mirror of
https://github.com/alibaba/EasyCV.git
synced 2025-06-03 14:49:00 +08:00
394 lines
12 KiB
Python
394 lines
12 KiB
Python
from __future__ import absolute_import, division, print_function
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from collections import namedtuple
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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 mmcv.cnn import constant_init, kaiming_init
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from torch.nn.modules.batchnorm import _BatchNorm
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from ..modelzoo import inceptionv4 as model_urls
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from ..registry import BACKBONES
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__all__ = ['Inception4']
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class BasicConv2d(nn.Module):
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def __init__(self,
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in_planes,
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out_planes,
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kernel_size,
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stride=1,
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padding=0):
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super(BasicConv2d, self).__init__()
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self.conv = nn.Conv2d(
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in_planes,
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out_planes,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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bias=False) # verify bias false
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self.bn = nn.BatchNorm2d(
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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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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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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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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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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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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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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(
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192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
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BasicConv2d(
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224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0)))
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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(
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192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)),
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BasicConv2d(
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192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
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BasicConv2d(
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224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)),
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BasicConv2d(
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224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)))
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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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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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self.branch1 = nn.Sequential(
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BasicConv2d(1024, 256, kernel_size=1, stride=1),
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BasicConv2d(
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256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)),
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BasicConv2d(
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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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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(
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384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
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self.branch1_1b = BasicConv2d(
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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(
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384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0))
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self.branch2_2 = BasicConv2d(
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448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1))
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self.branch2_3a = BasicConv2d(
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512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
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self.branch2_3b = BasicConv2d(
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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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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 InceptionAux(nn.Module):
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def __init__(self, in_channels, num_classes):
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super(InceptionAux, self).__init__()
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self.conv0 = BasicConv2d(in_channels, 128, kernel_size=1)
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self.conv1 = BasicConv2d(128, 768, kernel_size=5)
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self.conv1.stddev = 0.01
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self.fc = nn.Linear(768, num_classes)
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self.fc.stddev = 0.001
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def forward(self, x):
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# N x 768 x 17 x 17
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x = F.avg_pool2d(x, kernel_size=5, stride=3)
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# N x 768 x 5 x 5
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x = self.conv0(x)
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# N x 128 x 5 x 5
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x = self.conv1(x)
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# N x 768 x 1 x 1
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# Adaptive average pooling
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x = F.adaptive_avg_pool2d(x, (1, 1))
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# N x 768 x 1 x 1
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x = torch.flatten(x, 1)
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# N x 768
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x = self.fc(x)
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# N x 1000
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return x
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# class BasicConv2d(nn.Module):
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# def __init__(self, in_channels, out_channels, **kwargs):
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# super(BasicConv2d, self).__init__()
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# self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
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# self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
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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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# return F.relu(x, inplace=True)
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@BACKBONES.register_module
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class Inception4(nn.Module):
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"""InceptionV4 backbone.
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Args:
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num_classes (int): The num_classes of InceptionV4. An extra fc will be used if
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"""
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def __init__(self,
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num_classes: int = 0,
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p_dropout=0.2,
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aux_logits: bool = True):
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super(Inception4, self).__init__()
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self.aux_logits = aux_logits
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# Modules
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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(), # Mixed_6h 1024 x 17 x 17
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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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if aux_logits:
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self.AuxLogits = InceptionAux(1024, num_classes)
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self.dropout = nn.Dropout(p_dropout)
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self.last_linear = None
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if num_classes > 0:
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self.last_linear = nn.Linear(1536, num_classes)
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self.default_pretrained_model_path = model_urls[
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self.__class__.__name__]
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@property
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def fc(self):
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return self.last_linear
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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kaiming_init(m, mode='fan_in', nonlinearity='relu')
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elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
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constant_init(m, 1)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def logits(self, features):
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x = F.adaptive_avg_pool2d(features, output_size=(1, 1))
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# x = F.avg_pool2d(features, kernel_size=adaptiveAvgPoolWidth)
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x = x.view(x.size(0), -1) # B x 1536
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x = self.fc(x)
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# B x num_classes
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return x
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def forward(self, input: torch.Tensor):
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"""_summary_
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Args:
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input (torch.Tensor): A RGB image tensor with shape B x C x H x W
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Returns:
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torch.Tensor: A feature tensor or a logit tensor when num_classes is 0 (default)
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"""
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if self.training and self.aux_logits:
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x = self.features[:-4](input)
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aux = self.AuxLogits(x)
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x = self.features[-4:](x)
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else:
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x = self.features(input)
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aux = None
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if self.fc is not None:
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x = self.logits(x)
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return [aux, x]
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