提交 ac86d644 编写于 作者: M Megvii Engine Team

fix(mge/module): fix prelu error when use_symbolic_shape is true

GitOrigin-RevId: 25b9c4d41d0584922eaaf657b0b2d3c2d515b6e8
上级 4f6c5d8f
......@@ -239,12 +239,6 @@ class PReLU(Module):
self.weight = Parameter(data=[init])
def forward(self, inputs):
assert self.weight.shape == (1,) or self.weight.shape == (
1,
int(inputs.shape[1]),
1,
1,
), "invalid weight's shape"
return prelu(inputs, self.weight)
......
......@@ -7,9 +7,11 @@
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import numpy as np
import pytest
import megengine as mge
from megengine.module import LeakyReLU
from megengine.jit.tracing import set_symbolic_shape
from megengine.module import LeakyReLU, PReLU
def test_leaky_relu():
......@@ -21,3 +23,19 @@ def test_leaky_relu():
np_output = np.maximum(0, data) + negative_slope * np.minimum(0, data)
np.testing.assert_equal(output.numpy(), np_output)
@pytest.mark.parametrize("shape", [(1, 64, 15, 15), (64,)])
@pytest.mark.parametrize("use_symbolic", [False, True])
def test_prelu(shape, use_symbolic):
old_flag = set_symbolic_shape(use_symbolic)
data = np.random.random(size=shape)
num_channel = 1 if len(shape) == 1 else shape[1]
prelu = PReLU(num_parameters=num_channel, init=0.25)
output = prelu(mge.Tensor(data))
np_output = np.maximum(data, 0) + prelu.weight.numpy() * np.minimum(data, 0)
set_symbolic_shape(old_flag)
np.testing.assert_allclose(output.numpy(), np_output, atol=1e-5)
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