def _set_comet_experiment(configuration, config_key):
experiment = OfflineExperiment(
project_name='general',
workspace='benjaminbenoit',
offline_directory="../damic_comet_experiences")
experiment.set_name(config_key)
experiment.log_parameters(configuration)
return experiment
verbose = 10,
n_jobs = 2,
n_points = 2,
scoring = 'accuracy',
)
checkpoint_callback = skopt.callbacks.CheckpointSaver(f'D:\\FINKI\\8_dps\\Project\\MODELS\\skopt_checkpoints\\{EXPERIMENT_ID}.pkl')
hyperparameters_optimizer.fit(X_train, y_train, callback = [checkpoint_callback])
skopt.dump(hyperparameters_optimizer, f'saved_models\\{EXPERIMENT_ID}.pkl')
y_pred = hyperparameters_optimizer.best_estimator_.predict(X_test)
for i in range(len(hyperparameters_optimizer.cv_results_['params'])):
exp = OfflineExperiment(
api_key = 'A8Lg71j9LtIrsv0deBA0DVGcR',
project_name = ALGORITHM,
workspace = "8_dps",
auto_output_logging = 'native',
offline_directory = f'D:\\FINKI\\8_dps\\Project\\MODELS\\comet_ml_offline_experiments\\{EXPERIMENT_ID}'
)
exp.set_name(f'{EXPERIMENT_ID}_{i + 1}')
exp.add_tags([DS, SEGMENTS_LENGTH, ])
for k, v in hyperparameters_optimizer.cv_results_.items():
if k == "params": exp.log_parameters(dict(v[i]))
else: exp.log_metric(k, v[i])
exp.end()
class CometLogger(LightningLoggerBase):
r"""
Log using `Comet.ml <https://www.comet.ml>`_. Install it with pip:
.. code-block:: bash
pip install comet-ml
Comet requires either an API Key (online mode) or a local directory path (offline mode).
**ONLINE MODE**
.. code-block:: python
import os
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import CometLogger
# arguments made to CometLogger are passed on to the comet_ml.Experiment class
comet_logger = CometLogger(
api_key=os.environ.get('COMET_API_KEY'),
workspace=os.environ.get('COMET_WORKSPACE'), # Optional
save_dir='.', # Optional
project_name='default_project', # Optional
rest_api_key=os.environ.get('COMET_REST_API_KEY'), # Optional
experiment_name='default' # Optional
)
trainer = Trainer(logger=comet_logger)
**OFFLINE MODE**
.. code-block:: python
from pytorch_lightning.loggers import CometLogger
# arguments made to CometLogger are passed on to the comet_ml.Experiment class
comet_logger = CometLogger(
save_dir='.',
workspace=os.environ.get('COMET_WORKSPACE'), # Optional
project_name='default_project', # Optional
rest_api_key=os.environ.get('COMET_REST_API_KEY'), # Optional
experiment_name='default' # Optional
)
trainer = Trainer(logger=comet_logger)
Args:
api_key: Required in online mode. API key, found on Comet.ml. If not given, this
will be loaded from the environment variable COMET_API_KEY or ~/.comet.config
if either exists.
save_dir: Required in offline mode. The path for the directory to save local
comet logs. If given, this also sets the directory for saving checkpoints.
project_name: Optional. Send your experiment to a specific project.
Otherwise will be sent to Uncategorized Experiments.
If the project name does not already exist, Comet.ml will create a new project.
rest_api_key: Optional. Rest API key found in Comet.ml settings.
This is used to determine version number
experiment_name: Optional. String representing the name for this particular experiment on Comet.ml.
experiment_key: Optional. If set, restores from existing experiment.
offline: If api_key and save_dir are both given, this determines whether
the experiment will be in online or offline mode. This is useful if you use
save_dir to control the checkpoints directory and have a ~/.comet.config
file but still want to run offline experiments.
\**kwargs: Additional arguments like `workspace`, `log_code`, etc. used by
:class:`CometExperiment` can be passed as keyword arguments in this logger.
"""
def __init__(self,
api_key: Optional[str] = None,
save_dir: Optional[str] = None,
project_name: Optional[str] = None,
rest_api_key: Optional[str] = None,
experiment_name: Optional[str] = None,
experiment_key: Optional[str] = None,
offline: bool = False,
**kwargs):
if comet_ml is None:
raise ImportError(
"You want to use `comet_ml` logger which is not installed yet,"
" install it with `pip install comet-ml`.")
super().__init__()
self._experiment = None
# Determine online or offline mode based on which arguments were passed to CometLogger
api_key = api_key or comet_ml.config.get_api_key(
None, comet_ml.config.get_config())
if api_key is not None and save_dir is not None:
self.mode = "offline" if offline else "online"
self.api_key = api_key
self._save_dir = save_dir
elif api_key is not None:
self.mode = "online"
self.api_key = api_key
self._save_dir = None
elif save_dir is not None:
self.mode = "offline"
self._save_dir = save_dir
else:
# If neither api_key nor save_dir are passed as arguments, raise an exception
raise MisconfigurationException(
"CometLogger requires either api_key or save_dir during initialization."
)
log.info(f"CometLogger will be initialized in {self.mode} mode")
self._project_name = project_name
self._experiment_key = experiment_key
self._experiment_name = experiment_name
self._kwargs = kwargs
self._future_experiment_key = None
if rest_api_key is not None:
# Comet.ml rest API, used to determine version number
self.rest_api_key = rest_api_key
self.comet_api = API(self.rest_api_key)
else:
self.rest_api_key = None
self.comet_api = None
self._kwargs = kwargs
@property
@rank_zero_experiment
def experiment(self):
r"""
Actual Comet object. To use Comet features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_comet_function()
"""
if self._experiment is not None:
return self._experiment
if self._future_experiment_key is not None:
os.environ["COMET_EXPERIMENT_KEY"] = self._future_experiment_key
self._future_experiment_key = None
try:
if self.mode == "online":
if self._experiment_key is None:
self._experiment = CometExperiment(
api_key=self.api_key,
project_name=self._project_name,
**self._kwargs,
)
self._experiment_key = self._experiment.get_key()
else:
self._experiment = CometExistingExperiment(
api_key=self.api_key,
project_name=self._project_name,
previous_experiment=self._experiment_key,
**self._kwargs,
)
else:
self._experiment = CometOfflineExperiment(
offline_directory=self.save_dir,
project_name=self._project_name,
**self._kwargs,
)
finally:
os.environ.pop("COMET_EXPERIMENT_KEY", None)
if self._experiment_name:
self._experiment.set_name(self._experiment_name)
return self._experiment
@rank_zero_only
def log_hyperparams(self, params: Union[Dict[str, Any],
Namespace]) -> None:
params = self._convert_params(params)
params = self._flatten_dict(params)
self.experiment.log_parameters(params)
@rank_zero_only
def log_metrics(self,
metrics: Dict[str, Union[torch.Tensor, float]],
step: Optional[int] = None) -> None:
assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0"
# Comet.ml expects metrics to be a dictionary of detached tensors on CPU
for key, val in metrics.items():
if is_tensor(val):
metrics[key] = val.cpu().detach()
metrics_without_epoch = metrics.copy()
epoch = metrics_without_epoch.pop('epoch', None)
self.experiment.log_metrics(metrics_without_epoch,
step=step,
epoch=epoch)
def reset_experiment(self):
self._experiment = None
@rank_zero_only
def finalize(self, status: str) -> None:
r"""
When calling ``self.experiment.end()``, that experiment won't log any more data to Comet.
