def __init__(self,
args,
scale_factor=1.0,
div_flow=0.05,
num_scales=5,
num_highres_scales=2,
coarsest_resolution_loss_weight=0.32):
super().__init__()
self.args = args
self.div_flow = div_flow
self.num_scales = num_scales
self.scale_factor = scale_factor
# ---------------------------------------------------------------------
# start with initial scale
# for "low-resolution" scales we apply a scale factor of 4
# for "high-resolution" scales we apply a scale factor of 2
#
# e.g. [0.005, 0.01, 0.02, 0.08, 0.32]
# ---------------------------------------------------------------------
self.weights = [coarsest_resolution_loss_weight]
num_lowres_scales = num_scales - num_highres_scales
for k in range(num_lowres_scales - 1):
self.weights += [self.weights[-1] / 4]
for k in range(num_highres_scales):
self.weights += [self.weights[-1] / 2]
self.weights.reverse()
logging.value('MultiScaleEPE Weights: ', str(self.weights))
assert (len(self.weights) == num_scales) # sanity check
def load_state_dict_into_module(state_dict, module, strict=True):
own_state = module.state_dict()
for name, param in state_dict.items():
if name in own_state:
if isinstance(param, nn.Parameter):
# backwards compatibility for serialized parameters
param = param.data
try:
own_state[name].resize_as_(param)
own_state[name].copy_(param)
except Exception:
raise RuntimeError(
'While copying the parameter named {}, '
'whose dimensions in the model are {} and '
'whose dimensions in the checkpoint are {}.'.format(
name, own_state[name].size(), param.size()))
elif strict:
logging.info('Unexpected key "{}" in state_dict'.format(name))
raise KeyError('')
if strict:
missing = set(own_state.keys()) - set(state_dict.keys())
if len(missing) > 0:
logging.info('Missing keys in state_dict: ')
logging.value("{}".format(missing))
raise KeyError('')
def configure_tensorboard_summaries(save):
logdir = os.path.join(save, 'tb')
writer = summary.SummaryWriter(logdir,
flush_secs=constants.TENSORBOARD_FLUSH_SECS)
setattr(summary, "_summary_writer", writer)
with LoggingBlock("Tensorboard", emph=True):
logging.value(' flush_secs:', constants.TENSORBOARD_FLUSH_SECS)
logging.value(' logdir: ', logdir)
def try_register(name, module_class, registry, calling_frame):
if name in registry:
block_info = "Warning in {}[{}]:".format(calling_frame.filename,
calling_frame.lineno)
with logging.block(block_info):
code_info = "{} yields duplicate factory entry!".format(
calling_frame.code_context[0][0:-1])
logging.value(code_info)
registry[name] = module_class
def configure_lr_scheduler(args, optimizer):
with logging.block("Learning Rate Scheduler", emph=True):
logging.value(
"Scheduler: ",
args.lr_scheduler if args.lr_scheduler is not None else "None")
lr_scheduler = None
if args.lr_scheduler is not None:
kwargs = typeinf.kwargs_from_args(args, "lr_scheduler")
with logging.block():
logging.values(kwargs)
kwargs["optimizer"] = optimizer
lr_scheduler = typeinf.instance_from_kwargs(
args.lr_scheduler_class, kwargs=kwargs)
return lr_scheduler
def restore_module_from_filename(module,
filename,
key='state_dict',
include_params='*',
exclude_params=(),
translations=(),
fuzzy_translation_keys=()):
include_params = list(include_params)
exclude_params = list(exclude_params)
fuzzy_translation_keys = list(fuzzy_translation_keys)
translations = dict(translations)
# ------------------------------------------------------------------------------
# Make sure file exists
# ------------------------------------------------------------------------------
if not os.path.isfile(filename):
logging.info("Could not find checkpoint file '%s'!" % filename)
quit()
# ------------------------------------------------------------------------------
# Load checkpoint from file including the state_dict
# ------------------------------------------------------------------------------
cpu_device = torch.device('cpu')
checkpoint_dict = torch.load(filename, map_location=cpu_device)
checkpoint_state_dict = checkpoint_dict[key]
try:
restore_keys, actual_translations = restore_module_from_state_dict(
module,
checkpoint_state_dict,
include_params=include_params,
exclude_params=exclude_params,
translations=translations,
fuzzy_translation_keys=fuzzy_translation_keys)
except KeyError:
with logging.block('Checkpoint keys:'):
logging.value(checkpoint_state_dict.keys())
with logging.block('Module keys:'):
logging.value(module.state_dict().keys())
logging.info(
"Could not load checkpoint because of key errors. Checkpoint translations gone wrong?"
