def __init__(self,
vgg_name='VGG19',
in_channel=3,
out_channel=10,
drop_prob=0.0,
block_size=5,
forward='dropblock'):
super(VGG2, self).__init__()
# 修改了 make_layer 这个部分， 源码是在上面， 把中间分层处理
self.f1, self.f2, self.f3 = self._make_layers2(vggcfg[vgg_name],
in_channel=in_channel)
self.classifier = nn.Linear(512, out_channel)
self.dropblock = LinearScheduler(DropBlock2D(drop_prob=drop_prob,
block_size=block_size),
start_value=0.,
stop_value=drop_prob,
nr_steps=5e3)
self.wh = 2
self.wh2 = 4
self.align_sche = False
self.i = 0
# self.cr = CropAndResize(8, 8) # 8 is according to the real size
self.cr = RoIAlign(self.wh, self.wh, transform_fpcoor=True)
self.cr2 = RoIAlign(self.wh2, self.wh2, transform_fpcoor=True)
# 注释掉 其中的一个，
if forward == 'dropblock':
self.forward = self._forward_dropblock
print("------- VGG with Dropblock ---------\n")
else:
self.forward = self._forward_align
print("------- VGG with ROiAlign ---------\n")
def __init__(self,
block,
layers,
num_classes=1000,
drop_prob=0.,
block_size=5):
super(ResNet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.dropblock = LinearScheduler(DropBlock2D(drop_prob=drop_prob,
block_size=block_size),
start_value=0.,
stop_value=drop_prob,
nr_steps=5e3)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
self.wh = 2
self.wh2 = 8
self.align_sche = False
self.i = 0
# self.cr = CropAndResize(8, 8) # 8 is according to the real size
self.cr = RoIAlign(self.wh, self.wh, transform_fpcoor=True)
self.cr2 = RoIAlign(self.wh2, self.wh2, transform_fpcoor=True)
print("--------------------------------------------------------"
"\n-------- RoiAlign -------\n"
"--------------------------------------------------------")
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)