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values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
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train | SGD | Generate the implementation of SGD | example/bayesian-methods/algos.py | def SGD(sym, data_inputs, X, Y, X_test, Y_test, total_iter_num,
lr=None,
lr_scheduler=None, prior_precision=1,
out_grad_f=None,
initializer=None,
minibatch_size=100, dev=mx.gpu()):
"""Generate the implementation of SGD"""
if out_grad_f is None:
label_key = list(se... | def SGD(sym, data_inputs, X, Y, X_test, Y_test, total_iter_num,
lr=None,
lr_scheduler=None, prior_precision=1,
out_grad_f=None,
initializer=None,
minibatch_size=100, dev=mx.gpu()):
"""Generate the implementation of SGD"""
if out_grad_f is None:
label_key = list(se... | [
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train | SGLD | Generate the implementation of SGLD | example/bayesian-methods/algos.py | def SGLD(sym, X, Y, X_test, Y_test, total_iter_num,
data_inputs=None,
learning_rate=None,
lr_scheduler=None, prior_precision=1,
out_grad_f=None,
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dev=mx.gp... | def SGLD(sym, X, Y, X_test, Y_test, total_iter_num,
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lr_scheduler=None, prior_precision=1,
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train | DistilledSGLD | Generate the implementation of DistilledSGLD | example/bayesian-methods/algos.py | def DistilledSGLD(teacher_sym, student_sym,
teacher_data_inputs, student_data_inputs,
X, Y, X_test, Y_test, total_iter_num,
teacher_learning_rate, student_learning_rate,
teacher_lr_scheduler=None, student_lr_scheduler=None,
studen... | def DistilledSGLD(teacher_sym, student_sym,
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X, Y, X_test, Y_test, total_iter_num,
teacher_learning_rate, student_learning_rate,
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studen... | [
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train | get_platforms | Get a list of architectures given our dockerfiles | ci/build.py | def get_platforms(path: str = get_dockerfiles_path()) -> List[str]:
"""Get a list of architectures given our dockerfiles"""
dockerfiles = glob.glob(os.path.join(path, "Dockerfile.*"))
dockerfiles = list(filter(lambda x: x[-1] != '~', dockerfiles))
files = list(map(lambda x: re.sub(r"Dockerfile.(.*)", r"... | def get_platforms(path: str = get_dockerfiles_path()) -> List[str]:
"""Get a list of architectures given our dockerfiles"""
dockerfiles = glob.glob(os.path.join(path, "Dockerfile.*"))
dockerfiles = list(filter(lambda x: x[-1] != '~', dockerfiles))
files = list(map(lambda x: re.sub(r"Dockerfile.(.*)", r"... | [
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train | get_docker_tag | :return: docker tag to be used for the container | ci/build.py | def get_docker_tag(platform: str, registry: str) -> str:
""":return: docker tag to be used for the container"""
platform = platform if any(x in platform for x in ['build.', 'publish.']) else 'build.{}'.format(platform)
if not registry:
registry = "mxnet_local"
return "{0}/{1}".format(registry, p... | def get_docker_tag(platform: str, registry: str) -> str:
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platform = platform if any(x in platform for x in ['build.', 'publish.']) else 'build.{}'.format(platform)
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train | build_docker | Build a container for the given platform
:param platform: Platform
:param docker_binary: docker binary to use (docker/nvidia-docker)
:param registry: Dockerhub registry name
:param num_retries: Number of retries to build the docker image
:param no_cache: pass no-cache to docker to rebuild the images... | ci/build.py | def build_docker(platform: str, docker_binary: str, registry: str, num_retries: int, no_cache: bool) -> str:
"""
Build a container for the given platform
:param platform: Platform
:param docker_binary: docker binary to use (docker/nvidia-docker)
:param registry: Dockerhub registry name
:param nu... | def build_docker(platform: str, docker_binary: str, registry: str, num_retries: int, no_cache: bool) -> str:
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Build a container for the given platform
:param platform: Platform
:param docker_binary: docker binary to use (docker/nvidia-docker)
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train | _get_local_image_id | Get the image id of the local docker layer with the passed tag
:param docker_tag: docker tag
:return: Image id as string or None if tag does not exist | ci/build.py | def _get_local_image_id(docker_binary, docker_tag):
"""
Get the image id of the local docker layer with the passed tag
:param docker_tag: docker tag
:return: Image id as string or None if tag does not exist
"""
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image_id_b = check_output(cmd)
... | def _get_local_image_id(docker_binary, docker_tag):
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Get the image id of the local docker layer with the passed tag
:param docker_tag: docker tag
:return: Image id as string or None if tag does not exist
"""
cmd = [docker_binary, "images", "-q", docker_tag]
image_id_b = check_output(cmd)
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train | default_ccache_dir | :return: ccache directory for the current platform | ci/build.py | def default_ccache_dir() -> str:
""":return: ccache directory for the current platform"""
# Share ccache across containers
if 'CCACHE_DIR' in os.environ:
ccache_dir = os.path.realpath(os.environ['CCACHE_DIR'])
try:
os.makedirs(ccache_dir, exist_ok=True)
return ccache_... | def default_ccache_dir() -> str:
""":return: ccache directory for the current platform"""
# Share ccache across containers
if 'CCACHE_DIR' in os.environ:
ccache_dir = os.path.realpath(os.environ['CCACHE_DIR'])
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train | container_run | Run command in a container | ci/build.py | def container_run(platform: str,
nvidia_runtime: bool,
docker_registry: str,
shared_memory_size: str,
local_ccache_dir: str,
command: List[str],
cleanup: Cleanup,
environment: Dict[str, str],
... | def container_run(platform: str,
nvidia_runtime: bool,
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shared_memory_size: str,
local_ccache_dir: str,
command: List[str],
cleanup: Cleanup,
environment: Dict[str, str],
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train | load_docker_cache | Imports tagged container from the given docker registry | ci/build.py | def load_docker_cache(tag, docker_registry) -> None:
"""Imports tagged container from the given docker registry"""
if docker_registry:
# noinspection PyBroadException
try:
import docker_cache
logging.info('Docker cache download is enabled from registry %s', docker_registr... | def load_docker_cache(tag, docker_registry) -> None:
"""Imports tagged container from the given docker registry"""
if docker_registry:
# noinspection PyBroadException
try:
import docker_cache
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train | _load_general | Load a list of arrays into a list of arrays specified by slices. | python/mxnet/module/executor_group.py | def _load_general(data, targets, major_axis):
"""Load a list of arrays into a list of arrays specified by slices."""
