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“726337fc2ddf01bea512c58d9d3b0e7f4763891b”上不存在“...multi_language_interface/01.inference_implementation.html”
未验证 提交 27f49254 编写于 作者: Y yuehuayingxueluo 提交者： GitHub
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clear fluid apis in loss.py v_1 (#48132) 

* clear fluid apis: center_loss, bpr_loss, edit_distance, hsigmoid, sampled_softmax_with_cross_entropy, rank_loss, margin_rank_loss,  sigmoid_cross_entropy_with_logits, huber_loss

* fix python/paddle/fluid/layers/loss.py

* fix test_layers.py

* fix CI bug

* fix nn.py
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python/paddle/fluid/evaluator.py
浏览文件 @ 27f49254
......@@ -23,7 +23,6 @@ from .initializer import Constant
from .layers import detection
__all__ = [
'EditDistance',
'DetectionMAP',
]
......@@ -126,95 +125,6 @@ class Evaluator:
return state
class EditDistance(Evaluator):
"""
Warning: This would be deprecated in the future. Please use fluid.metrics.EditDistance
instead.
Accumulate edit distance sum and sequence number from mini-batches and
compute the average edit_distance and instance error of all batches.
Args:
input: the sequences predicted by network.
label: the target sequences which must have same sequence count
with input.
ignored_tokens(list of int): Tokens that should be removed before
calculating edit distance.
Examples:
.. code-block:: python
exe = fluid.executor(place)
distance_evaluator = fluid.Evaluator.EditDistance(input, label)
for epoch in PASS_NUM:
distance_evaluator.reset(exe)
for data in batches:
loss = exe.run(fetch_list=[cost])
distance, instance_error = distance_evaluator.eval(exe)
In the above example:
'distance' is the average of the edit distance in a pass.
'instance_error' is the instance error rate in a pass.
"""
def __init__(self, input, label, ignored_tokens=None, **kwargs):
super().__init__("edit_distance", **kwargs)
main_program = self.helper.main_program
if main_program.current_block().idx != 0:
raise ValueError("You can only invoke Evaluator in root block")
self.total_distance = self._create_state(
dtype='float32', shape=[1], suffix='total_distance'
)
self.seq_num = self._create_state(
dtype='int64', shape=[1], suffix='seq_num'
)
self.instance_error = self._create_state(
dtype='int64', shape=[1], suffix='instance_error'
)
distances, seq_num = layers.edit_distance(
input=input, label=label, ignored_tokens=ignored_tokens
)
zero = layers.fill_constant(shape=[1], value=0.0, dtype='float32')
compare_result = layers.equal(distances, zero)
compare_result_int = layers.cast(x=compare_result, dtype='int64')
seq_right_count = layers.reduce_sum(compare_result_int)
instance_error_count = layers.elementwise_sub(
x=seq_num, y=seq_right_count
)
total_distance = layers.reduce_sum(distances)
layers.sums(
input=[self.total_distance, total_distance], out=self.total_distance
)
layers.sums(input=[self.seq_num, seq_num], out=self.seq_num)
layers.sums(
input=[self.instance_error, instance_error_count],
out=self.instance_error,
)
self.metrics.append(total_distance)
self.metrics.append(instance_error_count)
def eval(self, executor, eval_program=None):
if eval_program is None:
eval_program = Program()
block = eval_program.current_block()
with program_guard(main_program=eval_program):
total_distance = _clone_var_(block, self.total_distance)
seq_num = _clone_var_(block, self.seq_num)
instance_error = _clone_var_(block, self.instance_error)
seq_num = layers.cast(x=seq_num, dtype='float32')
instance_error = layers.cast(x=instance_error, dtype='float32')
avg_distance = layers.elementwise_div(x=total_distance, y=seq_num)
avg_instance_error = layers.elementwise_div(
x=instance_error, y=seq_num
)
result = executor.run(
eval_program, fetch_list=[avg_distance, avg_instance_error]
)
return np.array(result[0]), np.array(result[1])
class DetectionMAP(Evaluator):
"""
Warning: This would be deprecated in the future. Please use fluid.metrics.DetectionMAP
......
python/paddle/fluid/layers/loss.py
浏览文件 @ 27f49254
......@@ -35,21 +35,12 @@ import warnings
from paddle import _C_ops, _legacy_C_ops
__all__ = [
'center_loss',
'bpr_loss',
'cross_entropy',
'square_error_cost',
'edit_distance',
'warpctc',
'nce',
'hsigmoid',
'sampled_softmax_with_cross_entropy',
'softmax_with_cross_entropy',
'rank_loss',
'margin_rank_loss',
'sigmoid_cross_entropy_with_logits',
'teacher_student_sigmoid_loss',
'huber_loss',
'kldiv_loss',
'npair_loss',
'mse_loss',
......@@ -58,159 +49,6 @@ __all__ = [
kIgnoreIndex = -100
def center_loss(
input, label, num_classes, alpha, param_attr, update_center=True
):
r"""
:api_attr: Static Graph
**Center loss Cost layer**
This OP accepts input (deep features,the output of the last hidden layer)
and target label and return the center loss cost. The average of the
distances of each sample in the mini-batch from the center of the
corresponding category is calculated as the center loss.
For deep features, :math:`X`, and target labels, :math:`Y`, the equation is:
.. math::
Out = \\frac{1}{2}(X - Y)^2
Args:
input (Variable): a 2-D tensor with shape[N x M]. Its dtype should be float32 or float64.
label (Variable): the groud truth which is a 2-D tensor
with shape[N x 1],where N is the batch size. Its dtype should be int32.
num_classes (int): the number of classification categories.
alpha (float|Variable): learning rate of centers.
param_attr (ParamAttr): Attribute initializer of centers.
update_center (bool): whether to update value of center.
Returns:
Variable: 2-D tensor with shape [N * 1]
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
input = fluid.data(name='x',shape=[20,30],dtype='float32')
label = fluid.data(name='y',shape=[20,1],dtype='int64')
num_classes = 1000
alpha = 0.01
param_attr = fluid.initializer.Xavier(uniform=False)
center_loss=fluid.layers.center_loss(input=input,
label=label,
num_classes=1000,
alpha=alpha,
param_attr=fluid.initializer.Xavier(uniform=False),
update_center=True)
"""
helper = LayerHelper('center_loss', **locals())
dtype = helper.input_dtype()
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'center_loss'
)
check_variable_and_dtype(label, 'label', ['int32', 'int64'], 'center_loss')
centers_shape = [num_classes, input.shape[1]]
centers_param = helper.create_parameter(
attr=param_attr, shape=centers_shape, dtype=dtype
)
centers_param.stop_gradient = True
if isinstance(alpha, Variable):
alpha_param = alpha
check_variable_and_dtype(
alpha, 'alpha', ['float32', 'float64'], 'center_loss'
)
else:
assert isinstance(alpha, float)
alpha_param = helper.create_variable(
name="centerloss_alpha",
shape=[1],
dtype="float32",
type=core.VarDesc.VarType.LOD_TENSOR,
persistable=True,
stop_gradient=True,
initializer=Constant(alpha),
)
centersdiff = helper.create_variable_for_type_inference(dtype=input.dtype)
loss = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='center_loss',
inputs={
'X': [input],
'Label': [label],
'Centers': [centers_param],
'CenterUpdateRate': [alpha_param],
},
outputs={
'SampleCenterDiff': [centersdiff],
'Loss': [loss],
'CentersOut': [centers_param],
},
attrs={'cluster_num': num_classes, 'need_update': update_center},
)
return loss
def bpr_loss(input, label, name=None):
r"""
**Bayesian Personalized Ranking Loss Operator**
This operator belongs to pairwise ranking loss. Label is the desired item.
