def execute(gpu, exp_batch, exp_alias, dataset_name, suppress_output):
latest = None
try:
# We set the visible cuda devices
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# At this point the log file with the correct naming is created.
merge_with_yaml(os.path.join('configs', exp_batch,
exp_alias + '.yaml'))
# The validation dataset is always fully loaded, so we fix a very high number of hours
g_conf.NUMBER_OF_HOURS = 10000
set_type_of_process('validation', dataset_name)
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
if suppress_output:
sys.stdout = open(os.path.join(
'_output_logs', exp_alias + '_' + g_conf.PROCESS_NAME + '_' +
str(os.getpid()) + ".out"),
"a",
buffering=1)
sys.stderr = open(os.path.join(
'_output_logs', exp_alias + '_err_' + g_conf.PROCESS_NAME +
'_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
# Define the dataset.
full_dataset = [
os.path.join(os.environ["COIL_DATASET_PATH"], dataset_name)
]
augmenter = Augmenter(None)
# Definition of the dataset to be used. Preload name is just the validation data name
dataset = CoILDataset(full_dataset,
transform=augmenter,
preload_names=[dataset_name])
# The data loader is the multi threaded module from pytorch that release a number of
# workers to get all the data.
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=g_conf.BATCH_SIZE,
shuffle=False,
num_workers=g_conf.NUMBER_OF_LOADING_WORKERS,
pin_memory=True)
# Create model.
model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
# The window used to keep track of the validation loss
l1_window = []
# If we have evaluated a checkpoint, get the validation losses of all the previously
# evaluated checkpoints (validation loss is used for early stopping)
latest = get_latest_evaluated_checkpoint()
if latest is not None: # When latest is noe
l1_window = coil_logger.recover_loss_window(dataset_name, None)
model.cuda()
best_mse = 1000
best_error = 1000
best_mse_iter = 0
best_error_iter = 0
# Loop to validate all checkpoints as they are saved during training
while not maximun_checkpoint_reach(latest, g_conf.TEST_SCHEDULE):
if is_next_checkpoint_ready(g_conf.TEST_SCHEDULE):
with torch.no_grad():
# Get and load latest checkpoint
latest = get_next_checkpoint(g_conf.TEST_SCHEDULE)
checkpoint = torch.load(
os.path.join('_logs', exp_batch, exp_alias,
'checkpoints',
str(latest) + '.pth'))
checkpoint_iteration = checkpoint['iteration']
print("Validation loaded ", checkpoint_iteration)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
accumulated_mse = 0
accumulated_error = 0
iteration_on_checkpoint = 0
if g_conf.USE_REPRESENTATION_LOSS:
accumulated_perception_rep_mse = 0
accumulated_speed_rep_mse = 0
accumulated_intentions_rep_mse = 0
accumulated_rep_mse = 0
accumulated_perception_rep_error = 0
accumulated_speed_rep_error = 0
accumulated_intentions_rep_error = 0
accumulated_rep_error = 0
# Validation loop
for data in data_loader:
# Compute the forward pass on a batch from the validation dataset
controls = data['directions']
# Run model forward and get outputs
# First case corresponds to squeeze network, second case corresponds to driving model without
# mimicking losses, last case corresponds to mimic network
if "seg" in g_conf.SENSORS.keys():
output = model.forward_branch(
data,
dataset.extract_inputs(data).cuda(), controls,
dataset.extract_intentions(data).cuda())
elif not g_conf.USE_REPRESENTATION_LOSS:
output = model.forward_branch(
data,
dataset.extract_inputs(data).cuda(), controls)
else:
output, intermediate_reps = model.forward_branch(
data,
dataset.extract_inputs(data).cuda(), controls)
write_regular_output(checkpoint_iteration, output)
# Compute control loss on current validation batch and accumulate it
targets_to_use = dataset.extract_targets(data)
mse = torch.mean(
(output - targets_to_use.cuda())**2).data.tolist()
mean_error = torch.mean(