for d_src, d_targets, axis in zip(data, targets, major_axis): # pylint: disable=too-many-nested-blocks
if isinstance(d_targets, nd.NDArray):
d_src.copyto(d_targets)
el... | def _load_general(data, targets, major_axis):
"""Load a list of arrays into a list of arrays specified by slices."""
for d_src, d_targets, axis in zip(data, targets, major_axis): # pylint: disable=too-many-nested-blocks
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train | _load_data | Load data into sliced arrays. | python/mxnet/module/executor_group.py | def _load_data(batch, targets, major_axis):
"""Load data into sliced arrays."""
if isinstance(batch, list):
new_batch = []
for i in range(len(targets)):
new_batch.append([b.data[i] for b in batch])
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"""Load data into sliced arrays."""
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train | _merge_multi_context | Merge outputs that lives on multiple context into one, so that they look
like living on one context. | python/mxnet/module/executor_group.py | def _merge_multi_context(outputs, major_axis):
"""Merge outputs that lives on multiple context into one, so that they look
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"""
rets = []
for tensors, axis in zip(outputs, major_axis):
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# pylint: disable=no-member,protected-access
... | def _merge_multi_context(outputs, major_axis):
"""Merge outputs that lives on multiple context into one, so that they look
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rets = []
for tensors, axis in zip(outputs, major_axis):
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train | _prepare_group2ctxs | Prepare the group2contexts, will duplicate the context
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"""
if group2ctxs is None:
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train | DataParallelExecutorGroup.decide_slices | Decide the slices for each context according to the workload.
Parameters
----------
data_shapes : list
list of (name, shape) specifying the shapes for the input data or label. | python/mxnet/module/executor_group.py | def decide_slices(self, data_shapes):
"""Decide the slices for each context according to the workload.
Parameters
----------
data_shapes : list
list of (name, shape) specifying the shapes for the input data or label.
"""
assert len(data_shapes) > 0
ma... | def decide_slices(self, data_shapes):
"""Decide the slices for each context according to the workload.
Parameters
----------
data_shapes : list
list of (name, shape) specifying the shapes for the input data or label.
"""
assert len(data_shapes) > 0
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train | DataParallelExecutorGroup._collect_arrays | Collect internal arrays from executors. | python/mxnet/module/executor_group.py | def _collect_arrays(self):
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# convenient data structures
self.data_arrays = [[(self.slices[i], e.arg_dict[name]) for i, e in enumerate(self.execs)]
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train | DataParallelExecutorGroup.bind_exec | Bind executors on their respective devices.
Parameters
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data_shapes : list
label_shapes : list
shared_group : DataParallelExecutorGroup
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"""Bind executors on their respective devices.
Parameters
----------
data_shapes : list
label_shapes : list
shared_group : DataParallelExecutorGroup
reshape : bool
"""
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"""Bind executors on their respective devices.
Parameters
----------
data_shapes : list
label_shapes : list
shared_group : DataParallelExecutorGroup
reshape : bool
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train | DataParallelExecutorGroup.reshape | Reshape executors.
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data_shapes : list
label_shapes : list
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train | DataParallelExecutorGroup.set_params | Assign, i.e. copy parameters to all the executors.
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arg_params : dict
A dictionary of name to `NDArray` parameter mapping.
aux_params : dict
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"""Assign, i.e. copy parameters to all the executors.
Parameters
----------
arg_params : dict
A dictionary of name to `NDArray` parameter mapping.
aux_params : dict
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A dictionary of name to `NDArray` parameter mapping.
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train | DataParallelExecutorGroup.get_params | Copy data from each executor to `arg_params` and `aux_params`.
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arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
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""" Copy data from each executor to `arg_params` and `aux_params`.
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arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
Target aux arrays.
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arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
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train | DataParallelExecutorGroup.forward | Split `data_batch` according to workload and run forward on each devices.
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data_batch : DataBatch
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is_train : bool
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"""Split `data_batch` according to workload and run forward on each devices.
Parameters
----------
data_batch : DataBatch
Or could be any object implementing similar interface.
is_train : bool
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train | DataParallelExecutorGroup.get_output_shapes | Get the shapes of the outputs. | python/mxnet/module/executor_group.py | def get_output_shapes(self):
"""Get the shapes of the outputs."""
outputs = self.execs[0].outputs
shapes = [out.shape for out in outputs]
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train | DataParallelExecutorGroup.get_outputs | Get outputs of the previous forward computation.
If begin or end is specified, return [begin, end)-th outputs,
otherwise return all outputs.
Parameters
----------
merge_multi_context : bool
Default is `True`. In the case when data-parallelism is used, the outputs
... | python/mxnet/module/executor_group.py | def get_outputs(self, merge_multi_context=True, begin=0, end=None):
"""Get outputs of the previous forward computation.
If begin or end is specified, return [begin, end)-th outputs,
otherwise return all outputs.
Parameters
----------
merge_multi_context : bool
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"""Get outputs of the previous forward computation.
If begin or end is specified, return [begin, end)-th outputs,
otherwise return all outputs.
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----------
merge_multi_context : bool
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train | DataParallelExecutorGroup.set_states | Set value for states. Only one of states & value can be specified.
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states : list of list of NDArrays
source states arrays formatted like [[state1_dev1, state1_dev2],
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value : number
a single scalar val... | python/mxnet/module/executor_group.py | def set_states(self, states=None, value=None):
"""Set value for states. Only one of states & value can be specified.
Parameters
----------
states : list of list of NDArrays
source states arrays formatted like [[state1_dev1, state1_dev2],
[state2_dev1, state2_dev2... | def set_states(self, states=None, value=None):
"""Set value for states. Only one of states & value can be specified.
