import torch.nn as nn
from train_test_lstm import train, test
from MusicDataset import Musicdata_LSTM
basic_dir = 'D:/OneDrive-UCalgary/OneDrive - University of Calgary/data/cal500/'
data_file = basic_dir + 'music-data-v7.csv'
label_file = basic_dir + 'labels-v5.csv'
record_file = 'record-lstm.txt'
model_path = 'lstm.pt'
net_name = 'lstm'
net = nn.LSTM(16, 18, 2)
train_set = Musicdata_LSTM(data_file=data_file,
label_file=label_file,
start=0,
total=2560)
test_set = Musicdata_LSTM(data_file=data_file,
label_file=label_file,
start=2560,
total=3219)
net = train(net, model_path=model_path, dataset=train_set)
test(net, net_name, dataset=test_set)
def __init__(self, input_size, hidden_size, output_size, num_layer):
super(net, self).__init__()
self.layer1 = nn.LSTM(input_size, hidden_size, num_layer)
self.layer2 = nn.Linear(hidden_size, output_size)
self.layer3 = nn.Softmax()
def __init__(self, input_size, hidden_size, output_size=1, num_layers=2):
super(LSTM, self).__init__()
self.lstm = nn.LSTM(input_size, hidden_size, num_layers)
self.out = nn.Linear(hidden_size, output_size)
def __init__(self, n_actions):
super(Network, self).__init__()
self.cnn = CNN(n_actions)
self.rnn = nn.LSTM(1447, 256, 1)
self.out = nn.Linear(256, n_actions)
self.to(device)
except OverflowError:
perplexity = float('inf')
if (epoch + 1) % pred_period == 0:
print('epoch %d, perplexity %f, time %.2f sec' %
(epoch + 1, perplexity, time.time() - start))
for prefix in prefixes:
print(
' -',
predict_rnn_pytorch(prefix, pred_len, model, vocab_size,
device, idx_to_char, char_to_idx))
(corpus_indices, char_to_idx, idx_to_char, vocab_size) = load_data_jay_lyrics()
# 初始化模型参数
num_inputs, num_hiddens, num_outputs = vocab_size, 256, vocab_size
print('will use', device)
# 训练并创作
num_epochs, num_steps, batch_size, lr, clipping_theta = 160, 35, 32, 1e-2, 1e-2
pred_period, pred_len, prefixes = 40, 50, ['分开', '不分开']
lstm_layer = nn.LSTM(input_size=vocab_size, hidden_size=num_hiddens)
model = RNNModel(lstm_layer, vocab_size)
train_and_predict_rnn_pytorch(model, num_hiddens, vocab_size, device,
corpus_indices, idx_to_char, char_to_idx,
num_epochs, num_steps, lr, clipping_theta,
batch_size, pred_period, pred_len, prefixes)
"""
https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
torch.manual_seed(1)
lstm = nn.LSTM(3, 4) # Input dim is 3, output dim is 3
inputs = [torch.randn(1, 3) for _ in range(5)] # make a sequence of length 5
# initialize the hidden state.
hidden = (torch.randn(1, 1, 4), torch.randn(1, 1, 4))
for i in inputs:
# Step through the sequence one element at a time.
# after each step, hidden contains the hidden state.
out, hidden = lstm(i.view(1, 1, -1), hidden)
print(out)
print(hidden)
print('@')
