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PaddleDetection
提交 880b2e80 编写于 作者: T tensor-tang
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Merge remote-tracking branch 'upstream/develop' into context

上级 5bf5650d 89bbc4f6
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Showing with 108 addition and 169 deletion
paddle/framework/op_desc.cc
浏览文件 @ 880b2e80
......@@ -260,7 +260,13 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
void operator()(int v) const { attr_->set_i(v); }
void operator()(float v) const { attr_->set_f(v); }
void operator()(const std::string &v) const { attr_->set_s(v); }
void operator()(bool b) const { attr_->set_b(b); }
// Please refer to https://github.com/PaddlePaddle/Paddle/issues/7162
template <class T,
class = typename std::enable_if<std::is_same<bool, T>::value>::type>
void operator()(T b) const {
attr_->set_b(b);
}
void operator()(const std::vector<int> &v) const {
VectorToRepeated(v, attr_->mutable_ints());
......@@ -274,9 +280,7 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
void operator()(const std::vector<bool> &v) const {
VectorToRepeated(v, attr_->mutable_bools());
}
void operator()(proto::BlockDesc *desc) const {
attr_->set_block_idx(desc->idx());
}
void operator()(BlockDesc *desc) const { attr_->set_block_idx(desc->ID()); }
void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }
};
......
paddle/framework/op_registry.h
浏览文件 @ 880b2e80
......@@ -37,8 +37,8 @@ class Registrar {
public:
// In our design, various kinds of classes, e.g., operators and kernels,
// have their corresponding registry and registrar. The action of
// registration is in the constructor of a global registrar variable, which,
// however, are not used in the code that calls package framework, and would
// registration is in the constructor of a global registrar variable, which
// are not used in the code that calls package framework, and would
// be removed from the generated binary file by the linker. To avoid such
// removal, we add Touch to all registrar classes and make USE_OP macros to
// call this method. So, as long as the callee code calls USE_OP, the global
......
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ 880b2e80
......@@ -1472,7 +1472,8 @@ TEST(Layer, RecurrentLayer) {
for (auto reversed : {false, true}) {
config.layerConfig.set_reversed(reversed);
config.testState = !reversed;
testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu);
testLayerGrad(
config, "recurrent", 50, /* trans= */ false, useGpu, false, 1.0);
}
}
}
......@@ -1494,7 +1495,8 @@ TEST(Layer, LstmLayer) {
for (auto reversed : {false, true}) {
config.layerConfig.set_reversed(reversed);
config.testState = !reversed;
testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu);
testLayerGrad(
config, "lstmemory", 100, /* trans= */ false, useGpu, false, 0.02);
}
}
for (auto useGpu : {true}) {
......
paddle/operators/CMakeLists.txt
浏览文件 @ 880b2e80
......@@ -61,106 +61,28 @@ function(op_library TARGET)
${op_common_deps})
endif()
# net_op doesn't need pybind
if ("${TARGET}" STREQUAL "net_op")
set(pybind_flag 1)
endif()
if ("${TARGET}" STREQUAL "compare_op")
set(pybind_flag 1)
file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(equal);\n")
endif()
# conv_op contains several operators
if ("${TARGET}" STREQUAL "conv_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
endif()
# conv_cudnn_op contains several operators
if ("${TARGET}" STREQUAL "conv_cudnn_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(conv2d_cudnn);\n")
endif()
# pool_op contains several operators
if ("${TARGET}" STREQUAL "pool_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(pool2d);\n")
endif()
# pool_cudnn_op contains several operators
if ("${TARGET}" STREQUAL "pool_cudnn_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
endif()
if ("${TARGET}" STREQUAL "logical_op")
set(pybind_flag 1)
file(APPEND ${pybind_file} "USE_OP(logical_and);\n")
endif()
# pool_with_index_op contains several operators
if ("${TARGET}" STREQUAL "pool_with_index_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n")
endif()
# conv_transpose_op contains several operators
if ("${TARGET}" STREQUAL "conv_transpose_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(conv2d_transpose);\n")
endif()
# conv_transpose_cudnn_op contains two operators
if ("${TARGET}" STREQUAL "conv_transpose_cudnn_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(conv2d_transpose_cudnn);\n")
endif()
# save_restore_op contains several operators
if ("${TARGET}" STREQUAL "save_restore_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_NO_KERNEL_OP(save);\n")
endif()
# activation_op contains several operators
if ("${TARGET}" STREQUAL "activation_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(sigmoid);\n")
endif()
# nccl_op contains several operators
if ("${TARGET}" STREQUAL "nccl_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(ncclAllReduce);\n")
endif()
# reduce_op contains several operators
if ("${TARGET}" STREQUAL "reduce_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(reduce_sum);\n")
endif()