The loss at a given point in one session is defined as:
.. math::
Y[i] = 1/(N[i] - 1) * \sum_j{\log(\sigma(X[i, Label[i]]-X[i, j]))}
Learn more details by reading paper <session-based recommendations with recurrent
neural networks>.
Args:
input (Variable|list): a 2-D tensor with shape [N x D], where N is the
batch size and D is the number of positive classes and negative classes
This input is not probability but logits.
label (Variable|list): the ground truth which is a 2-D tensor. `label`
is a tensor<int64> with shape [N x 1].
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically. Default: None.
Returns:
A 2-D tensor with shape [N x 1], the bpr loss.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
neg_size = 10
label = fluid.data(
name="label", shape=[3, 1], dtype="int64")
predict = fluid.data(
name="predict", shape=[3, neg_size + 1], dtype="float32")
cost = fluid.layers.bpr_loss(input=predict, label=label)
"""
helper = LayerHelper('bpr_loss', **locals())
out = helper.create_variable_for_type_inference(dtype=input.dtype)
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64'], 'bpr_loss'
)
helper.append_op(
type='bpr_loss',
inputs={'X': [input], 'Label': [label]},
outputs={'Y': [out]},
)
return out
def cross_entropy(input, label, soft_label=False, ignore_index=kIgnoreIndex):
r"""
:alias_main: paddle.nn.functional.cross_entropy
......@@ -347,86 +185,6 @@ def square_error_cost(input, label):
return paddle.nn.functional.square_error_cost(input, label)
def edit_distance(
input,
label,
normalized=True,
ignored_tokens=None,
input_length=None,
label_length=None,
):
"""
This op computes the edit distances, also called Levenshtein distance, between a batch of
hypothesis strings and their references. It measures how dissimilar two strings are by counting
the minimum number of operations to transform one string into another.
The operations include insertion, deletion, and substitution.
For example, given hypothesis string A = "kitten" and reference
B = "sitting", A will be transformed into B
at least after two substitutions and one insertion:
"kitten" -> "sitten" -> "sittin" -> "sitting"
So the edit distance between A and B is 3.
The input is a Tensor, the input_length and label_length should be supported.
The `batch_size` of labels should be same as `input`.
The output include the edit distance value between every pair of input and related label, and the number of sequence.
If Attr(normalized) is true,
the edit distance value will be divided by the length of label.
Parameters:
input(Tensor): The input tensor, its rank should be equal to 2 and its data type should be int64.
label(Tensor): The label tensor, its rank should be equal to 2 and its data type should be int64.
normalized(bool, default True): Indicated whether to normalize the edit distance.
ignored_tokens(list<int>, default None): Tokens that will be removed before
calculating edit distance.
input_length(Tensor): The length for each sequence in `input` if it's of Tensor type, it should have shape `(batch_size, )` and its data type should be int64.
label_length(Tensor): The length for each sequence in `label` if it's of Tensor type, it should have shape `(batch_size, )` and its data type should be int64.
NOTE: To be avoid unexpected result, the value of every elements in input_length and label_length should be equal to the value of the second dimension of input and label. For example, The input: [[1,2,3,4],[5,6,7,8],[9,10,11,12]], the shape of input is [3,4] and the input_length should be [4,4,4]
NOTE: This Api is different from fluid.metrics.EditDistance
Returns:
Tuple:
distance(Tensor): edit distance result, its data type is float32, and its shape is (batch_size, 1).
sequence_num(Tensor): sequence number, its data type is float32, and its shape is (1,).
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
input = paddle.to_tensor([[1,2,3],[4,5,6],[4,4,4],[1,1,1]], dtype='int64')
label = paddle.to_tensor([[1,3,4,1],[4,5,8,1],[7,7,7,1],[1,1,1,1]], dtype='int64')
input_len = paddle.to_tensor([3,3,3,3], dtype='int64')
label_len = paddle.to_tensor([4,4,4,4], dtype='int64')
distance, sequence_num = F.loss.edit_distance(input=input, label=label, input_length=input_len, label_length=label_len, normalized=False)
# print(distance)
# [[3.]
# [2.]
# [4.]
# [1.]]
# if set normalized to True
# [[0.75]
# [0.5 ]
# [1. ]
# [0.25]
#
# print(sequence_num)
# [4]
"""
return paddle.nn.functional.loss.edit_distance(
input, label, normalized, ignored_tokens, input_length, label_length
)
def warpctc(
input,
label,
......@@ -837,363 +595,6 @@ def nce(
return cost / (num_neg_samples + 1)
def hsigmoid(
input,
label,
num_classes,
param_attr=None,
bias_attr=None,
name=None,
path_table=None,
path_code=None,
is_custom=False,
is_sparse=False,
):
"""
:api_attr: Static Graph
The hierarchical sigmoid organizes the classes into a complete binary tree to reduce the computational complexity
and speed up the model training, especially the training of language model.
Each leaf node of the complete binary tree represents a class(word) and each non-leaf node acts as a binary classifier.
For each class(word), there's a unique path from root to itself, hsigmoid calculate the cost for each non-leaf node on
the path, and sum them to get a total cost.
Comparing to softmax, the OP can reduce the computational complexity from :math:`O(N)` to :math:`O(logN)`, where :math:`N`
represents the number of classes or the size of word dict.
The OP supports default tree and custom tree. For the default tree, you can refer to `Hierarchical Probabilistic Neural
Network Language Model <http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>`. For the custom
tree, you need to set :attr:`is_custom` to True, and do the following steps (take the language model as an example):
1. Using a custom word dict to build a binary tree, each leaf node should be an word in the word dict.
2. Creating a dict map word_id -> path that from the word to the root node, we call it path_table.
3. Creating a dict map word_id -> code of path that from the word to the root node, we call it path_code.
Code means the label of each binary classifier, 1 indicate true, 0 indicate false.
4. Now, each word should has its path and code along the path, you can pass a batch of path and code related
to the same batch of inputs.
Parameters:
input (Variable): A tensor with the shape [N, D], where N is the size of mini-batch,
and D is the feature size. Its data type supports float32 and float64.
label (Variable): A tensor contains the labels of training data. Its shape is [N, 1]
and data type is int64.
num_classes (int): The number of classes or the size of word dict, must be greater than 2.
