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apache/incubator-mxnet | python/mxnet/kvstore.py | _ctype_dict | def _ctype_dict(param_dict):
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... | python | def _ctype_dict(param_dict):
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apache/incubator-mxnet | python/mxnet/kvstore.py | _updater_wrapper | def _updater_wrapper(updater):
"""A wrapper for the user-defined handle."""
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"""A wrapper for the user-defined handle."""
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apache/incubator-mxnet | python/mxnet/kvstore.py | create | def create(name='local'):
"""Creates a new KVStore.
For single machine training, there are two commonly used types:
``local``: Copies all gradients to CPU memory and updates weights there.
``device``: Aggregates gradients and updates weights on GPUs. With this setting,
the KVStore also attempts t... | python | def create(name='local'):
"""Creates a new KVStore.
For single machine training, there are two commonly used types:
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``device``: Aggregates gradients and updates weights on GPUs. With this setting,
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.init | def init(self, key, value):
""" Initializes a single or a sequence of key-value pairs into the store.
For each key, one must `init` it before calling `push` or `pull`.
When multiple workers invoke `init` for the same key, only
the value supplied by worker with rank `0` is used. This fun... | python | def init(self, key, value):
""" Initializes a single or a sequence of key-value pairs into the store.
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.push | def push(self, key, value, priority=0):
""" Pushes a single or a sequence of key-value pairs into the store.
This function returns immediately after adding an operator to the engine.
The actual operation is executed asynchronously. If there are consecutive
pushes to the same key, there ... | python | def push(self, key, value, priority=0):
""" Pushes a single or a sequence of key-value pairs into the store.
This function returns immediately after adding an operator to the engine.
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.pull | def pull(self, key, out=None, priority=0, ignore_sparse=True):
""" Pulls a single value or a sequence of values from the store.
This function returns immediately after adding an operator to the engine.
Subsequent attempts to read from the `out` variable will be blocked until the
pull op... | python | def pull(self, key, out=None, priority=0, ignore_sparse=True):
""" Pulls a single value or a sequence of values from the store.
This function returns immediately after adding an operator to the engine.
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.row_sparse_pull | def row_sparse_pull(self, key, out=None, priority=0, row_ids=None):
""" Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \
from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull \
is invoked just once and the result is broadcast t... | python | def row_sparse_pull(self, key, out=None, priority=0, row_ids=None):
""" Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \
from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull \
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.set_gradient_compression | def set_gradient_compression(self, compression_params):
""" Specifies type of low-bit quantization for gradient compression \
and additional arguments depending on the type of compression being used.
2bit Gradient Compression takes a positive float `threshold`.
The technique works by t... | python | def set_gradient_compression(self, compression_params):
""" Specifies type of low-bit quantization for gradient compression \
and additional arguments depending on the type of compression being used.
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.set_optimizer | def set_optimizer(self, optimizer):
""" Registers an optimizer with the kvstore.
When using a single machine, this function updates the local optimizer.
If using multiple machines and this operation is invoked from a worker node,
it will serialized the optimizer with pickle and send it ... | python | def set_optimizer(self, optimizer):
""" Registers an optimizer with the kvstore.
When using a single machine, this function updates the local optimizer.
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.type | def type(self):
""" Returns the type of this kvstore.
Returns
-------
type : str
the string type
"""
kv_type = ctypes.c_char_p()
check_call(_LIB.MXKVStoreGetType(self.handle, ctypes.byref(kv_type)))
return py_str(kv_type.value) | python | def type(self):
""" Returns the type of this kvstore.
Returns
-------
type : str
the string type
"""
kv_type = ctypes.c_char_p()
check_call(_LIB.MXKVStoreGetType(self.handle, ctypes.byref(kv_type)))
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.rank | def rank(self):
""" Returns the rank of this worker node.
Returns
-------
rank : int
The rank of this node, which is in range [0, num_workers())
"""
rank = ctypes.c_int()
check_call(_LIB.MXKVStoreGetRank(self.handle, ctypes.byref(rank)))
retur... | python | def rank(self):
""" Returns the rank of this worker node.
Returns
-------
rank : int
The rank of this node, which is in range [0, num_workers())
"""
rank = ctypes.c_int()
check_call(_LIB.MXKVStoreGetRank(self.handle, ctypes.byref(rank)))
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.num_workers | def num_workers(self):
"""Returns the number of worker nodes.
Returns
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size :int
The number of worker nodes.
"""
size = ctypes.c_int()
check_call(_LIB.MXKVStoreGetGroupSize(self.handle, ctypes.byref(size)))
return size.value | python | def num_workers(self):
"""Returns the number of worker nodes.
Returns
-------
size :int
The number of worker nodes.
"""
size = ctypes.c_int()
check_call(_LIB.MXKVStoreGetGroupSize(self.handle, ctypes.byref(size)))
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.save_optimizer_states | def save_optimizer_states(self, fname, dump_optimizer=False):
"""Saves the optimizer (updater) state to a file. This is often used when checkpointing
the model during training.
Parameters
----------
fname : str
Path to the output states file.
dump_optimizer :... | python | def save_optimizer_states(self, fname, dump_optimizer=False):
"""Saves the optimizer (updater) state to a file. This is often used when checkpointing
the model during training.
Parameters
----------
fname : str
Path to the output states file.
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore.load_optimizer_states | def load_optimizer_states(self, fname):
"""Loads the optimizer (updater) state from the file.
Parameters
----------
fname : str
Path to input states file.
"""
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self._update... | python | def load_optimizer_states(self, fname):
"""Loads the optimizer (updater) state from the file.
Parameters
----------
fname : str
Path to input states file.
"""
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore._set_updater | def _set_updater(self, updater):
"""Sets a push updater into the store.
This function only changes the local store. When running on multiple machines one must
use `set_optimizer`.
Parameters
----------
updater : function
The updater function.
Exampl... | python | def _set_updater(self, updater):
"""Sets a push updater into the store.
This function only changes the local store. When running on multiple machines one must
use `set_optimizer`.
Parameters
----------
updater : function
The updater function.
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apache/incubator-mxnet | python/mxnet/kvstore.py | KVStore._send_command_to_servers | def _send_command_to_servers(self, head, body):
"""Sends a command to all server nodes.
Sending command to a server node will cause that server node to invoke
``KVStoreServer.controller`` to execute the command.
