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apache/incubator-mxnet | python/mxnet/autograd.py | is_recording | def is_recording():
"""Get status on recording/not recording.
Returns
-------
Current state of recording.
"""
curr = ctypes.c_bool()
check_call(_LIB.MXAutogradIsRecording(ctypes.byref(curr)))
return curr.value | python | def is_recording():
"""Get status on recording/not recording.
Returns
-------
Current state of recording.
"""
curr = ctypes.c_bool()
check_call(_LIB.MXAutogradIsRecording(ctypes.byref(curr)))
return curr.value | [
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apache/incubator-mxnet | python/mxnet/autograd.py | is_training | def is_training():
"""Get status on training/predicting.
Returns
-------
Current state of training/predicting.
"""
curr = ctypes.c_bool()
check_call(_LIB.MXAutogradIsTraining(ctypes.byref(curr)))
return curr.value | python | def is_training():
"""Get status on training/predicting.
Returns
-------
Current state of training/predicting.
"""
curr = ctypes.c_bool()
check_call(_LIB.MXAutogradIsTraining(ctypes.byref(curr)))
return curr.value | [
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apache/incubator-mxnet | python/mxnet/autograd.py | mark_variables | def mark_variables(variables, gradients, grad_reqs='write'):
"""Mark NDArrays as variables to compute gradient for autograd.
Parameters
----------
variables: NDArray or list of NDArray
gradients: NDArray or list of NDArray
grad_reqs: str or list of str
"""
if isinstance(variables, NDArr... | python | def mark_variables(variables, gradients, grad_reqs='write'):
"""Mark NDArrays as variables to compute gradient for autograd.
Parameters
----------
variables: NDArray or list of NDArray
gradients: NDArray or list of NDArray
grad_reqs: str or list of str
"""
if isinstance(variables, NDArr... | [
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apache/incubator-mxnet | python/mxnet/autograd.py | _parse_head | def _parse_head(heads, head_grads):
"""parse head gradient for backward and grad."""
if isinstance(heads, NDArray):
heads = [heads]
if isinstance(head_grads, NDArray):
head_grads = [head_grads]
head_handles = c_handle_array(heads)
if head_grads is None:
hgrad_handles = ctyp... | python | def _parse_head(heads, head_grads):
"""parse head gradient for backward and grad."""
if isinstance(heads, NDArray):
heads = [heads]
if isinstance(head_grads, NDArray):
head_grads = [head_grads]
head_handles = c_handle_array(heads)
if head_grads is None:
hgrad_handles = ctyp... | [
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apache/incubator-mxnet | python/mxnet/autograd.py | backward | def backward(heads, head_grads=None, retain_graph=False, train_mode=True): #pylint: disable=redefined-outer-name
"""Compute the gradients of heads w.r.t previously marked variables.
Parameters
----------
heads: NDArray or list of NDArray
Output NDArray(s)
head_grads: NDArray or list of NDAr... | python | def backward(heads, head_grads=None, retain_graph=False, train_mode=True): #pylint: disable=redefined-outer-name
"""Compute the gradients of heads w.r.t previously marked variables.
Parameters
----------
heads: NDArray or list of NDArray
Output NDArray(s)
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apache/incubator-mxnet | python/mxnet/autograd.py | grad | def grad(heads, variables, head_grads=None, retain_graph=None, create_graph=False,
train_mode=True): #pylint: disable=redefined-outer-name
"""Compute the gradients of heads w.r.t variables. Gradients will be
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train_mode=True): #pylint: disable=redefined-outer-name
"""Compute the gradients of heads w.r.t variables. Gradients will be
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apache/incubator-mxnet | python/mxnet/autograd.py | get_symbol | def get_symbol(x):
"""Retrieve recorded computation history as `Symbol`.
Parameters
----------
x : NDArray
Array representing the head of computation graph.
Returns
-------
Symbol
The retrieved Symbol.
"""
hdl = SymbolHandle()
check_call(_LIB.MXAutogradGetSymbol... | python | def get_symbol(x):
"""Retrieve recorded computation history as `Symbol`.
Parameters
----------
x : NDArray
Array representing the head of computation graph.
Returns
-------
Symbol
The retrieved Symbol.
"""
hdl = SymbolHandle()
check_call(_LIB.MXAutogradGetSymbol... | [
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apache/incubator-mxnet | example/recommenders/movielens_data.py | load_mldataset | def load_mldataset(filename):
"""Not particularly fast code to parse the text file and load it into three NDArray's
and product an NDArrayIter
"""
user = []
item = []
score = []
with open(filename) as f:
for line in f:
tks = line.strip().split('\t')
if len(tks... | python | def load_mldataset(filename):
"""Not particularly fast code to parse the text file and load it into three NDArray's
and product an NDArrayIter
"""
user = []
item = []
score = []
with open(filename) as f:
for line in f:
tks = line.strip().split('\t')
if len(tks... | [
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apache/incubator-mxnet | cpp-package/scripts/OpWrapperGenerator.py | ParseAllOps | def ParseAllOps():
"""
MXNET_DLL int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
AtomicSymbolCreator **out_array);
MXNET_DLL int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
const char **nam... | python | def ParseAllOps():
"""
MXNET_DLL int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
AtomicSymbolCreator **out_array);
MXNET_DLL int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
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apache/incubator-mxnet | tools/caffe_translator/scripts/convert_caffe_model.py | main | def main():
"""Read .caffemodel path and .params path as input from command line
and use CaffeModelConverter to do the conversion"""
parser = argparse.ArgumentParser(description='.caffemodel to MXNet .params converter.')
parser.add_argument('caffemodel', help='Path to the .caffemodel file to convert.')