That's why, if you need to log any more data, you need to create an ExistingCometExperiment.
For example, to log data when testing your model after training, because when training is
finalized :meth:`CometLogger.finalize` is called.
This happens automatically in the :meth:`~CometLogger.experiment` property, when
``self._experiment`` is set to ``None``, i.e. ``self.reset_experiment()``.
"""
self.experiment.end()
self.reset_experiment()
@property
def save_dir(self) -> Optional[str]:
return self._save_dir
@property
def name(self) -> str:
# Don't create an experiment if we don't have one
if self._experiment is not None and self._experiment.project_name is not None:
return self._experiment.project_name
if self._project_name is not None:
return self._project_name
return "comet-default"
@property
def version(self) -> str:
# Don't create an experiment if we don't have one
if self._experiment is not None:
return self._experiment.id
if self._experiment_key is not None:
return self._experiment_key
if self._future_experiment_key is not None:
return self._future_experiment_key
# Pre-generate an experiment key
self._future_experiment_key = comet_ml.generate_guid()
return self._future_experiment_key
def __getstate__(self):
state = self.__dict__.copy()
# Save the experiment id in case an experiment object already exists,
# this way we could create an ExistingExperiment pointing to the same
# experiment
state[
"_experiment_key"] = self._experiment.id if self._experiment is not None else None
# Remove the experiment object as it contains hard to pickle objects
# (like network connections), the experiment object will be recreated if
# needed later
state["_experiment"] = None
return state
def main(args):
torch.manual_seed(0)
# Get device
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Get dataset
dataset = Dataset("train.txt")
loader = DataLoader(dataset,
batch_size=hp.batch_size**2,
shuffle=True,
collate_fn=dataset.collate_fn,
drop_last=True,
num_workers=hp.num_workers)
speaker_encoder = None
if hp.speaker_encoder_path != "":
speaker_encoder = load_speaker_encoder(Path(hp.speaker_encoder_path),
device).to(device)
for param in speaker_encoder.parameters():
param.requires_grad = False
else:
speaker_encoder.train()
# Define model
fastspeech_model = FastSpeech2(speaker_encoder).to(device)
model = nn.DataParallel(fastspeech_model).to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of FastSpeech2 Parameters:', num_param)
# Optimizer and loss
optimizer = torch.optim.Adam(model.parameters(),
lr=1e-4,
betas=hp.betas,
eps=hp.eps,
weight_decay=hp.weight_decay)
scheduled_optim = ScheduledOptim(optimizer, hp.decoder_hidden,
hp.n_warm_up_step, args.restore_step)
Loss = FastSpeech2Loss().to(device)
print("Optimizer and Loss Function Defined.")
# Load checkpoint if exists
checkpoint_path = os.path.join(hp.checkpoint_path)
try:
checkpoint = torch.load(
os.path.join(checkpoint_path,
'checkpoint_{}.pth.tar'.format(args.restore_step)))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step {}---\n".format(args.restore_step))
except:
print("\n---Start New Training---\n")
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
# Load vocoder
if hp.vocoder == 'melgan':
vocoder = utils.get_melgan()
vocoder_infer = utils.melgan_infer
elif hp.vocoder == 'waveglow':
vocoder = utils.get_waveglow()
vocoder_infer = utils.waveglow_infer
else:
raise ValueError("Vocoder '%s' is not supported", hp.vocoder)
comet_experiment = None
use_comet = int(os.getenv("USE_COMET", default=0))
if use_comet != 0:
if use_comet == 1:
offline_dir = os.path.join(hp.models_path, "comet")
os.makedirs(offline_dir, exist_ok=True)
comet_experiment = OfflineExperiment(
project_name="mlp-project",
workspace="ino-voice",
offline_directory=offline_dir,
)
elif use_comet == 2:
comet_experiment = Experiment(
api_key="BtyTwUoagGMh3uN4VZt6gMOn8",
project_name="mlp-project",
workspace="ino-voice",
)
comet_experiment.set_name(args.experiment_name)
comet_experiment.log_parameters(hp)
comet_experiment.log_html(args.m)
start_time = time.perf_counter()
first_mel_train_loss, first_postnet_train_loss, first_d_train_loss, first_f_train_loss, first_e_train_loss = \
None, None, None, None, None
for epoch in range(hp.epochs):
total_step = hp.epochs * len(loader) * hp.batch_size
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
model = model.train()
current_step = i * hp.batch_size + j + args.restore_step + epoch * len(
loader) * hp.batch_size + 1
# Get Data
text = torch.from_numpy(
data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).long().to(device)
log_D = torch.from_numpy(
data_of_batch["log_D"]).float().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
# text = torch.from_numpy(data_of_batch["text"]).long()
# mel_target = torch.from_numpy(data_of_batch["mel_target"]).float()
# D = torch.from_numpy(data_of_batch["D"]).long()
# log_D = torch.from_numpy(data_of_batch["log_D"]).float()
# f0 = torch.from_numpy(data_of_batch["f0"]).float()
# energy = torch.from_numpy(data_of_batch["energy"]).float()
# src_len = torch.from_numpy(data_of_batch["src_len"]).long()
# mel_len = torch.from_numpy(data_of_batch["mel_len"]).long()
# max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
# max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
# Forward
mel_output, mel_postnet_output, log_duration_output, f0_output, energy_output, src_mask, mel_mask, _ = \
model(text, src_len, mel_target, mel_len, D, f0, energy, max_src_len, max_mel_len)
# Cal Loss
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
log_duration_output, log_D, f0_output, f0, energy_output,
energy, mel_output, mel_postnet_output, mel_target,
~src_mask, ~mel_mask)
total_loss = mel_loss + mel_postnet_loss + d_loss + f_loss + e_loss
# Set initial values for scaling
if first_mel_train_loss is None:
first_mel_train_loss = mel_loss
first_postnet_train_loss = mel_postnet_loss
first_d_train_loss = d_loss
first_f_train_loss = f_loss
first_e_train_loss = e_loss