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states : list of list of NDArrays
source states arrays formatted like [[state1_dev1, state1_dev2],
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train | DataParallelExecutorGroup.get_input_grads | Get the gradients with respect to the inputs of the module.
Parameters
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merge_multi_context : bool
Defaults to ``True``. In the case when data-parallelism is used, the outputs
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"""Get the gradients with respect to the inputs of the module.
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----------
merge_multi_context : bool
Defaults to ``True``. In the case when data-parallelism is used, the outputs
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train | DataParallelExecutorGroup.backward | Run backward on all devices. A backward should be called after
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Parameters
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out_grads : NDArray or list of NDArray, optional
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"""Run backward on all devices. A backward should be called after
a call to the forward function. Backward cannot be called unless
``self.for_training`` is ``True``.
Parameters
----------
out_grads : NDArray or list of NDArray, optiona... | def backward(self, out_grads=None):
"""Run backward on all devices. A backward should be called after
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train | DataParallelExecutorGroup.update_metric | Accumulate the performance according to `eval_metric` on all devices
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outputs.
Parameters
----------
eval_metric : EvalMetric
The metric used for evaluation.
labels : list of NDArray
... | python/mxnet/module/executor_group.py | def update_metric(self, eval_metric, labels, pre_sliced):
"""Accumulate the performance according to `eval_metric` on all devices
by comparing outputs from [begin, end) to labels. By default use all
outputs.
Parameters
----------
eval_metric : EvalMetric
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"""Accumulate the performance according to `eval_metric` on all devices
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train | DataParallelExecutorGroup._bind_ith_exec | Internal utility function to bind the i-th executor.
This function utilizes simple_bind python interface. | python/mxnet/module/executor_group.py | def _bind_ith_exec(self, i, data_shapes, label_shapes, shared_group):
"""Internal utility function to bind the i-th executor.
This function utilizes simple_bind python interface.
"""
shared_exec = None if shared_group is None else shared_group.execs[i]
context = self.contexts[i]
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"""Internal utility function to bind the i-th executor.
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shared_exec = None if shared_group is None else shared_group.execs[i]
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train | DataParallelExecutorGroup._sliced_shape | Get the sliced shapes for the i-th executor.
Parameters
----------
shapes : list of (str, tuple)
The original (name, shape) pairs.
i : int
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"""Get the sliced shapes for the i-th executor.
Parameters
----------
shapes : list of (str, tuple)
The original (name, shape) pairs.
i : int
Which executor we are dealing with.
"""
sliced_sh... | def _sliced_shape(self, shapes, i, major_axis):
"""Get the sliced shapes for the i-th executor.
Parameters
----------
shapes : list of (str, tuple)
The original (name, shape) pairs.
i : int
Which executor we are dealing with.
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train | parse_class_names | parse # classes and class_names if applicable | example/ssd/train.py | def parse_class_names(args):
""" parse # classes and class_names if applicable """
num_class = args.num_class
if len(args.class_names) > 0:
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""" parse # classes and class_names if applicable """
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train | _has_instance | Return True if ``data`` has instance of ``dtype``.
This function is called after _init_data.
``data`` is a list of (str, NDArray) | python/mxnet/io/utils.py | def _has_instance(data, dtype):
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train | _getdata_by_idx | Shuffle the data. | python/mxnet/io/utils.py | def _getdata_by_idx(data, idx):
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if (isinstance(v, h5py.Dataset) if h5py else False):
shuffle_data.append((k, v))
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train | get_mobilenet | r"""MobileNet model from the
`"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
<https://arxiv.org/abs/1704.04861>`_ paper.
Parameters
----------
multiplier : float
The width multiplier for controling the model size. Only multipliers that are no
le... | python/mxnet/gluon/model_zoo/vision/mobilenet.py | def get_mobilenet(multiplier, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""MobileNet model from the
`"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
<https://arxiv.org/abs/1704.04861>`_ paper.
Parameters... | def get_mobilenet(multiplier, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""MobileNet model from the
`"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
<https://arxiv.org/abs/1704.04861>`_ paper.
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If the user specifies a name,
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When user does not specify a name, we automatically generate a
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"""Get the canonical name for a symbol.
This is the default implementation.
If the user specifies a name,
the user-specified name will be used.
When user does not specify a name, we automatically generate a
name based on the hint string.
... | def get(self, name, hint):
"""Get the canonical name for a symbol.
This is the default implementation.
If the user specifies a name,
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When user does not specify a name, we automatically generate a
name based on the hint string.
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train | LogUniformSampler.draw | Draw samples from log uniform distribution and returns sampled candidates,
expected count for true classes and sampled classes. | example/rnn/large_word_lm/sampler.py | def draw(self, true_classes):
"""Draw samples from log uniform distribution and returns sampled candidates,
expected count for true classes and sampled classes."""
range_max = self.range_max
num_sampled = self.num_sampled
ctx = true_classes.context
log_range = math.log(ra... | def draw(self, true_classes):
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train | get_inception_score | Inception_score function.
The images will be divided into 'splits' parts, and calculate each inception_score separately,
then return the mean and std of inception_scores of these parts.
:param images: Images(num x c x w x h) that needs to calculate inception_score.
:param splits:
:return: me... | example/gluon/dc_gan/inception_score.py | def get_inception_score(images, splits=10):
"""
Inception_score function.
The images will be divided into 'splits' parts, and calculate each inception_score separately,
then return the mean and std of inception_scores of these parts.
:param images: Images(num x c x w x h) that needs to calcu... | def get_inception_score(images, splits=10):
"""
Inception_score function.
The images will be divided into 'splits' parts, and calculate each inception_score separately,
then return the mean and std of inception_scores of these parts.
