def train_pixelcnn_prior(params, checkpoint_dir, recover=True, force=False):
if force and os.path.exists(checkpoint_dir):
shutil.rmtree(checkpoint_dir)
logger.set_logger_dir(checkpoint_dir)
dataset_params = params['dataset']
model_params = params['model']
trainer_params = params['trainer']
train_ds, val_ds, sample_train, sample_val, sample_train_label, \
sample_val_label = get_dataflow(
dataset_params['path'], False,
dataset_params['train_val_split'], trainer_params['batch_size'],
trainer_params['num_parallel'])
vqvae_checkpoint_path = trainer_params['vqvae_checkpoint_path']
vqvae_config_path = os.path.join(os.path.split(vqvae_checkpoint_path)[0],
'config.json')
model_params['vqvae_model_params'] = vqvae_config_path
latent_shape = model_params['latent_shape']
num_labels = model_params['num_labels']
params.to_file(os.path.join(logger.get_logger_dir(), 'config.json'))
model = BasePixelCNNPrior.from_params(model_params)
trainer = SyncMultiGPUTrainerParameterServer(
gpus=trainer_params['num_gpus'], ps_device=None)
trainer_config = AutoResumeTrainConfig(
always_resume=recover,
model=model,
dataflow=train_ds,
callbacks=[
SequentialSampling(trainer_params['num_examples_to_generate'],
latent_shape, num_labels, model,
os.path.join(checkpoint_dir, 'images')),
Reconstruct(model, sample_train, sample_val,
os.path.join(checkpoint_dir, 'images'),
sample_train_label, sample_val_label),
ModelSaver(max_to_keep=5, checkpoint_dir=checkpoint_dir),
InferenceRunner(input=val_ds,
infs=ScalarStats(['loss'])),
MinSaver(monitor_stat='validation_loss'),
CompressResource(os.path.join(checkpoint_dir, 'images'),
os.path.join(checkpoint_dir, 'images.zip')),
RestoreWeights(vqvae_checkpoint_path),
Notification('Training status', 'Complete')
],
steps_per_epoch=trainer_params['steps_per_epoch'],
max_epoch=trainer_params['max_epochs']
)
launch_train_with_config(trainer_config, trainer)
def run_once(self, opt, sess_init=None, save_dir=None):
####
train_datagen = self.get_datagen(opt['train_batch_size'], mode='train')
valid_datagen = self.get_datagen(opt['infer_batch_size'], mode='valid')
###### must be called before ModelSaver
if save_dir is None:
logger.set_logger_dir(self.save_dir)
else:
logger.set_logger_dir(save_dir)
######
model_flags = opt['model_flags']
model = self.get_model()(**model_flags)
######
callbacks = [
ModelSaver(max_to_keep=1, keep_checkpoint_every_n_hours=None),
]
for param_name, param_info in opt['manual_parameters'].items():
model.add_manual_variable(param_name, param_info[0])
callbacks.append(
ScheduledHyperParamSetter(param_name, param_info[1]))
# multi-GPU inference (with mandatory queue prefetch)
infs = [StatCollector()]
callbacks.append(
DataParallelInferenceRunner(valid_datagen, infs,
list(range(nr_gpus))))
if self.model_mode == 'seg_gland':
callbacks.append(MaxSaver('valid_dice_obj'))
elif self.model_mode == 'seg_nuc':
callbacks.append(MaxSaver('valid_dice_np'))
else:
callbacks.append(MaxSaver('valid_auc'))
######
steps_per_epoch = train_datagen.size() // nr_gpus
config = TrainConfig(
model=model,
callbacks=callbacks,
dataflow=train_datagen,
steps_per_epoch=steps_per_epoch,
max_epoch=opt['nr_epochs'],
)
config.session_init = sess_init
launch_train_with_config(config,
SyncMultiGPUTrainerParameterServer(nr_gpus))
tf.reset_default_graph(
) # remove the entire graph in case of multiple runs
return
def train_image_embedding_softmax(params, checkpoint_dir, recover=True,
force=False):
if force and os.path.exists(checkpoint_dir):
shutil.rmtree(checkpoint_dir)
logger.set_logger_dir(checkpoint_dir)
dataset_params = params['dataset']
model_params = params['model']
trainer_params = params['trainer']
train_ds, val_ds, _, _, _, _ = get_dataflow(
dataset_params['path'], False, dataset_params['train_val_split'],
