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import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvLSTMCell(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size, bias, device):
"""
Initialize ConvLSTM cell.
Parameters
----------
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(ConvLSTMCell, self).__init__()
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.device = device
self.conv = nn.Conv2d(
in_channels=self.input_dim + self.hidden_dim,
out_channels=4 * self.hidden_dim,
kernel_size=self.kernel_size,
padding=self.padding,
bias=self.bias,
)
def __initStates(self, size):
return torch.zeros(size).to(self.device), torch.zeros(size).to(self.device)
# return torch.zeros(size).cuda(), torch.zeros(size).cuda()
def forward(self, input_tensor, cur_state):
if cur_state == None:
h_cur, c_cur = self.__initStates(
[
input_tensor.shape[0],
self.hidden_dim,
input_tensor.shape[2],
input_tensor.shape[3],
]
)
else:
h_cur, c_cur = cur_state
combined = torch.cat(
[input_tensor, h_cur], dim=1
) # concatenate along channel axis
combined_conv = self.conv(combined)
cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1)
i = torch.sigmoid(cc_i)
f = torch.sigmoid(cc_f)
o = torch.sigmoid(cc_o)
g = torch.tanh(cc_g)
c_next = f * c_cur + i * g
h_next = o * torch.tanh(c_next)
return h_next, c_next
def init_hidden(self, batch_size, image_size):
height, width = image_size
return (
torch.zeros(
batch_size,
self.hidden_dim,
height,
width,
device=self.conv.weight.device,
),
torch.zeros(
batch_size,
self.hidden_dim,
height,
width,
device=self.conv.weight.device,
),
)
class ConvLSTM(nn.Module):
"""
Parameters:
input_dim: Number of channels in input
hidden_dim: Number of hidden channels
kernel_size: Size of kernel in convolutions
num_layers: Number of LSTM layers stacked on each other
batch_first: Whether or not dimension 0 is the batch or not
bias: Bias or no bias in Convolution
return_all_layers: Return the list of computations for all layers
Note: Will do same padding.
Input:
A tensor of size B, T, C, H, W or T, B, C, H, W
Output:
A tuple of two lists of length num_layers (or length 1 if return_all_layers is False).
0 - layer_output_list is the list of lists of length T of each output
1 - last_state_list is the list of last states
each element of the list is a tuple (h, c) for hidden state and memory
Example:
>> x = torch.rand((32, 10, 64, 128, 128))
>> convlstm = ConvLSTM(64, 16, 3, 1, True, True, False)
>> _, last_states = convlstm(x)
>> h = last_states[0][0] # 0 for layer index, 0 for h index
"""
def __init__(
self,
input_dim,
hidden_dim,
kernel_size,
num_layers,
batch_first=False,
bias=True,
return_all_layers=False,
):
super(ConvLSTM, 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.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(
ConvLSTMCell(
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):
"""
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
Returns
-------
last_state_list, layer_output
"""
if not self.batch_first:
# (t, b, c, h, w) -> (b, t, c, h, w)
input_tensor = input_tensor.permute(1, 0, 2, 3, 4)
b, _, _, h, w = input_tensor.size()
# Implement stateful ConvLSTM
if hidden_state is not None:
raise NotImplementedError()
else:
# Since the init is done in forward. Can send image size here
hidden_state = self._init_hidden(batch_size=b, image_size=(h, w))
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, c = hidden_state[layer_idx]
output_inner = []
for t in range(seq_len):
h, c = self.cell_list[layer_idx](
input_tensor=cur_layer_input[:, t, :, :, :], cur_state=[h, c]
)
output_inner.append(h)
layer_output = torch.stack(output_inner, dim=1)
cur_layer_input = layer_output
layer_output_list.append(layer_output)
last_state_list.append([h, c])
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, image_size):
init_states = []
for i in range(self.num_layers):
init_states.append(self.cell_list[i].init_hidden(batch_size, image_size))
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
def normal_init(m, mean, std):
