def test_adda(self, epoch):
print('\nTesting, epoch: %d' % epoch)
performance_estimators = self.create_test_performance_estimators()
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
self.reset_before_test_epoch()
validation_loader_subset = self.problem.validation_loader_range(
0, self.args.num_validation)
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(validation_loader_subset),
device=self.device,
batch_names=["validation"],
requires_grad={"validation": []},
vectors_to_keep=["input", "softmaxGenotype"])
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
input_s = data_dict["validation"]["input"]
target_s = data_dict["validation"]["softmaxGenotype"]
self.net.eval()
self.test_one_batch(performance_estimators, batch_idx, input_s,
target_s)
if ((batch_idx + 1) *
self.mini_batch_size) > self.max_validation_examples:
break
# print()
finally:
data_provider.close()
self.compute_after_test_epoch()
return performance_estimators
def train_supervised_mixup(self, epoch):
performance_estimators = self.create_training_performance_estimators()
print('\nTraining, epoch: %d' % epoch)
self.net.train()
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
unsupervised_loss_acc = 0
num_batches = 0
train_loader_subset_1 = self.problem.train_loader_subset_range(
0, self.args.num_training)
train_loader_subset_2 = self.problem.train_loader_subset_range(
0, self.args.num_training)
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(train_loader_subset_1, train_loader_subset_2),
is_cuda=self.use_cuda,
batch_names=["training_1", "training_2"],
requires_grad={
"training_1": ["input"],
"training_2": ["input"]
},
volatile={
"training_1": ["metaData"],
"training_2": ["metaData"]
},
recode_functions={
"softmaxGenotype":
lambda x: recode_for_label_smoothing(x, self.epsilon),
"input":
self.normalize_inputs
})
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
input_s_1 = data_dict["training_1"]["input"]
target_s_1 = data_dict["training_1"]["softmaxGenotype"]
input_s_2 = data_dict["training_2"]["input"]
target_s_2 = data_dict["training_2"]["softmaxGenotype"]
metadata_1 = data_dict["training_1"]["metaData"]
metadata_2 = data_dict["training_2"]["metaData"]
num_batches += 1
self.train_one_batch(performance_estimators, batch_idx,
input_s_1, input_s_2, target_s_1,
target_s_2, metadata_1, metadata_2)
if (batch_idx +
1) * self.mini_batch_size > self.max_training_examples:
break
finally:
data_provider.close()
return performance_estimators
def train_adda(self, epoch):
performance_estimators = self.create_training_performance_estimators()
self.reset_before_train_epoch()
print('\nTraining, epoch: %d' % epoch)
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
unsupervised_loss_acc = 0
num_batches = 0
# Use the entire training set to draw examples, even num_training is limiting the length of an epoch.
train_loader_subset = self.problem.train_loader_subset_range(
0, len(self.problem.train_set()))
unlabeled_loader_subset = self.problem.unlabeled_loader()
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(train_loader_subset, unlabeled_loader_subset),
is_cuda=self.use_cuda,
batch_names=["training", "unlabeled"],
requires_grad={
"training": ["input"],
"unlabeled": ["input"]
},
volatile={
"training": ["metaData"],
"unlabeled": ["metaData"]
},
)
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
input_s_1 = data_dict["training"]["input"]
input_u_2 = data_dict["unlabeled"]["input"]
num_batches += 1
# allow some epochs of pre-training the critic without training the encoder:
self.do_train_encoder = True #epoch>2
self.train_one_batch(performance_estimators, batch_idx,
input_s_1, input_u_2)
if (batch_idx +
1) * self.mini_batch_size > self.max_training_examples:
break
finally:
data_provider.close()
self.training_perfs = performance_estimators
# Apply learning rate schedule:
test_metric = performance_estimators.get_metric(
self.get_test_metric_name())
assert test_metric is not None, (
self.get_test_metric_name() +
"must be found among estimated performance metrics")
if not self.args.constant_learning_rates:
self.scheduler_train.step(test_metric, epoch)
return performance_estimators
def test_semi_sup(self, epoch):
print('\nTesting, epoch: %d' % epoch)
performance_estimators = PerformanceList()
performance_estimators += [LossHelper("test_supervised_loss")]
performance_estimators += [LossHelper("test_reconstruction_loss")]
performance_estimators += [AccuracyHelper("test_")]
self.net.eval()