This function returns after the command has been executed on all server
... | python | def _send_command_to_servers(self, head, body):
"""Sends a command to all server nodes.
Sending command to a server node will cause that server node to invoke
``KVStoreServer.controller`` to execute the command.
This function returns after the command has been executed on all server
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.add | def add(self, module, **kwargs):
"""Add a module to the chain.
Parameters
----------
module : BaseModule
The new module to add.
kwargs : ``**keywords``
All the keyword arguments are saved as meta information
for the added module. The currently... | python | def add(self, module, **kwargs):
"""Add a module to the chain.
Parameters
----------
module : BaseModule
The new module to add.
kwargs : ``**keywords``
All the keyword arguments are saved as meta information
for the added module. The currently... | [
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.get_params | def get_params(self):
"""Gets current parameters.
Returns
-------
(arg_params, aux_params)
A pair of dictionaries each mapping parameter names to NDArray values. This
is a merged dictionary of all the parameters in the modules.
"""
assert self.bin... | python | def get_params(self):
"""Gets current parameters.
Returns
-------
(arg_params, aux_params)
A pair of dictionaries each mapping parameter names to NDArray values. This
is a merged dictionary of all the parameters in the modules.
"""
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.init_params | def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None,
allow_missing=False, force_init=False, allow_extra=False):
"""Initializes parameters.
Parameters
----------
initializer : Initializer
arg_params : dict
Default ``No... | python | def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None,
allow_missing=False, force_init=False, allow_extra=False):
"""Initializes parameters.
Parameters
----------
initializer : Initializer
arg_params : dict
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.bind | def bind(self, data_shapes, label_shapes=None, for_training=True,
inputs_need_grad=False, force_rebind=False, shared_module=None,
grad_req='write'):
"""Binds the symbols to construct executors. This is necessary before one
can perform computation with the module.
Param... | python | def bind(self, data_shapes, label_shapes=None, for_training=True,
inputs_need_grad=False, force_rebind=False, shared_module=None,
grad_req='write'):
"""Binds the symbols to construct executors. This is necessary before one
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.init_optimizer | def init_optimizer(self, kvstore='local', optimizer='sgd',
optimizer_params=(('learning_rate', 0.01),),
force_init=False):
"""Installs and initializes optimizers.
Parameters
----------
kvstore : str or KVStore
Default `'local'`.
... | python | def init_optimizer(self, kvstore='local', optimizer='sgd',
optimizer_params=(('learning_rate', 0.01),),
force_init=False):
"""Installs and initializes optimizers.
Parameters
----------
kvstore : str or KVStore
Default `'local'`.
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.forward | def forward(self, data_batch, is_train=None):
"""Forward computation.
Parameters
----------
data_batch : DataBatch
is_train : bool
Default is ``None``, in which case `is_train` is take as ``self.for_training``.
"""
assert self.binded and self.params_i... | python | def forward(self, data_batch, is_train=None):
"""Forward computation.
Parameters
----------
data_batch : DataBatch
is_train : bool
Default is ``None``, in which case `is_train` is take as ``self.for_training``.
"""
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.backward | def backward(self, out_grads=None):
"""Backward computation."""
assert self.binded and self.params_initialized
for i_layer, module in reversed(list(zip(range(len(self._modules)), self._modules))):
module.backward(out_grads=out_grads)
if i_layer == 0:
brea... | python | def backward(self, out_grads=None):
"""Backward computation."""
assert self.binded and self.params_initialized
for i_layer, module in reversed(list(zip(range(len(self._modules)), self._modules))):
module.backward(out_grads=out_grads)
if i_layer == 0:
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.update | def update(self):
"""Updates parameters according to installed optimizer and the gradient computed
in the previous forward-backward cycle.
"""
assert self.binded and self.params_initialized and self.optimizer_initialized
for module in self._modules:
module.update() | python | def update(self):
"""Updates parameters according to installed optimizer and the gradient computed
in the previous forward-backward cycle.
"""
assert self.binded and self.params_initialized and self.optimizer_initialized
for module in self._modules:
module.update() | [
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.get_outputs | def get_outputs(self, merge_multi_context=True):
"""Gets outputs from a previous forward computation.
Parameters
----------
merge_multi_context : bool
Default is ``True``. In the case when data-parallelism is used, the outputs
will be collected from multiple devi... | python | def get_outputs(self, merge_multi_context=True):
"""Gets outputs from a previous forward computation.
Parameters
----------
merge_multi_context : bool
Default is ``True``. In the case when data-parallelism is used, the outputs
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.get_input_grads | def get_input_grads(self, merge_multi_context=True):
"""Gets the gradients with respect to the inputs of the module.
Parameters
----------
merge_multi_context : bool
Default is ``True``. In the case when data-parallelism is used, the outputs
will be collected fro... | python | def get_input_grads(self, merge_multi_context=True):
"""Gets the gradients with respect to the inputs of the module.
Parameters
----------
merge_multi_context : bool
Default is ``True``. In the case when data-parallelism is used, the outputs
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.update_metric | def update_metric(self, eval_metric, labels, pre_sliced=False):
"""Evaluates and accumulates evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically ``data_batch.label``.
... | python | def update_metric(self, eval_metric, labels, pre_sliced=False):
"""Evaluates and accumulates evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically ``data_batch.label``.
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apache/incubator-mxnet | python/mxnet/module/sequential_module.py | SequentialModule.install_monitor | def install_monitor(self, mon):
"""Installs monitor on all executors."""
assert self.binded
for module in self._modules:
module.install_monitor(mon) | python | def install_monitor(self, mon):
"""Installs monitor on all executors."""
assert self.binded
for module in self._modules:
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apache/incubator-mxnet | example/caffe/data.py | get_iterator | def get_iterator(data_shape, use_caffe_data):
"""Generate the iterator of mnist dataset"""
def get_iterator_impl_mnist(args, kv):
"""return train and val iterators for mnist"""
# download data
get_mnist_ubyte()
flat = False if len(data_shape) != 1 else True
train = mx.io... | python | def get_iterator(data_shape, use_caffe_data):
"""Generate the iterator of mnist dataset"""
def get_iterator_impl_mnist(args, kv):
"""return train and val iterators for mnist"""
# download data
get_mnist_ubyte()
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apache/incubator-mxnet | example/gluon/audio/urban_sounds/predict.py | predict | def predict(prediction_dir='./Test'):
"""The function is used to run predictions on the audio files in the directory `pred_directory`.
