class CometExperimentLogger(ExperimentLogger):
def __init__(self, exp_name, online=True, **kwargs):
super(CometExperimentLogger, self).__init__(exp_name, **kwargs)
if online:
self.comet = Experiment(project_name=exp_name, **kwargs)
else:
self.comet = OfflineExperiment(project_name=exp_name, **kwargs)
def log_metric(self, tag, value, step, **kwargs):
self.comet.log_metric(tag, value, step=step, **kwargs)
def log_image(self, tag, img, step, **kwargs):
self.comet.log_image(img, name=tag, step=step, **kwargs)
def log_plt(self, tag, plt, step, **kwargs):
self.comet.log_figure(figure=plt, figure_name=tag, step=step, **kwargs)
def log_text(self, tag, text, **kwargs):
self.comet.log_text(text, **kwargs)
def log_parameters(self, params, **kwargs):
self.comet.log_parameters(params, **kwargs)
def start_epoch(self, **kwargs):
super(CometExperimentLogger, self).start_epoch()
def end_epoch(self, **kwargs):
super(CometExperimentLogger, self).end_epoch()
self.comet.log_epoch_end(self.epoch, **kwargs)
def end_experiment(self):
self.comet.end()
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()
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 AbstractTrainer(ABC):
"""Abstract class that fits the given model"""
def __init__(
self,
model,
optimizer,
cfg,
train_loader,
valid_loader,
test_loader,
device,
output_dir,
hyper_params,
max_patience=5,
):
"""
:param model: pytorch model
:param optimizer: pytorch optimizaer
:param cfg: config instance
:param train_loader: train data loader
:param valid_loade: valid data laoder
:param device: gpu device used (ex: cuda:0)
:param output_dir: output directory where the model and the results
will be located
:param hyper_params: hyper parameters
:param max_patience: max number of iteration without seeing
improvement in accuracy
"""
self.model = model.to(device)
self.optimizer = optimizer
self.cfg = cfg
self.train_loader = train_loader
self.valid_loader = valid_loader
self.test_loader = test_loader
self.device = device
self.stats = StatsRecorder()
self.performance_evaluator = PerformanceEvaluator(self.valid_loader)
self.train_evaluator = PerformanceEvaluator(self.train_loader)
self.valid_evaluator = PerformanceEvaluator(self.valid_loader)
self.test_evaluator = PerformanceEvaluator(self.test_loader)
self.output_dir = output_dir
self.best_model = None
self.hyper_params = hyper_params
self.max_patience = max_patience
self.current_patience = 0
self.epoch = 0
self.comet_ml_experiment = None
self.last_checkpoint_filename = None
def initialize_cometml_experiment(self, hyper_params):
"""
Initialize the comet_ml experiment (only if enabled in config file)
:param hyper_params: current hyper parameters dictionary
:return:
"""
if (
self.comet_ml_experiment is None
and self.cfg.COMET_ML_UPLOAD is True
):
# Create an experiment
self.comet_ml_experiment = Experiment(
api_key=os.environ["COMET_API_KEY"],
project_name="general",
workspace="proguoram",
)
if self.comet_ml_experiment.disabled is True:
# There is problably no internet (in the cluster for example)
# So we create a offline experiment
self.comet_ml_experiment = OfflineExperiment(
workspace="proguoram",
project_name="general",
offline_directory=self.output_dir,
)
self.comet_ml_experiment.log_parameters(hyper_params)
def fit(self, current_hyper_params, hyper_param_search_state=None):
"""
Fit function applied train, val, test to all models.