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
validation_loader_subset = self.problem.validation_loader_range(0, self.args.num_validation)
data_provider = MultiThreadedCpuGpuDataProvider(iterator=zip(validation_loader_subset), is_cuda=self.use_cuda,
batch_names=["validation"],
requires_grad={"validation": []},
volatile={"validation": ["input", "softmaxGenotype"]})
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
input_s = data_dict["validation"]["input"]
target_s = data_dict["validation"]["softmaxGenotype"]
# we need copies of the same tensors:
input_u, target_u = Variable(input_s.data, volatile=True), Variable(input_s.data, volatile=True)
output_s = self.net(input_s)
output_u = self.net.autoencoder(input_u)
output_s_p = self.get_p(output_s)
_, target_index = torch.max(target_s, dim=1)
supervised_loss = self.criterion_classifier(output_s, target_s)
reconstruction_loss = self.criterion_autoencoder(output_u, target_u)
performance_estimators.set_metric(batch_idx, "test_supervised_loss", supervised_loss.data[0])
performance_estimators.set_metric(batch_idx, "test_reconstruction_loss", reconstruction_loss.data[0])
performance_estimators.set_metric_with_outputs(batch_idx, "test_accuracy", supervised_loss.data[0],
output_s_p, targets=target_index)
progress_bar(batch_idx * self.mini_batch_size, self.max_validation_examples,
performance_estimators.progress_message(["test_supervised_loss", "test_reconstruction_loss",
"test_accuracy"]))
if ((batch_idx + 1) * self.mini_batch_size) > self.max_validation_examples:
break
# print()
finally:
data_provider.close()
test_metric = performance_estimators.get_metric(self.get_test_metric_name())
assert test_metric is not None, self.get_test_metric_name() + "must be found among estimated performance metrics"
if not self.args.constant_learning_rates:
self.scheduler_train.step(test_metric, epoch)
return performance_estimators
def test_semisupervised_mixup(self, epoch):
print('\nTesting, epoch: %d' % epoch)
errors = None
performance_estimators = self.create_test_performance_estimators()
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
validation_loader_subset = self.problem.validation_loader_range(
0, self.args.num_validation)
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(validation_loader_subset),
device=self.device,
batch_names=["validation"],
requires_grad={"validation": []},
recode_functions={"input": self.normalize_inputs},
vectors_to_keep=["softmaxGenotype"])
if self.best_model is None:
self.best_model = self.net
self.reset_before_test_epoch()
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
input_s = data_dict["validation"]["input"]
target_s = data_dict["validation"]["softmaxGenotype"]
self.net.eval()
self.test_one_batch(performance_estimators,
batch_idx,
input_s,
target_s,
errors=None)
if ((batch_idx + 1) *
self.mini_batch_size) > self.max_validation_examples:
break
# print()
finally:
data_provider.close()
print("test errors by class: ", str(errors))
# Apply learning rate schedule:
test_metric = performance_estimators.get_metric(
self.get_test_metric_name())
assert test_metric is not None, (
self.get_test_metric_name() +
"must be found among estimated performance metrics")
if not self.args.constant_learning_rates:
self.scheduler_train.step(test_metric, epoch)
self.compute_after_test_epoch()
return performance_estimators
def test_semisup_aae(self, epoch, performance_estimators=None):
print('\nTesting, epoch: %d' % epoch)
if performance_estimators is None:
performance_estimators = self.create_test_performance_estimators()
self.net.eval()
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
validation_loader_subset = self.problem.validation_loader_range(
0, self.args.num_validation)
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(validation_loader_subset),
is_cuda=self.use_cuda,
batch_names=["validation"],
requires_grad={"validation": []},
volatile={
"validation": ["input", "softmaxGenotype"],
},
recode_functions={"input": self.normalize_inputs})
self.reset_before_test_epoch()
errors = None
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
input_s = data_dict["validation"]["input"]
target_s = data_dict["validation"]["softmaxGenotype"]
meta_data = data_dict["validation"]["metaData"]
self.test_one_batch(performance_estimators, batch_idx, input_s,
target_s, meta_data, errors)
if ((batch_idx + 1) *
self.mini_batch_size) > self.max_validation_examples:
break
# print()
finally:
data_provider.close()