mel_l = mel_loss.item() / first_mel_train_loss
mel_postnet_l = mel_postnet_loss.item(
) / first_postnet_train_loss
d_l = d_loss.item() / first_d_train_loss
f_l = f_loss.item() / first_f_train_loss
e_l = e_loss.item() / first_e_train_loss
# Logger
if comet_experiment is not None:
comet_experiment.log_metric(
"total_loss", mel_l + mel_postnet_l + d_l + f_l + e_l,
current_step)
comet_experiment.log_metric("mel_loss", mel_l,
current_step)
comet_experiment.log_metric("mel_postnet_loss",
mel_postnet_l, current_step)
comet_experiment.log_metric("duration_loss", d_l,
current_step)
comet_experiment.log_metric("f0_loss", f_l, current_step)
comet_experiment.log_metric("energy_loss", e_l,
current_step)
# Backward
total_loss = total_loss / hp.acc_steps
total_loss.backward()
if current_step % hp.acc_steps != 0:
continue
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(model.parameters(),
hp.grad_clip_thresh)
# Update weights
scheduled_optim.step_and_update_lr()
scheduled_optim.zero_grad()
# Print
if current_step % hp.log_step == 0:
now = time.perf_counter()
print("\nEpoch [{}/{}], Step [{}/{}]:".format(
epoch + 1, hp.epochs, current_step, total_step))
print(
"Total Loss: {:.4f}, Mel Loss: {:.5f}, Mel PostNet Loss: {:.5f}, Duration Loss: {:.5f}, "
"F0 Loss: {:.5f}, Energy Loss: {:.5f};".format(
mel_l + mel_postnet_l + d_l + f_l + e_l, mel_l,
mel_postnet_l, d_l, f_l, e_l))
print("Time Used: {:.3f}s".format(now - start_time))
start_time = now
if current_step % hp.checkpoint == 0:
file_path = os.path.join(
checkpoint_path,
'checkpoint_{}.pth.tar'.format(current_step))
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
}, file_path)
print("saving model at to {}".format(file_path))
if current_step % hp.synth_step == 0:
length = mel_len[0].item()
mel_target_torch = mel_target[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_target = mel_target[
0, :length].detach().cpu().transpose(0, 1)
mel_torch = mel_output[0, :length].detach().unsqueeze(
0).transpose(1, 2)
mel = mel_output[0, :length].detach().cpu().transpose(0, 1)
mel_postnet_torch = mel_postnet_output[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_postnet = mel_postnet_output[
0, :length].detach().cpu().transpose(0, 1)
if comet_experiment is not None:
comet_experiment.log_audio(
audiotools.inv_mel_spec(mel), hp.sampling_rate,
"step_{}_griffin_lim.wav".format(current_step))
comet_experiment.log_audio(
audiotools.inv_mel_spec(mel_postnet),
hp.sampling_rate,
"step_{}_postnet_griffin_lim.wav".format(
current_step))
comet_experiment.log_audio(
vocoder_infer(mel_torch,
vocoder), hp.sampling_rate,
'step_{}_{}.wav'.format(current_step, hp.vocoder))
comet_experiment.log_audio(
vocoder_infer(mel_postnet_torch, vocoder),
hp.sampling_rate, 'step_{}_postnet_{}.wav'.format(
current_step, hp.vocoder))
comet_experiment.log_audio(
vocoder_infer(mel_target_torch,
vocoder), hp.sampling_rate,
'step_{}_ground-truth_{}.wav'.format(
current_step, hp.vocoder))
f0 = f0[0, :length].detach().cpu().numpy()
energy = energy[0, :length].detach().cpu().numpy()
f0_output = f0_output[
0, :length].detach().cpu().numpy()
energy_output = energy_output[
0, :length].detach().cpu().numpy()
utils.plot_data(
[(mel_postnet.numpy(), f0_output, energy_output),
(mel_target.numpy(), f0, energy)],
comet_experiment, [
'Synthesized Spectrogram',
'Ground-Truth Spectrogram'
])
if current_step % hp.eval_step == 0:
model.eval()
with torch.no_grad():
if comet_experiment is not None:
with comet_experiment.validate():
d_l, f_l, e_l, m_l, m_p_l = evaluate(
model, current_step, comet_experiment)
t_l = d_l + f_l + e_l + m_l + m_p_l
comet_experiment.log_metric(
"total_loss", t_l, current_step)
comet_experiment.log_metric(
"mel_loss", m_l, current_step)
comet_experiment.log_metric(
"mel_postnet_loss", m_p_l, current_step)
comet_experiment.log_metric(
"duration_loss", d_l, current_step)
comet_experiment.log_metric(
"F0_loss", f_l, current_step)
comet_experiment.log_metric(
"energy_loss", e_l, current_step)
class CometLogger(LightningLoggerBase):
r"""
Log using `Comet.ml <https://www.comet.ml>`_. Install it with pip:
.. code-block:: bash
pip install comet-ml
Comet requires either an API Key (online mode) or a local directory path (offline mode).
**ONLINE MODE**
Example:
>>> import os
>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.loggers import CometLogger
>>> # arguments made to CometLogger are passed on to the comet_ml.Experiment class
>>> comet_logger = CometLogger(
... api_key=os.environ.get('COMET_API_KEY'),
... workspace=os.environ.get('COMET_WORKSPACE'), # Optional
... save_dir='.', # Optional
... project_name='default_project', # Optional
... rest_api_key=os.environ.get('COMET_REST_API_KEY'), # Optional
... experiment_name='default' # Optional
... )
>>> trainer = Trainer(logger=comet_logger)
**OFFLINE MODE**
Example:
>>> from pytorch_lightning.loggers import CometLogger
>>> # arguments made to CometLogger are passed on to the comet_ml.Experiment class
>>> comet_logger = CometLogger(
... save_dir='.',
... workspace=os.environ.get('COMET_WORKSPACE'), # Optional
... project_name='default_project', # Optional
... rest_api_key=os.environ.get('COMET_REST_API_KEY'), # Optional
... experiment_name='default' # Optional
... )
>>> trainer = Trainer(logger=comet_logger)
Args:
api_key: Required in online mode. API key, found on Comet.ml
save_dir: Required in offline mode. The path for the directory to save local comet logs
workspace: Optional. Name of workspace for this user
project_name: Optional. Send your experiment to a specific project.
Otherwise will be sent to Uncategorized Experiments.
If the project name does not already exist, Comet.ml will create a new project.
rest_api_key: Optional. Rest API key found in Comet.ml settings.
This is used to determine version number
experiment_name: Optional. String representing the name for this particular experiment on Comet.ml.
experiment_key: Optional. If set, restores from existing experiment.