Each train/val/test may differe for each model
I/O is the same, so wrap them with this method and do logging
"""
self.initialize_cometml_experiment(current_hyper_params)
print("# Start training #")
since = time.time()
summary_writer = TBSummaryWriter(self.output_dir, current_hyper_params)
for epoch in range(self.epoch, self.cfg.TRAIN.NUM_EPOCHS, 1):
self.train(current_hyper_params)
self.train_evaluator.evaluate(
self.model, self.device, self.stats, mode="train")
self.validate(self.model)
self.epoch = epoch
print(
"\nEpoch: {}/{}".format(epoch + 1, self.cfg.TRAIN.NUM_EPOCHS)
)
self.stats.print_last_epoch_stats()
summary_writer.add_stats(self.stats, epoch)
if self.cfg.COMET_ML_UPLOAD is True:
self.stats.upload_to_comet_ml(self.comet_ml_experiment, epoch)
if self.early_stopping_check(self.model, hyper_param_search_state):
break
time_elapsed = time.time() - since
self.add_plots_summary(summary_writer)
print(
"\n\nTraining complete in {:.0f}m {:.0f}s".format(
time_elapsed // 60, time_elapsed % 60
)
)
# manually uncomment in training
# comment this out in hyper_param_train
print("Force save after final epoch")
model_filename = self.output_dir + "/checkpoint_{}_ep_{}.pth".format(
self.stats.valid_best_accuracy, self.epoch
)
self.save_current_best_model(model_filename, hyper_param_search_state)
@classmethod
@abstractmethod
def train(self, current_hyper_params):
"""
Abstract method for the training
:param current_hyper_params: current hyper parameters dictionary
"""
pass
def validate(self, model):
"""
Validate the model
:param model: pytorch model
"""
self.valid_evaluator.evaluate(
model, self.device, self.stats, mode="valid"
)
def test(self, model):
"""
Test the model
:param model: pytorch model
"""
model = model.to(self.device)
self.test_evaluator.evaluate(
model, self.device, self.stats, mode="test"
)
def early_stopping_check(self, model, hyper_param_search_state=None):
"""
Early stop check
:param model: pytorch model
:param current_hyper_params: current hyper parameters dictionary
:return: True if need to stop. False if continue the training
"""
last_accuracy_computed = self.stats.valid_accuracies[-1]
if last_accuracy_computed > self.stats.valid_best_accuracy:
self.stats.valid_best_accuracy = last_accuracy_computed
self.best_model = copy.deepcopy(model)
print("Checkpointing new model...")
model_filename = self.output_dir + "/checkpoint_{}.pth".format(
self.stats.valid_best_accuracy
)
self.save_current_best_model(
model_filename, hyper_param_search_state
)
if self.last_checkpoint_filename is not None:
os.remove(self.last_checkpoint_filename)
self.last_checkpoint_filename = model_filename
self.current_patience = 0
else:
self.current_patience += 1
if self.current_patience > self.max_patience:
return True
return False
def compute_loss(
self, length_logits, digits_logits, length_labels, digits_labels
):
"""
Multi loss computing function
:param length_logits: length logits tensor (N x 7)
:param digits_logits: digits legits tensor (N x 5 x 10)
:param length_labels: length labels (N x 5 x 1)
:param digits_labels: length labels tensor (N x 1)
:return: loss tensor value
"""
loss = torch.nn.functional.cross_entropy(length_logits, length_labels)
for i in range(digits_labels.shape[1]):
loss = loss + torch.nn.functional.cross_entropy(
digits_logits[i], digits_labels[:, i], ignore_index=-1
)
return loss
def load_state_dict(self, state_dict):
"""
Loads the previous state of the trainer
Should be overriden in the children classes if needed (see LRSchedulerTrainer for an example)
:param state_dict: state dictionary
"""
self.epoch = state_dict["epoch"]
self.stats = state_dict["stats"]
self.current_patience = state_dict["current_patience"]
self.best_model = self.model
def get_state_dict(self, hyper_param_search_state=None):
"""
Gets the current state of the trainer
Should be overriden in the children classes if needed (see LRSchedulerTrainer for an example)
:param hyper_param_search_state: hyper param search state if we are doing an hyper params serach
(None by default)
:return state_dict
"""
seed = np.random.get_state()[1][0]
return {
"epoch": self.epoch + 1,
"model_state_dict": self.best_model.state_dict(),
"optimizer_state_dict": self.optimizer.state_dict(),
"stats": self.stats,
"seed": seed,
"current_patience": self.current_patience,
"hyper_param_search_state": hyper_param_search_state,
"hyper_params": self.hyper_params,
}
def save_current_best_model(self, out_path, hyper_param_search_state=None):
"""
Saves the current best model
:param out_path: output path string
:param hyper_param_search_state: hyper param search state if we are doing an hyper params serach
(None by default)
"""
state_dict = self.get_state_dict(hyper_param_search_state)
torch.save(state_dict, out_path)
print("Model saved!")
