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import torch
import torch.nn as nn
from torch.nn.modules.normalization import LayerNorm
import random
import numpy as np
from utilities.constants import *
from utilities.device import get_device
from .positional_encoding import PositionalEncoding
from .rpr import TransformerDecoderRPR, TransformerDecoderLayerRPR
from datetime import datetime
import json
class VideoMusicTransformer(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_video=300, max_sequence_chord=300, total_vf_dim = 0, rpr=False):
super(VideoMusicTransformer, self).__init__()
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_video = max_sequence_video
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_root = nn.Embedding(CHORD_ROOT_SIZE, self.d_model)
self.embedding_attr = nn.Embedding(CHORD_ATTR_SIZE, self.d_model)
self.total_vf_dim = total_vf_dim
self.Linear_vis = nn.Linear(self.total_vf_dim, self.d_model)
self.Linear_chord = nn.Linear(self.d_model+1, self.d_model)
# Positional encoding
self.positional_encoding = PositionalEncoding(self.d_model, self.dropout, self.max_seq_chord)
self.positional_encoding_video = PositionalEncoding(self.d_model, self.dropout, self.max_seq_video)
# Add condition (minor or major)
self.condition_linear = nn.Linear(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=self.nlayers, dropout=self.dropout, # activation=self.ff_activ,
dim_feedforward=self.d_ff
)
# RPR Transformer
else:
decoder_norm = LayerNorm(self.d_model)
decoder_layer = TransformerDecoderLayerRPR(self.d_model, self.nhead, self.d_ff, self.dropout, er_len=self.max_seq_chord)
decoder = TransformerDecoderRPR(decoder_layer, self.nlayers, decoder_norm)
self.transformer = nn.Transformer(
d_model=self.d_model, nhead=self.nhead, num_encoder_layers=self.nlayers,
num_decoder_layers=self.nlayers, dropout=self.dropout, # activation=self.ff_activ,
dim_feedforward=self.d_ff, custom_decoder=decoder
)
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)
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def forward(self, x, x_root, x_attr, feature_semantic_list, feature_key, feature_scene_offset, feature_motion, feature_emotion, mask=True):
if(mask is True):
mask = self.transformer.generate_square_subsequent_mask(x.shape[1]).to(self.device)
else:
mask = None
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(self.device)
x = torch.cat([x, feature_key_padded], dim=-1)
xf = self.Linear_chord(x)
### Video (SemanticList + SceneOffset + Motion + Emotion) (ENCODER) ###
vf_concat = feature_semantic_list[0].float()
for i in range(1, len(feature_semantic_list)):
vf_concat = torch.cat( (vf_concat, feature_semantic_list[i].float()), dim=2)
vf_concat = torch.cat([vf_concat, feature_scene_offset.unsqueeze(-1).float()], dim=-1) # -> (max_seq_video, batch_size, d_model+1)
vf_concat = torch.cat([vf_concat, feature_motion.unsqueeze(-1).float()], dim=-1) # -> (max_seq_video, batch_size, d_model+1)
vf_concat = torch.cat([vf_concat, feature_emotion.float()], dim=-1) # -> (max_seq_video, batch_size, d_model+1)
vf = self.Linear_vis(vf_concat)
### POSITIONAL ENCODING ###
xf = xf.permute(1,0,2) # -> (max_seq-1, batch_size, d_model)
vf = vf.permute(1,0,2) # -> (max_seq_video, batch_size, d_model)
xf = self.positional_encoding(xf)
vf = self.positional_encoding_video(vf)
### TRANSFORMER ###
x_out = self.transformer(src=vf, 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
def generate(self, feature_semantic_list = [], feature_key=None, feature_scene_offset=None, feature_motion=None, feature_emotion=None,
primer=None, primer_root=None, primer_attr=None, target_seq_length=300, beam=0,
beam_chance=1.0, max_conseq_N = 0, max_conseq_chord = 2):
assert (not self.training), "Cannot generate while in training mode"
print("Generating sequence of max length:", target_seq_length)
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)
gen_seq = torch.full((1,target_seq_length), CHORD_PAD, dtype=TORCH_LABEL_TYPE, device=self.device)
gen_seq_root = torch.full((1,target_seq_length), CHORD_ROOT_PAD, dtype=TORCH_LABEL_TYPE, device=self.device)
gen_seq_attr = torch.full((1,target_seq_length), CHORD_ATTR_PAD, dtype=TORCH_LABEL_TYPE, device=self.device)
num_primer = len(primer)
gen_seq[..., :num_primer] = primer.type(TORCH_LABEL_TYPE).to(self.device)
gen_seq_root[..., :num_primer] = primer_root.type(TORCH_LABEL_TYPE).to(self.device)
gen_seq_attr[..., :num_primer] = primer_attr.type(TORCH_LABEL_TYPE).to(self.device)
cur_i = num_primer
while(cur_i < target_seq_length):
y = self.softmax( self.forward( gen_seq[..., :cur_i], gen_seq_root[..., :cur_i], gen_seq_attr[..., :cur_i],
feature_semantic_list, feature_key, feature_scene_offset, feature_motion, feature_emotion) )[..., :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:
# token_probs.shape : [1, 157]
# 0: N, 1: C, ... , 156: B:maj7
# 157 chordEnd 158 padding
if max_conseq_N == 0:
token_probs[0][0] = 0.0
isMaxChord = True
if cur_i >= max_conseq_chord :
preChord = gen_seq[0][cur_i-1].item()
for k in range (1, max_conseq_chord):
if preChord != gen_seq[0][cur_i-1-k].item():
isMaxChord = False
else:
isMaxChord = False
if isMaxChord:
preChord = gen_seq[0][cur_i-1].item()
token_probs[0][preChord] = 0.0
distrib = torch.distributions.categorical.Categorical(probs=token_probs)
next_token = distrib.sample()
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(self.device)
chordAttrID = torch.tensor([chordAttrID]).to(self.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(self.device)
chordAttrID = torch.tensor([chordAttrID]).to(self.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]
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