Parameters
----------
net:
The model that has been trained.
prediction_dir: string, default ./Test
The directory that contains the audio files on wh... | python | def predict(prediction_dir='./Test'):
"""The function is used to run predictions on the audio files in the directory `pred_directory`.
Parameters
----------
net:
The model that has been trained.
prediction_dir: string, default ./Test
The directory that contains the audio files on wh... | [
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apache/incubator-mxnet | example/ctc/multiproc_data.py | MPData._proc_loop | def _proc_loop(proc_id, alive, queue, fn):
"""Thread loop for generating data
Parameters
----------
proc_id: int
Process id
alive: multiprocessing.Value
variable for signaling whether process should continue or not
queue: multiprocessing.Queue
... | python | def _proc_loop(proc_id, alive, queue, fn):
"""Thread loop for generating data
Parameters
----------
proc_id: int
Process id
alive: multiprocessing.Value
variable for signaling whether process should continue or not
queue: multiprocessing.Queue
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apache/incubator-mxnet | example/ctc/multiproc_data.py | MPData._init_proc | def _init_proc(self):
"""Start processes if not already started"""
if not self.proc:
self.proc = [
mp.Process(target=self._proc_loop, args=(i, self.alive, self.queue, self.fn))
for i in range(self.num_proc)
]
self.alive.value = True
... | python | def _init_proc(self):
"""Start processes if not already started"""
if not self.proc:
self.proc = [
mp.Process(target=self._proc_loop, args=(i, self.alive, self.queue, self.fn))
for i in range(self.num_proc)
]
self.alive.value = True
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apache/incubator-mxnet | example/ctc/multiproc_data.py | MPData.reset | def reset(self):
"""Resets the generator by stopping all processes"""
self.alive.value = False
qsize = 0
try:
while True:
self.queue.get(timeout=0.1)
qsize += 1
except QEmptyExcept:
pass
print("Queue size on reset: {... | python | def reset(self):
"""Resets the generator by stopping all processes"""
self.alive.value = False
qsize = 0
try:
while True:
self.queue.get(timeout=0.1)
qsize += 1
except QEmptyExcept:
pass
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apache/incubator-mxnet | python/mxnet/base.py | with_metaclass | def with_metaclass(meta, *bases):
"""Create a base class with a metaclass."""
# This requires a bit of explanation: the basic idea is to make a dummy
# metaclass for one level of class instantiation that replaces itself with
# the actual metaclass.
class metaclass(type):
def __new__(cls, na... | python | def with_metaclass(meta, *bases):
"""Create a base class with a metaclass."""
# This requires a bit of explanation: the basic idea is to make a dummy
# metaclass for one level of class instantiation that replaces itself with
# the actual metaclass.
class metaclass(type):
def __new__(cls, na... | [
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apache/incubator-mxnet | python/mxnet/base.py | _load_lib | def _load_lib():
"""Load library by searching possible path."""
lib_path = libinfo.find_lib_path()
lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL)
# DMatrix functions
lib.MXGetLastError.restype = ctypes.c_char_p
return lib | python | def _load_lib():
"""Load library by searching possible path."""
lib_path = libinfo.find_lib_path()
lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL)
# DMatrix functions
lib.MXGetLastError.restype = ctypes.c_char_p
return lib | [
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apache/incubator-mxnet | python/mxnet/base.py | c_array | def c_array(ctype, values):
"""Create ctypes array from a Python array.
Parameters
----------
ctype : ctypes data type
Data type of the array we want to convert to, such as mx_float.
values : tuple or list
Data content.
Returns
-------
out : ctypes array
Create... | python | def c_array(ctype, values):
"""Create ctypes array from a Python array.
Parameters
----------
ctype : ctypes data type
Data type of the array we want to convert to, such as mx_float.
values : tuple or list
Data content.
Returns
-------
out : ctypes array
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Data type of the array we want to convert to, such as mx_float.
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Data content.
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-... | [
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apache/incubator-mxnet | python/mxnet/base.py | c_handle_array | def c_handle_array(objs):
"""Create ctypes const void ** from a list of MXNet objects with handles.
Parameters
----------
objs : list of NDArray/Symbol.
MXNet objects.
Returns
-------
(ctypes.c_void_p * len(objs))
A void ** pointer that can be passed to C API.
"""
a... | python | def c_handle_array(objs):
"""Create ctypes const void ** from a list of MXNet objects with handles.
Parameters
----------
objs : list of NDArray/Symbol.
MXNet objects.
Returns
-------
(ctypes.c_void_p * len(objs))
A void ** pointer that can be passed to C API.
"""
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apache/incubator-mxnet | python/mxnet/base.py | ctypes2numpy_shared | def ctypes2numpy_shared(cptr, shape):
"""Convert a ctypes pointer to a numpy array.
The resulting NumPy array shares the memory with the pointer.
Parameters
----------
cptr : ctypes.POINTER(mx_float)
pointer to the memory region
shape : tuple
Shape of target `NDArray`.
Re... | python | def ctypes2numpy_shared(cptr, shape):
"""Convert a ctypes pointer to a numpy array.
The resulting NumPy array shares the memory with the pointer.
Parameters
----------
cptr : ctypes.POINTER(mx_float)
pointer to the memory region
shape : tuple
Shape of target `NDArray`.
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apache/incubator-mxnet | python/mxnet/base.py | build_param_doc | def build_param_doc(arg_names, arg_types, arg_descs, remove_dup=True):
"""Build argument docs in python style.
arg_names : list of str
Argument names.
arg_types : list of str
Argument type information.
arg_descs : list of str
Argument description information.
remove_dup :... | python | def build_param_doc(arg_names, arg_types, arg_descs, remove_dup=True):
"""Build argument docs in python style.
arg_names : list of str
Argument names.
arg_types : list of str
Argument type information.
arg_descs : list of str
Argument description information.
remove_dup :... | [
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Whether remove duplication or not.
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apache/incubator-mxnet | python/mxnet/base.py | add_fileline_to_docstring | def add_fileline_to_docstring(module, incursive=True):
"""Append the definition position to each function contained in module.