# lstm = nn.LSTM(3, 4) # Input dim is 3, output dim is 3
# alternatively, we can do the entire sequence all at once.
# the first value returned by LSTM is all of the hidden states throughout
# the sequence. the second is just the most recent hidden state
# (compare the last slice of "out" with "hidden" below, they are the same)
# The reason for this is that:
# "out" will give you access to all hidden states in the sequence
def __init__(self, args):
super(LSTM, self).__init__()
self.args = args
self.lstm = nn.LSTM(args.embed_size, args.lstm_hidden_size, batch_first=True,
dropout=args.drop_out_lstm, num_layers=args.lstm_num_layers,bidirectional = args.bidirectional)
def __init__(self, input_dim, hidden_dim, output_dim):
super(LSTMTagger, self).__init__()
self.lstm = nn.LSTM(input_dim, hidden_dim)
self.hidden2tag = nn.Linear(hidden_dim, output_dim)
def __init__(self, in_dim, hidden_dim, n_layer, n_class):
super(RNN, self).__init__()
self.n_layer = n_layer
self.hidden = hidden_dim
self.lstm = nn.LSTM(in_dim, hidden_dim, n_layer, batch_first=True)
self.classifier = nn.Linear(hidden_dim, n_class)
def __init__(self, hps, *_):
super(BiLSTMTagger, self).__init__()
batch_size = hps['batch_size']
lstm_hidden_dim = hps['sent_hdim']
sent_embedding_dim = 3 * hps['sent_edim'] + 1 * hps['pos_edim']
# for the region mark
sent_embedding_dim += 1
role_embedding_dim = hps['role_edim']
frame_embedding_dim = role_embedding_dim
vocab_size = hps['vword']
self.tagset_size = hps['vbio']
self.pos_size = hps['vpos']
self.dep_size = hps['vdep']
self.frameset_size = hps['vframe']
self.num_layers = hps['rec_layers']
self.batch_size = batch_size
self.hidden_dim = lstm_hidden_dim
self.word_emb_dim = hps['sent_edim']
self.specific_dep_size = hps['svdep']
self.word_embeddings = nn.Embedding(vocab_size, hps['sent_edim'])
self.pos_embeddings = nn.Embedding(self.pos_size, hps['pos_edim'])
self.dep_embeddings = nn.Embedding(self.dep_size, hps['pos_edim'])
self.p_lemma_embeddings = nn.Embedding(self.frameset_size,
hps['sent_edim'])
# self.lr_dep_embeddings = nn.Embedding(self.lr_dep_size, hps[])
self.word_fixed_embeddings = nn.Embedding(vocab_size, hps['sent_edim'])
self.word_fixed_embeddings.weight.data.copy_(
torch.from_numpy(hps['word_embeddings']))
self.role_embeddings = nn.Embedding(self.tagset_size,
role_embedding_dim)
self.frame_embeddings = nn.Embedding(self.frameset_size,
frame_embedding_dim)
self.hidden2tag_M = nn.Linear(100, 200)
self.hidden2tag_H = nn.Linear(100, 200)
self.MLP = nn.Linear(200, self.dep_size)
self.hidden2tag_spe = nn.Linear(100, 100)
self.MLP_spe = nn.Linear(100, 4)
self.word_emb_dropout = nn.Dropout(p=0.3)
self.hidden_state_dropout = nn.Dropout(p=0.3)
self.label_dropout = nn.Dropout(p=0.5)
self.link_dropout = nn.Dropout(p=0.5)
self.Label_hidden2hidden = nn.Linear(100, 20)
# The LSTM takes word embeddings as inputs, and outputs hidden states
# with dimensionality hidden_dim.
self.num_layers = 2
self.BiLSTM_share = nn.LSTM(input_size=sent_embedding_dim,
hidden_size=lstm_hidden_dim,
batch_first=True,
bidirectional=True,
num_layers=self.num_layers)
init.orthogonal_(self.BiLSTM_share.all_weights[0][0])
init.orthogonal_(self.BiLSTM_share.all_weights[0][1])
init.orthogonal_(self.BiLSTM_share.all_weights[1][0])
init.orthogonal_(self.BiLSTM_share.all_weights[1][1])
self.num_layers = 1
self.BiLSTM_Spe = nn.LSTM(input_size=lstm_hidden_dim * 2,
hidden_size=lstm_hidden_dim,
batch_first=True,
bidirectional=True,
num_layers=self.num_layers)
init.orthogonal_(self.BiLSTM_Spe.all_weights[0][0])
init.orthogonal_(self.BiLSTM_Spe.all_weights[0][1])
init.orthogonal_(self.BiLSTM_Spe.all_weights[1][0])
init.orthogonal_(self.BiLSTM_Spe.all_weights[1][1])
self.num_layers = 1
self.BiLSTM_SRL = nn.LSTM(input_size=lstm_hidden_dim * 2 + 20,
hidden_size=lstm_hidden_dim,
