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experiment_musictimbre.py
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import os
from abc import ABC
import pytorch_lightning as pl
import torch
import torchvision.utils as vutils
from torch import optim
from models import BaseVAE, MusicTimbreVAE
from models.types_ import *
class MusicTimbreVAELightningModule(pl.LightningModule, ABC):
def __init__(self,
vae_model: MusicTimbreVAE, # contains MusicTimbre vae
config: dict) -> None:
super(MusicTimbreVAELightningModule, self).__init__()
self.save_hyperparameters() # Added by me, to test
self.model = vae_model
self.config = config
self.curr_device = None
self.hold_graph = False
try:
self.hold_graph = self.config['retain_first_backpass']
except:
pass
def forward(self, input: Tensor, **kwargs) -> Tensor:
return self.model(input, **kwargs)
def training_step(self, batch_item, batch_idx, optimizer_idx=0):
print(f'\n=== Training step. batchidx: {batch_idx}, optimizeridx: {optimizer_idx} ===')
batch, batch_di, key, offset = batch_item
batch = torch.squeeze(batch, 0)
# print(f"batch: {batch.shape}, batch_di: {batch_di.shape}, key: {key}, offset: {offset}")
self.curr_device = batch.device
self.model.set_device(self.curr_device)
if optimizer_idx == 0:
music_results = self.model.forward_music(batch)
self.z_music = music_results[4].cpu().detach().numpy()
# print(f"z_music: {self.z_music.shape}")
music_train_loss = self.model.loss_function_music(*music_results,
M_N=self.config['kld_weight'], # al_img.shape[0]/ self.num_train_imgs,
optimizer_idx=optimizer_idx,
batch_idx=batch_idx)
self.log_dict({key: val.item() for key, val in music_train_loss.items()}, sync_dist=True)
print(music_train_loss)
return music_train_loss['loss']
if optimizer_idx == 1:
timbre_results = self.model.forward_timbre(batch, self.z_music)
timbre_train_loss = self.model.loss_function_timbre(*timbre_results,
M_N=self.config['kld_weight'], # al_img.shape[0]/ self.num_train_imgs,
optimizer_idx=optimizer_idx,
batch_idx=batch_idx)
self.log_dict({key: val.item() for key, val in timbre_train_loss.items()}, sync_dist=True)
print(timbre_train_loss)
return timbre_train_loss['loss']
def validation_step(self, batch_item, batch_idx, optimizer_idx):
print(f'\n=== Validation step. batchidx: {batch_idx}, optimizeridx: {optimizer_idx} ===')
batch, batch_di, key, offset = batch_item
batch = torch.squeeze(batch, 0)
self.curr_device = batch.device
self.model.set_device(self.curr_device)
if optimizer_idx == 0:
music_results = self.model.forward_music(batch)
self.z_music = music_results[4].cpu().detach().numpy()
# print(f"z_music: {self.z_music.shape}")
music_val_loss = self.model.loss_function_music(*music_results,
M_N=self.config['kld_weight'], # al_img.shape[0]/ self.num_train_imgs,
optimizer_idx=optimizer_idx,
batch_idx=batch_idx)
self.log_dict({f"val_{key}": val.item() for key, val in music_val_loss.items()}, sync_dist=True)
if optimizer_idx == 1:
timbre_results = self.model.forward_timbre(batch, self.z_music)
timbre_val_loss = self.model.loss_function_timbre(*timbre_results,
M_N=self.config['kld_weight'], # al_img.shape[0]/ self.num_train_imgs,
optimizer_idx=optimizer_idx,
batch_idx=batch_idx)
self.log_dict({f"val_{key}": val.item() for key, val in timbre_val_loss.items()}, sync_dist=True)
def configure_optimizers(self):
optims = []
music_optimizer = optim.Adam(self.model.music_vae.parameters(),
lr=self.config['LR'],
weight_decay=self.config['weight_decay'])
timbre_optimizer = optim.Adam(self.model.timbre_vae.parameters(),
lr=self.config['LR'],
weight_decay=self.config['weight_decay'])
optims.append(music_optimizer)
optims.append(timbre_optimizer)
return optims