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train | load_param | same as mx.model.load_checkpoint, but do not load symnet and will convert context | example/rcnn/symnet/model.py | def load_param(params, ctx=None):
"""same as mx.model.load_checkpoint, but do not load symnet and will convert context"""
if ctx is None:
ctx = mx.cpu()
save_dict = mx.nd.load(params)
arg_params = {}
aux_params = {}
for k, v in save_dict.items():
tp, name = k.split(':', 1)
... | def load_param(params, ctx=None):
"""same as mx.model.load_checkpoint, but do not load symnet and will convert context"""
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ctx = mx.cpu()
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train | rnn_unroll | Deprecated. Please use cell.unroll instead | python/mxnet/rnn/rnn.py | def rnn_unroll(cell, length, inputs=None, begin_state=None, input_prefix='', layout='NTC'):
"""Deprecated. Please use cell.unroll instead"""
warnings.warn('rnn_unroll is deprecated. Please call cell.unroll directly.')
return cell.unroll(length=length, inputs=inputs, begin_state=begin_state,
... | def rnn_unroll(cell, length, inputs=None, begin_state=None, input_prefix='', layout='NTC'):
"""Deprecated. Please use cell.unroll instead"""
warnings.warn('rnn_unroll is deprecated. Please call cell.unroll directly.')
return cell.unroll(length=length, inputs=inputs, begin_state=begin_state,
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train | save_rnn_checkpoint | Save checkpoint for model using RNN cells.
Unpacks weight before saving.
Parameters
----------
cells : mxnet.rnn.RNNCell or list of RNNCells
The RNN cells used by this symbol.
prefix : str
Prefix of model name.
epoch : int
The epoch number of the model.
symbol : Symb... | python/mxnet/rnn/rnn.py | def save_rnn_checkpoint(cells, prefix, epoch, symbol, arg_params, aux_params):
"""Save checkpoint for model using RNN cells.
Unpacks weight before saving.
Parameters
----------
cells : mxnet.rnn.RNNCell or list of RNNCells
The RNN cells used by this symbol.
prefix : str
Prefix o... | def save_rnn_checkpoint(cells, prefix, epoch, symbol, arg_params, aux_params):
"""Save checkpoint for model using RNN cells.
Unpacks weight before saving.
Parameters
----------
cells : mxnet.rnn.RNNCell or list of RNNCells
The RNN cells used by this symbol.
prefix : str
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train | load_rnn_checkpoint | Load model checkpoint from file.
Pack weights after loading.
Parameters
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cells : mxnet.rnn.RNNCell or list of RNNCells
The RNN cells used by this symbol.
prefix : str
Prefix of model name.
epoch : int
Epoch number of model we would like to load.
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... | python/mxnet/rnn/rnn.py | def load_rnn_checkpoint(cells, prefix, epoch):
"""Load model checkpoint from file.
Pack weights after loading.
Parameters
----------
cells : mxnet.rnn.RNNCell or list of RNNCells
The RNN cells used by this symbol.
prefix : str
Prefix of model name.
epoch : int
Epoch ... | def load_rnn_checkpoint(cells, prefix, epoch):
"""Load model checkpoint from file.
Pack weights after loading.
Parameters
----------
cells : mxnet.rnn.RNNCell or list of RNNCells
The RNN cells used by this symbol.
prefix : str
Prefix of model name.
epoch : int
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train | do_rnn_checkpoint | Make a callback to checkpoint Module to prefix every epoch.
unpacks weights used by cells before saving.
Parameters
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cells : mxnet.rnn.RNNCell or list of RNNCells
The RNN cells used by this symbol.
prefix : str
The file prefix to checkpoint to
period : int
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unpacks weights used by cells before saving.
Parameters
----------
cells : mxnet.rnn.RNNCell or list of RNNCells
The RNN cells used by this symbol.
prefix : str
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train | Sequential.hybridize | Activates or deactivates `HybridBlock` s recursively. Has no effect on
non-hybrid children.
Parameters
----------
active : bool, default True
Whether to turn hybrid on or off.
**kwargs : string
Additional flags for hybridized operator. | python/mxnet/gluon/nn/basic_layers.py | def hybridize(self, active=True, **kwargs):
"""Activates or deactivates `HybridBlock` s recursively. Has no effect on
non-hybrid children.
Parameters
----------
active : bool, default True
Whether to turn hybrid on or off.
**kwargs : string
Additi... | def hybridize(self, active=True, **kwargs):
"""Activates or deactivates `HybridBlock` s recursively. Has no effect on
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Parameters
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active : bool, default True
Whether to turn hybrid on or off.
**kwargs : string
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train | read_img | Reads image specified by path into numpy.ndarray | example/ctc/lstm_ocr_infer.py | def read_img(path):
""" Reads image specified by path into numpy.ndarray"""
img = cv2.resize(cv2.imread(path, 0), (80, 30)).astype(np.float32) / 255
img = np.expand_dims(img.transpose(1, 0), 0)
return img | def read_img(path):
""" Reads image specified by path into numpy.ndarray"""
img = cv2.resize(cv2.imread(path, 0), (80, 30)).astype(np.float32) / 255
img = np.expand_dims(img.transpose(1, 0), 0)
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train | lstm_init_states | Returns a tuple of names and zero arrays for LSTM init states | example/ctc/lstm_ocr_infer.py | def lstm_init_states(batch_size):
""" Returns a tuple of names and zero arrays for LSTM init states"""
hp = Hyperparams()
init_shapes = lstm.init_states(batch_size=batch_size, num_lstm_layer=hp.num_lstm_layer, num_hidden=hp.num_hidden)
init_names = [s[0] for s in init_shapes]
init_arrays = [mx.nd.ze... | def lstm_init_states(batch_size):
""" Returns a tuple of names and zero arrays for LSTM init states"""
hp = Hyperparams()
init_shapes = lstm.init_states(batch_size=batch_size, num_lstm_layer=hp.num_lstm_layer, num_hidden=hp.num_hidden)
init_names = [s[0] for s in init_shapes]
init_arrays = [mx.nd.ze... | [
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train | load_module | Loads the model from checkpoint specified by prefix and epoch, binds it
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"""Loads the model from checkpoint specified by prefix and epoch, binds it
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sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
# We don't need CTC loss ... | def load_module(prefix, epoch, data_names, data_shapes):
"""Loads the model from checkpoint specified by prefix and epoch, binds it
