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import torch
import torch.nn as nn
class ConvLSTMCell(nn.Module):
def __init__(self, in_channel, num_hidden, height, width, filter_size, stride, layer_norm):
super(ConvLSTMCell, self).__init__()
self.num_hidden = num_hidden
self.padding = filter_size // 2
self._forget_bias = 1.0
if layer_norm:
self.conv_x = nn.Sequential(
nn.Conv2d(in_channel, num_hidden * 4, kernel_size=filter_size,
stride=stride, padding=self.padding, bias=False),
nn.LayerNorm([num_hidden * 4, height, width])
)
self.conv_h = nn.Sequential(
nn.Conv2d(num_hidden, num_hidden * 4, kernel_size=filter_size,
stride=stride, padding=self.padding, bias=False),
nn.LayerNorm([num_hidden * 4, height, width])
)
self.conv_o = nn.Sequential(
nn.Conv2d(num_hidden * 2, num_hidden, kernel_size=filter_size,
stride=stride, padding=self.padding, bias=False),
nn.LayerNorm([num_hidden, height, width])
)
else:
self.conv_x = nn.Sequential(
nn.Conv2d(in_channel, num_hidden * 4, kernel_size=filter_size,
stride=stride, padding=self.padding, bias=False),
)
self.conv_h = nn.Sequential(
nn.Conv2d(num_hidden, num_hidden * 4, kernel_size=filter_size,
stride=stride, padding=self.padding, bias=False),
)
self.conv_o = nn.Sequential(
nn.Conv2d(num_hidden * 2, num_hidden, kernel_size=filter_size,
stride=stride, padding=self.padding, bias=False),
)
self.conv_last = nn.Conv2d(num_hidden * 2, num_hidden, kernel_size=1,
stride=1, padding=0, bias=False)
def forward(self, x_t, h_t, c_t):
x_concat = self.conv_x(x_t)
h_concat = self.conv_h(h_t)
i_x, f_x, g_x, o_x = torch.split(x_concat, self.num_hidden, dim=1)
i_h, f_h, g_h, o_h = torch.split(h_concat, self.num_hidden, dim=1)
i_t = torch.sigmoid(i_x + i_h)
f_t = torch.sigmoid(f_x + f_h)
g_t = torch.tanh(g_x + g_h)
c_new = f_t * c_t + i_t * g_t
o_t = torch.sigmoid(o_x + o_h)
h_new = o_t * torch.tanh(c_new)
return h_new, c_new |