If the default tree is used (:attr:`is_custom` is set to False), :attr:`num_classes`
should not be None. If the custom tree is used (:attr:`is_custom` is set to True),
:attr:`num_classes` should be the number of non-leaf nodes, which indicates the num of
classes using by the binary classifier.
param_attr (ParamAttr, optional): The parameter attribute for the learnable parameters/weights
of hsigmoid. If it is set to None or one attribute of ParamAttr, hsigmoid will create a
ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is
initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool, optional): The parameter attribute for the bias of hsigmoid. If it
is set to False, no bias will be added. If it is set to None or one attribute of ParamAttr,
hsigmoid will create a ParamAttr as bias_attr. If the Initializer of the bias_attr is not
set, the bias is initialized zero. Default: None.
name (str, optional): Normally there is no need for user to set this property. For more information,
please refer to :ref:`api_guide_Name`. Default: None.
path_table (Variable, optional): A tensor that stores each batch of samples' path from leaf to root
node, its shape is [N, L] and data type is int64, where L is the length of path. For each sample i,
path_table[i] is a np.array like structure and each element in this array is the indexes in parent
nodes' weight matrix. Default: None.
path_code (Variable, optional): A tensor that stores each batch of samples' code of path from leaf
to root node, its shape is [N, L] and data type is int64, which is the same as :attr:`path_table`.
Each code of path is consisted with the code of nodes from leaf to root node. Default: None.
is_custom (bool, optional): Whether use custom binary tree. If it's True, :attr:`path_table`,
:attr:`path_code` and :attr:`num_classes` should be set, otherwise :attr:`num_classes` should
be set. Default: False.
is_sparse (bool, optional): Whether use sparse updating instead of dense updating, if it's True, the
gradient of W and input will be sparse. Default: False.
Returns:
Variable: A tensor with the cost of hierarchical sigmoid, its shape is [N, 1] and data type is the same as :attr:`input`.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.fill_constant(shape=[4, 3], value=0.9, dtype='float32')
# x = [[0.9, 0.9, 0.9], [0.9, 0.9, 0.9], [0.9, 0.9, 0.9], [0.9, 0.9, 0.9]]
y = fluid.layers.fill_constant(
shape=[4, 1], value=1, dtype='int64')
# y = [[1], [1], [1], [1]]
out = fluid.layers.hsigmoid(input=x, label=y, num_classes=2, param_attr=fluid.initializer.Constant(
value=0.05), bias_attr=fluid.initializer.Constant(value=.0))
# out = [[0.62792355], [0.62792355], [0.62792355], [0.62792355]]
"""
check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'hsigmoid')
check_variable_and_dtype(label, 'label', ['int64'], 'hsigmoid')
helper = LayerHelper('hierarchical_sigmoid', **locals())
dtype = helper.input_dtype()
out = helper.create_variable_for_type_inference(dtype)
pre_out = helper.create_variable_for_type_inference(dtype)
dim = input.shape[1]
if ((num_classes is None) or (num_classes < 2)) and (not is_custom):
raise ValueError(
"num_classes must not be less than 2 with default tree"
)
if (not is_custom) and (is_sparse):
print("Sparse mode should not be used without custom tree")
is_sparse = False
if (not is_custom) and (
(path_table is not None) or (path_code is not None)
):
raise ValueError(
"only num_classes should be passed without custom tree"
)
if (is_custom) and (path_code is None):
raise ValueError("path_code should not be None with custom tree")
elif (is_custom) and (path_table is None):
raise ValueError("path_table should not be None with custom tree")
elif (is_custom) and (num_classes is None):
raise ValueError("num_classes should not be None with custom tree")
else:
pass
weights = None
remote_prefetch = is_sparse
print(
"With sparse mode, if your models has only small parameter prefetch may cause speed down"
)
if not is_custom:
weights = helper.create_parameter(
attr=helper.param_attr,
shape=[num_classes - 1, dim],
is_bias=False,
dtype=input.dtype,
)
else:
weights = helper.create_parameter(
attr=helper.param_attr,
shape=[num_classes, dim],
is_bias=False,
dtype=input.dtype,
)
inputs = {
"X": input,
"W": weights,
"PathTable": path_table,
"PathCode": path_code,
"Label": label,
}
if helper.bias_attr:
if not is_custom:
bias = helper.create_parameter(
attr=helper.bias_attr,
shape=[num_classes - 1, 1],
is_bias=True,
dtype=input.dtype,
)
inputs['Bias'] = bias
else:
bias = helper.create_parameter(
attr=helper.bias_attr,
shape=[num_classes, 1],
is_bias=True,
dtype=input.dtype,
)
inputs['Bias'] = bias
helper.append_op(
type="hierarchical_sigmoid",
inputs=inputs,
outputs={"Out": out, "PreOut": pre_out, "W_Out": weights},
attrs={
"num_classes": num_classes,
"is_sparse": is_sparse,
"remote_prefetch": remote_prefetch,
},
)
return out
def sampled_softmax_with_cross_entropy(
logits,
label,
num_samples,
num_true=1,
remove_accidental_hits=True,
use_customized_samples=False,
customized_samples=None,
customized_probabilities=None,
seed=0,
):
"""
**Sampled Softmax With Cross Entropy Operator.**
Cross entropy loss with sampled softmax is used as the output layer for
larger output classes extensively. This operator samples a number of samples
for all examples, and computes the softmax normalized values for each
row of the sampled tensor, after which cross-entropy loss is computed.
Because this operator performs a softmax on logits internally, it expects
unscaled logits. This operator should not be used with the output of
softmax operator since that would produce incorrect results.
For examples with T true labels (T >= 1), we assume that each true label has
a probability of 1/T. For each sample, S samples are generated using a
log uniform distribution. True labels are concatenated with these samples to
form T + S samples for each example. So, assume the shape of logits is
[N x K], the shape for samples is [N x (T+S)]. For each sampled label, a
probability is calculated, which corresponds to the Q(y|x) in
[Jean et al., 2014](http://arxiv.org/abs/1412.2007).
Logits are sampled according to the sampled labels. Then if
remove_accidental_hits is True, if a sample[i, j] accidentally hits true
labels, then the corresponding sampled_logits[i, j] is minus by 1e20 to
make its softmax result close to zero. Then sampled logits are subtracted by
logQ(y|x), these sampled logits and re-indexed labels are used to compute
a softmax with cross entropy.