... | python | def main():
"""Read .caffemodel path and .params path as input from command line
and use CaffeModelConverter to do the conversion"""
parser = argparse.ArgumentParser(description='.caffemodel to MXNet .params converter.')
parser.add_argument('caffemodel', help='Path to the .caffemodel file to convert.')
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apache/incubator-mxnet | tools/caffe_translator/scripts/convert_caffe_model.py | CaffeModelConverter.add_param | def add_param(self, param_name, layer_index, blob_index):
"""Add a param to the .params file"""
blobs = self.layers[layer_index].blobs
self.dict_param[param_name] = mx.nd.array(caffe.io.blobproto_to_array(blobs[blob_index])) | python | def add_param(self, param_name, layer_index, blob_index):
"""Add a param to the .params file"""
blobs = self.layers[layer_index].blobs
self.dict_param[param_name] = mx.nd.array(caffe.io.blobproto_to_array(blobs[blob_index])) | [
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apache/incubator-mxnet | tools/caffe_translator/scripts/convert_caffe_model.py | CaffeModelConverter.add_arg_param | def add_arg_param(self, param_name, layer_index, blob_index):
"""Add an arg param to .params file. Example: weights of a fully connected layer."""
self.add_param('arg:%s' % param_name, layer_index, blob_index) | python | def add_arg_param(self, param_name, layer_index, blob_index):
"""Add an arg param to .params file. Example: weights of a fully connected layer."""
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apache/incubator-mxnet | tools/caffe_translator/scripts/convert_caffe_model.py | CaffeModelConverter.add_aux_param | def add_aux_param(self, param_name, layer_index, blob_index):
"""Add an aux param to .params file. Example: moving_mean in BatchNorm layer """
self.add_param('aux:%s' % param_name, layer_index, blob_index) | python | def add_aux_param(self, param_name, layer_index, blob_index):
"""Add an aux param to .params file. Example: moving_mean in BatchNorm layer """
self.add_param('aux:%s' % param_name, layer_index, blob_index) | [
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apache/incubator-mxnet | tools/caffe_translator/scripts/convert_caffe_model.py | CaffeModelConverter.add_optional_arg_param | def add_optional_arg_param(self, param_name, layer_index, blob_index):
"""Add an arg param. If there is no such param in .caffemodel fie, silently ignore it."""
blobs = self.layers[layer_index].blobs
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self.add_arg_param(param_name, layer_index, blob_index) | python | def add_optional_arg_param(self, param_name, layer_index, blob_index):
"""Add an arg param. If there is no such param in .caffemodel fie, silently ignore it."""
blobs = self.layers[layer_index].blobs
if blob_index < len(blobs):
self.add_arg_param(param_name, layer_index, blob_index) | [
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apache/incubator-mxnet | tools/caffe_translator/scripts/convert_caffe_model.py | CaffeModelConverter.convert | def convert(self, caffemodel_path, outmodel_path):
"""Convert a Caffe .caffemodel file to MXNet .params file"""
net_param = caffe_pb2.NetParameter()
with open(caffemodel_path, 'rb') as caffe_model_file:
net_param.ParseFromString(caffe_model_file.read())
layers = net_param.la... | python | def convert(self, caffemodel_path, outmodel_path):
"""Convert a Caffe .caffemodel file to MXNet .params file"""
net_param = caffe_pb2.NetParameter()
with open(caffemodel_path, 'rb') as caffe_model_file:
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apache/incubator-mxnet | example/rcnn/symnet/proposal_target.py | sample_rois | def sample_rois(rois, gt_boxes, num_classes, rois_per_image, fg_rois_per_image, fg_overlap, box_stds):
"""
generate random sample of ROIs comprising foreground and background examples
:param rois: [n, 5] (batch_index, x1, y1, x2, y2)
:param gt_boxes: [n, 5] (x1, y1, x2, y2, cls)
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"""
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:param rois: [n, 5] (batch_index, x1, y1, x2, y2)
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apache/incubator-mxnet | python/mxnet/operator.py | register | def register(reg_name):
"""Register a subclass of CustomOpProp to the registry with name reg_name."""
def do_register(prop_cls):
"""Register a subclass of CustomOpProp to the registry."""
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"""Register a subclass of CustomOpProp to the registry with name reg_name."""
def do_register(prop_cls):
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apache/incubator-mxnet | python/mxnet/operator.py | NDArrayOp.declare_backward_dependency | def declare_backward_dependency(self, out_grad, in_data, out_data):
"""Declare dependencies of this operator for backward pass.
Parameters
----------
out_grad : list of int
ids of out_grad blobs.
in_data : list of int
ids of in_data blobs.
