def __init__(self, backbone1, backbone2, drop, pretrained=True):
super(MultiModalNet, self).__init__()
self.visit_model = DPN26()
if backbone1 == 'se_resnext101_32x4d':
self.img_encoder = se_resnext101_32x4d(9, None)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'se_resnext50_32x4d':
self.img_encoder = se_resnext50_32x4d(9, None)
print(
"load pretrained model from /home/zxw/2019BaiduXJTU/se_resnext50_32x4d-a260b3a4.pth"
)
state_dict = torch.load(
'/home/zxw/2019BaiduXJTU/se_resnext50_32x4d-a260b3a4.pth')
state_dict.pop('last_linear.bias')
state_dict.pop('last_linear.weight')
self.img_encoder.load_state_dict(state_dict, strict=False)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'se_resnext26_32x4d':
self.img_encoder = se_resnext26_32x4d(9, None)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'multiscale_se_resnext':
self.img_encoder = multiscale_se_resnext(9)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'multiscale_se_resnext_cat':
self.img_encoder = multiscale_se_resnext(9)
self.img_fc = nn.Linear(1024, 256)
elif backbone1 == 'multiscale_se_resnext_HR':
self.img_encoder = multiscale_se_resnext_HR(9)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'se_resnet50':
self.img_encoder = se_resnet50(9, None)
print(
"load pretrained model from /home/zxw/2019BaiduXJTU/se_resnet50-ce0d4300.pth"
)
state_dict = torch.load(
'/home/zxw/2019BaiduXJTU/se_resnet50-ce0d4300.pth')
state_dict.pop('last_linear.bias')
state_dict.pop('last_linear.weight')
self.img_encoder.load_state_dict(state_dict, strict=False)
self.img_fc = nn.Linear(2048, 256)
self.dropout = nn.Dropout(0.5)
self.cls = nn.Linear(512, 9)
import torch
import torch.nn as nn
#from senet import se_resnext50_32x4d
from basenet.multimodal import MultiModalNet
from basenet.senet import se_resnet50, se_resnext101_32x4d, se_resnext50_32x4d
#from oct_resnet import oct_resnet26,oct_resnet101
from basenet.nasnet import nasnetalarge
from basenet.multiscale_resnet import multiscale_resnet
from basenet.DPN import DPN92, DPN26
from basenet.multiscale_se_resnext import multiscale_se_resnext
import torch.backends.cudnn as cudnn
torch.set_default_tensor_type('torch.cuda.FloatTensor')
cudnn.benchmark = True
model = multiscale_se_resnext(9)
print(model(torch.randn(16, 3, 100, 100).float().cuda()).size())
"""
model = MultiModalNet('se_resnext26_32x4d', 'DPN26', 0.5)
print(model(torch.randn(16,3,100,100).float().cuda(),torch.randn(16,7,24,26).float().cuda()))
"""
def __init__(self, backbone1, backbone2, drop, pretrained=True):
super(MultiModalNet, self).__init__()
self.visit_model = DPN26()
if backbone1 == 'se_resnext101_32x4d':
self.img_encoder = se_resnext101_32x4d(9, None)
# print("load pretrained model from pth/se_resnext101_32x4d-3b2fe3d8.pth")
# state_dict = torch.load('pth/se_resnext101_32x4d-3b2fe3d8.pth')
# state_dict.pop('last_linear.bias')
# state_dict.pop('last_linear.weight')
# self.img_encoder.load_state_dict(state_dict, strict=False)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'densenet169':
self.img_encoder = densenet169(1000, None)
self.img_fc = nn.Linear(1000, 256)
elif backbone1 == 'inceptionv3':
self.img_encoder = inceptionv3(9, None)
print("load pretrained model from pth inceptionv3")
state_dict = torch.load('pth/inception_v3_google-1a9a5a14.pth')
state_dict.pop('fc.bias')
state_dict.pop('fc.weight')
self.img_encoder.load_state_dict(state_dict, strict=False)
self.img_fc = nn.Linear(1000, 256)
elif backbone1 == 'densenet121':
self.img_encoder = densenet121(9, None)
print("load pretrained model from pth/densenet121-fbdb23505.pth")
state_dict = torch.load('pth/densenet121-fbdb23505.pth')
state_dict.pop('classifier.bias')
state_dict.pop('classifier.weight')
self.img_encoder.load_state_dict(state_dict, strict=False)
self.img_fc = nn.Linear(1000, 256)
elif backbone1 == 'senet154':
