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model.py
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model.py
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import torch
import torch.nn as nn
from torch.autograd import Variable
device = "cuda" if torch.cuda.is_available else "cpu"
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)
self.Wxc = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True)
self.Whc = nn.Conv2d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False)
self.Wxo = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True)
self.Who = nn.Conv2d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False)
self.Wci = None
self.Wcf = None
self.Wco = None
def forward(self, x, h, c):
ci = torch.sigmoid(self.Wxi(x) + self.Whi(h) + c * self.Wci)
cf = torch.sigmoid(self.Wxf(x) + self.Whf(h) + c * self.Wcf)
cc = cf * c + ci * torch.tanh(self.Wxc(x) + self.Whc(h))
co = torch.sigmoid(self.Wxo(x) + self.Who(h) + cc * self.Wco)
ch = co * torch.tanh(cc)
return ch, cc
def init_hidden(self, batch_size, hidden, shape):
if self.Wci is None:
self.Wci = Variable(torch.zeros(1, hidden, shape[0], shape[1])).to("cuda")
self.Wcf = Variable(torch.zeros(1, hidden, shape[0], shape[1])).to("cuda")
self.Wco = Variable(torch.zeros(1, hidden, shape[0], shape[1])).to("cuda")
else:
assert shape[0] == self.Wci.size()[2], 'Input Height Mismatched!'
assert shape[1] == self.Wci.size()[3], 'Input Width Mismatched!'
return (Variable(torch.zeros(batch_size, hidden, shape[0], shape[1])).to("cuda"),
Variable(torch.zeros(batch_size, hidden, shape[0], shape[1])).to("cuda"))
class ConvLSTM(nn.Module):
# input_channels corresponds to the first input feature map
# hidden state is a list of succeeding lstm layers.
def __init__(self, input_channels, hidden_channels, kernel_size, step=1, effective_step=[1]):
super(ConvLSTM, self).__init__()
self.input_channels = [input_channels] + hidden_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.num_layers = len(hidden_channels)
self.step = step
self.effective_step = effective_step
self._all_layers = []
for i in range(self.num_layers):
name = 'cell{}'.format(i)
cell = ConvLSTMCell(self.input_channels[i], self.hidden_channels[i], self.kernel_size)
setattr(self, name, cell)
self._all_layers.append(cell)
def forward(self, input_matrix):
internal_state = []
outputs = []
for step in range(self.step):
x = input_matrix
for i in range(self.num_layers):
# all cells are initialized in the first step
name = 'cell{}'.format(i)
if step == 0:
bsize, _, height, width = x.size()
(h, c) = getattr(self, name).init_hidden(batch_size=bsize, hidden=self.hidden_channels[i],
shape=(height, width))
internal_state.append((h, c))
# do forward
(h, c) = internal_state[i]
x, new_c = getattr(self, name)(x, h, c)
internal_state[i] = (x, new_c)
# only record effective steps
if step in self.effective_step:
outputs.append(x)
# return outputs
return outputs, (x, new_c)
def attention(ConvLstm_out):
attention_w = []
for k in range(5):
attention_w.append(torch.sum(torch.mul(ConvLstm_out[k], ConvLstm_out[-1])) / 5)
m = nn.Softmax(dim=0)
attention_w = torch.reshape(m(torch.stack(attention_w)), (-1, 5))
cl_out_shape = ConvLstm_out.shape
ConvLstm_out = torch.reshape(ConvLstm_out, (5, -1))
