未验证 提交 ee69f437 编写于 作者: T Tao Luo 提交者: GitHub

add gradient unittest and update code example for max/min (#38393)

* add gradient unittest and update code example for max/min

* update docs

* remove _get_reduce_all_value
上级 a4d07bb9
# Copyright (c) 2019 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
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import Program, program_guard
from op_test import OpTest
paddle.enable_static()
class TestMaxMinAPI(unittest.TestCase):
def setUp(self):
self.init_case()
self.cal_np_out_and_gradient()
self.place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
) else fluid.CPUPlace()
def init_case(self):
self.x_np = np.array([[0.2, 0.3, 0.5, 0.9], [0.1, 0.2, 0.6, 0.7]])
self.shape = [2, 4]
self.dtype = 'float64'
self.axis = None
self.keepdim = False
# If there are multiple minimum or maximum elements, max/min/ is non-derivable,
# its gradient check is not supported by unittest framework,
# thus we calculate the gradient by numpy function.
def cal_np_out_and_gradient(self):
def _cal_np_out_and_gradient(func):
if func is 'max':
out = np.max(self.x_np, axis=self.axis, keepdims=self.keepdim)
elif func is 'min':
out = np.min(self.x_np, axis=self.axis, keepdims=self.keepdim)
else:
print('This unittest only test max/min, but now is', func)
self.np_out[func] = out
grad = np.zeros(self.shape)
out_b = np.broadcast_to(out, self.shape)
grad[self.x_np == out_b] = 1
self.np_grad[func] = grad
self.np_out = dict()
self.np_grad = dict()
_cal_np_out_and_gradient('max')
_cal_np_out_and_gradient('min')
def _choose_paddle_func(self, func, x):
if func is 'max':
out = paddle.max(x, self.axis, self.keepdim)
elif func is 'min':
out = paddle.min(x, self.axis, self.keepdim)
else:
print('This unittest only test max/min, but now is', func)
return out
# We check the output between paddle API and numpy in static graph.
def test_static_graph(self):
def _test_static_graph(func):
startup_program = fluid.Program()
train_program = fluid.Program()
with fluid.program_guard(startup_program, train_program):
x = fluid.data(name='input', dtype=self.dtype, shape=self.shape)
x.stop_gradient = False
out = self._choose_paddle_func(func, x)
exe = fluid.Executor(self.place)
res = exe.run(fluid.default_main_program(),
feed={'input': self.x_np},
fetch_list=[out])
self.assertTrue((np.array(res[0]) == self.np_out[func]).all())
_test_static_graph('max')
_test_static_graph('min')
# As dygraph is easy to compute gradient, we check the gradient between
# paddle API and numpy in dygraph.
def test_dygraph(self):
def _test_dygraph(func):
paddle.disable_static()
x = paddle.to_tensor(
self.x_np, dtype=self.dtype, stop_gradient=False)
out = self._choose_paddle_func(func, x)
grad_tensor = paddle.ones_like(x)
paddle.autograd.backward([out], [grad_tensor], True)
self.assertEqual(np.allclose(self.np_out[func], out.numpy()), True)
self.assertEqual(np.allclose(self.np_grad[func], x.grad), True)
paddle.enable_static()
_test_dygraph('max')
_test_dygraph('min')
# test multiple minimum or maximum elements
class TestMaxMinAPI2(TestMaxMinAPI):
def init_case(self):
self.x_np = np.array([[0.2, 0.3, 0.9, 0.9], [0.1, 0.1, 0.6, 0.7]])
self.shape = [2, 4]
self.dtype = 'float64'
self.axis = None
self.keepdim = False
# test different axis
class TestMaxMinAPI3(TestMaxMinAPI):
def init_case(self):
self.x_np = np.array([[0.2, 0.3, 0.9, 0.9], [0.1, 0.1, 0.6, 0.7]])
self.shape = [2, 4]
self.dtype = 'float64'
self.axis = 0
self.keepdim = False
# test keepdim = True
class TestMaxMinAPI4(TestMaxMinAPI):
def init_case(self):
self.x_np = np.array([[0.2, 0.3, 0.9, 0.9], [0.1, 0.1, 0.6, 0.7]])
self.shape = [2, 4]
self.dtype = 'float64'
self.axis = 1
self.keepdim = True
# test axis is tuple
class TestMaxMinAPI5(TestMaxMinAPI):
def init_case(self):
self.x_np = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]).astype(np.int32)
self.shape = [2, 2, 2]
self.dtype = 'int32'
self.axis = (0, 1)
self.keepdim = False
...@@ -1437,8 +1437,7 @@ def max(x, axis=None, keepdim=False, name=None): ...@@ -1437,8 +1437,7 @@ def max(x, axis=None, keepdim=False, name=None):
Computes the maximum of tensor elements over the given axis. Computes the maximum of tensor elements over the given axis.
