def __init__(self, num_class=2):
super(FusionNet, self).__init__()
self.color_moudle = Net(num_class=num_class, is_first_bn=True)
self.depth_moudle = Net(num_class=num_class, is_first_bn=True)
self.ir_moudle = Net(num_class=num_class, is_first_bn=True)
self.color_SE = SEModule(512, reduction=16)
self.depth_SE = SEModule(512, reduction=16)
self.ir_SE = SEModule(512, reduction=16)
self.bottleneck = nn.Sequential(
nn.Conv2d(512 * 3, 512, kernel_size=1, padding=0),
nn.BatchNorm2d(512), nn.ReLU(inplace=True))
self.res_0 = self._make_layer(SEResNeXtBottleneck,
planes=256,
blocks=2,
stride=2,
groups=32,
reduction=16,
downsample_kernel_size=1,
downsample_padding=0)
self.res_1 = self._make_layer(SEResNeXtBottleneck,
planes=512,
blocks=2,
stride=2,
groups=32,
reduction=16,
downsample_kernel_size=1,
downsample_padding=0)
self.fc = nn.Sequential(nn.Dropout(0.5), nn.Linear(2048, 256),
nn.ReLU(inplace=True),
nn.Linear(256, num_class))
def __init__(self, num_class=2, deploy=False, width_multiplier=[0.75, 0.75, 0.75, 2.5], num_blocks=[2, 4, 14, 1], override_groups_map=None):
super(FusionNet, self).__init__()
self.deploy = deploy
self.cur_layer_idx = 1
self.in_planes = 384
self.override_groups_map = override_groups_map or dict()
assert 0 not in self.override_groups_map
self.color_moudle = Net(num_class=num_class, is_first_bn=True)
self.depth_moudle = Net(num_class=num_class, is_first_bn=True)
self.ir_moudle = Net(num_class=num_class, is_first_bn=True)
self.color_SE = SEModule(128,reduction=16)
self.depth_SE = SEModule(128,reduction=16)
self.ir_SE = SEModule(128,reduction=16)
self.res_0 = self._make_layer(BasicBlock, 384, 256, 2, stride=2)
self.res_1 = self._make_layer(BasicBlock, 256, 512, 2, stride=2)
# self.res_0 = self._make_RepVGG_layer(384, num_blocks[2], stride=2)
# self.res_1 = self._make_RepVGG_layer(int(512 * width_multiplier[3]), num_blocks[3], stride=2)
self.fc = nn.Sequential(nn.Dropout(0.5),
# nn.Linear(int(512 * width_multiplier[3]), 256),
nn.Linear(int(512), 256),
nn.ReLU(inplace=True),
nn.Linear(256, num_class))
def __init__(self, num_class=2, modality='fusion'):
super(FusionNet, self).__init__()
# Net是model_baseline中的net，返回
# logit.shape: torch.Size([batch_size, 2])
# logit.shape: torch.Size([batch_size, 300])
# fea.shape: torch.Size([batch_size, 512])
self.modality = modality
if self.modality == 'fusion':
self.color_moudle = Net(num_class=num_class, is_first_bn=True)
self.depth_moudle = Net(num_class=num_class, is_first_bn=True)
self.ir_moudle = Net(num_class=num_class, is_first_bn=True)
# SEModule，输入channels和reduction,这个channel要和前一个网络的输出维度一致
self.color_SE = SEModule(128, reduction=16)
self.depth_SE = SEModule(128, reduction=16)
self.ir_SE = SEModule(128, reduction=16)
# 采用resnet的方式创建两个层
self.res_0 = self._make_layer(BasicBlock, 384, 256, 2, stride=2)
else:
self.color_moudle = Net(num_class=num_class, is_first_bn=True)
self.color_SE = SEModule(128, reduction=16)
self.res_0 = self._make_layer(BasicBlock, 128, 256, 2, stride=2)
self.res_1 = self._make_layer(BasicBlock, 256, 512, 2, stride=2)
self.fc = nn.Sequential(nn.Dropout(0.5), nn.Linear(512, 256),
nn.ReLU(inplace=True),
nn.Linear(256, num_class))
def __init__(self, num_class=2, deploy=False, width_multiplier=[0.75, 0.75, 0.75, 2.5], num_blocks=[2, 4, 14, 1], override_groups_map=None):
super(FusionNet, self).__init__()
self.deploy = deploy
# width_multiplier=[1, 1, 1, 1]
# num_blocks=[2, 2, 2, 2]
self.cur_layer_idx = 1
self.in_planes = int(256 * width_multiplier[2])
self.override_groups_map = override_groups_map or dict()
assert 0 not in self.override_groups_map
self.color_module = Net(num_class=num_class, is_first_bn=True)
self.depth_module = Net(num_class=num_class, is_first_bn=True)
self.ir_module = Net(num_class=num_class, is_first_bn=True)
self.color_SE = SEModule(96,reduction=8)
self.depth_SE = SEModule(96,reduction=8)
self.ir_SE = SEModule(96,reduction=8)
self.bottleneck = nn.Sequential(nn.Conv2d(96*3, int(256 * width_multiplier[2]), kernel_size=1, padding=0),
nn.BatchNorm2d(int(256 * width_multiplier[2])),
nn.ReLU(inplace=True))
self.res_0 = self._make_RepVGG_layer(int(256 * width_multiplier[2]), num_blocks[2], stride=2)
self.res_1 = self._make_RepVGG_layer(int(512 * width_multiplier[3]), num_blocks[3], stride=2)
self.fc = nn.Sequential(nn.Dropout(0.5),
nn.Linear(int(512 * width_multiplier[3]), 256),
nn.ReLU(inplace=True),
nn.Linear(256, num_class))
def __init__(self, num_class=2):
super(FusionNet, self).__init__()
self.color_moudle = Net(num_class=num_class, is_first_bn=True)
self.depth_moudle = Net(num_class=num_class, is_first_bn=True)
self.ir_moudle = Net(num_class=num_class, is_first_bn=True)
self.color_SE = SEModule(128, reduction=16)
self.depth_SE = SEModule(128, reduction=16)
self.ir_SE = SEModule(128, reduction=16)
self.res_0 = self._make_layer(BasicBlock, 384, 256, 2, stride=2)
self.res_1 = self._make_layer(BasicBlock, 256, 512, 2, stride=2)
self.fc = nn.Sequential(nn.Dropout(0.5), nn.Linear(512, 256),
nn.ReLU(inplace=True),
nn.Linear(256, num_class))
def __init__(self, num_class=2):
super(FusionNet, self).__init__()
self.color_moudle = Net(num_class=num_class, is_first_bn=True)
self.depth_moudle = Net(num_class=num_class, is_first_bn=True)
self.ir_moudle = Net(num_class=num_class, is_first_bn=True)
self.color_SE = SEModule(512, reduction=16)
self.depth_SE = SEModule(512, reduction=16)
self.ir_SE = SEModule(512, reduction=16)
self.bottleneck = nn.Sequential(
nn.Conv2d(512 * 3, 128 * 3, kernel_size=1, padding=0),
nn.BatchNorm2d(128 * 3), nn.ReLU(inplace=True))
self.res_0 = self._make_layer(BasicBlock, 128 * 3, 256, 2, stride=2)
self.res_1 = self._make_layer(BasicBlock, 256, 512, 2, stride=2)
self.fc = nn.Sequential(nn.Dropout(0.5), nn.Linear(512, 256),
nn.ReLU(inplace=True),
nn.Linear(256, num_class))