Examples
--------
# Put the following codes at the end of a file
add_fileline_to_docstring(__name__)
"""
def _add_fileline(obj):
"""Add fileinto to a objec... | python | def add_fileline_to_docstring(module, incursive=True):
"""Append the definition position to each function contained in module.
Examples
--------
# Put the following codes at the end of a file
add_fileline_to_docstring(__name__)
"""
def _add_fileline(obj):
"""Add fileinto to a objec... | [
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apache/incubator-mxnet | python/mxnet/base.py | _init_op_module | def _init_op_module(root_namespace, module_name, make_op_func):
"""
Registers op functions created by `make_op_func` under
`root_namespace.module_name.[submodule_name]`,
where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`.
Parameters
----------
root_namespace : str
Top level ... | python | def _init_op_module(root_namespace, module_name, make_op_func):
"""
Registers op functions created by `make_op_func` under
`root_namespace.module_name.[submodule_name]`,
where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`.
Parameters
----------
root_namespace : str
Top level ... | [
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apache/incubator-mxnet | python/mxnet/base.py | _generate_op_module_signature | def _generate_op_module_signature(root_namespace, module_name, op_code_gen_func):
"""
Generate op functions created by `op_code_gen_func` and write to the source file
of `root_namespace.module_name.[submodule_name]`,
where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`.
Parameters
-------... | python | def _generate_op_module_signature(root_namespace, module_name, op_code_gen_func):
"""
Generate op functions created by `op_code_gen_func` and write to the source file
of `root_namespace.module_name.[submodule_name]`,
where `submodule_name` is one of `_OP_SUBMODULE_NAME_LIST`.
Parameters
-------... | [
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apache/incubator-mxnet | python/mxnet/base.py | set_np_compat | def set_np_compat(active):
"""
Turns on/off NumPy compatibility. NumPy-compatibility is turned off by default in backend.
Parameters
----------
active : bool
Indicates whether to turn on/off NumPy compatibility.
Returns
-------
A bool value indicating the previous state of ... | python | def set_np_compat(active):
"""
Turns on/off NumPy compatibility. NumPy-compatibility is turned off by default in backend.
Parameters
----------
active : bool
Indicates whether to turn on/off NumPy compatibility.
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Indicates whether to turn on/off NumPy compatibility.
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apache/incubator-mxnet | python/mxnet/base.py | is_np_compat | def is_np_compat():
"""
Checks whether the NumPy compatibility is currently turned on.
NumPy-compatibility is turned off by default in backend.
Returns
-------
A bool value indicating whether the NumPy compatibility is currently on.
"""
curr = ctypes.c_bool()
check_call(_LIB.MXI... | python | def is_np_compat():
"""
Checks whether the NumPy compatibility is currently turned on.
NumPy-compatibility is turned off by default in backend.
Returns
-------
A bool value indicating whether the NumPy compatibility is currently on.
"""
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apache/incubator-mxnet | python/mxnet/base.py | use_np_compat | def use_np_compat(func):
"""Wraps a function with an activated NumPy-compatibility scope. This ensures
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Example::
import mxnet as mx
@mx.use_np_compat
def sca... | python | def use_np_compat(func):
"""Wraps a function with an activated NumPy-compatibility scope. This ensures
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apache/incubator-mxnet | example/multivariate_time_series/src/metrics.py | rse | def rse(label, pred):
"""computes the root relative squared error (condensed using standard deviation formula)"""
numerator = np.sqrt(np.mean(np.square(label - pred), axis = None))
denominator = np.std(label, axis = None)
return numerator / denominator | python | def rse(label, pred):
"""computes the root relative squared error (condensed using standard deviation formula)"""
numerator = np.sqrt(np.mean(np.square(label - pred), axis = None))
denominator = np.std(label, axis = None)
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apache/incubator-mxnet | example/multivariate_time_series/src/metrics.py | rae | def rae(label, pred):
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denominator = np.mean(np.abs(label - np.mean(label, axis=None)), axis=None)
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"""computes the relative absolute error (condensed using standard deviation formula)"""
numerator = np.mean(np.abs(label - pred), axis=None)
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apache/incubator-mxnet | example/multivariate_time_series/src/metrics.py | corr | def corr(label, pred):
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"""computes the empirical correlation coefficient"""
numerator1 = label - np.mean(label, axis=0)
numerator2 = pred - np.mean(pred, axis = 0)
numerator = np.mean(numerator1 * numerator2, axis=0)
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apache/incubator-mxnet | example/multivariate_time_series/src/metrics.py | get_custom_metrics | def get_custom_metrics():
"""
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"""
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_corr = mx.metric.create(corr)
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"""
:return: mxnet metric object
"""
_rse = mx.metric.create(rse)
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apache/incubator-mxnet | example/ssd/tools/caffe_converter/convert_symbol.py | _get_input | def _get_input(proto):
"""Get input size
"""
layer = caffe_parser.get_layers(proto)
if len(proto.input_dim) > 0:
input_dim = proto.input_dim
elif len(proto.input_shape) > 0:
input_dim = proto.input_shape[0].dim
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input_dim = layer[0].input_par... | python | def _get_input(proto):
"""Get input size
"""
layer = caffe_parser.get_layers(proto)
if len(proto.input_dim) > 0:
input_dim = proto.input_dim
elif len(proto.input_shape) > 0:
input_dim = proto.input_shape[0].dim
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apache/incubator-mxnet | example/ssd/tools/caffe_converter/convert_symbol.py | _convert_conv_param | def _convert_conv_param(param):
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"""
param_string = "num_filter=%d" % param.num_output
pad_w = 0
pad_h = 0
if isinstance(param.pad, int):
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param_string += ", pad=(%d, %d)" % (pad, pad)
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"""
Convert convolution layer parameter from Caffe to MXNet
"""
param_string = "num_filter=%d" % param.num_output
pad_w = 0
pad_h = 0
if isinstance(param.pad, int):
pad = param.pad
param_string += ", pad=(%d, %d)" % (pad, pad)
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apache/incubator-mxnet | example/ssd/tools/caffe_converter/convert_symbol.py | _convert_pooling_param | def _convert_pooling_param(param):
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"""Convert the pooling layer parameter
"""
param_string = "pooling_convention='full', "
if param.global_pooling:
param_string += "global_pool=True, kernel=(1,1)"
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apache/incubator-mxnet | example/ssd/tools/caffe_converter/convert_symbol.py | _parse_proto | def _parse_proto(prototxt_fname):
"""Parse Caffe prototxt into symbol string
"""
proto = caffe_parser.read_prototxt(prototxt_fname)
# process data layer
input_name, input_dim, layers = _get_input(proto)
# only support single input, so always use `data` as the input data
mapping = {input_nam... | python | def _parse_proto(prototxt_fname):
"""Parse Caffe prototxt into symbol string
"""
proto = caffe_parser.read_prototxt(prototxt_fname)
# process data layer
input_name, input_dim, layers = _get_input(proto)
# only support single input, so always use `data` as the input data
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apache/incubator-mxnet | example/ssd/tools/caffe_converter/convert_symbol.py | convert_symbol | def convert_symbol(prototxt_fname):
"""Convert caffe model definition into Symbol
Parameters
----------
prototxt_fname : str
Filename of the prototxt file
Returns
-------
Symbol
Converted Symbol
tuple
Input shape
"""
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"""Convert caffe model definition into Symbol
Parameters
----------
prototxt_fname : str
Filename of the prototxt file
Returns
-------
Symbol
Converted Symbol
tuple
Input shape
"""
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apache/incubator-mxnet | example/reinforcement-learning/parallel_actor_critic/train.py | train_episode | def train_episode(agent, envs, preprocessors, t_max, render):
"""Complete an episode's worth of training for each environment."""