trainer_params['batch_size'], trainer_params['num_parallel'])
vqvae_checkpoint_path = trainer_params['vqvae_checkpoint_path']
vqvae_config_path = os.path.join(os.path.split(vqvae_checkpoint_path)[0],
'config.json')
model_params['vqvae_model_params'] = vqvae_config_path
params.to_file(os.path.join(logger.get_logger_dir(), 'config.json'))
model = BaseImageEmbedding.from_params(model_params)
trainer = SyncMultiGPUTrainerParameterServer(
gpus=trainer_params['num_gpus'], ps_device=None)
trainer_config = AutoResumeTrainConfig(
always_resume=recover,
model=model,
dataflow=train_ds,
callbacks=[
InferenceRunner(input=val_ds, infs=[
ScalarStats('loss'),
ClassificationError('correct_prediction',
'val-correct_prediction')]),
ModelSaver(max_to_keep=5, checkpoint_dir=checkpoint_dir),
MinSaver(monitor_stat='val-correct_prediction'),
RestoreWeights(vqvae_checkpoint_path),
SendStat('Training status', [
'loss', 'accuracy',
'validation_loss', 'val-correct_prediction'],
after_every=2),
Notification('Training status', 'Complete')
],
steps_per_epoch=trainer_params['steps_per_epoch'],
max_epoch=trainer_params['max_epochs']
)
launch_train_with_config(trainer_config, trainer)
def run_once(self, opt, sess_init=None, save_dir=None):
####
train_datagen = self.get_datagen(opt["train_batch_size"], mode="train")
valid_datagen = self.get_datagen(opt["infer_batch_size"], mode="valid")
###### must be called before ModelSaver
if save_dir is None:
logger.set_logger_dir(self.save_dir)
else:
logger.set_logger_dir(save_dir)
######
model_flags = opt["model_flags"]
model = self.get_model()(**model_flags)
######
callbacks = [
# ModelSaver(max_to_keep=20), # TODO dynamic this
ModelSaver(max_to_keep=opt["nr_epochs"]),
# InjectShell(file='/tools/hover_net/src/config.yml', shell='ipython'),
]
for param_name, param_info in opt["manual_parameters"].items():
model.add_manual_variable(param_name, param_info[0])
callbacks.append(ScheduledHyperParamSetter(param_name, param_info[1]))
# multi-GPU inference (with mandatory queue prefetch)
infs = [StatCollector()]
callbacks.append(
DataParallelInferenceRunner(valid_datagen, infs, list(range(nr_gpus)))
)
callbacks.append(MaxSaver("valid_dice"))
######
steps_per_epoch = train_datagen.size() // nr_gpus
config = TrainConfig(
model=model,
callbacks=callbacks,
dataflow=train_datagen,
steps_per_epoch=steps_per_epoch,
max_epoch=opt["nr_epochs"],
)
config.session_init = sess_init
launch_train_with_config(config, SyncMultiGPUTrainerParameterServer(nr_gpus))
tf.reset_default_graph() # remove the entire graph in case of multiple runs
# TODO: save
return
def run(model):
instance = Model(model, model.conf.data_format)
if not model.conf.is_train:
batch = 64
dataset = get_data(model.conf.data_dir, 'val', batch)
eval_on_ILSVRC12(
instance,
get_model_loader(model.conf.logdir + '/' + model.conf.test_step),
dataset)
else:
logger.set_logger_dir(os.path.join(model.conf.logdir))
config = get_config(instance, model.conf)
if model.conf.reload_step:
config.session_init = get_model_loader(model.conf.logdir + '/' +
model.conf.reload_step)
trainer = SyncMultiGPUTrainerParameterServer(max(get_nr_gpu(), 1))
launch_train_with_config(config, trainer)
def train_vae(params, checkpoint_dir, recover=True, force=False):
if force and os.path.exists(checkpoint_dir):
shutil.rmtree(checkpoint_dir)
logger.set_logger_dir(checkpoint_dir)
dataset_params = params['dataset']
model_params = params['model']
trainer_params = params['trainer']
train_ds, val_ds, sample_train, sample_val, _, _ = \
get_dataflow(dataset_params['path'],
dataset_params['binarizer'],
dataset_params['train_val_split'],
trainer_params['batch_size'],
trainer_params['num_parallel'])
params.to_file(os.path.join(logger.get_logger_dir(), 'config.json'))
latent_dim = model_params['latent_dim']
