Ross Wightman
5 years ago
parent
f632190fd9
commit
da52fcf78a
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .registry import register_model
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from .helpers import load_pretrained
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from .adaptive_avgmax_pool import SelectAdaptivePool2d
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__all__ = ['NASNetALarge']
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default_cfgs = {
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'nasnetalarge': {
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'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
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'input_size': (3, 331, 331),
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'pool_size': (11, 11),
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'crop_pct': 0.875,
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'interpolation': 'bicubic',
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'mean': (0.5, 0.5, 0.5),
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'std': (0.5, 0.5, 0.5),
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'num_classes': 1001,
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'first_conv': 'conv_0.conv',
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'classifier': 'last_linear',
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},
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}
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class MaxPoolPad(nn.Module):
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def __init__(self):
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super(MaxPoolPad, self).__init__()
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self.pad = nn.ZeroPad2d((1, 0, 1, 0))
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self.pool = nn.MaxPool2d(3, stride=2, padding=1)
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def forward(self, x):
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x = self.pad(x)
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x = self.pool(x)
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x = x[:, :, 1:, 1:]
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return x
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class AvgPoolPad(nn.Module):
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def __init__(self, stride=2, padding=1):
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super(AvgPoolPad, self).__init__()
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self.pad = nn.ZeroPad2d((1, 0, 1, 0))
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self.pool = nn.AvgPool2d(3, stride=stride, padding=padding, count_include_pad=False)
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def forward(self, x):
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x = self.pad(x)
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x = self.pool(x)
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x = x[:, :, 1:, 1:]
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return x
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class SeparableConv2d(nn.Module):
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def __init__(self, in_channels, out_channels, dw_kernel, dw_stride, dw_padding, bias=False):
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super(SeparableConv2d, self).__init__()
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self.depthwise_conv2d = nn.Conv2d(
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in_channels, in_channels, dw_kernel,
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stride=dw_stride, padding=dw_padding,
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bias=bias, groups=in_channels)
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self.pointwise_conv2d = nn.Conv2d(in_channels, out_channels, 1, stride=1, bias=bias)
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def forward(self, x):
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x = self.depthwise_conv2d(x)
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x = self.pointwise_conv2d(x)
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return x
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class BranchSeparables(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias=False):
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super(BranchSeparables, self).__init__()
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self.relu = nn.ReLU()
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self.separable_1 = SeparableConv2d(in_channels, in_channels, kernel_size, stride, padding, bias=bias)
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self.bn_sep_1 = nn.BatchNorm2d(in_channels, eps=0.001, momentum=0.1, affine=True)
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self.relu1 = nn.ReLU()
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self.separable_2 = SeparableConv2d(in_channels, out_channels, kernel_size, 1, padding, bias=bias)
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self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1, affine=True)
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def forward(self, x):
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x = self.relu(x)
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x = self.separable_1(x)
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x = self.bn_sep_1(x)
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x = self.relu1(x)
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x = self.separable_2(x)
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x = self.bn_sep_2(x)
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return x
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class BranchSeparablesStem(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias=False):
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super(BranchSeparablesStem, self).__init__()
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self.relu = nn.ReLU()
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self.separable_1 = SeparableConv2d(in_channels, out_channels, kernel_size, stride, padding, bias=bias)
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self.bn_sep_1 = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1, affine=True)
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self.relu1 = nn.ReLU()
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self.separable_2 = SeparableConv2d(out_channels, out_channels, kernel_size, 1, padding, bias=bias)
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self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1, affine=True)
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def forward(self, x):
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x = self.relu(x)
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x = self.separable_1(x)
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x = self.bn_sep_1(x)
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x = self.relu1(x)
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x = self.separable_2(x)
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x = self.bn_sep_2(x)
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return x
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class BranchSeparablesReduction(BranchSeparables):
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def __init__(self, in_channels, out_channels, kernel_size, stride, padding, z_padding=1, bias=False):
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BranchSeparables.__init__(self, in_channels, out_channels, kernel_size, stride, padding, bias)
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self.padding = nn.ZeroPad2d((z_padding, 0, z_padding, 0))
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def forward(self, x):
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x = self.relu(x)
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x = self.padding(x)
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x = self.separable_1(x)
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x = x[:, :, 1:, 1:].contiguous()
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x = self.bn_sep_1(x)
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x = self.relu1(x)
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x = self.separable_2(x)
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x = self.bn_sep_2(x)
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return x
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class CellStem0(nn.Module):
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def __init__(self, stem_size, num_channels=42):