num_envs = len(envs)
# Buffers to hold trajectories, e.g. `env_xs[i]` will hold the observations
# for environment `i`.
env_xs, env_as = _2d_list(num_envs), _2d_list(num... | python | def train_episode(agent, envs, preprocessors, t_max, render):
"""Complete an episode's worth of training for each environment."""
num_envs = len(envs)
# Buffers to hold trajectories, e.g. `env_xs[i]` will hold the observations
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apache/incubator-mxnet | example/ssd/tools/caffe_converter/caffe_parse/parse_from_protobuf.py | parse_caffemodel | def parse_caffemodel(file_path):
"""
parses the trained .caffemodel file
filepath: /path/to/trained-model.caffemodel
returns: layers
"""
f = open(file_path, 'rb')
contents = f.read()
net_param = caffe_pb2.NetParameter()
net_param.ParseFromString(contents)
layers = find_layers... | python | def parse_caffemodel(file_path):
"""
parses the trained .caffemodel file
filepath: /path/to/trained-model.caffemodel
returns: layers
"""
f = open(file_path, 'rb')
contents = f.read()
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apache/incubator-mxnet | example/speech_recognition/stt_datagenerator.py | DataGenerator.featurize | def featurize(self, audio_clip, overwrite=False, save_feature_as_csvfile=False):
""" For a given audio clip, calculate the log of its Fourier Transform
Params:
audio_clip(str): Path to the audio clip
"""
return spectrogram_from_file(
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""" For a given audio clip, calculate the log of its Fourier Transform
Params:
audio_clip(str): Path to the audio clip
"""
return spectrogram_from_file(
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apache/incubator-mxnet | example/speech_recognition/stt_datagenerator.py | DataGenerator.load_metadata_from_desc_file | def load_metadata_from_desc_file(self, desc_file, partition='train',
max_duration=16.0,):
""" Read metadata from the description file
(possibly takes long, depending on the filesize)
Params:
desc_file (str): Path to a JSON-line file that cont... | python | def load_metadata_from_desc_file(self, desc_file, partition='train',
max_duration=16.0,):
""" Read metadata from the description file
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desc_file (str): Path to a JSON-line file that cont... | [
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apache/incubator-mxnet | example/speech_recognition/stt_datagenerator.py | DataGenerator.prepare_minibatch | def prepare_minibatch(self, audio_paths, texts, overwrite=False,
is_bi_graphemes=False, seq_length=-1, save_feature_as_csvfile=False):
""" Featurize a minibatch of audio, zero pad them and return a dictionary
Params:
audio_paths (list(str)): List of paths to audio f... | python | def prepare_minibatch(self, audio_paths, texts, overwrite=False,
is_bi_graphemes=False, seq_length=-1, save_feature_as_csvfile=False):
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apache/incubator-mxnet | example/speech_recognition/stt_datagenerator.py | DataGenerator.sample_normalize | def sample_normalize(self, k_samples=1000, overwrite=False):
""" Estimate the mean and std of the features from the training set
Params:
k_samples (int): Use this number of samples for estimation
"""
log = logUtil.getlogger()
log.info("Calculating mean and std from sa... | python | def sample_normalize(self, k_samples=1000, overwrite=False):
""" Estimate the mean and std of the features from the training set
Params:
k_samples (int): Use this number of samples for estimation
"""
log = logUtil.getlogger()
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apache/incubator-mxnet | example/speech_recognition/stt_layer_gru.py | gru | def gru(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0., is_batchnorm=False, gamma=None, beta=None, name=None):
"""
GRU Cell symbol
Reference:
* Chung, Junyoung, et al. "Empirical evaluation of gated recurrent neural
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"""
GRU Cell symbol
Reference:
* Chung, Junyoung, et al. "Empirical evaluation of gated recurrent neural
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apache/incubator-mxnet | example/gluon/sn_gan/utils.py | save_image | def save_image(data, epoch, image_size, batch_size, output_dir, padding=2):
""" save image """
data = data.asnumpy().transpose((0, 2, 3, 1))
datanp = np.clip(
(data - np.min(data))*(255.0/(np.max(data) - np.min(data))), 0, 255).astype(np.uint8)
x_dim = min(8, batch_size)
y_dim = int(math.cei... | python | def save_image(data, epoch, image_size, batch_size, output_dir, padding=2):
""" save image """
data = data.asnumpy().transpose((0, 2, 3, 1))
datanp = np.clip(
(data - np.min(data))*(255.0/(np.max(data) - np.min(data))), 0, 255).astype(np.uint8)
x_dim = min(8, batch_size)
y_dim = int(math.cei... | [
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apache/incubator-mxnet | tools/im2rec.py | list_image | def list_image(root, recursive, exts):
"""Traverses the root of directory that contains images and
generates image list iterator.
Parameters
----------
root: string
recursive: bool
exts: string
Returns
-------
image iterator that contains all the image under the specified path
... | python | def list_image(root, recursive, exts):
"""Traverses the root of directory that contains images and
generates image list iterator.