torch.abs(output -
targets_to_use.cuda())).data.tolist()
accumulated_error += mean_error
accumulated_mse += mse
error = torch.abs(output - targets_to_use.cuda())
# Compute mimicking losses on current validation batch and accumulate it
if g_conf.USE_REPRESENTATION_LOSS:
expert_reps = dataset.extract_representations(data)
# First L1 losses (seg mask, speed, intention mimicking losses)
if g_conf.USE_PERCEPTION_REP_LOSS:
perception_rep_loss = torch.sum(
torch.abs(intermediate_reps[0] -
expert_reps[0].cuda())
).data.tolist() / (3 * output.shape[0])
else:
perception_rep_loss = 0
if g_conf.USE_SPEED_REP_LOSS:
speed_rep_loss = torch.sum(
torch.abs(intermediate_reps[1] -
expert_reps[1].cuda())
).data.tolist() / (3 * output.shape[0])
else:
speed_rep_loss = 0
if g_conf.USE_INTENTION_REP_LOSS:
intentions_rep_loss = torch.sum(
torch.abs(intermediate_reps[2] -
expert_reps[2].cuda())
).data.tolist() / (3 * output.shape[0])
else:
intentions_rep_loss = 0
rep_error = g_conf.REP_LOSS_WEIGHT * (
perception_rep_loss + speed_rep_loss +
intentions_rep_loss)
accumulated_perception_rep_error += perception_rep_loss
accumulated_speed_rep_error += speed_rep_loss
accumulated_intentions_rep_error += intentions_rep_loss
accumulated_rep_error += rep_error
# L2 losses now
if g_conf.USE_PERCEPTION_REP_LOSS:
perception_rep_loss = torch.sum(
(intermediate_reps[0] -
expert_reps[0].cuda())**
2).data.tolist() / (3 * output.shape[0])
else:
perception_rep_loss = 0
if g_conf.USE_SPEED_REP_LOSS:
speed_rep_loss = torch.sum(
(intermediate_reps[1] -
expert_reps[1].cuda())**
2).data.tolist() / (3 * output.shape[0])
else:
speed_rep_loss = 0
if g_conf.USE_INTENTION_REP_LOSS:
intentions_rep_loss = torch.sum(
(intermediate_reps[2] -
expert_reps[2].cuda())**
2).data.tolist() / (3 * output.shape[0])
else:
intentions_rep_loss = 0
rep_mse = g_conf.REP_LOSS_WEIGHT * (
perception_rep_loss + speed_rep_loss +
intentions_rep_loss)
accumulated_perception_rep_mse += perception_rep_loss
accumulated_speed_rep_mse += speed_rep_loss
accumulated_intentions_rep_mse += intentions_rep_loss
accumulated_rep_mse += rep_mse
# Log a random position
position = random.randint(
0,
len(output.data.tolist()) - 1)
# Logging
if g_conf.USE_REPRESENTATION_LOSS:
total_mse = mse + rep_mse
total_error = mean_error + rep_error
coil_logger.add_message(
'Iterating', {
'Checkpoint':
latest,
'Iteration':
(str(iteration_on_checkpoint * 120) + '/' +
str(len(dataset))),
'MeanError':
mean_error,
'MSE':
mse,
'RepMeanError':
rep_error,
'RepMSE':
rep_mse,
'MeanTotalError':
total_error,
'TotalMSE':
total_mse,
'Output':
output[position].data.tolist(),
'GroundTruth':
targets_to_use[position].data.tolist(),
'Error':
error[position].data.tolist(),
'Inputs':
dataset.extract_inputs(
data)[position].data.tolist()
}, latest)
else:
coil_logger.add_message(
'Iterating', {
'Checkpoint':
latest,
'Iteration':
(str(iteration_on_checkpoint * 120) + '/' +
str(len(dataset))),
'MeanError':
mean_error,
'MSE':
mse,
'Output':
output[position].data.tolist(),
'GroundTruth':
targets_to_use[position].data.tolist(),
'Error':
error[position].data.tolist(),
'Inputs':
dataset.extract_inputs(
data)[position].data.tolist()
}, latest)
iteration_on_checkpoint += 1
if g_conf.USE_REPRESENTATION_LOSS:
print("Iteration %d on Checkpoint %d : Error %f" %
(iteration_on_checkpoint,
checkpoint_iteration, total_error))
else:
print("Iteration %d on Checkpoint %d : Error %f" %
(iteration_on_checkpoint,
checkpoint_iteration, mean_error))
"""
########
Finish a round of validation, write results, wait for the next
########
"""
# Compute average L1 and L2 losses over whole round of validation and log them
checkpoint_average_mse = accumulated_mse / (
len(data_loader))
checkpoint_average_error = accumulated_error / (
len(data_loader))
coil_logger.add_scalar('L2 Loss', checkpoint_average_mse,
latest, True)
