequentialCell([
nn.AvgPool2d(kernel_size=1, stride=2, pad_mode='valid'),
nn.Conv2d(in_channels=in_channels_left, out_channels=out_channels_left, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False)])
self.path_2 = nn.CellList([])
self.path_2.append(nn.Pad(paddings=((0, 0), (0, 0), (0, 1), (0, 1)), mode="CONSTANT"))
self.path_2.append(
nn.AvgPool2d(kernel_size=1, stride=2, pad_mode='valid')
)
self.path_2.append(
nn.Conv2d(in_channels=in_channels_left, out_channels=out_channels_left, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False)
)
self.final_path_bn = nn.BatchNorm2d(num_features=out_channels_left * 2, eps=0.001, momentum=0.9, affine=True)
self.comb_iter_0_left = BranchSeparables(
out_channels_right, out_channels_right, 5, 1, 2, bias=False
)
self.comb_iter_0_right = BranchSeparables(
out_channels_right, out_channels_right, 3, 1, 1, bias=False
)
self.comb_iter_1_left = BranchSeparables(
out_channels_right, out_channels_right, 5, 1, 2, bias=False
)
self.comb_iter_1_right = BranchSeparables(
out_channels_right, out_channels_right, 3, 1, 1, bias=False
)
self.comb_iter_2_left = nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same')
self.comb_iter_3_left = nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same')
self.comb_iter_3_right = nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same')
self.comb_iter_4_left = BranchSeparables(
out_channels_right, out_channels_right, 3, 1, 1, bias=False
)
def construct(self, x: Tensor, x_prev: Tensor) -> Tensor:
x_relu = self.relu(x_prev)
x_path1 = self.path_1(x_relu)
x_path2 = self.path_2[0](x_relu)
x_path2 = x_path2[:, :, 1:, 1:]
x_path2 = self.path_2[1](x_path2)
x_path2 = self.path_2[2](x_path2)
# final path
x_left = self.final_path_bn(ops.concat((x_path1, x_path2), axis=1))
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_left)
x_comb_iter_1_right = self.comb_iter_1_right(x_left)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_right)
x_comb_iter_2 = x_comb_iter_2_left + x_left
x_comb_iter_3_left = self.comb_iter_3_left(x_left)
x_comb_iter_3_right = self.comb_iter_3_right(x_left)
x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
x_comb_iter_4_left = self.comb_iter_4_left(x_right)
x_comb_iter_4 = x_comb_iter_4_left + x_right
x_out = ops.concat((x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4), axis=1)
return x_out
class NormalCell(nn.Cell):
"""NasNet model basic architecture"""
def __init__(self,
in_channels_left: int,
out_channels_left: int,
in_channels_right: int,
out_channels_right: int) -> None:
super().__init__()
self.conv_prev_1x1 = nn.SequentialCell([
nn.ReLU(),
nn.Conv2d(in_channels=in_channels_left, out_channels=out_channels_left, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False),
nn.BatchNorm2d(num_features=out_channels_left, eps=0.001, momentum=0.9, affine=True)])
self.conv_1x1 = nn.SequentialCell([
nn.ReLU(),
nn.Conv2d(in_channels=in_channels_right, out_channels=out_channels_right, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False),
nn.BatchNorm2d(num_features=out_channels_right, eps=0.001, momentum=0.9, affine=True)])
self.comb_iter_0_left = BranchSeparables(
out_channels_right, out_channels_right, 5, 1, 2, bias=False
)
self.comb_iter_0_right = BranchSeparables(
out_channels_left, out_channels_left, 3, 1, 1, bias=False
)
self.comb_iter_1_left = BranchSeparables(
out_channels_left, out_channels_left, 5, 1, 2, bias=False
)
self.comb_iter_1_right = BranchSeparables(
out_channels_left, out_channels_left, 3, 1, 1, bias=False
)
self.comb_iter_2_left = nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same')
self.comb_iter_3_left = nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same')
self.comb_iter_3_right = nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same')
self.comb_iter_4_left = BranchSeparables(
out_channels_right, out_channels_right, 3, 1, 1, bias=False
)
def construct(self, x: Tensor, x_prev: Tensor) -> Tensor:
x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_left)
x_comb_iter_1_right = self.comb_iter_1_right(x_left)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_right)
x_comb_iter_2 = x_comb_iter_2_left + x_left
x_comb_iter_3_left = self.comb_iter_3_left(x_left)
x_comb_iter_3_right = self.comb_iter_3_right(x_left)
x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
x_comb_iter_4_left = self.comb_iter_4_left(x_right)
x_comb_iter_4 = x_comb_iter_4_left + x_right
x_out = ops.concat((x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4), axis=1)
return x_out
class ReductionCell0(nn.Cell):