Parameters
----------
root: string
recursive: bool
exts: string
Returns
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image iterator that contains all the image under the specified path
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apache/incubator-mxnet | tools/im2rec.py | write_list | def write_list(path_out, image_list):
"""Hepler function to write image list into the file.
The format is as below,
integer_image_index \t float_label_index \t path_to_image
Note that the blank between number and tab is only used for readability.
Parameters
----------
path_out: string
im... | python | def write_list(path_out, image_list):
"""Hepler function to write image list into the file.
The format is as below,
integer_image_index \t float_label_index \t path_to_image
Note that the blank between number and tab is only used for readability.
Parameters
----------
path_out: string
im... | [
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apache/incubator-mxnet | tools/im2rec.py | make_list | def make_list(args):
"""Generates .lst file.
Parameters
----------
args: object that contains all the arguments
"""
image_list = list_image(args.root, args.recursive, args.exts)
image_list = list(image_list)
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random.seed(100)
random.shuffle(image_l... | python | def make_list(args):
"""Generates .lst file.
Parameters
----------
args: object that contains all the arguments
"""
image_list = list_image(args.root, args.recursive, args.exts)
image_list = list(image_list)
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apache/incubator-mxnet | tools/im2rec.py | read_list | def read_list(path_in):
"""Reads the .lst file and generates corresponding iterator.
Parameters
----------
path_in: string
Returns
-------
item iterator that contains information in .lst file
"""
with open(path_in) as fin:
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line = fin.readline()
... | python | def read_list(path_in):
"""Reads the .lst file and generates corresponding iterator.
Parameters
----------
path_in: string
Returns
-------
item iterator that contains information in .lst file
"""
with open(path_in) as fin:
while True:
line = fin.readline()
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apache/incubator-mxnet | tools/im2rec.py | image_encode | def image_encode(args, i, item, q_out):
"""Reads, preprocesses, packs the image and put it back in output queue.
Parameters
----------
args: object
i: int
item: list
q_out: queue
"""
fullpath = os.path.join(args.root, item[1])
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header... | python | def image_encode(args, i, item, q_out):
"""Reads, preprocesses, packs the image and put it back in output queue.
Parameters
----------
args: object
i: int
item: list
q_out: queue
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fullpath = os.path.join(args.root, item[1])
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apache/incubator-mxnet | tools/im2rec.py | read_worker | def read_worker(args, q_in, q_out):
"""Function that will be spawned to fetch the image
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Parameters
----------
args: object
q_in: queue
q_out: queue
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break... | python | def read_worker(args, q_in, q_out):
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----------
args: object
q_in: queue
q_out: queue
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apache/incubator-mxnet | tools/im2rec.py | write_worker | def write_worker(q_out, fname, working_dir):
"""Function that will be spawned to fetch processed image
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Parameters
----------
q_out: queue
fname: string
working_dir: string
"""
pre_time = time.time()
count = 0
fname = os.path.b... | python | def write_worker(q_out, fname, working_dir):
"""Function that will be spawned to fetch processed image
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----------
q_out: queue
fname: string
working_dir: string
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apache/incubator-mxnet | tools/im2rec.py | parse_args | def parse_args():
"""Defines all arguments.
Returns
-------
args object that contains all the params
"""
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Create an image list or \
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apache/incubator-mxnet | example/gluon/embedding_learning/data.py | transform | def transform(data, target_wd, target_ht, is_train, box):
"""Crop and normnalize an image nd array."""
if box is not None:
x, y, w, h = box
data = data[y:min(y+h, data.shape[0]), x:min(x+w, data.shape[1])]
# Resize to target_wd * target_ht.
data = mx.image.imresize(data, target_wd, targ... | python | def transform(data, target_wd, target_ht, is_train, box):
"""Crop and normnalize an image nd array."""
if box is not None:
x, y, w, h = box
data = data[y:min(y+h, data.shape[0]), x:min(x+w, data.shape[1])]
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apache/incubator-mxnet | example/gluon/embedding_learning/data.py | cub200_iterator | def cub200_iterator(data_path, batch_k, batch_size, data_shape):
"""Return training and testing iterator for the CUB200-2011 dataset."""
return (CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=True),
CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=False)) | python | def cub200_iterator(data_path, batch_k, batch_size, data_shape):
"""Return training and testing iterator for the CUB200-2011 dataset."""
return (CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=True),
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apache/incubator-mxnet | example/gluon/embedding_learning/data.py | CUB200Iter.get_image | def get_image(self, img, is_train):
"""Load and transform an image."""
img_arr = mx.image.imread(img)
img_arr = transform(img_arr, 256, 256, is_train, self.boxes[img])
return img_arr | python | def get_image(self, img, is_train):
"""Load and transform an image."""
img_arr = mx.image.imread(img)
img_arr = transform(img_arr, 256, 256, is_train, self.boxes[img])
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apache/incubator-mxnet | example/gluon/embedding_learning/data.py | CUB200Iter.sample_train_batch | def sample_train_batch(self):
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"""Sample a training batch (data and label)."""
batch = []
labels = []
num_groups = self.batch_size // self.batch_k
# For CUB200, we use the first 100 classes for training.
sampled_classes = np.random.choice(100, num_groups, replace=False)
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apache/incubator-mxnet | example/gluon/embedding_learning/data.py | CUB200Iter.next | def next(self):
"""Return a batch."""
