Convolutional Gated Recurrent Unit (ConvGRU) in PyTorch
These modules implement an individual ConvGRUCell and the corresponding multi-cell ConvGRU wrapper in PyTorch.
The ConvGRU is implemented as described in Ballas et. al. 2015: Delving Deeper into Convolutional Networks for Learning Video Representations.
The ConvGRUCell was largely borrowed from @halochou. The ConvGRU wrapper is based on the PyTorch RNN source.
Usage
from convgru import ConvGRU
# Generate a ConvGRU with 3 cells
# input_size and hidden_sizes reflect feature map depths.
# Height and Width are preserved by zero padding within the module.
model = ConvGRU(input_size=8, hidden_sizes=[32,64,16],
kernel_sizes=[3, 5, 3], n_layers=3)
x = Variable(torch.FloatTensor(1,8,64,64))
output = model(x)
# output is a list of sequential hidden representation tensors
print(type(output)) # list
# final output size
print(output[-1].size()) # torch.Size([1, 16, 64, 64])Development
This tool is a product of the Laboratory of Cell Geometry at the University of California, San Francisco.