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"""Declare dependencies of this operator for backward pass.
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ids of out_grad blobs.
in_data : list of int
ids of in_data blobs.
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apache/incubator-mxnet | python/mxnet/operator.py | CustomOp.assign | def assign(self, dst, req, src):
"""Helper function for assigning into dst depending on requirements."""
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dst[:] = src
elif req == 'add':
dst[:] += src | python | def assign(self, dst, req, src):
"""Helper function for assigning into dst depending on requirements."""
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return
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apache/incubator-mxnet | python/mxnet/operator.py | CustomOpProp.infer_type | def infer_type(self, in_type):
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list of argument types in the same order as
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apache/incubator-mxnet | python/mxnet/operator.py | CustomOpProp.infer_storage_type | def infer_storage_type(self, in_stype):
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apache/incubator-mxnet | python/mxnet/operator.py | CustomOpProp.infer_storage_type_backward | def infer_storage_type_backward(self, ograd_stype, in_stype, out_stype, igrad_stype, aux_stype):
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apache/incubator-mxnet | python/mxnet/operator.py | CustomOpProp.declare_backward_dependency | def declare_backward_dependency(self, out_grad, in_data, out_data):
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out_grad : list of int
ids of out_grad blobs.
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apache/incubator-mxnet | python/mxnet/operator.py | _Registry.inc | def inc(self):
"""Get index for new entry."""
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"""Get index for new entry."""
self.lock.acquire()
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apache/incubator-mxnet | tools/rec2idx.py | IndexCreator.close | def close(self):
"""Closes the record and index files."""
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"""Closes the record and index files."""
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apache/incubator-mxnet | tools/rec2idx.py | IndexCreator.tell | def tell(self):
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apache/incubator-mxnet | docs/mxdoc.py | _run_cmd | def _run_cmd(cmds):
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raise err | python | def _run_cmd(cmds):
"""Run commands, raise exception if failed"""
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apache/incubator-mxnet | docs/mxdoc.py | generate_doxygen | def generate_doxygen(app):
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apache/incubator-mxnet | docs/mxdoc.py | build_mxnet | def build_mxnet(app):
"""Build mxnet .so lib"""
if not os.path.exists(os.path.join(app.builder.srcdir, '..', 'config.mk')):
_run_cmd("cd %s/.. && cp make/config.mk config.mk && make -j$(nproc) USE_MKLDNN=0 USE_CPP_PACKAGE=1 " %
app.builder.srcdir)
else:
_run_cmd("cd %s/.. && ... | python | def build_mxnet(app):
"""Build mxnet .so lib"""
if not os.path.exists(os.path.join(app.builder.srcdir, '..', 'config.mk')):
_run_cmd("cd %s/.. && cp make/config.mk config.mk && make -j$(nproc) USE_MKLDNN=0 USE_CPP_PACKAGE=1 " %
app.builder.srcdir)
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apache/incubator-mxnet | docs/mxdoc.py | build_r_docs | def build_r_docs(app):
"""build r pdf"""
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pdf_path = app.builder.srcdir + '/api/r/mxnet-r-reference-manual.pdf'
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"""build r pdf"""
r_root = app.builder.srcdir + '/../R-package'
pdf_path = app.builder.srcdir + '/api/r/mxnet-r-reference-manual.pdf'
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apache/incubator-mxnet | docs/mxdoc.py | build_scala | def build_scala(app):
"""build scala for scala docs, java docs, and clojure docs to use"""
if any(v in _BUILD_VER for v in ['1.2.', '1.3.', '1.4.']):
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else:
_run_c... | python | def build_scala(app):
"""build scala for scala docs, java docs, and clojure docs to use"""
if any(v in _BUILD_VER for v in ['1.2.', '1.3.', '1.4.']):
_run_cmd("cd %s/.. && make scalapkg" % app.builder.srcdir)
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apache/incubator-mxnet | docs/mxdoc.py | build_scala_docs | def build_scala_docs(app):
"""build scala doc and then move the outdir"""
scala_path = app.builder.srcdir + '/../scala-package'
scala_doc_sources = 'find . -type f -name "*.scala" | egrep \"\.\/core|\.\/infer\" | egrep -v \"\/javaapi\" | egrep -v \"Suite\"'
scala_doc_classpath = ':'.join([
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"""build scala doc and then move the outdir"""
scala_path = app.builder.srcdir + '/../scala-package'
scala_doc_sources = 'find . -type f -name "*.scala" | egrep \"\.\/core|\.\/infer\" | egrep -v \"\/javaapi\" | egrep -v \"Suite\"'
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apache/incubator-mxnet | docs/mxdoc.py | build_java_docs | def build_java_docs(app):
"""build java docs and then move the outdir"""
java_path = app.builder.srcdir + '/../scala-package'
java_doc_sources = 'find . -type f -name "*.scala" | egrep \"\.\/core|\.\/infer\" | egrep \"\/javaapi\" | egrep -v \"Suite\"'
java_doc_classpath = ':'.join([
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"""build java docs and then move the outdir"""
java_path = app.builder.srcdir + '/../scala-package'
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apache/incubator-mxnet | docs/mxdoc.py | build_clojure_docs | def build_clojure_docs(app):
"""build clojure doc and then move the outdir"""
clojure_path = app.builder.srcdir + '/../contrib/clojure-package'