Args:
logits (Variable): The unscaled log probabilities, which is a 2-D tensor
with shape [N x K]. N is the batch_size, and K is the class number.
label (Variable): The ground truth which is a 2-D tensor. Label is a
Tensor<int64> with shape [N x T], where T is the number of true
labels per example.
num_samples (int): The number for each example, num_samples should be
less than the number of class.
num_true(int): The number of target classes per training example.
remove_accidental_hits (bool): A flag indicating whether to remove
accidental hits when sampling. If True and if a sample[i, j]
accidentally hits true labels, then the corresponding
sampled_logits[i, j] is minus by 1e20 to make its softmax result
close to zero. Default is True.
use_customized_samples (bool): Whether to use custom samples and probabities to sample
logits.
customized_samples (Variable): User defined samples, which is a 2-D tensor
with shape [N, T + S]. S is the num_samples, and T is the number of true
labels per example.
customized_probabilities (Variable): User defined probabilities of samples,
a 2-D tensor which has the same shape with customized_samples.
seed (int): The random seed for generating random number, which is used
in the process of sampling. Default is 0.
Returns:
Variable: Return the cross entropy loss which is a 2-D tensor with shape
[N x 1].
Examples:
.. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name='data', shape=[256], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
fc = fluid.layers.fc(input=input, size=100)
out = fluid.layers.sampled_softmax_with_cross_entropy(
logits=fc, label=label, num_samples=25)
"""
if _non_static_mode():
sample_logits_attrs = (
'use_customized_samples',
use_customized_samples,
'uniq',
True,
'remove_accidental_hits',
remove_accidental_hits,
'num_samples',
num_samples,
'seed',
seed,
)
(
_,
_,
_,
_,
sampled_logits_out,
sampled_label_out,
) = _legacy_C_ops.sample_logits(logits, label, *sample_logits_attrs)
depth = num_samples + 1
sampled_softlabel_out = _legacy_C_ops.one_hot(
sampled_label_out, 'depth', depth
)
softmax_with_cross_entropy_attrs = (
'soft_label',
True,
'numeric_stable_mode',
False,
)
_, loss = _legacy_C_ops.softmax_with_cross_entropy(
sampled_logits_out,
sampled_softlabel_out,
*softmax_with_cross_entropy_attrs
)
return loss / num_true
helper = LayerHelper('sample_logits', **locals())
samples = (
customized_samples
if use_customized_samples
else helper.create_variable_for_type_inference(dtype='int64')
)
probabilities = (
customized_probabilities
if use_customized_samples
else helper.create_variable_for_type_inference(dtype=logits.dtype)
)
sampled_logits = helper.create_variable_for_type_inference(
dtype=logits.dtype
)
sampled_label = helper.create_variable_for_type_inference(dtype='int64')
sampled_softlabel = helper.create_variable_for_type_inference(
dtype=logits.dtype
)
logits_dim = helper.create_variable_for_type_inference(dtype=logits.dtype)
labels_dim = helper.create_variable_for_type_inference(dtype=label.type)
helper.append_op(
type='sample_logits',
inputs={
'Logits': logits,
'Labels': label,
'CustomizedSamples': customized_samples,
'CustomizedProbabilities': customized_probabilities,
},
outputs={
'Samples': samples,
'Probabilities': probabilities,
'SampledLabels': sampled_label,
'SampledLogits': sampled_logits,
'LogitsDim': logits_dim,
'LabelsDim': labels_dim,
},
attrs={
'use_customized_samples': use_customized_samples,
'uniq': True,
'remove_accidental_hits': remove_accidental_hits,
'num_samples': num_samples,
'seed': seed,
},
)
loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
helper.append_op(
type='one_hot',
inputs={'X': sampled_label},
attrs={'depth': num_samples + 1},
outputs={'Out': sampled_softlabel},
)
helper.append_op(
type='softmax_with_cross_entropy',
inputs={'Logits': sampled_logits, 'Label': sampled_softlabel},
outputs={'Softmax': softmax, 'Loss': loss},
attrs={
'soft_label': True,
'ignore_index': False,
'numeric_stable_mode': False,
},
)
return loss / num_true
def softmax_with_cross_entropy(
logits,
label,
......@@ -1364,118 +765,6 @@ def identity_loss(x, reduction="none"):
return out
def rank_loss(label, left, right, name=None):
r"""
This operator implements the sort loss layer in the RankNet model. RankNet is a pairwise ranking model
with a training sample consisting of a pair of documents (A and B), The label (P)
indicates whether A is ranked higher than B or not. Please refer to more details:
`RankNet <http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf>`_
Rank loss layer takes three inputs: left ( :math:`o_i` ), right ( :math:`o_j` ) and
label ( :math:`P_{i,j}` ). The inputs respectively represent RankNet's output scores
for documents A and B and the value of label P. Rank loss layer takes batch inputs
with size batch_size (batch_size >= 1), P = {0, 1} or {0, 0.5, 1},
where 0.5 means that there is no information about the rank of the input pair.
The following equation computes rank loss C_{i,j} from the inputs:
.. math::
C_{i,j} &= -\\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\\\
.. math::
o_{i,j} &= o_i - o_j \\\\
.. math::
\\tilde{P_{i,j}} &= \\left \{0, 0.5, 1 \\right \} \ or \ \\left \{0, 1 \\right \}
Parameters:
label (Variable): 2-D ``Tensor`` with the shape of :math:`[batch,1]`, the data type is float32, batch indicates the size of the data. Indicats whether A ranked higher than B or not.
left (Variable): 2-D ``Tensor`` with the shape of :math:`[batch,1]`, the data type is float32. RankNet's output score for doc A.
right (Variable): 2-D ``Tensor`` with the shape of :math:`[batch,1]`, the data type is float32. RankNet's output score for doc B.
name(str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` .
Returns:
Variable: ``Tensor`` indicating the output value of the sort loss layer, the data type is float32, and the return value's shape is :math:`[batch,1]` .
Raises:
ValueError: Any of label, left, and right is not a ``Variable`` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
label = fluid.data(name="label", shape=[-1, 1], dtype="float32")
left = fluid.data(name="left", shape=[-1, 1], dtype="float32")
right = fluid.data(name="right", shape=[-1, 1], dtype="float32")
out = fluid.layers.rank_loss(label, left, right)
"""
helper = LayerHelper('rank_loss', **locals())
check_variable_and_dtype(label, 'label', ['float32'], "rank_loss")
check_variable_and_dtype(left, 'left', ['float32'], "rank_loss")
check_variable_and_dtype(right, 'right', ['float32'], "rank_loss")
out = helper.create_variable_for_type_inference("float32")
helper.append_op(
type='rank_loss',
inputs={"Label": label, "Left": left, "Right": right},
outputs={'Out': out},
)
return out
def margin_rank_loss(label, left, right, margin=0.1, name=None):
r"""
Margin Ranking Loss Layer for ranking problem,
which compares left score and right score passed in.
The ranking loss can be defined as following equation:
.. math::
rank\_loss = max(0, -label * (left - right) + margin)
Args:
label (Variable): Indicates whether the left is ranked higher than the right or not.
Data type is float32.
left (Variable): Ranking score for left. Data type float32.
right (Variable): Ranking score for right. Data type float32.
margin (float): Indicates the given margin.
name(str|None): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default.
Returns:
Variable: The ranking loss.
Raises:
ValueError: Any of label, left, and right is not a Variable.
