def __init__(self, cfg):
super(MGNBackbone, self).__init__()
resnet = create_backbone(cfg)
self.backbone = nn.Sequential(
resnet.conv1,
resnet.bn1,
resnet.relu,
resnet.maxpool,
resnet.layer1,
resnet.layer2,
resnet.layer3[0],
)
res_conv4 = nn.Sequential(*resnet.layer3[1:])
res_g_conv5 = resnet.layer4
res_p_conv5 = nn.Sequential(
Bottleneck(1024,
512,
downsample=nn.Sequential(
nn.Conv2d(1024, 2048, 1, bias=False),
nn.BatchNorm2d(2048))), Bottleneck(2048, 512),
Bottleneck(2048, 512))
# res_p_conv5.load_state_dict(resnet.layer4.state_dict())
load_state_dict(res_p_conv5, resnet.layer4.state_dict())
self.p0 = nn.Sequential(copy.deepcopy(res_conv4),
copy.deepcopy(res_g_conv5))
self.p1 = nn.Sequential(copy.deepcopy(res_conv4),
copy.deepcopy(res_p_conv5))
self.p2 = nn.Sequential(copy.deepcopy(res_conv4),
copy.deepcopy(res_p_conv5))
def __init__(self, cfg):
super(PyramidalBackbone, self).__init__()
resnet = create_backbone(cfg)
self.backbone = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu,
resnet.maxpool, resnet.layer1,
resnet.layer2, resnet.layer3,
resnet.layer4)
def __init__(self, cfg):
super(PGFABackbone, self).__init__()
# cfg.model.backbone.last_conv_stride = 1
resnet = create_backbone(cfg)
# resnet.layer4[0].downsample[0].stride = (1,1)
# resnet.layer4[0].conv2.stride = (1,1)
self.backbone = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu,
resnet.maxpool, resnet.layer1,
resnet.layer2, resnet.layer3,
resnet.layer4)
def __init__(self, cfg):
super(MDRSBackbone, self).__init__()
resnet = create_backbone(cfg)
self.cfg = cfg
self.backbone = nn.Sequential(
resnet.conv1,
resnet.bn1,
resnet.relu,
resnet.maxpool,
resnet.layer1,
resnet.layer2,
resnet.layer3[0],
)
self.out_c = resnet.out_c
res_conv4 = nn.Sequential(*resnet.layer3[1:])
global_conv5 = resnet.layer4
# Option 1
# local_conv5_1 = nn.Sequential(
# Bottleneck(1024, 512, stride=2, downsample=nn.Sequential(nn.Conv2d(1024, 2048, 1, 2, bias=False), nn.BatchNorm2d(2048))),
# Bottleneck(2048, 512),
# Bottleneck(2048, 512))
#
# load_state_dict(local_conv5_1, resnet.layer4.state_dict())
#
# local_conv5_2 = nn.Sequential(
# Bottleneck(1024, 512, downsample=nn.Sequential(nn.Conv2d(1024, 2048, 1, bias=False), nn.BatchNorm2d(2048))),
# Bottleneck(2048, 512),
# Bottleneck(2048, 512))
#
# load_state_dict(res_p_conv5_2, resnet.layer4.state_dict())
#
# self.p0 = nn.Sequential(copy.deepcopy(res_conv4), copy.deepcopy(global_conv5))
# self.p1 = nn.Sequential(copy.deepcopy(res_conv4), copy.deepcopy(local_conv5_1))
# self.p2 = nn.Sequential(copy.deepcopy(res_conv4), copy.deepcopy(local_conv5_2))
# Option 2
local_conv5 = nn.Sequential(
Bottleneck(1024,
512,
downsample=nn.Sequential(
nn.Conv2d(1024, 2048, 1, bias=False),
nn.BatchNorm2d(2048))), Bottleneck(2048, 512),
Bottleneck(2048, 512))
local_conv5.load_state_dict(resnet.layer4.state_dict())
self.p0 = nn.Sequential(copy.deepcopy(res_conv4),
copy.deepcopy(global_conv5))
self.p1 = nn.Sequential(copy.deepcopy(res_conv4),
copy.deepcopy(local_conv5))
self.p2 = nn.Sequential(copy.deepcopy(res_conv4),
copy.deepcopy(local_conv5))
def __init__(self, cfg):
super(HOReIDEncoder, self).__init__()
self.cfg = cfg
# backbone and optimize its architecture
# cfg.model.backbone.last_conv_stride = 1
resnet = create_backbone(cfg)
# resnet.layer4[0].downsample[0].stride = (1,1)
# resnet.layer4[0].conv2.stride = (1,1)
# cnn backbone
self.resnet_conv = nn.Sequential(
resnet.conv1,
resnet.bn1,
resnet.maxpool, # no relu
resnet.layer1,
resnet.layer2,
resnet.layer3,
resnet.layer4)