Spaces:
Sleeping
Sleeping
File size: 8,048 Bytes
4e46a55 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
import torch
import torch.nn as nn
from torch.nn.modules.normalization import LayerNorm
import random
from utilities.constants import *
from utilities.device import get_device
from .positional_encoding import PositionalEncoding
from .rpr import TransformerEncoderRPR, TransformerEncoderLayerRPR
import json
# MusicTransformer
class MusicTransformer(nn.Module):
def __init__(self, n_layers=6, num_heads=8, d_model=512, dim_feedforward=1024,
dropout=0.1, max_sequence_midi=2048, max_sequence_chord=300, rpr=False):
super(MusicTransformer, self).__init__()
self.dummy = DummyDecoder()
self.nlayers = n_layers
self.nhead = num_heads
self.d_model = d_model
self.d_ff = dim_feedforward
self.dropout = dropout
self.max_seq_midi = max_sequence_midi
self.max_seq_chord = max_sequence_chord
self.rpr = rpr
# Input embedding for video and music features
self.embedding = nn.Embedding(CHORD_SIZE, self.d_model)
# self.embedding_key = nn.Embedding(1, self.d_model)
self.embedding_root = nn.Embedding(CHORD_ROOT_SIZE, self.d_model)
self.embedding_attr = nn.Embedding(CHORD_ATTR_SIZE, self.d_model)
self.positional_encoding = PositionalEncoding(self.d_model, self.dropout, self.max_seq_chord)
self.Linear_chord = nn.Linear(self.d_model+1, self.d_model)
# Base transformer
if(not self.rpr):
self.transformer = nn.Transformer(
d_model=self.d_model, nhead=self.nhead, num_encoder_layers=self.nlayers,
num_decoder_layers=0, dropout=self.dropout, # activation=self.ff_activ,
dim_feedforward=self.d_ff, custom_decoder=self.dummy
)
# RPR Transformer
else:
encoder_norm = LayerNorm(self.d_model)
encoder_layer = TransformerEncoderLayerRPR(self.d_model, self.nhead, self.d_ff, self.dropout, er_len=self.max_seq_chord)
encoder = TransformerEncoderRPR(encoder_layer, self.nlayers, encoder_norm)
self.transformer = nn.Transformer(
d_model=self.d_model, nhead=self.nhead, num_encoder_layers=self.nlayers,
num_decoder_layers=0, dropout=self.dropout, # activation=self.ff_activ,
dim_feedforward=self.d_ff, custom_decoder=self.dummy, custom_encoder=encoder
)
# Final output is a softmaxed linear layer
self.Wout = nn.Linear(self.d_model, CHORD_SIZE)
self.Wout_root = nn.Linear(self.d_model, CHORD_ROOT_SIZE)
self.Wout_attr = nn.Linear(self.d_model, CHORD_ATTR_SIZE)
self.softmax = nn.Softmax(dim=-1)
# forward
def forward(self, x, x_root, x_attr, feature_key, mask=True):
if(mask is True):
mask = self.transformer.generate_square_subsequent_mask(x.shape[1]).to(get_device())
else:
mask = None
### Chord + Key (DECODER) ###
# x = self.embedding(x)
x_root = self.embedding_root(x_root)
x_attr = self.embedding_attr(x_attr)
x = x_root + x_attr
feature_key_padded = torch.full((x.shape[0], x.shape[1], 1), feature_key.item())
feature_key_padded = feature_key_padded.to(get_device())
x = torch.cat([x, feature_key_padded], dim=-1)
xf = self.Linear_chord(x)
### POSITIONAL ENCODING ###
xf = xf.permute(1,0,2) # -> (max_seq-1, batch_size, d_model)
xf = self.positional_encoding(xf)
### TRANSFORMER ###
x_out = self.transformer(src=xf, tgt=xf, tgt_mask=mask)
x_out = x_out.permute(1,0,2)
if IS_SEPERATED:
y_root = self.Wout_root(x_out)
