Skip to content

P
Paddle

urhero / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 0
Fork 0
P
Paddle

体验新版 GitCode，发现更多精彩内容 >>

提交 83ca657f 编写于 作者: T Tao Luo
浏览文件
操作

Merge branch 'develop' into resnet50_ut

上级 21ee3059 35b713c3
隐藏空白更改
内联 并排
Showing with 288 addition and 195 deletion
paddle/fluid/API.spec
浏览文件 @ 83ca657f
......@@ -145,14 +145,14 @@ paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, key
paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None))
paddle.fluid.layers.expand ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_concat ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.scale ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'out', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None, None))
paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.scale ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None))
paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0))
paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype', 'use_mkldnn'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32', False))
paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
......@@ -160,6 +160,12 @@ paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shap
paddle.fluid.layers.sum ArgSpec(args=['x', 'use_mkldnn'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.logical_and ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.logical_or ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.logical_xor ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.logical_not ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......@@ -225,12 +231,6 @@ paddle.fluid.layers.is_empty ArgSpec(args=['x', 'cond'], varargs=None, keywords=
paddle.fluid.layers.mean ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.mul ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.clip ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.clip_by_norm ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_and ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_or ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_xor ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.maxout ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
......
paddle/fluid/framework/scope.cc
浏览文件 @ 83ca657f
......@@ -20,13 +20,6 @@ limitations under the License. */
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/string/printf.h"
// The mutex is not needed by training and inference, only for distribution.
#if PADDLE_WITH_DISTRIBUTE
#define WITH_LOCK 1
#else
#define WITH_LOCK 0
#endif
DEFINE_bool(benchmark, false,
"Doing memory benchmark. It will make deleting scope synchronized, "
"and add some memory usage logs."
......@@ -56,24 +49,18 @@ int64_t GetEagerDeletionThreshold() {
Scope::~Scope() { DropKids(); }
Scope& Scope::NewScope() const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
kids_.push_back(new Scope(this));
return *kids_.back();
}
Variable* Scope::Var(const std::string& name) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return VarInternal(name);
}
Variable* Scope::Var(std::string* name) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
if (name != nullptr) {
*name = new_name;
......@@ -82,39 +69,29 @@ Variable* Scope::Var(std::string* name) {
}
Variable* Scope::FindVar(const std::string& name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return FindVarInternal(name);
}
const Scope* Scope::FindScope(const Variable* var) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
return FindScopeInternal(var);
}
void Scope::DropKids() {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
for (Scope* s : kids_) delete s;
kids_.clear();
}
bool Scope::HasKid(const Scope* scope) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
return it != this->kids_.end();
}
std::vector<std::string> Scope::LocalVarNames() const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
std::vector<std::string> known_vars;
known_vars.reserve(this->vars_.size());
for (auto& p : vars_) {
......@@ -124,9 +101,7 @@ std::vector<std::string> Scope::LocalVarNames() const {
}
void Scope::DeleteScope(Scope* scope) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
PADDLE_ENFORCE(it != this->kids_.end(), "Cannot find %p as kid scope", scope);
this->kids_.erase(it);
......@@ -139,9 +114,7 @@ void Scope::DeleteScope(Scope* scope) const {
}
void Scope::EraseVars(const std::vector<std::string>& var_names) {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) {
......@@ -154,16 +127,12 @@ void Scope::EraseVars(const std::vector<std::string>& var_names) {
void Scope::Rename(const std::string& origin_name,
const std::string& new_name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
RenameInternal(origin_name, new_name);
}
std::string Scope::Rename(const std::string& origin_name) const {
#if WITH_LOCK
std::unique_lock<std::mutex> lock(mutex_);
#endif
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
RenameInternal(origin_name, new_name);
return new_name;
......
paddle/fluid/platform/dynload/cublas.h
浏览文件 @ 83ca657f
......@@ -55,7 +55,7 @@ extern void *cublas_dso_handle;
struct DynLoad__##__name { \
template <typename... Args> \
inline cublasStatus_t operator()(Args... args) { \
return __name(args...); \
return ::__name(args...); \
} \
}; \
extern DynLoad__##__name __name
......
