from __future__ import division, absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
__all__ = ['nasnetamobile']
"""
NASNet Mobile
Thanks to Anastasiia (https://github.com/DagnyT) for the great help, support and motivation!
------------------------------------------------------------------------------------
Architecture | Top-1 Acc | Top-5 Acc | Multiply-Adds | Params (M)
------------------------------------------------------------------------------------
| NASNet-A (4 @ 1056) | 74.08% | 91.74% | 564 M | 5.3 |
------------------------------------------------------------------------------------
# References:
- [Learning Transferable Architectures for Scalable Image Recognition]
(https://arxiv.org/abs/1707.07012)
"""
"""
Code imported from https://github.com/Cadene/pretrained-models.pytorch
"""
pretrained_settings = {
'nasnetamobile': {
'imagenet': {
# 'url': 'https://github.com/veronikayurchuk/pretrained-models.pytorch/releases/download/v1.0/nasnetmobile-7e03cead.pth.tar',
'url':
'http://data.lip6.fr/cadene/pretrainedmodels/nasnetamobile-7e03cead.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224], # resize 256
'input_range': [0, 1],
'mean': [0.5, 0.5, 0.5],
'std': [0.5, 0.5, 0.5],
'num_classes': 1000
},
# 'imagenet+background': {
# # 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
# 'input_space': 'RGB',
# 'input_size': [3, 224, 224], # resize 256
# 'input_range': [0, 1],
# 'mean': [0.5, 0.5, 0.5],
# 'std': [0.5, 0.5, 0.5],
# 'num_classes': 1001
# }
}
}
class MaxPoolPad(nn.Module):
def __init__(self):
super(MaxPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(3, stride=2, padding=1)
def forward(self, x):
x = self.pad(x)
x = self.pool(x)
x = x[:, :, 1:, 1:].contiguous()
return x
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(
3, stride=stride, padding=padding, count_include_pad=False
)
def forward(self, x):
x = self.pad(x)
x = self.pool(x)
x = x[:, :, 1:, 1:].contiguous()
return x
class SeparableConv2d(nn.Module):
def __init__(
self,
in_channels,
out_channels,
dw_kernel,
dw_stride,
dw_padding,
bias=False
):
super(SeparableConv2d, self).__init__()
self.depthwise_conv2d = nn.Conv2d(
in_channels,
in_channels,
dw_kernel,
stride=dw_stride,
padding=dw_padding,
bias=bias,
groups=in_channels
)
self.pointwise_conv2d = nn.Conv2d(
in_channels, out_channels, 1, stride=1, bias=bias
)
def forward(self, x):
x = self.depthwise_conv2d(x)
x = self.pointwise_conv2d(x)
return x
class BranchSeparables(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
name=None,
bias=False
):
super(BranchSeparables, self).__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(
in_channels, eps=0.001, momentum=0.1, 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(
out_channels, eps=0.001, momentum=0.1, affine=True
)
self.name = name
def forward(self, x):
x = self.relu(x)
if self.name == 'specific':
x = nn.ZeroPad2d((1, 0, 1, 0))(x)
x = self.separable_1(x)
if self.name == 'specific':
x = x[:, :, 1:, 1:].contiguous()
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.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
bias=False
):
super(BranchSeparablesStem, self).__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(
out_channels, eps=0.001, momentum=0.1, 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(
out_channels, eps=0.001, momentum=0.1, affine=True
)
def forward(self, x):
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):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
z_padding=1,
bias=False
):
BranchSeparables.__init__(
self, in_channels, out_channels, kernel_size, stride, padding, bias
)
self.padding = nn.ZeroPad2d((z_padding, 0, z_padding, 0))
def forward(self, x):
x = self.relu(x)
x = self.padding(x)
x = self.separable_1(x)
x = x[:, :, 1:, 1:].contiguous()
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.Module):
def __init__(self, stem_filters, num_filters=42):
super(CellStem0, self).__init__()
self.num_filters = num_filters
self.stem_filters = stem_filters
self.conv_1x1 = nn.Sequential()
self.conv_1x1.add_module('relu', nn.ReLU())
self.conv_1x1.add_module(
'conv',
nn.Conv2d(
self.stem_filters, self.num_filters, 1, stride=1, bias=False