batch_first=True,
bidirectional=True,
num_layers=self.num_layers)
init.orthogonal_(self.BiLSTM_SRL.all_weights[0][0])
init.orthogonal_(self.BiLSTM_SRL.all_weights[0][1])
init.orthogonal_(self.BiLSTM_SRL.all_weights[1][0])
init.orthogonal_(self.BiLSTM_SRL.all_weights[1][1])
# non-linear map to role embedding
self.role_map = nn.Linear(in_features=role_embedding_dim * 2,
out_features=self.hidden_dim * 4)
# Init hidden state
self.hidden = self.init_hidden_share()
self.hidden_2 = self.init_hidden_spe()
self.hidden_3 = self.init_hidden_spe()
self.hidden_4 = self.init_hidden_spe()
def create_lstm():
return [nn.LSTM(*get_size(l), 1) for l in range(n_layers)]
def __init__(self, n_char, char_dim, char_hidden):
super(char_lstm, self).__init__()
self.char_embed = nn.Embedding(n_char, char_dim)
self.lstm = nn.LSTM(char_dim, char_hidden)
def __init__(self, vocab_size, embed_size, hidden_size):
super().__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size, hidden_size, num_layers=2, bidirectional=True)
self.linear = nn.Linear(hidden_size * 2, 5)
self.softmax = nn.Softmax(dim=2)
def __init__(self, options):
super(AileverModel, self).__init__()
self.lstm = nn.LSTM(2, options.dataset.sequence, 3, batch_first=True)
self.linear = nn.Linear(options.dataset.sequence,
options.dataset.prediction)
def __init__(self, nIn, nHidden, nOut):
super(BidirectionalLSTM, self).__init__()
self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
self.embedding = nn.Linear(nHidden * 2, nOut)
def __init__(self, vocab_size, embedding_dim, hidden_dim):
super(PoetryModel, self).__init__()
self.hidden_dim = hidden_dim
self.embeddings = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, self.hidden_dim, num_layers=2)
self.linear1 = nn.Linear(self.hidden_dim, vocab_size)
def __init__(self, hidden_size, n_layers=1):
super(LSTM_Encoder, self).__init__()
self.n_layers = n_layers
self.hidden_size = hidden_size
self.lstm = nn.LSTM(hidden_size, hidden_size)
embedding_dim=50).cuda()
pos_u_embeddings = torch.nn.Embedding(num_embeddings=len(posUni) + 3,
embedding_dim=10).cuda()
pos_p_embeddings = torch.nn.Embedding(num_embeddings=len(posFine) + 3,
embedding_dim=10).cuda()
state_embeddings = torch.nn.Embedding(num_embeddings=len(itos_state),
embedding_dim=50).cuda()
#baseline = torch.nn.Embedding(num_embeddings = vocab_size+3, embedding_dim=1).cuda()
#baseline_upos = torch.nn.Embedding(num_embeddings = len(posUni)+3, embedding_dim=1).cuda()
#baseline_ppos = torch.nn.Embedding(num_embeddings = len(posFine)+3, embedding_dim=1).cuda()
dropout = nn.Dropout(0.3).cuda()
rnn = nn.LSTM(70, 128, 1).cuda()
for name, param in rnn.named_parameters():
if 'bias' in name:
nn.init.constant(param, 0.0)
elif 'weight' in name:
nn.init.xavier_normal(param)
rnn_state = nn.LSTM(50, 128, 1).cuda()
for name, param in rnn_state.named_parameters():
if 'bias' in name:
nn.init.constant(param, 0.0)
elif 'weight' in name:
nn.init.xavier_normal(param)
vocab_size_states = len(itos_state)
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(simpleLSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, num_classes)
def __init__(self, inputdim,hid_dim, layers): super(Decoder,self).__init__() self.hidden = hid_dim self.n_layers = layers self.lstm = nn.LSTM(inputdim, hid_dim, layers)
def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1):
super(DecoderRNN, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
self.linear = nn.Linear(hidden_size, vocab_size)
def __init__(self, embed_size, hidden_size, vocab, dropout_rate=0.2):
""" Init NMT Model.
@param embed_size (int): Embedding size (dimensionality)
@param hidden_size (int): Hidden Size, the size of hidden states (dimensionality)
@param vocab (Vocab): Vocabulary object containing src and tgt languages
See vocab.py for documentation.