_run_cmd('cd ' + clojure_path + '; lein codox')
dest_path = app.builder.outdir + '/api/clojure/docs'
_run_cmd('rm -rf ' + dest_path)
_run_cmd('mkdir -p ... | python | def build_clojure_docs(app):
"""build clojure doc and then move the outdir"""
clojure_path = app.builder.srcdir + '/../contrib/clojure-package'
_run_cmd('cd ' + clojure_path + '; lein codox')
dest_path = app.builder.outdir + '/api/clojure/docs'
_run_cmd('rm -rf ' + dest_path)
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apache/incubator-mxnet | docs/mxdoc.py | _convert_md_table_to_rst | def _convert_md_table_to_rst(table):
"""Convert a markdown table to rst format"""
if len(table) < 3:
return ''
out = '```eval_rst\n.. list-table::\n :header-rows: 1\n\n'
for i,l in enumerate(table):
cols = l.split('|')[1:-1]
if i == 0:
ncol = len(cols)
else:... | python | def _convert_md_table_to_rst(table):
"""Convert a markdown table to rst format"""
if len(table) < 3:
return ''
out = '```eval_rst\n.. list-table::\n :header-rows: 1\n\n'
for i,l in enumerate(table):
cols = l.split('|')[1:-1]
if i == 0:
ncol = len(cols)
else:... | [
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apache/incubator-mxnet | docs/mxdoc.py | convert_table | def convert_table(app, docname, source):
"""Find tables in a markdown and then convert them into the rst format"""
num_tables = 0
for i,j in enumerate(source):
table = []
output = ''
in_table = False
for l in j.split('\n'):
r = l.strip()
if r.startswit... | python | def convert_table(app, docname, source):
"""Find tables in a markdown and then convert them into the rst format"""
num_tables = 0
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table = []
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apache/incubator-mxnet | docs/mxdoc.py | _parse_code_lines | def _parse_code_lines(lines):
"""A iterator that returns if a line is within a code block
Returns
-------
iterator of (str, bool, str, int)
- line: the line
- in_code: if this line is in a code block
- lang: the code block langunage
- indent: the code indent
"""
... | python | def _parse_code_lines(lines):
"""A iterator that returns if a line is within a code block
Returns
-------
iterator of (str, bool, str, int)
- line: the line
- in_code: if this line is in a code block
- lang: the code block langunage
- indent: the code indent
"""
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apache/incubator-mxnet | docs/mxdoc.py | _get_blocks | def _get_blocks(lines):
"""split lines into code and non-code blocks
Returns
-------
iterator of (bool, str, list of str)
- if it is a code block
- source language
- lines of source
"""
cur_block = []
pre_lang = None
pre_in_code = None
for (l, in_code, cur_lang, _)... | python | def _get_blocks(lines):
"""split lines into code and non-code blocks
Returns
-------
iterator of (bool, str, list of str)
- if it is a code block
- source language
- lines of source
"""
cur_block = []
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apache/incubator-mxnet | docs/mxdoc.py | _get_python_block_output | def _get_python_block_output(src, global_dict, local_dict):
"""Evaluate python source codes
Returns
(bool, str):
- True if success
- output
"""
src = '\n'.join([l for l in src.split('\n')
if not l.startswith('%') and not 'plt.show()' in l])
ret_status = True
... | python | def _get_python_block_output(src, global_dict, local_dict):
"""Evaluate python source codes
Returns
(bool, str):
- True if success
- output
"""
src = '\n'.join([l for l in src.split('\n')
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ret_status = True
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apache/incubator-mxnet | docs/mxdoc.py | copy_artifacts | def copy_artifacts(app):
"""Copies artifacts needed for website presentation"""
dest_path = app.builder.outdir + '/error'
source_path = app.builder.srcdir + '/build_version_doc/artifacts'
_run_cmd('cd ' + app.builder.srcdir)
_run_cmd('rm -rf ' + dest_path)
_run_cmd('mkdir -p ' + dest_path)
_... | python | def copy_artifacts(app):
"""Copies artifacts needed for website presentation"""
dest_path = app.builder.outdir + '/error'
source_path = app.builder.srcdir + '/build_version_doc/artifacts'
_run_cmd('cd ' + app.builder.srcdir)
_run_cmd('rm -rf ' + dest_path)
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apache/incubator-mxnet | tools/caffe_converter/convert_caffe_modelzoo.py | download_caffe_model | def download_caffe_model(model_name, meta_info, dst_dir='./model'):
"""Download caffe model into disk by the given meta info """
if not os.path.isdir(dst_dir):
os.mkdir(dst_dir)
model_name = os.path.join(dst_dir, model_name)
assert 'prototxt' in meta_info, "missing prototxt url"
proto_url, ... | python | def download_caffe_model(model_name, meta_info, dst_dir='./model'):
"""Download caffe model into disk by the given meta info """
if not os.path.isdir(dst_dir):
os.mkdir(dst_dir)
model_name = os.path.join(dst_dir, model_name)
assert 'prototxt' in meta_info, "missing prototxt url"
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apache/incubator-mxnet | tools/caffe_converter/convert_caffe_modelzoo.py | convert_caffe_model | def convert_caffe_model(model_name, meta_info, dst_dir='./model'):
"""Download, convert and save a caffe model"""
(prototxt, caffemodel, mean) = download_caffe_model(model_name, meta_info, dst_dir)
model_name = os.path.join(dst_dir, model_name)
convert_model(prototxt, caffemodel, model_name)
if isi... | python | def convert_caffe_model(model_name, meta_info, dst_dir='./model'):
"""Download, convert and save a caffe model"""
(prototxt, caffemodel, mean) = download_caffe_model(model_name, meta_info, dst_dir)
model_name = os.path.join(dst_dir, model_name)
convert_model(prototxt, caffemodel, model_name)
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apache/incubator-mxnet | example/gluon/lipnet/utils/multi.py | multi_p_run | def multi_p_run(tot_num, _func, worker, params, n_process):
"""
Run _func with multi-process using params.