)
)
self.conv_1x1.add_module(
'bn',
nn.BatchNorm2d(
self.num_filters, eps=0.001, momentum=0.1, 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(3, stride=2, padding=1)
self.comb_iter_1_right = BranchSeparablesStem(
self.stem_filters, self.num_filters, 7, 2, 3, bias=False
)
self.comb_iter_2_left = nn.AvgPool2d(
3, stride=2, padding=1, count_include_pad=False
)
self.comb_iter_2_right = BranchSeparablesStem(
self.stem_filters, self.num_filters, 5, 2, 2, bias=False
)
self.comb_iter_3_right = nn.AvgPool2d(
3, stride=1, padding=1, count_include_pad=False
)
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, padding=1)
def forward(self, x):
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 = torch.cat(
[x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
)
return x_out
class CellStem1(nn.Module):
def __init__(self, stem_filters, num_filters):
super(CellStem1, self).__init__()
self.num_filters = num_filters
self.stem_filters = stem_filters
self.conv_1x1 = nn.Sequential()
self.conv_1x1.add_module('relu', nn.ReLU())
self.conv_1x1.add_module(
'conv',
nn.Conv2d(
2 * self.num_filters,
self.num_filters,
1,
stride=1,
bias=False
)
)
self.conv_1x1.add_module(
'bn',
nn.BatchNorm2d(
self.num_filters, eps=0.001, momentum=0.1, affine=True
)
)
self.relu = nn.ReLU()
self.path_1 = nn.Sequential()
self.path_1.add_module(
'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
)
self.path_1.add_module(
'conv',
nn.Conv2d(
self.stem_filters,
self.num_filters // 2,
1,
stride=1,
bias=False
)
)
self.path_2 = nn.ModuleList()
self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
self.path_2.add_module(
'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
)
self.path_2.add_module(
'conv',
nn.Conv2d(
self.stem_filters,
self.num_filters // 2,
1,
stride=1,
bias=False
)
)
self.final_path_bn = nn.BatchNorm2d(
self.num_filters, eps=0.001, momentum=0.1, affine=True
)
self.comb_iter_0_left = BranchSeparables(
self.num_filters,
self.num_filters,
5,
2,
2,
name='specific',
bias=False
)
self.comb_iter_0_right = BranchSeparables(
self.num_filters,
self.num_filters,
7,
2,
3,
name='specific',
bias=False
)
# self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
self.comb_iter_1_left = MaxPoolPad()
self.comb_iter_1_right = BranchSeparables(
self.num_filters,
self.num_filters,
7,
2,
3,
name='specific',
bias=False
)
# self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
self.comb_iter_2_left = AvgPoolPad()
self.comb_iter_2_right = BranchSeparables(
self.num_filters,
self.num_filters,
5,
2,
2,
name='specific',
bias=False
)
self.comb_iter_3_right = nn.AvgPool2d(
3, stride=1, padding=1, count_include_pad=False
)
self.comb_iter_4_left = BranchSeparables(
self.num_filters,
self.num_filters,
3,
1,
1,
name='specific',
bias=False
)
# self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
self.comb_iter_4_right = MaxPoolPad()
def forward(self, x_conv0, x_stem_0):
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.pad(x_relu)
x_path2 = x_path2[:, :, 1:, 1:]
x_path2 = self.path_2.avgpool(x_path2)
x_path2 = self.path_2.conv(x_path2)
# final path
x_right = self.final_path_bn(torch.cat([x_path1, x_path2], 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 = torch.cat(
[x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
)
return x_out
class FirstCell(nn.Module):
def __init__(
self, in_channels_left, out_channels_left, in_channels_right,
out_channels_right
):
super(FirstCell, self).__init__()
self.conv_1x1 = nn.Sequential()
self.conv_1x1.add_module('relu', nn.ReLU())
self.conv_1x1.add_module(
'conv',
nn.Conv2d(
in_channels_right, out_channels_right, 1, stride=1, bias=False
)
)
self.conv_1x1.add_module(
'bn',
nn.BatchNorm2d(
out_channels_right, eps=0.001, momentum=0.1, affine=True
)
)
self.relu = nn.ReLU()
self.path_1 = nn.Sequential()
self.path_1.add_module(
'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
)
self.path_1.add_module(
'conv',
nn.Conv2d(
in_channels_left, out_channels_left, 1, stride=1, bias=False
)
)
self.path_2 = nn.ModuleList()
self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
self.path_2.add_module(
