def main(args):
if args.cuda:
torch.set_default_tensor_type("torch.cuda.FloatTensor")
logging.info("Loading data")
data = poly.load_data(poly.JSB_CHORALES)
logging.info("-" * 40)
model = models[args.model]
logging.info("Training {} on {} sequences".format(
model.__name__, len(data["train"]["sequences"])))
sequences = data["train"]["sequences"]
lengths = data["train"]["sequence_lengths"]
# find all the notes that are present at least once in the training set
present_notes = (sequences == 1).sum(0).sum(0) > 0
# remove notes that are never played (we remove 37/88 notes)
sequences = sequences[..., present_notes]
if args.truncate:
lengths = lengths.clamp(max=args.truncate)
sequences = sequences[:, :args.truncate]
num_observations = float(lengths.sum())
pyro.set_rng_seed(args.seed)
pyro.clear_param_store()
# We'll train using MAP Baum-Welch, i.e. MAP estimation while marginalizing
# out the hidden state x. This is accomplished via an automatic guide that
# learns point estimates of all of our conditional probability tables,
# named probs_*.
guide = AutoDelta(
poutine.block(model,
expose_fn=lambda msg: msg["name"].startswith("probs_")))
# To help debug our tensor shapes, let's print the shape of each site's
# distribution, value, and log_prob tensor. Note this information is
# automatically printed on most errors inside SVI.
if args.print_shapes:
first_available_dim = -2 if model is model_0 else -3
guide_trace = poutine.trace(guide).get_trace(
sequences, lengths, args=args, batch_size=args.batch_size)
model_trace = poutine.trace(
poutine.replay(poutine.enum(model, first_available_dim),
guide_trace)).get_trace(sequences,
lengths,
args=args,
batch_size=args.batch_size)
logging.info(model_trace.format_shapes())
# Enumeration requires a TraceEnum elbo and declaring the max_plate_nesting.
# All of our models have two plates: "data" and "tones".
optim = Adam({"lr": args.learning_rate})
if args.tmc:
if args.jit:
raise NotImplementedError(
"jit support not yet added for TraceTMC_ELBO")
elbo = TraceTMC_ELBO(max_plate_nesting=1 if model is model_0 else 2)
tmc_model = poutine.infer_config(
model,
lambda msg: {
"num_samples": args.tmc_num_samples,
"expand": False
} if msg["infer"].get("enumerate", None) == "parallel" else {},
) # noqa: E501
svi = SVI(tmc_model, guide, optim, elbo)
else:
Elbo = JitTraceEnum_ELBO if args.jit else TraceEnum_ELBO
elbo = Elbo(
max_plate_nesting=1 if model is model_0 else 2,
strict_enumeration_warning=(model is not model_7),
jit_options={"time_compilation": args.time_compilation},
)
svi = SVI(model, guide, optim, elbo)
# We'll train on small minibatches.
logging.info("Step\tLoss")
for step in range(args.num_steps):
loss = svi.step(sequences,
lengths,
args=args,
batch_size=args.batch_size)
logging.info("{: >5d}\t{}".format(step, loss / num_observations))
if args.jit and args.time_compilation:
logging.debug("time to compile: {} s.".format(
elbo._differentiable_loss.compile_time))
# We evaluate on the entire training dataset,
# excluding the prior term so our results are comparable across models.
train_loss = elbo.loss(model,
guide,
sequences,
lengths,
args,
include_prior=False)
logging.info("training loss = {}".format(train_loss / num_observations))
# Finally we evaluate on the test dataset.
logging.info("-" * 40)
logging.info("Evaluating on {} test sequences".format(
len(data["test"]["sequences"])))
sequences = data["test"]["sequences"][..., present_notes]
lengths = data["test"]["sequence_lengths"]
if args.truncate:
lengths = lengths.clamp(max=args.truncate)
num_observations = float(lengths.sum())
# note that since we removed unseen notes above (to make the problem a bit easier and for
# numerical stability) this test loss may not be directly comparable to numbers
# reported on this dataset elsewhere.
test_loss = elbo.loss(model,
guide,
sequences,
lengths,
args=args,
include_prior=False)
logging.info("test loss = {}".format(test_loss / num_observations))