# Define operators that don't need pybind here.
foreach(manual_pybind_op "net_op" "compare_op" "logical_op" "nccl_op" "tensor_array_read_write_op")
if ("${TARGET}" STREQUAL "${manual_pybind_op}")
set(pybind_flag 1)
endif()
endforeach()
if ("${TARGET}" STREQUAL "tensor_array_read_write_op")
set(pybind_flag 1)
file(APPEND ${pybind_file} "USE_NO_KERNEL_OP(read_from_array);\nUSE_NO_KERNEL_OP(write_to_array);\n")
# The registration of USE_OP, please refer to paddle/framework/op_registry.h.
# Note that it's enough to just adding one operator to pybind in a *_op.cc file.
# And for detail pybind information, please see generated paddle/pybind/pybind.h.
file(READ ${TARGET}.cc TARGET_CONTENT)
string(REGEX MATCH "REGISTER_OP\\(.*REGISTER_OP\\(" multi_register "${TARGET_CONTENT}")
string(REGEX MATCH "REGISTER_OP\\([a-z0-9_]*," one_register "${multi_register}")
if (one_register STREQUAL "")
string(REPLACE "_op" "" TARGET "${TARGET}")
else ()
string(REPLACE "REGISTER_OP(" "" TARGET "${one_register}")
string(REPLACE "," "" TARGET "${TARGET}")
endif()
# pybind USE_NO_KERNEL_OP
# HACK: if REGISTER_OP_CPU_KERNEL presents the operator must have kernel
file(READ ${TARGET}.cc TARGET_CONTENT)
string(REGEX MATCH "REGISTER_OP_CPU_KERNEL" regex_result "${TARGET_CONTENT}")
string(REPLACE "_op" "" TARGET "${TARGET}")
if (${pybind_flag} EQUAL 0 AND regex_result STREQUAL "")
......@@ -171,7 +93,6 @@ function(op_library TARGET)
# pybind USE_CPU_ONLY_OP
list(LENGTH cu_srcs cu_srcs_len)
list(LENGTH cu_cc_srcs cu_cc_srcs_len)
if (${pybind_flag} EQUAL 0 AND ${cu_srcs_len} EQUAL 0 AND ${cu_cc_srcs_len} EQUAL 0)
file(APPEND ${pybind_file} "USE_CPU_ONLY_OP(${TARGET});\n")
set(pybind_flag 1)
......@@ -188,6 +109,7 @@ add_subdirectory(nccl)
if(WITH_GPU)
op_library(nccl_op DEPS nccl_common)
file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(ncclAllReduce);\n")
else()
set(DEPS_OPS ${DEPS_OPS} nccl_op)
endif()
......@@ -238,6 +160,8 @@ list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
op_library(${src})
endforeach()
file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\n")
set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
......
python/paddle/v2/fluid/layers/nn.py
浏览文件 @ 880b2e80
......@@ -151,7 +151,7 @@ def embedding(input, size, is_sparse=False, param_attr=None, dtype='float32'):
Args:
input(Variable): Input to the function
size(tuple|list|None): Shape of the look up table parameter
size(tuple|list|None): Shape of the look up table parameter
is_sparse(bool): Boolean flag that specifying whether the input is sparse
param_attr(ParamAttr): Parameters for this layer
dtype(np.dtype|core.DataType|str): The type of data : float32, float_16, int etc
......@@ -366,9 +366,9 @@ def cross_entropy(input, label, **kwargs):
1) One-hot cross-entropy:
`soft_label = False`, `Label[i, 0]` indicates the class index for sample i:
.. math::
Y[i] = -\log(X[i, Label[i]])
2) Soft-label cross-entropy:
......@@ -386,15 +386,15 @@ def cross_entropy(input, label, **kwargs):
As a special case of 2), when each row of 'label' has only one
non-zero element which is equal to 1, soft-label cross-entropy degenerates
to a one-hot cross-entropy with one-hot label representation.