"""NasNet model Residual Connections"""
def __init__(self,
in_channels_left: int,
out_channels_left: int,
in_channels_right: int,
out_channels_right: int) -> None:
super().__init__()
self.conv_prev_1x1 = nn.SequentialCell([
nn.ReLU(),
nn.Conv2d(in_channels=in_channels_left, out_channels=out_channels_left, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False),
nn.BatchNorm2d(num_features=out_channels_left, eps=0.001, momentum=0.9, affine=True)])
self.conv_1x1 = nn.SequentialCell([
nn.ReLU(),
nn.Conv2d(in_channels=in_channels_right, out_channels=out_channels_right, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False),
nn.BatchNorm2d(num_features=out_channels_right, eps=0.001, momentum=0.9, affine=True)])
self.comb_iter_0_left = BranchSeparablesReduction(
out_channels_right, out_channels_right, 5, 2, 2, bias=False
)
self.comb_iter_0_right = BranchSeparablesReduction(
out_channels_right, out_channels_right, 7, 2, 3, bias=False
)
self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, pad_mode='same')
self.comb_iter_1_right = BranchSeparablesReduction(
out_channels_right, out_channels_right, 7, 2, 3, bias=False
)
self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, pad_mode='same')
self.comb_iter_2_right = BranchSeparablesReduction(
out_channels_right, out_channels_right, 5, 2, 2, bias=False
)
self.comb_iter_3_right = nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same')
self.comb_iter_4_left = BranchSeparablesReduction(
out_channels_right, out_channels_right, 3, 1, 1, bias=False
)
self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, pad_mode='same')
def construct(self, x: Tensor, x_prev: Tensor) -> Tensor:
x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_right)
x_comb_iter_1_right = self.comb_iter_1_right(x_left)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_right)
x_comb_iter_2_right = self.comb_iter_2_right(x_left)
x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
x_comb_iter_4_right = self.comb_iter_4_right(x_right)
x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
x_out = ops.concat((x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4), axis=1)
return x_out
class ReductionCell1(nn.Cell):
"""NasNet model Residual Connections"""
def __init__(self,
in_channels_left: int,
out_channels_left: int,
in_channels_right: int,
out_channels_right: int) -> None:
super().__init__()
self.conv_prev_1x1 = nn.SequentialCell([
nn.ReLU(),
nn.Conv2d(in_channels=in_channels_left, out_channels=out_channels_left, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False),
nn.BatchNorm2d(num_features=out_channels_left, eps=0.001, momentum=0.9, affine=True)])
self.conv_1x1 = nn.SequentialCell([
nn.ReLU(),
nn.Conv2d(in_channels=in_channels_right, out_channels=out_channels_right, kernel_size=1, stride=1,
pad_mode='pad', has_bias=False),
nn.BatchNorm2d(num_features=out_channels_right, eps=0.001, momentum=0.9, affine=True)])
self.comb_iter_0_left = BranchSeparables(
out_channels_right,
out_channels_right,
5,
2,
2,
bias=False
)
self.comb_iter_0_right = BranchSeparables(
out_channels_right,
out_channels_right,
7,
2,
3,
bias=False
)
self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, pad_mode='same')
self.comb_iter_1_right = BranchSeparables(
out_channels_right,
out_channels_right,
7,
2,
3,
bias=False
)
self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, pad_mode='same')
self.comb_iter_2_right = BranchSeparables(
out_channels_right,
out_channels_right,
5,
2,
2,
bias=False
)
self.comb_iter_3_right = nn.AvgPool2d(kernel_size=3, stride=1, pad_mode='same')
self.comb_iter_4_left = BranchSeparables(
out_channels_right,
out_channels_right,
3,
1,
1,
bias=False
)
self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, pad_mode='same')
def construct(self, x: Tensor, x_prev: Tensor) -> Tensor:
x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_right)
x_comb_iter_1_right = self.comb_iter_1_right(x_left)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_right)
x_comb_iter_2_right = self.comb_iter_2_right(x_left)
x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
x_comb_iter_4_right = self.comb_iter_4_right(x_right)
x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
x_out = ops.concat((x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4), axis=1)
return x_out
[文档]class NASNetAMobile(nn.Cell):
r"""NasNet model class, based on
`"Learning Transferable Architectures for Scalable Image Recognition" <https://arxiv.org/pdf/1707.07012v4.pdf>`_
Args:
num_classes: number of classification classes.
