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from hypothesis import assume, given, settings
import hypothesis.strategies as st
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
class TestDropout(serial.SerializedTestCase):
@serial.given(X=hu.tensor(),
in_place=st.booleans(),
ratio=st.floats(0, 0.999),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs_cpu_only)
def test_dropout_is_test(self, X, in_place, ratio, engine, gc, dc):
"""Test with is_test=True for a deterministic reference impl."""
# TODO(lukeyeager): enable this path when the GPU path is fixed
if in_place:
# Skip if trying in-place on GPU
assume(not (gc.device_type in {caffe2_pb2.CUDA, caffe2_pb2.HIP} and engine == ''))
# If in-place on CPU, don't compare with GPU
dc = dc[:1]
op = core.CreateOperator("Dropout", ["X"],
["X" if in_place else "Y"],
ratio=ratio, engine=engine, is_test=True)
self.assertDeviceChecks(dc, op, [X], [0])
# No sense in checking gradients for test phase
def reference_dropout_test(x):
return x, np.ones(x.shape, dtype=np.bool)
self.assertReferenceChecks(
gc, op, [X], reference_dropout_test,
# The 'mask' output may be uninitialized
outputs_to_check=[0])
@given(X=hu.tensor(),
in_place=st.booleans(),
output_mask=st.booleans(),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs_cpu_only)
@settings(deadline=10000)
def test_dropout_ratio0(self, X, in_place, output_mask, engine, gc, dc):
"""Test with ratio=0 for a deterministic reference impl."""
# TODO(lukeyeager): enable this path when the op is fixed
if in_place:
# Skip if trying in-place on GPU
assume(gc.device_type not in {caffe2_pb2.CUDA, caffe2_pb2.HIP})
# If in-place on CPU, don't compare with GPU
dc = dc[:1]
is_test = not output_mask
op = core.CreateOperator("Dropout", ["X"],
["X" if in_place else "Y"] +
(["mask"] if output_mask else []),
ratio=0.0, engine=engine,
is_test=is_test)
self.assertDeviceChecks(dc, op, [X], [0])
if not is_test:
self.assertGradientChecks(gc, op, [X], 0, [0])
def reference_dropout_ratio0(x):
return (x,) if is_test else (x, np.ones(x.shape, dtype=np.bool))
self.assertReferenceChecks(
gc, op, [X], reference_dropout_ratio0,
# Don't check the mask with cuDNN because it's packed data
outputs_to_check=None if engine != 'CUDNN' else [0])
@given(X=hu.tensor(),
in_place=st.booleans(),
output_mask=st.booleans(),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs_cpu_only)
@settings(deadline=10000)
def test_dropout_ratio1(self, X, in_place, output_mask, engine, gc, dc):
"""Test with ratio=0 for a deterministic reference impl."""
if in_place:
# Skip if trying in-place on GPU
assume(gc.device_type not in {caffe2_pb2.CUDA, caffe2_pb2.HIP})
# If in-place on CPU, don't compare with GPU
dc = dc[:1]
is_test = not output_mask
op = core.CreateOperator("Dropout", ["X"],
["X" if in_place else "Y"] +
(["mask"] if output_mask else []),
ratio=1.0, engine=engine,
is_test=is_test)
self.assertDeviceChecks(dc, op, [X], [0])
if not is_test:
self.assertGradientChecks(gc, op, [X], 0, [0])
def reference_dropout_ratio1(x):
return (x,) if is_test else (np.zeros(x.shape, dtype=np.float), np.zeros(x.shape, dtype=np.bool))
self.assertReferenceChecks(
gc, op, [X], reference_dropout_ratio1,
# Don't check the mask with cuDNN because it's packed data
outputs_to_check=None if engine != 'CUDNN' else [0])
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