def _train_batch(self, batch):
"""
Basic batch method (called by children class normally
:param batch: batch
:return: loss tensor value
"""
inputs, targets = batch["image"], batch["target"]
inputs = inputs.to(self.device)
targets = targets.long().to(self.device)
target_ndigits = targets[:, 0]
# Zero the gradient buffer
self.optimizer.zero_grad()
# Forward
pred_length, pred_sequences = self.model(inputs)
# For each digit predicted
target_digit = targets[:, 1:]
loss = self.compute_loss(
pred_length, pred_sequences, target_ndigits, target_digit
)
# Backward
loss.backward()
# Optimize
self.optimizer.step()
return loss
def add_plots_summary(self, summary_writer):
"""
Add plotting values for tensor board
:param summary_writer: Summary writer object from tensor board
"""
# plot loss curves
loss_dict = {
"Train loss": self.stats.train_loss_history,
"Valid loss": self.stats.valid_losses,
}
axis_labels = {"x": "Epochs", "y": "Loss"}
summary_writer.plot_curves(loss_dict, "Learning curves", axis_labels)
# plot accuracy curves
acc_dict = {
"Valid accuracy": self.stats.valid_accuracies,
"Length accuracy": self.stats.length_accuracy,
}
axis_labels = {"x": "Epochs", "y": "Accuracy"}
summary_writer.plot_curves(acc_dict, "Accuracy curves", axis_labels)
class Logger:
def __init__(self, send_logs, tags, parameters, experiment=None):
self.stations = 5
self.send_logs = send_logs
if self.send_logs:
if experiment is None:
json_loc = glob.glob("./**/comet_token.json")[0]
with open(json_loc, "r") as f:
kwargs = json.load(f)
self.experiment = OfflineExperiment(**kwargs)
else:
self.experiment = experiment
self.sent_mb = 0
self.speed_window = deque(maxlen=100)
self.step_time = None
self.current_speed = 0
if self.send_logs:
if tags is not None:
self.experiment.add_tags(tags)
if parameters is not None:
self.experiment.log_parameters(parameters)
def begin_logging(self, episode_count, steps_per_ep, sigma, theta, step_time):
self.step_time = step_time
if self.send_logs:
self.experiment.log_parameter("Episode count", episode_count)
self.experiment.log_parameter("Steps per episode", steps_per_ep)
self.experiment.log_parameter("theta", theta)
self.experiment.log_parameter("sigma", sigma)
def log_round(self, states, reward, cumulative_reward, info, loss, observations, step):
self.experiment.log_histogram_3d(states, name="Observations", step=step)
info = [[j for j in i.split("|")] for i in info]
info = np.mean(np.array(info, dtype=np.float32), axis=0)
try:
round_mb = info[0]
except Exception as e:
print(info)
print(reward)
raise e
self.speed_window.append(round_mb)
self.current_speed = np.mean(np.asarray(self.speed_window)/self.step_time)
self.sent_mb += round_mb
CW = info[1]
CW_ax = info[2]
self.stations = info[3]
fairness = info[4]
if self.send_logs:
self.experiment.log_metric("Round reward", np.mean(reward), step=step)
self.experiment.log_metric("Per-ep reward", np.mean(cumulative_reward), step=step)
self.experiment.log_metric("Megabytes sent", self.sent_mb, step=step)
self.experiment.log_metric("Round megabytes sent", round_mb, step=step)
self.experiment.log_metric("Chosen CW for legacy devices", CW, step=step)
self.experiment.log_metric("Chosen CW for 802.11ax devices", CW_ax, step=step)
self.experiment.log_metric("Station count", self.stations, step=step)
self.experiment.log_metric("Current throughput", self.current_speed, step=step)
self.experiment.log_metric("Fairness index", fairness, step=step)
for i, obs in enumerate(observations):
self.experiment.log_metric(f"Observation {i}", obs, step=step)
self.experiment.log_metrics(loss, step=step)
def log_episode(self, cumulative_reward, speed, step):