stem_filters: number of stem filters. Default: 32.
penultimate_filters: number of penultimate filters. Default: 1056.
filters_multiplier: size of filters multiplier. Default: 2.
"""
def __init__(self,
in_channels: int = 3,
num_classes: int = 1000,
stem_filters: int = 32,
penultimate_filters: int = 1056,
filters_multiplier: int = 2) -> None:
super().__init__()
self.stem_filters = stem_filters
self.penultimate_filters = penultimate_filters
self.filters_multiplier = filters_multiplier
filters = self.penultimate_filters // 24
# 24 is default value for the architecture
self.conv0 = nn.SequentialCell([
nn.Conv2d(in_channels=in_channels, out_channels=self.stem_filters, kernel_size=3, stride=2, pad_mode='pad',
padding=0,
has_bias=False),
nn.BatchNorm2d(num_features=self.stem_filters, eps=0.001, momentum=0.9, affine=True)
])
self.cell_stem_0 = CellStem0(
self.stem_filters, num_filters=filters // (filters_multiplier ** 2)
)
self.cell_stem_1 = CellStem1(
self.stem_filters, num_filters=filters // filters_multiplier
)
self.cell_0 = FirstCell(
in_channels_left=filters,
out_channels_left=filters // 2, # 1, 0.5
in_channels_right=2 * filters,
out_channels_right=filters
) # 2, 1
self.cell_1 = NormalCell(
in_channels_left=2 * filters,
out_channels_left=filters, # 2, 1
in_channels_right=6 * filters,
out_channels_right=filters
) # 6, 1
self.cell_2 = NormalCell(
in_channels_left=6 * filters,
out_channels_left=filters, # 6, 1
in_channels_right=6 * filters,
out_channels_right=filters
) # 6, 1
self.cell_3 = NormalCell(
in_channels_left=6 * filters,
out_channels_left=filters, # 6, 1
in_channels_right=6 * filters,
out_channels_right=filters
) # 6, 1
self.reduction_cell_0 = ReductionCell0(
in_channels_left=6 * filters,
out_channels_left=2 * filters, # 6, 2
in_channels_right=6 * filters,
out_channels_right=2 * filters
) # 6, 2
self.cell_6 = FirstCell(
in_channels_left=6 * filters,
out_channels_left=filters, # 6, 1
in_channels_right=8 * filters,
out_channels_right=2 * filters
) # 8, 2
self.cell_7 = NormalCell(
in_channels_left=8 * filters,
out_channels_left=2 * filters, # 8, 2
in_channels_right=12 * filters,
out_channels_right=2 * filters
) # 12, 2
self.cell_8 = NormalCell(
in_channels_left=12 * filters,
out_channels_left=2 * filters, # 12, 2
in_channels_right=12 * filters,
out_channels_right=2 * filters
) # 12, 2
self.cell_9 = NormalCell(
in_channels_left=12 * filters,
out_channels_left=2 * filters, # 12, 2
in_channels_right=12 * filters,
out_channels_right=2 * filters
) # 12, 2
self.reduction_cell_1 = ReductionCell1(
in_channels_left=12 * filters,
out_channels_left=4 * filters, # 12, 4
in_channels_right=12 * filters,
out_channels_right=4 * filters
) # 12, 4
self.cell_12 = FirstCell(
in_channels_left=12 * filters,
out_channels_left=2 * filters, # 12, 2
in_channels_right=16 * filters,