to an executor, and sets its parameters and returns a mx.mod.Module
"""
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
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train | main | Program entry point | example/ctc/lstm_ocr_infer.py | def main():
"""Program entry point"""
parser = argparse.ArgumentParser()
parser.add_argument("path", help="Path to the CAPTCHA image file")
parser.add_argument("--prefix", help="Checkpoint prefix [Default 'ocr']", default='ocr')
parser.add_argument("--epoch", help="Checkpoint epoch [Default 100]", t... | def main():
"""Program entry point"""
parser = argparse.ArgumentParser()
parser.add_argument("path", help="Path to the CAPTCHA image file")
parser.add_argument("--prefix", help="Checkpoint prefix [Default 'ocr']", default='ocr')
parser.add_argument("--epoch", help="Checkpoint epoch [Default 100]", t... | [
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train | bbox_flip | invalid value in bbox_transform if this wrong (no overlap), note index 0 and 2
also note need to save before assignment
:param bbox: [n][x1, y1, x2, y2]
:param width: cv2 (height, width, channel)
:param flip_x: will flip x1 and x2
:return: flipped box | example/rcnn/symdata/bbox.py | def bbox_flip(bbox, width, flip_x=False):
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invalid value in bbox_transform if this wrong (no overlap), note index 0 and 2
also note need to save before assignment
:param bbox: [n][x1, y1, x2, y2]
:param width: cv2 (height, width, channel)
:param flip_x: will flip x1 and x2
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invalid value in bbox_transform if this wrong (no overlap), note index 0 and 2
also note need to save before assignment
:param bbox: [n][x1, y1, x2, y2]
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train | bbox_overlaps | determine overlaps between boxes and query_boxes
:param boxes: n * 4 bounding boxes
:param query_boxes: k * 4 bounding boxes
:return: overlaps: n * k overlaps | example/rcnn/symdata/bbox.py | def bbox_overlaps(boxes, query_boxes):
"""
determine overlaps between boxes and query_boxes
:param boxes: n * 4 bounding boxes
:param query_boxes: k * 4 bounding boxes
:return: overlaps: n * k overlaps
"""
n_ = boxes.shape[0]
k_ = query_boxes.shape[0]
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"""
determine overlaps between boxes and query_boxes
:param boxes: n * 4 bounding boxes
:param query_boxes: k * 4 bounding boxes
:return: overlaps: n * k overlaps
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n_ = boxes.shape[0]
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train | clip_boxes | Clip boxes to image boundaries.
:param boxes: [N, 4* num_classes]
:param im_shape: tuple of 2
:return: [N, 4* num_classes] | example/rcnn/symdata/bbox.py | def clip_boxes(boxes, im_shape):
"""
Clip boxes to image boundaries.
:param boxes: [N, 4* num_classes]
:param im_shape: tuple of 2
:return: [N, 4* num_classes]
"""
# x1 >= 0
boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)
# y1 >= 0
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"""
Clip boxes to image boundaries.
:param boxes: [N, 4* num_classes]
:param im_shape: tuple of 2
:return: [N, 4* num_classes]
"""
# x1 >= 0
boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)
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train | bbox_transform | compute bounding box regression targets from ex_rois to gt_rois
:param ex_rois: [N, 4]
:param gt_rois: [N, 4]
:return: [N, 4] | example/rcnn/symdata/bbox.py | def bbox_transform(ex_rois, gt_rois, box_stds):
"""
compute bounding box regression targets from ex_rois to gt_rois
:param ex_rois: [N, 4]
:param gt_rois: [N, 4]
:return: [N, 4]
"""
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ex_widths = ex_rois[:, 2] - ex_ro... | def bbox_transform(ex_rois, gt_rois, box_stds):
"""
compute bounding box regression targets from ex_rois to gt_rois
:param ex_rois: [N, 4]
:param gt_rois: [N, 4]
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train | bbox_pred | Transform the set of class-agnostic boxes into class-specific boxes
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:param boxes: !important [N 4]
:param box_deltas: [N, 4 * num_classes]
:return: [N 4 * num_classes] | example/rcnn/symdata/bbox.py | def bbox_pred(boxes, box_deltas, box_stds):
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:param boxes: !important [N 4]
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train | nms | greedily select boxes with high confidence and overlap with current maximum <= thresh
rule out overlap >= thresh
:param dets: [[x1, y1, x2, y2 score]]
:param thresh: retain overlap < thresh
:return: indexes to keep | example/rcnn/symdata/bbox.py | def nms(dets, thresh):
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train | im_detect | rois (nroi, 4), scores (nrois, nclasses), bbox_deltas (nrois, 4 * nclasses), im_info (3) | example/rcnn/symdata/bbox.py | def im_detect(rois, scores, bbox_deltas, im_info,
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rois = rois.asnumpy()
scores = scores.asnumpy()
bbox_deltas = bbox_deltas.asnumpy()
im_info = im_info.a... | def im_detect(rois, scores, bbox_deltas, im_info,
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rois = rois.asnumpy()
scores = scores.asnumpy()
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train | c_str | Convert a python string to C string. | amalgamation/python/mxnet_predict.py | def c_str(string):
""""Convert a python string to C string."""
if not isinstance(string, str):
string = string.decode('ascii')
return ctypes.c_char_p(string.encode('utf-8')) | def c_str(string):
""""Convert a python string to C string."""
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train | _find_lib_path | Find mxnet library. | amalgamation/python/mxnet_predict.py | def _find_lib_path():
"""Find mxnet library."""
curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
amalgamation_lib_path = os.path.join(curr_path, '../../lib/libmxnet_predict.so')
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"""Find mxnet library."""
curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
amalgamation_lib_path = os.path.join(curr_path, '../../lib/libmxnet_predict.so')
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train | _load_lib | Load libary by searching possible path. | amalgamation/python/mxnet_predict.py | def _load_lib():
"""Load libary by searching possible path."""
lib_path = _find_lib_path()
lib = ctypes.cdll.LoadLibrary(lib_path[0])
# DMatrix functions
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lib_path = _find_lib_path()
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train | load_ndarray_file | Load ndarray file and return as list of numpy array.