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super(CellStem0, self).__init__()
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self.num_channels = num_channels
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self.stem_size = stem_size
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self.conv_1x1 = nn.Sequential()
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self.conv_1x1.add_module('relu', nn.ReLU())
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self.conv_1x1.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels, 1, stride=1, bias=False))
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self.conv_1x1.add_module('bn', nn.BatchNorm2d(self.num_channels, eps=0.001, momentum=0.1, affine=True))
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self.comb_iter_0_left = BranchSeparables(self.num_channels, self.num_channels, 5, 2, 2)
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self.comb_iter_0_right = BranchSeparablesStem(self.stem_size, self.num_channels, 7, 2, 3, bias=False)
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self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
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self.comb_iter_1_right = BranchSeparablesStem(self.stem_size, self.num_channels, 7, 2, 3, bias=False)
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self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
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self.comb_iter_2_right = BranchSeparablesStem(self.stem_size, self.num_channels, 5, 2, 2, bias=False)
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self.comb_iter_3_right = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)
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self.comb_iter_4_left = BranchSeparables(self.num_channels, self.num_channels, 3, 1, 1, bias=False)
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self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
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def forward(self, x):
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x1 = self.conv_1x1(x)
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x_comb_iter_0_left = self.comb_iter_0_left(x1)
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x_comb_iter_0_right = self.comb_iter_0_right(x)
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x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
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x_comb_iter_1_left = self.comb_iter_1_left(x1)
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x_comb_iter_1_right = self.comb_iter_1_right(x)
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x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
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x_comb_iter_2_left = self.comb_iter_2_left(x1)
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x_comb_iter_2_right = self.comb_iter_2_right(x)
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x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
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x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
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x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
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x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
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x_comb_iter_4_right = self.comb_iter_4_right(x1)
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x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
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x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
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return x_out
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class CellStem1(nn.Module):
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def __init__(self, stem_size, num_channels):
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super(CellStem1, self).__init__()
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self.num_channels = num_channels
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self.stem_size = stem_size
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self.conv_1x1 = nn.Sequential()
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self.conv_1x1.add_module('relu', nn.ReLU())
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self.conv_1x1.add_module('conv', nn.Conv2d(2*self.num_channels, self.num_channels, 1, stride=1, bias=False))
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self.conv_1x1.add_module('bn', nn.BatchNorm2d(self.num_channels, eps=0.001, momentum=0.1, affine=True))
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self.relu = nn.ReLU()
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self.path_1 = nn.Sequential()
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self.path_1.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
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self.path_1.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels//2, 1, stride=1, bias=False))
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self.path_2 = nn.ModuleList()
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self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
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self.path_2.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
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self.path_2.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels//2, 1, stride=1, bias=False))
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self.final_path_bn = nn.BatchNorm2d(self.num_channels, eps=0.001, momentum=0.1, affine=True)
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self.comb_iter_0_left = BranchSeparables(self.num_channels, self.num_channels, 5, 2, 2, bias=False)
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self.comb_iter_0_right = BranchSeparables(self.num_channels, self.num_channels, 7, 2, 3, bias=False)
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self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
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self.comb_iter_1_right = BranchSeparables(self.num_channels, self.num_channels, 7, 2, 3, bias=False)
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self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
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self.comb_iter_2_right = BranchSeparables(self.num_channels, self.num_channels, 5, 2, 2, bias=False)
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self.comb_iter_3_right = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)
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self.comb_iter_4_left = BranchSeparables(self.num_channels, self.num_channels, 3, 1, 1, bias=False)
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self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
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def forward(self, x_conv0, x_stem_0):
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x_left = self.conv_1x1(x_stem_0)
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x_relu = self.relu(x_conv0)
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# path 1
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x_path1 = self.path_1(x_relu)
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# path 2
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x_path2 = self.path_2.pad(x_relu)
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x_path2 = x_path2[:, :, 1:, 1:]
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x_path2 = self.path_2.avgpool(x_path2)
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x_path2 = self.path_2.conv(x_path2)
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# final path
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x_right = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
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x_comb_iter_0_left = self.comb_iter_0_left(x_left)
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x_comb_iter_0_right = self.comb_iter_0_right(x_right)
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x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
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x_comb_iter_1_left = self.comb_iter_1_left(x_left)
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x_comb_iter_1_right = self.comb_iter_1_right(x_right)
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x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
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x_comb_iter_2_left = self.comb_iter_2_left(x_left)
