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mvcnn_att.py
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import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
__all__ = ['MVCNN_ATT','mvcnn_att']
model_urls = {
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class MVCNN_ATT(nn.Module):
def __init__(self, num_classes=1000):
super(MVCNN_ATT, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
# self.classifier = nn.Sequential(
# nn.Dropout(),
# nn.Linear(256*6*6, 4096),
# nn.ReLU(inplace=True),
# nn.Dropout(),
# nn.Linear(4096, 4096),
# nn.ReLU(inplace=True),
# nn.Linear(4096, num_classes),
# )
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256*6*6, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_classes),
)
self.V=nn.Sequential(
nn.Linear(256*6*6,1024),
nn.ReLU(inplace=True),
nn.Linear(1024,1),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = x.transpose(0, 1)
view_pool = []
weights=[]
for v in x:
v = self.features(v)
v = v.view(v.size(0), 256 * 6 * 6) #(batch x 9216)
w=self.V(v) #(batch x 1)
view_pool.append(v)
weights.append(w)
weights=F.softmax(torch.cat(weights,1),1) #batch x 20
#print(weights.shape)
view_pool=torch.stack(view_pool,1) #batch x 20 x 9216
#print(view_pool.shape)
#weights=weights.repeat(1,1,256*6*6) #batch x 20 x 9216
weights = torch.stack([weights]*256*6*6,2)
#print(weights.shape)
pooled_view=(weights*view_pool).sum(1)
pooled_view = self.classifier(pooled_view)
return pooled_view
def mvcnn_att(pretrained=False, **kwargs):
r"""MVCNN model architecture from the
`"Multi-view Convolutional..." <hhttp://vis-www.cs.umass.edu/mvcnn/docs/su15mvcnn.pdf>`_ paper.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = MVCNN_ATT(**kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['alexnet'])
model_dict = model.state_dict()
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model.load_state_dict(model_dict)
return model