)
quit()
return checkpoint_dict, restore_keys, actual_translations
def msra_(mod_or_modules, mode='fan_out', nonlinearity='relu'):
logging.value("Initializing MSRA")
modules = _2modules(mod_or_modules)
uninitialized_modules = {}
for layer in modules:
if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.ConvTranspose2d):
nn.init.kaiming_normal_(layer.weight, mode=mode, nonlinearity=nonlinearity)
if layer.bias is not None:
nn.init.constant_(layer.bias, 0.0)
elif isinstance(layer, nn.BatchNorm2d):
identity_(layer)
else:
uninitialized_modules = _check_uninitialized(uninitialized_modules, layer)
for name, num_params in uninitialized_modules.items():
logging.value("Found uninitialized layer of type '{}' [{} params]".format(name, num_params))
def configure_visualizers(args, model_and_loss, optimizer, param_scheduler,
lr_scheduler, train_loader, validation_loader):
with logging.block("Runtime Visualizers", emph=True):
logging.value(
"Visualizer: ",
args.visualizer if args.visualizer is not None else "None")
visualizer = None
if args.visualizer is not None:
kwargs = typeinf.kwargs_from_args(args, "visualizer")
logging.values(kwargs)
kwargs["args"] = args
kwargs["model_and_loss"] = model_and_loss
kwargs["optimizer"] = optimizer
kwargs["param_scheduler"] = param_scheduler
kwargs["lr_scheduler"] = lr_scheduler
kwargs["train_loader"] = train_loader
kwargs["validation_loader"] = validation_loader
visualizer = typeinf.instance_from_kwargs(args.visualizer_class,
kwargs=kwargs)
return visualizer
def _log_statistics(loader, dataset):
example_dict = loader.first_item(
) # get sizes from first dataset example
for key, value in sorted(example_dict.items()):
if key == "index" or "name" in key: # no need to display these
continue
if isinstance(value, str):
logging.value("%s: " % key, value)
elif isinstance(value, list) or isinstance(value, tuple):
logging.value("%s: " % key, _sizes_to_str(value[0]))
else:
logging.value("%s: " % key, _sizes_to_str(value))
logging.value("num_examples: ", len(dataset))
def configure_runtime_augmentations(args):
with logging.block("Runtime Augmentations", emph=True):
training_augmentation = None
validation_augmentation = None
# ----------------------------------------------------
# Training Augmentation
# ----------------------------------------------------
if args.training_augmentation is not None:
kwargs = typeinf.kwargs_from_args(args, "training_augmentation")
logging.value("training_augmentation: ",
args.training_augmentation)
with logging.block():
logging.values(kwargs)
kwargs["args"] = args
training_augmentation = typeinf.instance_from_kwargs(
args.training_augmentation_class, kwargs=kwargs)
training_augmentation = training_augmentation.to(args.device)
else:
logging.info("training_augmentation: None")
# ----------------------------------------------------
# Training Augmentation
# ----------------------------------------------------
if args.validation_augmentation is not None:
kwargs = typeinf.kwargs_from_args(args, "validation_augmentation")
logging.value("validation_augmentation: ",
args.training_augmentation)
with logging.block():
logging.values(kwargs)
kwargs["args"] = args
validation_augmentation = typeinf.instance_from_kwargs(
args.validation_augmentation_class, kwargs=kwargs)
validation_augmentation = validation_augmentation.to(args.device)
else:
logging.info("validation_augmentation: None")
return training_augmentation, validation_augmentation
def fanmax_(mod_or_modules):
logging.value("Initializing FAN_MAX")
modules = _2modules(mod_or_modules)
uninitialized_modules = {}
for layer in modules:
if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.ConvTranspose2d):
m = layer.in_channels
n = layer.out_channels
k = layer.kernel_size
stddev = np.sqrt(2.0 / (np.maximum(m, n)) * np.prod(k))
nn.init.normal_(layer.weight, mean=0.0, std=stddev)
if layer.bias is not None:
nn.init.constant_(layer.bias, 0.0)
elif isinstance(layer, nn.BatchNorm2d):
identity_(layer)
else:
uninitialized_modules = _check_uninitialized(uninitialized_modules, layer)
for name, num_params in uninitialized_modules.items():
logging.value("Found uninitialized layer of type '{}' [{} params]".format(name, num_params))
def initialize(self, filename):
if not os.path.isfile(filename):
logging.info("Could not find {}".format(filename))
else:
logging.info("Loading Telegram tokens from {}".format(filename))
bots = None
with open(filename, "r") as f:
lines = f.readlines()
try:
bots = json.loads(''.join(lines), encoding='utf-8')
except Exception:
raise ValueError('Could not read %s. %s' %
(filename, sys.exc_info()[1]))
if bots is not None:
hostname = system.hostname()
logging.value("Found Host: ", hostname)
if "chat_id" in bots.keys():
self.chat_id = bots["chat_id"]
if "machines" in bots.keys():
machines = bots["machines"]
if hostname in machines.keys():
self.token = machines[hostname]
if self.token is not None:
try: # try out once
from telegrambotapiwrapper import Api
Api(token=self.token)
except Exception:
bots = None
self.chat_id = None
self.token = None
logging.info(
"Token seems to be invalid (or internet access is restricted)!"
