405 lines
12 KiB
Python
405 lines
12 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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__all__ = ['inceptionresnetv2']
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import torch
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import torch.nn as nn
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from torch.nn import functional as F
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import torch.utils.model_zoo as model_zoo
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import os
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import sys
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"""
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Code imported from https://github.com/Cadene/pretrained-models.pytorch
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"""
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pretrained_settings = {
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'inceptionresnetv2': {
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'imagenet': {
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'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.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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},
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'imagenet+background': {
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'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.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': 1001
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}
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}
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}
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class BasicConv2d(nn.Module):
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def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
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super(BasicConv2d, self).__init__()
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self.conv = nn.Conv2d(in_planes, out_planes,
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kernel_size=kernel_size, stride=stride,
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padding=padding, bias=False) # verify bias false
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self.bn = nn.BatchNorm2d(out_planes,
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eps=0.001, # value found in tensorflow
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momentum=0.1, # default pytorch value
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affine=True)
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self.relu = nn.ReLU(inplace=False)
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.relu(x)
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return x
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class Mixed_5b(nn.Module):
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def __init__(self):
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super(Mixed_5b, self).__init__()
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self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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BasicConv2d(192, 48, kernel_size=1, stride=1),
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BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
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)
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self.branch2 = nn.Sequential(
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BasicConv2d(192, 64, kernel_size=1, stride=1),
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BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
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BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
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)
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self.branch3 = nn.Sequential(
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nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
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BasicConv2d(192, 64, kernel_size=1, stride=1)
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)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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x2 = self.branch2(x)
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x3 = self.branch3(x)
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out = torch.cat((x0, x1, x2, x3), 1)
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return out
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class Block35(nn.Module):
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def __init__(self, scale=1.0):
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super(Block35, self).__init__()
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self.scale = scale
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self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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BasicConv2d(320, 32, kernel_size=1, stride=1),
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BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
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)
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self.branch2 = nn.Sequential(
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BasicConv2d(320, 32, kernel_size=1, stride=1),
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BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
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BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
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)
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self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
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self.relu = nn.ReLU(inplace=False)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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x2 = self.branch2(x)
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out = torch.cat((x0, x1, x2), 1)
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out = self.conv2d(out)
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out = out * self.scale + x
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out = self.relu(out)
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return out
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class Mixed_6a(nn.Module):
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def __init__(self):
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super(Mixed_6a, self).__init__()
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self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)
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self.branch1 = nn.Sequential(
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BasicConv2d(320, 256, kernel_size=1, stride=1),
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BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
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BasicConv2d(256, 384, kernel_size=3, stride=2)
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)
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self.branch2 = nn.MaxPool2d(3, stride=2)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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x2 = self.branch2(x)
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out = torch.cat((x0, x1, x2), 1)
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return out
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class Block17(nn.Module):
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def __init__(self, scale=1.0):
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super(Block17, self).__init__()
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self.scale = scale
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self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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BasicConv2d(1088, 128, kernel_size=1, stride=1),
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BasicConv2d(128, 160, kernel_size=(1,7), stride=1, padding=(0,3)),
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BasicConv2d(160, 192, kernel_size=(7,1), stride=1, padding=(3,0))
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)
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self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
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self.relu = nn.ReLU(inplace=False)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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out = torch.cat((x0, x1), 1)
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out = self.conv2d(out)
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out = out * self.scale + x
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out = self.relu(out)
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return out
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class Mixed_7a(nn.Module):
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def __init__(self):
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super(Mixed_7a, self).__init__()
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self.branch0 = nn.Sequential(
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BasicConv2d(1088, 256, kernel_size=1, stride=1),
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BasicConv2d(256, 384, kernel_size=3, stride=2)
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)
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self.branch1 = nn.Sequential(
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BasicConv2d(1088, 256, kernel_size=1, stride=1),
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BasicConv2d(256, 288, kernel_size=3, stride=2)
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)
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self.branch2 = nn.Sequential(
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BasicConv2d(1088, 256, kernel_size=1, stride=1),
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BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
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BasicConv2d(288, 320, kernel_size=3, stride=2)
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)
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self.branch3 = nn.MaxPool2d(3, stride=2)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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x2 = self.branch2(x)
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x3 = self.branch3(x)
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out = torch.cat((x0, x1, x2, x3), 1)
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return out
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class Block8(nn.Module):
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def __init__(self, scale=1.0, noReLU=False):
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super(Block8, self).__init__()
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self.scale = scale
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self.noReLU = noReLU
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self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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BasicConv2d(2080, 192, kernel_size=1, stride=1),