model = BaseVAE.from_params(model_params)
trainer = SyncMultiGPUTrainerParameterServer(
gpus=trainer_params['num_gpus'], ps_device=None)
trainer_config = AutoResumeTrainConfig(
always_resume=recover,
model=model,
dataflow=train_ds,
callbacks=[
Sampling(model, trainer_params['num_examples_to_generate'],
latent_dim, os.path.join(checkpoint_dir, 'images')),
Reconstruct(model, sample_train, sample_val,
os.path.join(checkpoint_dir, 'images')),
ModelSaver(max_to_keep=5, checkpoint_dir=checkpoint_dir),
InferenceRunner(input=val_ds,
infs=ScalarStats(['avg_logpx_z', 'neg_elbo'])),
MinSaver(monitor_stat='validation_neg_elbo'),
CompressResource(os.path.join(checkpoint_dir, 'images'),
os.path.join(checkpoint_dir, 'images.zip')),
Notification('Training status', 'Complete')
],
steps_per_epoch=trainer_params['steps_per_epoch'],
max_epoch=trainer_params['max_epochs']
)
launch_train_with_config(trainer_config, trainer)
def run_once(self, nr_gpus, freeze, sess_init=None, save_dir=None):
####
train_datagen = self.get_datagen(mode='train')
valid_datagen = self.get_datagen(mode='valid')
###### must be called before ModelSaver
if save_dir is None:
logger.set_logger_dir(self.save_dir)
else:
logger.set_logger_dir(save_dir)
callbacks = [
ModelSaver(max_to_keep=200),
ScheduledHyperParamSetter('learning_rate', self.lr_sched),
]
######
# multi-GPU inference (with mandatory queue prefetch)
infs = [StatCollector()]
callbacks.append(
DataParallelInferenceRunner(valid_datagen, infs,
list(range(nr_gpus))))
######
steps_per_epoch = train_datagen.size() // nr_gpus
MODEL_MAKER = Model_NP_XY if self.model_mode == 'np+xy' else Model_NP_DIST
config = TrainConfig(
model=MODEL_MAKER(freeze),
callbacks=callbacks,
dataflow=train_datagen,
steps_per_epoch=steps_per_epoch,
max_epoch=self.nr_epochs,
)
config.session_init = sess_init
launch_train_with_config(config,
SyncMultiGPUTrainerParameterServer(nr_gpus))
tf.reset_default_graph(
) # remove the entire graph in case of multiple runs
return
def train_image_embedding_triplet(params, checkpoint_dir, recover=True,
force=False):
if force and os.path.exists(checkpoint_dir):
shutil.rmtree(checkpoint_dir)
logger.set_logger_dir(checkpoint_dir)
dataset_params = params['dataset']
model_params = params['model']
trainer_params = params['trainer']
train_ds = get_triplet_dataflow(
dataset_params['path'], trainer_params['items_per_batch'],
trainer_params['images_per_item'], trainer_params['num_parallel'])
vqvae_checkpoint_path = trainer_params['vqvae_checkpoint_path']
vqvae_config_path = os.path.join(os.path.split(vqvae_checkpoint_path)[0],
'config.json')
model_params['vqvae_model_params'] = vqvae_config_path
params.to_file(os.path.join(logger.get_logger_dir(), 'config.json'))
model = BaseImageEmbedding.from_params(model_params)
trainer = SyncMultiGPUTrainerParameterServer(
gpus=trainer_params['num_gpus'], ps_device=None)
trainer_config = AutoResumeTrainConfig(
always_resume=recover,
model=model,
dataflow=train_ds,
callbacks=[
ModelSaver(max_to_keep=5, checkpoint_dir=checkpoint_dir),
MinSaver(monitor_stat='loss'),
RestoreWeights(vqvae_checkpoint_path),
SendStat('Training status', ['loss', 'pos_triplet_frac'],
after_every=2),
Notification('Training status', 'Complete')
],
steps_per_epoch=trainer_params['steps_per_epoch'],
max_epoch=trainer_params['max_epochs']
)
launch_train_with_config(trainer_config, trainer)
help='variants of resnet to use',
default='resnet')
parser.add_argument('--lp', choices=['2', 'inf'])
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.mode == 'se':
assert args.depth >= 50
nr_tower = max(get_nr_gpu(), 1)
batch_size = TOTAL_BATCH_SIZE // nr_tower
model = Model(args.image_size, args.depth, args.data_format, args.mode)
if args.eval:
batch = 128 # something that can run on one gpu
