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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 | |
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 | |