Examples:
.. code-block:: python
import paddle.fluid as fluid
label = fluid.data(name="label", shape=[-1, 1], dtype="float32")
left = fluid.data(name="left", shape=[-1, 1], dtype="float32")
right = fluid.data(name="right", shape=[-1, 1], dtype="float32")
out = fluid.layers.margin_rank_loss(label, left, right)
"""
helper = LayerHelper('margin_rank_loss', **locals())
check_variable_and_dtype(label, 'label', ['float32'], 'margin_rank_loss')
check_variable_and_dtype(label, 'left', ['float32'], 'margin_rank_loss')
check_variable_and_dtype(label, 'right', ['float32'], 'margin_rank_loss')
out = helper.create_variable_for_type_inference(left.dtype)
act = helper.create_variable_for_type_inference(left.dtype)
helper.append_op(
type='margin_rank_loss',
inputs={"Label": label, "X1": left, "X2": right},
outputs={'Out': out, 'Activated': act},
attrs={'margin': margin},
)
return out
@templatedoc()
def sigmoid_cross_entropy_with_logits(
x, label, ignore_index=kIgnoreIndex, name=None, normalize=False
......@@ -1539,144 +828,6 @@ def sigmoid_cross_entropy_with_logits(
return out
def teacher_student_sigmoid_loss(
input, label, soft_max_up_bound=15.0, soft_max_lower_bound=-15.0
):
"""
**Teacher Student Log Loss Layer**
This layer accepts input predictions and target label and returns the
teacher_student loss. Z is click or not, z' is value of teacher loss, label = {-2, -1, [0, 2]}
when z' is not exist, clk = 0 : label = -2; when z' is not exist, clk = 1 : label = -1;
when z' is exist , clk = 0 : label = 0 + z'; when z' is exist , clk = 1 : label = 1 + z'
.. math::
loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' + log(1 + exp(-abs(x)))
Args:
input (Variable|list): a 2-D tensor with shape [N x 1], where N is the
batch size. This input is a probability computed
by the previous operator.
label (Variable|list): the ground truth which is a 2-D tensor with
shape [N x 1], where N is the batch size.
soft_max_up_bound (float): if input > soft_max_up_bound, will be bound
soft_max_lower_bound (float): if input < soft_max_lower_bound, will be bound
Returns:
Variable: A 2-D tensor with shape [N x 1], the teacher_student_sigmoid_loss.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
batch_size = 64
label = fluid.data(
name="label", shape=[batch_size, 1], dtype="int64")
similarity = fluid.data(
name="similarity", shape=[batch_size, 1], dtype="float32")
cost = fluid.layers.teacher_student_sigmoid_loss(input=similarity, label=label)
"""
check_variable_and_dtype(
input,
"input",
['float32', 'float64', 'int32', 'int64'],
'teacher_student_sigmoid_loss',
)
check_variable_and_dtype(
label,
"label",
['float32', 'float64', 'int32', 'int64'],
'teacher_student_sigmoid_loss',
)
helper = LayerHelper('teacher_student_sigmoid_loss', **locals())
out = helper.create_variable(dtype=input.dtype)
helper.append_op(
type='teacher_student_sigmoid_loss',
inputs={'X': [input], 'Label': [label]},
outputs={'Y': [out]},
attrs={
"soft_max_lower_bound": float(soft_max_lower_bound),
"soft_max_up_bound": float(soft_max_up_bound),
},
)
return out
def huber_loss(input, label, delta):
r"""
This operator computes the Huber loss between input and label.
Huber loss is commonly used in regression tasks. Compared to square_error_cost, Huber loss is more robust and less sensitivity to outliers.
When the absolute difference between input and label is greater than delta, the linear error is calculated:
.. math::
huber\_loss = delta * (label - input) - 0.5 * delta * delta
When the absolute difference between input and label is greater than delta, the square error is calculated:
.. math::
huber\_loss = 0.5 * (label - input) * (label - input)
Args:
input (Variable): Predicted data, 2D-Tensor with the shape of [batch_size, 1]. The data type should be float32.
label (Variable): Ground truth label, 2D-Tensor with the shape of [batch_size, 1]. The data type should be float32.
delta (float): The threshold for Huber loss, which is used to control the balance between the linear error and square error. The data type should be float32.
Returns:
Variable: The huber loss, a tensor with the same shape and data type as input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
DATATYPE='float32'
input_data = np.array([[1.],[2.],[3.],[4.]]).astype(DATATYPE)
label_data = np.array([[3.],[3.],[4.],[4.]]).astype(DATATYPE)
x = fluid.data(name='input', shape=[None, 1], dtype=DATATYPE)
y = fluid.data(name='label', shape=[None, 1], dtype=DATATYPE)
loss = fluid.layers.huber_loss(input=x, label=y, delta=1.0)
place = fluid.CPUPlace()
#place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
HuberLoss, = exe.run(feed={'input':input_data ,'label':label_data}, fetch_list=[loss.name])
print(HuberLoss) #[[1.5], [0.5], [0.5], [0. ]], dtype=float32
"""
if in_dygraph_mode():
out, residual = _C_ops.huber_loss(input, label, delta)
return out
helper = LayerHelper('huber_loss', **locals())
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'huber_loss'
)
check_variable_and_dtype(
label, 'label', ['float32', 'float64'], 'huber_loss'
)
residual = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='huber_loss',
inputs={'X': input, 'Y': label},
outputs={'Out': out, 'Residual': residual},
attrs={'delta': delta},
)
return out
@deprecated(since="2.0.0", update_to="paddle.nn.functional.kl_div")
@templatedoc()
def kldiv_loss(x, target, reduction='mean', name=None):
......
python/paddle/fluid/metrics.py
浏览文件 @ 27f49254
......@@ -702,7 +702,7 @@ class EditDistance(MetricBase):
"""
if self.seq_num == 0:
raise ValueError(
"There is no data in EditDistance Metric. Please check layers.edit_distance output has been added to EditDistance."
"There is no data in EditDistance Metric. Please check paddle.nn.functional.loss.edit_distance output has been added to EditDistance."