# We expect models with higher capacity to perform better,
# but eventually overfit to the training set.
capacity = sum(
value.reshape(-1).size(0) for value in pyro.get_param_store().values())
logging.info("{} capacity = {} parameters".format(model.__name__,
capacity))
def main(args):
## ドレミ
def easyTones():
training_seq_lengths = torch.tensor([8]*1)
training_data_sequences = torch.zeros(1,8,88)
for i in range(1):
training_data_sequences[i][0][int(70-i*10) ] = 1
training_data_sequences[i][1][int(70-i*10)+2] = 1
training_data_sequences[i][2][int(70-i*10)+4] = 1
training_data_sequences[i][3][int(70-i*10)+5] = 1
training_data_sequences[i][4][int(70-i*10)+7] = 1
training_data_sequences[i][5][int(70-i*10)+9] = 1
training_data_sequences[i][6][int(70-i*10)+11] = 1
training_data_sequences[i][7][int(70-i*10)+12] = 1
return training_seq_lengths, training_data_sequences
def superEasyTones():
training_seq_lengths = torch.tensor([8]*10)
training_data_sequences = torch.zeros(10,8,88)
for i in range(10):
for j in range(8):
training_data_sequences[i][j][int(30+i*5)] = 1
return training_seq_lengths, training_data_sequences
## ドドド、ドドド、ドドド
def easiestTones():
training_seq_lengths = torch.tensor([8]*10)
training_data_sequences = torch.zeros(10,8,88)
for i in range(10):
for j in range(8):
training_data_sequences[i][j][int(70)] = 1
return training_seq_lengths, training_data_sequences
# setup logging
logging.basicConfig(level=logging.DEBUG, format='%(message)s', filename=args.log, filemode='w')
console = logging.StreamHandler()
console.setLevel(logging.INFO)
logging.getLogger('').addHandler(console)
logging.info(args)
data = poly.load_data(poly.JSB_CHORALES)
training_seq_lengths = data['train']['sequence_lengths']
training_data_sequences = data['train']['sequences']
training_seq_lengths, training_data_sequences = easiestTones()
test_seq_lengths = data['test']['sequence_lengths']
test_data_sequences = data['test']['sequences']
test_seq_lengths, test_data_sequences = easiestTones()
val_seq_lengths = data['valid']['sequence_lengths']
val_data_sequences = data['valid']['sequences']
val_seq_lengths, val_data_sequences = easiestTones()
N_train_data = len(training_seq_lengths)
N_train_time_slices = float(torch.sum(training_seq_lengths))
N_mini_batches = int(N_train_data / args.mini_batch_size +
int(N_train_data % args.mini_batch_size > 0))
logging.info("N_train_data: %d avg. training seq. length: %.2f N_mini_batches: %d" %
(N_train_data, training_seq_lengths.float().mean(), N_mini_batches))
# how often we do validation/test evaluation during training
val_test_frequency = 50
# the number of samples we use to do the evaluation
n_eval_samples = 1
# package repeated copies of val/test data for faster evaluation
# (i.e. set us up for vectorization)
def rep(x):
rep_shape = torch.Size([x.size(0) * n_eval_samples]) + x.size()[1:]
repeat_dims = [1] * len(x.size())
repeat_dims[0] = n_eval_samples
return x.repeat(repeat_dims).reshape(n_eval_samples, -1).transpose(1, 0).reshape(rep_shape)