Args: Args:
x(Tensor): A tensor, the data type is float32, x(Tensor): A tensor, the data type is float32, float64, int32, int64.
float64, int32, int64.
axis(int|list|tuple, optional): The axis along which the maximum is computed. axis(int|list|tuple, optional): The axis along which the maximum is computed.
If :attr:`None`, compute the maximum over all elements of If :attr:`None`, compute the maximum over all elements of
`x` and return a Tensor with a single element, `x` and return a Tensor with a single element,
...@@ -1462,34 +1461,47 @@ def max(x, axis=None, keepdim=False, name=None): ...@@ -1462,34 +1461,47 @@ def max(x, axis=None, keepdim=False, name=None):
# data_x is a Tensor with shape [2, 4] # data_x is a Tensor with shape [2, 4]
# the axis is a int element # the axis is a int element
x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9], x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9],
[0.1, 0.2, 0.6, 0.7]]) [0.1, 0.2, 0.6, 0.7]],
dtype='float64', stop_gradient=False)
result1 = paddle.max(x) result1 = paddle.max(x)
print(result1) result1.backward()
#[0.9] print(result1, x.grad)
#[0.9], [[0., 0., 0., 1.], [0., 0., 0., 0.]]
x.clear_grad()
result2 = paddle.max(x, axis=0) result2 = paddle.max(x, axis=0)
print(result2) result2.backward()
#[0.2 0.3 0.6 0.9] print(result2, x.grad)
#[0.2, 0.3, 0.6, 0.9], [[1., 1., 0., 1.], [0., 0., 1., 0.]]
x.clear_grad()
result3 = paddle.max(x, axis=-1) result3 = paddle.max(x, axis=-1)
print(result3) result3.backward()
#[0.9 0.7] print(result3, x.grad)
#[0.9, 0.7], [[0., 0., 0., 1.], [0., 0., 0., 1.]]
x.clear_grad()
result4 = paddle.max(x, axis=1, keepdim=True) result4 = paddle.max(x, axis=1, keepdim=True)
print(result4) result4.backward()
#[[0.9] print(result4, x.grad)
# [0.7]] #[[0.9], [0.7]], [[0., 0., 0., 1.], [0., 0., 0., 1.]]
# data_y is a Tensor with shape [2, 2, 2] # data_y is a Tensor with shape [2, 2, 2]
# the axis is list # the axis is list
y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]], y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]],
[[5.0, 6.0], [7.0, 8.0]]]) [[5.0, 6.0], [7.0, 8.0]]],
dtype='float64', stop_gradient=False)
result5 = paddle.max(y, axis=[1, 2]) result5 = paddle.max(y, axis=[1, 2])
print(result5) result5.backward()
#[4. 8.] print(result5, y.grad)
#[4., 8.], [[[0., 0.], [0., 1.]], [[0., 0.], [0., 1.]]]
y.clear_grad()
result6 = paddle.max(y, axis=[0, 1]) result6 = paddle.max(y, axis=[0, 1])
print(result6) result6.backward()
#[7. 8.] print(result6, y.grad)
#[7., 8.], [[[0., 0.], [0., 0.]], [[0., 0.], [1., 1.]]]
