import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.model_zoo as model_zoo from torch.autograd import Variable pretrained_settings = { 'nasnetalarge': { 'imagenet': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth', 'input_space': 'RGB', 'input_size': [3, 331, 331], # resize 354 '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, 331, 331], # resize 354 '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:] 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): super(CellStem0, self).__init__() self.conv_1x1 = nn.Sequential() self.conv_1x1.add_module('relu', nn.ReLU()) self.conv_1x1.add_module('conv', nn.Conv2d(96, 42, 1, stride=1, bias=False)) self.conv_1x1.add_module('bn', nn.BatchNorm2d(42, eps=0.001, momentum=0.1, affine=True)) self.comb_iter_0_left = BranchSeparables(42, 42, 5, 2, 2) self.comb_iter_0_right = BranchSeparablesStem(96, 42, 7, 2, 3, bias=False) self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1) self.comb_iter_1_right = BranchSeparablesStem(96, 42, 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(96, 42, 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(42, 42, 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): super(CellStem1, self).__init__() self.conv_1x1 = nn.Sequential() self.conv_1x1.add_module('relu', nn.ReLU()) self.conv_1x1.add_module('conv', nn.Conv2d(168, 84, 1, stride=1, bias=False)) self.conv_1x1.add_module('bn', nn.BatchNorm2d(84, 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(96, 42, 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(96, 42, 1, stride=1, bias=False)) self.final_path_bn = nn.BatchNorm2d(84, eps=0.001, momentum=0.1, affine=True) self.comb_iter_0_left = BranchSeparables(84, 84, 5, 2, 2, bias=False) self.comb_iter_0_right = BranchSeparables(84, 84, 7, 2, 3, bias=False) self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1) self.comb_iter_1_right = BranchSeparables(84, 84, 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(84, 84, 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(84, 84, 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): def __init__(self, use_classifier=False, num_classes=1001): super(NASNetALarge, self).__init__() self.use_classifier,self.num_classes = use_classifier,num_classes self.conv0 = nn.Sequential() self.conv0.add_module('conv', nn.Conv2d(in_channels=3, out_channels=96, kernel_size=3, padding=0, stride=2, bias=False)) self.conv0.add_module('bn', nn.BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True)) self.cell_stem_0 = CellStem0() self.cell_stem_1 = CellStem1() self.cell_0 = FirstCell(in_channels_left=168, out_channels_left=84, in_channels_right=336, out_channels_right=168) self.cell_1 = NormalCell(in_channels_left=336, out_channels_left=168, in_channels_right=1008, out_channels_right=168) self.cell_2 = NormalCell(in_channels_left=1008, out_channels_left=168, in_channels_right=1008, out_channels_right=168) self.cell_3 = NormalCell(in_channels_left=1008, out_channels_left=168, in_channels_right=1008, out_channels_right=168) self.cell_4 = NormalCell(in_channels_left=1008, out_channels_left=168, in_channels_right=1008, out_channels_right=168) self.cell_5 = NormalCell(in_channels_left=1008, out_channels_left=168, in_channels_right=1008, out_channels_right=168) self.reduction_cell_0 = ReductionCell0(in_channels_left=1008, out_channels_left=336, in_channels_right=1008, out_channels_right=336) self.cell_6 = FirstCell(in_channels_left=1008, out_channels_left=168, in_channels_right=1344, out_channels_right=336) self.cell_7 = NormalCell(in_channels_left=1344, out_channels_left=336, in_channels_right=2016, out_channels_right=336) self.cell_8 = NormalCell(in_channels_left=2016, out_channels_left=336, in_channels_right=2016, out_channels_right=336) self.cell_9 = NormalCell(in_channels_left=2016, out_channels_left=336, in_channels_right=2016, out_channels_right=336) self.cell_10 = NormalCell(in_channels_left=2016, out_channels_left=336, in_channels_right=2016, out_channels_right=336) self.cell_11 = NormalCell(in_channels_left=2016, out_channels_left=336, in_channels_right=2016, out_channels_right=336) self.reduction_cell_1 = ReductionCell1(in_channels_left=2016, out_channels_left=672, in_channels_right=2016, out_channels_right=672) self.cell_12 = FirstCell(in_channels_left=2016, out_channels_left=336, in_channels_right=2688, out_channels_right=672) self.cell_13 = NormalCell(in_channels_left=2688, out_channels_left=672, in_channels_right=4032, out_channels_right=672) self.cell_14 = NormalCell(in_channels_left=4032, out_channels_left=672, in_channels_right=4032, out_channels_right=672) self.cell_15 = NormalCell(in_channels_left=4032, out_channels_left=672, in_channels_right=4032, out_channels_right=672) self.cell_16 = NormalCell(in_channels_left=4032, out_channels_left=672, in_channels_right=4032, out_channels_right=672) self.cell_17 = NormalCell(in_channels_left=4032, out_channels_left=672, in_channels_right=4032, out_channels_right=672) self.relu = nn.ReLU() self.dropout = nn.Dropout() self.last_linear = nn.Linear(4032, self.num_classes) def 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) return self.relu(x_cell_17) def classifier(self, x): x = F.adaptive_max_pool2d(x, 1) x = x.view(x.size(0), -1) x = self.dropout(x) return F.log_softmax(self.linear(x)) def forward(self, x): x = self.features(x) if self.use_classifier: x = self.classifier(x) return x def nasnetalarge(num_classes=1000, pretrained='imagenet'): r"""NASNetALarge model architecture from the `"NASNet" `_ paper. """ if pretrained: settings = pretrained_settings['nasnetalarge'][pretrained] assert num_classes == settings['num_classes'], \ "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes) # both 'imagenet'&'imagenet+background' are loaded from same parameters model = NASNetALarge(num_classes=1001) model.load_state_dict(model_zoo.load_url(settings['url'])) if pretrained == 'imagenet': new_last_linear = nn.Linear(model.last_linear.in_features, 1000) new_last_linear.weight.data = model.last_linear.weight.data[1:] new_last_linear.bias.data = model.last_linear.bias.data[1:] model.last_linear = new_last_linear model.input_space = settings['input_space'] model.input_size = settings['input_size'] model.input_range = settings['input_range'] model.mean = settings['mean'] model.std = settings['std'] else: model = NASNetALarge(num_classes=num_classes) return model