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238 lines
7.5 KiB
238 lines
7.5 KiB
"""
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Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
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@author: tstandley
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Adapted by cadene
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Creates an Xception Model as defined in:
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Francois Chollet
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Xception: Deep Learning with Depthwise Separable Convolutions
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https://arxiv.org/pdf/1610.02357.pdf
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This weights ported from the Keras implementation. Achieves the following performance on the validation set:
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Loss:0.9173 Prec@1:78.892 Prec@5:94.292
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REMEMBER to set your image size to 3x299x299 for both test and validation
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normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
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std=[0.5, 0.5, 0.5])
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The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
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"""
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from __future__ import print_function, division, absolute_import
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import math
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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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import torch.utils.model_zoo as model_zoo
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from torch.nn import init
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__all__ = ['xception']
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pretrained_config = {
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'xception': {
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'imagenet': {
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'url': 'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth',
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'input_space': 'RGB',
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'input_size': [3, 299, 299],
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'input_range': [0, 1],
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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': 1000,
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'scale': 0.8975
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# The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
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}
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}
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}
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class SeparableConv2d(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False):
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super(SeparableConv2d, self).__init__()
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self.conv1 = nn.Conv2d(
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in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=bias)
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self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias)
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def forward(self, x):
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x = self.conv1(x)
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x = self.pointwise(x)
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return x
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class Block(nn.Module):
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def __init__(self, in_filters, out_filters, reps, strides=1, start_with_relu=True, grow_first=True):
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super(Block, self).__init__()
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if out_filters != in_filters or strides != 1:
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self.skip = nn.Conv2d(in_filters, out_filters, 1, stride=strides, bias=False)
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self.skipbn = nn.BatchNorm2d(out_filters)
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else:
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self.skip = None
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self.relu = nn.ReLU(inplace=True)
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rep = []
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filters = in_filters
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if grow_first:
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rep.append(self.relu)
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rep.append(SeparableConv2d(in_filters, out_filters, 3, stride=1, padding=1, bias=False))
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rep.append(nn.BatchNorm2d(out_filters))
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filters = out_filters
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for i in range(reps - 1):
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rep.append(self.relu)
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rep.append(SeparableConv2d(filters, filters, 3, stride=1, padding=1, bias=False))
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rep.append(nn.BatchNorm2d(filters))
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if not grow_first:
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rep.append(self.relu)
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rep.append(SeparableConv2d(in_filters, out_filters, 3, stride=1, padding=1, bias=False))
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rep.append(nn.BatchNorm2d(out_filters))
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if not start_with_relu:
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rep = rep[1:]
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else:
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rep[0] = nn.ReLU(inplace=False)
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if strides != 1:
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rep.append(nn.MaxPool2d(3, strides, 1))
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self.rep = nn.Sequential(*rep)
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def forward(self, inp):
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x = self.rep(inp)
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if self.skip is not None:
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skip = self.skip(inp)
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skip = self.skipbn(skip)
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else:
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skip = inp
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x += skip
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return x
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class Xception(nn.Module):
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"""
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Xception optimized for the ImageNet dataset, as specified in
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https://arxiv.org/pdf/1610.02357.pdf
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"""
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def __init__(self, num_classes=1000):
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""" Constructor
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Args:
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num_classes: number of classes
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"""
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super(Xception, self).__init__()
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self.num_classes = num_classes
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self.conv1 = nn.Conv2d(3, 32, 3, 2, 0, bias=False)
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self.bn1 = nn.BatchNorm2d(32)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
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self.bn2 = nn.BatchNorm2d(64)
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# do relu here
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self.block1 = Block(64, 128, 2, 2, start_with_relu=False, grow_first=True)
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self.block2 = Block(128, 256, 2, 2, start_with_relu=True, grow_first=True)
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self.block3 = Block(256, 728, 2, 2, start_with_relu=True, grow_first=True)
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self.block4 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
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self.block5 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
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self.block6 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
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self.block7 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
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self.block8 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
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self.block9 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
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self.block10 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
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self.block11 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
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self.block12 = Block(728, 1024, 2, 2, start_with_relu=True, grow_first=False)
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self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
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self.bn3 = nn.BatchNorm2d(1536)
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# do relu here
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self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
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self.bn4 = nn.BatchNorm2d(2048)
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self.fc = nn.Linear(2048, num_classes)
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# #------- init weights --------
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# for m in self.modules():
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# if isinstance(m, nn.Conv2d):
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# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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# m.weight.data.normal_(0, math.sqrt(2. / n))
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# elif isinstance(m, nn.BatchNorm2d):
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# m.weight.data.fill_(1)
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# m.bias.data.zero_()
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# #-----------------------------
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def forward_features(self, input):
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x = self.conv1(input)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.conv2(x)
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x = self.bn2(x)
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x = self.relu(x)
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x = self.block1(x)
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x = self.block2(x)
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x = self.block3(x)
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x = self.block4(x)
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x = self.block5(x)
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x = self.block6(x)
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x = self.block7(x)
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x = self.block8(x)
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x = self.block9(x)
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x = self.block10(x)
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x = self.block11(x)
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x = self.block12(x)
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x = self.conv3(x)
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x = self.bn3(x)
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x = self.relu(x)
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x = self.conv4(x)
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x = self.bn4(x)
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return x
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def logits(self, features):
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x = self.relu(features)
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x = F.adaptive_avg_pool2d(x, (1, 1))
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x = x.view(x.size(0), -1)
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x = self.last_linear(x)
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return x
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def forward(self, input):
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x = self.forward_features(input)
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x = self.logits(x)
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return x
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def xception(num_classes=1000, pretrained='imagenet'):
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model = Xception(num_classes=num_classes)
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if pretrained:
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config = pretrained_config['xception'][pretrained]
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assert num_classes == config['num_classes'], \
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"num_classes should be {}, but is {}".format(config['num_classes'], num_classes)
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model = Xception(num_classes=num_classes)
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model.load_state_dict(model_zoo.load_url(config['url']))
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model.input_space = config['input_space']
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model.input_size = config['input_size']
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model.input_range = config['input_range']
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model.mean = config['mean']
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model.std = config['std']
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# TODO: ugly
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model.last_linear = model.fc
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del model.fc
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return model
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