"""
from multiprocessing import Process, Queue
out_q = Queue()
procs = []
split_num = split_seq(list(range(0, tot_num)), n_process)
print(tot_num, ">>", split_num)
split_len ... | python | def multi_p_run(tot_num, _func, worker, params, n_process):
"""
Run _func with multi-process using params.
"""
from multiprocessing import Process, Queue
out_q = Queue()
procs = []
split_num = split_seq(list(range(0, tot_num)), n_process)
print(tot_num, ">>", split_num)
split_len ... | [
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apache/incubator-mxnet | example/gluon/lipnet/utils/multi.py | split_seq | def split_seq(sam_num, n_tile):
"""
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import math
print(sam_num)
print(n_tile)
start_num = sam_num[0::int(math.ceil(len(sam_num) / (n_tile)))]
end_num = start_num[1::]
end_num.append(len(sam_num))
return [[i, j] for i, j in zip(s... | python | def split_seq(sam_num, n_tile):
"""
Split the number(sam_num) into numbers by n_tile
"""
import math
print(sam_num)
print(n_tile)
start_num = sam_num[0::int(math.ceil(len(sam_num) / (n_tile)))]
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apache/incubator-mxnet | example/gluon/lipnet/utils/multi.py | put_worker | def put_worker(func, from_idx, to_idx, params, out_q):
"""
put worker
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put worker
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apache/incubator-mxnet | example/ssd/config/utils.py | namedtuple_with_defaults | def namedtuple_with_defaults(typename, field_names, default_values=()):
""" create a namedtuple with default values """
T = collections.namedtuple(typename, field_names)
T.__new__.__defaults__ = (None, ) * len(T._fields)
if isinstance(default_values, collections.Mapping):
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""" create a namedtuple with default values """
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apache/incubator-mxnet | example/ssd/config/utils.py | merge_dict | def merge_dict(a, b):
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c = a.copy()
c.update(b)
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""" merge dict a, b, with b overriding keys in a """
c = a.copy()
c.update(b)
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apache/incubator-mxnet | example/ssd/config/utils.py | zip_namedtuple | def zip_namedtuple(nt_list):
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nt_list = [nt_list]
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if not nt_list:
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apache/incubator-mxnet | example/ssd/config/utils.py | config_as_dict | def config_as_dict(cfg):
""" convert raw configuration to unified dictionary """
ret = cfg.__dict__.copy()
# random cropping params
del ret['rand_crop_samplers']
assert isinstance(cfg.rand_crop_samplers, list)
ret = merge_dict(ret, zip_namedtuple(cfg.rand_crop_samplers))
num_crop_sampler = l... | python | def config_as_dict(cfg):
""" convert raw configuration to unified dictionary """
ret = cfg.__dict__.copy()
# random cropping params
del ret['rand_crop_samplers']
assert isinstance(cfg.rand_crop_samplers, list)
ret = merge_dict(ret, zip_namedtuple(cfg.rand_crop_samplers))
num_crop_sampler = l... | [
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/import_model.py | import_model | def import_model(model_file):
"""Imports the ONNX model file, passed as a parameter, into MXNet symbol and parameters.
Operator support and coverage -
https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
Parameters
----------
model_file : str
ONNX model file name
... | python | def import_model(model_file):
"""Imports the ONNX model file, passed as a parameter, into MXNet symbol and parameters.
