假设强烈抱怨这是缓慢的:
@composite
def f_and_g_and_padding(draw, in_channels = channel_ints, out_channels = channel_ints, fs = shapes_2d, fill=None, elements=well_behaved_floats):
shape_f = draw(basic_shape)
padding = draw(shapes_2d)
fs = draw(fs)
in_channels = draw(in_channels)
out_channels = draw(out_channels)
batch_size = draw(shape_ints)
shape_f = (batch_size, in_channels, fs[0], fs[1])
f = draw(stnp.arrays(dt_numpy, shape_f, elements=elements, fill=fill))
h_in = f.shape[2] + padding[0] * 2
w_in = f.shape[3] + padding[1] * 2
shape_g = (out_channels, in_channels, h_in, w_in)
g = draw(stnp.arrays(dt_numpy, shape_g, elements=elements, fill=fill))
return (f, g, padding)
我试图找出原因,但失败了。见:How to use pytest, hypothesis and line_profiler / kernprof together?
因此,我的问题仍然是:为什么?
以下是使用的其他策略:
well_behaved_floats = stnp.from_dtype(dtype=dt_numpy, allow_infinity=False, allow_nan=False)
small_floats = stnp.from_dtype(dtype=dt_numpy, min_value=-10000, max_value=10000, allow_infinity=False, allow_nan=False)
floats_0_1 = stnp.from_dtype(dtype=dt_numpy, min_value=-1, max_value=1, allow_infinity=False, allow_nan=False)
small_ints = stnp.from_dtype(dtype=numpy.dtype("i4"), allow_infinity=False, allow_nan=False, min_value=-10, max_value=10)
small_positive_ints = stnp.from_dtype(dtype=numpy.dtype("i4"), allow_infinity=False, allow_nan=False, min_value=0, max_value=10)
one_or_greater = st.integers(min_value=1)
shape_ints = st.integers(min_value=1, max_value=4)
channel_ints = st.integers(min_value=1, max_value=10)
basic_shape = stnp.array_shapes(min_dims=4, max_dims=4, min_side=1, max_side=10)
ones = st.integers(min_value=1, max_value=1)
shapes_2d = stnp.array_shapes(min_dims=2, max_dims=2, min_side=1, max_side=4)
这样使用:
@given(f_and_g_and_padding(elements=ones))
def test_padding(f_g_padding: Tuple[numpy.ndarray, numpy.ndarray, Tuple[int, int]]):
f, g, padding = f_g_padding
run_test(Tensor(f), Tensor(g), padding=padding)
没有过滤,只有简单的绘图和numpy数组
fwiw以下是假设配置:
hypothesis.settings.register_profile("default",
derandomize=True,
deadline=None,
print_blob=True,
report_multiple_bugs=False,
suppress_health_check=[HealthCheck.too_slow])
我认为你的
basic_shapes
策略是罪魁祸首;如果至少有四个维度,那么平均边长中就有n^4个元素,这会很慢。考虑减少该策略的^ {CD2}};如果这是不可接受的,您可能需要使用假设生成形状,但使用numpy.random
生成元素我还建议不要将
allow_infinity=False, allow_nan=False
传递给整数或有界浮点的策略——在这两种情况下,非有限数都已经被排除在外,因此,尽管它们没有做任何事情,但这对可读性是一个打击相关问题 更多 >
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