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script_train_dmelodies.py
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script_train_dmelodies.py
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import os
import numpy as np
import json
import torch
from dmelodies_torch_dataloader import DMelodiesTorchDataset
from src.dmelodiesvae.dmelodies_vae import DMelodiesVAE
from src.dmelodiesvae.dmelodies_cnnvae import DMelodiesCNNVAE
from src.dmelodiesvae.dmelodies_vae_trainer import DMelodiesVAETrainer
from src.dmelodiesvae.dmelodies_cnnvae_trainer import DMelodiesCNNVAETrainer
from src.dmelodiesvae.interp_vae import InterpVAE
from src.dmelodiesvae.interp_vae_trainer import InterpVAETrainer
from src.dmelodiesvae.s2_vae import S2VAE
from src.dmelodiesvae.s2_vae_trainer import S2VAETrainer
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_type",
type=str,
default='beta-VAE',
choices=['beta-VAE', 'ar-VAE', 'interp-VAE', 's2-VAE']
)
parser.add_argument("--net_type", type=str, default='cnn', choices=['rnn', 'cnn'])
parser.add_argument("--gamma", type=float, default=None)
parser.add_argument("--delta", type=float, default=10.0)
parser.add_argument("--interp_num_dims", type=int, default=1)
parser.add_argument("--no_log", action='store_false')
args = parser.parse_args()
# Select the Type of VAE-model and the network architecture
m = args.model_type
net_type = args.net_type
# Specify training params
seed_list = [0, 1, 2]
model_dict = {
'beta-VAE': {
'capacity_list': [50.0],
'beta_list': [0.2, 1.0, 4.0],
'gamma_list': [1.0]
},
'ar-VAE': {
'capacity_list': [50.0],
'beta_list': [0.2],
'gamma_list': [0.1, 1.0, 10.0],
'delta': args.delta,
},
'interp-VAE': {
'capacity_list': [50.0],
'beta_list': [0.2],
'gamma_list': [0.1, 1.0, 10.0],
'num_dims': args.interp_num_dims
},
's2-VAE': {
'capacity_list': [50.0],
'beta_list': [0.2],
'gamma_list': [0.1, 1.0, 10.0],
}
}
num_epochs = 100
batch_size = 512
# Specify the network and trainer classes
if m == 'interp-VAE':
model = InterpVAE
trainer = InterpVAETrainer
elif m == 's2-VAE':
model = S2VAE
trainer = S2VAETrainer
else:
if net_type == 'cnn':
model = DMelodiesCNNVAE
trainer = DMelodiesCNNVAETrainer
else:
model = DMelodiesVAE
trainer = DMelodiesVAETrainer
c_list = model_dict[m]['capacity_list']
b_list = model_dict[m]['beta_list']
g_list = model_dict[m]['gamma_list']
for seed in seed_list:
for c in c_list:
for b in b_list:
for g in g_list:
dataset = DMelodiesTorchDataset(seed=seed)
if m == 'interp-VAE':
vae_model = model(dataset, vae_type=net_type, num_dims=model_dict[m]['num_dims'])
elif m == 's2-VAE':
vae_model = model(dataset, vae_type=net_type)
else:
vae_model = model(dataset)
if torch.cuda.is_available():
vae_model.cuda()
trainer_args = {
'model_type': m,
'beta': b,
'capacity': c,
'lr': 1e-4,
'rand': seed
}
if m == 'ar-VAE':
trainer_args.update({'gamma': g})
trainer_args.update({'delta': model_dict[m]['delta']})
elif m == 'interp-VAE' or m == 's2-VAE':
trainer_args.update({'gamma': g})
vae_trainer = trainer(
dataset,
vae_model,
**trainer_args
)
if not os.path.exists(vae_model.filepath):
vae_trainer.train_model(batch_size=batch_size, num_epochs=num_epochs, log=args.no_log)
else:
print('Model exists. Running evaluation.')
vae_trainer.load_model()
metrics = vae_trainer.compute_eval_metrics()
print(f"Model: {net_type}_{trainer_args}")
print(json.dumps(metrics, indent=2))
print(vae_trainer.test_model(batch_size=512))
vae_trainer.update_reg_dim_limits()
vae_trainer.evaluate_latent_interpolations()