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convgru.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class ConvGRUCell(nn.Module):
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias=True, activation=F.tanh, batchnorm=False):
"""
Initialize ConvGRU cell.
Parameters
----------
input_size: (int, int)
Height and width of input tensor as (height, width).
input_dim: int
Number of channels of input tensor.
hidden_dim: int
Number of channels of hidden state.
kernel_size: (int, int)
Size of the convolutional kernel.
bias: bool
Whether or not to add the bias.
"""
super(ConvGRUCell, self).__init__()
self.height, self.width = input_size
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.kernel_size = kernel_size
self.padding = kernel_size[0] // 2, kernel_size[1] // 2
self.bias = bias
self.activation = activation
self.batchnorm = batchnorm
self.conv_zr = nn.Conv2d(in_channels=self.input_dim + self.hidden_dim,
out_channels=2 * self.hidden_dim,
kernel_size=self.kernel_size,
padding=self.padding,
bias=self.bias)
self.conv_h1 = nn.Conv2d(in_channels=self.input_dim,
out_channels=self.hidden_dim,
kernel_size=self.kernel_size,
padding=self.padding,
bias=self.bias)
self.conv_h2 = nn.Conv2d(in_channels=self.hidden_dim,
out_channels=self.hidden_dim,
kernel_size=self.kernel_size,
padding=self.padding,
bias=self.bias)
self.reset_parameters()
def forward(self, input, h_prev):
combined = torch.cat((input, h_prev), dim=1) # concatenate along channel axis
combined_conv = F.sigmoid(self.conv_zr(combined))
z, r = torch.split(combined_conv, self.hidden_dim, dim=1)
h_ = self.activation(self.conv_h1(input) + r * self.conv_h2(h_prev))
h_cur = (1 - z) * h_ + z * h_prev
return h_cur
def init_hidden(self, batch_size, cuda=True):
state = torch.zeros(batch_size, self.hidden_dim, self.height, self.width)
if cuda:
state = state.cuda()
return state
def reset_parameters(self):
#self.conv.reset_parameters()
nn.init.xavier_uniform_(self.conv_zr.weight, gain=nn.init.calculate_gain('tanh'))
self.conv_zr.bias.data.zero_()
nn.init.xavier_uniform_(self.conv_h1.weight, gain=nn.init.calculate_gain('tanh'))
self.conv_h1.bias.data.zero_()
nn.init.xavier_uniform_(self.conv_h2.weight, gain=nn.init.calculate_gain('tanh'))
self.conv_h2.bias.data.zero_()
if self.batchnorm:
self.bn1.reset_parameters()
self.bn2.reset_parameters()
class ConvGRU(nn.Module):
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, num_layers, batch_first=True, bias=True, activation=F.tanh, batchnorm=False):
super(ConvGRU, self).__init__()
self._check_kernel_size_consistency(kernel_size)
# Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers
kernel_size = self._extend_for_multilayer(kernel_size, num_layers)
hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers)
activation = self._extend_for_multilayer(activation, num_layers)
if not len(kernel_size) == len(hidden_dim) == len(activation) == num_layers:
raise ValueError('Inconsistent list length.')
self.height, self.width = input_size
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.kernel_size = kernel_size
self.num_layers = num_layers
self.batch_first = batch_first
self.bias = bias
cell_list = []
for i in range(0, self.num_layers):
cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i-1]
cell_list.append(ConvGRUCell(input_size=(self.height, self.width),
input_dim=cur_input_dim,
hidden_dim=self.hidden_dim[i],
kernel_size=self.kernel_size[i],
bias=self.bias,
activation=activation[i],
batchnorm=batchnorm))
self.cell_list = nn.ModuleList(cell_list)
self.reset_parameters()
def forward(self, input, hidden_state):
"""
Parameters
----------
input_tensor:
5-D Tensor either of shape (t, b, c, h, w) or (b, t, c, h, w)
hidden_state:
Returns
-------
last_state_list, layer_output
"""
cur_layer_input = torch.unbind(input, dim=int(self.batch_first))
if not hidden_state:
hidden_state = self.get_init_states(cur_layer_input[0].size(0))
seq_len = len(cur_layer_input)
layer_output_list = []
last_state_list = []
for layer_idx in range(self.num_layers):
h = hidden_state[layer_idx]
output_inner = []
for t in range(seq_len):
h = self.cell_list[layer_idx](input=cur_layer_input[t],
h_prev=h)
output_inner.append(h)
cur_layer_input = output_inner
last_state_list.append(h)
layer_output = torch.stack(output_inner, dim=int(self.batch_first))
return layer_output, last_state_list
def reset_parameters(self):
for c in self.cell_list:
c.reset_parameters()
def get_init_states(self, batch_size, cuda=True):
init_states = []
for i in range(self.num_layers):
init_states.append(self.cell_list[i].init_hidden(batch_size, cuda))
return init_states
@staticmethod
def _check_kernel_size_consistency(kernel_size):
if not (isinstance(kernel_size, tuple) or (isinstance(kernel_size, list)
and all([isinstance(elem, tuple) for elem in kernel_size]))):
raise ValueError('`kernel_size` must be tuple or list of tuples')
@staticmethod
def _extend_for_multilayer(param, num_layers):
if not isinstance(param, list):
param = [param] * num_layers
return param