)
if bots is None or self.chat_id is None or self.token is None:
logging.info("Could not set up telegram bot for some reason !")
def configure_parameter_scheduler(args, model_and_loss):
param_groups = args.param_scheduler_group
with logging.block("Parameter Scheduler", emph=True):
if param_groups is None:
logging.info("None")
else:
logging.value("Info: ",
"Please set lr=0 for scheduled parameters!")
scheduled_parameter_groups = []
with logging.block("parameter_groups:"):
for group_kwargs in param_groups:
group_match = group_kwargs["params"]
group_args = {
key: value
for key, value in group_kwargs.items()
if key != "params"
}
with logging.block("%s: %s" % (group_match, group_args)):
gnames, gparams = _param_names_and_trainable_generator(
model_and_loss, match=group_match)
for n in sorted(gnames):
logging.info(n)
group_args['params'] = gparams
scheduled_parameter_groups.append(group_args)
# create schedulers for every parameter group
schedulers = [
_configure_parameter_scheduler_group(kwargs)
for kwargs in scheduled_parameter_groups
]
# create container of parameter schedulers
scheduler = facade.ParameterSchedulerContainer(schedulers)
return scheduler
return None
def configure_random_seed(args):
with logging.block("Random Seeds", emph=True):
seed = args.seed
if seed is not None:
# python
random.seed(seed)
logging.value("Python seed: ", seed)
# numpy
seed += 1
np.random.seed(seed)
logging.value("Numpy seed: ", seed)
# torch
seed += 1
torch.manual_seed(seed)
logging.value("Torch CPU seed: ", seed)
# torch cuda
seed += 1
torch.cuda.manual_seed(seed)
logging.value("Torch CUDA seed: ", seed)
else:
logging.info("None")
def configure_model_and_loss(args):
with logging.block("Model and Loss", emph=True):
kwargs = typeinf.kwargs_from_args(args, "model")
kwargs["args"] = args
model = typeinf.instance_from_kwargs(args.model_class, kwargs=kwargs)
loss = None
if args.loss is not None:
kwargs = typeinf.kwargs_from_args(args, "loss")
kwargs["args"] = args
loss = typeinf.instance_from_kwargs(args.loss_class, kwargs=kwargs)
else:
logging.info("Loss is None; you need to pick a loss!")