convLstmOut = torch.matmul(attention_w, ConvLstm_out)
convLstmOut = torch.reshape(convLstmOut, (cl_out_shape[1], cl_out_shape[2], cl_out_shape[3]))
return convLstmOut
class CnnEncoder(nn.Module):
def __init__(self, in_channels_encoder, paddings):
super(CnnEncoder, self).__init__()
# đầu vào, đầu ra, kernel, stride, padding
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels_encoder, 32, kernel_size=3, stride=1, padding=paddings[0]),
nn.SELU()
)
self.conv2 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3, stride=(2, 2), padding=paddings[1]),
nn.SELU()
)
self.conv3 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=2, stride=(2, 2), padding=paddings[2]),
nn.SELU()
)
self.conv4 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=2, stride=(2, 2), padding=paddings[3]),
nn.SELU()
)
def forward(self, X):
conv1_out = self.conv1(X)
conv2_out = self.conv2(conv1_out)
conv3_out = self.conv3(conv2_out)
conv4_out = self.conv4(conv3_out)
return conv1_out, conv2_out, conv3_out, conv4_out
class Conv_LSTM(nn.Module):
def __init__(self):
super(Conv_LSTM, self).__init__()
#### head 1 #####
self.conv1_lstm_head1 = ConvLSTM(input_channels=32, hidden_channels=[32],
kernel_size=3, step=5, effective_step=[4])
self.conv2_lstm_head1 = ConvLSTM(input_channels=64, hidden_channels=[64],
kernel_size=3, step=5, effective_step=[4])
self.conv3_lstm_head1 = ConvLSTM(input_channels=128, hidden_channels=[128],
kernel_size=3, step=5, effective_step=[4])
self.conv4_lstm_head1 = ConvLSTM(input_channels=256, hidden_channels=[256],
kernel_size=3, step=5, effective_step=[4])
#### head 2 #####
#self.conv1_lstm_head2 = ConvLSTM(input_channels=32, hidden_channels=[32],
# kernel_size=3, step=5, effective_step=[4])
#self.conv2_lstm_head2 = ConvLSTM(input_channels=64, hidden_channels=[64],
# kernel_size=3, step=5, effective_step=[4])
#self.conv3_lstm_head2 = ConvLSTM(input_channels=128, hidden_channels=[128],
# kernel_size=3, step=5, effective_step=[4])
#self.conv4_lstm_head2 = ConvLSTM(input_channels=256, hidden_channels=[256],
# kernel_size=3, step=5, effective_step=[4])
#### head 3 #####
#self.conv1_lstm_head3 = ConvLSTM(input_channels=32, hidden_channels=[32],
# kernel_size=3, step=5, effective_step=[4])
#self.conv2_lstm_head3 = ConvLSTM(input_channels=64, hidden_channels=[64],
# kernel_size=3, step=5, effective_step=[4])
#self.conv3_lstm_head3 = ConvLSTM(input_channels=128, hidden_channels=[128],
# kernel_size=3, step=5, effective_step=[4])
#self.conv4_lstm_head3 = ConvLSTM(input_channels=256, hidden_channels=[256],
# kernel_size=3, step=5, effective_step=[4])
#### head 4 #####
#self.conv1_lstm_head4 = ConvLSTM(input_channels=32, hidden_channels=[32],
# kernel_size=3, step=5, effective_step=[4])
#self.conv2_lstm_head4 = ConvLSTM(input_channels=64, hidden_channels=[64],
# kernel_size=3, step=5, effective_step=[4])
#self.conv3_lstm_head4 = ConvLSTM(input_channels=128, hidden_channels=[128],
# kernel_size=3, step=5, effective_step=[4])
#self.conv4_lstm_head4 = ConvLSTM(input_channels=256, hidden_channels=[256],
# kernel_size=3, step=5, effective_step=[4])
#### head 5 #####
#self.conv1_lstm_head5 = ConvLSTM(input_channels=32, hidden_channels=[32],
