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networks.py
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Created
Tue, Jul 23, 04:17
Size
5 KB
Mime Type
text/x-python
Expires
Thu, Jul 25, 04:17 (1 d, 23 h)
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blob
Format
Raw Data
Handle
19237201
Attached To
R11789 DED Contrastive Learning
networks.py
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import
torch.nn
as
nn
import
torch.nn.functional
as
F
# class EmbeddingNet(nn.Module):
# def __init__(self):
# super(EmbeddingNet, self).__init__()
# self.convnet = nn.Sequential(nn.Conv2d(1, 32, 5), nn.PReLU(),
# nn.MaxPool2d(2, stride=2),
# nn.Conv2d(32, 64, 5), nn.PReLU(),
# nn.MaxPool2d(2, stride=2))
# self.fc = nn.Sequential(nn.Linear(64 * 4 * 4, 256),
# nn.PReLU(),
# nn.Linear(256, 256),
# nn.PReLU(),
# nn.Linear(256, 2)
# )
# def forward(self, x):
# output = self.convnet(x)
# #print(output.shape)
# output = output.view(output.size()[0], -1)
# output = self.fc(output)
# return output
# def get_embedding(self, x):
# return self.forward(x)
# class EmbeddingNet(nn.Module):
# def __init__(self,dropout):
# super(EmbeddingNet, self).__init__()
# self.convnet = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=4, kernel_size=3),
# nn.BatchNorm2d(4),
# nn.ReLU(),
# nn.MaxPool2d(2,2),
# nn.Dropout(dropout),
# nn.Conv2d(in_channels=4, out_channels=8, kernel_size=3),
# nn.BatchNorm2d(8),
# nn.ReLU(),
# nn.MaxPool2d(2,2),
# nn.Dropout(dropout),
# nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3),
# nn.BatchNorm2d(16),
# nn.ReLU(),
# nn.MaxPool2d(2,2),
# nn.Dropout(dropout),
# nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3),
# nn.BatchNorm2d(32),
# nn.ReLU(),
# nn.MaxPool2d(2,2),
# nn.Dropout(dropout))
# self.fc = nn.Sequential(nn.Linear(32 *18 * 28, 2096),
# nn.ReLU(),
# nn.Dropout(dropout),
# nn.Linear(2096, 512),
# nn.ReLU(),
# nn.Dropout(dropout),
# nn.Linear(512, 64),
# nn.ReLU(),
# nn.Linear(64, 4)
# )
# def forward(self, x):
# output = self.convnet(x)
# output = output.view(-1, 32 *28 * 18)
# output = self.fc(output)
# return output
# def get_embedding(self, x):
# return self.forward(x)
class
EmbeddingNet
(
nn
.
Module
):
def
__init__
(
self
,
dropout
):
super
(
EmbeddingNet
,
self
)
.
__init__
()
self
.
convnet
=
nn
.
Sequential
(
nn
.
Conv2d
(
in_channels
=
3
,
out_channels
=
4
,
kernel_size
=
3
),
nn
.
BatchNorm2d
(
4
),
nn
.
ReLU
(),
nn
.
MaxPool2d
(
2
,
2
),
nn
.
Dropout
(
dropout
),
nn
.
Conv2d
(
in_channels
=
4
,
out_channels
=
8
,
kernel_size
=
3
),
nn
.
BatchNorm2d
(
8
),
nn
.
ReLU
(),
nn
.
MaxPool2d
(
2
,
2
),
nn
.
Dropout
(
dropout
),
nn
.
Conv2d
(
in_channels
=
8
,
out_channels
=
16
,
kernel_size
=
3
),
nn
.
BatchNorm2d
(
16
),
nn
.
ReLU
(),
nn
.
MaxPool2d
(
2
,
2
),
nn
.
Dropout
(
dropout
),
nn
.
Conv2d
(
in_channels
=
16
,
out_channels
=
32
,
kernel_size
=
3
),
nn
.
BatchNorm2d
(
32
),
nn
.
ReLU
(),
nn
.
MaxPool2d
(
2
,
2
),
nn
.
Dropout
(
dropout
),
nn
.
Conv2d
(
in_channels
=
32
,
out_channels
=
64
,
kernel_size
=
3
),
nn
.
BatchNorm2d
(
64
),
nn
.
ReLU
(),
nn
.
MaxPool2d
(
2
,
2
),
nn
.
Dropout
(
dropout
))
self
.
fc
=
nn
.
Sequential
(
nn
.
Linear
(
64
*
8
*
13
,
512
),
nn
.
ReLU
(),
nn
.
Dropout
(
dropout
),
nn
.
Linear
(
512
,
64
),
nn
.
ReLU
(),
nn
.
Dropout
(
dropout
),
nn
.
Linear
(
64
,
4
)
)
def
forward
(
self
,
x
):
output
=
self
.
convnet
(
x
)
output
=
output
.
view
(
-
1
,
64
*
8
*
13
)
output
=
self
.
fc
(
output
)
return
output
def
get_embedding
(
self
,
x
):
return
self
.
forward
(
x
)
class
TripletNet
(
nn
.
Module
):
def
__init__
(
self
,
embedding_net
):
super
(
TripletNet
,
self
)
.
__init__
()
self
.
embedding_net
=
embedding_net
def
forward
(
self
,
x1
,
x2
,
x3
):
output1
=
self
.
embedding_net
(
x1
)
output2
=
self
.
embedding_net
(
x2
)
output3
=
self
.
embedding_net
(
x3
)
return
output1
,
output2
,
output3
def
get_embedding
(
self
,
x
):
return
self
.
embedding_net
(
x
)
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