Initialize stateful ConvLSTM cell. Parameters ---------- input_channels : ``int`` Number of channels of input tensor. hidden_channels : ``int`` Number of channels of hidden state. kernel_size : ``int`` Size of the c
(self, input_channels, hidden_channels, kernel_size)
| 5 | |
| 6 | class ConvLSTMCell(nn.Module): |
| 7 | def __init__(self, input_channels, hidden_channels, kernel_size): |
| 8 | """Initialize stateful ConvLSTM cell. |
| 9 | |
| 10 | Parameters |
| 11 | ---------- |
| 12 | input_channels : ``int`` |
| 13 | Number of channels of input tensor. |
| 14 | hidden_channels : ``int`` |
| 15 | Number of channels of hidden state. |
| 16 | kernel_size : ``int`` |
| 17 | Size of the convolutional kernel. |
| 18 | |
| 19 | Paper |
| 20 | ----- |
| 21 | https://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting.pdf |
| 22 | |
| 23 | Referenced code |
| 24 | --------------- |
| 25 | https://github.com/automan000/Convolution_LSTM_PyTorch/blob/master/convolution_lstm.py |
| 26 | """ |
| 27 | super(ConvLSTMCell, self).__init__() |
| 28 | |
| 29 | assert hidden_channels % 2 == 0 |
| 30 | |
| 31 | self.input_channels = input_channels |
| 32 | self.hidden_channels = hidden_channels |
| 33 | self.kernel_size = kernel_size |
| 34 | self.num_features = 4 |
| 35 | |
| 36 | self.padding = int((kernel_size - 1) / 2) |
| 37 | |
| 38 | self.Wxi = nn.Conv2d( |
| 39 | self.input_channels, |
| 40 | self.hidden_channels, |
| 41 | self.kernel_size, |
| 42 | 1, |
| 43 | self.padding, |
| 44 | bias=True, |
| 45 | ) |
| 46 | self.Whi = nn.Conv2d( |
| 47 | self.hidden_channels, |
| 48 | self.hidden_channels, |
| 49 | self.kernel_size, |
| 50 | 1, |
| 51 | self.padding, |
| 52 | bias=False, |
| 53 | ) |
| 54 | self.Wxf = nn.Conv2d( |
| 55 | self.input_channels, |
| 56 | self.hidden_channels, |
| 57 | self.kernel_size, |
| 58 | 1, |
| 59 | self.padding, |
| 60 | bias=True, |
| 61 | ) |
| 62 | self.Whf = nn.Conv2d( |
| 63 | self.hidden_channels, |
| 64 | self.hidden_channels, |