# kernel_size=3, step=5, effective_step=[4])
#self.conv2_lstm_head5 = ConvLSTM(input_channels=64, hidden_channels=[64],
# kernel_size=3, step=5, effective_step=[4])
#self.conv3_lstm_head5 = ConvLSTM(input_channels=128, hidden_channels=[128],
# kernel_size=3, step=5, effective_step=[4])
#self.conv4_lstm_head5 = ConvLSTM(input_channels=256, hidden_channels=[256],
# kernel_size=3, step=5, effective_step=[4])
def forward(self, conv1_out, conv2_out,
conv3_out, conv4_out):
# head 1
conv1_lstm_out_head1 = self.conv1_lstm_head1(conv1_out)
conv1_lstm_out_att_head1 = attention(conv1_lstm_out_head1[0][0])
conv2_lstm_out_head1 = self.conv2_lstm_head1(conv2_out)
conv2_lstm_out_att_head1 = attention(conv2_lstm_out_head1[0][0])
conv3_lstm_out_head1 = self.conv3_lstm_head1(conv3_out)
conv3_lstm_out_att_head1 = attention(conv3_lstm_out_head1[0][0])
conv4_lstm_out_head1 = self.conv4_lstm_head1(conv4_out)
conv4_lstm_out_att_head1 = attention(conv4_lstm_out_head1[0][0])
# head 2
#conv1_lstm_out_head2 = self.conv1_lstm_head2(conv1_out)
#conv1_lstm_out_att_head2 = attention(conv1_lstm_out_head2[0][0])
#conv2_lstm_out_head2 = self.conv2_lstm_head2(conv2_out)
#conv2_lstm_out_att_head2 = attention(conv2_lstm_out_head2[0][0])
#conv3_lstm_out_head2 = self.conv3_lstm_head2(conv3_out)
#conv3_lstm_out_att_head2 = attention(conv3_lstm_out_head2[0][0])
#conv4_lstm_out_head2 = self.conv4_lstm_head2(conv4_out)
#conv4_lstm_out_att_head2 = attention(conv4_lstm_out_head2[0][0])
# head 3
#conv1_lstm_out_head3 = self.conv1_lstm_head3(conv1_out)
#conv1_lstm_out_att_head3 = attention(conv1_lstm_out_head3[0][0])
#conv2_lstm_out_head3 = self.conv2_lstm_head3(conv2_out)
#conv2_lstm_out_att_head3 = attention(conv2_lstm_out_head3[0][0])
#conv3_lstm_out_head3 = self.conv3_lstm_head3(conv3_out)
#conv3_lstm_out_att_head3 = attention(conv3_lstm_out_head3[0][0])
#conv4_lstm_out_head3 = self.conv4_lstm_head3(conv4_out)
#conv4_lstm_out_att_head3 = attention(conv4_lstm_out_head3[0][0])
# head 4
#conv1_lstm_out_head4 = self.conv1_lstm_head4(conv1_out)
#conv1_lstm_out_att_head4 = attention(conv1_lstm_out_head4[0][0])
#conv2_lstm_out_head4 = self.conv2_lstm_head4(conv2_out)
#conv2_lstm_out_att_head4 = attention(conv2_lstm_out_head4[0][0])
#conv3_lstm_out_head4 = self.conv3_lstm_head4(conv3_out)
#conv3_lstm_out_att_head4 = attention(conv3_lstm_out_head4[0][0])
#conv4_lstm_out_head4 = self.conv4_lstm_head4(conv4_out)
#conv4_lstm_out_att_head4 = attention(conv4_lstm_out_head4[0][0])
# head 5
#conv1_lstm_out_head5 = self.conv1_lstm_head5(conv1_out)
#conv1_lstm_out_att_head5 = attention(conv1_lstm_out_head5[0][0])
#conv2_lstm_out_head5 = self.conv2_lstm_head5(conv2_out)
#conv2_lstm_out_att_head5 = attention(conv2_lstm_out_head5[0][0])
#conv3_lstm_out_head5 = self.conv3_lstm_head5(conv3_out)
#conv3_lstm_out_att_head5 = attention(conv3_lstm_out_head5[0][0])
#conv4_lstm_out_head5 = self.conv4_lstm_head5(conv4_out)
#conv4_lstm_out_att_head5 = attention(conv4_lstm_out_head5[0][0])
conv1_lstm_out_att_5_step = [conv1_lstm_out_att_head1.unsqueeze(0), ]
# conv1_lstm_out_att_head2.unsqueeze(0),
# conv1_lstm_out_att_head3.unsqueeze(0),
# conv1_lstm_out_att_head4.unsqueeze(0),
# conv1_lstm_out_att_head5.unsqueeze(0)]
conv2_lstm_out_att_5_step = [conv2_lstm_out_att_head1.unsqueeze(0), ]
