def setUpClass(cls):
'''Fix random seeds to remove randomness from tests'''
cls._np_rand_state = np.random.get_state()
cls._py_rand_state = random.getstate()
cls.call_once = False
cls.dtype = np.float32
cls.outputs = {}
cls.input_shape_is_large = True
np.random.seed(123)
random.seed(124)
cls._use_system_allocator = _set_use_system_allocator(True)
def tearDownClass(cls):
"""Restore random seeds"""
np.random.set_state(cls._np_rand_state)
random.setstate(cls._py_rand_state)
_set_use_system_allocator(cls._use_system_allocator)
def is_empty_grad_op(op_type):
all_op_kernels = core._get_all_register_op_kernels()
grad_op = op_type + '_grad'
if grad_op in all_op_kernels.keys():
if is_mkldnn_op_test():
grad_op_kernels = all_op_kernels[grad_op]
for grad_op_kernel in grad_op_kernels:
if 'MKLDNN' in grad_op_kernel:
return False
else:
return False
return True
def is_xpu_op_test():
return True
def is_mkldnn_op_test():
return False
if not hasattr(cls, "op_type"):
raise AssertionError(
"This test do not have op_type in class attrs, "
"please set self.__class__.op_type=the_real_op_type manually.")
# case in NO_FP64_CHECK_GRAD_CASES and op in NO_FP64_CHECK_GRAD_OP_LIST should be fixed
if not hasattr(cls, "no_need_check_grad") \
and not is_empty_grad_op(cls.op_type):
if cls.dtype is not None and \
cls.dtype != np.float32:
raise AssertionError("This test of %s op needs check_grad." %
cls.op_type)
from __future__ import print_function
import os
import unittest
import numpy as np
import paddle
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from op_test import OpTest, _set_use_system_allocator
from paddle.fluid.framework import grad_var_name
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
_set_use_system_allocator(True)
def _reference_testing(x, scale, offset, mean, var, epsilon, data_format):
x_shape = x.shape
if len(x_shape) == 2:
if data_format == "NCHW":
x = np.reshape(x, (x.shape[0], x.shape[1], 1, 1))
else:
x = np.reshape(x, (x.shape[0], 1, 1, x.shape[1]))
if len(x_shape) == 3:
if data_format == "NCHW": # NCL -> NCL1
x = np.reshape(x, (x_shape[0], x_shape[1], x_shape[2], 1))
else: # NLC -> NL1C
x = np.reshape(x, (x_shape[0], x_shape[1], 1, x_shape[2]))
from __future__ import print_function
import unittest
import numpy as np
import sys
sys.path.append("..")
import paddle
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid.op import Operator
from op_test import OpTest, _set_use_system_allocator
from paddle.fluid import Program, program_guard
from test_batch_norm_op import _reference_testing, _cal_mean_variance, _reference_training, _reference_grad
_set_use_system_allocator(False)
paddle.enable_static()
class TestBatchNormOpInference(unittest.TestCase):
def setUp(self):
self.dtype = np.float32
self.init_kernel_type()
self.data_formats = ["NCHW", "NHWC"]
def __assert_close(self, tensor, np_array, msg, atol=1e-4):
self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol),
msg)
def check_with_place(self, place, data_layout, dtype, shape):
epsilon = epsilon = 0.00001