out_channels_right=4 * filters
) # 16, 4
self.cell_13 = NormalCell(
in_channels_left=16 * filters,
out_channels_left=4 * filters, # 16, 4
in_channels_right=24 * filters,
out_channels_right=4 * filters
) # 24, 4
self.cell_14 = NormalCell(
in_channels_left=24 * filters,
out_channels_left=4 * filters, # 24, 4
in_channels_right=24 * filters,
out_channels_right=4 * filters
) # 24, 4
self.cell_15 = NormalCell(
in_channels_left=24 * filters,
out_channels_left=4 * filters, # 24, 4
in_channels_right=24 * filters,
out_channels_right=4 * filters
) # 24, 4
self.relu = nn.ReLU()
self.dropout = nn.Dropout(keep_prob=0.5)
self.classifier = nn.Dense(in_channels=24 * filters, out_channels=num_classes)
self.pool = GlobalAvgPooling()
self._initialize_weights()
def _initialize_weights(self):
"""Initialize weights for cells."""
self.init_parameters_data()
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(math.sqrt(2. / n), 0),
cell.weight.shape, cell.weight.dtype))
if cell.bias is not None:
cell.bias.set_data(init.initializer(init.Zero(),
cell.bias.shape, cell.bias.dtype))
elif isinstance(cell, nn.BatchNorm2d):
cell.gamma.set_data(init.initializer(init.One(),
cell.gamma.shape, cell.gamma.dtype))
cell.beta.set_data(init.initializer(init.Zero(),
cell.beta.shape, cell.beta.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(init.Zero(),
cell.bias.shape, cell.bias.dtype))
[文档] def forward_features(self, x: Tensor) -> Tensor:
"""Network forward feature extraction."""
x_conv0 = self.conv0(x)
x_stem_0 = self.cell_stem_0(x_conv0)
x_stem_1 = self.cell_stem_1(x_conv0, x_stem_0)
x_cell_0 = self.cell_0(x_stem_1, x_stem_0)
x_cell_1 = self.cell_1(x_cell_0, x_stem_1)
x_cell_2 = self.cell_2(x_cell_1, x_cell_0)
x_cell_3 = self.cell_3(x_cell_2, x_cell_1)
x_reduction_cell_0 = self.reduction_cell_0(x_cell_3, x_cell_2)
x_cell_6 = self.cell_6(x_reduction_cell_0, x_cell_3)
x_cell_7 = self.cell_7(x_cell_6, x_reduction_cell_0)
x_cell_8 = self.cell_8(x_cell_7, x_cell_6)
x_cell_9 = self.cell_9(x_cell_8, x_cell_7)
x_reduction_cell_1 = self.reduction_cell_1(x_cell_9, x_cell_8)
x_cell_12 = self.cell_12(x_reduction_cell_1, x_cell_9)
x_cell_13 = self.cell_13(x_cell_12, x_reduction_cell_1)
x_cell_14 = self.cell_14(x_cell_13, x_cell_12)
x_cell_15 = self.cell_15(x_cell_14, x_cell_13)
x_cell_15 = self.relu(x_cell_15)
return x_cell_15
[文档] def forward_head(self, x: Tensor) -> Tensor:
x = self.pool(x) # global average pool
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 nasnet(pretrained: bool = False, num_classes: int = 1000, in_channels: int = 3, **kwargs) -> NASNetAMobile:
"""Get NasNet model.
Refer to the base class `models.NASNetAMobile` for more details."""
default_cfg = default_cfgs['nasnet']
model = NASNetAMobile(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