)
avg_distance = self.total_distance / self.seq_num
avg_instance_error = self.instance_error / float(self.seq_num)
......
python/paddle/fluid/tests/unittests/ipu/test_huber_loss_op_ipu.py 已删除 100644 → 0
浏览文件 @ cbdc86b5
# Copyright (c) 2022 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.
import unittest
import numpy as np
import paddle
import paddle.static
from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
class TestBase(IPUOpTest):
def setUp(self):
self.set_atol()
self.set_training()
self.set_data_feed()
self.set_feed_attr()
self.set_op_attrs()
def set_data_feed(self):
x = np.random.uniform(size=[3, 4, 2, 2])
target = np.random.uniform(size=[3, 4, 2, 2])
self.feed_fp32 = {
"x": x.astype(np.float32),
"target": target.astype(np.float32),
}
self.feed_fp16 = {
"x": x.astype(np.float16),
"target": target.astype(np.float16),
}
def set_feed_attr(self):
self.feed_shape = [x.shape for x in self.feed_fp32.values()]
self.feed_list = list(self.feed_fp32.keys())
def set_op_attrs(self):
self.attrs = {
'delta': 1.0,
}
@IPUOpTest.static_graph
def build_model(self, on_ipu):
x = paddle.static.data(
name=self.feed_list[0], shape=self.feed_shape[0], dtype="float32"
)
target = paddle.static.data(
name=self.feed_list[1], shape=self.feed_shape[1], dtype='float32'
)
out = paddle.fluid.layers.huber_loss(x, target, **self.attrs)
self.fetch_list = [out.name]
def run_model(self, exec_mode):
self.run_op_test(exec_mode)
def test(self):
for m in IPUOpTest.ExecutionMode:
if not self.skip_mode(m):
self.build_model(self.is_ipu_mode(m))
self.run_model(m)
self.check()
class TestCase1(TestBase):
def set_op_attrs(self):
self.attrs = {
'delta': 0.5,
}
class TestCase2(TestBase):
def set_op_attrs(self):
self.attrs = {
'delta': 0.0,
}
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/ipu/test_margin_rank_loss_op_ipu.py
浏览文件 @ 27f49254
......@@ -63,7 +63,7 @@ class TestBase(IPUOpTest):
right = paddle.static.data(
name=self.feed_list[2], shape=self.feed_shape[2], dtype='float32'
)
out = paddle.fluid.layers.margin_rank_loss(label, left, right)
out = paddle.nn.functional.margin_ranking_loss(left, right, label)
self.fetch_list = [out.name]
def run_model(self, exec_mode):
......
python/paddle/fluid/tests/unittests/ipu/test_rank_loss_op_ipu.py 已删除 100644 → 0
浏览文件 @ cbdc86b5
# Copyright (c) 2022 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.
import unittest
import numpy as np
import paddle
import paddle.static
from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
class TestBase(IPUOpTest):
def setUp(self):
self.set_atol()
self.set_training()
self.set_data_feed()
self.set_feed_attr()
def set_data_feed(self):
label = np.random.uniform(size=[3, 1])
left = np.random.uniform(size=[3, 1])
right = np.random.uniform(size=[3, 1])
self.feed_fp32 = {
"label": label.astype(np.float32),
"left": left.astype(np.float32),
"right": right.astype(np.float32),
}
self.feed_fp16 = {
"label": label.astype(np.float16),
"left": left.astype(np.float16),
"right": right.astype(np.float16),
}
def set_feed_attr(self):
self.feed_shape = [x.shape for x in self.feed_fp32.values()]
self.feed_list = list(self.feed_fp32.keys())
@IPUOpTest.static_graph
def build_model(self, on_ipu):
label = paddle.static.data(
name=self.feed_list[0], shape=self.feed_shape[0], dtype="float32"
)
left = paddle.static.data(
name=self.feed_list[1], shape=self.feed_shape[1], dtype='float32'
)
right = paddle.static.data(
name=self.feed_list[2], shape=self.feed_shape[2], dtype='float32'
)
out = paddle.fluid.layers.rank_loss(label, left, right)
self.fetch_list = [out.name]
def run_model(self, exec_mode):
self.run_op_test(exec_mode)
def test(self):
for m in IPUOpTest.ExecutionMode:
if not self.skip_mode(m):
self.build_model(self.is_ipu_mode(m))
self.run_model(m)
self.check()
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/mlu/test_huber_loss_op_mlu.py
浏览文件 @ 27f49254
......@@ -37,7 +37,6 @@ class TestHuberLossOp(OpTest):
def setUp(self):
self.op_type = 'huber_loss'
self.set_mlu()
self.python_api = paddle.fluid.layers.huber_loss
self.python_out_sig = ["Out"]
self.delta = 1.0
self.init_input()
......@@ -103,28 +102,5 @@ def TestHuberLossOp3(TestHuberLossOp):
return (6, 6, 1)
class TestHuberLossOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
# the input and label must be Variable
xw = np.random.random((6, 6)).astype("float32")
xr = fluid.data(name='xr', shape=[None, 6], dtype="float32")
lw = np.random.random((6, 6)).astype("float32")
lr = fluid.data(name='lr', shape=[None, 6], dtype="float32")
delta = 1.0
self.assertRaises(TypeError, fluid.layers.huber_loss, xr, lw, delta)
self.assertRaises(TypeError, fluid.layers.huber_loss, xw, lr, delta)
# the dtype of input and label must be float32 or float64
xw2 = fluid.data(name='xw2', shape=[None, 6], dtype="int32")
lw2 = fluid.data(name='lw2', shape=[None, 6], dtype="int32")
self.assertRaises(
TypeError, fluid.layers.huber_loss, xw2, lr, delta
)
self.assertRaises(
TypeError, fluid.layers.huber_loss, xr, lw2, delta
)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/npu/test_huber_loss_op_npu.py
浏览文件 @ 27f49254
......@@ -126,27 +126,6 @@ def TestHuberLossOpFP16(TestHuberLossOp):
@unittest.skipIf(
not paddle.is_compiled_with_npu(), "core is not compiled with NPU"
)
class TestHuberLossOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
# the input and label must be Variable
xw = np.random.random((6, 6)).astype("float32")
xr = fluid.data(name='xr', shape=[None, 6], dtype="float32")
lw = np.random.random((6, 6)).astype("float32")
lr = fluid.data(name='lr', shape=[None, 6], dtype="float32")
delta = 1.0
self.assertRaises(TypeError, fluid.layers.huber_loss, xr, lw, delta)
self.assertRaises(TypeError, fluid.layers.huber_loss, xw, lr, delta)
# the dtype of input and label must be float32 or float64
xw2 = fluid.data(name='xw2', shape=[None, 6], dtype="int32")
lw2 = fluid.data(name='lw2', shape=[None, 6], dtype="int32")
self.assertRaises(
TypeError, fluid.layers.huber_loss, xw2, lr, delta
)
self.assertRaises(
TypeError, fluid.layers.huber_loss, xr, lw2, delta
)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_center_loss.py
浏览文件 @ 27f49254
......@@ -15,7 +15,7 @@
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
import paddle
class TestCenterLossOp(OpTest):
......@@ -89,72 +89,6 @@ class TestCenterLossOpNoUpdate(TestCenterLossOp):
self.need_update = False
class BadInputTestCenterLoss(unittest.TestCase):
def test_error(self):
with fluid.program_guard(fluid.Program()):
def test_bad_x():
data = [[1, 2, 3, 4], [5, 6, 7, 8]]
label = fluid.layers.data(
name='label', shape=[2, 1], dtype='int32'
)
res = fluid.layers.center_loss(
data,
label,
num_classes=1000,
alpha=0.2,
param_attr=fluid.initializer.Xavier(uniform=False),
update_center=True,
)
self.assertRaises(TypeError, test_bad_x)
def test_bad_y():
data = fluid.layers.data(
name='data', shape=[2, 32], dtype='float32'
)
label = [[2], [3]]
res = fluid.layers.center_loss(
data,
label,
num_classes=1000,
alpha=0.2,
param_attr=fluid.initializer.Xavier(uniform=False),
update_center=True,
)
self.assertRaises(TypeError, test_bad_y)
def test_bad_alpha():
data = fluid.layers.data(
name='data2',
shape=[2, 32],
dtype='float32',
append_batch_size=False,
)
label = fluid.layers.data(
name='label2',
shape=[2, 1],
dtype='int32',
append_batch_size=False,
)
alpha = fluid.layers.data(
name='alpha',
shape=[1],
dtype='int64',
append_batch_size=False,
)
res = fluid.layers.center_loss(
data,
label,
num_classes=1000,
alpha=alpha,
param_attr=fluid.initializer.Xavier(uniform=False),
update_center=True,
)
self.assertRaises(TypeError, test_bad_alpha)
if __name__ == "__main__":
paddle.enable_static()
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ 27f49254
......@@ -1440,14 +1440,18 @@ class TestRemoteHsigmoid(TestDistLookupTableBase):
),
)
cost = fluid.layers.hsigmoid(
loss = paddle.nn.HSigmoidLoss(
feature_size=emb.shape[1],
num_classes=num_total_classes,
is_custom=True,
is_sparse=is_sparse,
)
cost = loss(
input=emb,
label=label,
num_classes=num_total_classes,
path_table=path_table,
path_code=path_code,
is_custom=True,
is_sparse=is_sparse,
)
avg_cost = paddle.mean(cost)
# optimizer
......