coil_logger.add_scalar('Loss', checkpoint_average_error,
latest, True)
if g_conf.USE_REPRESENTATION_LOSS:
checkpoint_average_perception_rep_mse = accumulated_perception_rep_mse / (
len(data_loader))
checkpoint_average_speed_rep_mse = accumulated_speed_rep_mse / (
len(data_loader))
checkpoint_average_intentions_rep_mse = accumulated_intentions_rep_mse / (
len(data_loader))
checkpoint_average_rep_mse = accumulated_rep_mse / (
len(data_loader))
checkpoint_average_total_mse = checkpoint_average_mse + checkpoint_average_rep_mse
checkpoint_average_perception_rep_error = accumulated_perception_rep_error / (
len(data_loader))
checkpoint_average_speed_rep_error = accumulated_speed_rep_error / (
len(data_loader))
checkpoint_average_intentions_rep_error = accumulated_intentions_rep_error / (
len(data_loader))
checkpoint_average_rep_error = accumulated_rep_error / (
len(data_loader))
checkpoint_average_total_error = checkpoint_average_error + checkpoint_average_rep_mse
# Log L1/L2 loss terms
coil_logger.add_scalar(
'Perception Rep Loss',
checkpoint_average_perception_rep_mse, latest,
True)
coil_logger.add_scalar(
'Speed Rep Loss', checkpoint_average_speed_rep_mse,
latest, True)
coil_logger.add_scalar(
'Intentions Rep Loss',
checkpoint_average_intentions_rep_mse, latest,
True)
coil_logger.add_scalar('Overall Rep Loss',
checkpoint_average_rep_mse,
latest, True)
coil_logger.add_scalar('Total L2 Loss',
checkpoint_average_total_mse,
latest, True)
coil_logger.add_scalar(
'Perception Rep Error',
checkpoint_average_perception_rep_error, latest,
True)
coil_logger.add_scalar(
'Speed Rep Error',
checkpoint_average_speed_rep_error, latest, True)
coil_logger.add_scalar(
'Intentions Rep Error',
checkpoint_average_intentions_rep_error, latest,
True)
coil_logger.add_scalar('Total Rep Error',
checkpoint_average_rep_error,
latest, True)
coil_logger.add_scalar('Total Loss',
checkpoint_average_total_error,
latest, True)
else:
checkpoint_average_total_mse = checkpoint_average_mse
checkpoint_average_total_error = checkpoint_average_error
if checkpoint_average_total_mse < best_mse:
best_mse = checkpoint_average_total_mse
best_mse_iter = latest
if checkpoint_average_total_error < best_error:
best_error = checkpoint_average_total_error
best_error_iter = latest
# Print for logging / to terminal validation results
if g_conf.USE_REPRESENTATION_LOSS:
coil_logger.add_message(
'Iterating', {
'Summary': {
'Control Error': checkpoint_average_error,
'Control Loss': checkpoint_average_mse,
'Rep Error': checkpoint_average_rep_error,
'Rep Loss': checkpoint_average_rep_mse,
'Error': checkpoint_average_total_error,
'Loss': checkpoint_average_total_mse,
'BestError': best_error,
'BestMSE': best_mse,
'BestMSECheckpoint': best_mse_iter,
'BestErrorCheckpoint': best_error_iter
},
'Checkpoint': latest
}, latest)
else:
coil_logger.add_message(
'Iterating', {
'Summary': {
'Error': checkpoint_average_error,
'Loss': checkpoint_average_mse,
'BestError': best_error,
'BestMSE': best_mse,
'BestMSECheckpoint': best_mse_iter,
'BestErrorCheckpoint': best_error_iter
},
'Checkpoint': latest
}, latest)
# Save validation loss history (validation loss is used for early stopping)
l1_window.append(checkpoint_average_total_error)
coil_logger.write_on_error_csv(
dataset_name, checkpoint_average_total_error)
# Early stopping
if g_conf.FINISH_ON_VALIDATION_STALE is not None:
if dlib.count_steps_without_decrease(l1_window) > 3 and \
dlib.count_steps_without_decrease_robust(l1_window) > 3:
coil_logger.write_stop(dataset_name, latest)
break
else:
latest = get_latest_evaluated_checkpoint()
time.sleep(1)
coil_logger.add_message('Loading',
{'Message': 'Waiting Checkpoint'})
print("Waiting for the next Validation")
coil_logger.add_message('Finished', {})
except KeyboardInterrupt:
coil_logger.add_message('Error', {'Message': 'Killed By User'})