# Apply learning rate schedules:
test_metric = performance_estimators.get_metric(
self.get_test_metric_name())
assert test_metric is not None, (
self.get_test_metric_name() +
"must be found among estimated performance metrics")
if not self.args.constant_learning_rates:
for scheduler in self.schedulers:
scheduler.step(test_metric, epoch)
self.compute_after_test_epoch()
return performance_estimators
def predict(self, iterator, output_filename, max_examples=sys.maxsize):
self.model.eval()
if self.processing_type == "multithreaded":
# Enable fake_GPU_on_CPU to debug on CPU
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(iterator),
is_cuda=self.use_cuda,
batch_names=["unlabeled"],
volatile={"unlabeled": [self.input_name]},
recode_functions=self.recode_fn,
fake_gpu_on_cpu=False)
elif self.processing_type == "sequential":
data_provider = DataProvider(
iterator=zip(iterator),
is_cuda=self.use_cuda,
batch_names=["unlabeled"],
volatile={"unlabeled": [self.input_name]},
recode_functions=self.recode_fn)
else:
raise Exception("Unrecognized processing type {}".format(
self.processing_type))
with VectorWriterBinary(sample_id=0,
path_with_basename=output_filename,
tensor_names=self.problem.get_output_names(),
domain_descriptor=self.domain_descriptor,
feature_mapper=self.feature_mapper,
samples=self.samples,
input_files=self.input_files,
problem=self.problem,
model=self.model) as writer:
for batch_idx, (indices_dict,
data_dict) in enumerate(data_provider):
input_u = data_dict["unlabeled"][self.input_name]
idxs_u = indices_dict["unlabeled"]
outputs = self.model(input_u)
writer.append(list(idxs_u), outputs, inverse_logit=True)
progress_bar(batch_idx * self.mini_batch_size, max_examples)
if ((batch_idx + 1) * self.mini_batch_size) > max_examples:
break
data_provider.close()
print("Done")
def test_supervised(self, epoch):
print('\nTesting, epoch: %d' % epoch)
errors = None
performance_estimators = self.create_test_performance_estimators()
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
validation_loader_subset = self.problem.validation_loader_range(
0, self.args.num_validation)
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(validation_loader_subset),
is_cuda=self.use_cuda,
batch_names=["validation"],
requires_grad={"validation": []},
volatile={"validation": ["sbi", "softmaxGenotype"]},
)
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
sbi = data_dict["validation"]["sbi"]
target_s = data_dict["validation"]["softmaxGenotype"]
self.net.eval()
self.test_one_batch(performance_estimators,
batch_idx,
sbi,
target_s,
errors=errors)
if ((batch_idx + 1) *
self.mini_batch_size) > self.max_validation_examples:
break
# print()
finally:
data_provider.close()
print("test errors by class: ", str(errors))
if self.reweight_by_validation_error:
self.reweight_by_val_errors(errors)
# Apply learning rate schedule:
test_metric = performance_estimators.get_metric(
self.get_test_metric_name())
assert test_metric is not None, (
self.get_test_metric_name() +
"must be found among estimated performance metrics")
if not self.args.constant_learning_rates:
self.scheduler_train.step(test_metric, epoch)
return performance_estimators
def train_supervised(self, epoch):
performance_estimators = PerformanceList()
performance_estimators += [FloatHelper("supervised_loss")]
performance_estimators += [AccuracyHelper("train_")]
if self.use_cuda:
self.tensor_cache.cuda()
print('\nTraining, epoch: %d' % epoch)
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
unsupervised_loss_acc = 0
num_batches = 0
train_loader_subset = self.problem.train_loader_subset_range(
0, self.args.num_training)
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(train_loader_subset),
is_cuda=self.use_cuda,
batch_names=["training"],
requires_grad={"training": ["sbi"]},
volatile={"training": ["metaData"]},
recode_functions={
"softmaxGenotype":
lambda x: recode_for_label_smoothing(x, self.epsilon),
})
cudnn.benchmark = False
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
sbi = data_dict["training"]["sbi"]
target_s = data_dict["training"]["softmaxGenotype"]
metadata = data_dict["training"]["metaData"]
self.train_one_batch(performance_estimators, batch_idx, sbi,
target_s, metadata)
if (batch_idx +
1) * self.mini_batch_size > self.max_training_examples:
break
finally:
data_provider.close()
return performance_estimators
def train_semisup(self, epoch):
performance_estimators = PerformanceList()
performance_estimators += [FloatHelper("optimized_loss")]