# conv2_lstm_out_att_head2.unsqueeze(0),
# conv2_lstm_out_att_head3.unsqueeze(0),
# conv2_lstm_out_att_head4.unsqueeze(0),
# conv2_lstm_out_att_head5.unsqueeze(0)]
conv3_lstm_out_att_5_step = [conv3_lstm_out_att_head1.unsqueeze(0), ]
# conv3_lstm_out_att_head2.unsqueeze(0),
# conv3_lstm_out_att_head3.unsqueeze(0),
# conv3_lstm_out_att_head4.unsqueeze(0),
# conv3_lstm_out_att_head5.unsqueeze(0) ]
conv4_lstm_out_att_5_step = [conv4_lstm_out_att_head1.unsqueeze(0), ]
# conv4_lstm_out_att_head2.unsqueeze(0),
# conv4_lstm_out_att_head3.unsqueeze(0),
# conv4_lstm_out_att_head4.unsqueeze(0),
# conv4_lstm_out_att_head5.unsqueeze(0) ]
#print(conv1_lstm_out_att_5_step[0] == conv1_lstm_out_att_5_step[1])
return torch.cat(conv1_lstm_out_att_5_step, dim=0), \
torch.cat(conv2_lstm_out_att_5_step, dim=0), \
torch.cat(conv3_lstm_out_att_5_step, dim=0), \
torch.cat(conv4_lstm_out_att_5_step, dim=0)
class CnnDecoder(nn.Module):
def __init__(self, in_channels, paddings, output_paddings):
super(CnnDecoder, self).__init__()
self.deconv4 = nn.Sequential(
nn.ConvTranspose2d(in_channels, 128, kernel_size=2, stride=2, padding=paddings[0],
output_padding=output_paddings[0]),
nn.SELU()
)
self.deconv3 = nn.Sequential(
nn.ConvTranspose2d(256, 64, kernel_size=2, stride=2, padding=paddings[1],
output_padding=output_paddings[1]),
nn.SELU()
)
self.deconv2 = nn.Sequential(
nn.ConvTranspose2d(128, 32, kernel_size=3, stride=2, padding=paddings[2],
output_padding=output_paddings[2]),
nn.SELU()
)
self.deconv1 = nn.Sequential(
nn.ConvTranspose2d(64, 3, kernel_size=3, stride=1, padding=paddings[3],
output_padding=output_paddings[3]),
nn.SELU()
)
def forward(self, conv1_lstm_out, conv2_lstm_out, conv3_lstm_out, conv4_lstm_out):
deconv4 = self.deconv4(conv4_lstm_out)
deconv4_concat = torch.cat((deconv4, conv3_lstm_out), dim=1)
deconv3 = self.deconv3(deconv4_concat)
deconv3_concat = torch.cat((deconv3, conv2_lstm_out), dim=1)
deconv2 = self.deconv2(deconv3_concat)
deconv2_concat = torch.cat((deconv2, conv1_lstm_out), dim=1)
deconv1 = self.deconv1(deconv2_concat)
return deconv1
class RCLEDmodel(nn.Module):
# Autoencoder model
def __init__(self,
num_vars,
in_channels_ENCODER,
in_channels_DECODER
):
self.dtype = torch.float32
super().__init__()
if num_vars == 25:
ENCODER_paddings = [1, 1, 1, 1]
DECODER_paddings = [1, 1, 1, 1]
DECODER_out_paddings = [1, 1, 0, 0]
if num_vars == 30:
ENCODER_paddings = [1, 1, 1, 0]
DECODER_paddings = [0, 1, 1, 1]
DECODER_out_paddings = [0, 1, 1, 0]
if num_vars == 55:
ENCODER_paddings = [1, 1, 1, 1]
DECODER_paddings = [1, 1, 1, 1]
DECODER_out_paddings = [1, 0, 0, 0]
self.cnn_encoder = CnnEncoder(in_channels_ENCODER, ENCODER_paddings)
self.conv_lstm = Conv_LSTM()
self.cnn_decoder = CnnDecoder(in_channels_DECODER, DECODER_paddings, DECODER_out_paddings)
def forward(self, x):
conv1_out, conv2_out, conv3_out, conv4_out = self.cnn_encoder(x)
conv1_lstm_out, conv2_lstm_out, conv3_lstm_out, conv4_lstm_out = self.conv_lstm(
conv1_out, conv2_out, conv3_out, conv4_out)
gen_x = self.cnn_decoder(conv1_lstm_out, conv2_lstm_out,
conv3_lstm_out, conv4_lstm_out)
return torch.cat([gen_x, gen_x, gen_x, gen_x, gen_x], dim=0)
if __name__ == '__main__':
print('Convolutional LSTM Encoder Decoder')