quit()
model_and_loss = facade.ModelAndLoss(args, model, loss)
logging.value("Batch Size: ", args.batch_size)
if loss is not None:
logging.value("Loss: ", args.loss)
logging.value("Network: ", args.model)
logging.value("Number of parameters: ",
model_and_loss.num_parameters())
if loss is not None:
logging.value("Training Key: ", args.training_key)
if args.validation_dataset is not None:
logging.value("Validation Keys: ", args.validation_keys)
logging.value("Validation Modes: ", args.validation_modes)
return model_and_loss
def configure_data_loaders(args):
with logging.block("Datasets", emph=True):
def _sizes_to_str(value):
if np.isscalar(value):
return '1L'
else:
sizes = str([d for d in value.size()])
return ' '.join([strings.replace_index(sizes, 1, '#')])
def _log_statistics(loader, dataset):
example_dict = loader.first_item(
) # get sizes from first dataset example
for key, value in sorted(example_dict.items()):
if key == "index" or "name" in key: # no need to display these
continue
if isinstance(value, str):
logging.value("%s: " % key, value)
elif isinstance(value, list) or isinstance(value, tuple):
logging.value("%s: " % key, _sizes_to_str(value[0]))
else:
logging.value("%s: " % key, _sizes_to_str(value))
logging.value("num_examples: ", len(dataset))
# -----------------------------------------------------------------------------------------
# GPU parameters
# -----------------------------------------------------------------------------------------
gpuargs = {
"pin_memory": constants.DATALOADER_PIN_MEMORY
} if args.cuda else {}
train_loader_and_collation = None
validation_loader_and_collation = None
# -----------------------------------------------------------------
# This figures out from the args alone, whether we need batch collcation
# -----------------------------------------------------------------
train_collation, validation_collation = configure_collation(args)
# -----------------------------------------------------------------------------------------
# Training dataset
# -----------------------------------------------------------------------------------------
if args.training_dataset is not None:
# ----------------------------------------------
# Figure out training_dataset arguments
# ----------------------------------------------
kwargs = typeinf.kwargs_from_args(args, "training_dataset")
kwargs["args"] = args
# ----------------------------------------------
# Create training dataset and loader
# ----------------------------------------------
logging.value("Training Dataset: ", args.training_dataset)
with logging.block():
train_dataset = typeinf.instance_from_kwargs(
args.training_dataset_class, kwargs=kwargs)
if args.batch_size > len(train_dataset):
logging.info(
"Problem: batch_size bigger than number of training dataset examples!"
)
quit()
train_loader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=constants.TRAINING_DATALOADER_SHUFFLE,
drop_last=constants.TRAINING_DATALOADER_DROP_LAST,
num_workers=args.training_dataset_num_workers,
**gpuargs)
train_loader_and_collation = facade.LoaderAndCollation(
args, loader=train_loader, collation=train_collation)
_log_statistics(train_loader_and_collation, train_dataset)
# -----------------------------------------------------------------------------------------
# Validation dataset
# -----------------------------------------------------------------------------------------
if args.validation_dataset is not None:
# ----------------------------------------------
# Figure out validation_dataset arguments
# ----------------------------------------------
kwargs = typeinf.kwargs_from_args(args, "validation_dataset")
kwargs["args"] = args
# ------------------------------------------------------
# per default batch_size is the same as for training,
# unless a validation_batch_size is specified.
# -----------------------------------------------------
validation_batch_size = args.batch_size
if args.validation_batch_size > 0:
validation_batch_size = args.validation_batch_size
# ----------------------------------------------
# Create validation dataset and loader
# ----------------------------------------------
logging.value("Validation Dataset: ", args.validation_dataset)
with logging.block():
validation_dataset = typeinf.instance_from_kwargs(
args.validation_dataset_class, kwargs=kwargs)
if validation_batch_size > len(validation_dataset):
logging.info(
"Problem: validation_batch_size bigger than number of validation dataset examples!"
)
quit()
validation_loader = DataLoader(
validation_dataset,
batch_size=validation_batch_size,
shuffle=constants.VALIDATION_DATALOADER_SHUFFLE,
drop_last=constants.VALIDATION_DATALOADER_DROP_LAST,
num_workers=args.validation_dataset_num_workers,
**gpuargs)
validation_loader_and_collation = facade.LoaderAndCollation(
args,
loader=validation_loader,
collation=validation_collation)
_log_statistics(validation_loader_and_collation,
validation_dataset)
return train_loader_and_collation, validation_loader_and_collation
def configure_optimizer(args, model_and_loss):
optimizer = None
with logging.block("Optimizer", emph=True):
logging.value("Algorithm: ",
args.optimizer if args.optimizer is not None else "None")
if args.optimizer is not None:
if model_and_loss.num_parameters() == 0:
logging.info("No trainable parameters detected.")
logging.info("Setting optimizer to None.")