if self.send_logs:
self.experiment.log_metric("Cumulative reward", cumulative_reward, step=step)
self.experiment.log_metric("Speed", speed, step=step)
self.sent_mb = 0
self.last_speed = speed
self.speed_window = deque(maxlen=100)
self.current_speed = 0
def end(self):
if self.send_logs:
self.experiment.end()
parse_args=False,
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]
print(e)
print("Ignoring argument", u)
for o in dir(opts):
if not o.startswith("_"):
if o in config:
print("Overwriting {:20} {:30} -> {:}".format(
o, config[k], getattr(opts, o)))
config[o] = getattr(opts, o)
comet_exp.log_asset(opts.config)
max_iter = config["max_iter"]
display_size = config["display_size"]
config["vgg_model_path"] = opts.output_path
comet_exp.log_parameters(config)
print("Using model", opts.trainer)
# Setup model and data loader
if opts.trainer == "MUNIT":
trainer = MUNIT_Trainer(config, comet_exp)
elif opts.trainer == "UNIT":
trainer = UNIT_Trainer(config)
elif opts.trainer == "DoubleMUNIT":
trainer = DoubleMUNIT_Trainer(config, comet_exp)
else:
sys.exit("Only support MUNIT|UNIT|DOubleMUNIT")
trainer.cuda()
train_loader_a, train_loader_b, test_loader_a, test_loader_b = get_all_data_loaders(
config)
train_display_images_a = torch.stack(
# 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
elif encoding_model == "cvae":
encoding_model = CVAE(latent_dim=latent_dim).to(device)
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()
comet_exp = OfflineExperiment(
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)
if __name__ == '__main__':
SEED = 1234
random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
config = get_config(sys.argv[1])
# experiment = Experiment("wXwnV8LZOtVfxqnRxr65Lv7C2")
comet_dir_path = os.path.join(config["result_directory"], config["model"])
makedirs(comet_dir_path)
experiment = OfflineExperiment(
project_name="DeepGenomics",
offline_directory=comet_dir_path)
experiment.log_parameters(config)
if torch.cuda.is_available():
# torch.cuda.set_device(str(os.environ["CUDA_VISIBLE_DEVICES"]))
device = torch.device('cuda:{}'.format(os.environ["CUDA_VISIBLE_DEVICES"]))
else:
device = torch.device('cpu')
print(device)
number_of_examples = len(get_filenames(os.path.join(config["data"], "x")))
list_ids = [str(i) for i in range(number_of_examples)]
random.shuffle(list_ids)
t_ind, v_ind = round(number_of_examples * 0.7), round(number_of_examples * 0.9)
train_indices, validation_indices, test_indices = list_ids[:t_ind], list_ids[t_ind:v_ind], list_ids[v_ind:]
params = {'batch_size': config["training"]["batch_size"],
'shuffle': config["training"]["shuffle"],
'num_workers': config["training"]["num_workers"]}
class CometLogger(Logger):
def __init__(
self,
batch_size: int,
snapshot_dir: Optional[str] = None,
snapshot_mode: str = "last",
snapshot_gap: int = 1,
exp_set: Optional[str] = None,
use_print_exp: bool = False,
saved_exp: Optional[str] = None,
**kwargs,
):
"""
:param kwargs: passed to comet's Experiment at init.
"""
if use_print_exp:
self.experiment = PrintExperiment()
else:
from comet_ml import Experiment, ExistingExperiment, OfflineExperiment
if saved_exp:
self.experiment = ExistingExperiment(
previous_experiment=saved_exp, **kwargs
)
else:
try:
self.experiment = Experiment(**kwargs)
except ValueError: # no API key
log_dir = Path.home() / "logs"
log_dir.mkdir(exist_ok=True)
self.experiment = OfflineExperiment(offline_directory=str(log_dir))
self.experiment.log_parameter("complete", False)
if exp_set:
self.experiment.log_parameter("exp_set", exp_set)
if snapshot_dir:
snapshot_dir = Path(snapshot_dir) / self.experiment.get_key()