performance_estimators += [FloatHelper("supervised_loss")]
performance_estimators += [FloatHelper("reconstruction_loss")]
performance_estimators += [AccuracyHelper("train_")]
print('\nTraining, epoch: %d' % epoch)
self.net.train()
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
unsupervised_loss_acc = 0
num_batches = 0
train_loader_subset = self.problem.train_loader_subset_range(0, self.args.num_training)
unlabeled_loader = self.problem.unlabeled_loader()
data_provider = MultiThreadedCpuGpuDataProvider(iterator=zip(train_loader_subset, unlabeled_loader),is_cuda=self.use_cuda,
batch_names=["training", "unlabeled"],
requires_grad={"training": ["input"], "unlabeled": ["input"]},
volatile={"training": ["metaData"], "unlabeled": []},
recode_functions={"softmaxGenotype": lambda x: recode_for_label_smoothing(x,self.epsilon)})
self.net.autoencoder.train()
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
input_s = data_dict["training"]["input"]
metadata = data_dict["training"]["metaData"]
target_s = data_dict["training"]["softmaxGenotype"]
input_u = data_dict["unlabeled"]["input"]
num_batches += 1
# need a copy of input_u and input_s as output:
target_u = Variable(input_u.data, requires_grad=False)
target_output_s = Variable(input_s.data, requires_grad=False)
# outputs used to calculate the loss of the supervised model
# must be done with the model prior to regularization:
# Zero gradients:
self.net.zero_grad()
self.net.autoencoder.zero_grad()
self.optimizer_training.zero_grad()
output_s = self.net(input_s)
output_u = self.net.autoencoder(input_u)
input_output_s = self.net.autoencoder(input_s)
output_s_p = self.get_p(output_s)
_, target_index = torch.max(target_s, dim=1)
supervised_loss = self.criterion_classifier(output_s, target_s)
reconstruction_loss_unsup = self.criterion_autoencoder(output_u, target_u)
reconstruction_loss_sup = self.criterion_autoencoder(input_output_s, target_output_s)
reconstruction_loss = self.args.gamma * reconstruction_loss_unsup+reconstruction_loss_sup
optimized_loss = supervised_loss + reconstruction_loss
optimized_loss.backward()
self.optimizer_training.step()
performance_estimators.set_metric(batch_idx, "supervised_loss", supervised_loss.data[0])
performance_estimators.set_metric(batch_idx, "reconstruction_loss", reconstruction_loss.data[0])
performance_estimators.set_metric(batch_idx, "optimized_loss", optimized_loss.data[0])
performance_estimators.set_metric_with_outputs(batch_idx, "train_accuracy", supervised_loss.data[0],
output_s_p, targets=target_index)
progress_bar(batch_idx * self.mini_batch_size,
self.max_training_examples,
performance_estimators.progress_message(["supervised_loss", "reconstruction_loss",
"train_accuracy"]))
if (batch_idx + 1) * self.mini_batch_size > self.max_training_examples:
break
finally:
data_provider.close()
return performance_estimators
def train_semisup_aae(self, epoch,
performance_estimators=None):
if performance_estimators is None:
performance_estimators = self.create_training_performance_estimators()
print('\nTraining, epoch: %d' % epoch)
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
self.net.train()
supervised_grad_norm = 1.
unsupervised_loss_acc = 0
num_batches = 0
train_loader_subset = self.problem.train_loader_subset_range(0, self.args.num_training)
unlabeled_loader = self.problem.unlabeled_loader()
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(train_loader_subset, unlabeled_loader),
device=self.device,
batch_names=["training", "unlabeled"],
requires_grad={"training": ["input"], "unlabeled": ["input"]},
recode_functions={
"softmaxGenotype": lambda x: recode_for_label_smoothing(x, self.epsilon),
"input": self.normalize_inputs
},
vectors_to_keep=["metaData"]
)
self.reset_before_train_epoch()
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
input_s = data_dict["training"]["input"]
target_s = data_dict["training"]["softmaxGenotype"]
input_u = data_dict["unlabeled"]["input"]
meta_data = data_dict["training"]["metaData"]
num_batches += 1
self.train_one_batch( performance_estimators, batch_idx, input_s, target_s, meta_data, input_u)
if ((batch_idx + 1) * self.mini_batch_size) > self.max_training_examples:
break
finally:
data_provider.close()
latent_code_device = torch.device("cpu")
if self.args.latent_code_output is not None:
# Each dimension in latent code should be Gaussian distributed, so take histogram of each column
# Plot histograms later to see how they compare to Gaussian
latent_code_tensor = torch.stack(self.latent_codes).to(latent_code_device)