"""
def __init__(self,
api_key: Optional[str] = None,
save_dir: Optional[str] = None,
workspace: Optional[str] = None,
project_name: Optional[str] = None,
rest_api_key: Optional[str] = None,
experiment_name: Optional[str] = None,
experiment_key: Optional[str] = None,
force_offline: bool = False,
**kwargs):
if not _COMET_AVAILABLE:
raise ImportError(
'You want to use `comet_ml` logger which is not installed yet,'
' install it with `pip install comet-ml`.')
super().__init__()
self._experiment = None
self.save_dir = save_dir
self.api_key = api_key
# Determine online or offline mode based on which arguments were passed to CometLogger
if api_key is None and save_dir is None:
# If neither api_key nor save_dir are passed as arguments, raise an exception
raise MisconfigurationException(
"CometLogger requires either api_key or save_dir during initialization."
)
elif api_key is None:
self.mode = "offline"
elif save_dir is None:
self.mode = "online"
else: # both given so need explicit argument
self.mode = "offline" if force_offline else "online"
log.info(f"CometLogger will be initialized in {self.mode} mode")
self.workspace = workspace
self.project_name = project_name
self.experiment_name = experiment_name
self.experiment_key = experiment_key
self._kwargs = kwargs
if rest_api_key is not None:
# Comet.ml rest API, used to determine version number
self.rest_api_key = rest_api_key
self.comet_api = API(self.rest_api_key)
else:
self.rest_api_key = None
self.comet_api = None
if self.experiment_name is None:
self._version = self.experiment.id
else:
# ensure that the directory name is unique by appending a number to the end
root_save_dir = Path(self.save_dir) / self.name
root_save_dir.mkdir(exist_ok=True, parents=True)
max_i = -1
for path in root_save_dir.glob("*"):
name = path.name
if name.startswith(self.experiment_name):
try:
i = int(name.split("_")[-1])
max_i = max(max_i, i)
except (IndexError, ValueError): # no _ or no int at end
continue
self._version = f"{self.experiment_name}_{max_i + 1}"
self._kwargs = kwargs
@property
def experiment(self) -> CometBaseExperiment:
r"""
Actual Comet object. To use Comet features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_comet_function()
"""
if self._experiment is not None:
return self._experiment
if self.mode == "online":
if self.experiment_key is None:
self._experiment = CometExperiment(
api_key=self.api_key,
workspace=self.workspace,
project_name=self.project_name,
**self._kwargs)
self.experiment_key = self._experiment.get_key()
else:
self._experiment = CometExistingExperiment(
api_key=self.api_key,
workspace=self.workspace,
project_name=self.project_name,
previous_experiment=self.experiment_key,
**self._kwargs)
else:
save_dir = Path(self.save_dir) / self.name / self.version
save_dir.mkdir(exist_ok=True, parents=True)
self._experiment = CometOfflineExperiment(
offline_directory=save_dir,
workspace=self.workspace,
project_name=self.project_name,
**self._kwargs)
if self.experiment_name is not None:
self._experiment.set_name(self.experiment_name)
return self._experiment
@rank_zero_only
def log_hyperparams(self, params: Union[Dict[str, Any],
Namespace]) -> None:
params = self._convert_params(params)
params = self._flatten_dict(params)
self.experiment.log_parameters(params)
@rank_zero_only
def log_metrics(self,
metrics: Dict[str, Union[torch.Tensor, float]],
step: Optional[int] = None) -> None:
# Comet.ml expects metrics to be a dictionary of detached tensors on CPU
for key, val in metrics.items():
if is_tensor(val):
metrics[key] = val.cpu().detach()
self.experiment.log_metrics(metrics, step=step)
def reset_experiment(self):
self._experiment = None
@rank_zero_only
def finalize(self, status: str) -> None:
r"""
When calling ``self.experiment.end()``, that experiment won't log any more data to Comet.
That's why, if you need to log any more data, you need to create an ExistingCometExperiment.
For example, to log data when testing your model after training, because when training is
finalized :meth:`CometLogger.finalize` is called.
This happens automatically in the :meth:`~CometLogger.experiment` property, when
``self._experiment`` is set to ``None``, i.e. ``self.reset_experiment()``.
"""
self.experiment.end()
self.reset_experiment()
@property
def name(self) -> str:
return str(self.project_name)
# no setter because you can't change the project name of an experiment (I don't think)
@property
def version(self) -> str:
return self._version
project_name='swissroll-' + args.tag,
workspace="wronnyhuang")
else:
experiment = Experiment(api_key="vPCPPZrcrUBitgoQkvzxdsh9k",
parse_args=False,
project_name='swissroll-' + args.tag,
workspace="wronnyhuang")
open(join(logdir, 'comet_expt_key.txt'), 'w+').write(experiment.get_key())
if any([a.find('nhidden1') != -1 for a in sys.argv[1:]]):
args.nhidden = [
args.nhidden1, args.nhidden2, args.nhidden3, args.nhidden4,
args.nhidden5, args.nhidden6
]
experiment.log_parameters(vars(args))
experiment.set_name(args.sugg)
print(sys.argv)
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
np.random.seed(args.seed)
tf.set_random_seed(args.seed)
# make dataset
X, y = twospirals(args.ndata // 2, noise=args.noise)
order = np.random.permutation(len(X))
X = X[order]
y = y[order]
splitIdx = int(.5 * len(X))
xtrain, ytrain = X[:splitIdx], y[:splitIdx, None]
xtest, ytest = X[splitIdx:], y[splitIdx:, None]
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Set all seeds for full reproducibility
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
# Set up Comet Experiment tracking
experiment = OfflineExperiment("z15Um8oxWZwiXQXZxZKGh48cl",
workspace='swechhachoudhary',
offline_directory="../swechhas_experiments")
experiment.set_name(
name=args.config +
"_dim={}_overlapped={}".format(latent_dim, train_split))
experiment.log_parameters(configuration)
if encoding_model == 'pca':
encoding_model = PCAEncoder(seed)
flattened = True
elif encoding_model == 'vae':
encoding_model = VAE(latent_dim=latent_dim).to(device)
flattened = True
elif encoding_model == "ae":
encoding_model = AE(latent_dim=latent_dim).to(device)
flattened = True
elif encoding_model == "cae":
encoding_model = CAE(latent_dim=latent_dim).to(device)
flattened = False
class CometWriter:
def __init__(self,
logger,
project_name: Optional[str] = None,
experiment_name: Optional[str] = None,
api_key: Optional[str] = None,
log_dir: Optional[str] = None,
offline: bool = False,
**kwargs):
if not _COMET_AVAILABLE:
raise ImportError(
"You want to use `comet_ml` logger which is not installed yet,"
" install it with `pip install comet-ml`.")
self.project_name = project_name
self.experiment_name = experiment_name
self.kwargs = kwargs
self.timer = Timer()
if (api_key is not None) and (log_dir is not None):
self.mode = "offline" if offline else "online"
self.api_key = api_key
self.log_dir = log_dir
elif api_key is not None:
self.mode = "online"
self.api_key = api_key
self.log_dir = None
elif log_dir is not None:
self.mode = "offline"
self.log_dir = log_dir
else:
logger.warning(
"CometLogger requires either api_key or save_dir during initialization."
