def LoadModel(path, model, use_ideep):
'''
Load pretrained model from file
'''
log.info("Loading path: {}".format(path))
meta_net_def = pred_exp.load_from_db(path, 'minidb')
init_net = core.Net(pred_utils.GetNet(
meta_net_def, predictor_constants.GLOBAL_INIT_NET_TYPE))
predict_init_net = core.Net(pred_utils.GetNet(
meta_net_def, predictor_constants.PREDICT_INIT_NET_TYPE))
if use_ideep:
predict_init_net.RunAllOnIDEEP()
else:
predict_init_net.RunAllOnGPU()
if use_ideep:
init_net.RunAllOnIDEEP()
else:
init_net.RunAllOnGPU()
assert workspace.RunNetOnce(predict_init_net)
assert workspace.RunNetOnce(init_net)
# Hack: fix iteration counter which is in CUDA context after load model
itercnt = workspace.FetchBlob("optimizer_iteration")
workspace.FeedBlob(
"optimizer_iteration",
itercnt,
device_option=core.DeviceOption(caffe2_pb2.CPU, 0)
)
def test_meta_net_def_net_runs(self):
for param, value in self.params.items():
workspace.FeedBlob(param, value)
extra_init_net = core.Net('extra_init')
extra_init_net.ConstantFill('data', 'data', value=1.0)
pem = pe.PredictorExportMeta(
predict_net=self.predictor_export_meta.predict_net,
parameters=self.predictor_export_meta.parameters,
inputs=self.predictor_export_meta.inputs,
outputs=self.predictor_export_meta.outputs,
shapes=self.predictor_export_meta.shapes,
extra_init_net=extra_init_net,
)
db_type = 'minidb'
db_file = tempfile.NamedTemporaryFile(delete=False,
suffix=".{}".format(db_type))
pe.save_to_db(db_type=db_type,
db_destination=db_file.name,
predictor_export_meta=pem)
workspace.ResetWorkspace()
meta_net_def = pe.load_from_db(
db_type=db_type,
filename=db_file.name,
)
self.assertTrue("data" not in workspace.Blobs())
self.assertTrue("y" not in workspace.Blobs())
init_net = pred_utils.GetNet(meta_net_def, pc.PREDICT_INIT_NET_TYPE)
# 0-fills externalblobs blobs and runs extra_init_net
workspace.RunNetOnce(init_net)
self.assertTrue("data" in workspace.Blobs())
self.assertTrue("y" in workspace.Blobs())
print(workspace.FetchBlob("data"))
np.testing.assert_array_equal(workspace.FetchBlob("data"),
np.ones(shape=(1, 5)))
np.testing.assert_array_equal(workspace.FetchBlob("y"),
np.zeros(shape=(1, 10)))
# Load parameters from DB
global_init_net = pred_utils.GetNet(meta_net_def,
pc.GLOBAL_INIT_NET_TYPE)
workspace.RunNetOnce(global_init_net)
# Run the net with a reshaped input and verify we are
# producing good numbers (with our custom implementation)
workspace.FeedBlob("data", np.random.randn(2, 5).astype(np.float32))
predict_net = pred_utils.GetNet(meta_net_def, pc.PREDICT_NET_TYPE)
workspace.RunNetOnce(predict_net)
np.testing.assert_array_almost_equal(
workspace.FetchBlob("y"),
workspace.FetchBlob("data").dot(self.params["y_w"].T) +
self.params["y_b"])
def ConvertModel(args):
meta_net_def = pred_exp.load_from_db(args.load_model_path, args.db_type)
net = core.Net(
pred_utils.GetNet(meta_net_def, predictor_constants.PREDICT_NET_TYPE))
init_net = core.Net(
pred_utils.GetNet(meta_net_def,
predictor_constants.GLOBAL_INIT_NET_TYPE))
init_net.RunAllOnGPU()
assert workspace.RunNetOnce(init_net)
pred_params = list(set(net.Proto().external_input) - set(['gpu_0/data']))
save_params = [str(param) for param in pred_params]
save_blobs = {}
for param in save_params:
scoped_blob_name = str(param)
unscoped_blob_name = unscope_name(scoped_blob_name)
if unscoped_blob_name not in save_blobs:
save_blobs[unscoped_blob_name] = workspace.FetchBlob(
scoped_blob_name)