Operator support and coverage -
https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
Parameters
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model_file : str
ONNX model file name
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/import_model.py | get_model_metadata | def get_model_metadata(model_file):
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Notes
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This method is available when you ``import mxnet.contrib.onnx``
Parameters
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model_file : str
ONNX model file name
... | python | def get_model_metadata(model_file):
"""
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Notes
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model_file : str
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apache/incubator-mxnet | example/ssd/symbol/common.py | conv_act_layer | def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new la... | python | def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
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continue on which layer
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"""
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apache/incubator-mxnet | example/ssd/symbol/common.py | multi_layer_feature | def multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128):
"""Wrapper function to extract features from base network, attaching extra
layers and SSD specific layers
Parameters
----------
from_layers : list of str
feature extraction layers, use '' for add extra l... | python | def multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128):
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apache/incubator-mxnet | example/ssd/symbol/common.py | multibox_layer | def multibox_layer(from_layers, num_classes, sizes=[.2, .95],
ratios=[1], normalization=-1, num_channels=[],
clip=False, interm_layer=0, steps=[]):
"""
the basic aggregation module for SSD detection. Takes in multiple layers,
generate multiple object detection targets... | python | def multibox_layer(from_layers, num_classes, sizes=[.2, .95],
ratios=[1], normalization=-1, num_channels=[],
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apache/incubator-mxnet | python/mxnet/gluon/loss.py | _apply_weighting | def _apply_weighting(F, loss, weight=None, sample_weight=None):
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Parameters
----------
loss : Symbol
The loss to be weighted.
weight : float or None
Global scalar weight for loss.
sample_weight : Symbol or None
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loss : Symbol
The loss to be weighted.
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Global scalar weight for loss.
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apache/incubator-mxnet | python/mxnet/gluon/loss.py | _reshape_like | def _reshape_like(F, x, y):
"""Reshapes x to the same shape as y."""
return x.reshape(y.shape) if F is ndarray else F.reshape_like(x, y) | python | def _reshape_like(F, x, y):
"""Reshapes x to the same shape as y."""
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"""create TV gradient executor with input binded on img
"""
if tv_weight <= 0.0:
return None
nchannel = img.shape[1]
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"""create TV gradient executor with input binded on img
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apache/incubator-mxnet | example/neural-style/nstyle.py | train_nstyle | def train_nstyle(args, callback=None):
"""Train a neural style network.
Args are from argparse and control input, output, hyper-parameters.
callback allows for display of training progress.
"""
# input
dev = mx.gpu(args.gpu) if args.gpu >= 0 else mx.cpu()
content_np = PreprocessContentImage(... | python | def train_nstyle(args, callback=None):
"""Train a neural style network.
Args are from argparse and control input, output, hyper-parameters.
callback allows for display of training progress.
"""
# input
dev = mx.gpu(args.gpu) if args.gpu >= 0 else mx.cpu()
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apache/incubator-mxnet | example/ssd/dataset/iterator.py | DetIter._get_batch | def _get_batch(self):
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batch_data = mx.nd.zeros((self.batch_size, 3, self._data_shape[0], self._data_shape[1]))
batch_label = []
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"""
Load data/label from dataset
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batch_data = mx.nd.zeros((self.batch_size, 3, self._data_shape[0], self._data_shape[1]))
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apache/incubator-mxnet | example/ssd/dataset/iterator.py | DetIter._data_augmentation | def _data_augmentation(self, data, label):
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rand_crops = []
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apache/incubator-mxnet | example/deep-embedded-clustering/data.py | get_mnist | def get_mnist():
""" Gets MNIST dataset """
np.random.seed(1234) # set seed for deterministic ordering
mnist_data = mx.test_utils.get_mnist()
X = np.concatenate([mnist_data['train_data'], mnist_data['test_data']])
Y = np.concatenate([mnist_data['train_label'], mnist_data['test_label']])
p = np.... | python | def get_mnist():
""" Gets MNIST dataset """
np.random.seed(1234) # set seed for deterministic ordering
mnist_data = mx.test_utils.get_mnist()
X = np.concatenate([mnist_data['train_data'], mnist_data['test_data']])
Y = np.concatenate([mnist_data['train_label'], mnist_data['test_label']])
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apache/incubator-mxnet | python/mxnet/executor_manager.py | _split_input_slice | def _split_input_slice(batch_size, work_load_list):
"""Get input slice from the input shape.
Parameters
----------
batch_size : int
The number of samples in a mini-batch.
work_load_list : list of float or int, optional
The list of work load for different devices,
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"""Get input slice from the input shape.
Parameters
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batch_size : int
The number of samples in a mini-batch.
work_load_list : list of float or int, optional
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apache/incubator-mxnet | python/mxnet/executor_manager.py | _check_arguments | def _check_arguments(symbol):
"""Check the argument names of symbol.
This function checks the duplication of arguments in Symbol.
The check is done for feedforward net for now.
Parameters
----------
symbol : Symbol
The network configuration.
"""
arg_set = set()
arg_names = s... | python | def _check_arguments(symbol):
"""Check the argument names of symbol.
This function checks the duplication of arguments in Symbol.
The check is done for feedforward net for now.
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symbol : Symbol
The network configuration.
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apache/incubator-mxnet | python/mxnet/executor_manager.py | _load_general | def _load_general(data, targets):
"""Load a list of arrays into a list of arrays specified by slices."""
for d_src, d_targets in zip(data, targets):
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d_src.copyto(d_targets)
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apache/incubator-mxnet | python/mxnet/executor_manager.py | _bind_exec | def _bind_exec(sym, ctx, input_shapes, param_names, need_grad=False,
base_exec=None, shared_data_arrays=None, input_types=None, logger=logging):
"""bind executor for bucketing, potentially sharing data with an existing executor."""
arg_shape, _, aux_shape = sym.infer_shape(**input_shapes)
ass... | python | def _bind_exec(sym, ctx, input_shapes, param_names, need_grad=False,
base_exec=None, shared_data_arrays=None, input_types=None, logger=logging):
"""bind executor for bucketing, potentially sharing data with an existing executor."""
arg_shape, _, aux_shape = sym.infer_shape(**input_shapes)
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apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorGroup.load_data_batch | def load_data_batch(self, data_batch):
"""Load data and labels into arrays."""