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 classes. This input is a probability
input (Variable|list): a 2-D tensor with shape [N x D], where N is the
batch size and D is the number of classes. This input is a probability
computed by the previous operator, which is almost always the result
of a softmax operator.
label (Variable|list): the ground truth which is a 2-D tensor. When
`soft_label` is set to `False`, `label` is a tensor<int64> with shape
[N x 1]. When `soft_label` is set to `True`, `label` is a
label (Variable|list): the ground truth which is a 2-D tensor. When
`soft_label` is set to `False`, `label` is a tensor<int64> with shape
[N x 1]. When `soft_label` is set to `True`, `label` is a
tensor<float/double> with shape [N x D].
soft_label (bool, via `**kwargs`): a flag indicating whether to interpretate
the given labels as soft labels, default `False`.
......@@ -403,7 +403,7 @@ def cross_entropy(input, label, **kwargs):
A 2-D tensor with shape [N x 1], the cross entropy loss.
Raises:
`ValueError`: 1) the 1st dimension of `input` and `label` are not equal; 2) when \
`ValueError`: 1) the 1st dimension of `input` and `label` are not equal; 2) when \
`soft_label == True`, and the 2nd dimension of `input` and `label` are not \
equal; 3) when `soft_label == False`, and the 2nd dimension of `label` is not 1.
......@@ -727,9 +727,9 @@ def conv2d(input,
def sequence_pool(input, pool_type, **kwargs):
"""
This function add the operator for sequence pooling.
It pools features of all time-steps of each instance, and is applied
on top of the input using pool_type mentioned in the parameters.
This function add the operator for sequence pooling.
It pools features of all time-steps of each instance, and is applied
on top of the input using pool_type mentioned in the parameters.
It supports four pool_type:
......@@ -758,7 +758,7 @@ def sequence_pool(input, pool_type, **kwargs):
Args:
input(variable): The input variable which is a LoDTensor.
pool_type (string): The pooling type of sequence_pool.
pool_type (string): The pooling type of sequence_pool.
It supports average, sum, sqrt and max.
Returns:
......@@ -768,7 +768,7 @@ def sequence_pool(input, pool_type, **kwargs):
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
......@@ -816,7 +816,7 @@ def sequence_first_step(input, **kwargs):
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_first_step = fluid.layers.sequence_first_step(input=x)
"""
......@@ -849,7 +849,7 @@ def sequence_last_step(input, **kwargs):
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_last_step = fluid.layers.sequence_last_step(input=x)
"""
......@@ -1168,25 +1168,26 @@ def lstm_unit(x_t,
.. math::
i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)
i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)
f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)
f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)
c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)
c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)
o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)
o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)
h_t & = o_t tanh(c_t)
The inputs of lstm unit includes :math:`x_t`, :math:`h_{t-1}` and
:math:`c_{t-1}`. The implementation separates the linear transformation
and non-linear transformation apart. Here, we take :math:`i_t` as an
example. The linear transformation is applied by calling a `fc` layer and
the equation is:
The inputs of lstm unit include :math:`x_t`, :math:`h_{t-1}` and
:math:`c_{t-1}`. The 2nd dimensions of :math:`h_{t-1}` and :math:`c_{t-1}`
should be same. The implementation separates the linear transformation and
non-linear transformation apart. Here, we take :math:`i_t` as an example.
The linear transformation is applied by calling a `fc` layer and the
equation is:
.. math::
L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i
L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i
The non-linear transformation is applied by calling `lstm_unit_op` and the
equation is:
......@@ -1198,9 +1199,12 @@ def lstm_unit(x_t,
This layer has two outputs including :math:`h_t` and :math:`o_t`.
Args:
x_t (Variable): The input value of current step.
hidden_t_prev (Variable): The hidden value of lstm unit.
cell_t_prev (Variable): The cell value of lstm unit.
x_t (Variable): The input value of current step, a 2-D tensor with shape
M x N, M for batch size and N for input size.
hidden_t_prev (Variable): The hidden value of lstm unit, a 2-D tensor
with shape M x S, M for batch size and S for size of lstm unit.
cell_t_prev (Variable): The cell value of lstm unit, a 2-D tensor with
shape M x S, M for batch size and S for size of lstm unit.
forget_bias (float): The forget bias of lstm unit.
param_attr (ParamAttr): The attributes of parameter weights, used to set
initializer, name etc.
