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"""
Modified from https://github.com/TUM-LMF/MTLCC-pytorch/blob/master/src/models/convlstm/convlstm.py
authors: TUM-LMF
"""
import torch.nn as nn
from torch.autograd import Variable
import torch
class ConvGRUCell(nn.Module):
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias):
"""
Initialize ConvLSTM 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.in_conv = 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.out_conv = nn.Conv2d(
in_channels=self.input_dim + self.hidden_dim,
out_channels=self.hidden_dim,
kernel_size=self.kernel_size,
padding=self.padding,
bias=self.bias,
)
def forward(self, input_tensor, cur_state):
combined = torch.cat([input_tensor, cur_state], dim=1)
z, r = torch.sigmoid(self.in_conv(combined)).chunk(2, dim=1)
h = torch.tanh(self.out_conv(torch.cat([input_tensor, r * cur_state], dim=1)))
new_state = (1 - z) * cur_state + z * h
return new_state
def init_hidden(self, batch_size, device):
return Variable(
torch.zeros(batch_size, self.hidden_dim, self.height, self.width)
).to(device)
class ConvGRU(nn.Module):
def __init__(
self,
input_size,
input_dim,
hidden_dim,
kernel_size,
num_layers=1,
batch_first=True,
bias=True,
return_all_layers=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)
if not len(kernel_size) == len(hidden_dim) == 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
self.return_all_layers = return_all_layers
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,
)
)
self.cell_list = nn.ModuleList(cell_list)
def forward(
self, input_tensor, hidden_state=None, pad_mask=None, batch_positions=None
):
"""
Parameters
----------
input_tensor: todo
5-D Tensor either of shape (t, b, c, h, w) or (b, t, c, h, w)
hidden_state: todo
None. todo implement stateful
pad_maks (b , t)
Returns
-------
last_state_list, layer_output
"""
if not self.batch_first:
# (t, b, c, h, w) -> (b, t, c, h, w)
input_tensor.permute(1, 0, 2, 3, 4)
# Implement stateful ConvLSTM
if hidden_state is not None:
raise NotImplementedError()
else:
hidden_state = self._init_hidden(
batch_size=input_tensor.size(0), device=input_tensor.device
)
layer_output_list = []
last_state_list = []
seq_len = input_tensor.size(1)
cur_layer_input = input_tensor
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_tensor=cur_layer_input[:, t, :, :, :], cur_state=h
)
output_inner.append(h)
layer_output = torch.stack(output_inner, dim=1)
if pad_mask is not None:
last_positions = (~pad_mask).sum(dim=1) - 1
layer_output = layer_output[:, last_positions, :, :, :]
cur_layer_input = layer_output
layer_output_list.append(layer_output)
last_state_list.append(h)
if not self.return_all_layers:
layer_output_list = layer_output_list[-1]
last_state_list = last_state_list[-1]
return layer_output_list, last_state_list
def _init_hidden(self, batch_size, device):
init_states = []
for i in range(self.num_layers):
init_states.append(self.cell_list[i].init_hidden(batch_size, device))
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
class ConvGRU_Seg(nn.Module):
def __init__(
self, num_classes, input_size, input_dim, hidden_dim, kernel_size, pad_value=0
):
super(ConvGRU_Seg, self).__init__()
self.convgru_encoder = ConvGRU(
input_dim=input_dim,
input_size=input_size,
hidden_dim=hidden_dim,
kernel_size=kernel_size,
return_all_layers=False,
)
self.classification_layer = nn.Conv2d(
in_channels=hidden_dim,
out_channels=num_classes,
kernel_size=kernel_size,
padding=1,
)
self.pad_value = pad_value
def forward(self, input, batch_positions=None):
pad_mask = (
(input == self.pad_value).all(dim=-1).all(dim=-1).all(dim=-1)
) # BxT pad mask
pad_mask = pad_mask if pad_mask.any() else None
_, out = self.convgru_encoder(input, pad_mask=pad_mask)
out = self.classification_layer(out)
return out
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