# log_traj_window (int): How many trajectories to hold in deque for computing performance statistics.
self.log_traj_window = 100
self._cum_metrics = {
"n_unsafe_actions": 0,
"constraint_used": 0,
"cum_completed_trajs": 0,
"logging_time": 0,
}
self._new_completed_trajs = 0
self._last_step = 0
self._start_time = self._last_time = time()
self._last_snapshot_upload = 0
self._snaphot_upload_time = 30 * 60
super().__init__(batch_size, snapshot_dir, snapshot_mode, snapshot_gap)
def log_fast(
self,
step: int,
traj_infos: Sequence[Dict[str, float]],
opt_info: Optional[Tuple[Sequence[float], ...]] = None,
test: bool = False,
) -> None:
if not traj_infos:
return
start = time()
self._new_completed_trajs += len(traj_infos)
self._cum_metrics["cum_completed_trajs"] += len(traj_infos)
# TODO: do we need to support sum(t[k]) if key in k?
# without that, this doesn't include anything from extra eval samplers
for key in self._cum_metrics:
if key == "cum_completed_trajs":
continue
self._cum_metrics[key] += sum(t.get(key, 0) for t in traj_infos)
self._cum_metrics["logging_time"] += time() - start
def log(
self,
step: int,
traj_infos: Sequence[Dict[str, float]],
opt_info: Optional[Tuple[Sequence[float], ...]] = None,
test: bool = False,
):
self.log_fast(step, traj_infos, opt_info, test)
start = time()
with (self.experiment.test() if test else nullcontext()):
step *= self.batch_size
if opt_info is not None:
# grad norm is left on the GPU for some reason
# https://github.com/astooke/rlpyt/issues/163
self.experiment.log_metrics(
{
k: np.mean(v)
for k, v in opt_info._asdict().items()
if k != "gradNorm"
},
step=step,
)
if traj_infos:
agg_vals = {}
for key in traj_infos[0].keys():
if key in self._cum_metrics:
continue
agg_vals[key] = sum(t[key] for t in traj_infos) / len(traj_infos)
self.experiment.log_metrics(agg_vals, step=step)
if not test:
now = time()
self.experiment.log_metrics(
{
"new_completed_trajs": self._new_completed_trajs,
"steps_per_second": (step - self._last_step)
/ (now - self._last_time),
},
step=step,
)
self._last_time = now
self._last_step = step
self._new_completed_trajs = 0
self.experiment.log_metrics(self._cum_metrics, step=step)
self._cum_metrics["logging_time"] += time() - start
def log_metric(self, name, val):
self.experiment.log_metric(name, val)
def log_parameters(self, parameters):
self.experiment.log_parameters(parameters)
def log_config(self, config):
self.experiment.log_parameter("config", json.dumps(convert_dict(config)))
def upload_snapshot(self):
if self.snapshot_dir:
self.experiment.log_asset(self._previous_snapshot_fname)
def save_itr_params(
self, step: int, params: Dict[str, Any], metric: Optional[float] = None
) -> None:
super().save_itr_params(step, params, metric)
now = time()
if now - self._last_snapshot_upload > self._snaphot_upload_time:
self._last_snapshot_upload = now
self.upload_snapshot()
def shutdown(self, error: bool = False) -> None:
if not error:
self.upload_snapshot()
self.experiment.log_parameter("complete", True)
self.experiment.end()
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
"sequence_length": 28,
"input_size": 28,
"hidden_size": 128,
"num_layers": 2,
"num_classes": 10,
"batch_size": 100,
"num_epochs": 3,
"learning_rate": 0.01
}
optimizer = Optimizer("pA3Hqc1pEswNvXOPtSoRobt7C")
experiment = OfflineExperiment(project_name="horoma",
offline_directory="./experiments",
disabled=False)
experiment.log_parameters(hyper_params)
# MNIST Dataset
train_dataset = dsets.MNIST(root='./data/',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = dsets.MNIST(root='./data/',
train=False,
transform=transforms.ToTensor())
# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(
dataset=train_dataset, batch_size=hyper_params['batch_size'], shuffle=True)
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))