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @ 83ca657f
......@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#define GLOG_NO_ABBREVIATED_SEVERITIES
#define GOOGLE_GLOG_DLL_DECL
#include <glog/logging.h>
#include <cudnn.h>
#include <mutex> // NOLINT
......@@ -47,13 +50,13 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
#else
#define DECLARE_DYNAMIC_LOAD_CUDNN_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
return __name(args...); \
} \
}; \
#define DECLARE_DYNAMIC_LOAD_CUDNN_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
inline cudnnStatus_t operator()(Args... args) { \
return ::__name(args...); \
} \
}; \
extern DynLoad__##__name __name
#endif
......
paddle/fluid/platform/dynload/curand.h
浏览文件 @ 83ca657f
......@@ -44,7 +44,7 @@ extern void *curand_dso_handle;
struct DynLoad__##__name { \
template <typename... Args> \
curandStatus_t operator()(Args... args) { \
return __name(args...); \
return ::__name(args...); \
} \
}; \
extern DynLoad__##__name __name
......
paddle/fluid/platform/dynload/dynamic_loader.cc
浏览文件 @ 83ca657f
......@@ -107,7 +107,11 @@ static inline void* GetDsoHandleFromDefaultPath(const std::string& dso_path,
static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
const std::string& dso_name,
bool throw_on_error = true) {
#if !defined(_WIN32)
int dynload_flags = RTLD_LAZY | RTLD_LOCAL;
#else
int dynload_flags = 0;
#endif // !_WIN32
void* dso_handle = nullptr;
std::string dlPath = dso_name;
......@@ -117,10 +121,15 @@ static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
// search xxx.so from custom path
dlPath = join(search_root, dso_name);
dso_handle = dlopen(dlPath.c_str(), dynload_flags);
#if !defined(_WIN32)
auto errorno = dlerror();
#else
auto errorno = GetLastError();
#endif // !_WIN32
// if not found, search from default path
if (nullptr == dso_handle) {
LOG(WARNING) << "Failed to find dynamic library: " << dlPath << " ("
<< dlerror() << ")";
<< errorno << ")";
if (dlPath.find("nccl") != std::string::npos) {
std::cout
<< "You may need to install 'nccl2' from NVIDIA official website: "
......@@ -139,10 +148,15 @@ static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
"export LD_LIBRARY_PATH=... \n Note: After Mac OS 10.11, "
"using the DYLD_LIBRARY_PATH is impossible unless System "
"Integrity Protection (SIP) is disabled.";
#if !defined(_WIN32)
auto errorno = dlerror();
#else
auto errorno = GetLastError();
#endif // !_WIN32
if (throw_on_error) {
PADDLE_ENFORCE(nullptr != dso_handle, error_msg, dlPath, dlerror());
PADDLE_ENFORCE(nullptr != dso_handle, error_msg, dlPath, errorno);
} else if (nullptr == dso_handle) {
LOG(WARNING) << string::Sprintf(error_msg, dlPath, dlerror());
LOG(WARNING) << string::Sprintf(error_msg, dlPath, errorno);
}
return dso_handle;
......
paddle/scripts/paddle_build.sh
浏览文件 @ 83ca657f
......@@ -395,7 +395,7 @@ EOF
ctest --output-on-failure -j $1
# make install should also be test when unittest
make install -j 8
pip install /usr/local/opt/paddle/share/wheels/*.whl
pip install ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]] ; then
paddle version
fi
......@@ -750,7 +750,7 @@ function main() {
cmake_gen ${PYTHON_ABI:-""}
build
run_test
assert_api_not_changed
assert_api_not_changed ${PYTHON_ABI:-""}
;;
*)
print_usage
......