@param dropout_rate (float): Dropout probability, for attention
"""
super(NMT, self).__init__()
self.model_embeddings = ModelEmbeddings(embed_size, vocab)
self.hidden_size = hidden_size
self.dropout_rate = dropout_rate
self.vocab = vocab
# default values
self.encoder = None
self.decoder = None
self.h_projection = None
self.c_projection = None
self.att_projection = None
self.combined_output_projection = None
self.target_vocab_projection = None
self.dropout = None
# For sanity check only, not relevant to implementation
self.gen_sanity_check = False
self.counter = 0
### YOUR CODE HERE (~8 Lines)
### TODO - Initialize the following variables:
### self.encoder (Bidirectional LSTM with bias)
### self.decoder (LSTM Cell with bias)
### self.h_projection (Linear Layer with no bias), called W_{h} in the PDF.
### self.c_projection (Linear Layer with no bias), called W_{c} in the PDF.
### self.att_projection (Linear Layer with no bias), called W_{attProj} in the PDF.
### self.combined_output_projection (Linear Layer with no bias), called W_{u} in the PDF.
### self.target_vocab_projection (Linear Layer with no bias), called W_{vocab} in the PDF.
### self.dropout (Dropout Layer)
###
### Use the following docs to properly initialize these variables:
### LSTM:
### https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM
### LSTM Cell:
### https://pytorch.org/docs/stable/nn.html#torch.nn.LSTMCell
### Linear Layer:
### https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
### Dropout Layer:
### https://pytorch.org/docs/stable/nn.html#torch.nn.Dropout
self.encoder = nn.LSTM(input_size=embed_size,
hidden_size=self.hidden_size,
bias=True,
bidirectional=True)
self.decoder = nn.LSTMCell(input_size=embed_size + hidden_size,
hidden_size=self.hidden_size,
bias=True)
self.h_projection = nn.Linear(in_features=2 * self.hidden_size,
out_features=self.hidden_size,
bias=False)
self.c_projection = nn.Linear(in_features=2 * self.hidden_size,
out_features=self.hidden_size,
bias=False)
self.att_projection = nn.Linear(in_features=2 * self.hidden_size,
out_features=self.hidden_size,
bias=False)
self.combined_output_projection = nn.Linear(
in_features=3 * self.hidden_size,
out_features=self.hidden_size,
bias=False)
self.target_vocab_projection = nn.Linear(in_features=self.hidden_size,
out_features=len(
self.vocab.tgt),
bias=False)
self.dropout = nn.Dropout(p=self.dropout_rate)
def __init__(self, x_tasks, model_config):
self.criterion = self.avg_sharpe_ratio
self.X_train_tasks = x_tasks
self.tsteps = model_config["tsteps"]
self.tasks_tsteps = model_config["tasks_tsteps"]
self.batch_size = model_config["batch_size"]
self.seq_len = model_config["seq_len"]
self.device = model_config["device"]
self.export_path = model_config["export_path"]
self.export_label = model_config["export_label"]
self.opt_lr = model_config["global_lstm_lstm"]["opt_lr"]
self.amsgrad = model_config["global_lstm_lstm"]["amsgrad"]
self.export_model = model_config["global_lstm_lstm"]["export_model"]
self.in_n_layers = model_config["global_lstm_lstm"]["in_n_layers"]
self.out_n_layers = model_config["global_lstm_lstm"]["out_n_layers"]
self.out_nhi = model_config["global_lstm_lstm"]["out_nhi"]
self.dropout = model_config["global_lstm_lstm"]["drop_rate"]
self.in_transfer_dim = model_config["global_lstm_lstm"][
"in_transfer_dim"]
self.out_transfer_dim = model_config["global_lstm_lstm"][
"out_transfer_dim"]
self.transfer_layers = model_config["global_lstm_lstm"]["n_layers"]
self.dropout_transfer = model_config["global_lstm_lstm"][
"drop_rate_transfer"]
self.mtl_list = self.X_train_tasks.keys()
(
self.sub_mtl_list,
self.transfer_lstm_dict,
self.model_in_dict,
self.model_out_dict,
self.model_lin_dict,
self.opt_dict,