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x_comb_iter_2_right = self.comb_iter_2_right(x_right)
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x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
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x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
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x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
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x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
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x_comb_iter_4_right = self.comb_iter_4_right(x_left)
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x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
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x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
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return x_out
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class FirstCell(nn.Module):
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def __init__(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right):
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super(FirstCell, self).__init__()
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self.conv_1x1 = nn.Sequential()
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self.conv_1x1.add_module('relu', nn.ReLU())
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self.conv_1x1.add_module('conv', nn.Conv2d(in_channels_right, out_channels_right, 1, stride=1, bias=False))
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self.conv_1x1.add_module('bn', nn.BatchNorm2d(out_channels_right, eps=0.001, momentum=0.1, affine=True))
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self.relu = nn.ReLU()
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self.path_1 = nn.Sequential()
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self.path_1.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
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self.path_1.add_module('conv', nn.Conv2d(in_channels_left, out_channels_left, 1, stride=1, bias=False))
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self.path_2 = nn.ModuleList()
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self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
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self.path_2.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
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self.path_2.add_module('conv', nn.Conv2d(in_channels_left, out_channels_left, 1, stride=1, bias=False))
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self.final_path_bn = nn.BatchNorm2d(out_channels_left * 2, eps=0.001, momentum=0.1, affine=True)
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self.comb_iter_0_left = BranchSeparables(out_channels_right, out_channels_right, 5, 1, 2, bias=False)
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self.comb_iter_0_right = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)
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self.comb_iter_1_left = BranchSeparables(out_channels_right, out_channels_right, 5, 1, 2, bias=False)
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self.comb_iter_1_right = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)
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self.comb_iter_2_left = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)
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self.comb_iter_3_left = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)
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self.comb_iter_3_right = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)
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self.comb_iter_4_left = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)
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def forward(self, x, x_prev):
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x_relu = self.relu(x_prev)
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# path 1
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x_path1 = self.path_1(x_relu)
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# path 2
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x_path2 = self.path_2.pad(x_relu)
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x_path2 = x_path2[:, :, 1:, 1:]
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x_path2 = self.path_2.avgpool(x_path2)
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x_path2 = self.path_2.conv(x_path2)
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# final path
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x_left = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
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x_right = self.conv_1x1(x)
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x_comb_iter_0_left = self.comb_iter_0_left(x_right)
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x_comb_iter_0_right = self.comb_iter_0_right(x_left)
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x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
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x_comb_iter_1_left = self.comb_iter_1_left(x_left)
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x_comb_iter_1_right = self.comb_iter_1_right(x_left)
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x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
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x_comb_iter_2_left = self.comb_iter_2_left(x_right)
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x_comb_iter_2 = x_comb_iter_2_left + x_left
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x_comb_iter_3_left = self.comb_iter_3_left(x_left)
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x_comb_iter_3_right = self.comb_iter_3_right(x_left)
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x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
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x_comb_iter_4_left = self.comb_iter_4_left(x_right)
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x_comb_iter_4 = x_comb_iter_4_left + x_right
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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)
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return x_out
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class NormalCell(nn.Module):
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def __init__(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right):
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super(NormalCell, self).__init__()
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self.conv_prev_1x1 = nn.Sequential()
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self.conv_prev_1x1.add_module('relu', nn.ReLU())
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self.conv_prev_1x1.add_module('conv', nn.Conv2d(in_channels_left, out_channels_left, 1, stride=1, bias=False))
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self.conv_prev_1x1.add_module('bn', nn.BatchNorm2d(out_channels_left, eps=0.001, momentum=0.1, affine=True))
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self.conv_1x1 = nn.Sequential()
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self.conv_1x1.add_module('relu', nn.ReLU())
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self.conv_1x1.add_module('conv', nn.Conv2d(in_channels_right, out_channels_right, 1, stride=1, bias=False))
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self.conv_1x1.add_module('bn', nn.BatchNorm2d(out_channels_right, eps=0.001, momentum=0.1, affine=True))
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self.comb_iter_0_left = BranchSeparables(out_channels_right, out_channels_right, 5, 1, 2, bias=False)
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self.comb_iter_0_right = BranchSeparables(out_channels_left, out_channels_left, 3, 1, 1, bias=False)
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self.comb_iter_1_left = BranchSeparables(out_channels_left, out_channels_left, 5, 1, 2, bias=False)
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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 forward_features(self, input, pool=True):
|
||||
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_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)
|
||||
if pool:
|
||||
x = self.global_pool(x)
|
||||
x = x.view(x.size(0), -1)
|
||||
return x
|
||||
|
||||
def forward(self, input):
|
||||
x = self.forward_features(input)
|
||||
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
|
||||
Loading…
Reference in new issue