else:
with logging.block():
# -------------------------------------------
# Figure out all optimizer arguments
# -------------------------------------------
all_kwargs = typeinf.kwargs_from_args(args, "optimizer")
# -------------------------------------------
# Get the split of param groups
# -------------------------------------------
kwargs_without_groups = {
key: value
for key, value in all_kwargs.items() if key != "group"
}
param_groups = all_kwargs["group"]
# ----------------------------------------------------------------------
# Print arguments (without groups)
# ----------------------------------------------------------------------
logging.values(kwargs_without_groups)
# ----------------------------------------------------------------------
# Construct actual optimizer params
# ----------------------------------------------------------------------
kwargs = dict(kwargs_without_groups)
if param_groups is None:
# ---------------------------------------------------------
# Add all trainable parameters if there is no param groups
# ---------------------------------------------------------
all_trainable_parameters = _generate_trainable_params(
model_and_loss)
kwargs["params"] = all_trainable_parameters
else:
# -------------------------------------------
# Add list of parameter groups instead
# -------------------------------------------
trainable_parameter_groups = []
dnames, dparams = _param_names_and_trainable_generator(
model_and_loss)
dnames = set(dnames)
dparams = set(list(dparams))
with logging.block("parameter_groups:"):
for group in param_groups:
# log group settings
group_match = group["params"]
group_args = {
key: value
for key, value in group.items()
if key != "params"
}
with logging.block("%s: %s" %
(group_match, group_args)):
# retrieve parameters by matching name
gnames, gparams = _param_names_and_trainable_generator(
model_and_loss, match=group_match)
# log all names affected
for n in sorted(gnames):
logging.info(n)
# set generator for group
group_args["params"] = gparams
# append parameter group
trainable_parameter_groups.append(
group_args)
# update remaining trainable parameters
dnames -= set(gnames)
dparams -= set(list(gparams))
# append default parameter group
trainable_parameter_groups.append(
{"params": list(dparams)})
# and log its parameter names
with logging.block("default:"):
for dname in sorted(dnames):
logging.info(dname)
# set params in optimizer kwargs
kwargs["params"] = trainable_parameter_groups
# -------------------------------------------
# Create optimizer instance
# -------------------------------------------
optimizer = typeinf.instance_from_kwargs(
args.optimizer_class, kwargs=kwargs)
return optimizer
def exec_runtime(args, checkpoint_saver, model_and_loss, optimizer,
lr_scheduler, param_scheduler, train_loader,
validation_loader, training_augmentation,
validation_augmentation, visualizer):
# --------------------------------------------------------------------------------
# Validation schedulers are a bit special:
# They need special treatment as they want to be called with a validation loss..
# --------------------------------------------------------------------------------
validation_scheduler = (lr_scheduler is not None
and args.lr_scheduler == "ReduceLROnPlateau")
# --------------------------------------------------------
# Log some runtime info
# --------------------------------------------------------
with logging.block("Runtime", emph=True):
logging.value("start_epoch: ", args.start_epoch)
logging.value("total_epochs: ", args.total_epochs)
# ---------------------------------------
# Total progress bar arguments
# ---------------------------------------
progressbar_args = {
"desc": "Total",
"initial": args.start_epoch - 1,
"invert_iterations": True,
"iterable": range(1, args.total_epochs + 1),
"logging_on_close": True,
"logging_on_update": True,
"unit": "ep",
"track_eta": True
}
# --------------------------------------------------------
# Total progress bar
# --------------------------------------------------------
print(''), logging.logbook('')
total_progress = create_progressbar(**progressbar_args)
total_progress_stats = {}
print("\n")
# -------------------------------------------------k-------
# Remember validation losses
# --------------------------------------------------------
best_validation_losses = None
store_as_best = None
if validation_loader is not None:
num_validation_losses = len(args.validation_keys)
best_validation_losses = [
float("inf")
if args.validation_modes[i] == 'min' else -float("inf")
for i in range(num_validation_losses)
]
store_as_best = [False for _ in range(num_validation_losses)]
# ----------------------------------------------------------------
# Send Telegram message
# ----------------------------------------------------------------
logging.telegram(format_telegram_status_update(args, epoch=0))
avg_loss_dict = {}
for epoch in range(args.start_epoch, args.total_epochs + 1):
# --------------------------------
# Make Epoch %i/%i header message
# --------------------------------
epoch_header = "Epoch {}/{}{}{}".format(
epoch, args.total_epochs, " " * 24,
format_epoch_header_machine_stats(args))
with logger.LoggingBlock(epoch_header, emph=True):
