You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
258 lines
8.0 KiB
258 lines
8.0 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import print_function
|
|
|
|
import unittest
|
|
import numpy as np
|
|
from op_test import OpTest
|
|
import paddle
|
|
import paddle.fluid.core as core
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid import Program, program_guard
|
|
|
|
np.random.seed(10)
|
|
|
|
|
|
class TestMeanOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "mean"
|
|
self.dtype = np.float64
|
|
self.init_dtype_type()
|
|
self.inputs = {'X': np.random.random((10, 10)).astype(self.dtype)}
|
|
self.outputs = {'Out': np.mean(self.inputs["X"])}
|
|
|
|
def init_dtype_type(self):
|
|
pass
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_checkout_grad(self):
|
|
self.check_grad(['X'], 'Out')
|
|
|
|
|
|
class TestMeanOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
# The input type of mean_op must be Variable.
|
|
input1 = 12
|
|
self.assertRaises(TypeError, fluid.layers.mean, input1)
|
|
# The input dtype of mean_op must be float16, float32, float64.
|
|
input2 = fluid.layers.data(
|
|
name='input2', shape=[12, 10], dtype="int32")
|
|
self.assertRaises(TypeError, fluid.layers.mean, input2)
|
|
input3 = fluid.layers.data(
|
|
name='input3', shape=[4], dtype="float16")
|
|
fluid.layers.softmax(input3)
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestFP16MeanOp(TestMeanOp):
|
|
def init_dtype_type(self):
|
|
self.dtype = np.float16
|
|
|
|
def test_check_output(self):
|
|
place = core.CUDAPlace(0)
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(place, atol=2e-3)
|
|
|
|
def test_checkout_grad(self):
|
|
place = core.CUDAPlace(0)
|
|
if core.is_float16_supported(place):
|
|
self.check_grad_with_place(
|
|
place, ['X'], 'Out', max_relative_error=0.8)
|
|
|
|
|
|
def ref_reduce_mean(x, axis=None, keepdim=False, reduce_all=False):
|
|
if isinstance(axis, list):
|
|
axis = tuple(axis)
|
|
if reduce_all:
|
|
axis = None
|
|
return np.mean(x, axis=axis, keepdims=keepdim)
|
|
|
|
|
|
class TestReduceMeanOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = 'reduce_mean'
|
|
self.dtype = 'float64'
|
|
self.shape = [2, 3, 4, 5]
|
|
self.axis = [0]
|
|
self.keepdim = False
|
|
self.reduce_all = False
|
|
self.set_attrs()
|
|
|
|
np.random.seed(10)
|
|
x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
|
|
out_np = ref_reduce_mean(x_np, self.axis, self.keepdim, self.reduce_all)
|
|
self.inputs = {'X': x_np}
|
|
self.outputs = {'Out': out_np}
|
|
self.attrs = {
|
|
'dim': self.axis,
|
|
'keep_dim': self.keepdim,
|
|
'reduce_all': self.reduce_all
|
|
}
|
|
|
|
def set_attrs(self):
|
|
pass
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['X'], ['Out'])
|
|
|
|
|
|
class TestReduceMeanOpDefaultAttrs(TestReduceMeanOp):
|
|
def setUp(self):
|
|
self.op_type = 'reduce_mean'
|
|
self.dtype = 'float64'
|
|
self.shape = [2, 3, 4, 5]
|
|
|
|
x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
|
|
out_np = np.mean(x_np, axis=0)
|
|
self.inputs = {'X': x_np}
|
|
self.outputs = {'Out': out_np}
|
|
|
|
|
|
class TestReduceMeanOpFloat32(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.dtype = 'float32'
|
|
|
|
|
|
class TestReduceMeanOpShape1D(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.shape = [100]
|
|
|
|
|
|
class TestReduceMeanOpShape6D(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.shape = [2, 3, 4, 5, 6, 7]