log.info('{:s} -> {:s}'.format(scoped_blob_name,
unscoped_blob_name))
log.info('saving weights to {}'.format(args.save_model_path))
with open(args.save_model_path, 'w') as fwrite:
pickle.dump(dict(blobs=save_blobs), fwrite, pickle.HIGHEST_PROTOCOL)
def LoadModel(path):
meta_net_def = pred_exp.load_from_db(path, 'minidb')
init_net = core.Net(
pred_utils.GetNet(meta_net_def,
predictor_constants.GLOBAL_INIT_NET_TYPE))
predict_init_net = core.Net(
pred_utils.GetNet(meta_net_def,
predictor_constants.PREDICT_INIT_NET_TYPE))
predict_init_net.RunAllOnGPU()
init_net.RunAllOnGPU()
assert workspace.RunNetOnce(predict_init_net)
assert workspace.RunNetOnce(init_net)
def LoadModel(path, model):
'''
Load pretrained model from file
'''
log.info("Loading path: {}".format(path))
meta_net_def = pred_exp.load_from_db(path, 'minidb')
init_net = core.Net(pred_utils.GetNet(
meta_net_def, predictor_constants.GLOBAL_INIT_NET_TYPE))
predict_init_net = core.Net(pred_utils.GetNet(
meta_net_def, predictor_constants.PREDICT_INIT_NET_TYPE))
predict_init_net.RunAllOnGPU()
init_net.RunAllOnGPU()
assert workspace.RunNetOnce(predict_init_net)
assert workspace.RunNetOnce(init_net)
def LoadModel(path, model):
log.info("Loading path: {}".format(path))
meta_net_def = pred_exp.load_from_db(path, 'minidb')
init_net = core.Net(
pred_utils.GetNet(meta_net_def,
predictor_constants.GLOBAL_INIT_NET_TYPE))
predict_init_net = core.Net(
pred_utils.GetNet(meta_net_def,
predictor_constants.PREDICT_INIT_NET_TYPE))
predict_init_net.RunAllOnGPU()
init_net.RunAllOnGPU()
itercnt = workspace.FetchBlob("optimizer_iteration")
workspace.FeedBlob("optimizer_iteration",
itercnt,
device_option=core.DeviceOption(caffe2_pb2.CPU, 0))
def prepare_prediction_net(filename, db_type, device_option=None):
'''
Helper function which loads all required blobs from the db
and returns prediction net ready to be used
'''
metanet_def = load_from_db(filename, db_type, device_option)
global_init_net = utils.GetNet(
metanet_def, predictor_constants.GLOBAL_INIT_NET_TYPE)
workspace.RunNetOnce(global_init_net)
predict_init_net = utils.GetNet(
metanet_def, predictor_constants.PREDICT_INIT_NET_TYPE)
workspace.RunNetOnce(predict_init_net)
predict_net = core.Net(
utils.GetNet(metanet_def, predictor_constants.PREDICT_NET_TYPE))
workspace.CreateNet(predict_net)
return predict_net
def test_load_device_scope(self):
for param, value in self.params.items():
workspace.FeedBlob(param, value)
pem = pe.PredictorExportMeta(
predict_net=self.predictor_export_meta.predict_net,
parameters=self.predictor_export_meta.parameters,
inputs=self.predictor_export_meta.inputs,
outputs=self.predictor_export_meta.outputs,
shapes=self.predictor_export_meta.shapes,
net_type='dag',
)
db_type = 'minidb'
db_file = tempfile.NamedTemporaryFile(delete=False,
suffix=".{}".format(db_type))
pe.save_to_db(db_type=db_type,
db_destination=db_file.name,
predictor_export_meta=pem)
workspace.ResetWorkspace()
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 1)):
meta_net_def = pe.load_from_db(
db_type=db_type,
filename=db_file.name,
)
init_net = core.Net(
pred_utils.GetNet(meta_net_def, pc.GLOBAL_INIT_NET_TYPE))
predict_init_net = core.Net(
pred_utils.GetNet(meta_net_def, pc.PREDICT_INIT_NET_TYPE))
# check device options
for op in list(init_net.Proto().op) + list(
predict_init_net.Proto().op):
self.assertEqual(1, op.device_option.device_id)
self.assertEqual(caffe2_pb2.CPU, op.device_option.device_type)