""" """
if axis is not None and not isinstance(axis, list): if axis is not None and not isinstance(axis, list):
...@@ -1552,34 +1564,49 @@ def min(x, axis=None, keepdim=False, name=None): ...@@ -1552,34 +1564,49 @@ def min(x, axis=None, keepdim=False, name=None):
import paddle import paddle
# x is a tensor with shape [2, 4] # data_x is a Tensor with shape [2, 4]
# the axis is a int element # the axis is a int element
x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9], x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9],
[0.1, 0.2, 0.6, 0.7]]) [0.1, 0.2, 0.6, 0.7]],
dtype='float64', stop_gradient=False)
result1 = paddle.min(x) result1 = paddle.min(x)
print(result1) result1.backward()
#[0.1] print(result1, x.grad)
#[0.1], [[0., 0., 0., 0.], [1., 0., 0., 0.]]
x.clear_grad()
result2 = paddle.min(x, axis=0) result2 = paddle.min(x, axis=0)
print(result2) result2.backward()
#[0.1 0.2 0.5 0.7] print(result2, x.grad)
#[0.1, 0.2, 0.5, 0.7], [[0., 0., 1., 0.], [1., 1., 0., 1.]]
x.clear_grad()
result3 = paddle.min(x, axis=-1) result3 = paddle.min(x, axis=-1)
print(result3) result3.backward()
#[0.2 0.1] print(result3, x.grad)
#[0.2, 0.1], [[1., 0., 0., 0.], [1., 0., 0., 0.]]
x.clear_grad()
result4 = paddle.min(x, axis=1, keepdim=True) result4 = paddle.min(x, axis=1, keepdim=True)
print(result4) result4.backward()
#[[0.2] print(result4, x.grad)
# [0.1]] #[[0.2], [0.1]], [[1., 0., 0., 0.], [1., 0., 0., 0.]]
# y is a Tensor with shape [2, 2, 2] # data_y is a Tensor with shape [2, 2, 2]
# the axis is list # the axis is list
y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]], y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]],
[[5.0, 6.0], [7.0, 8.0]]]) [[5.0, 6.0], [7.0, 8.0]]],
dtype='float64', stop_gradient=False)
result5 = paddle.min(y, axis=[1, 2]) result5 = paddle.min(y, axis=[1, 2])
print(result5) result5.backward()
#[1. 5.] print(result5, y.grad)
#[1., 5.], [[[1., 0.], [0., 0.]], [[1., 0.], [0., 0.]]]
y.clear_grad()
result6 = paddle.min(y, axis=[0, 1]) result6 = paddle.min(y, axis=[0, 1])
print(result6) result6.backward()
#[1. 2.] print(result6, y.grad)
#[1., 2.], [[[1., 1.], [0., 0.]], [[0., 0.], [0., 0.]]]
""" """
if axis is not None and not isinstance(axis, list): if axis is not None and not isinstance(axis, list):
...@@ -1590,6 +1617,7 @@ def min(x, axis=None, keepdim=False, name=None): ...@@ -1590,6 +1617,7 @@ def min(x, axis=None, keepdim=False, name=None):
else: else:
raise TypeError( raise TypeError(
"The type of axis must be int, list or tuple, but received {}".format(type(axis))) "The type of axis must be int, list or tuple, but received {}".format(type(axis)))
reduce_all = True if axis == None or axis == [] else False reduce_all = True if axis == None or axis == [] else False
axis = axis if axis != None and axis != [] else [0] axis = axis if axis != None and axis != [] else [0]
if in_dygraph_mode(): if in_dygraph_mode():
...@@ -1613,7 +1641,6 @@ def min(x, axis=None, keepdim=False, name=None): ...@@ -1613,7 +1641,6 @@ def min(x, axis=None, keepdim=False, name=None):
}) })
return out return out
def log1p(x, name=None): def log1p(x, name=None):
r""" r"""
Calculates the natural log of the given input tensor, element-wise. Calculates the natural log of the given input tensor, element-wise.
......
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