if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d):
m.weight.data.normal_(mean, std)
m.bias.data.zero_()
class Generator(nn.Module):
def __init__(self, device, inputChannels=4, outputChannels=3, d=64):
super().__init__()
self.d = d
self.device = device
self.conv1 = nn.Conv2d(inputChannels, d, 3, 2, 1)
self.conv2 = nn.Conv2d(d, d * 2, 3, 2, 1)
self.conv3 = nn.Conv2d(d * 2, d * 4, 3, 2, 1)
self.conv4 = nn.Conv2d(d * 4, d * 8, 3, 2, 1)
self.conv5 = nn.Conv2d(d * 8, d * 8, 3, 2, 1)
self.conv6 = nn.Conv2d(d * 8, d * 8, 3, 2, 1)
self.conv7 = nn.Conv2d(d * 8, d * 8, 3, 2, 1)
self.conv_lstm_d1 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
self.conv_lstm_d2 = ConvLSTMCell(d * 8 * 2, d * 8, (3, 3), False, device)
self.conv_lstm_d3 = ConvLSTMCell(d * 8 * 2, d * 8, (3, 3), False, device)
self.conv_lstm_d4 = ConvLSTMCell(d * 8 * 2, d * 4, (3, 3), False, device)
self.conv_lstm_d5 = ConvLSTMCell(d * 4 * 2, d * 2, (3, 3), False, device)
self.conv_lstm_d6 = ConvLSTMCell(d * 2 * 2, d, (3, 3), False, device)
self.conv_lstm_d7 = ConvLSTMCell(d * 2, d, (3, 3), False, device)
self.conv_lstm_e1 = ConvLSTMCell(d, d, (3, 3), False, device)
self.conv_lstm_e2 = ConvLSTMCell(d * 2, d * 2, (3, 3), False, device)
self.conv_lstm_e3 = ConvLSTMCell(d * 4, d * 4, (3, 3), False, device)
self.conv_lstm_e4 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
self.conv_lstm_e5 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
self.conv_lstm_e6 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
self.conv_lstm_e7 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
self.up = nn.Upsample(scale_factor=2)
self.conv_out = nn.Conv2d(d, outputChannels, 3, 1, 1)
self.slope = 0.2
def weight_init(self, mean, std):
for m in self._modules:
normal_init(self._modules[m], mean, std)
def forward_step(self, input, states_encoder, states_decoder):
e1 = self.conv1(input)
states_e1 = self.conv_lstm_e1(e1, states_encoder[0])
e2 = self.conv2(F.leaky_relu(states_e1[0], self.slope))
states_e2 = self.conv_lstm_e2(e2, states_encoder[1])
e3 = self.conv3(F.leaky_relu(states_e2[0], self.slope))
states_e3 = self.conv_lstm_e3(e3, states_encoder[2])
e4 = self.conv4(F.leaky_relu(states_e3[0], self.slope))
states_e4 = self.conv_lstm_e4(e4, states_encoder[3])
e5 = self.conv5(F.leaky_relu(states_e4[0], self.slope))
states_e5 = self.conv_lstm_e5(e5, states_encoder[4])
e6 = self.conv6(F.leaky_relu(states_e5[0], self.slope))
states_e6 = self.conv_lstm_e6(e6, states_encoder[5])
e7 = self.conv7(F.leaky_relu(states_e6[0], self.slope))
states1 = self.conv_lstm_d1(F.relu(e7), states_decoder[0])
d1 = self.up(states1[0])
d1 = torch.cat([d1, e6], 1)
states2 = self.conv_lstm_d2(F.relu(d1), states_decoder[1])
d2 = self.up(states2[0])
d2 = torch.cat([d2, e5], 1)
states3 = self.conv_lstm_d3(F.relu(d2), states_decoder[2])
d3 = self.up(states3[0])
d3 = torch.cat([d3, e4], 1)
states4 = self.conv_lstm_d4(F.relu(d3), states_decoder[3])
d4 = self.up(states4[0])
d4 = torch.cat([d4, e3], 1)
states5 = self.conv_lstm_d5(F.relu(d4), states_decoder[4])
d5 = self.up(states5[0])
d5 = torch.cat([d5, e2], 1)
states6 = self.conv_lstm_d6(F.relu(d5), states_decoder[5])
d6 = self.up(states6[0])
d6 = torch.cat([d6, e1], 1)
states7 = self.conv_lstm_d7(F.relu(d6), states_decoder[6])
d7 = self.up(states7[0])
o = torch.clip(torch.tanh(self.conv_out(d7)), min=-0.0, max=1)
states_e = [states_e1, states_e2, states_e3, states_e4, states_e5, states_e6]
states_d = [states1, states2, states3, states4, states5, states6, states7]
return o, (states_e, states_d)
def forward(self, tensor):
states_encoder = (None, None, None, None, None, None, None)
states_decoder = (None, None, None, None, None, None, None)
output = torch.empty_like(tensor)
for timeStep in range(tensor.shape[4]):
output[:, :, :, :, timeStep], states = self.forward_step(
tensor[:, :, :, :, timeStep], states_encoder, states_decoder
)
states_encoder, states_decoder = states[0], states[1]
return output, states
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