import torch
import torch.nn as nn
from torch.autograd import Variable
class ConvLSTMCell(nn.Module):
def __init__(self, input_channels, hidden_channels, kernel_size):
super(ConvLSTMCell, self).__init__()
assert hidden_channels % 2 == 0
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.num_features = 4
self.padding = int((kernel_size - 1) / 2)
self.Wxi = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True)
self.Whi = nn.Conv2d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False)
self.Wxf = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True)
self.Whf = nn.Conv2d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False)
Convolutional LSTM PyTorch
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转载自blog.csdn.net/tony2278/article/details/105296300
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