self.signal_layer,
self.losses,
) = ({}, {}, {}, {}, {}, {}, {}, {})
self.global_transfer_lstm = (nn.LSTM(
self.in_transfer_dim,
self.out_transfer_dim,
self.transfer_layers,
batch_first=True,
dropout=self.dropout_transfer,
).double().to(self.device))
for tk in self.mtl_list:
(
self.model_in_dict[tk],
self.model_out_dict[tk],
self.model_lin_dict[tk],
self.signal_layer[tk],
self.opt_dict[tk],
self.losses[tk],
) = ({}, {}, {}, {}, {}, {})
self.sub_mtl_list[tk] = self.X_train_tasks[tk].keys()
for sub_tk in self.sub_mtl_list[tk]:
self.losses[tk][sub_tk] = []
nin = self.X_train_tasks[tk][sub_tk].shape[1]
nout = self.X_train_tasks[tk][sub_tk].shape[1]
in_n_layers, out_n_layers, out_nhi = (
self.in_n_layers,
self.out_n_layers,
self.out_nhi,
)
self.model_in_dict[tk][sub_tk] = (nn.LSTM(
nin,
self.in_transfer_dim,
in_n_layers,
batch_first=True,
dropout=self.dropout,
).double().to(self.device))
self.model_out_dict[tk][sub_tk] = (nn.LSTM(
self.out_transfer_dim,
out_nhi,
out_n_layers,
batch_first=True,
dropout=self.dropout,
).double().to(self.device))
self.model_lin_dict[tk][sub_tk] = (nn.Linear(
out_nhi, nout).double().to(self.device))
self.signal_layer[tk][sub_tk] = nn.Tanh().to(self.device)
self.opt_dict[tk][sub_tk] = torch.optim.Adam(
list(self.model_in_dict[tk][sub_tk].parameters()) +
list(self.model_out_dict[tk][sub_tk].parameters()) +
list(self.model_lin_dict[tk][sub_tk].parameters()) +
list(self.global_transfer_lstm.parameters()) +
list(self.signal_layer[tk][sub_tk].parameters()),
lr=self.opt_lr,
amsgrad=self.amsgrad,
)
print(
tk,
sub_tk,
self.model_in_dict[tk][sub_tk],
self.model_out_dict[tk][sub_tk],
self.model_lin_dict[tk][sub_tk],
self.global_transfer_lstm,
self.signal_layer[tk][sub_tk],
self.opt_dict[tk][sub_tk],
)
def __init__(self, embed_dim, vocab_len, output_len, lstm_layers) :
super(Decoder1, self).__init__()
self.embedding = nn.Embedding(vocab_len, embed_dim)
self.lstm = nn.LSTM(embed_dim, output_len, lstm_layers, bidirectional=True)
self.final_layer = nn.Linear(2*output_len, vocab_len)
def __init__(
self,
embed_size,
hidden_size,
vocab_size=None,
embeddings=None,
lstm_reduction="max",
freeze=False,
skip_embeddings=False,
bidirectional=True,
verbose=True,
seed=123,
lstm_num_layers=1,
**kwargs,
):
"""
Args:
embed_size: The (integer) size of the input at each time
step; usually this is the size of the embeddings
hidden_size: The size of the hidden layer in the LSTM
vocab_size: The size of the vocabulary of the embeddings
If embeddings=None, this helps to set the size of the randomly
initilialized embeddings
If embeddings!=None, this is used to double check that the
provided embeddings have the intended size
embeddings: An optional embedding Tensor
lstm_reduction: One of ['mean', 'max', 'last', 'attention']
denoting what to return as the output of the LSTMLayer
freeze: If False, allow the embeddings to be updated
skip_embeddings: If True, directly accept X without using embeddings
"""
super().__init__()
self.lstm_reduction = lstm_reduction
self.output_dim = hidden_size * 2 if bidirectional else hidden_size
self.verbose = verbose
self.skip_embeddings = skip_embeddings
if not self.skip_embeddings:
# Load provided embeddings or randomly initialize new ones
if embeddings is None:
# Note: Need to set seed here for deterministic init
if seed is not None:
self._set_seed(seed)
self.embeddings = nn.Embedding(vocab_size, embed_size)
if self.verbose:
print(f"Using randomly initialized embeddings.")
else:
self.embeddings = self._load_pretrained(embeddings)
if self.verbose:
print(f"Using pretrained embeddings.")
# Freeze or not
self.embeddings.weight.requires_grad = not freeze
if self.verbose:
if self.skip_embeddings:
print("Skipping embeddings and using direct input.")