# -------------------------------------------------------------------------------
# Let TensorBoard know where we are..
# -------------------------------------------------------------------------------
summary.set_global_step(epoch)
# -----------------------------------------------------------------
# Update standard learning scheduler and get current learning rate
# -----------------------------------------------------------------
# Starting with PyTorch 1.1 the expected validation order is:
# optimize(...)
# validate(...)
# scheduler.step()..
# ---------------------------------------------------------------------
# Update parameter schedule before the epoch
# Note: Parameter schedulers are tuples of (optimizer, schedule)
# ---------------------------------------------------------------------
if param_scheduler is not None:
param_scheduler.step(epoch=epoch)
# -----------------------------------------------------------------
# Get current learning rate from either optimizer or scheduler
# -----------------------------------------------------------------
lr = args.optimizer_lr if args.optimizer is not None else "None"
if lr_scheduler is not None:
lr = [group['lr'] for group in optimizer.param_groups] \
if args.optimizer is not None else "None"
# --------------------------------------------------------
# Current Epoch header stats
# --------------------------------------------------------
logging.info(format_epoch_header_stats(args, lr))
# -------------------------------------------
# Create and run a training epoch
# -------------------------------------------
if train_loader is not None:
if visualizer is not None:
visualizer.on_epoch_init(lr,
train=True,
epoch=epoch,
total_epochs=args.total_epochs)
ema_loss_dict = RuntimeEpoch(
args,
desc="Train",
augmentation=training_augmentation,
loader=train_loader,
model_and_loss=model_and_loss,
optimizer=optimizer,
visualizer=visualizer).run(train=True)
if visualizer is not None:
visualizer.on_epoch_finished(
ema_loss_dict,
train=True,
epoch=epoch,
total_epochs=args.total_epochs)
# -------------------------------------------
# Create and run a validation epoch
# -------------------------------------------
if validation_loader is not None:
if visualizer is not None:
visualizer.on_epoch_init(lr,
train=False,
epoch=epoch,
total_epochs=args.total_epochs)
# ---------------------------------------------------
# Construct holistic recorder for epoch
# ---------------------------------------------------
epoch_recorder = configure_holistic_epoch_recorder(
args, epoch=epoch, loader=validation_loader)
with torch.no_grad():
avg_loss_dict = RuntimeEpoch(
args,
desc="Valid",
augmentation=validation_augmentation,
loader=validation_loader,
model_and_loss=model_and_loss,
recorder=epoch_recorder,
visualizer=visualizer).run(train=False)
try:
epoch_recorder.add_scalars("evaluation_losses",
avg_loss_dict)
except Exception:
pass
if visualizer is not None:
visualizer.on_epoch_finished(
avg_loss_dict,
train=False,
epoch=epoch,
total_epochs=args.total_epochs)
# ----------------------------------------------------------------
# Evaluate valdiation losses
# ----------------------------------------------------------------
validation_losses = [
avg_loss_dict[vkey] for vkey in args.validation_keys
]
for i, (vkey, vmode) in enumerate(
zip(args.validation_keys, args.validation_modes)):
if vmode == 'min':
store_as_best[i] = validation_losses[
i] < best_validation_losses[i]
else:
store_as_best[i] = validation_losses[
i] > best_validation_losses[i]
if store_as_best[i]:
best_validation_losses[i] = validation_losses[i]
# ----------------------------------------------------------------
# Update validation scheduler, if one is in place
# We use the first key in validation keys as the relevant one
# ----------------------------------------------------------------
if lr_scheduler is not None:
if validation_scheduler:
lr_scheduler.step(validation_losses[0], epoch=epoch)
else:
lr_scheduler.step(epoch=epoch)
# ----------------------------------------------------------------
# Also show best loss on total_progress
# ----------------------------------------------------------------
total_progress_stats = {
"best_" + vkey + "_avg":
"%1.4f" % best_validation_losses[i]
for i, vkey in enumerate(args.validation_keys)
}
total_progress.set_postfix(total_progress_stats)
# ----------------------------------------------------------------
# Bump total progress
# ----------------------------------------------------------------
total_progress.update()
print('')
# ----------------------------------------------------------------
# Get ETA string for display in loggers
# ----------------------------------------------------------------
eta_str = total_progress.eta_str()
# ----------------------------------------------------------------
# Send Telegram status udpate
# ----------------------------------------------------------------
total_progress_stats['lr'] = format_learning_rate(lr)
logging.telegram(
format_telegram_status_update(
args,
eta_str=eta_str,
epoch=epoch,
total_progress_stats=total_progress_stats))
# ----------------------------------------------------------------
# Update ETA in progress title
# ----------------------------------------------------------------
eta_proctitle = "{} finishes in {}".format(args.proctitle, eta_str)
proctitles.setproctitle(eta_proctitle)
# ----------------------------------------------------------------
# Store checkpoint
# ----------------------------------------------------------------
if checkpoint_saver is not None and validation_loader is not None:
checkpoint_saver.save_latest(
directory=args.save,
model_and_loss=model_and_loss,
stats_dict=dict(avg_loss_dict, epoch=epoch),
store_as_best=store_as_best,
store_prefixes=args.validation_keys)
# ----------------------------------------------------------------
# Vertical space between epochs
# ----------------------------------------------------------------
print(''), logging.logbook('')
# ----------------------------------------------------------------
# Finish up
# ----------------------------------------------------------------
logging.telegram_flush()
total_progress.close()
logging.info("Finished.")