......@@ -1213,14 +1217,15 @@ def lstm_unit(x_t,
Raises:
ValueError: The ranks of **x_t**, **hidden_t_prev** and **cell_t_prev**\
not be 2 or the 1st dimensions of **x_t**, **hidden_t_prev** \
and **cell_t_prev** not be the same.
and **cell_t_prev** not be the same or the 2nd dimensions of \
**hidden_t_prev** and **cell_t_prev** not be the same.
Examples:
.. code-block:: python
x_t = fluid.layers.fc(input=x_t_data, size=10)
prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=20)
prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
hidden_t_prev=prev_hidden,
......@@ -1239,7 +1244,11 @@ def lstm_unit(x_t,
if x_t.shape[0] != hidden_t_prev.shape[0] or x_t.shape[
0] != cell_t_prev.shape[0]:
raise ValueError("The 1s dimension of x_t, hidden_t_prev and "
raise ValueError("The 1st dimensions of x_t, hidden_t_prev and "
"cell_t_prev must be the same.")
if hidden_t_prev.shape[1] != cell_t_prev.shape[1]:
raise ValueError("The 2nd dimensions of hidden_t_prev and "
"cell_t_prev must be the same.")
if bias_attr is None:
......@@ -1268,17 +1277,17 @@ def lstm_unit(x_t,
def reduce_sum(input, dim=None, keep_dim=False):
"""
Computes the sum of tensor elements over the given dimension.
Computes the sum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the sum is performed. If
:attr:`None`, sum all elements of :attr:`input` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`,
dim (int|None): The dimension along which the sum is performed. If
:attr:`None`, sum all elements of :attr:`input` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`,
the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
Returns:
......@@ -1312,17 +1321,17 @@ def reduce_sum(input, dim=None, keep_dim=False):
def reduce_mean(input, dim=None, keep_dim=False):
"""
Computes the mean of tensor elements over the given dimension.
Computes the mean of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the mean is computed. If
:attr:`None`, compute the mean over all elements of :attr:`input`
and return a Tensor variable with a single element, otherwise
must be in the range :math:`[-rank(input), rank(input))`. If
dim (int|None): The dimension along which the mean is computed. If
:attr:`None`, compute the mean over all elements of :attr:`input`
and return a Tensor variable with a single element, otherwise
must be in the range :math:`[-rank(input), rank(input))`. If
:math:`dim < 0`, the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
Returns:
......@@ -1356,22 +1365,22 @@ def reduce_mean(input, dim=None, keep_dim=False):
def reduce_max(input, dim=None, keep_dim=False):
"""
Computes the maximum of tensor elements over the given dimension.
Computes the maximum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the maximum is computed.
If :attr:`None`, compute the maximum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
dim (int|None): The dimension along which the maximum is computed.
If :attr:`None`, compute the maximum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
If :math:`dim < 0`, the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
......@@ -1400,22 +1409,22 @@ def reduce_max(input, dim=None, keep_dim=False):
def reduce_min(input, dim=None, keep_dim=False):
"""
Computes the minimum of tensor elements over the given dimension.
Computes the minimum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the minimum is computed.
If :attr:`None`, compute the minimum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
dim (int|None): The dimension along which the minimum is computed.
If :attr:`None`, compute the minimum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
If :math:`dim < 0`, the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
......
python/paddle/v2/fluid/tests/test_layers.py
浏览文件 @ 880b2e80
......@@ -177,8 +177,8 @@ class TestBook(unittest.TestCase):
name='x_t_data', shape=[10, 10], dtype='float32')
x_t = layers.fc(input=x_t_data, size=10)
prev_hidden_data = layers.data(
name='prev_hidden_data', shape=[10, 20], dtype='float32')
prev_hidden = layers.fc(input=prev_hidden_data, size=20)
name='prev_hidden_data', shape=[10, 30], dtype='float32')
prev_hidden = layers.fc(input=prev_hidden_data, size=30)
prev_cell_data = layers.data(
name='prev_cell', shape=[10, 30], dtype='float32')
prev_cell = layers.fc(input=prev_cell_data, size=30)
......
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