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https://github.com/kamo-naoyuki/pytorch_convolutional_rnn
PyTorch implementation of Convolutional Recurrent Neural Network
https://github.com/kamo-naoyuki/pytorch_convolutional_rnn
cnn crnn deep-learning pytorch rnn
Last synced: 17 days ago
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PyTorch implementation of Convolutional Recurrent Neural Network
- Host: GitHub
- URL: https://github.com/kamo-naoyuki/pytorch_convolutional_rnn
- Owner: kamo-naoyuki
- License: mit
- Created: 2018-04-30T09:16:17.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-02-22T10:37:43.000Z (over 1 year ago)
- Last Synced: 2024-10-20T20:27:07.860Z (24 days ago)
- Topics: cnn, crnn, deep-learning, pytorch, rnn
- Language: Python
- Homepage:
- Size: 22.5 KB
- Stars: 132
- Watchers: 4
- Forks: 34
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# pytorch_convolutional_rnn
The pytorch implemenation for convolutional rnn is alreaedy exisitng other than my module, for example.
- https://github.com/ndrplz/ConvLSTM_pytorch
- https://github.com/jacobkimmel/pytorch_convgru
However, there are no modules supporting neither variable length tensor nor bidirectional rnn.
I implemented ``AutogradConvRNN`` by referring to ``AutogradRNN`` at https://github.com/pytorch/pytorch/blob/master/torch/nn/_functions/rnn.py, so my convolutional RNN modules have similar structure to ``torch.nn.RNN`` and supports the above features as it has.
The benefit of using ``AutogradConvRNN`` is not only that it enables my modules to have the same interface as ``torch.nn.RNN``, but makes it very easy to implement many kinds of CRNN, such as ``CLSTM``, ``CGRU``.
## Require
- python3 (Not supporting python2 because I prefer type annotation)
- pytorch0.4.0, python1.0.0
## Feature
- Implemented at python level, without any additional CUDA kernel, c++ codes.
- Convolutional RNN, Convolutional LSTM, Convolutional Peephole LSTM, Convolutional GRU
- Unidirectional, Bidirectional
- 1d, 2d, 3d
- Supporting PackedSequence (Supporting variable length tensor)
- Supporting nlayers RNN and RNN Cell, both.
- Not supporting different hidden sizes for each layers (But, it is very easy to implement it by stacking 1-layer-CRNNs)
## Example
- With `pack_padded_sequence`
```python
import torch
import convolutional_rnn
from torch.nn.utils.rnn import pack_padded_sequence
in_channels = 2
net = convolutional_rnn.Conv3dGRU(in_channels=in_channels, # Corresponds to input size
out_channels=5, # Corresponds to hidden size
kernel_size=(3, 4, 6), # Int or List[int]
num_layers=2,
bidirectional=True,
dilation=2, stride=2, dropout=0.5)
length = 3
batchsize = 2
lengths = [3, 1]
shape = (10, 14, 18)
x = pack_padded_sequence(torch.randn(length, batchsize, in_channels, *shape), lengths, batch_first=False)
h = None
y, h = net(x, h)
```
- Without `pack_padded_sequence`
```python
import torch
import convolutional_rnn
from torch.nn.utils.rnn import pack_padded_sequence
in_channels = 2
net = convolutional_rnn.Conv2dLSTM(in_channels=in_channels, # Corresponds to input size
out_channels=5, # Corresponds to hidden size
kernel_size=3, # Int or List[int]
num_layers=2,
bidirectional=True,
dilation=2, stride=2, dropout=0.5,
batch_first=True)
length = 3
batchsize = 2
shape = (10, 14)
x = torch.randn(batchsize, length, in_channels, *shape)
h = None
y, h = net(x, h)
```
- With `Cell`
```python
import torch
import convolutional_rnn
cell = convolutional_rnn.Conv2dLSTMCell(in_channels=3, out_channels=5, kernel_size=3).cuda()
time = 6
input = torch.randn(time, 16, 3, 10, 10).cuda()
output = []
for i in range(time):
if i == 0:
hx, cx = cell(input[i])
else:
hx, cx = cell(input[i], (hx, cx))
output.append(hx)
```