# get the validation/test data ready for the dmm: pack into sequences, etc.
val_seq_lengths = rep(val_seq_lengths)
test_seq_lengths = rep(test_seq_lengths)
val_batch, val_batch_reversed, val_batch_mask, val_seq_lengths = poly.get_mini_batch(
torch.arange(n_eval_samples * val_data_sequences.shape[0]), rep(val_data_sequences),
val_seq_lengths, cuda=args.cuda)
test_batch, test_batch_reversed, test_batch_mask, test_seq_lengths = poly.get_mini_batch(
torch.arange(n_eval_samples * test_data_sequences.shape[0]), rep(test_data_sequences),
test_seq_lengths, cuda=args.cuda)
# instantiate the dmm
dmm = DMM(rnn_dropout_rate=args.rnn_dropout_rate, num_iafs=args.num_iafs,
iaf_dim=args.iaf_dim, use_cuda=args.cuda)
# setup optimizer
adam_params = {"lr": args.learning_rate, "betas": (args.beta1, args.beta2),
"clip_norm": args.clip_norm, "lrd": args.lr_decay,
"weight_decay": args.weight_decay}
adam = ClippedAdam(adam_params)
# setup inference algorithm
if args.tmc:
if args.jit:
raise NotImplementedError("no JIT support yet for TMC")
tmc_loss = TraceTMC_ELBO()
dmm_guide = config_enumerate(dmm.guide, default="parallel", num_samples=args.tmc_num_samples, expand=False)
svi = SVI(dmm.model, dmm_guide, adam, loss=tmc_loss)
elif args.tmcelbo:
if args.jit:
raise NotImplementedError("no JIT support yet for TMC ELBO")
elbo = TraceEnum_ELBO()
dmm_guide = config_enumerate(dmm.guide, default="parallel", num_samples=args.tmc_num_samples, expand=False)
svi = SVI(dmm.model, dmm_guide, adam, loss=elbo)
else:
elbo = JitTrace_ELBO() if args.jit else Trace_ELBO()
svi = SVI(dmm.model, dmm.guide, adam, loss=elbo)
# now we're going to define some functions we need to form the main training loop
# saves the model and optimizer states to disk
def save_checkpoint():
logging.info("saving model to %s..." % args.save_model)
torch.save(dmm.state_dict(), args.save_model)
# logging.info("saving optimizer states to %s..." % args.save_opt)
# adam.save(args.save_opt)
logging.info("done saving model and optimizer checkpoints to disk.")
# loads the model and optimizer states from disk
def load_checkpoint():
assert exists(args.load_opt) and exists(args.load_model), \
"--load-model and/or --load-opt misspecified"
logging.info("loading model from %s..." % args.load_model)
dmm.load_state_dict(torch.load(args.load_model))
logging.info("loading optimizer states from %s..." % args.load_opt)
adam.load(args.load_opt)
logging.info("done loading model and optimizer states.")
# prepare a mini-batch and take a gradient step to minimize -elbo
def process_minibatch(epoch, which_mini_batch, shuffled_indices):
if args.annealing_epochs > 0 and epoch < args.annealing_epochs:
# compute the KL annealing factor approriate for the current mini-batch in the current epoch
min_af = args.minimum_annealing_factor
annealing_factor = min_af + (1.0 - min_af) * \
(float(which_mini_batch + epoch * N_mini_batches + 1) /
float(args.annealing_epochs * N_mini_batches))
else:
# by default the KL annealing factor is unity
annealing_factor = 1.0
# compute which sequences in the training set we should grab
mini_batch_start = (which_mini_batch * args.mini_batch_size)
mini_batch_end = np.min([(which_mini_batch + 1) * args.mini_batch_size, N_train_data])
mini_batch_indices = shuffled_indices[mini_batch_start:mini_batch_end]
# grab a fully prepped mini-batch using the helper function in the data loader
mini_batch, mini_batch_reversed, mini_batch_mask, mini_batch_seq_lengths \
= poly.get_mini_batch(mini_batch_indices, training_data_sequences,
training_seq_lengths, cuda=args.cuda)
# do an actual gradient step
loss = svi.step(mini_batch, mini_batch_reversed, mini_batch_mask,
mini_batch_seq_lengths, annealing_factor)
# keep track of the training loss
return loss
# helper function for doing evaluation
def do_evaluation():
# put the RNN into evaluation mode (i.e. turn off drop-out if applicable)
dmm.rnn.eval()
# compute the validation and test loss n_samples many times
val_nll = svi.evaluate_loss(val_batch, val_batch_reversed, val_batch_mask,
val_seq_lengths) / float(torch.sum(val_seq_lengths))
test_nll = svi.evaluate_loss(test_batch, test_batch_reversed, test_batch_mask,