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
浏览文件 @ 27f49254
......@@ -17,7 +17,6 @@ import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.nn.functional as F
from paddle.fluid import Program, program_guard
import paddle.fluid.initializer as I
import math
from op_test import OpTest, skip_check_grad_ci
......@@ -305,15 +304,19 @@ class TestHSigmoidOpWithSparseGrad(unittest.TestCase):
),
)
cost = fluid.layers.hsigmoid(
loss = paddle.nn.HSigmoidLoss(
feature_size=emb.shape[1],
num_classes=3,
bias_attr=True,
is_custom=True,
is_sparse=is_sparse,
)
cost = loss(
input=emb,
label=label,
bias_attr=True,
num_classes=3,
path_table=path_table,
path_code=path_code,
is_custom=True,
is_sparse=is_sparse,
)
avg_cost = fluid.layers.reduce_mean(cost)
......@@ -633,16 +636,19 @@ class TestHSigmoidLossAPI(unittest.TestCase):
path_code = fluid.data('path_code', [-1, -1], 'int64')
weight_attr = I.NumpyArrayInitializer(self.weight_np)
bias_attr = I.NumpyArrayInitializer(self.bias_np)
out = fluid.layers.hsigmoid(
x,
labels,
self.num_classes,
weight_attr,
bias_attr,
'out',
path_table,
path_code,
self.is_custom,
loss = paddle.nn.HSigmoidLoss(
feature_size=x.shape[1],
num_classes=self.num_classes,
weight_attr=weight_attr,
bias_attr=bias_attr,
is_custom=self.is_custom,
name='out',
)
out = loss(
input=x,
label=labels,
path_table=path_table,
path_code=path_code,
)
exe = fluid.Executor(self.place)
......@@ -730,28 +736,6 @@ class TestHSigmoidLossAPI(unittest.TestCase):
self.assertRaises(ValueError, F.hsigmoid_loss, x, label, 0, weight)
paddle.enable_static()
# test paddle.fluid.layers.hsigmoid
with program_guard(Program()):
label = fluid.data('label', [4, 1], 'int64')
# The input type must be Variable.
self.assertRaises(TypeError, fluid.layers.hsigmoid, 1, label, 2)
# The input dtype must be float16, float32, float64.
x_int32 = fluid.data(name='x_int32', shape=[4, 3], dtype='int32')
self.assertRaises(
TypeError, fluid.layers.hsigmoid, x_int32, label, 2
)
# support the input dtype is float32
x_fp32 = fluid.data(name='x_fp32', shape=[4, 3], dtype='float32')
fluid.layers.hsigmoid(x_fp32, label, 2)
# The label type must be Variable.
self.assertRaises(TypeError, fluid.layers.hsigmoid, x_fp32, 1, 2)
# The label dtype must be int64.
label_int32 = fluid.data('label_int32', [4, 1], 'int32')
self.assertRaises(
TypeError, fluid.layers.hsigmoid, x_fp32, label_int32, 2
)
class TestHSigmoidLossAPICustom(TestHSigmoidLossAPI):
def set_attrs(self):
......
python/paddle/fluid/tests/unittests/test_huber_loss_op.py
浏览文件 @ 27f49254
......@@ -15,9 +15,7 @@
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
import paddle
from paddle.fluid import Program, program_guard
def huber_loss_forward(val, delta):
......@@ -31,7 +29,6 @@ def huber_loss_forward(val, delta):
class TestHuberLossOp(OpTest):
def setUp(self):
self.op_type = 'huber_loss'
self.python_api = paddle.fluid.layers.huber_loss
self.python_out_sig = ["Out"]
self.delta = 1.0
self.init_input()
......@@ -54,10 +51,10 @@ class TestHuberLossOp(OpTest):
return (100, 1)
def test_check_output(self):
self.check_output(check_eager=True)
self.check_output(check_eager=False)
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', check_eager=True)
self.check_grad(['X', 'Y'], 'Out', check_eager=False)
def test_check_grad_ingore_x(self):
self.check_grad(
......@@ -85,29 +82,6 @@ def TestHuberLossOp3(TestHuberLossOp):
return (6, 6, 1)
class TestHuberLossOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
# the input and label must be Variable
xw = np.random.random((6, 6)).astype("float32")
xr = fluid.data(name='xr', shape=[None, 6], dtype="float32")
lw = np.random.random((6, 6)).astype("float32")
lr = fluid.data(name='lr', shape=[None, 6], dtype="float32")
delta = 1.0
self.assertRaises(TypeError, fluid.layers.huber_loss, xr, lw, delta)
self.assertRaises(TypeError, fluid.layers.huber_loss, xw, lr, delta)
# the dtype of input and label must be float32 or float64
xw2 = fluid.data(name='xw2', shape=[None, 6], dtype="int32")
lw2 = fluid.data(name='lw2', shape=[None, 6], dtype="int32")
self.assertRaises(
TypeError, fluid.layers.huber_loss, xw2, lr, delta
)
self.assertRaises(
TypeError, fluid.layers.huber_loss, xr, lw2, delta
)
if __name__ == '__main__':
paddle.enable_static()
unittest.main()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 27f49254
......@@ -2984,7 +2984,6 @@ class TestBook(LayerTest):
"make_gaussian_random_batch_size_like",
"make_kldiv_loss",
"make_prelu",
"make_sampled_softmax_with_cross_entropy",
"make_sampling_id",
"make_uniform_random_batch_size_like",
}
......@@ -3091,18 +3090,6 @@ class TestBook(LayerTest):
append_batch_size=append_batch_size,
)
def make_sampled_softmax_with_cross_entropy(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
logits = self._get_data(name='Logits', shape=[256], dtype='float32')
label = self._get_data(name='Label', shape=[1], dtype='int64')
num_samples = 25
output = layers.sampled_softmax_with_cross_entropy(
logits, label, num_samples
)
return output
def make_fit_a_line(self):
with program_guard(
fluid.default_main_program(),
......@@ -3237,33 +3224,6 @@ class TestBook(LayerTest):
x=dat, label=lbl, ignore_index=ignore_index
)
def make_hsigmoid(self):
self._force_to_use_cpu = True
with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
x = self._get_data(name='x', shape=[2], dtype='float32')
y = self._get_data(name='y', shape=[2], dtype='int64')
return layers.hsigmoid(input=x, label=y, num_classes=2)
# test hsigmod with custom tree structure
program2 = Program()
with program_guard(program2):
