def __init__(self, phase, base, extras, head, num_classes):
super(SSD, self).__init__()
self.phase = phase
self.num_classes = num_classes
self.cfg = Config
self.vgg = nn.ModuleList(base)
self.L2Norm = L2Norm(512, 20)
self.extras = nn.ModuleList(extras)
self.priorbox = PriorBox(self.cfg)
with torch.no_grad():
self.priors = Variable(self.priorbox.forward())
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
if phase == 'test':
self.softmax = nn.Softmax(dim=-1)
self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
self.SE1 = SEModule(512)
self.SE2 = SEModule(1024)
self.SE3 = SEModule(512)
self.SE4 = SEModule(256)
self.SE5 = SEModule(256)
self.SE6 = SEModule(256)
def __init__(self, phase, base, extras, head, num_classes):
super(SSD, self).__init__()
self.phase = phase
self.num_classes = num_classes
self.cfg = Config
self.vgg = nn.ModuleList(base)
self.L2Norm = L2Norm(512, 20)
self.extras = nn.ModuleList(extras)
self.priorbox = PriorBox(self.cfg)
with torch.no_grad():
self.priors = self.priorbox.forward()
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
if phase == 'test':
self.softmax = nn.Softmax(dim=-1)
self.detect = Detect(self)
def __init__(self, phase, base, extras, head, num_classes):
super(SSD, self).__init__()
self.phase = phase
self.num_classes = num_classes
self.cfg = Config
self.vgg = nn.ModuleList(base)
self.L2Norm = L2Norm(512, 20)
self.extras = nn.ModuleList(extras)
self.priorbox = PriorBox(self.cfg)
with torch.no_grad():
self.priors = Variable(self.priorbox.forward())
# self.priors = self.priorbox.forward() # 这一行改成这样也能正常运行
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
self.relu_list4cxq = nn.ModuleList([torch.nn.ReLU(True) for i in range(8)]) # 自己修改后的方式
self.feature_maps4cxq = None # 用于grad cam
self.scores4cxq = None # 用于grad cam
if phase == 'test':
self.softmax = nn.Softmax(dim=-1)
self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
def __init__(self, phase, base, extras, head, num_classes):
super(SSD, self).__init__()
self.phase = phase
self.num_classes = num_classes
self.cfg = Config
self.vgg = nn.ModuleList(base)
self.L2Norm = L2Norm(512, 20)
self.extras = nn.ModuleList(extras)
self.priorbox = PriorBox(self.cfg)
with torch.no_grad():
self.priors = Variable(self.priorbox.forward())
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
if phase == 'test':
self.softmax = nn.Softmax(dim=-1)
self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
self.upsample_256_256 = Upsample(10)
self.conv_256_512 = nn.Conv2d(in_channels=256,
out_channels=512,
kernel_size=1,
stride=1)
#conv8_2 -> conv8_2
self.conv_512_512_1 = nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=1,
stride=1)
self.upsample_512_512 = Upsample(19)
self.conv_512_1024 = nn.Conv2d(in_channels=512,
out_channels=1024,
kernel_size=1,
stride=1)
self.conv_1024_1024 = nn.Conv2d(in_channels=1024,
out_channels=1024,
kernel_size=1,
stride=1)
self.upsample_1024_1024 = Upsample(38)
self.conv_1024_512 = nn.Conv2d(in_channels=1024,
out_channels=512,
kernel_size=1,
stride=1)
self.conv_512_512_2 = nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=1,
stride=1)
self.smooth = nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1)
self.smooth1 = nn.Conv2d(1024,
1024,
kernel_size=3,
padding=1,
stride=1)
if USE_CBAM:
self.CBAM1 = Bottleneck(512)
self.CBAM2 = Bottleneck(1024)
self.CBAM3 = Bottleneck(512)
self.CBAM4 = Bottleneck(256)
self.CBAM5 = Bottleneck(256)
self.CBAM6 = Bottleneck(256)
if USE_SE:
self.SE1 = SEModule(512)
self.SE2 = SEModule(1024)
self.SE3 = SEModule(512)
self.SE4 = SEModule(256)
self.SE5 = SEModule(256)
self.SE6 = SEModule(256)
def __init__(self, phase, base, extras, head, num_classes):
super(SSD, self).__init__()
self.phase = phase
self.num_classes = num_classes
self.cfg = Config
self.vgg = nn.ModuleList(base)
self.L2Norm = L2Norm(512, 20)
self.extras = nn.ModuleList(extras)
self.priorbox = PriorBox(self.cfg)
with torch.no_grad():
self.priors = Variable(self.priorbox.forward())
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
if phase == 'test':
self.softmax = nn.Softmax(dim=-1)
self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
self.DilationConv_128_128 = nn.Conv2d(in_channels=128,
out_channels=128,
kernel_size=3,
padding=2,
dilation=2,
stride=2)
self.conv_512_256 = nn.Conv2d(in_channels=512,
out_channels=256,
kernel_size=1,
stride=1)
self.upsample_1024_1024 = Upsample(38)
self.conv_1024_128 = nn.Conv2d(in_channels=1024,
out_channels=128,
kernel_size=1,
stride=1)
self.DilationConv_512_256 = nn.Conv2d(in_channels=512,
out_channels=256,
kernel_size=3,
padding=2,
dilation=2,
stride=2)
self.conv_1024_512 = nn.Conv2d(in_channels=1024,
out_channels=512,
kernel_size=1,
stride=1)
self.upsample_512_512 = Upsample(19)
self.conv_512_256_fc7 = nn.Conv2d(in_channels=512,
out_channels=256,
kernel_size=1,
stride=1)
self.DilationConv_512_128_2 = nn.Conv2d(in_channels=512,
out_channels=128,
kernel_size=3,
padding=2,
dilation=2,
stride=2)
self.conv_512_256_2 = nn.Conv2d(in_channels=512,
out_channels=256,
kernel_size=1,
stride=1)
self.upsample_256_256_2 = Upsample(10)
self.conv_256_128_2 = nn.Conv2d(in_channels=256,
out_channels=128,
kernel_size=1,
stride=1)
self.smooth = nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1)
self.smooth2 = nn.Conv2d(1024,
1024,
kernel_size=3,
padding=1,
stride=1)
self.bn = nn.BatchNorm2d(128)
self.bn1 = nn.BatchNorm2d(256)
if USE_SE:
self.SE1 = SEModule(512)
self.SE2 = SEModule(512)
self.SE3 = SEModule(512)
self.SE4 = SEModule(256)
self.SE5 = SEModule(256)
self.SE6 = SEModule(256)
if USE_ECA:
self.ECA1 = ECAModule(512)
self.ECA2 = ECAModule(1024)
self.ECA3 = ECAModule(512)
self.ECA4 = ECAModule(256)