latent_code_histograms = [torch.histc(latent_code_tensor[:, col_idx],
bins=self.args.latent_code_bins).data.numpy()
for col_idx in range(latent_code_tensor.size()[1])]
gaussian_code_tensor = torch.stack(self.gaussian_codes).to(latent_code_device)
gaussian_code_histograms = [torch.histc(gaussian_code_tensor[:, col_idx],
bins=self.args.latent_code_bins).data.numpy()
for col_idx in range(gaussian_code_tensor.size()[1])]
torch.save({
"latent": latent_code_histograms,
"gaussian": gaussian_code_histograms,
}, "{}_{}.pt".format(self.args.latent_code_output, epoch))
return performance_estimators
# First, get the sum of all of the example vectors
for index, dataset in enumerate(datasets):
train_loader_subset = problem.loader_for_dataset(dataset, shuffle=True)
print("Summing dataset {}/{}".format(index + 1, len(datasets)))
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(train_loader_subset),
is_cuda=False,
batch_names=["dataset"],
volatile={"dataset": [args.vector_name]},
recode_functions={args.vector_name: add_to_sum})
batch_index = 0
for example in data_provider:
batch_index += 1
if batch_index * args.mini_batch_size > args.n:
break
data_provider.close()
# Calculate the mean
print("Calculating the mean")
mean = sum_n / n
# Calculate the sum of squared deviations from means (sum_sdm)
for index, dataset in enumerate(datasets):
train_loader_subset = problem.loader_for_dataset(dataset, shuffle=True)
print("Calculating sum of squared deviations for dataset {}/{}".format(
index + 1, len(datasets)))
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(train_loader_subset),
is_cuda=False,
batch_names=["dataset"],
volatile={"dataset": [args.vector_name]},
recode_functions={args.vector_name: add_to_sum_sdm})
batch_index = 0
def train_semisup_aae(self, epoch, performance_estimators=None):
if performance_estimators is None:
performance_estimators = PerformanceList()
performance_estimators += [FloatHelper("reconstruction_loss")]
performance_estimators += [FloatHelper("discriminator_loss")]
performance_estimators += [FloatHelper("generator_loss")]
performance_estimators += [FloatHelper("supervised_loss")]
performance_estimators += [FloatHelper("weight")]
print('\nTraining, epoch: %d' % epoch)
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
self.net.train()
supervised_grad_norm = 1.
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
unsupervised_loss_acc = 0
num_batches = 0
train_loader_subset1 = self.problem.train_loader_subset_range(
0, self.args.num_training)
train_loader_subset2 = self.problem.train_loader_subset_range(
0, self.args.num_training)
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(train_loader_subset1, train_loader_subset2),
is_cuda=self.use_cuda,
batch_names=["training1", "training2"],
requires_grad={
"training1": ["input"],
"training2": ["input"]
},
volatile={
"training1": ["metaData"],
"training2": ["metaData"]
},
recode_functions={
"softmaxGenotype": recode_for_label_smoothing,
"input": self.normalize_inputs
})
indel_weight = self.args.indel_weight_factor
snp_weight = 1.0
latent_codes = []
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
input_s1 = data_dict["training1"]["input"]
input_s2 = data_dict["training2"]["input"]
target_s1 = data_dict["training1"]["softmaxGenotype"]
target_s2 = data_dict["training2"]["softmaxGenotype"]
meta_data1 = data_dict["training1"]["metaData"]
meta_data2 = data_dict["training2"]["metaData"]
num_batches += 1
self.zero_grad_all_optimizers()
# input_s=normalize_mean_std(input_s)
# input_u=normalize_mean_std(input_u)
# print(torch.mean(input_s,dim=0))
# Train reconstruction phase:
self.net.decoder.train()
reconstruction_loss = self.net.get_crossconstruction_loss(
input_s1, input_s2, target_s2)
reconstruction_loss.backward()
for opt in [self.decoder_opt, self.encoder_reconstruction_opt]:
opt.step()
# Train discriminators:
self.net.encoder.train()
self.net.discriminator_cat.train()
self.net.discriminator_prior.train()
self.zero_grad_all_optimizers()
genotype_frequencies = self.class_frequencies[
"softmaxGenotype"]
category_prior = (genotype_frequencies /
torch.sum(genotype_frequencies)).numpy()
discriminator_loss = self.net.get_discriminator_loss(
input_s1, category_prior=category_prior)
discriminator_loss.backward()
for opt in [
self.discriminator_cat_opt,
self.discriminator_prior_opt
]:
opt.step()
self.zero_grad_all_optimizers()