'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
)
self.path_2.add_module(
'conv',
nn.Conv2d(
in_channels_left, out_channels_left, 1, stride=1, bias=False
)
)
self.final_path_bn = nn.BatchNorm2d(
out_channels_left * 2, eps=0.001, momentum=0.1, 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(
3, stride=1, padding=1, count_include_pad=False
)
self.comb_iter_3_left = nn.AvgPool2d(
3, stride=1, padding=1, count_include_pad=False
)
self.comb_iter_3_right = nn.AvgPool2d(
3, stride=1, padding=1, count_include_pad=False
)
self.comb_iter_4_left = BranchSeparables(
out_channels_right, out_channels_right, 3, 1, 1, bias=False
)
def forward(self, x, x_prev):
x_relu = self.relu(x_prev)
# path 1
x_path1 = self.path_1(x_relu)
# path 2
x_path2 = self.path_2.pad(x_relu)
x_path2 = x_path2[:, :, 1:, 1:]
x_path2 = self.path_2.avgpool(x_path2)
x_path2 = self.path_2.conv(x_path2)
# final path
x_left = self.final_path_bn(torch.cat([x_path1, x_path2], 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 = torch.cat(
[
x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2,
x_comb_iter_3, x_comb_iter_4
], 1
)
return x_out
class NormalCell(nn.Module):
def __init__(
self, in_channels_left, out_channels_left, in_channels_right,
out_channels_right
):
super(NormalCell, self).__init__()
self.conv_prev_1x1 = nn.Sequential()
self.conv_prev_1x1.add_module('relu', nn.ReLU())
self.conv_prev_1x1.add_module(
'conv',
nn.Conv2d(
in_channels_left, out_channels_left, 1, stride=1, bias=False
)
)
self.conv_prev_1x1.add_module(
'bn',
nn.BatchNorm2d(
out_channels_left, eps=0.001, momentum=0.1, affine=True
)
)
self.conv_1x1 = nn.Sequential()
self.conv_1x1.add_module('relu', nn.ReLU())
self.conv_1x1.add_module(
'conv',
nn.Conv2d(
in_channels_right, out_channels_right, 1, stride=1, bias=False
)
)
self.conv_1x1.add_module(
'bn',
nn.BatchNorm2d(
out_channels_right, eps=0.001, momentum=0.1, 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(
3, stride=1, padding=1, count_include_pad=False
)
self.comb_iter_3_left = nn.AvgPool2d(
3, stride=1, padding=1, count_include_pad=False
)
self.comb_iter_3_right = nn.AvgPool2d(
3, stride=1, padding=1, count_include_pad=False
)
self.comb_iter_4_left = BranchSeparables(
out_channels_right, out_channels_right, 3, 1, 1, bias=False
)
def forward(self, x, x_prev):
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 = torch.cat(
[
x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2,
x_comb_iter_3, x_comb_iter_4
], 1
)
return x_out
class ReductionCell0(nn.Module):
def __init__(
self, in_channels_left, out_channels_left, in_channels_right,
out_channels_right
):
super(ReductionCell0, self).__init__()
self.conv_prev_1x1 = nn.Sequential()
self.conv_prev_1x1.add_module('relu', nn.ReLU())
self.conv_prev_1x1.add_module(
'conv',
nn.Conv2d(
in_channels_left, out_channels_left, 1, stride=1, bias=False
)
)
self.conv_prev_1x1.add_module(
'bn',
nn.BatchNorm2d(
out_channels_left, eps=0.001, momentum=0.1, affine=True
)
)
self.conv_1x1 = nn.Sequential()
self.conv_1x1.add_module('relu', nn.ReLU())
self.conv_1x1.add_module(
'conv',
nn.Conv2d(
in_channels_right, out_channels_right, 1, stride=1, bias=False
)
)
self.conv_1x1.add_module(
'bn',
nn.BatchNorm2d(
out_channels_right, eps=0.001, momentum=0.1, 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 = MaxPoolPad()
self.comb_iter_1_right = BranchSeparablesReduction(
out_channels_right, out_channels_right, 7, 2, 3, bias=False
)
self.comb_iter_2_left = AvgPoolPad()
self.comb_iter_2_right = BranchSeparablesReduction(
out_channels_right, out_channels_right, 5, 2, 2, bias=False
)
self.comb_iter_3_right = nn.AvgPool2d(
3, stride=1, padding=1, count_include_pad=False
)
self.comb_iter_4_left = BranchSeparablesReduction(
out_channels_right, out_channels_right, 3, 1, 1, bias=False
)
self.comb_iter_4_right = MaxPoolPad()
def forward(self, x, x_prev):
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 = torch.cat(
[x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
)
return x_out
class ReductionCell1(nn.Module):
def __init__(
self, in_channels_left, out_channels_left, in_channels_right,
out_channels_right
):
super(ReductionCell1, self).__init__()
