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# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from typing import Optional, Tuple
import numpy as np
from numpy.random import RandomState # type: ignore
from onnx.reference.op_run import OpRun
def _dropout(
X: np.ndarray,
drop_probability: float = 0.5,
seed: Optional[int] = None,
training_mode: bool = False,
return_mask: bool = False,
) -> Tuple[np.ndarray]:
if drop_probability == 0 or not training_mode:
if return_mask:
return X, np.ones(X.shape, dtype=bool) # type: ignore
return (X,)
rnd = RandomState(seed)
mask = rnd.uniform(0, 1.0, X.shape) >= drop_probability
scale = 1.0 / (1.0 - drop_probability)
return (mask * X * scale, mask.astype(bool)) if return_mask else (mask * X * scale,) # type: ignore
class DropoutBase(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
self.n_outputs = len(onnx_node.output)
def _private_run(
self,
X: np.ndarray,
seed: Optional[int] = None,
ratio: float = 0.5,
training_mode: bool = False,
) -> Tuple[np.ndarray]:
return _dropout(
X,
ratio,
seed=seed, # type: ignore
return_mask=self.n_outputs == 2,
training_mode=training_mode,
)
class Dropout_7(DropoutBase):
def _run(self, X, ratio=None): # type: ignore
return self._private_run(X, ratio)
class Dropout_12(DropoutBase):
def _run(self, *inputs, seed=None): # type: ignore
X = inputs[0]
ratio = 0.5 if len(inputs) <= 1 else inputs[1]
training_mode = False if len(inputs) <= 2 else inputs[2]
return self._private_run(
X, seed=seed, ratio=ratio, training_mode=training_mode # type: ignore
)
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