def main():
# ---------------------------------------------------
# Set working directory to folder containing main.py
# ---------------------------------------------------
os.chdir(os.path.dirname(os.path.realpath(__file__)))
# ----------------------------------------------------------------
# Activate syntax highlighting in tracebacks for better debugging
# ----------------------------------------------------------------
colored_traceback.add_hook()
# -----------------------------------------------------------
# Configure logging
# -----------------------------------------------------------
logging_filename = os.path.join(commandline.parse_save_dir(),
constants.LOGGING_LOGBOOK_FILENAME)
logger.configure_logging(logging_filename)
# ----------------------------------------------------------------
# Register type factories before parsing the commandline.
# NOTE: We decided to explicitly call these init() functions, to
# have more precise control over the timeline
# ----------------------------------------------------------------
with logging.block("Registering factories", emph=True):
augmentations.init()
datasets.init()
losses.init()
models.init()
optim.init()
visualizers.init()
logging.info('Done!')
# -----------------------------------------------------------
# Parse commandline after factories have been filled
# -----------------------------------------------------------
args = commandline.parse_arguments(blocktitle="Commandline Arguments")
# -----------------------
# Telegram configuration
# -----------------------
with logging.block("Telegram", emph=True):
logger.configure_telegram(constants.LOGGING_TELEGRAM_MACHINES_FILENAME)
# ----------------------------------------------------------------------
# Log git repository hash and make a compressed copy of the source code
# ----------------------------------------------------------------------
with logging.block("Source Code", emph=True):
logging.value("Git Hash: ", system.git_hash())
# Zip source code and copy to save folder
filename = os.path.join(args.save,
constants.LOGGING_ZIPSOURCE_FILENAME)
zipsource.create_zip(filename=filename, directory=os.getcwd())
logging.value("Archieved code: ", filename)
# ----------------------------------------------------
# Change process title for `top` and `pkill` commands
# This is more "informative" in `nvidia-smi` ;-)
# ----------------------------------------------------
args = config.configure_proctitle(args)
# -------------------------------------------------
# Set random seed for python, numpy, torch, cuda..
# -------------------------------------------------
config.configure_random_seed(args)
# -----------------------------------------------------------
# Machine stats
# -----------------------------------------------------------
with logging.block("Machine Statistics", emph=True):
if args.cuda:
args.device = torch.device("cuda:0")
logging.value("Cuda: ", torch.version.cuda)
logging.value("Cuda device count: ", torch.cuda.device_count())
logging.value("Cuda device name: ", torch.cuda.get_device_name(0))
logging.value("CuDNN: ", torch.backends.cudnn.version())
device_no = 0
if 'CUDA_VISIBLE_DEVICES' in os.environ.keys():
device_no = os.environ['CUDA_VISIBLE_DEVICES']
args.actual_device = "gpu:%s" % device_no
else:
args.device = torch.device("cpu")
args.actual_device = "cpu"
logging.value("Hostname: ", system.hostname())
logging.value("PyTorch: ", torch.__version__)
logging.value("PyTorch device: ", args.actual_device)
# ------------------------------------------------------
# Fetch data loaders. Quit if no data loader is present
# ------------------------------------------------------
train_loader, validation_loader = config.configure_data_loaders(args)
# -------------------------------------------------------------------------
# Check whether any dataset could be found
# -------------------------------------------------------------------------
success = any(loader is not None
for loader in [train_loader, validation_loader])
if not success:
logging.info(
"No dataset could be loaded successfully. Please check dataset paths!"