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nd_bytes : str or bytes
The internal ndarray bytes
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out : dict of str to numpy array or list of numpy array
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train | Predictor.forward | Perform forward to get the output.
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**kwargs
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"""Perform forward to get the output.
Parameters
----------
**kwargs
Keyword arguments of input variable name to data.
Examples
--------
>>> predictor.forward(data=mydata)
>>> out = predictor.get_output(0)
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Keyword arguments of input variable name to data.
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>>> predictor.forward(data=mydata)
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train | Predictor.reshape | Change the input shape of the predictor.
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Parameters
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The new shape of input data.
Examples
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>>> predictor.reshape({'data':data_shape_tuple})
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train | Predictor.get_output | Get the index-th output.
Parameters
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index : int
The index of output.
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out : numpy array.
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index : int
The index of output.
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out : numpy array.
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train | AtariGame.begin_episode | Begin an episode of a game instance. We can play the game for a maximum of
`max_episode_step` and after that, we are forced to restart | example/reinforcement-learning/dqn/atari_game.py | def begin_episode(self, max_episode_step=DEFAULT_MAX_EPISODE_STEP):
"""
Begin an episode of a game instance. We can play the game for a maximum of
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Begin an episode of a game instance. We can play the game for a maximum of
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train | RecurrentCell.reset | Reset before re-using the cell for another graph. | python/mxnet/gluon/rnn/rnn_cell.py | def reset(self):
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self._init_counter = -1
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cell.reset() | def reset(self):
"""Reset before re-using the cell for another graph."""
self._init_counter = -1
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train | RecurrentCell.begin_state | Initial state for this cell.
Parameters
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func : callable, default symbol.zeros
Function for creating initial state.
For Symbol API, func can be `symbol.zeros`, `symbol.uniform`,
`symbol.var etc`. Use `symbol.var` if you want to directly
... | python/mxnet/gluon/rnn/rnn_cell.py | def begin_state(self, batch_size=0, func=ndarray.zeros, **kwargs):
"""Initial state for this cell.
Parameters
----------
func : callable, default symbol.zeros
Function for creating initial state.
For Symbol API, func can be `symbol.zeros`, `symbol.uniform`,
... | def begin_state(self, batch_size=0, func=ndarray.zeros, **kwargs):
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func : callable, default symbol.zeros
Function for creating initial state.
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train | RecurrentCell.unroll | Unrolls an RNN cell across time steps.
Parameters
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length : int
Number of steps to unroll.
inputs : Symbol, list of Symbol, or None
If `inputs` is a single Symbol (usually the output
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Parameters
----------
length : int
Number of steps to unroll.
inputs : Symbol, list of Symbol, or None
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----------
length : int
Number of steps to unroll.
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train | RecurrentCell._get_activation | Get activation function. Convert if is string | python/mxnet/gluon/rnn/rnn_cell.py | def _get_activation(self, F, inputs, activation, **kwargs):
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func = {'tanh': F.tanh,
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train | RecurrentCell.forward | Unrolls the recurrent cell for one time step.
Parameters
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inputs : sym.Variable
Input symbol, 2D, of shape (batch_size * num_units).
states : list of sym.Variable
RNN state from previous step or the output of begin_state().
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train | _check_input_names | Check that all input names are in symbol's arguments. | python/mxnet/module/base_module.py | def _check_input_names(symbol, names, typename, throw):
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args = symbol.list_arguments()
for name in names:
if name in args:
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train | _check_names_match | Check that input names matches input data descriptors. | python/mxnet/module/base_module.py | def _check_names_match(data_names, data_shapes, name, throw):
"""Check that input names matches input data descriptors."""
actual = [x[0] for x in data_shapes]
if sorted(data_names) != sorted(actual):
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train | _parse_data_desc | parse data_attrs into DataDesc format and check that names match | python/mxnet/module/base_module.py | def _parse_data_desc(data_names, label_names, data_shapes, label_shapes):
"""parse data_attrs into DataDesc format and check that names match"""
data_shapes = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in data_shapes]
_check_names_match(data_names, data_shapes, 'data', True)
if label_shapes i... | def _parse_data_desc(data_names, label_names, data_shapes, label_shapes):
"""parse data_attrs into DataDesc format and check that names match"""
data_shapes = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in data_shapes]
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train | BaseModule.forward_backward | A convenient function that calls both ``forward`` and ``backward``. | python/mxnet/module/base_module.py | def forward_backward(self, data_batch):
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train | BaseModule.score | Runs prediction on ``eval_data`` and evaluates the performance according to
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Checkout `Module Tutorial <http://mxnet.io/tutorials/basic/module.html>`_ to see
a end-to-end use-case.
Parameters
----------
eval_data : DataIter
Evaluati... | python/mxnet/module/base_module.py | def score(self, eval_data, eval_metric, num_batch=None, batch_end_callback=None,
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train | BaseModule.iter_predict | Iterates over predictions.
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... # i_batch is a integer
... # batch is the data batch from the data iterator
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... | python/mxnet/module/base_module.py | def iter_predict(self, eval_data, num_batch=None, reset=True, sparse_row_id_fn=None):
"""Iterates over predictions.
Examples
--------
>>> for pred, i_batch, batch in module.iter_predict(eval_data):
... # pred is a list of outputs from the module
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train | BaseModule.predict | Runs prediction and collects the outputs.
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When `merge_batches` is ``True`` (by default), the return value will be a list
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train | BaseModule.set_params | Assigns parameter and aux state values.
Parameters
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arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
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"""Assigns parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
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"""Assigns parameter and aux state values.
Parameters
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arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
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train | BaseModule.save_params | Saves model parameters to file.
Parameters
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fname : str
Path to output param file.
Examples
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>>> # An example of saving module parameters.
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Parameters
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fname : str
Path to output param file.
Examples
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>>> # An example of saving module parameters.
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Parameters
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fname : str
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>>> # An example of saving module parameters.
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train | BaseModule.load_params | Loads model parameters from file.
Parameters
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fname : str
Path to input param file.
Examples
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>>> # An example of loading module parameters.