)
if self.mode == "online":
self.experiment = CometExperiment(
api_key=self.api_key,
project_name=self.project_name,
**self.kwargs,
)
else:
self.experiment = CometOfflineExperiment(
offline_directory=self.log_dir,
project_name=self.project_name,
**self.kwargs,
)
if self.experiment_name:
self.experiment.set_name(self.experiment_name)
def set_step(self, step, epoch=None, mode='train') -> None:
self.mode = mode
self.step = step
self.epoch = epoch
if step == 0:
self.timer.reset()
else:
duration = self.timer.check()
self.add_scalar({'steps_per_sec': 1 / duration})
def log_hyperparams(self, params: Dict[str, Any]) -> None:
self.experiment.log_parameters(params)
def log_code(self, file_name=None, folder='models/') -> None:
self.experiment.log_code(file_name=file_name, folder=folder)
def add_scalar(self,
metrics: Dict[str, Union[torch.Tensor, float]],
step: Optional[int] = None,
epoch: Optional[int] = None) -> None:
metrics_renamed = {}
for key, val in metrics.items():
tag = '{}/{}'.format(key, self.mode)
if is_tensor(val):
metrics_renamed[tag] = val.cpu().detach()
else:
metrics_renamed[tag] = val
if epoch is None:
self.experiment.log_metrics(metrics_renamed,
step=self.step,
epoch=self.epoch)
else:
self.experiment.log_metrics(metrics_renamed, epoch=epoch)
def add_plot(self, figure_name, figure):
"""
Primarily for log gate plots
"""
self.experiment.log_figure(figure_name=figure_name, figure=figure)
def add_hist3d(self, hist, name):
"""
Primarily for log gate plots
"""
self.experiment.log_histogram_3d(hist, name=name)
def reset_experiment(self):
self.experiment = None
def finalize(self) -> None:
self.experiment.end()
self.reset_experiment()
# Parse configuration file
batch_size = configuration['batch_size']
seed = configuration['seed']
n_epochs = configuration['n_epochs']
# Set all seeds for full reproducibility
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
latent_dim = configuration['Zdim']
if not os.path.exists('experiments'):
print('mkdir ', 'experiments')
os.mkdir('experiments')
if configuration['encode']:
experiment = OfflineExperiment(project_name="ali", workspace='timothynest',
offline_directory=str('../experiments/' + configuration['experiment']))
elif configuration['cluster']:
experiment = OfflineExperiment(project_name="ali", workspace='timothynest', offline_directory=str(
'../experiments/' + configuration['experiment'] + '/cluster'))
experiment.set_name(name=configuration['experiment'])
experiment.log_parameters(configuration)
experiment.add_tag(configuration['experiment'])
# Initiate experiment
main(datapath, configuration, experiment)
def train(config, weights, ntrain, ntest, nepochs, recreate, prefix, plot_freq,
customize, comet_offline):
# tf.debugging.enable_check_numerics()
"""Train a model defined by config"""
config_file_path = config
config, config_file_stem = parse_config(config,
nepochs=nepochs,
weights=weights)
if plot_freq:
config["callbacks"]["plot_freq"] = plot_freq
if customize:
config = customization_functions[customize](config)
# Decide tf.distribute.strategy depending on number of available GPUs
horovod_enabled = config["setup"]["horovod_enabled"]
if horovod_enabled:
num_gpus = initialize_horovod()
else:
strategy, num_gpus = get_strategy()
outdir = ""
if not horovod_enabled or hvd.rank() == 0:
outdir = create_experiment_dir(prefix=prefix + config_file_stem + "_",
suffix=platform.node())
shutil.copy(
config_file_path, outdir + "/config.yaml"
) # Copy the config file to the train dir for later reference
try:
if comet_offline:
print("Using comet-ml OfflineExperiment, saving logs locally.")
from comet_ml import OfflineExperiment
experiment = OfflineExperiment(
project_name="particleflow-tf",
auto_metric_logging=True,
auto_param_logging=True,
auto_histogram_weight_logging=True,
auto_histogram_gradient_logging=False,
auto_histogram_activation_logging=False,
offline_directory=outdir + "/cometml",
)
else:
print("Using comet-ml Experiment, streaming logs to www.comet.ml.")
from comet_ml import Experiment
experiment = Experiment(
project_name="particleflow-tf",
auto_metric_logging=True,
auto_param_logging=True,
auto_histogram_weight_logging=True,
auto_histogram_gradient_logging=False,
auto_histogram_activation_logging=False,
)
except Exception as e:
print("Failed to initialize comet-ml dashboard: {}".format(e))
experiment = None
if experiment:
experiment.set_name(outdir)
experiment.log_code("mlpf/tfmodel/model.py")
experiment.log_code("mlpf/tfmodel/utils.py")
experiment.log_code(config_file_path)
ds_train, num_train_steps = get_datasets(config["train_test_datasets"],
config, num_gpus, "train")
ds_test, num_test_steps = get_datasets(config["train_test_datasets"],
config, num_gpus, "test")
ds_val, ds_info = get_heptfds_dataset(
config["validation_datasets"][0],
config,
num_gpus,
"test",
config["setup"]["num_events_validation"],
supervised=False,
)
ds_val = ds_val.batch(5)
if ntrain:
ds_train = ds_train.take(ntrain)
num_train_steps = ntrain
if ntest:
ds_test = ds_test.take(ntest)
num_test_steps = ntest
print("num_train_steps", num_train_steps)
print("num_test_steps", num_test_steps)
total_steps = num_train_steps * config["setup"]["num_epochs"]
print("total_steps", total_steps)
if horovod_enabled:
model, optim_callbacks, initial_epoch = model_scope(
config, total_steps, weights, horovod_enabled)
else:
with strategy.scope():
model, optim_callbacks, initial_epoch = model_scope(
config, total_steps, weights)
callbacks = prepare_callbacks(
config,
outdir,
ds_val,
comet_experiment=experiment,
horovod_enabled=config["setup"]["horovod_enabled"])
verbose = 1
if horovod_enabled:
callbacks.append(hvd.callbacks.BroadcastGlobalVariablesCallback(0))
callbacks.append(hvd.callbacks.MetricAverageCallback())
verbose = 1 if hvd.rank() == 0 else 0
num_train_steps /= hvd.size()
num_test_steps /= hvd.size()
callbacks.append(optim_callbacks)
model.fit(
ds_train.repeat(),
validation_data=ds_test.repeat(),
epochs=initial_epoch + config["setup"]["num_epochs"],
callbacks=callbacks,
steps_per_epoch=num_train_steps,
validation_steps=num_test_steps,
initial_epoch=initial_epoch,
verbose=verbose,
)
api_key="hIXq6lDzWzz24zgKv7RYz6blo",
project_name="supercyclecons",
workspace="cinjon",
auto_metric_logging=True,
auto_output_logging=None,
auto_param_logging=False,
offline_directory=params['local_comet_dir'])
else:
comet_exp = CometExperiment(api_key="hIXq6lDzWzz24zgKv7RYz6blo",
project_name="supercyclecons",
workspace="cinjon",
auto_metric_logging=True,
auto_output_logging=None,
auto_param_logging=False)
comet_exp.log_parameters(vars(args))
comet_exp.set_name(params['name'])
def partial_load(pretrained_dict, model):
model_dict = model.state_dict()
# 1. filter out unnecessary keys
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model.load_state_dict(pretrained_dict)
def run_experiment_iter(i, experiment, train_iter, nExp, agent_list, env,
video, user_seed, experiment_name, log_params, debug,
project_name, sps, sps_es, **kwargs):
"""
Function used to paralelize the run_experiment calculations.