)
quit()
# -------------------------------------------------------------------------
# Configure runtime augmentations
# -------------------------------------------------------------------------
training_augmentation, validation_augmentation = config.configure_runtime_augmentations(
args)
# ----------------------------------------------------------
# Configure model and loss.
# ----------------------------------------------------------
model_and_loss = config.configure_model_and_loss(args)
# --------------------------------------------------------
# Print model visualization
# --------------------------------------------------------
if args.logging_model_graph:
with logging.block("Model Graph", emph=True):
logger.log_module_info(model_and_loss.model)
if args.logging_loss_graph:
with logging.block("Loss Graph", emph=True):
logger.log_module_info(model_and_loss.loss)
# -------------------------------------------------------------------------
# Possibly resume from checkpoint
# -------------------------------------------------------------------------
checkpoint_saver, checkpoint_stats = config.configure_checkpoint_saver(
args, model_and_loss)
if checkpoint_stats is not None:
with logging.block():
logging.info("Checkpoint Statistics:")
with logging.block():
logging.values(checkpoint_stats)
# ---------------------------------------------------------------------
# Set checkpoint stats
# ---------------------------------------------------------------------
if args.checkpoint_mode in ["resume_from_best", "resume_from_latest"]:
args.start_epoch = checkpoint_stats["epoch"]
# ---------------------------------------------------------------------
# Checkpoint and save directory
# ---------------------------------------------------------------------
with logging.block("Save Directory", emph=True):
if args.save is None:
logging.info("No 'save' directory specified!")
quit()
logging.value("Save directory: ", args.save)
if not os.path.exists(args.save):
os.makedirs(args.save)
# ------------------------------------------------------------
# If this is just an evaluation: overwrite savers and epochs
# ------------------------------------------------------------
if args.training_dataset is None and args.validation_dataset is not None:
args.start_epoch = 1
args.total_epochs = 1
train_loader = None
checkpoint_saver = None
args.optimizer = None
args.lr_scheduler = None
# ----------------------------------------------------
# Tensorboard summaries
# ----------------------------------------------------
logger.configure_tensorboard_summaries(args.save)
# -------------------------------------------------------------------
# From PyTorch API:
# If you need to move a model to GPU via .cuda(), please do so before
# constructing optimizers for it. Parameters of a model after .cuda()
# will be different objects with those before the call.
# In general, you should make sure that optimized parameters live in
# consistent locations when optimizers are constructed and used.
# -------------------------------------------------------------------
model_and_loss = model_and_loss.to(args.device)
# ----------------------------------------------------------
# Configure optimizer
# ----------------------------------------------------------
optimizer = config.configure_optimizer(args, model_and_loss)
# ----------------------------------------------------------
# Configure learning rate
# ----------------------------------------------------------
lr_scheduler = config.configure_lr_scheduler(args, optimizer)
# --------------------------------------------------------------------------
# Configure parameter scheduling
# --------------------------------------------------------------------------
param_scheduler = config.configure_parameter_scheduler(
args, model_and_loss)
# quit()
# ----------------------------------------------------------
# Cuda optimization
# ----------------------------------------------------------
if args.cuda:
torch.backends.cudnn.benchmark = constants.CUDNN_BENCHMARK
# ----------------------------------------------------------
# Configurate runtime visualization
# ----------------------------------------------------------
visualizer = config.configure_visualizers(
args,
model_and_loss=model_and_loss,
optimizer=optimizer,
param_scheduler=param_scheduler,
lr_scheduler=lr_scheduler,
train_loader=train_loader,
validation_loader=validation_loader)
if visualizer is not None:
visualizer = visualizer.to(args.device)
# ----------------------------------------------------------
# Kickoff training, validation and/or testing
# ----------------------------------------------------------
return runtime.exec_runtime(
args,
checkpoint_saver=checkpoint_saver,
lr_scheduler=lr_scheduler,
param_scheduler=param_scheduler,
model_and_loss=model_and_loss,
optimizer=optimizer,
train_loader=train_loader,
training_augmentation=training_augmentation,
validation_augmentation=validation_augmentation,
validation_loader=validation_loader,
visualizer=visualizer)