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BasicConv2d(192, 224, kernel_size=(1,3), stride=1, padding=(0,1)),
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BasicConv2d(224, 256, kernel_size=(3,1), stride=1, padding=(1,0))
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)
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self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
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if not self.noReLU:
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self.relu = nn.ReLU(inplace=False)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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out = torch.cat((x0, x1), 1)
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out = self.conv2d(out)
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out = out * self.scale + x
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if not self.noReLU:
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out = self.relu(out)
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return out
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def inceptionresnetv2(num_classes=1000, pretrained='imagenet'):
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r"""InceptionResNetV2 model architecture from the
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`"InceptionV4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
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"""
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if pretrained:
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settings = pretrained_settings['inceptionresnetv2'][pretrained]
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assert num_classes == settings['num_classes'], \
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'num_classes should be {}, but is {}'.format(settings['num_classes'], num_classes)
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# both 'imagenet'&'imagenet+background' are loaded from same parameters
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model = InceptionResNetV2(num_classes=1001)
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model.load_state_dict(model_zoo.load_url(settings['url']))
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if pretrained == 'imagenet':
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new_last_linear = nn.Linear(1536, 1000)
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new_last_linear.weight.data = model.last_linear.weight.data[1:]
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new_last_linear.bias.data = model.last_linear.bias.data[1:]
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model.last_linear = new_last_linear
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model.input_space = settings['input_space']
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model.input_size = settings['input_size']
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model.input_range = settings['input_range']
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model.mean = settings['mean']
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model.std = settings['std']
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else:
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model = InceptionResNetV2(num_classes=num_classes)
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return model
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##################### Model Definition #########################
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class InceptionResNetV2(nn.Module):
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"""Inception-ResNet-V2.
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Reference:
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Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
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Connections on Learning. AAAI 2017.
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Public keys:
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- ``inceptionresnetv2``: Inception-ResNet-V2.
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"""
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def __init__(self, num_classes, loss='softmax', **kwargs):
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super(InceptionResNetV2, self).__init__()
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self.loss = loss
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# Modules
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self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
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self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
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self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
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self.maxpool_3a = nn.MaxPool2d(3, stride=2)
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self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
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self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
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self.maxpool_5a = nn.MaxPool2d(3, stride=2)
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self.mixed_5b = Mixed_5b()
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self.repeat = nn.Sequential(
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Block35(scale=0.17),
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Block35(scale=0.17),
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Block35(scale=0.17),
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Block35(scale=0.17),
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Block35(scale=0.17),
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Block35(scale=0.17),
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Block35(scale=0.17),
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Block35(scale=0.17),
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Block35(scale=0.17),
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Block35(scale=0.17)
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)
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self.mixed_6a = Mixed_6a()
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self.repeat_1 = nn.Sequential(
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10),
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Block17(scale=0.10)
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)
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self.mixed_7a = Mixed_7a()
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self.repeat_2 = nn.Sequential(
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Block8(scale=0.20),
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Block8(scale=0.20),
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Block8(scale=0.20),
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Block8(scale=0.20),
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Block8(scale=0.20),
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Block8(scale=0.20),
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Block8(scale=0.20),
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Block8(scale=0.20),
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Block8(scale=0.20)
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)
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self.block8 = Block8(noReLU=True)
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self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
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self.global_avgpool = nn.AdaptiveAvgPool2d(1)
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self.classifier = nn.Linear(1536, num_classes)
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def load_imagenet_weights(self):
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settings = pretrained_settings['inceptionresnetv2']['imagenet']
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pretrain_dict = model_zoo.load_url(settings['url'])
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model_dict = self.state_dict()
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pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
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model_dict.update(pretrain_dict)
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self.load_state_dict(model_dict)
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def featuremaps(self, x):
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x = self.conv2d_1a(x)
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x = self.conv2d_2a(x)
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x = self.conv2d_2b(x)
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x = self.maxpool_3a(x)
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x = self.conv2d_3b(x)
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x = self.conv2d_4a(x)
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x = self.maxpool_5a(x)
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x = self.mixed_5b(x)
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x = self.repeat(x)
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x = self.mixed_6a(x)
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x = self.repeat_1(x)
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x = self.mixed_7a(x)
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x = self.repeat_2(x)
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x = self.block8(x)
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x = self.conv2d_7b(x)
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return x
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def forward(self, x):
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f = self.featuremaps(x)
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v = self.global_avgpool(f)
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v = v.view(v.size(0), -1)
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if not self.training:
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return v
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y = self.classifier(v)
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if self.loss == 'softmax':
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return y
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elif self.loss == 'triplet':
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return y, v
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else:
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raise KeyError('Unsupported loss: {}'.format(self.loss))
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def inceptionresnetv2(num_classes, loss='softmax', pretrained=True, **kwargs):
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model = InceptionResNetV2(
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num_classes=num_classes,
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loss=loss,
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**kwargs
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)
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if pretrained:
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model.load_imagenet_weights()
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return model |