def test_restore_checkpoint():
# Create Model
model = model_builder.create(cfg.MODEL.TYPE, train=True)
add_momentum_init_ops(model)
init_weights(model)
# Fill input blobs
roidb = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES
)
model_builder.add_training_inputs(model, roidb=roidb)
workspace.CreateNet(model.net)
# Bookkeeping for checkpoint creation
iter_num = 0
checkpoints = {}
output_dir = get_output_dir(cfg.TRAIN.DATASETS, training=True)
chk_file_path = os.path.join(output_dir, 'model_iter{}.pkl'.format(iter_num))
checkpoints[iter_num] = chk_file_path
# Save model weights
nu.save_model_to_weights_file(checkpoints[iter_num], model)
orig_gpu_0_params, orig_all_params = get_params(model)
# Change the model weights
init_weights(model)
# Reload the weights in the model
nu.initialize_gpu_from_weights_file(model, chk_file_path, gpu_id=0)
nu.broadcast_parameters(model)
shutil.rmtree(cfg.OUTPUT_DIR)
_, restored_all_params = get_params(model)
# Check if all params are loaded correctly
for scoped_name, blob in orig_all_params.items():
np.testing.assert_array_equal(blob, restored_all_params[scoped_name])
# Check if broadcast_parameters works
for scoped_name, blob in restored_all_params.items():
unscoped_name = c2_utils.UnscopeName(scoped_name)
np.testing.assert_array_equal(blob, orig_gpu_0_params[unscoped_name])
def setup_model_for_training(model, output_dir):
"""Loaded saved weights and create the network in the C2 workspace."""
logger = logging.getLogger(__name__)
add_model_training_inputs(model)
if cfg.TRAIN.WEIGHTS:
# Override random weight initialization with weights from a saved model
nu.initialize_gpu_0_from_weights_file(model, cfg.TRAIN.WEIGHTS)
# Even if we're randomly initializing we still need to synchronize
# parameters across GPUs
nu.broadcast_parameters(model)
workspace.CreateNet(model.net)
logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
dump_proto_files(model, output_dir)
# Start loading mini-batches and enqueuing blobs
model.roi_data_loader.register_sigint_handler()
model.roi_data_loader.start(prefill=True)
return output_dir
def setup_model_for_training(model, output_dir):
"""Loaded saved weights and create the network in the C2 workspace."""
logger = logging.getLogger(__name__)
add_model_training_inputs(model)
if cfg.TRAIN.WEIGHTS:
# Override random weight initialization with weights from a saved model
nu.initialize_gpu_from_weights_file(model, cfg.TRAIN.WEIGHTS, gpu_id=0)
# Even if we're randomly initializing we still need to synchronize
# parameters across GPUs
nu.broadcast_parameters(model)
workspace.CreateNet(model.net)
logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
dump_proto_files(model, output_dir)
# Start loading mini-batches and enqueuing blobs
model.roi_data_loader.register_sigint_handler()
model.roi_data_loader.start(prefill=True)
return output_dir
def init_weights(model):
# init weights in gpu_id = 0 and then broadcast
workspace.RunNetOnce(model.param_init_net)
nu.broadcast_parameters(model)
def net_trainer():
model, start_iter, checkpoints = create_model()
if 'final' in checkpoints:
return checkpoints
add_model_inputs(model)
if cfg.TRAIN.WEIGHTS:
nu.initialize_gpu_0_from_weights_file(model, cfg.TRAIN.WEIGHTS)
# Even if we're randomly initializing we still need to synchronize
# parameters across GPUs
nu.broadcast_parameters(model)
workspace.CreateNet(model.net)
output_dir = get_output_dir(training=True)
logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
dump_proto_files(model, output_dir)
json_out_file = os.path.join(output_dir, 'json_stats.log')
# Start loading mini-batches and enqueuing blobs
model.roi_data_loader.register_sigint_handler()
# DEBUG data loading
if cfg.DEBUG.DATA_LOADING:
for _ in range(10000000):
# this was with threading...
# model.roi_data_loader._get_next_minibatch()
model.roi_data_loader._get_next_minibatch2(
model.roi_data_loader.shared_readonly_dict,
model.roi_data_loader._lock,
model.roi_data_loader.mp_cur,
model.roi_data_loader.mp_perm)
sys.exit(0)
model.roi_data_loader.start(prefill=True)
smoothed_values = {
key: SmoothedValue(WIN_SZ) for key in model.losses + model.metrics}
iter_values = {key: 0 for key in model.losses + model.metrics}
total_loss = SmoothedValue(WIN_SZ)
iter_time = SmoothedValue(WIN_SZ)
mb_qsize = SmoothedValue(WIN_SZ)
iter_timer = Timer()
checkpoints = {}
for i in range(start_iter, cfg.SOLVER.MAX_ITER):
iter_timer.tic()
lr = model.UpdateWorkspaceLr(i)
workspace.RunNet(model.net.Proto().name)
if i == start_iter:
nu.print_net(model)
iter_time.AddValue(iter_timer.toc(average=False))
for k in iter_values.keys():
if k in model.losses:
iter_values[k] = nu.sum_multi_gpu_blob(k)
else:
iter_values[k] = nu.average_multi_gpu_blob(k)
for k, v in smoothed_values.items():
v.AddValue(iter_values[k])
loss = np.sum(np.array([iter_values[k] for k in model.losses]))
total_loss.AddValue(loss)
mb_qsize.AddValue(model.roi_data_loader._minibatch_queue.qsize())
if i % LOG_PERIOD == 0 or i == cfg.SOLVER.MAX_ITER - 1:
eta_seconds = iter_timer.average_time * (cfg.SOLVER.MAX_ITER - i)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
mem_stats = c2_utils.GetGPUMemoryUsageStats()
mem_usage = np.max(mem_stats['max_by_gpu'][:cfg.NUM_GPUS])
stats = dict(
iter=i,
lr=float(lr),
time=iter_timer.average_time,
loss=total_loss.GetMedianValue(),
eta=eta,
mb_qsize=int(np.round(mb_qsize.GetMedianValue())),
mem=int(np.ceil(mem_usage / 1024 / 1024)))
for k, v in smoothed_values.items():
stats[k] = v.GetMedianValue()
log_json_stats(stats, json_out_file=json_out_file)
if cfg.DEBUG.STOP_TRAIN_ITER:
import pdb
pdb.set_trace()
if ((i + 1) % int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS) == 0 and
i > start_iter):
checkpoints[i] = os.path.join(
output_dir, 'model_iter{}.pkl'.format(i))
nu.save_model_to_weights_file(checkpoints[i], model)
if i == start_iter + LOG_PERIOD:
# Reset the iter timer after the first LOG_PERIOD iterations to
# discard initial iterations that have outlier timings
iter_timer.reset()
if np.isnan(loss):
logger.critical('Loss is NaN, exiting...')
os._exit(0) # FB: use code 0 to avoid flow retries
# Save the final model
checkpoints['final'] = os.path.join(output_dir, 'model_final.pkl')
nu.save_model_to_weights_file(checkpoints['final'], model)
# Shutdown data loading threads
model.roi_data_loader.shutdown()
return checkpoints