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data, labels = self.get_test_batch()
self.test_count += 1
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"""Return a batch."""
if self.is_train:
data, labels = self.sample_train_batch()
else:
if self.test_count * self.batch_size < len(self.test_image_files):
data, labels = self.get_test_batch()
self.test_count += 1
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apache/incubator-mxnet | example/bayesian-methods/data_loader.py | load_mnist | def load_mnist(training_num=50000):
"""Load mnist dataset"""
data_path = os.path.join(os.path.dirname(os.path.realpath('__file__')), 'mnist.npz')
if not os.path.isfile(data_path):
from six.moves import urllib
origin = (
'https://github.com/sxjscience/mxnet/raw/master/example/baye... | python | def load_mnist(training_num=50000):
"""Load mnist dataset"""
data_path = os.path.join(os.path.dirname(os.path.realpath('__file__')), 'mnist.npz')
if not os.path.isfile(data_path):
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origin = (
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apache/incubator-mxnet | python/mxnet/runtime.py | feature_list | def feature_list():
"""
Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc
Returns
-------
list
List of :class:`.Feature` objects
"""
lib_features_c_array = ctypes.POINTER(Feature)()
lib_features_size = ctypes.c_size_t()
... | python | def feature_list():
"""
Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc
Returns
-------
list
List of :class:`.Feature` objects
"""
lib_features_c_array = ctypes.POINTER(Feature)()
lib_features_size = ctypes.c_size_t()
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apache/incubator-mxnet | python/mxnet/runtime.py | Features.is_enabled | def is_enabled(self, feature_name):
"""
Check for a particular feature by name
Parameters
----------
feature_name: str
The name of a valid feature as string for example 'CUDA'
Returns
-------
Boolean
True if it's enabled, False if... | python | def is_enabled(self, feature_name):
"""
Check for a particular feature by name
Parameters
----------
feature_name: str
The name of a valid feature as string for example 'CUDA'
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-------
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apache/incubator-mxnet | example/ssd/dataset/pascal_voc.py | PascalVoc.cache_path | def cache_path(self):
"""
make a directory to store all caches
Returns:
---------
cache path
"""
cache_path = os.path.join(os.path.dirname(__file__), '..', 'cache')
if not os.path.exists(cache_path):
os.mkdir(cache_path)
return cac... | python | def cache_path(self):
"""
make a directory to store all caches
Returns:
---------
cache path
"""
cache_path = os.path.join(os.path.dirname(__file__), '..', 'cache')
if not os.path.exists(cache_path):
os.mkdir(cache_path)
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apache/incubator-mxnet | example/ssd/dataset/pascal_voc.py | PascalVoc._load_image_set_index | def _load_image_set_index(self, shuffle):
"""
find out which indexes correspond to given image set (train or val)
Parameters:
----------
shuffle : boolean
whether to shuffle the image list
Returns:
----------
entire list of images specified in... | python | def _load_image_set_index(self, shuffle):
"""
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shuffle : boolean
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apache/incubator-mxnet | example/ssd/dataset/pascal_voc.py | PascalVoc.image_path_from_index | def image_path_from_index(self, index):
"""
given image index, find out full path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of this image
"""
assert self.image_set_index is not No... | python | def image_path_from_index(self, index):
"""
given image index, find out full path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of this image
"""
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apache/incubator-mxnet | example/ssd/dataset/pascal_voc.py | PascalVoc._label_path_from_index | def _label_path_from_index(self, index):
"""
given image index, find out annotation path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of annotation file
"""
label_file = os.path.joi... | python | def _label_path_from_index(self, index):
"""
given image index, find out annotation path
Parameters:
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index: int
index of a specific image
Returns:
----------
full path of annotation file
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apache/incubator-mxnet | example/ssd/dataset/pascal_voc.py | PascalVoc._load_image_labels | def _load_image_labels(self):
"""
preprocess all ground-truths
Returns:
----------
labels packed in [num_images x max_num_objects x 5] tensor
"""
temp = []
# load ground-truth from xml annotations
for idx in self.image_set_index:
labe... | python | def _load_image_labels(self):
"""
preprocess all ground-truths
Returns:
----------
labels packed in [num_images x max_num_objects x 5] tensor
"""
temp = []
# load ground-truth from xml annotations
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apache/incubator-mxnet | example/ssd/dataset/pascal_voc.py | PascalVoc.evaluate_detections | def evaluate_detections(self, detections):
"""
top level evaluations
Parameters:
----------
detections: list
result list, each entry is a matrix of detections
Returns:
----------
None
"""
# make all these folders for results... | python | def evaluate_detections(self, detections):
"""
top level evaluations
Parameters:
----------
detections: list
result list, each entry is a matrix of detections
Returns:
----------
None
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apache/incubator-mxnet | example/ssd/dataset/pascal_voc.py | PascalVoc.get_result_file_template | def get_result_file_template(self):
"""
this is a template
VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
Returns:
----------
a string template
"""
res_file_folder = os.path.join(self.devkit_path, 'results', 'VOC' + self.year, 'Main')... | python | def get_result_file_template(self):
"""
this is a template
VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
Returns:
----------
a string template
"""
res_file_folder = os.path.join(self.devkit_path, 'results', 'VOC' + self.year, 'Main')... | [
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apache/incubator-mxnet | example/ssd/dataset/pascal_voc.py | PascalVoc.write_pascal_results | def write_pascal_results(self, all_boxes):
"""
write results files in pascal devkit path
Parameters:
----------
all_boxes: list
boxes to be processed [bbox, confidence]
Returns:
----------
None
"""
for cls_ind, cls in enumerate(... | python | def write_pascal_results(self, all_boxes):
"""
write results files in pascal devkit path
Parameters:
----------
all_boxes: list
boxes to be processed [bbox, confidence]
Returns:
----------
None
"""
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apache/incubator-mxnet | example/ssd/dataset/pascal_voc.py | PascalVoc.do_python_eval | def do_python_eval(self):
"""
python evaluation wrapper
Returns:
----------
None
"""
annopath = os.path.join(self.data_path, 'Annotations', '{:s}.xml')
imageset_file = os.path.join(self.data_path, 'ImageSets', 'Main', self.image_set + '.txt')
cach... | python | def do_python_eval(self):
"""
python evaluation wrapper
Returns:
----------
None
"""
annopath = os.path.join(self.data_path, 'Annotations', '{:s}.xml')
imageset_file = os.path.join(self.data_path, 'ImageSets', 'Main', self.image_set + '.txt')
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apache/incubator-mxnet | example/ssd/dataset/pascal_voc.py | PascalVoc._get_imsize | def _get_imsize(self, im_name):
"""
get image size info
Returns:
----------
tuple of (height, width)
"""
img = cv2.imread(im_name)
return (img.shape[0], img.shape[1]) | python | def _get_imsize(self, im_name):
"""
get image size info
Returns:
----------
tuple of (height, width)
"""
img = cv2.imread(im_name)
return (img.shape[0], img.shape[1]) | [
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apache/incubator-mxnet | example/image-classification/common/fit.py | add_fit_args | def add_fit_args(parser):
"""
parser : argparse.ArgumentParser
return a parser added with args required by fit
"""
train = parser.add_argument_group('Training', 'model training')
train.add_argument('--network', type=str,
help='the neural network to use')
train.add_argu... | python | def add_fit_args(parser):
"""
parser : argparse.ArgumentParser
return a parser added with args required by fit
"""
train = parser.add_argument_group('Training', 'model training')
train.add_argument('--network', type=str,
help='the neural network to use')
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apache/incubator-mxnet | example/image-classification/common/fit.py | fit | def fit(args, network, data_loader, **kwargs):
"""
train a model
args : argparse returns
network : the symbol definition of the nerual network
data_loader : function that returns the train and val data iterators
"""
# kvstore
kv = mx.kvstore.create(args.kv_store)
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"""
train a model
args : argparse returns
network : the symbol definition of the nerual network
data_loader : function that returns the train and val data iterators
"""
# kvstore
kv = mx.kvstore.create(args.kv_store)
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apache/incubator-mxnet | python/mxnet/image/detection.py | CreateMultiRandCropAugmenter | def CreateMultiRandCropAugmenter(min_object_covered=0.1, aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0), min_eject_coverage=0.3,
max_attempts=50, skip_prob=0):
"""Helper function to create multiple random crop augmenters.
