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# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx import helper
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def dropout(X, drop_probability=0.5, seed=0, training_mode=False, return_mask=False): # type: ignore
if drop_probability == 0 or training_mode is False:
if return_mask is True:
return X, np.ones(X.shape, dtype=bool)
else:
return X
np.random.seed(seed)
mask = np.random.uniform(0, 1.0, X.shape) >= drop_probability
scale = 1 / (1 - drop_probability)
if return_mask:
return mask * X * scale, mask.astype(bool)
return mask * X * scale
class Dropout(Base):
# Inferencing tests.
@staticmethod
def export_default() -> None:
seed = np.int64(0)
node = onnx.helper.make_node("Dropout", inputs=["x"], outputs=["y"], seed=seed)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = dropout(x)
expect(node, inputs=[x], outputs=[y], name="test_dropout_default")
@staticmethod
def export_default_ratio() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r"], outputs=["y"], seed=seed
)
r = np.float32(0.1)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = dropout(x, r)
expect(node, inputs=[x, r], outputs=[y], name="test_dropout_default_ratio")
@staticmethod
def export_default_mask() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y, z = dropout(x, return_mask=True)
expect(node, inputs=[x], outputs=[y, z], name="test_dropout_default_mask")
@staticmethod
def export_default_mask_ratio() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r"], outputs=["y", "z"], seed=seed
)
r = np.float32(0.1)
x = np.random.randn(3, 4, 5).astype(np.float32)
y, z = dropout(x, r, return_mask=True)
expect(
node, inputs=[x, r], outputs=[y, z], name="test_dropout_default_mask_ratio"
)
# Training tests.
@staticmethod
def export_training_default() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.5)
t = np.bool_(True)
y = dropout(x, r, training_mode=t)
expect(
node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_default"
)
@staticmethod
def export_training_default_ratio_mask() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.5)
t = np.bool_(True)
y, z = dropout(x, r, training_mode=t, return_mask=True)
expect(
node,
inputs=[x, r, t],
outputs=[y, z],
name="test_training_dropout_default_mask",
)
@staticmethod
def export_training() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.75)
t = np.bool_(True)
y = dropout(x, r, training_mode=t)
expect(node, inputs=[x, r, t], outputs=[y], name="test_training_dropout")
@staticmethod
def export_training_ratio_mask() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.75)
t = np.bool_(True)
y, z = dropout(x, r, training_mode=t, return_mask=True)
expect(
node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_mask"
)
@staticmethod
def export_training_default_zero_ratio() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.0)
t = np.bool_(True)
y = dropout(x, r, training_mode=t)
expect(
node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_zero_ratio"
)
@staticmethod
def export_training_default_zero_ratio_mask() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.0)
t = np.bool_(True)
y, z = dropout(x, r, training_mode=t, return_mask=True)
expect(
node,
inputs=[x, r, t],
outputs=[y, z],
name="test_training_dropout_zero_ratio_mask",
)
# Old dropout tests
@staticmethod
def export_default_old() -> None:
node = onnx.helper.make_node(
"Dropout",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = x
expect(
node,
inputs=[x],
outputs=[y],
name="test_dropout_default_old",
opset_imports=[helper.make_opsetid("", 11)],
)
@staticmethod
def export_random_old() -> None:
node = onnx.helper.make_node(
"Dropout",
inputs=["x"],
outputs=["y"],
ratio=0.2,
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = x
expect(
node,
inputs=[x],
outputs=[y],
name="test_dropout_random_old",
opset_imports=[helper.make_opsetid("", 11)],
)
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