_load_data(data_batch, self.data_arrays)
_load_label(data_batch, self.label_arrays) | python | def load_data_batch(self, data_batch):
"""Load data and labels into arrays."""
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apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorGroup.forward | def forward(self, is_train=False):
"""Perform a forward pass on each executor."""
for texec in self.train_execs:
texec.forward(is_train=is_train) | python | def forward(self, is_train=False):
"""Perform a forward pass on each executor."""
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apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorGroup.update_metric | def update_metric(self, metric, labels, pre_sliced=False):
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labels_slice = [label[islice] for label in labels]
... | python | def update_metric(self, metric, labels, pre_sliced=False):
"""Update evaluation metric with label and current outputs."""
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apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorManager.install_monitor | def install_monitor(self, monitor):
"""Install monitor on all executors."""
if self.sym_gen is not None:
raise NotImplementedError("Monitoring is not implemented for bucketing")
for train_exec in self.execgrp.train_execs:
monitor.install(train_exec) | python | def install_monitor(self, monitor):
"""Install monitor on all executors."""
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apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorManager.set_params | def set_params(self, arg_params, aux_params):
"""Set parameter and aux values.
Parameters
----------
arg_params : list of NDArray
Source parameter arrays
aux_params : list of NDArray
Source aux arrays.
"""
for texec in self.execgrp.train_... | python | def set_params(self, arg_params, aux_params):
"""Set parameter and aux values.
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arg_params : list of NDArray
Source parameter arrays
aux_params : list of NDArray
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apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorManager.load_data_batch | def load_data_batch(self, data_batch):
"""Load data and labels into arrays."""
if self.sym_gen is not None:
key = data_batch.bucket_key
if key not in self.execgrp_bucket:
# create new bucket entry
symbol = self.sym_gen(key)
execgrp ... | python | def load_data_batch(self, data_batch):
"""Load data and labels into arrays."""
if self.sym_gen is not None:
key = data_batch.bucket_key
if key not in self.execgrp_bucket:
# create new bucket entry
symbol = self.sym_gen(key)
execgrp ... | [
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apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorManager.update_metric | def update_metric(self, metric, labels, pre_sliced=False):
"""Update metric with the current executor."""
self.curr_execgrp.update_metric(metric, labels, pre_sliced) | python | def update_metric(self, metric, labels, pre_sliced=False):
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apache/incubator-mxnet | example/reinforcement-learning/dqn/replay_memory.py | ReplayMemory.clear | def clear(self):
"""
Clear all contents in the relay memory
"""
self.states[:] = 0
self.actions[:] = 0
self.rewards[:] = 0
self.terminate_flags[:] = 0
self.top = 0
self.size = 0 | python | def clear(self):
"""
Clear all contents in the relay memory
"""
self.states[:] = 0
self.actions[:] = 0
self.rewards[:] = 0
self.terminate_flags[:] = 0
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apache/incubator-mxnet | cpp-package/scripts/lint.py | get_header_guard_dmlc | def get_header_guard_dmlc(filename):
"""Get Header Guard Convention for DMLC Projects.
For headers in include, directly use the path
For headers in src, use project name plus path
Examples: with project-name = dmlc
include/dmlc/timer.h -> DMLC_TIMTER_H_
src/io/libsvm_parser.h -> DMLC_IO_... | python | def get_header_guard_dmlc(filename):
"""Get Header Guard Convention for DMLC Projects.
For headers in include, directly use the path
For headers in src, use project name plus path
Examples: with project-name = dmlc
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apache/incubator-mxnet | cpp-package/scripts/lint.py | process | def process(fname, allow_type):
"""Process a file."""
fname = str(fname)
# HACK: ignore op.h which is automatically generated
if fname.endswith('op.h'):
return
arr = fname.rsplit('.', 1)
if fname.find('#') != -1 or arr[-1] not in allow_type:
return
if arr[-1] in CXX_SUFFIX:
... | python | def process(fname, allow_type):
"""Process a file."""
fname = str(fname)
# HACK: ignore op.h which is automatically generated
if fname.endswith('op.h'):
return
arr = fname.rsplit('.', 1)
if fname.find('#') != -1 or arr[-1] not in allow_type:
return
if arr[-1] in CXX_SUFFIX:
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apache/incubator-mxnet | cpp-package/scripts/lint.py | main | def main():
"""Main entry function."""
if len(sys.argv) < 3:
print('Usage: <project-name> <filetype> <list-of-path to traverse>')
print('\tfiletype can be python/cpp/all')
exit(-1)
_HELPER.project_name = sys.argv[1]
file_type = sys.argv[2]
allow_type = []
if file_type == ... | python | def main():
"""Main entry function."""