x2 = self._get_data(name='x2', shape=[4, 8], dtype='float32')
y2 = self._get_data(name='y2', shape=[4], dtype='int64')
path_table = self._get_data(
name='path_table', shape=[4, 6], dtype='int64'
)
path_code = self._get_data(
name='path_code', shape=[4, 6], dtype='int64'
)
return layers.hsigmoid(
input=x2,
label=y2,
num_classes=6,
path_table=path_table,
path_code=path_code,
is_custom=True,
)
def make_pool2d(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
......@@ -3597,31 +3557,6 @@ class TestBook(LayerTest):
return out
return ids
def make_rank_loss(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
label = self._get_data(
name='label',
append_batch_size=False,
shape=[16, 1],
dtype="float32",
)
left = self._get_data(
name='left',
append_batch_size=False,
shape=[16, 1],
dtype="float32",
)
right = self._get_data(
name='right',
append_batch_size=False,
shape=[16, 1],
dtype="float32",
)
out = layers.rank_loss(label, left, right, name="rank_loss")
return out
def make_shape(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
......@@ -3691,14 +3626,6 @@ class TestBook(LayerTest):
out = layers.cross_entropy(x, label, False, 4)
return out
def make_bpr_loss(self):
self._force_to_use_cpu = True
with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
x = self._get_data(name="x", shape=[30, 10], dtype="float32")
label = self._get_data(name="label", shape=[30, 1], dtype="int64")
out = layers.bpr_loss(x, label)
return out
def make_expand(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
......@@ -4585,17 +4512,6 @@ class TestBook(LayerTest):
)
return output
def test_edit_distance(self):
with self.static_graph():
predict = layers.data(
name='predict', shape=[-1, 1], dtype='int64', lod_level=1
)
label = layers.data(
name='label', shape=[-1, 1], dtype='int64', lod_level=1
)
evaluator = fluid.evaluator.EditDistance(predict, label)
return evaluator.metrics
def test_basic_gru(self):
input_size = 128
hidden_size = 256
......
python/paddle/fluid/tests/unittests/test_margin_rank_loss_op.py
浏览文件 @ 27f49254
......@@ -16,6 +16,7 @@ import unittest
import numpy as np
from op_test import OpTest
from paddle import fluid
import paddle
class TestMarginRankLossOp(OpTest):
......@@ -87,7 +88,9 @@ class TestMarginRankLossLayer(unittest.TestCase):
label = fluid.data("label", (self.batch_size, 1), "float32")
x1 = fluid.data("x1", (self.batch_size, 1), "float32")
x2 = fluid.data("x2", (self.batch_size, 1), "float32")
out = fluid.layers.margin_rank_loss(label, x1, x2, self.margin)
out = paddle.nn.functional.margin_ranking_loss(
x1, x2, label, self.margin, 'none'
)
exe = fluid.Executor(place)
exe.run(start)
......
python/paddle/fluid/tests/unittests/test_rank_loss_op.py
浏览文件 @ 27f49254
......@@ -15,8 +15,6 @@
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
class TestRankLossOp(OpTest):
......@@ -84,31 +82,5 @@ class TestRankLossOp5(TestRankLossOp):
return (batch_size), (batch_size), (batch_size)
class TestRankLossOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
label = fluid.data(name="label", shape=[16, 1], dtype="float32")
left = fluid.data(name="left", shape=[16, 1], dtype="float32")
right = fluid.data(name="right", shape=[16, 1], dtype="float32")
def test_label_Variable():
label_data = np.random.rand(16, 1).astype("float32")
out = fluid.layers.rank_loss(label_data, left, right)
self.assertRaises(TypeError, test_label_Variable)
def test_left_Variable():
left_data = np.random.rand(16, 1).astype("float32")
out = fluid.layers.rank_loss(label, left_data, right)
self.assertRaises(TypeError, test_left_Variable)
def test_right_Variable():
right_data = np.random.rand(16, 1).astype("float32")
out = fluid.layers.rank_loss(label, left, right_data)
self.assertRaises(TypeError, test_right_Variable)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_teacher_student_sigmoid_loss_op.py
浏览文件 @ 27f49254
......@@ -17,8 +17,6 @@ from math import log
from math import exp
from op_test import OpTest
from scipy.special import logit
import unittest
import paddle.fluid as fluid
class TestTeacherStudentSigmoidLossOp(OpTest):
......@@ -71,20 +69,3 @@ class TestTeacherStudentSigmoidLossOp(OpTest):
def test_check_grad(self):
self.check_grad(["X"], "Y", numeric_grad_delta=0.005)
class TestTeacherStudentSigmoidLossInvalidInput(unittest.TestCase):
def test_error(self):
def test_invalid_input():
input = [512, 1]
label = fluid.data(name='label', shape=[None, 1], dtype='float32')
loss = fluid.layers.teacher_student_sigmoid_loss(input, label)
self.assertRaises(TypeError, test_invalid_input)
def test_invalid_label():
input = fluid.data(name='input1', shape=[None, 1], dtype='float32')
label = [512, 1]
loss = fluid.layers.teacher_student_sigmoid_loss(input, label)
self.assertRaises(TypeError, test_invalid_label)
python/paddle/nn/functional/loss.py
浏览文件 @ 27f49254
......@@ -506,7 +506,6 @@ def edit_distance(
input_length(Tensor): The length for each sequence in `input` if it's of Tensor type, it should have shape `(batch_size, )` and its data type should be int64.
label_length(Tensor): The length for each sequence in `label` if it's of Tensor type, it should have shape `(batch_size, )` and its data type should be int64.
NOTE: To be avoid unexpected result, the value of every elements in input_length and label_length should be equal to the value of the second dimension of input and label. For example, The input: [[1,2,3,4],[5,6,7,8],[9,10,11,12]], the shape of input is [3,4] and the input_length should be [4,4,4]
NOTE: This Api is different from fluid.metrics.EditDistance
Returns:
Tuple:
......
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