|
|
|
|
|
|
class TestReduceMeanOpAxisAll(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = [0, 1, 2, 3]
|
|
|
|
|
|
class TestReduceMeanOpAxisTuple(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = (0, 1, 2)
|
|
|
|
|
|
class TestReduceMeanOpAxisNegative(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = [-2, -1]
|
|
|
|
|
|
class TestReduceMeanOpKeepdimTrue1(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.keepdim = True
|
|
|
|
|
|
class TestReduceMeanOpKeepdimTrue2(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.axis = [0, 1, 2, 3]
|
|
self.keepdim = True
|
|
|
|
|
|
class TestReduceMeanOpReduceAllTrue(TestReduceMeanOp):
|
|
def set_attrs(self):
|
|
self.reduce_all = True
|
|
|
|
|
|
class TestMeanAPI(unittest.TestCase):
|
|
# test paddle.tensor.stat.mean
|
|
|
|
def setUp(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
|
|
self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
|
|
else paddle.CPUPlace()
|
|
|
|
def test_api_static(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.fluid.data('X', self.x_shape)
|
|
out1 = paddle.mean(x)
|
|
out2 = paddle.tensor.mean(x)
|
|
out3 = paddle.tensor.stat.mean(x)
|
|
axis = np.arange(len(self.x_shape)).tolist()
|
|
out4 = paddle.mean(x, axis)
|
|
out5 = paddle.mean(x, tuple(axis))
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
res = exe.run(feed={'X': self.x},
|
|
fetch_list=[out1, out2, out3, out4, out5])
|
|
out_ref = np.mean(self.x)
|
|
for out in res:
|
|
self.assertEqual(np.allclose(out, out_ref, rtol=1e-04), True)
|
|
|
|
def test_api_dygraph(self):
|
|
paddle.disable_static(self.place)
|
|
|
|
def test_case(x, axis=None, keepdim=False):
|
|
x_tensor = paddle.to_tensor(x)
|
|
out = paddle.mean(x_tensor, axis, keepdim)
|
|
if isinstance(axis, list):
|
|
axis = tuple(axis)
|
|
if len(axis) == 0:
|
|
axis = None
|
|
out_ref = np.mean(x, axis, keepdims=keepdim)
|
|
self.assertEqual(
|
|
np.allclose(
|
|
out.numpy(), out_ref, rtol=1e-04), True)
|
|
|
|
test_case(self.x)
|
|
test_case(self.x, [])
|
|
test_case(self.x, -1)
|
|
test_case(self.x, keepdim=True)
|
|
test_case(self.x, 2, keepdim=True)
|
|
test_case(self.x, [0, 2])
|
|
test_case(self.x, (0, 2))
|
|
test_case(self.x, [0, 1, 2, 3])
|
|
paddle.enable_static()
|
|
|
|
def test_fluid_api(self):
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
x = fluid.data("x", shape=[10, 10], dtype="float32")
|
|
out = fluid.layers.reduce_mean(input=x, dim=1)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
x_np = np.random.rand(10, 10).astype(np.float32)
|
|
res = exe.run(feed={"x": x_np}, fetch_list=[out])
|
|
self.assertEqual(np.allclose(res[0], np.mean(x_np, axis=1)), True)
|
|
|
|
with fluid.dygraph.guard():
|
|
x_np = np.random.rand(10, 10).astype(np.float32)
|
|
x = fluid.dygraph.to_variable(x_np)
|
|
out = fluid.layers.reduce_mean(input=x, dim=1)
|
|
self.assertEqual(np.allclose(out.numpy(), np.mean(x_np, axis=1)), True)
|
|
|
|
def test_errors(self):
|
|
paddle.disable_static()
|
|
x = np.random.uniform(-1, 1, [10, 12]).astype('float32')
|
|
x = paddle.to_tensor(x)
|
|
self.assertRaises(Exception, paddle.mean, x, -3)
|
|
self.assertRaises(Exception, paddle.mean, x, 2)
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.fluid.data('X', [10, 12], 'int32')
|
|
self.assertRaises(TypeError, paddle.mean, x)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|