test_seq_lengths) / float(torch.sum(test_seq_lengths))
# put the RNN back into training mode (i.e. turn on drop-out if applicable)
dmm.rnn.train()
return val_nll, test_nll
# if checkpoint files provided, load model and optimizer states from disk before we start training
if args.load_opt != '' and args.load_model != '':
load_checkpoint()
#################
# TRAINING LOOP #
#################
times = [time.time()]
for epoch in range(args.num_epochs):
# if specified, save model and optimizer states to disk every checkpoint_freq epochs
if args.checkpoint_freq > 0 and epoch > 0 and epoch % args.checkpoint_freq == 0:
save_checkpoint()
# accumulator for our estimate of the negative log likelihood (or rather -elbo) for this epoch
epoch_nll = 0.0
# prepare mini-batch subsampling indices for this epoch
shuffled_indices = torch.randperm(N_train_data)
# process each mini-batch; this is where we take gradient steps
for which_mini_batch in range(N_mini_batches):
epoch_nll += process_minibatch(epoch, which_mini_batch, shuffled_indices)
# report training diagnostics
times.append(time.time())
epoch_time = times[-1] - times[-2]
logging.info("[training epoch %04d] %.4f \t\t\t\t(dt = %.3f sec)" %
(epoch, epoch_nll / N_train_time_slices, epoch_time))
# do evaluation on test and validation data and report results
if val_test_frequency > 0 and epoch > 0 and epoch % val_test_frequency == 0:
val_nll, test_nll = do_evaluation()
logging.info("[val/test epoch %04d] %.4f %.4f" % (epoch, val_nll, test_nll))
def main(args):
pyro.set_rng_seed(0)
pyro.clear_param_store()
pyro.enable_validation(__debug__)
# load data
if args.dataset == "dipper":
capture_history_file = os.path.dirname(
os.path.abspath(__file__)) + '/dipper_capture_history.csv'
elif args.dataset == "vole":
capture_history_file = os.path.dirname(
os.path.abspath(__file__)) + '/meadow_voles_capture_history.csv'
else:
raise ValueError("Available datasets are \'dipper\' and \'vole\'.")
capture_history = torch.tensor(
np.genfromtxt(capture_history_file, delimiter=',')).float()[:, 1:]
N, T = capture_history.shape
print(
"Loaded {} capture history for {} individuals collected over {} time periods."
.format(args.dataset, N, T))
if args.dataset == "dipper" and args.model in ["4", "5"]:
sex_file = os.path.dirname(
os.path.abspath(__file__)) + '/dipper_sex.csv'
sex = torch.tensor(np.genfromtxt(sex_file, delimiter=',')).float()[:,
1]
print("Loaded dipper sex data.")
elif args.dataset == "vole" and args.model in ["4", "5"]:
raise ValueError(
"Cannot run model_{} on meadow voles data, since we lack sex " +
"information for these animals.".format(args.model))
else:
sex = None
model = models[args.model]
# we use poutine.block to only expose the continuous latent variables
# in the models to AutoDiagonalNormal (all of which begin with 'phi'
# or 'rho')
def expose_fn(msg):
return msg["name"][0:3] in ['phi', 'rho']
# we use a mean field diagonal normal variational distributions (i.e. guide)
# for the continuous latent variables.
guide = AutoDiagonalNormal(poutine.block(model, expose_fn=expose_fn))
# since we enumerate the discrete random variables,
# we need to use TraceEnum_ELBO or TraceTMC_ELBO.
optim = Adam({'lr': args.learning_rate})
if args.tmc:
elbo = TraceTMC_ELBO(max_plate_nesting=1)
tmc_model = poutine.infer_config(model, lambda msg: {
"num_samples": args.tmc_num_samples,
"expand": False
} if msg["infer"].get("enumerate", None) == "parallel" else {}
) # noqa: E501
svi = SVI(tmc_model, guide, optim, elbo)
else:
elbo = TraceEnum_ELBO(max_plate_nesting=1,
num_particles=20,
vectorize_particles=True)
svi = SVI(model, guide, optim, elbo)
losses = []
print(
"Beginning training of model_{} with Stochastic Variational Inference."
.format(args.model))
for step in range(args.num_steps):
loss = svi.step(capture_history, sex)
losses.append(loss)
if step % 20 == 0 and step > 0 or step == args.num_steps - 1:
print("[iteration %03d] loss: %.3f" %
(step, np.mean(losses[-20:])))
# evaluate final trained model
elbo_eval = TraceEnum_ELBO(max_plate_nesting=1,
num_particles=2000,
vectorize_particles=True)
svi_eval = SVI(model, guide, optim, elbo_eval)
print("Final loss: %.4f" % svi_eval.evaluate_loss(capture_history, sex))