349 lines
11 KiB
Python
349 lines
11 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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__all__ = ['inceptionv4']
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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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'inceptionv4': {
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'imagenet': {
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'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.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/inceptionv4-8e4777a0.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=True)
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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_3a(nn.Module):
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def __init__(self):
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super(Mixed_3a, self).__init__()
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self.maxpool = nn.MaxPool2d(3, stride=2)
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self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)
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def forward(self, x):
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x0 = self.maxpool(x)
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x1 = self.conv(x)
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out = torch.cat((x0, x1), 1)
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return out
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class Mixed_4a(nn.Module):
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def __init__(self):
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super(Mixed_4a, self).__init__()
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self.branch0 = nn.Sequential(
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BasicConv2d(160, 64, kernel_size=1, stride=1),
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BasicConv2d(64, 96, kernel_size=3, stride=1)
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)
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self.branch1 = nn.Sequential(
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BasicConv2d(160, 64, kernel_size=1, stride=1),
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BasicConv2d(64, 64, kernel_size=(1,7), stride=1, padding=(0,3)),
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BasicConv2d(64, 64, kernel_size=(7,1), stride=1, padding=(3,0)),
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BasicConv2d(64, 96, kernel_size=(3,3), 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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out = torch.cat((x0, x1), 1)
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return out
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class Mixed_5a(nn.Module):
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def __init__(self):
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super(Mixed_5a, self).__init__()
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self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
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self.maxpool = nn.MaxPool2d(3, stride=2)
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def forward(self, x):
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x0 = self.conv(x)
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x1 = self.maxpool(x)
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out = torch.cat((x0, x1), 1)
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return out
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class Inception_A(nn.Module):
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def __init__(self):
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super(Inception_A, self).__init__()
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self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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BasicConv2d(384, 64, kernel_size=1, stride=1),
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BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
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)
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self.branch2 = nn.Sequential(
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BasicConv2d(384, 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(384, 96, 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 Reduction_A(nn.Module):
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def __init__(self):
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super(Reduction_A, self).__init__()
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self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)
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self.branch1 = nn.Sequential(
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BasicConv2d(384, 192, kernel_size=1, stride=1),
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BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
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BasicConv2d(224, 256, 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 Inception_B(nn.Module):
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def __init__(self):
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super(Inception_B, self).__init__()
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self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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BasicConv2d(1024, 192, kernel_size=1, stride=1),
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BasicConv2d(192, 224, kernel_size=(1,7), stride=1, padding=(0,3)),
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BasicConv2d(224, 256, kernel_size=(7,1), stride=1, padding=(3,0))
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)
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self.branch2 = nn.Sequential(
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BasicConv2d(1024, 192, kernel_size=1, stride=1),
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BasicConv2d(192, 192, kernel_size=(7,1), stride=1, padding=(3,0)),
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BasicConv2d(192, 224, kernel_size=(1,7), stride=1, padding=(0,3)),
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BasicConv2d(224, 224, kernel_size=(7,1), stride=1, padding=(3,0)),
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BasicConv2d(224, 256, kernel_size=(1,7), stride=1, padding=(0,3))
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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(1024, 128, 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 Reduction_B(nn.Module):
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def __init__(self):
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super(Reduction_B, self).__init__()
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self.branch0 = nn.Sequential(
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BasicConv2d(1024, 192, kernel_size=1, stride=1),
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BasicConv2d(192, 192, kernel_size=3, stride=2)
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)
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self.branch1 = nn.Sequential(
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BasicConv2d(1024, 256, kernel_size=1, stride=1),
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BasicConv2d(256, 256, kernel_size=(1,7), stride=1, padding=(0,3)),
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BasicConv2d(256, 320, kernel_size=(7,1), stride=1, padding=(3,0)),
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BasicConv2d(320, 320, 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 Inception_C(nn.Module):
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def __init__(self):
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super(Inception_C, self).__init__()
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self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
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self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
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self.branch1_1a = BasicConv2d(384, 256, kernel_size=(1,3), stride=1, padding=(0,1))
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self.branch1_1b = BasicConv2d(384, 256, kernel_size=(3,1), stride=1, padding=(1,0))
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self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
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self.branch2_1 = BasicConv2d(384, 448, kernel_size=(3,1), stride=1, padding=(1,0))
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self.branch2_2 = BasicConv2d(448, 512, kernel_size=(1,3), stride=1, padding=(0,1))
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self.branch2_3a = BasicConv2d(512, 256, kernel_size=(1,3), stride=1, padding=(0,1))
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self.branch2_3b = BasicConv2d(512, 256, kernel_size=(3,1), stride=1, padding=(1,0))
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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(1536, 256, 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_0 = self.branch1_0(x)
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x1_1a = self.branch1_1a(x1_0)
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x1_1b = self.branch1_1b(x1_0)
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x1 = torch.cat((x1_1a, x1_1b), 1)
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x2_0 = self.branch2_0(x)
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x2_1 = self.branch2_1(x2_0)
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x2_2 = self.branch2_2(x2_1)
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x2_3a = self.branch2_3a(x2_2)
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x2_3b = self.branch2_3b(x2_2)
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x2 = torch.cat((x2_3a, x2_3b), 1)
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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 InceptionV4(nn.Module):
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"""Inception-v4.
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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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- ``inceptionv4``: InceptionV4.
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"""
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def __init__(self, num_classes, loss, **kwargs):
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super(InceptionV4, self).__init__()
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self.loss = loss
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self.features = nn.Sequential(
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BasicConv2d(3, 32, kernel_size=3, stride=2),
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BasicConv2d(32, 32, kernel_size=3, stride=1),
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BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
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Mixed_3a(),
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Mixed_4a(),
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Mixed_5a(),
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Inception_A(),
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Inception_A(),
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Inception_A(),
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Inception_A(),
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Reduction_A(), # Mixed_6a
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Inception_B(),
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Inception_B(),
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Inception_B(),
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Inception_B(),
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Inception_B(),
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Inception_B(),
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Inception_B(),
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Reduction_B(), # Mixed_7a
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Inception_C(),
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Inception_C(),
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Inception_C()
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)
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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 forward(self, x):
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f = self.features(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 init_pretrained_weights(model, model_url):
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"""Initializes model with pretrained weights.
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Layers that don't match with pretrained layers in name or size are kept unchanged.
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"""
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pretrain_dict = model_zoo.load_url(model_url)
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model_dict = model.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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model.load_state_dict(model_dict)
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def inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs):
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model = InceptionV4(num_classes, loss, **kwargs)
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if pretrained:
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model_url = pretrained_settings['inceptionv4']['imagenet']['url']
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init_pretrained_weights(model, model_url)
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return model
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