Parameters
... | python | def CreateMultiRandCropAugmenter(min_object_covered=0.1, aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0), min_eject_coverage=0.3,
max_attempts=50, skip_prob=0):
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Parameters
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apache/incubator-mxnet | python/mxnet/image/detection.py | CreateDetAugmenter | def CreateDetAugmenter(data_shape, resize=0, rand_crop=0, rand_pad=0, rand_gray=0,
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saturation=0, pca_noise=0, hue=0, inter_method=2, min_object_covered=0.1,
aspect_ratio_range=(0.75, 1.... | python | def CreateDetAugmenter(data_shape, resize=0, rand_crop=0, rand_pad=0, rand_gray=0,
rand_mirror=False, mean=None, std=None, brightness=0, contrast=0,
saturation=0, pca_noise=0, hue=0, inter_method=2, min_object_covered=0.1,
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apache/incubator-mxnet | python/mxnet/image/detection.py | DetRandomSelectAug.dumps | def dumps(self):
"""Override default."""
return [self.__class__.__name__.lower(), [x.dumps() for x in self.aug_list]] | python | def dumps(self):
"""Override default."""
return [self.__class__.__name__.lower(), [x.dumps() for x in self.aug_list]] | [
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apache/incubator-mxnet | python/mxnet/image/detection.py | DetRandomCropAug._calculate_areas | def _calculate_areas(self, label):
"""Calculate areas for multiple labels"""
heights = np.maximum(0, label[:, 3] - label[:, 1])
widths = np.maximum(0, label[:, 2] - label[:, 0])
return heights * widths | python | def _calculate_areas(self, label):
"""Calculate areas for multiple labels"""
heights = np.maximum(0, label[:, 3] - label[:, 1])
widths = np.maximum(0, label[:, 2] - label[:, 0])
return heights * widths | [
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apache/incubator-mxnet | python/mxnet/image/detection.py | DetRandomCropAug._intersect | def _intersect(self, label, xmin, ymin, xmax, ymax):
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left = np.maximum(label[:, 0], xmin)
right = np.minimum(label[:, 2], xmax)
top = np.maximum(label[:, 1], ymin)
bot = np.minimum(label[:, 3], ymax)
invalid = np.where(np.logic... | python | def _intersect(self, label, xmin, ymin, xmax, ymax):
"""Calculate intersect areas, normalized."""
left = np.maximum(label[:, 0], xmin)
right = np.minimum(label[:, 2], xmax)
top = np.maximum(label[:, 1], ymin)
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apache/incubator-mxnet | python/mxnet/image/detection.py | DetRandomCropAug._check_satisfy_constraints | def _check_satisfy_constraints(self, label, xmin, ymin, xmax, ymax, width, height):
"""Check if constrains are satisfied"""
if (xmax - xmin) * (ymax - ymin) < 2:
return False # only 1 pixel
x1 = float(xmin) / width
y1 = float(ymin) / height
x2 = float(xmax) / width
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"""Check if constrains are satisfied"""
if (xmax - xmin) * (ymax - ymin) < 2:
return False # only 1 pixel
x1 = float(xmin) / width
y1 = float(ymin) / height
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apache/incubator-mxnet | python/mxnet/image/detection.py | DetRandomCropAug._update_labels | def _update_labels(self, label, crop_box, height, width):
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xmin = float(crop_box[0]) / width
ymin = float(crop_box[1]) / height
w = float(crop_box[2]) / width
h = float(crop_box[3]) / height
out = label.copy()
out[:, (1, 3... | python | def _update_labels(self, label, crop_box, height, width):
"""Convert labels according to crop box"""
xmin = float(crop_box[0]) / width
ymin = float(crop_box[1]) / height
w = float(crop_box[2]) / width
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apache/incubator-mxnet | python/mxnet/image/detection.py | DetRandomCropAug._random_crop_proposal | def _random_crop_proposal(self, label, height, width):
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"""Propose cropping areas"""
from math import sqrt
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min_area = self.area_range[0] * height * width
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out[:, (1, 3)] = (out[:, (1, 3)] * width + pad_box[0]) / pad_box[2]
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return out | python | def _update_labels(self, label, pad_box, height, width):
"""Update label according to padding region"""
out = label.copy()
out[:, (1, 3)] = (out[:, (1, 3)] * width + pad_box[0]) / pad_box[2]
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... | Update label according to padding region | [
"Update",
"label",
"according",
"to",
"padding",
"region"
] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/image/detection.py#L378-L383 | train |
apache/incubator-mxnet | python/mxnet/image/detection.py | DetRandomPadAug._random_pad_proposal | def _random_pad_proposal(self, label, height, width):
"""Generate random padding region"""
from math import sqrt
if not self.enabled or height <= 0 or width <= 0:
return ()
min_area = self.area_range[0] * height * width
max_area = self.area_range[1] * height * width
... | python | def _random_pad_proposal(self, label, height, width):
"""Generate random padding region"""
from math import sqrt
if not self.enabled or height <= 0 or width <= 0:
return ()
min_area = self.area_range[0] * height * width
max_area = self.area_range[1] * height * width
... | [
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"min... | Generate random padding region | [
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/image/detection.py#L385-L414 | train |
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