self.img_encoder = senet154(9, None)
# not right
# print("load pretrained model from pth/senet154-c7b49a05.pth")
# state_dict = torch.load('pth/senet154-c7b49a05.pth')
# state_dict.pop('last_linear.bias')
# state_dict.pop('last_linear.weight')
# self.img_encoder.load_state_dict(state_dict, strict=False)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'nasnetalarge':
self.img_encoder = nasnetalarge(2048, None)
#not right
print(
"load pretrained model from pth/nasnetalarge-a1897284.pth in multimodal.py"
)
state_dict = torch.load('pth/nasnetalarge-a1897284.pth')
#print(state_dict.keys())
state_dict.pop('last_linear.bias')
state_dict.pop('last_linear.weight')
self.img_encoder.load_state_dict(state_dict, strict=False)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'nasnetamobile':
self.img_encoder = nasnetamobile(2048, None)
# not right
print("load pretrained model from pth nasnetamobile")
state_dict = torch.load('pth/nasnetamobile-7e03cead.pth')
# print(state_dict.keys())
state_dict.pop('last_linear.bias')
state_dict.pop('last_linear.weight')
self.img_encoder.load_state_dict(state_dict, strict=False)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'ResNeXt101_64x4d':
self.img_encoder = se_resnext101_64x4d(9, None)
# print("load pretrained model from pth/resnext101_64x4d-e77a0586.pth")
# state_dict = torch.load('pth/resnext101_64x4d-e77a0586.pth')
# state_dict.pop('last_linear.bias')
# state_dict.pop('last_linear.weight')
# self.img_encoder.load_state_dict(state_dict, strict=False)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'se_resnext50_32x4d':
self.img_encoder = se_resnext50_32x4d(9, None)
# print("load pretrained model from pth/se_resnext50_32x4d-a260b3a4.pth")
# state_dict = torch.load('pth/se_resnext50_32x4d-a260b3a4.pth')
# print("load pretrained model from weights_82/BDXJTU2019_SGD_82.pth")
# state_dict1 = torch.load('weights_82/BDXJTU2019_SGD_82.pth')
#
# key1=state_dict1.keys()
# dict_img={}
# dict_vis={}
# dict_fc={}
# dict_cls={}
#
# key_img=[]
# key_vis=[]
# key_fc=[]
# key_cls=[]
# for key in key1:
# if key.count("img_encoder")>0:
# key_img.append(key)
# elif key.count("visit_model")>0:
# key_vis.append(key)
# elif key.count("img_fc") > 0:
# key_fc.append(key)
# elif key.count("cls") > 0:
# key_cls.append(key)
# else:
# print(key)
# dict_img.fromkeys(key_img)
# dict_vis.fromkeys(key_vis)
# dict_fc.fromkeys(key_fc)
# dict_cls.fromkeys(key_cls)
# for key in key1:
# if key.count("img_encoder")>0:
# dict_img[key]=state_dict1[key]
# elif key.count("visit_model")>0:
# dict_vis[key]=state_dict1[key]
# elif key.count("img_fc")>0:
# dict_fc[key]=state_dict1[key]
# elif key.count("cls") > 0:
# dict_cls[key]=state_dict1[key]
# else:
# print(key)
# state_dict.pop('last_linear.bias')
# state_dict.pop('last_linear.weight')
#self.img_encoder.load_state_dict(dict_img, strict = False)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'se_resnext26_32x4d':
self.img_encoder = se_resnext26_32x4d(9, None)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'multiscale_se_resnext':
self.img_encoder = multiscale_se_resnext(9)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'multiscale_se_resnext_cat':
self.img_encoder = multiscale_se_resnext(9)
self.img_fc = nn.Linear(1024, 256)
elif backbone1 == 'multiscale_se_resnext_HR':
self.img_encoder = multiscale_se_resnext_HR(9)
self.img_fc = nn.Linear(2048, 256)
elif backbone1 == 'se_resnet50':
self.img_encoder = se_resnet50(9, None)
# print("load pretrained model from pth/se_resnet50-ce0d4300.pth")
# state_dict = torch.load('pth/se_resnet50-ce0d4300.pth')
#
# state_dict.pop('last_linear.bias')
# state_dict.pop('last_linear.weight')
# self.img_encoder.load_state_dict(state_dict, strict = False)
self.img_fc = nn.Linear(2048, 256)
self.dropout = nn.Dropout(0.5)
self.cls = nn.Linear(512, 9)