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