Parameters
----------
i : int
Index of the agent being trained.
Raises
------
NotImplementedError
In case Comet is used, raises this error to signal where user intervention
is required (namely to set the api_key and the workspace).
Returns
-------
rewards : array
An array with the cumulative rewards, where each column corresponds to
an agent (random seed), and each row to a training iteration.
arms : array
An array with the number of agent arms, where each column corresponds
to an agent (random seed), and each row to a training iteration.
agent : Agent
The trained agent.
"""
if debug:
start = time.time()
print("Experiment {0} out of {1}...".format(i + 1, nExp))
if not user_seed:
seed = int.from_bytes(os.urandom(4), 'big')
else:
seed = user_seed
if experiment_name:
raise NotImplementedError(
"Before using Comet, you need to come here and set your API key")
experiment = Experiment(api_key=None,
project_name=project_name,
workspace=None,
display_summary=False,
offline_directory="offline")
experiment.add_tag(experiment_name)
experiment.set_name("{0}_{1}".format(experiment_name, i))
# Sometimes adding the tag fails
log_params["experiment_tag"] = experiment_name
experiment.log_parameters(log_params)
agent = agent_list[i]
if sps_es: # This one overrides sps
rewards, arms, agent = run_sps_es_experiment(agent,
env,
train_iter,
seed=seed,
video=video,
experiment=experiment,
**kwargs)
elif sps:
rewards, arms, agent = run_sps_experiment(agent,
env,
train_iter,
seed=seed,
video=video,
experiment=experiment,
**kwargs)
else:
rewards, arms, agent = run_aql_experiment(agent,
env,
train_iter,
seed=seed,
video=video,
experiment=experiment,
**kwargs)
agent_list[i] = agent
if experiment:
experiment.end()
if debug:
end = time.time()
elapsed = end - start
units = "secs"
if elapsed > 3600:
elapsed /= 3600
units = "hours"
elif elapsed > 60:
elapsed /= 60
units = "mins"
print("Time elapsed: {0:.02f} {1}".format(elapsed, units))
return rewards, arms, agent
logging.info(opt)
if opt.resume and not opt.load_checkpoint:
parser.error(
'load_checkpoint argument is required to resume training from checkpoint'
)
if not opt.attention and opt.attention_method:
parser.error("Attention method provided, but attention is not turned on")
if opt.attention and not opt.attention_method:
parser.error("Attention turned on, but no attention method provided")
if opt.exp_name is None and not comet_args.get('disabled'):
parser.error('Please provide exp_name if logging to CometML')
experiment.set_name(opt.exp_name)
if torch.cuda.is_available():
logging.info("Cuda device set to %i" % opt.cuda_device)
torch.cuda.set_device(opt.cuda_device)
if opt.attention:
if not opt.attention_method:
logging.info("No attention method provided. Using DOT method.")
opt.attention_method = 'dot'
# Set random seed
if opt.random_seed:
random.seed(opt.random_seed)
np.random.seed(opt.random_seed)
torch.manual_seed(opt.random_seed)
def main(args):
print('Pretrain? ', not args.not_pretrain)
print(args.model)
start_time = time.time()
if opt['local_comet_dir']:
comet_exp = OfflineExperiment(api_key="hIXq6lDzWzz24zgKv7RYz6blo",
project_name="selfcifar",
workspace="cinjon",
auto_metric_logging=True,
auto_output_logging=None,
auto_param_logging=False,
offline_directory=opt['local_comet_dir'])
else:
comet_exp = CometExperiment(api_key="hIXq6lDzWzz24zgKv7RYz6blo",
project_name="selfcifar",
workspace="cinjon",
auto_metric_logging=True,
auto_output_logging=None,
auto_param_logging=False)
comet_exp.log_parameters(vars(args))
comet_exp.set_name(args.name)
# Build model
# path = "/misc/kcgscratch1/ChoGroup/resnick/spaceofmotion/zeping/bsn"
linear_cls = NonLinearModel if args.do_nonlinear else LinearModel
if args.model == "amdim":
hparams = load_hparams_from_tags_csv(
'/checkpoint/cinjon/amdim/meta_tags.csv')
# hparams = load_hparams_from_tags_csv(os.path.join(path, "meta_tags.csv"))
model = AMDIMModel(hparams)
if not args.not_pretrain:
# _path = os.path.join(path, "_ckpt_epoch_434.ckpt")
_path = '/checkpoint/cinjon/amdim/_ckpt_epoch_434.ckpt'
model.load_state_dict(torch.load(_path)["state_dict"])
else:
print("AMDIM not loading checkpoint") # Debug
linear_model = linear_cls(AMDIM_OUTPUT_DIM, args.num_classes)
elif args.model == "ccc":
model = CCCModel(None)
if not args.not_pretrain:
# _path = os.path.join(path, "TimeCycleCkpt14.pth")
_path = '/checkpoint/cinjon/spaceofmotion/bsn/TimeCycleCkpt14.pth'
checkpoint = torch.load(_path)
base_dict = {
'.'.join(k.split('.')[1:]): v
for k, v in list(checkpoint['state_dict'].items())
}
model.load_state_dict(base_dict)
else:
print("CCC not loading checkpoint") # Debug
linear_model = linaer_cls(CCC_OUTPUT_DIM,
args.num_classes) #.to(device)
elif args.model == "corrflow":
model = CORRFLOWModel(None)
if not args.not_pretrain:
_path = '/checkpoint/cinjon/spaceofmotion/supercons/corrflow.kineticsmodel.pth'
# _path = os.path.join(path, "corrflow.kineticsmodel.pth")
checkpoint = torch.load(_path)
base_dict = {
'.'.join(k.split('.')[1:]): v
for k, v in list(checkpoint['state_dict'].items())
}
model.load_state_dict(base_dict)
else:
print("CorrFlow not loading checkpoing") # Debug
linear_model = linear_cls(CORRFLOW_OUTPUT_DIM, args.num_classes)
elif args.model == "resnet":
if not args.not_pretrain:
resnet = torchvision.models.resnet50(pretrained=True)
else:
resnet = torchvision.models.resnet50(pretrained=False)
print("ResNet not loading checkpoint") # Debug
modules = list(resnet.children())[:-1]
model = nn.Sequential(*modules)
linear_model = linear_cls(RESNET_OUTPUT_DIM, args.num_classes)
else:
raise Exception("model type has to be amdim, ccc, corrflow or resnet")
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model).to(device)
linear_model = nn.DataParallel(linear_model).to(device)