>>> mod.load_params('myfile') | python/mxnet/module/base_module.py | def load_params(self, fname):
"""Loads model parameters from file.
Parameters
----------
fname : str
Path to input param file.
Examples
--------
>>> # An example of loading module parameters.
>>> mod.load_params('myfile')
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fname : str
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>>> # An example of loading module parameters.
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train | BaseModule.bind | Binds the symbols to construct executors. This is necessary before one
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Parameters
----------
data_shapes : list of (str, tuple) or DataDesc objects
Typically is ``data_iter.provide_data``. Can also be a list of
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train | find_lib_path | Find MXNet dynamic library files.
Returns
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Returns
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lib_path : list(string)
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"""
lib_from_env = os.environ.get('MXNET_LIBRARY_PATH')
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train | find_include_path | Find MXNet included header files.
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train | CaptchaGen.image | Generate a greyscale captcha image representing number string
Parameters
----------
captcha_str: str
string a characters for captcha image
Returns
-------
numpy.ndarray
Generated greyscale image in np.ndarray float type with values normalized to ... | example/ctc/captcha_generator.py | def image(self, captcha_str):
"""Generate a greyscale captcha image representing number string
Parameters
----------
captcha_str: str
string a characters for captcha image
Returns
-------
numpy.ndarray
Generated greyscale image in np.ndar... | def image(self, captcha_str):
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Parameters
----------
captcha_str: str
string a characters for captcha image
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-------
numpy.ndarray
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train | DigitCaptcha.get_rand | Generates a character string of digits. Number of digits are
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Returns
-------
str | example/ctc/captcha_generator.py | def get_rand(num_digit_min, num_digit_max):
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Returns
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buf = ""
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train | DigitCaptcha._gen_sample | Generate a random captcha image sample
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(numpy.ndarray, str)
Tuple of image (numpy ndarray) and character string of digits used to generate the image | example/ctc/captcha_generator.py | def _gen_sample(self):
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Returns
-------
(numpy.ndarray, str)
Tuple of image (numpy ndarray) and character string of digits used to generate the image
"""
num_str = self.get_rand(self.num_digit_min, self.num_digit_max)
... | def _gen_sample(self):
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Tuple of image (numpy ndarray) and character string of digits used to generate the image
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num_str = self.get_rand(self.num_digit_min, self.num_digit_max)
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train | Optimizer.register | Registers a new optimizer.
Once an optimizer is registered, we can create an instance of this
optimizer with `create_optimizer` later.
Examples
--------
>>> @mx.optimizer.Optimizer.register
... class MyOptimizer(mx.optimizer.Optimizer):
... pass
>>>... | python/mxnet/optimizer/optimizer.py | def register(klass):
"""Registers a new optimizer.
Once an optimizer is registered, we can create an instance of this
optimizer with `create_optimizer` later.
Examples
--------
>>> @mx.optimizer.Optimizer.register
... class MyOptimizer(mx.optimizer.Optimizer):
... | def register(klass):
"""Registers a new optimizer.
Once an optimizer is registered, we can create an instance of this
optimizer with `create_optimizer` later.
Examples
--------
>>> @mx.optimizer.Optimizer.register
... class MyOptimizer(mx.optimizer.Optimizer):
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train | Optimizer.create_optimizer | Instantiates an optimizer with a given name and kwargs.
.. note:: We can use the alias `create` for ``Optimizer.create_optimizer``.
Parameters
----------
name: str
Name of the optimizer. Should be the name
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k... | python/mxnet/optimizer/optimizer.py | def create_optimizer(name, **kwargs):
"""Instantiates an optimizer with a given name and kwargs.
.. note:: We can use the alias `create` for ``Optimizer.create_optimizer``.
Parameters
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name: str
Name of the optimizer. Should be the name
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train | Optimizer.create_state_multi_precision | Creates auxiliary state for a given weight, including FP32 high
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This method is provided to perform automatic mixed precision training
for optimizers that do not support it themselves.
Parameters
----------
index : int
... | python/mxnet/optimizer/optimizer.py | def create_state_multi_precision(self, index, weight):
"""Creates auxiliary state for a given weight, including FP32 high
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This method is provided to perform automatic mixed precision training
for optimizers that do not support it themselves.
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"""Creates auxiliary state for a given weight, including FP32 high
precision copy if original weight is FP16.
This method is provided to perform automatic mixed precision training
for optimizers that do not support it themselves.
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train | Optimizer.update_multi_precision | Updates the given parameter using the corresponding gradient and state.
Mixed precision version.
Parameters
----------
index : int
The unique index of the parameter into the individual learning
rates and weight decays. Learning rates and weight decay
... | python/mxnet/optimizer/optimizer.py | def update_multi_precision(self, index, weight, grad, state):
"""Updates the given parameter using the corresponding gradient and state.
Mixed precision version.
Parameters
----------
index : int
The unique index of the parameter into the individual learning
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The unique index of the parameter into the individual learning
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train | Optimizer.set_lr_mult | Sets an individual learning rate multiplier for each parameter.
If you specify a learning rate multiplier for a parameter, then
the learning rate for the parameter will be set as the product of
the global learning rate `self.lr` and its multiplier.
.. note:: The default learning rate m... | python/mxnet/optimizer/optimizer.py | def set_lr_mult(self, args_lr_mult):
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train | Optimizer.set_wd_mult | Sets an individual weight decay multiplier for each parameter.
By default, if `param_idx2name` was provided in the
constructor, the weight decay multipler is set as 0 for all
parameters whose name don't end with ``_weight`` or
``_gamma``.
.. note:: The default weight decay mult... | python/mxnet/optimizer/optimizer.py | def set_wd_mult(self, args_wd_mult):
"""Sets an individual weight decay multiplier for each parameter.
By default, if `param_idx2name` was provided in the
constructor, the weight decay multipler is set as 0 for all
parameters whose name don't end with ``_weight`` or
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By default, if `param_idx2name` was provided in the
constructor, the weight decay multipler is set as 0 for all
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train | Optimizer._set_current_context | Sets the number of the currently handled device.
