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pytorch-image-models/timm/models/nasnet.py

621 lines
29 KiB

import torch
import torch.nn as nn
import torch.nn.functional as F
from .registry import register_model
from .helpers import load_pretrained
from .layers import SelectAdaptivePool2d
__all__ = ['NASNetALarge']
default_cfgs = {
'nasnetalarge': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
'input_size': (3, 331, 331),
'pool_size': (11, 11),
'crop_pct': 0.875,
'interpolation': 'bicubic',
'mean': (0.5, 0.5, 0.5),
'std': (0.5, 0.5, 0.5),
'num_classes': 1001,
'first_conv': 'conv_0.conv',
'classifier': 'last_linear',
},
}
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:]
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:]
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, 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)
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 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_size, num_channels=42):
super(CellStem0, self).__init__()
self.num_channels = num_channels
self.stem_size = stem_size
self.conv_1x1 = nn.Sequential()
self.conv_1x1.add_module('relu', nn.ReLU())
self.conv_1x1.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels, 1, stride=1, bias=False))
self.conv_1x1.add_module('bn', nn.BatchNorm2d(self.num_channels, eps=0.001, momentum=0.1, affine=True))
self.comb_iter_0_left = BranchSeparables(self.num_channels, self.num_channels, 5, 2, 2)
self.comb_iter_0_right = BranchSeparablesStem(self.stem_size, self.num_channels, 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_size, self.num_channels, 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_size, self.num_channels, 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_channels, self.num_channels, 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_size, num_channels):
super(CellStem1, self).__init__()
self.num_channels = num_channels
self.stem_size = stem_size
self.conv_1x1 = nn.Sequential()
self.conv_1x1.add_module('relu', nn.ReLU())
self.conv_1x1.add_module('conv', nn.Conv2d(2*self.num_channels, self.num_channels, 1, stride=1, bias=False))
self.conv_1x1.add_module('bn', nn.BatchNorm2d(self.num_channels, 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_size, self.num_channels//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_size, self.num_channels//2, 1, stride=1, bias=False))
self.final_path_bn = nn.BatchNorm2d(self.num_channels, eps=0.001, momentum=0.1, affine=True)
self.comb_iter_0_left = BranchSeparables(self.num_channels, self.num_channels, 5, 2, 2, bias=False)
self.comb_iter_0_right = BranchSeparables(self.num_channels, self.num_channels, 7, 2, 3, bias=False)
self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
self.comb_iter_1_right = BranchSeparables(self.num_channels, self.num_channels, 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 = BranchSeparables(self.num_channels, self.num_channels, 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_channels, self.num_channels, 3, 1, 1, bias=False)
self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
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, 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, padding=1)
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, padding=1, count_include_pad=False)
self.comb_iter_2_right = BranchSeparables(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 = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)
self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
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 NASNetALarge(nn.Module):
"""NASNetALarge (6 @ 4032) """
def __init__(self, num_classes=1000, in_chans=1, stem_size=96, num_features=4032, channel_multiplier=2,
drop_rate=0., global_pool='avg'):
super(NASNetALarge, self).__init__()
self.num_classes = num_classes
self.stem_size = stem_size
self.num_features = num_features
self.channel_multiplier = channel_multiplier
self.drop_rate = drop_rate
channels = self.num_features // 24
# 24 is default value for the architecture
self.conv0 = nn.Sequential()
self.conv0.add_module('conv', nn.Conv2d(
in_channels=in_chans, out_channels=self.stem_size, kernel_size=3, padding=0, stride=2, bias=False))
self.conv0.add_module('bn', nn.BatchNorm2d(self.stem_size, eps=0.001, momentum=0.1, affine=True))
self.cell_stem_0 = CellStem0(self.stem_size, num_channels=channels // (channel_multiplier ** 2))
self.cell_stem_1 = CellStem1(self.stem_size, num_channels=channels // channel_multiplier)
