未验证 提交 82ec247a 编写于 作者: A Aurelius84 提交者: GitHub

fix random failed on GPU test=develop (#25293)

上级 0371cf6f
......@@ -32,6 +32,10 @@ import unittest
import numpy as np
from PIL import Image, ImageOps
import os
# Use GPU:0 to elimate the influence of other tasks.
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph import to_variable, declarative, ProgramTranslator
......@@ -327,6 +331,11 @@ class conv2d(fluid.dygraph.Layer):
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=stddev)),
bias_attr=con_bias_attr)
# Note(Aurelius84): The calculation of GPU kernel in BN is non-deterministic,
# failure rate is 1/100 in Dev but seems incremental in CE platform.
# If on GPU, we disable BN temporarily.
if fluid.is_compiled_with_cuda():
norm = False
if norm:
self.bn = BatchNorm(
use_global_stats=True, # set True to use deterministic algorithm
......@@ -383,6 +392,8 @@ class DeConv2D(fluid.dygraph.Layer):
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=stddev)),
bias_attr=de_bias_attr)
if fluid.is_compiled_with_cuda():
norm = False
if norm:
self.bn = BatchNorm(
use_global_stats=True, # set True to use deterministic algorithm
......@@ -606,8 +617,16 @@ class TestCycleGANModel(unittest.TestCase):
def test_train(self):
st_out = self.train(to_static=True)
dy_out = self.train(to_static=False)
assert_func = np.allclose
# Note(Aurelius84): Because we disable BN on GPU,
# but here we enhance the check on CPU by `np.array_equal`
# which means the dy_out and st_out shall be exactly same.
if not fluid.is_compiled_with_cuda():
assert_func = np.array_equal
self.assertTrue(
np.allclose(dy_out, st_out),
assert_func(dy_out, st_out),
msg="dy_out:\n {}\n st_out:\n{}".format(dy_out, st_out))
......
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