def predict_ids(model_name, vg, vd):
workspace.ResetWorkspace()
# preproc the input
vg = vg.astype(np.float32)
vd = vd.astype(np.float32)
#if len(self.preproc_param) == 0:
preproc_param = pickle.load(open(model_name + '_preproc_param.p', "rb"))
dummy_ids = np.zeros(len(vg))
preproc_data_arrays = preproc.dc_iv_preproc(
vg,
vd,
dummy_ids,
preproc_param['scale'],
preproc_param['vg_shift'],
)
# print(preproc_data_arrays[0].shape)
_preproc_data_arrays = [
np.expand_dims(x, axis=1) if len(x.shape) < 2 else x
for x in preproc_data_arrays
]
workspace.FeedBlob('DBInput_train/sig_input', _preproc_data_arrays[0])
workspace.FeedBlob('DBInput_train/tanh_input', _preproc_data_arrays[1])
pred_net = exporter.load_net(model_name + '_init', model_name + '_predict')
workspace.RunNet(pred_net)
_ids = np.squeeze(workspace.FetchBlob('prediction'))
restore_id_func, get_restore_id_grad_func = preproc.get_restore_id_func(
preproc_param['scale'])
ids = restore_id_func(_ids)
return ids
def predict_ids(self, vg, vd):
# preproc the input
vg = vg.astype(np.float32)
vd = vd.astype(np.float32)
if len(self.preproc_param) == 0:
self.preproc_param = pickle.load(open(self.pickle_file_name, "rb"))
dummy_ids = np.zeros(len(vg))
preproc_data_arrays = preproc.dc_iv_preproc(
vg,
vd,
dummy_ids,
self.preproc_param['scale'],
self.preproc_param['vg_shift'],
)
_preproc_data_arrays = [
np.expand_dims(x, axis=1) for x in preproc_data_arrays
]
workspace.FeedBlob('DBInput_train/sig_input', _preproc_data_arrays[0])
workspace.FeedBlob('DBInput_train/tanh_input', _preproc_data_arrays[1])
pred_net = self.net_store['pred_net']
workspace.RunNet(pred_net)
_ids = np.squeeze(schema.FetchRecord(self.pred).get())
restore_id_func, _ = preproc.get_restore_id_func(
self.preproc_param['scale'],
self.preproc_param['vg_shift'],
)
ids = restore_id_func(_ids)
return _ids, ids
def predict_ids(self, vg, vd):
# preproc the input
vg = vg.astype(np.float32)
vd = vd.astype(np.float32)
if len(self.preproc_param) == 0:
self.preproc_param = pickle.load(open(self.pickle_file_name, "rb"))
dummy_ids = np.zeros(len(vg))
preproc_data_arrays = [vg, vd, dummy_ids]
if self.train_target == TrainTarget.ORIGIN:
preproc_data_arrays = preproc.dc_iv_preproc(
vg,
vd,
dummy_ids,
self.preproc_param['scale'],
self.preproc_param['vg_shift'],
)
_preproc_data_arrays = [
np.expand_dims(x, axis=1) if x.ndim == 1 else x
for x in preproc_data_arrays
]
workspace.FeedBlob('DBInput_train/sig_input', _preproc_data_arrays[0])
workspace.FeedBlob('DBInput_train/tanh_input', _preproc_data_arrays[1])
if self.train_target == TrainTarget.ADJOINT:
adjoint_input = np.ones((vg.shape[0], 1))
workspace.FeedBlob('DBInput_train/adjoint_input', adjoint_input)
pred_net = self.net_store['pred_net']
workspace.RunNet(pred_net)
_ids = np.squeeze(schema.FetchRecord(self.pred).get())
ids = _ids
if self.train_target == TrainTarget.ORIGIN:
restore_id_func, _ = preproc.get_restore_id_func(
self.preproc_param['scale'],
self.preproc_param['vg_shift'],
)
ids = restore_id_func(_ids)
return _ids, ids
def predict_ids_grads(model_name, vg, vd):
workspace.ResetWorkspace()
# preproc the input
vg = vg.astype(np.float32)
vd = vd.astype(np.float32)
#if len(self.preproc_param) == 0:
preproc_param = pickle.load(open(model_name + '_preproc_param.p', "rb"))
dummy_ids = np.zeros(len(vg))
preproc_data_arrays = preproc.dc_iv_preproc(
vg,
vd,
dummy_ids,
preproc_param['scale'],
preproc_param['vg_shift'],
)
_preproc_data_arrays = [
np.expand_dims(x, axis=1) for x in preproc_data_arrays
]
workspace.FeedBlob('DBInput_train/sig_input', _preproc_data_arrays[0])
workspace.FeedBlob('DBInput_train/tanh_input', _preproc_data_arrays[1])
adjoint_input = np.ones((_preproc_data_arrays[0].shape[0], 1))
workspace.FeedBlob('adjoint_input', adjoint_input)
pred_net = exporter.load_net(model_name + '_init', model_name + '_predict')
workspace.RunNet(pred_net)
_ids = np.squeeze(workspace.FetchBlob('origin/Mul/origin_pred'))
_sig_grad = np.squeeze(
workspace.FetchBlob('adjoint/sig_fc_layer_0/output'))
_tanh_grad = np.squeeze(
workspace.FetchBlob('adjoint/tanh_fc_layer_0/output'))
restore_id_func, get_restore_id_grad_func = preproc.get_restore_id_func(
preproc_param['scale'])
ids = restore_id_func(_ids)
sig_grad, tanh_grad = get_restore_id_grad_func(_sig_grad, _tanh_grad)
return ids, sig_grad, tanh_grad