self.cell_0 = FirstCell(in_channels_left=channels, out_channels_left=channels//2,
in_channels_right=2*channels, out_channels_right=channels)
self.cell_1 = NormalCell(in_channels_left=2*channels, out_channels_left=channels,
in_channels_right=6*channels, out_channels_right=channels)
self.cell_2 = NormalCell(in_channels_left=6*channels, out_channels_left=channels,
in_channels_right=6*channels, out_channels_right=channels)
self.cell_3 = NormalCell(in_channels_left=6*channels, out_channels_left=channels,
in_channels_right=6*channels, out_channels_right=channels)
self.cell_4 = NormalCell(in_channels_left=6*channels, out_channels_left=channels,
in_channels_right=6*channels, out_channels_right=channels)
self.cell_5 = NormalCell(in_channels_left=6*channels, out_channels_left=channels,
in_channels_right=6*channels, out_channels_right=channels)
self.reduction_cell_0 = ReductionCell0(in_channels_left=6*channels, out_channels_left=2*channels,
in_channels_right=6*channels, out_channels_right=2*channels)
self.cell_6 = FirstCell(in_channels_left=6*channels, out_channels_left=channels,
in_channels_right=8*channels, out_channels_right=2*channels)
self.cell_7 = NormalCell(in_channels_left=8*channels, out_channels_left=2*channels,
in_channels_right=12*channels, out_channels_right=2*channels)
self.cell_8 = NormalCell(in_channels_left=12*channels, out_channels_left=2*channels,
in_channels_right=12*channels, out_channels_right=2*channels)
self.cell_9 = NormalCell(in_channels_left=12*channels, out_channels_left=2*channels,
in_channels_right=12*channels, out_channels_right=2*channels)
self.cell_10 = NormalCell(in_channels_left=12*channels, out_channels_left=2*channels,
in_channels_right=12*channels, out_channels_right=2*channels)
self.cell_11 = NormalCell(in_channels_left=12*channels, out_channels_left=2*channels,
in_channels_right=12*channels, out_channels_right=2*channels)
self.reduction_cell_1 = ReductionCell1(in_channels_left=12*channels, out_channels_left=4*channels,
in_channels_right=12*channels, out_channels_right=4*channels)
self.cell_12 = FirstCell(in_channels_left=12*channels, out_channels_left=2*channels,
in_channels_right=16*channels, out_channels_right=4*channels)
self.cell_13 = NormalCell(in_channels_left=16*channels, out_channels_left=4*channels,
in_channels_right=24*channels, out_channels_right=4*channels)
self.cell_14 = NormalCell(in_channels_left=24*channels, out_channels_left=4*channels,
in_channels_right=24*channels, out_channels_right=4*channels)
self.cell_15 = NormalCell(in_channels_left=24*channels, out_channels_left=4*channels,
in_channels_right=24*channels, out_channels_right=4*channels)
self.cell_16 = NormalCell(in_channels_left=24*channels, out_channels_left=4*channels,
in_channels_right=24*channels, out_channels_right=4*channels)
self.cell_17 = NormalCell(in_channels_left=24*channels, out_channels_left=4*channels,
in_channels_right=24*channels, out_channels_right=4*channels)
self.relu = nn.ReLU()
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.last_linear = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
def get_classifier(self):
return self.last_linear
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
del self.last_linear
self.last_linear = nn.Linear(
self.num_features * self.global_pool.feat_mult(), num_classes) if num_classes else None
def forward_features(self, x):
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_cell_4 = self.cell_4(x_cell_3, x_cell_2)
x_cell_5 = self.cell_5(x_cell_4, x_cell_3)
x_reduction_cell_0 = self.reduction_cell_0(x_cell_5, x_cell_4)
x_cell_6 = self.cell_6(x_reduction_cell_0, x_cell_4)
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_cell_10 = self.cell_10(x_cell_9, x_cell_8)
x_cell_11 = self.cell_11(x_cell_10, x_cell_9)
x_reduction_cell_1 = self.reduction_cell_1(x_cell_11, x_cell_10)
x_cell_12 = self.cell_12(x_reduction_cell_1, x_cell_10)
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_16 = self.cell_16(x_cell_15, x_cell_14)
x_cell_17 = self.cell_17(x_cell_16, x_cell_15)
x = self.relu(x_cell_17)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.global_pool(x).flatten(1)
if self.drop_rate > 0:
x = F.dropout(x, self.drop_rate, training=self.training)
x = self.last_linear(x)
return x
@register_model
def nasnetalarge(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""NASNet-A large model architecture.
"""
default_cfg = default_cfgs['nasnetalarge']
model = NASNetALarge(num_classes=1000, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model