def __init__(self,
input_dim=None,
hidden_dim=200,
output_dim=100,
batch_size=1,
p_dropout=0.2,
num_layers=2):
super(LSTMModel, self).__init__()
self.hidden_dim = hidden_dim
self.batch_size = batch_size
self.bidirectional = True
self.num_layers = num_layers
self.bidir_mult = 2 if self.bidirectional else 1
self.dimension_mult = self.num_layers * self.bidir_mult
# The LSTM takes sequences of spectrograms/MFCCs as inputs, and outputs hidden states
# with dimensionality hidden_dim.
self.lstm = to_gpu(
nn.LSTM(input_dim,
hidden_dim,
bidirectional=self.bidirectional,
num_layers=self.num_layers,
dropout=p_dropout))
self.dropout_1 = nn.Dropout(p_dropout)
# The linear layer that maps from hidden state space to tag space
self.hidden2tag = to_gpu(
nn.Linear(hidden_dim * self.bidir_mult, output_dim))
self.reset_hidden()
def __getitem__(self, idx):
if idx < self.batches:
x = to_gpu(torch.randn(self.x_shape))
y = to_gpu(self.model(Variable(x)).data)
return (x, y)
else:
raise StopIteration()
def init_hidden(self, batch_size=None):
if batch_size is None:
batch_size = self.batch_size
# Before we've done anything, we dont have any hidden state.
# Refer to the Pytorch documentation to see exactly
# why they have this dimensionality.
# The axes semantics are (num_layers, minibatch_size, hidden_dim)
return (autograd.Variable(
to_gpu(
torch.zeros(self.dimension_mult, batch_size,
self.hidden_dim))),
autograd.Variable(
to_gpu(
torch.zeros(self.dimension_mult, batch_size,
self.hidden_dim))))
def forward(self, last_action=None, last_action_pos=None):
'''
One step of the RNN model
:param enc_output: batch x z_size, so don't support sequences
:param last_action: batch of ints, all equaling None for first step
:param last_action_pos: ignored, used by the attention decoder, here just to get the signature right
:return:
'''
if self.hidden is None: # first step after reset
# need to do it here as batch size might be different for each sequence
self.hidden = self.init_hidden(batch_size=self.batch_size)
self.one_hot_action = to_gpu(
torch.zeros(self.batch_size, self.output_feature_size))
encoded = self.encode(self.enc_output, last_action)
# copy the latent state to length of sequence, instead of sampling inputs
embedded = F.relu(self.fc_input(self.batch_norm(encoded))) \
.view(self.batch_size, 1, self.hidden_n) \
.repeat(1, self.max_seq_length, 1)
embedded = self.dropout_1(embedded)
# run the GRU on it
out_3, self.hidden = self.gru_1(embedded, self.hidden)
# tmp has dim (batch_size*seq_len)xhidden_n, so we can apply the linear transform to it
tmp = self.dropout_2(out_3.contiguous().view(-1, self.hidden_n))
out = self.fc_out(tmp).view(self.batch_size, self.max_seq_length,
self.output_feature_size)
# just return the logits
#self.hidden = None
return out #, hidden_1
def encode(self, x):
'''
:param x: a numpy array batch x seq x feature
:return:
'''
out, hidden = self.forward(to_gpu(Variable(FloatTensor(x))))
return out.data.cpu().numpy()
def gen():
iter = self.iterable.__iter__()
while True:
# TODO: cast to float earlier?
x = to_gpu(next(iter).float())
yield (x, x)
def init_hidden(self, batch_size):
h1 = Variable(to_gpu(
torch.zeros(self.dimension_mult, batch_size, self.hidden_n)),
requires_grad=False)
return h1
def init_hidden(self, batch_size):
# NOTE: assume only 1 layer no bi-direction
h1 = Variable(to_gpu(
torch.zeros(self.num_layers, batch_size, self.hidden_n)),
requires_grad=False)
return h1