python/paddle/fluid/clip.py
浏览文件 @ 83ca657f
......@@ -271,7 +271,8 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
"All parameters' 'clip_norm' of a same group should be the same"
)
local_norm_var = layers.reduce_sum(input=layers.pow(x=grad, factor=2.0))
square = grad * grad
local_norm_var = layers.cast(layers.reduce_sum(input=square), 'float64')
context[self.group_name].append(local_norm_var)
self.context = context
......@@ -281,6 +282,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
if group_scale_name not in self.context:
group_norm_var = layers.sums(input=self.context[self.group_name])
group_norm_var = layers.sqrt(x=group_norm_var)
group_norm_var = layers.cast(group_norm_var, 'float32')
clip_var = self.context[self.group_name + "_clip"]
group_scale_var = layers.elementwise_div(
x=clip_var,
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 83ca657f
......@@ -21,7 +21,7 @@ from .. import core
from ..framework import Program, Variable, Operator
from ..layer_helper import LayerHelper, unique_name
from ..initializer import force_init_on_cpu
from .ops import logical_and, logical_not, logical_or
from .nn import logical_and, logical_not, logical_or
import numpy
import warnings
import six
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 83ca657f
......@@ -51,7 +51,9 @@ __all__ = [
'expand', 'sequence_concat', 'scale', 'elementwise_add', 'elementwise_div',
'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min',
'elementwise_pow', 'uniform_random_batch_size_like', 'gaussian_random',
'sampling_id', 'gaussian_random_batch_size_like', 'sum', 'slice', 'shape'
'sampling_id', 'gaussian_random_batch_size_like', 'sum', 'slice', 'shape',
'logical_and', 'logical_or', 'logical_xor', 'logical_not', 'clip',
'clip_by_norm'
]
......@@ -953,8 +955,8 @@ def cross_entropy(input, label, soft_label=False, ignore_index=-100):
soft_label (bool): a flag indicating whether to
interpretate the given labels as soft
labels. Default: `False`.
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default: -100
Returns:
......@@ -2714,20 +2716,20 @@ def sequence_pad(x, pad_value, maxlen=None):
Args:
x(Variable): Input variable which should contain lod information.
pad_value(Variable): The Variable that holds values that will be fill
into padded steps. It can be a scalar or a tensor whose shape
equals to time steps in sequences. If it's a scalar, it will be
pad_value(Variable): The Variable that holds values that will be fill
into padded steps. It can be a scalar or a tensor whose shape
equals to time steps in sequences. If it's a scalar, it will be
automatically broadcasted to the shape of time step.
maxlen(int, default None): The length of padded sequences. It can be
None or any positive int. When it is None, all sequences will be
padded up to the length of the longest one among them; when it a
certain positive value, it must be greater than the length of the
maxlen(int, default None): The length of padded sequences. It can be
None or any positive int. When it is None, all sequences will be
padded up to the length of the longest one among them; when it a
certain positive value, it must be greater than the length of the
longest original sequence."
Returns:
Variable: The padded sequence batch and the original lengths before
Variable: The padded sequence batch and the original lengths before
padding. All sequences has the same length.
Examples:
.. code-block:: python
......@@ -4343,8 +4345,8 @@ def softmax_with_cross_entropy(logits,
soft_label is set to true, Label is a Tensor<float/double> with
soft_label (bool): A flag to indicate whether to interpretate the given
labels as soft labels. By default, `soft_label` is set to False.
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default: -100
Returns:
......@@ -4601,14 +4603,14 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
def squeeze(input, axes, name=None):
"""
Remove single-dimensional entries from the shape of a tensor. Takes a
parameter axes with a list of axes to squeeze. If axes is not provided, all
the single dimensions will be removed from the shape. If an axis is
Remove single-dimensional entries from the shape of a tensor. Takes a
parameter axes with a list of axes to squeeze. If axes is not provided, all
the single dimensions will be removed from the shape. If an axis is
selected with shape entry not equal to one, an error is raised.