# Train generator:
self.net.encoder.train()
generator_loss = self.net.get_generator_loss(input_s1)
generator_loss.backward()
for opt in [self.encoder_generator_opt]:
opt.step()
self.zero_grad_all_optimizers()
if self.use_pdf:
self.net.encoder.train()
_, latent_code = self.net.encoder(input_s1)
weight = self.estimate_example_density_weight(latent_code)
else:
weight = self.estimate_batch_weight(
meta_data1,
indel_weight=indel_weight,
snp_weight=snp_weight)
self.net.encoder.train()
supervised_loss = self.net.get_crossencoder_supervised_loss(
input_s1, target_s1) * weight
supervised_loss.backward()
for opt in [self.encoder_semisup_opt]:
opt.step()
self.zero_grad_all_optimizers()
performance_estimators.set_metric(batch_idx,
"reconstruction_loss",
reconstruction_loss.data[0])
performance_estimators.set_metric(batch_idx,
"discriminator_loss",
discriminator_loss.data[0])
performance_estimators.set_metric(batch_idx, "generator_loss",
generator_loss.data[0])
performance_estimators.set_metric(batch_idx, "supervised_loss",
supervised_loss.data[0])
performance_estimators.set_metric(batch_idx, "weight", weight)
if not self.args.no_progress:
progress_bar(
batch_idx * self.mini_batch_size,
self.max_training_examples,
performance_estimators.progress_message([
"reconstruction_loss", "discriminator_loss",
"generator_loss", "semisup_loss"
]))
if ((batch_idx + 1) *
self.mini_batch_size) > self.max_training_examples:
break
finally:
data_provider.close()
return performance_estimators
def test_semisup_aae(self, epoch, performance_estimators=None):
print('\nTesting, epoch: %d' % epoch)
if performance_estimators is None:
performance_estimators = PerformanceList()
performance_estimators += [FloatHelper("reconstruction_loss")]
performance_estimators += [LossHelper("test_loss")]
performance_estimators += [AccuracyHelper("test_")]
performance_estimators += [FloatHelper("weight")]
self.net.eval()
for performance_estimator in performance_estimators:
performance_estimator.init_performance_metrics()
validation_loader_subset = self.problem.validation_loader_range(
0, self.args.num_validation)
data_provider = MultiThreadedCpuGpuDataProvider(
iterator=zip(validation_loader_subset),
is_cuda=self.use_cuda,
batch_names=["validation"],
requires_grad={"validation": []},
volatile={
"validation": ["input", "softmaxGenotype"],
},
recode_functions={"input": self.normalize_inputs})
self.net.eval()
try:
for batch_idx, (_, data_dict) in enumerate(data_provider):
input_s = data_dict["validation"]["input"]
target_s = data_dict["validation"]["softmaxGenotype"]
# Estimate the reconstruction loss on validation examples:
reconstruction_loss = self.net.get_crossconstruction_loss(
input_s, input_s, target_s)
# now evaluate prediction of categories:
categories_predicted, latent_code = self.net.encoder(input_s)
# categories_predicted+=self.net.latent_to_categories(latent_code)
categories_predicted_p = self.get_p(categories_predicted)
categories_predicted_p[
categories_predicted_p != categories_predicted_p] = 0.0
_, target_index = torch.max(target_s, dim=1)
categories_loss = self.net.semisup_loss_criterion(
categories_predicted, target_s)
weight = self.estimate_example_density_weight(latent_code)
performance_estimators.set_metric(batch_idx,
"reconstruction_loss",
reconstruction_loss.data[0])
performance_estimators.set_metric(batch_idx, "weight", weight)
performance_estimators.set_metric_with_outputs(
batch_idx, "test_accuracy", reconstruction_loss.data[0],
categories_predicted_p, target_index)
performance_estimators.set_metric_with_outputs(
batch_idx, "test_loss", categories_loss.data[0] * weight,
categories_predicted_p, target_s)
if not self.args.no_progress:
progress_bar(
batch_idx * self.mini_batch_size,
self.max_validation_examples,
performance_estimators.progress_message([
"test_loss", "test_accuracy", "reconstruction_loss"
]))
if ((batch_idx + 1) *
self.mini_batch_size) > self.max_validation_examples:
break
# print()
finally:
data_provider.close()
# Apply learning rate schedules:
test_metric = performance_estimators.get_metric(
self.get_test_metric_name())
assert test_metric is not None, (
self.get_test_metric_name() +
"must be found among estimated performance metrics")
if not self.args.constant_learning_rates:
for scheduler in self.schedulers:
scheduler.step(test_metric, epoch)
# Run the garbage collector to try to release memory we no longer need:
import gc
gc.collect()
return performance_estimators