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sociallstm.py
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Created
Wed, May 29, 11:37
Size
2 KB
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text/x-python
Expires
Fri, May 31, 11:37 (2 d)
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blob
Format
Raw Data
Handle
17943545
Attached To
R6590 project14
sociallstm.py
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import
torch
import
torch.nn
as
nn
from
torch.autograd
import
Variable
import
project.data_utils
as
du
class
SocialLSTM
(
nn
.
Module
):
'''
Class representing the Social LSTM model
'''
def
__init__
(
self
):
super
(
SocialLSTM
,
self
)
.
__init__
()
# Store required sizes
self
.
hidden_size
=
128
#self.grid_size = args.grid_size
self
.
embedding_size
=
64
#self.pooling_size = args.pooling_size #pooling window
self
.
input_size
=
2
self
.
output_size
=
2
#parameters of bivariate distribution
#self.neighborhood_size = args.neighborhood_size
# The LSTM cell. (Social LSTM) embedding size = 64
self
.
lstm
=
nn
.
LSTM
(
2
*
self
.
embedding_size
,
self
.
hidden_size
,
dropout
=
0.2
)
self
.
lstm2
=
nn
.
LSTM
(
self
.
embedding_size
,
self
.
hidden_size
,
dropout
=
0.2
)
# Linear layer to embed the input position into LSTM
self
.
input_embedding_layer
=
nn
.
Linear
(
self
.
input_size
,
self
.
embedding_size
)
# Linear layer to embed the social tensor
#self.tensor_embedding_layer = nn.Linear(self.neighborhood_size*self.neighborhood_size*self.hidden_size, self.embedding_size)
# Linear layer to map the hidden state of LSTM to output
self
.
output_layer
=
nn
.
Linear
(
self
.
hidden_size
,
self
.
output_size
)
# ReLU and dropout unit
self
.
relu
=
nn
.
ReLU
()
#self.dropout = nn.Dropout(0.5)
def
forward
(
self
,
peds
,
social_tensor
,
future
=
0
):
'''
Forward pass for the model
params:
peds: pedestrian coords
'''
outputs
=
[]
hidden_states
=
Variable
(
torch
.
zeros
(
1
,
1
,
self
.
hidden_size
))
cell_states
=
Variable
(
torch
.
zeros
(
1
,
1
,
self
.
hidden_size
))
input
=
self
.
relu
(
self
.
input_embedding_layer
(
peds
))
if
social_tensor
is
None
:
h_peds
,
c_peds
=
self
.
lstm2
(
input
,
(
hidden_states
,
cell_states
))
else
:
social_embed
=
self
.
relu
(
self
.
input_embedding_layer
(
social_tensor
))
concat_embed
=
torch
.
cat
((
input
,
social_embed
),
2
)
h_peds
,
c_peds
=
self
.
lstm
(
concat_embed
,
(
hidden_states
,
cell_states
))
output
=
self
.
output_layer
(
h_peds
)
outputs
+=
[
output
]
for
i
in
range
(
future
):
#predict future
new_out
=
self
.
relu
(
self
.
input_embedding_layer
(
output
))
#concat_embed = torch.cat((new_out,social_embed),0)
h_peds
,
c_peds
=
self
.
lstm2
(
new_out
,
(
hidden_states
,
cell_states
))
output
=
self
.
output_layer
(
h_peds
)
outputs
+=
[
output
]
outputs
=
torch
.
cat
(
outputs
,
0
)
return
outputs
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