else:
model = model.to(device)
linear_model = linear_model.to(device)
# model = model.to(device)
# linear_model = linear_model.to(device)
# Freeze model
for p in model.parameters():
p.requires_grad = False
model.eval()
if args.optimizer == "Adam":
optimizer = optim.Adam(linear_model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
print("Optimizer: Adam with weight decay: {}".format(
args.weight_decay))
elif args.optimizer == "SGD":
optimizer = optim.SGD(linear_model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
print("Optimizer: SGD with weight decay: {} momentum: {}".format(
args.weight_decay, args.momentum))
else:
raise Exception("optimizer should be Adam or SGD")
optimizer.zero_grad()
# Set up log dir
now = datetime.datetime.now()
log_dir = '/checkpoint/cinjon/spaceofmotion/bsn/cifar-%d-weights/%s/%s' % (
args.num_classes, args.model, args.name)
# log_dir = "{}{:%Y%m%dT%H%M}".format(args.model, now)
# log_dir = os.path.join("weights", log_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
print("Saving to {}".format(log_dir))
batch_size = args.batch_size * torch.cuda.device_count()
# CIFAR-10
if args.num_classes == 10:
data_path = ("/private/home/cinjon/cifar-data/cifar-10-batches-py")
_train_dataset = CIFAR_dataset(glob(os.path.join(data_path, "data*")),
args.num_classes, args.model, True)
# _train_acc_dataset = CIFAR_dataset(
# glob(os.path.join(data_path, "data*")),
# args.num_classes,
# args.model,
# False)
train_dataloader = data.DataLoader(_train_dataset,
shuffle=True,
batch_size=batch_size,
num_workers=args.num_workers)
# train_split = int(len(_train_dataset) * 0.8)
# train_dev_split = int(len(_train_dataset) - train_split)
# train_dataset, train_dev_dataset = data.random_split(
# _train_dataset, [train_split, train_dev_split])
# train_acc_dataloader = data.DataLoader(
# train_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
# train_dev_acc_dataloader = data.DataLoader(
# train_dev_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
# train_dataset = data.Subset(_train_dataset, list(range(train_split)))
# train_dataloader = data.DataLoader(
# train_dataset, shuffle=True, batch_size=batch_size, num_workers=args.num_workers)
# train_acc_dataset = data.Subset(
# _train_acc_dataset, list(range(train_split)))
# train_acc_dataloader = data.DataLoader(
# train_acc_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
# train_dev_acc_dataset = data.Subset(
# _train_acc_dataset, list(range(train_split, len(_train_acc_dataset))))
# train_dev_acc_dataloader = data.DataLoader(
# train_dev_acc_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
_val_dataset = CIFAR_dataset([os.path.join(data_path, "test_batch")],
args.num_classes, args.model, False)
val_dataloader = data.DataLoader(_val_dataset,
shuffle=False,
batch_size=batch_size,
num_workers=args.num_workers)
# val_split = int(len(_val_dataset) * 0.8)
# val_dev_split = int(len(_val_dataset) - val_split)
# val_dataset, val_dev_dataset = data.random_split(
# _val_dataset, [val_split, val_dev_split])
# val_dataloader = data.DataLoader(
# val_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
# val_dev_dataloader = data.DataLoader(
# val_dev_dataset, shuffle=False, batch_size=batch_size, num_workers=args.num_workers)
# CIFAR-100
elif args.num_classes == 100:
data_path = ("/private/home/cinjon/cifar-data/cifar-100-python")
_train_dataset = CIFAR_dataset([os.path.join(data_path, "train")],
args.num_classes, args.model, True)
train_dataloader = data.DataLoader(_train_dataset,
shuffle=True,
batch_size=batch_size)
_val_dataset = CIFAR_dataset([os.path.join(data_path, "test")],
args.num_classes, args.model, False)
val_dataloader = data.DataLoader(_val_dataset,
shuffle=False,
batch_size=batch_size)
else:
raise Exception("num_classes should be 10 or 100")
best_acc = 0.0
best_epoch = 0
# Training
for epoch in range(1, args.epochs + 1):
current_lr = max(3e-4, args.lr *\
math.pow(0.5, math.floor(epoch / args.lr_interval)))
linear_model.train()
if args.optimizer == "Adam":
optimizer = optim.Adam(linear_model.parameters(),
lr=current_lr,
weight_decay=args.weight_decay)
elif args.optimizer == "SGD":
optimizer = optim.SGD(
linear_model.parameters(),
lr=current_lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
)
####################################################
# Train
t = time.time()
train_acc = 0
train_loss_sum = 0.0
for iter, input in enumerate(train_dataloader):
if time.time(
) - start_time > args.time * 3600 - 300 and comet_exp is not None:
comet_exp.end()
sys.exit(-1)
imgs = input[0].to(device)
if args.model != "resnet":
imgs = imgs.unsqueeze(1)
lbls = input[1].flatten().to(device)
# output = model(imgs)
# output = linear_model(output)
output = linear_model(model(imgs))
loss = F.cross_entropy(output, lbls)
train_loss_sum += float(loss.data)
train_acc += int(sum(torch.argmax(output, dim=1) == lbls))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# log_text = "train epoch {}/{}\titer {}/{} loss:{} {:.3f}s/iter"
if iter % 1500 == 0:
log_text = "train epoch {}/{}\titer {}/{} loss:{}"
print(log_text.format(epoch, args.epochs, iter + 1,
len(train_dataloader), loss.data,
time.time() - t),
flush=False)
t = time.time()
train_acc /= len(_train_dataset)
train_loss_sum /= len(train_dataloader)
with comet_exp.train():
comet_exp.log_metrics({
'acc': train_acc,
'loss': train_loss_sum
},
step=(epoch + 1) * len(train_dataloader),
epoch=epoch + 1)
print("train acc epoch {}/{} loss:{} train_acc:{}".format(
epoch, args.epochs, train_loss_sum, train_acc),
flush=True)
#######################################################################