# We erase the output that was unfinished due to some process stop.
if latest is not None:
coil_logger.erase_csv(latest)
except RuntimeError as e:
if latest is not None:
coil_logger.erase_csv(latest)
coil_logger.add_message('Error', {'Message': str(e)})
except:
traceback.print_exc()
coil_logger.add_message('Error', {'Message': 'Something Happened'})
# We erase the output that was unfinished due to some process stop.
if latest is not None:
coil_logger.erase_csv(latest)
def execute(gpu,
exp_batch,
exp_alias,
suppress_output=True,
number_of_workers=12):
"""
The main training function. This functions loads the latest checkpoint
for a given, exp_batch (folder) and exp_alias (experiment configuration).
With this checkpoint it starts from the beginning or continue some training.
Args:
gpu: The GPU number
exp_batch: the folder with the experiments
exp_alias: the alias, experiment name
suppress_output: if the output are going to be saved on a file
number_of_workers: the number of threads used for data loading
Returns:
None
"""
try:
# We set the visible cuda devices to select the GPU
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
g_conf.VARIABLE_WEIGHT = {}
# At this point the log file with the correct naming is created.
# You merge the yaml file with the global configuration structure.
merge_with_yaml(os.path.join('configs', exp_batch,
exp_alias + '.yaml'))
set_type_of_process('train')
# Set the process into loading status.
coil_logger.add_message('Loading', {'GPU': gpu})
# Seed RNGs
torch.manual_seed(g_conf.MAGICAL_SEED)
random.seed(g_conf.MAGICAL_SEED)
# Put the output to a separate file if it is the case
if suppress_output:
if not os.path.exists('_output_logs'):
os.mkdir('_output_logs')
sys.stdout = open(os.path.join(
'_output_logs', exp_alias + '_' + g_conf.PROCESS_NAME + '_' +
str(os.getpid()) + ".out"),
"a",
buffering=1)
sys.stderr = open(os.path.join(
'_output_logs', exp_alias + '_err_' + g_conf.PROCESS_NAME +
'_' + str(os.getpid()) + ".out"),
"a",
buffering=1)
if coil_logger.check_finish('train'):
coil_logger.add_message('Finished', {})
return
# Preload option
if g_conf.PRELOAD_MODEL_ALIAS is not None:
checkpoint = torch.load(
os.path.join('_logs', g_conf.PRELOAD_MODEL_BATCH,
g_conf.PRELOAD_MODEL_ALIAS, 'checkpoints',
str(g_conf.PRELOAD_MODEL_CHECKPOINT) + '.pth'))
# Get the latest checkpoint to be loaded
# returns none if there are no checkpoints saved for this model
checkpoint_file = get_latest_saved_checkpoint()
if checkpoint_file is not None:
checkpoint = torch.load(
os.path.join('_logs', exp_batch, exp_alias, 'checkpoints',
str(get_latest_saved_checkpoint())))
iteration = checkpoint['iteration']
best_loss = checkpoint['best_loss']
best_loss_iter = checkpoint['best_loss_iter']
else:
iteration = 0
best_loss = 10000.0
best_loss_iter = 0
# Define the dataset.
# Can specify a list of training datasets or just a single training dataset
if len(g_conf.TRAIN_DATASET_NAMES) == 0:
train_dataset_list = [g_conf.TRAIN_DATASET_NAME]
else:
train_dataset_list = g_conf.TRAIN_DATASET_NAMES
full_dataset = [
os.path.join(os.environ["COIL_DATASET_PATH"], dataset_name)
for dataset_name in train_dataset_list
]
# By instantiating the augmenter we get a callable that augment images and transform them
# into tensors.
augmenter = Augmenter(g_conf.AUGMENTATION)
# Instantiate the class used to read a dataset. The coil dataset generator
# can be found
dataset = CoILDataset(full_dataset,
transform=augmenter,
preload_names=[
str(g_conf.NUMBER_OF_HOURS) + 'hours_' +
dataset_name
for dataset_name in train_dataset_list
],
train_dataset=True)
print("Loaded dataset")
# Create dataloader, model, and optimizer
data_loader = select_balancing_strategy(dataset, iteration,
number_of_workers)
model = CoILModel(g_conf.MODEL_TYPE, g_conf.MODEL_CONFIGURATION)
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=g_conf.LEARNING_RATE)
# If we have a previous checkpoint, load model, optimizer, and record of previous
# train loss values (used for the learning rate schedule)
if checkpoint_file is not None or g_conf.PRELOAD_MODEL_ALIAS is not None:
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
accumulated_time = checkpoint['total_time']
loss_window = coil_logger.recover_loss_window('train', iteration)
else: # We accumulate iteration time and keep the average speed
accumulated_time = 0
loss_window = []
print("Before the loss")
# Define control loss function
criterion = Loss(g_conf.LOSS_FUNCTION)
if iteration == 0 and is_ready_to_save(iteration):
state = {
'iteration': iteration,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'total_time': accumulated_time,
'optimizer': optimizer.state_dict(),
'best_loss_iter': best_loss_iter
}
torch.save(
state,
os.path.join('_logs', exp_batch, exp_alias, 'checkpoints',
str(iteration) + '.pth'))
# Training loop
for data in data_loader:
# Basically in this mode of execution, we validate every X Steps, if it goes up 3 times,
# add a stop on the _logs folder that is going to be read by this process
if g_conf.FINISH_ON_VALIDATION_STALE is not None and \
check_loss_validation_stopped(iteration, g_conf.FINISH_ON_VALIDATION_STALE):
break
"""
####################################
Main optimization loop
####################################
"""
iteration += 1
# Adjust learning rate based on training loss
if iteration % 1000 == 0:
adjust_learning_rate_auto(optimizer, loss_window)
capture_time = time.time()
model.zero_grad()
controls = data['directions']
# Run model forward and get outputs
# First case corresponds to training squeeze network, second case corresponds to training driving model without
# mimicking losses, last case corresponds to training mimic network
if "seg" in g_conf.SENSORS.keys():
branches = model(data,
dataset.extract_inputs(data).cuda(),
dataset.extract_intentions(data).cuda())
elif not g_conf.USE_REPRESENTATION_LOSS:
branches = model(data, dataset.extract_inputs(data).cuda())
else:
branches, intermediate_reps = model(
data,
dataset.extract_inputs(data).cuda())
# Compute control loss
targets_to_use = dataset.extract_targets(data)
loss_function_params = {
'branches': branches,
'targets': targets_to_use.cuda(),
'controls': controls.cuda(),
'inputs': dataset.extract_inputs(data).cuda(),
'branch_weights': g_conf.BRANCH_LOSS_WEIGHT,
'variable_weights': g_conf.VARIABLE_WEIGHT
}
loss, _ = criterion(loss_function_params)
# Compute mimicking loss
if g_conf.USE_REPRESENTATION_LOSS:
expert_reps = dataset.extract_representations(data)
# Seg mask mimicking loss
if g_conf.USE_PERCEPTION_REP_LOSS:
perception_rep_loss_elementwise = (
intermediate_reps[0] - expert_reps[0].cuda())**2
perception_rep_loss = g_conf.PERCEPTION_REP_WEIGHT * torch.sum(
perception_rep_loss_elementwise) / branches[0].shape[0]
else:
perception_rep_loss = torch.tensor(0.).cuda()
# Speed mimicking loss
if g_conf.USE_SPEED_REP_LOSS:
speed_rep_loss_elementwise = (intermediate_reps[1] -
expert_reps[1].cuda())**2
speed_rep_loss = g_conf.SPEED_REP_WEIGHT * torch.sum(
speed_rep_loss_elementwise) / branches[0].shape[0]
else:
speed_rep_loss = torch.tensor(0.).cuda()
# Stop intentions mimicking loss
if g_conf.USE_INTENTION_REP_LOSS:
intentions_rep_loss_elementwise = (
intermediate_reps[2] - expert_reps[2].cuda())**2
intentions_rep_loss = g_conf.INTENTIONS_REP_WEIGHT * torch.sum(
intentions_rep_loss_elementwise) / branches[0].shape[0]
else:
intentions_rep_loss = torch.tensor(0.).cuda()
rep_loss = g_conf.REP_LOSS_WEIGHT * (
perception_rep_loss + speed_rep_loss + intentions_rep_loss)
overall_loss = loss + rep_loss
else:
overall_loss = loss
overall_loss.backward()
optimizer.step()
"""
####################################
Saving the model if necessary
####################################
"""
if is_ready_to_save(iteration):
state = {
'iteration': iteration,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'total_time': accumulated_time,
'optimizer': optimizer.state_dict(),
'best_loss_iter': best_loss_iter
}
torch.save(
state,
os.path.join('_logs', exp_batch, exp_alias, 'checkpoints',
str(iteration) + '.pth'))
"""
################################################
Adding tensorboard logs.