Examples:
Case 1:
Given
Given
X.shape = (1, 3, 1, 5)
and
axes = [0]
......@@ -4617,11 +4619,11 @@ def squeeze(input, axes, name=None):
Case 2:
Given
X.shape = (1, 3, 1, 5)
and
and
axes = []
we get:
Out.shape = (3, 5)
Args:
input (Variable): The input variable to be squeezed.
axes (list): List of integers, indicating the dimensions to be squeezed.
......@@ -4651,14 +4653,14 @@ def squeeze(input, axes, name=None):
def unsqueeze(input, axes, name=None):
"""
Insert single-dimensional entries to the shape of a tensor. Takes one
required argument axes, a list of dimensions that will be inserted.
Dimension indices in axes are as seen in the output tensor.
Insert single-dimensional entries to the shape of a tensor. Takes one
required argument axes, a list of dimensions that will be inserted.
Dimension indices in axes are as seen in the output tensor.
For example:
Given a tensor such that tensor with shape [3, 4, 5],
For example:
Given a tensor such that tensor with shape [3, 4, 5],
then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1].
Args:
input (Variable): The input variable to be unsqueezed.
axes (list): List of integers, indicating the dimensions to be inserted.
......@@ -5757,39 +5759,39 @@ def pad2d(input,
Example:
Given that X is a channel of image from input:
X = [[1, 2, 3],
[4, 5, 6]]
Case 0:
paddings = [0, 1, 2, 3],
mode = 'constant'
pad_value = 0
Out = [[0, 0, 1, 2, 3, 0, 0, 0]
[0, 0, 4, 5, 6, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0]]
Case 1:
paddings = [0, 1, 2, 1],
mode = 'reflect'
Out = [[3, 2, 1, 2, 3, 2]
[6, 5, 4, 5, 6, 5]
[3, 2, 1, 2, 3, 2]]
Case 2:
paddings = [0, 1, 2, 1],
mode = 'edge'
Out = [[1, 1, 1, 2, 3, 3]
[4, 4, 4, 5, 6, 6]
[4, 4, 4, 5, 6, 6]]
Args:
input (Variable): The input image with [N, C, H, W] format or [N, H, W, C] format.
paddings (tuple|list): The padding size. If padding is a tuple, it must
......@@ -5988,7 +5990,7 @@ def prelu(x, mode, param_attr=None, name=None):
channel:elements in a channel share same weight
element:each element has a weight
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
will be named automatically.
Returns:
Variable: The output tensor with the same shape as input.
......@@ -6166,10 +6168,10 @@ def flatten(x, axis=1, name=None):
def sequence_enumerate(input, win_size, pad_value=0, name=None):
"""
Generate a new sequence for the input index sequence, which enumerates all the
sub-sequences with length `win_size` of the input.
sub-sequences with length `win_size` of the input.
The enumerated sequence has the same 1st dimension with variable `input`, and
the 2nd dimension is `win_size`, padded by `pad_value` if necessary in generation.
Examples:
Case 1:
Input:
......@@ -6296,20 +6298,20 @@ def unstack(x, axis=0, num=None):
**UnStack Layer**
This layer unstacks input :code:`x` into several tensors along axis.
If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x)`.
If :code:`num` is None, it would be inferred from :code:`x.shape[axis]`,
and if :code:`x.shape[axis]` <= 0 or is unknown, :code:`ValueError` is
raised.
raised.
Args:
x (Variable): Input variable.
x (Variable): Input variable.
axis (int): The axis along which the input is unstacked.
num (int|None): The number of output variables.
Returns:
list(Variable): The unstacked variables.
"""
helper = LayerHelper('unstack', **locals())
......@@ -6342,21 +6344,21 @@ def expand(x, expand_times, name=None):
.. code-block:: text
Input(X) is a 3-D tensor with shape [2, 3, 1]:
[
[[1], [2], [3]],
[[4], [5], [6]]
]
Attr(expand_times): [1, 2, 2]
Output(Out) is a 3-D tensor with shape [2, 6, 2]:
[
[[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
[[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
]
Args:
x (Variable): A tensor with rank in [1, 6].
expand_times (list|tuple): Expand times number for each dimension.