Parameters
----------
device_id : int
The number of current device. | python/mxnet/optimizer/optimizer.py | def _set_current_context(self, device_id):
"""Sets the number of the currently handled device.
Parameters
----------
device_id : int
The number of current device.
"""
if device_id not in self._all_index_update_counts:
self._all_index_update_counts... | def _set_current_context(self, device_id):
"""Sets the number of the currently handled device.
Parameters
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device_id : int
The number of current device.
"""
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train | Optimizer._update_count | Updates num_update.
Parameters
----------
index : int or list of int
The index to be updated. | python/mxnet/optimizer/optimizer.py | def _update_count(self, index):
"""Updates num_update.
Parameters
----------
index : int or list of int
The index to be updated.
"""
if not isinstance(index, (list, tuple)):
index = [index]
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if idx not in self.... | def _update_count(self, index):
"""Updates num_update.
Parameters
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index : int or list of int
The index to be updated.
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train | Optimizer._get_lrs | Gets the learning rates given the indices of the weights.
Parameters
----------
indices : list of int
Indices corresponding to weights.
Returns
-------
lrs : list of float
Learning rates for those indices. | python/mxnet/optimizer/optimizer.py | def _get_lrs(self, indices):
"""Gets the learning rates given the indices of the weights.
Parameters
----------
indices : list of int
Indices corresponding to weights.
Returns
-------
lrs : list of float
Learning rates for those indices.
... | def _get_lrs(self, indices):
"""Gets the learning rates given the indices of the weights.
Parameters
----------
indices : list of int
Indices corresponding to weights.
Returns
-------
lrs : list of float
Learning rates for those indices.
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train | Optimizer._get_wds | Gets weight decays for indices.
Returns 0 for non-weights if the name of weights are provided for `__init__`.
Parameters
----------
indices : list of int
Indices of weights.
Returns
-------
wds : list of float
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"""Gets weight decays for indices.
Returns 0 for non-weights if the name of weights are provided for `__init__`.
Parameters
----------
indices : list of int
Indices of weights.
Returns
-------
wds : list of float
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Parameters
----------
indices : list of int
Indices of weights.
Returns
-------
wds : list of float
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train | Updater.sync_state_context | sync state context. | python/mxnet/optimizer/optimizer.py | def sync_state_context(self, state, context):
"""sync state context."""
if isinstance(state, NDArray):
return state.as_in_context(context)
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"""sync state context."""
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train | Updater.set_states | Sets updater states. | python/mxnet/optimizer/optimizer.py | def set_states(self, states):
"""Sets updater states."""
states = pickle.loads(states)
if isinstance(states, tuple) and len(states) == 2:
self.states, self.optimizer = states
else:
self.states = states
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train | Updater.get_states | Gets updater states.
Parameters
----------
dump_optimizer : bool, default False
Whether to also save the optimizer itself. This would also save optimizer
information such as learning rate and weight decay schedules. | python/mxnet/optimizer/optimizer.py | def get_states(self, dump_optimizer=False):
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Parameters
----------
dump_optimizer : bool, default False
Whether to also save the optimizer itself. This would also save optimizer
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----------
dump_optimizer : bool, default False
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train | preprocess | Preprocess: Convert a video into the mouth images | example/gluon/lipnet/utils/preprocess_data.py | def preprocess(from_idx, to_idx, _params):
"""
Preprocess: Convert a video into the mouth images
"""
source_exts = '*.mpg'
src_path = _params['src_path']
tgt_path = _params['tgt_path']
face_predictor_path = './shape_predictor_68_face_landmarks.dat'
succ = set()
fail = set()
for ... | def preprocess(from_idx, to_idx, _params):
"""
Preprocess: Convert a video into the mouth images
"""
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src_path = _params['src_path']
tgt_path = _params['tgt_path']
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fail = set()
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train | Video.from_frames | Read from frames | example/gluon/lipnet/utils/preprocess_data.py | def from_frames(self, path):
"""
Read from frames
"""
frames_path = sorted([os.path.join(path, x) for x in os.listdir(path)])
frames = [ndimage.imread(frame_path) for frame_path in frames_path]
self.handle_type(frames)
return self | def from_frames(self, path):
"""
Read from frames
"""
frames_path = sorted([os.path.join(path, x) for x in os.listdir(path)])
frames = [ndimage.imread(frame_path) for frame_path in frames_path]
self.handle_type(frames)
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train | Video.from_video | Read from videos | example/gluon/lipnet/utils/preprocess_data.py | def from_video(self, path):
"""
Read from videos
"""
frames = self.get_video_frames(path)
self.handle_type(frames)
return self | def from_video(self, path):
"""
Read from videos
"""
frames = self.get_video_frames(path)
self.handle_type(frames)
return self | [
"Read",
"from",
"videos"
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/gluon/lipnet/utils/preprocess_data.py#L80-L86 | [
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train | Video.handle_type | Config video types | example/gluon/lipnet/utils/preprocess_data.py | def handle_type(self, frames):
"""
Config video types
"""
if self.vtype == 'mouth':
self.process_frames_mouth(frames)
elif self.vtype == 'face':
self.process_frames_face(frames)
else:
raise Exception('Video type not found') | def handle_type(self, frames):
"""
Config video types
"""
if self.vtype == 'mouth':
self.process_frames_mouth(frames)
elif self.vtype == 'face':
self.process_frames_face(frames)
else:
raise Exception('Video type not found') | [
"Config",
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/gluon/lipnet/utils/preprocess_data.py#L95-L104 | [
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... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | Video.process_frames_face | Preprocess from frames using face detector | example/gluon/lipnet/utils/preprocess_data.py | def process_frames_face(self, frames):
"""
Preprocess from frames using face detector
"""
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(self.face_predictor_path)
mouth_frames = self.get_frames_mouth(detector, predictor, frames)
self.... | def process_frames_face(self, frames):
"""
Preprocess from frames using face detector
"""
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(self.face_predictor_path)
mouth_frames = self.get_frames_mouth(detector, predictor, frames)
self.... | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/gluon/lipnet/utils/preprocess_data.py#L106-L116 | [
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... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
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