self.conv_prev_1x1 = nn.Sequential()
self.conv_prev_1x1.add_module('relu', nn.ReLU())
self.conv_prev_1x1.add_module(
'conv',
nn.Conv2d(
in_channels_left, out_channels_left, 1, stride=1, bias=False
)
)
self.conv_prev_1x1.add_module(
'bn',
nn.BatchNorm2d(
out_channels_left, eps=0.001, momentum=0.1, affine=True
)
)
self.conv_1x1 = nn.Sequential()
self.conv_1x1.add_module('relu', nn.ReLU())
self.conv_1x1.add_module(
'conv',
nn.Conv2d(
in_channels_right, out_channels_right, 1, stride=1, bias=False
)
)
self.conv_1x1.add_module(
'bn',
nn.BatchNorm2d(
out_channels_right, eps=0.001, momentum=0.1, affine=True
)
)
self.comb_iter_0_left = BranchSeparables(
out_channels_right,
out_channels_right,
5,
2,
2,
name='specific',
bias=False
)
self.comb_iter_0_right = BranchSeparables(
out_channels_right,
out_channels_right,
7,
2,
3,
name='specific',
bias=False
)
# self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
self.comb_iter_1_left = MaxPoolPad()
self.comb_iter_1_right = BranchSeparables(
out_channels_right,
out_channels_right,
7,
2,
3,
name='specific',
bias=False
)
# self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
self.comb_iter_2_left = AvgPoolPad()
self.comb_iter_2_right = BranchSeparables(
out_channels_right,
out_channels_right,
5,
2,
2,
name='specific',
bias=False
)
self.comb_iter_3_right = nn.AvgPool2d(
3, stride=1, padding=1, count_include_pad=False
)
self.comb_iter_4_left = BranchSeparables(
out_channels_right,
out_channels_right,
3,
1,
1,
name='specific',
bias=False
)
# self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
self.comb_iter_4_right = MaxPoolPad()
def forward(self, x, x_prev):
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 = torch.cat(
[x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
)
return x_out
[docs]class NASNetAMobile(nn.Module):
"""Neural Architecture Search (NAS).
Reference:
Zoph et al. Learning Transferable Architectures
for Scalable Image Recognition. CVPR 2018.
Public keys:
- ``nasnetamobile``: NASNet-A Mobile.
"""
def __init__(
self,
num_classes,
loss,
stem_filters=32,
penultimate_filters=1056,
filters_multiplier=2,
**kwargs
):
super(NASNetAMobile, self).__init__()
self.stem_filters = stem_filters
self.penultimate_filters = penultimate_filters
self.filters_multiplier = filters_multiplier
self.loss = loss
filters = self.penultimate_filters // 24
# 24 is default value for the architecture
self.conv0 = nn.Sequential()
self.conv0.add_module(
'conv',
nn.Conv2d(
in_channels=3,
out_channels=self.stem_filters,
kernel_size=3,
padding=0,
stride=2,
bias=False
)
)
self.conv0.add_module(
'bn',
nn.BatchNorm2d(
self.stem_filters, eps=0.001, momentum=0.1, 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()
self.classifier = nn.Linear(24 * filters, num_classes)
self._init_params()
def _init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu'
)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def features(self, input):
x_conv0 = self.conv0(input)
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)
x_cell_15 = F.avg_pool2d(
x_cell_15,
x_cell_15.size()[2:]
) # global average pool
x_cell_15 = x_cell_15.view(x_cell_15.size(0), -1)
x_cell_15 = self.dropout(x_cell_15)
return x_cell_15
def forward(self, input):
v = self.features(input)
if not self.training:
return v
y = self.classifier(v)
if self.loss == 'softmax':
return y
elif self.loss == 'triplet':
return y, v
else:
raise KeyError('Unsupported loss: {}'.format(self.loss))
def init_pretrained_weights(model, model_url):
"""Initializes model with pretrained weights.
Layers that don't match with pretrained layers in name or size are kept unchanged.
"""
pretrain_dict = model_zoo.load_url(model_url)
model_dict = model.state_dict()
pretrain_dict = {
k: v
for k, v in pretrain_dict.items()
if k in model_dict and model_dict[k].size() == v.size()
}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
def nasnetamobile(num_classes, loss='softmax', pretrained=True, **kwargs):
model = NASNetAMobile(num_classes, loss, **kwargs)
if pretrained:
model_url = pretrained_settings['nasnetamobile']['imagenet']['url']
init_pretrained_weights(model, model_url)
return model