基于深度学习的驾驶行为识别.zip

from __future__ import print_function, division, absolute_import import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.model_zoo as model_zoo import os import sys __all__ = ['InceptionV4', 'inceptionv4'] pretrained_settings = { 'inceptionv4': { 'imagenet': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth', 'input_space': 'RGB', 'input_size': [3, 299, 299], 'input_range': [0, 1], 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'num_classes': 1000 }, 'imagenet+background': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth', 'input_space': 'RGB', 'input_size': [3, 299, 299], 'input_range': [0, 1], 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'num_classes': 1001 } } } class BasicConv2d(nn.Module): def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): super(BasicConv2d, self).__init__() self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False) # verify bias false self.bn = nn.BatchNorm2d(out_planes, eps=0.001, # value found in tensorflow momentum=0.1, # default pytorch value affine=True) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) return x class Mixed_3a(nn.Module): def __init__(self): super(Mixed_3a, self).__init__() self.maxpool = nn.MaxPool2d(3, stride=2) self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2) def forward(self, x): x0 = self.maxpool(x) x1 = self.conv(x) out = torch.cat((x0, x1), 1) return out class Mixed_4a(nn.Module): def __init__(self): super(Mixed_4a, self).__init__() self.branch0 = nn.Sequential( BasicConv2d(160, 64, kernel_size=1, stride=1), BasicConv2d(64, 96, kernel_size=3, stride=1) ) self.branch1 = nn.Sequential( BasicConv2d(160, 64, kernel_size=1, stride=1), BasicConv2d(64, 64, kernel_size=(1,7), stride=1, padding=(0,3)), BasicConv2d(64, 64, kernel_size=(7,1), stride=1, padding=(3,0)), BasicConv2d(64, 96, kernel_size=(3,3), stride=1) ) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) return out class Mixed_5a(nn.Module): def __init__(self): super(Mixed_5a, self).__init__() self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2) self.maxpool = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.conv(x) x1 = self.maxpool(x) out = torch.cat((x0, x1), 1) return out class Inception_A(nn.Module): def __init__(self): super(Inception_A, self).__init__() self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(384, 64, kernel_size=1, stride=1), BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1) ) self.branch2 = nn.Sequential( BasicConv2d(384, 64, kernel_size=1, stride=1), BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1), BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1) ) self.branch3 = nn.Sequential( nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), BasicConv2d(384, 96, kernel_size=1, stride=1) ) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out class Reduction_A(nn.Module): def __init__(self): super(Reduction_A, self).__init__() self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2) self.branch1 = nn.Sequential( BasicConv2d(384, 192, kernel_size=1, stride=1), BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1), BasicConv2d(224, 256, kernel_size=3, stride=2) ) self.branch2 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) return out class Inception_B(nn.Module): def __init__(self): super(Inception_B, self).__init__() self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(1024, 192, kernel_size=1, stride=1), BasicConv2d(192, 224, kernel_size=(1,7), stride=1, padding=(0,3)), BasicConv2d(224, 256, kernel_size=(7,1), stride=1, padding=(3,0)) ) self.branch2 = nn.Sequential( BasicConv2d(1024, 192, kernel_size=1, stride=1), BasicConv2d(192, 192, kernel_size=(7,1), stride=1, padding=(3,0)), BasicConv2d(192, 224, kernel_size=(1,7), stride=1, padding=(0,3)), BasicConv2d(224, 224, kernel_size=(7,1), stride=1, padding=(3,0)), BasicConv2d(224, 256, kernel_size=(1,7), stride=1, padding=(0,3)) ) self.branch3 = nn.Sequential( nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), BasicConv2d(1024, 128, kernel_size=1, stride=1) ) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out class Reduction_B(nn.Module): def __init__(self): super(Reduction_B, self).__init__() self.branch0 = nn.Sequential( BasicConv2d(1024, 192, kernel_size=1, stride=1), BasicConv2d(192, 192, kernel_size=3, stride=2) ) self.branch1 = nn.Sequential( BasicConv2d(1024, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=(1,7), stride=1, padding=(0,3)), BasicConv2d(256, 320, kernel_size=(7,1), stride=1, padding=(3,0)), BasicConv2d(320, 320, kernel_size=3, stride=2) ) self.branch2 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) return out class Inception_C(nn.Module): def __init__(self): super(Inception_C, self).__init__() self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1) self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1) self.branch1_1a = BasicConv2d(384, 256, kernel_size=(1,3), stride=1, padding=(0,1)) self.branch1_1b = BasicConv2d(384, 256, kernel_size=(3,1), stride=1, padding=(1,0)) self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1) self.branch2_1 = BasicConv2d(384, 448, kernel_size=(3,1), stride=1, padding=(1,0)) self.branch2_2 = BasicConv2d(448, 512, kernel_size=(1,3), stride=1, padding=(0,1)) self.branch2_3a = BasicConv2d(512, 256, kernel_size=(1,3), stride=1, padding=(0,1)) self.branch2_3b = BasicConv2d(512, 256, kernel_size=(3,1), stride=1, padding=(1,0)) self.branch3 = nn.Sequential( nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), BasicConv2d(1536, 256, kernel_size=1, stride=1) ) def forward(self, x): x0 = self.branch0(x) x1_0 = self.branch1_0(x) x1_1a =