......@@ -6630,14 +6632,12 @@ def _elementwise_op(helper):
assert y is not None, 'y cannot be None in {}'.format(op_type)
axis = helper.kwargs.get('axis', -1)
use_mkldnn = helper.kwargs.get('use_mkldnn', False)
out = helper.kwargs.get('out', None)
if out is None:
name = helper.kwargs.get('name', None)
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
name = helper.kwargs.get('name', None)
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type=op_type,
......@@ -6650,13 +6650,7 @@ def _elementwise_op(helper):
@templatedoc()
def scale(x,
scale=1.0,
bias=0.0,
bias_after_scale=True,
out=None,
act=None,
name=None):
def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
"""
${comment}
......@@ -6665,21 +6659,19 @@ def scale(x,
scale(${scale_type}): ${scale_comment}
bias(${bias_type}): ${bias_comment}
bias_after_scale(${bias_after_scale_type}): ${bias_after_scale_comment}
out(Tensor): Output tensor.
act(basestring|None): Activation applied to the output.
name(basestring|None): Name of the output.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper('scale', **locals())
if out is None:
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type='scale',
......@@ -6693,73 +6685,31 @@ def scale(x,
return helper.append_activation(out)
def elementwise_add(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_add(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_add', **locals()))
def elementwise_div(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_div(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_div', **locals()))
def elementwise_sub(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_sub(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_sub', **locals()))
def elementwise_mul(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_mul(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_mul', **locals()))
def elementwise_max(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_max(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_max', **locals()))
def elementwise_min(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_min(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_min', **locals()))
def elementwise_pow(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
def elementwise_pow(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
......@@ -6771,7 +6721,168 @@ for func in [
func.__doc__ = _generate_doc_string_(
op_proto,
additional_args_lines=[
"out (Tensor): The output tensor of elementwise op.",
"act (basestring|None): Activation applied to the output.",
"name (basestring|None): Name of the output."
])
def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
helper = LayerHelper(op_name, **locals())
if binary_op:
assert x.dtype == y.dtype
if out is None:
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
if binary_op:
helper.append_op(
type=op_name, inputs={"X": x,
"Y": y}, outputs={"Out": out})
else:
helper.append_op(type=op_name, inputs={"X": x}, outputs={"Out": out})
return out
@templatedoc()
def logical_and(x, y, out=None, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_and", x=x, y=y, name=name, out=out, binary_op=True)
@templatedoc()
def logical_or(x, y, out=None, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_or", x=x, y=y, name=name, out=out, binary_op=True)
@templatedoc()
def logical_xor(x, y, out=None, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_xor", x=x, y=y, name=name, out=out, binary_op=True)
@templatedoc()
def logical_not(x, out=None, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_not", x=x, y=None, name=name, out=out, binary_op=False)
@templatedoc()
def clip(x, min, max, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
min(${min_type}): ${min_comment}
max(${max_type}): ${max_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper("clip", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="clip",
inputs={"X": x},
attrs={"min": min,
"max": max},
outputs={"Out": out})
return out
@templatedoc()
def clip_by_norm(x, max_norm, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
max_norm(${max_norm_type}): ${max_norm_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper("clip_by_norm", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="clip_by_norm",
inputs={"X": x},
attrs={"max_norm": max_norm},
outputs={"Out": out})
return out
python/paddle/fluid/layers/ops.py
浏览文件 @ 83ca657f
......@@ -39,12 +39,6 @@ __all__ = [
'mean',
'mul',
'sigmoid_cross_entropy_with_logits',
'clip',
'clip_by_norm',
'logical_and',
'logical_or',
'logical_xor',
'logical_not',
'maxout',
]
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ 83ca657f
......@@ -22,7 +22,7 @@ class TestDistSeResneXt2x2(TestDistBase):
self._sync_mode = True
self._use_reader_alloc = False
def test_dist_train(self):
def no_test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......@@ -40,7 +40,7 @@ class TestDistSeResneXt2x2Async(TestDistBase):
self._sync_mode = False
self._use_reader_alloc = False
def test_dist_train(self):
def no_test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或