ds = get_data('val', batch, args.image_size)
eval_on_ILSVRC12(model, get_model_loader(args.load), ds)
else:
logger.set_logger_dir(args.checkpoint_dir)
config = get_config(model,
args.checkpoint_dir,
args.image_size,
fake=args.fake)
if args.load:
config.session_init = get_model_loader(args.load)
trainer = SyncMultiGPUTrainerParameterServer(max(get_nr_gpu(), 1))
launch_train_with_config(config, trainer)
action='store_true')
parser.add_argument('--data_format',
help='specify NCHW or NHWC',
type=str,
default='NCHW')
parser.add_argument('-d',
'--depth',
help='resnet depth',
type=int,
default=18,
choices=[18, 34, 50, 101])
parser.add_argument('--eval', action='store_true')
args = parser.parse_args()
DEPTH = args.depth
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.eval:
BATCH_SIZE = 128 # something that can run on one gpu
ds = get_data('val')
eval_on_ILSVRC12(Model(), args.load, ds)
sys.exit()
logger.set_logger_dir(
os.path.join('train_log', 'imagenet-resnet-d' + str(DEPTH)))
config = get_config(fake=args.fake, data_format=args.data_format)
if args.load:
config.session_init = SaverRestore(args.load)
SyncMultiGPUTrainerParameterServer(config).train()
def train_child(model_cls, args, log_dir, child_dir, prev_dir):
"""
"""
if not os.path.exists(child_dir):
os.mkdir(child_dir)
if os.path.basename(child_dir) == "0" and args.use_init_model:
init_model_dir = os.path.join(args.data_dir, 'init_model',
args.ds_name)
if os.path.exists(init_model_dir):
# This implies that there exists init_model_dir, and we are in first model
# so we do not need to train. Copy the model and mark finished
logger.info("Skip first model as this model is fully trained.")
cmd = "mkdir -p {cdir} ; cp {pdir}/* {cdir}/ ".format(\
cdir=child_dir, pdir=args.init_model_dir)
_ = subprocess.check_output(cmd, shell=True)
return
# get training params for train-config
(model, args, starting_epoch, lr_schedule, ds_train, insrc_train,
train_cbs) = get_training_params(model_cls, args)
## Model callbacks
# loss weight update
ls_cbs_func = getattr(model, 'compute_loss_select_callbacks', None)
if callable(ls_cbs_func):
train_cbs.extend(ls_cbs_func())
# extra callback for general logging/ update.
extra_callbacks = DEFAULT_CALLBACKS()
if not args.do_remote_child_inf_runner:
extra_callbacks = \
[ecb for ecb in extra_callbacks if not isinstance(ecb, ProgressBar)]
logger.info("Extra callbacks are {}".format(
[ecb.__class__ for ecb in extra_callbacks]))
# Logging for analysis
model_str = model.net_info.to_str()
logger.info('LayerInfoListString is :\n {}'.format(model_str))
train_callbacks = [
ModelSaver(checkpoint_dir=child_dir,
max_to_keep=1,
keep_checkpoint_every_n_hours=100),
] + train_cbs
if lr_schedule:
train_callbacks.append(
ScheduledHyperParamSetter('learning_rate', lr_schedule))
logger.info('The updated params for training is \n{}'.format(args))
config = TrainConfig(
data=insrc_train,
dataflow=ds_train,
callbacks=train_callbacks,
extra_callbacks=extra_callbacks,
model=model,
monitors=[JSONWriter(), ScalarPrinter()], #, TFEventWriter()],
steps_per_epoch=args.steps_per_epoch,
max_epoch=args.max_epoch,
starting_epoch=starting_epoch)
for dn in [child_dir, prev_dir]:
if dn is None:
continue
ckpt = tf.train.latest_checkpoint(dn)
if ckpt:
if args.search_cat_based:
restore_cls = SaverRestoreSizeRelaxed
else:
restore_cls = SaverRestore
_ignore = [DYNAMIC_WEIGHTS_NAME]
_sess_init_load = restore_cls(ckpt, ignore=_ignore)
if dn == child_dir:
# loading from self keep global step
config.session_init = _sess_init_load
else:
# loading from others. Set global_step to 0
config.session_init = ChainInit([
_sess_init_load,
AssignGlobalStep(0),
])
break
launch_train_with_config(config,
SyncMultiGPUTrainerParameterServer(args.nr_gpu))
return model