y_attr = self.Wout_attr(x_out)
del mask
return y_root, y_attr
else:
y = self.Wout(x_out)
del mask
return y
# generate
def generate(self, feature_key=None, primer=None, primer_root=None, primer_attr=None, target_seq_length=300, beam=0, beam_chance=1.0):
assert (not self.training), "Cannot generate while in training mode"
with open('dataset/vevo_meta/chord_inv.json') as json_file:
chordInvDic = json.load(json_file)
with open('dataset/vevo_meta/chord_root.json') as json_file:
chordRootDic = json.load(json_file)
with open('dataset/vevo_meta/chord_attr.json') as json_file:
chordAttrDic = json.load(json_file)
print("Generating sequence of max length:", target_seq_length)
gen_seq = torch.full((1,target_seq_length), CHORD_PAD, dtype=TORCH_LABEL_TYPE, device=get_device())
gen_seq_root = torch.full((1,target_seq_length), CHORD_ROOT_PAD, dtype=TORCH_LABEL_TYPE, device=get_device())
gen_seq_attr = torch.full((1,target_seq_length), CHORD_ATTR_PAD, dtype=TORCH_LABEL_TYPE, device=get_device())
num_primer = len(primer)
gen_seq[..., :num_primer] = primer.type(TORCH_LABEL_TYPE).to(get_device())
gen_seq_root[..., :num_primer] = primer_root.type(TORCH_LABEL_TYPE).to(get_device())
gen_seq_attr[..., :num_primer] = primer_attr.type(TORCH_LABEL_TYPE).to(get_device())
cur_i = num_primer
while(cur_i < target_seq_length):
# gen_seq_batch = gen_seq.clone()
# y = self.softmax(self.forward(gen_seq[..., :cur_i]))[..., :CHORD_END]
y = self.softmax( self.forward( gen_seq[..., :cur_i], gen_seq_root[..., :cur_i], gen_seq_attr[..., :cur_i], feature_key) )[..., :CHORD_END]
token_probs = y[:, cur_i-1, :]
if(beam == 0):
beam_ran = 2.0
else:
beam_ran = random.uniform(0,1)
if(beam_ran <= beam_chance):
token_probs = token_probs.flatten()
top_res, top_i = torch.topk(token_probs, beam)
beam_rows = top_i // CHORD_SIZE
beam_cols = top_i % CHORD_SIZE
gen_seq = gen_seq[beam_rows, :]
gen_seq[..., cur_i] = beam_cols
else:
distrib = torch.distributions.categorical.Categorical(probs=token_probs)
next_token = distrib.sample()
#print("next token:",next_token)
gen_seq[:, cur_i] = next_token
gen_chord = chordInvDic[ str( next_token.item() ) ]
chord_arr = gen_chord.split(":")
if len(chord_arr) == 1:
chordRootID = chordRootDic[chord_arr[0]]
chordAttrID = 1
chordRootID = torch.tensor([chordRootID]).to(get_device())
chordAttrID = torch.tensor([chordAttrID]).to(get_device())
gen_seq_root[:, cur_i] = chordRootID
gen_seq_attr[:, cur_i] = chordAttrID
elif len(chord_arr) == 2:
chordRootID = chordRootDic[chord_arr[0]]
chordAttrID = chordAttrDic[chord_arr[1]]
chordRootID = torch.tensor([chordRootID]).to(get_device())
chordAttrID = torch.tensor([chordAttrID]).to(get_device())
gen_seq_root[:, cur_i] = chordRootID
gen_seq_attr[:, cur_i] = chordAttrID
# Let the transformer decide to end if it wants to
if(next_token == CHORD_END):
print("Model called end of sequence at:", cur_i, "/", target_seq_length)
break
cur_i += 1
if(cur_i % 50 == 0):
print(cur_i, "/", target_seq_length)
return gen_seq[:, :cur_i]
class DummyDecoder(nn.Module):
def __init__(self):
super(DummyDecoder, self).__init__()
def forward(self, tgt, memory, tgt_mask, memory_mask,tgt_key_padding_mask,memory_key_padding_mask):
return memory
|