else:
print(
f"Embeddings shape = ({self.embeddings.num_embeddings}, "
f"{self.embeddings.embedding_dim})"
)
print(f"The embeddings are {'' if freeze else 'NOT '}FROZEN")
print(f"Using lstm_reduction = '{lstm_reduction}'")
# Create lstm core
# NOTE: We only pass explicitly-named kwargs here; can always add more!
self.lstm = nn.LSTM(
embed_size,
hidden_size,
num_layers=lstm_num_layers,
batch_first=True,
bidirectional=bidirectional,
)
if lstm_reduction == "attention":
att_size = hidden_size * (self.lstm.bidirectional + 1)
att_param = nn.Parameter(torch.FloatTensor(att_size, 1))
nn.init.xavier_normal_(att_param)
self.attention_param = att_param
def __init__(self, embed_dim, vocab_len, output_len, lstm_layers) :
super(Encoder1, self).__init__()
self.output_len = output_len
self.embedding = nn.Embedding(vocab_len, embed_dim)
self.lstm = nn.LSTM(embed_dim, output_len, lstm_layers, bidirectional=True)
def LSTM(input_size, hidden_size, **kwargs):
m = nn.LSTM(input_size, hidden_size, **kwargs)
for name, param in m.named_parameters():
if 'weight' in name or 'bias' in name:
param.data.uniform_(-0.1, 0.1)
return m
def __init__(self, hidden_size, n_node):
super(DynamicScore, self).__init__()
self.hidden_size = hidden_size
self.state_lstm = nn.LSTM(self.hidden_size, self.hidden_size)
self.residual_lstm = nn.LSTM(self.hidden_size, self.hidden_size)
self.embedding = nn.Embedding(n_node, self.hidden_size)
def define_module(self):
self.embed = nn.Embedding(self.d_vocab, self.d_text_feature)
self.rnn_cell = nn.LSTM(self.d_text_feature, self.d_gen_hidden, self.d_gen_layers, dropout=(0 if self.d_gen_layers == 1 else self.gen_dropout), batch_first=True)
self.drop = nn.Dropout(self.gen_dropout)
self.fc_logits = nn.Linear(self.d_gen_hidden, self.d_vocab)
self.log_soft = nn.LogSoftmax(dim=-1)
def __init__(self,
ninputs,
d_fmaps,
kwidth,
activation,
audio_samples,
bnorm=True,
pooling=4,
SND=False,
pool_type='none',
dropout=0,
Genc=None,
pool_size=8,
num_spks=None):
super(Discriminator, self).__init__(name='Discriminator')
if Genc is None:
if not isinstance(activation, list):
activation = [activation] * len(d_fmaps)
self.disc = nn.ModuleList()
for d_i, d_fmap in enumerate(d_fmaps):
act = activation[d_i]
if d_i == 0:
inp = ninputs
else:
inp = d_fmaps[d_i - 1]
self.disc.append(DiscBlock(inp, kwidth, d_fmap, act,
pooling=4))
else:
print('Assigning Genc to D')
# Genc and Denc MUST be same dimensions
self.disc = Genc
self.pool_type = pool_type
if pool_type == 'none':
# resize tensor to fit into FC directly
pool_size *= d_fmaps[-1]
if isinstance(act, nn.LeakyReLU):
'''
Before feeding the audio to the FC layer module, it is scaled by 4. We will adapt the FC to the length
of the wav. Example, if we work with 1s audios at 16000, we have initially 16000 samples and after the
scaling, we would obtain 16000/4 = 4000 -> 4096.
'''
input_dim = [
2**i for i in range(0, 15)
] # defining powers of 2 until 16384, which would fit for 4 audio seconds.
input_dim.append(
3072
) # THIS IS HARDCODED! We have seen we need this input dimension when working with 0.7 s.
num_neurons = min(input_dim,
key=lambda x: abs(x - audio_samples / 4))
self.fc = nn.Sequential(nn.Linear(num_neurons, 256),
nn.ReLU(inplace=True),
nn.Linear(256, 128),
nn.ReLU(inplace=True),
nn.Linear(128, 128))
else:
self.fc = nn.Sequential(nn.Linear(pool_size, 256),
nn.PReLU(256), nn.Linear(256, 128),
nn.PReLU(128), nn.Linear(128, 128))
elif pool_type == 'rnn':
if bnorm:
self.ln = LayerNorm()
pool_size = 128
self.rnn = nn.LSTM(d_fmaps[-1],
pool_size,
batch_first=True,
bidirectional=True)
# bidirectional size
pool_size *= 2
self.fc = nn.Linear(pool_size, 1)
elif pool_type == 'conv':
self.pool_conv = nn.Conv1d(d_fmaps[-1], 1, 1)
self.fc = nn.Linear(pool_size, 1)
else:
raise TypeError('Unrecognized pool type: ', pool_type)
outs = 1
if num_spks is not None:
outs += num_spks