Making calculations for logging purposes.
These logs are monitored by the printer module.
#################################################
"""
coil_logger.add_scalar('Loss', loss.data, iteration)
if g_conf.USE_REPRESENTATION_LOSS:
coil_logger.add_scalar('Perception Rep Loss',
perception_rep_loss.data, iteration)
coil_logger.add_scalar('Speed Rep Loss', speed_rep_loss.data,
iteration)
coil_logger.add_scalar('Intentions Rep Loss',
intentions_rep_loss.data, iteration)
coil_logger.add_scalar('Overall Rep Loss', rep_loss.data,
iteration)
coil_logger.add_scalar('Total Loss', overall_loss.data,
iteration)
if 'rgb' in data:
coil_logger.add_image('Image', torch.squeeze(data['rgb']),
iteration)
if overall_loss.data < best_loss:
best_loss = overall_loss.data.tolist()
best_loss_iter = iteration
# Log a random position
position = random.randint(0, len(data) - 1)
output = model.extract_branch(torch.stack(branches[0:4]), controls)
error = torch.abs(output - targets_to_use.cuda())
accumulated_time += time.time() - capture_time
# Log to terminal and log file
if g_conf.USE_REPRESENTATION_LOSS:
coil_logger.add_message(
'Iterating', {
'Iteration':
iteration,
'Loss':
overall_loss.data.tolist(),
'Control Loss':
loss.data.tolist(),
'Rep Loss':
rep_loss.data.tolist(),
'Images/s':
(iteration * g_conf.BATCH_SIZE) / accumulated_time,
'BestLoss':
best_loss,
'BestLossIteration':
best_loss_iter,
'Output':
output[position].data.tolist(),
'GroundTruth':
targets_to_use[position].data.tolist(),
'Error':
error[position].data.tolist(),
'Inputs':
dataset.extract_inputs(data)[position].data.tolist()
}, iteration)
else:
coil_logger.add_message(
'Iterating', {
'Iteration':
iteration,
'Loss':
loss.data.tolist(),
'Images/s':
(iteration * g_conf.BATCH_SIZE) / accumulated_time,
'BestLoss':
best_loss,
'BestLossIteration':
best_loss_iter,
'Output':
output[position].data.tolist(),
'GroundTruth':
targets_to_use[position].data.tolist(),
'Error':
error[position].data.tolist(),
'Inputs':
dataset.extract_inputs(data)[position].data.tolist()
}, iteration)
# Save training loss history (useful for restoring training runs since learning rate is adjusted
# based on training loss)
loss_window.append(overall_loss.data.tolist())
coil_logger.write_on_error_csv('train', overall_loss.data)
print("Iteration: %d Loss: %f" % (iteration, overall_loss.data))
coil_logger.add_message('Finished', {})
except KeyboardInterrupt:
coil_logger.add_message('Error', {'Message': 'Killed By User'})
except RuntimeError as e:
coil_logger.add_message('Error', {'Message': str(e)})
except:
traceback.print_exc()
coil_logger.add_message('Error', {'Message': 'Something Happened'})