# Train acc
# linear_model.eval()
# train_acc = 0
# train_loss_sum = 0.0
# for iter, input in enumerate(train_acc_dataloader):
# imgs = input[0].to(device)
# if args.model != "resnet":
# imgs = imgs.unsqueeze(1)
# lbls = input[1].flatten().to(device)
#
# # output = model(imgs)
# # output = linear_model(output)
# output = linear_model(model(imgs))
# loss = F.cross_entropy(output, lbls)
# train_loss_sum += float(loss.data)
# train_acc += int(sum(torch.argmax(output, dim=1) == lbls))
#
# print("train acc epoch {}/{}\titer {}/{} loss:{} {:.3f}s/iter".format(
# epoch,
# args.epochs,
# iter+1,
# len(train_acc_dataloader),
# loss.data,
# time.time() - t),
# flush=True)
# t = time.time()
#
#
# train_acc /= len(train_acc_dataset)
# train_loss_sum /= len(train_acc_dataloader)
# print("train acc epoch {}/{} loss:{} train_acc:{}".format(
# epoch, args.epochs, train_loss_sum, train_acc), flush=True)
#######################################################################
# Train dev acc
# # linear_model.eval()
# train_dev_acc = 0
# train_dev_loss_sum = 0.0
# for iter, input in enumerate(train_dev_acc_dataloader):
# imgs = input[0].to(device)
# if args.model != "resnet":
# imgs = imgs.unsqueeze(1)
# lbls = input[1].flatten().to(device)
#
# output = model(imgs)
# output = linear_model(output)
# # output = linear_model(model(imgs))
# loss = F.cross_entropy(output, lbls)
# train_dev_loss_sum += float(loss.data)
# train_dev_acc += int(sum(torch.argmax(output, dim=1) == lbls))
#
# print("train dev acc epoch {}/{}\titer {}/{} loss:{} {:.3f}s/iter".format(
# epoch,
# args.epochs,
# iter+1,
# len(train_dev_acc_dataloader),
# loss.data,
# time.time() - t),
# flush=True)
# t = time.time()
#
# train_dev_acc /= len(train_dev_acc_dataset)
# train_dev_loss_sum /= len(train_dev_acc_dataloader)
# print("train dev epoch {}/{} loss:{} train_dev_acc:{}".format(
# epoch, args.epochs, train_dev_loss_sum, train_dev_acc), flush=True)
#######################################################################
# Val dev
# # linear_model.eval()
# val_dev_acc = 0
# val_dev_loss_sum = 0.0
# for iter, input in enumerate(val_dev_dataloader):
# imgs = input[0].to(device)
# if args.model != "resnet":
# imgs = imgs.unsqueeze(1)
# lbls = input[1].flatten().to(device)
#
# output = model(imgs)
# output = linear_model(output)
# loss = F.cross_entropy(output, lbls)
# val_dev_loss_sum += float(loss.data)
# val_dev_acc += int(sum(torch.argmax(output, dim=1) == lbls))
#
# print("val dev epoch {}/{} iter {}/{} loss:{} {:.3f}s/iter".format(
# epoch,
# args.epochs,
# iter+1,
# len(val_dev_dataloader),
# loss.data,
# time.time() - t),
# flush=True)
# t = time.time()
#
# val_dev_acc /= len(val_dev_dataset)
# val_dev_loss_sum /= len(val_dev_dataloader)
# print("val dev epoch {}/{} loss:{} val_dev_acc:{}".format(
# epoch, args.epochs, val_dev_loss_sum, val_dev_acc), flush=True)
#######################################################################
# Val
linear_model.eval()
val_acc = 0
val_loss_sum = 0.0
for iter, input in enumerate(val_dataloader):
if time.time(
) - start_time > args.time * 3600 - 300 and comet_exp is not None:
comet_exp.end()
sys.exit(-1)
imgs = input[0].to(device)
if args.model != "resnet":
imgs = imgs.unsqueeze(1)
lbls = input[1].flatten().to(device)
output = model(imgs)
output = linear_model(output)
loss = F.cross_entropy(output, lbls)
val_loss_sum += float(loss.data)
val_acc += int(sum(torch.argmax(output, dim=1) == lbls))
# log_text = "val epoch {}/{} iter {}/{} loss:{} {:.3f}s/iter"
if iter % 1500 == 0:
log_text = "val epoch {}/{} iter {}/{} loss:{}"
print(log_text.format(epoch, args.epochs, iter + 1,
len(val_dataloader), loss.data,
time.time() - t),
flush=False)
t = time.time()
val_acc /= len(_val_dataset)
val_loss_sum /= len(val_dataloader)
print("val epoch {}/{} loss:{} val_acc:{}".format(
epoch, args.epochs, val_loss_sum, val_acc))
with comet_exp.test():
comet_exp.log_metrics({
'acc': val_acc,
'loss': val_loss_sum
},
step=(epoch + 1) * len(train_dataloader),
epoch=epoch + 1)
if val_acc > best_acc:
best_acc = val_acc
best_epoch = epoch
linear_save_path = os.path.join(log_dir,
"{}.linear.pth".format(epoch))
model_save_path = os.path.join(log_dir,
"{}.model.pth".format(epoch))
torch.save(linear_model.state_dict(), linear_save_path)
torch.save(model.state_dict(), model_save_path)
# Check bias and variance
print(
"Epoch {} lr {} total: train_loss:{} train_acc:{} val_loss:{} val_acc:{}"
.format(epoch, current_lr, train_loss_sum, train_acc, val_loss_sum,
val_acc),
flush=True)
# print("Epoch {} lr {} total: train_acc:{} train_dev_acc:{} val_dev_acc:{} val_acc:{}".format(
# epoch, current_lr, train_acc, train_dev_acc, val_dev_acc, val_acc), flush=True)
print("The best epoch: {} acc: {}".format(best_epoch, best_acc))
def _get_comet_experiment():
experiment = OfflineExperiment(project_name='general',
workspace='benjaminbenoit',
offline_directory="../transformer_net_comet_experiences")
experiment.set_name("TransformerNet")
return experiment
# Set all seeds for full reproducibility
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
# Set up Comet Experiment tracking # Replace this with appropriate comet
# workspaces
experiment = OfflineExperiment("z15Um8oxWZwiXQXZxZKGh48cl",
workspace='swechhachoudhary',
offline_directory="../swechhas_experiments")
experiment.set_name(
name=args.config +
"_dim={}_split={}".format(latent_dim, train_unlabeled_split))
experiment.log_parameters(configuration)
if encoding_model == 'pca':
encoding_model = PCAEncoder(seed)
flattened = True
elif encoding_model == 'vae':
encoding_model = VAE(latent_dim=latent_dim).to(device)
flattened = True
elif encoding_model == "ae":
encoding_model = AE(latent_dim=latent_dim).to(device)
flattened = True
elif encoding_model == "cae":
encoding_model = CAE(latent_dim=latent_dim).to(device)
flattened = False