if len(sys.argv) < 3:
print('Usage: <project-name> <filetype> <list-of-path to traverse>')
print('\tfiletype can be python/cpp/all')
exit(-1)
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file_type = sys.argv[2]
allow_type = []
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apache/incubator-mxnet | cpp-package/scripts/lint.py | LintHelper._print_summary_map | def _print_summary_map(strm, result_map, ftype):
"""Print summary of certain result map."""
if len(result_map) == 0:
return 0
npass = len([x for k, x in result_map.iteritems() if len(x) == 0])
strm.write('=====%d/%d %s files passed check=====\n' % (npass, len(result_map), fty... | python | def _print_summary_map(strm, result_map, ftype):
"""Print summary of certain result map."""
if len(result_map) == 0:
return 0
npass = len([x for k, x in result_map.iteritems() if len(x) == 0])
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apache/incubator-mxnet | cpp-package/scripts/lint.py | LintHelper.process_cpp | def process_cpp(self, path, suffix):
"""Process a cpp file."""
_cpplint_state.ResetErrorCounts()
cpplint.ProcessFile(str(path), _cpplint_state.verbose_level)
_cpplint_state.PrintErrorCounts()
errors = _cpplint_state.errors_by_category.copy()
if suffix == 'h':
... | python | def process_cpp(self, path, suffix):
"""Process a cpp file."""
_cpplint_state.ResetErrorCounts()
cpplint.ProcessFile(str(path), _cpplint_state.verbose_level)
_cpplint_state.PrintErrorCounts()
errors = _cpplint_state.errors_by_category.copy()
if suffix == 'h':
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apache/incubator-mxnet | cpp-package/scripts/lint.py | LintHelper.process_python | def process_python(self, path):
"""Process a python file."""
(pylint_stdout, pylint_stderr) = epylint.py_run(
' '.join([str(path)] + self.pylint_opts), return_std=True)
emap = {}
print(pylint_stderr.read())
for line in pylint_stdout:
sys.stderr.write(line)... | python | def process_python(self, path):
"""Process a python file."""
(pylint_stdout, pylint_stderr) = epylint.py_run(
' '.join([str(path)] + self.pylint_opts), return_std=True)
emap = {}
print(pylint_stderr.read())
for line in pylint_stdout:
sys.stderr.write(line)... | [
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apache/incubator-mxnet | cpp-package/scripts/lint.py | LintHelper.print_summary | def print_summary(self, strm):
"""Print summary of lint."""
nerr = 0
nerr += LintHelper._print_summary_map(strm, self.cpp_header_map, 'cpp-header')
nerr += LintHelper._print_summary_map(strm, self.cpp_src_map, 'cpp-soruce')
nerr += LintHelper._print_summary_map(strm, self.python_... | python | def print_summary(self, strm):
"""Print summary of lint."""
nerr = 0
nerr += LintHelper._print_summary_map(strm, self.cpp_header_map, 'cpp-header')
nerr += LintHelper._print_summary_map(strm, self.cpp_src_map, 'cpp-soruce')
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apache/incubator-mxnet | python/mxnet/kvstore_server.py | _init_kvstore_server_module | def _init_kvstore_server_module():
"""Start server/scheduler."""
is_worker = ctypes.c_int()
check_call(_LIB.MXKVStoreIsWorkerNode(ctypes.byref(is_worker)))
if is_worker.value == 0:
kvstore = create('dist')
server = KVStoreServer(kvstore)
server.run()
sys.exit() | python | def _init_kvstore_server_module():
"""Start server/scheduler."""
is_worker = ctypes.c_int()
check_call(_LIB.MXKVStoreIsWorkerNode(ctypes.byref(is_worker)))
if is_worker.value == 0:
kvstore = create('dist')
server = KVStoreServer(kvstore)
server.run()
sys.exit() | [
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apache/incubator-mxnet | python/mxnet/kvstore_server.py | KVStoreServer._controller | def _controller(self):
"""Return the server controller."""
def server_controller(cmd_id, cmd_body, _):
"""Server controler."""
if not self.init_logginig:
# the reason put the codes here is because we cannot get
# kvstore.rank earlier
... | python | def _controller(self):
"""Return the server controller."""
def server_controller(cmd_id, cmd_body, _):
"""Server controler."""
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# the reason put the codes here is because we cannot get
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apache/incubator-mxnet | python/mxnet/kvstore_server.py | KVStoreServer.run | def run(self):
"""Run the server, whose behavior is like.
>>> while receive(x):
... if is_command x: controller(x)
... else if is_key_value x: updater(x)
"""
_ctrl_proto = ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.c_char_p, ctypes.c_void_p)
check_call(... | python | def run(self):
"""Run the server, whose behavior is like.
>>> while receive(x):
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"""
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apache/incubator-mxnet | python/mxnet/ndarray/register.py | _generate_ndarray_function_code | def _generate_ndarray_function_code(handle, name, func_name, signature_only=False):
"""Generate function for ndarray op by handle and function name."""
real_name = ctypes.c_char_p()
desc = ctypes.c_char_p()
num_args = mx_uint()
arg_names = ctypes.POINTER(ctypes.c_char_p)()
arg_types = ctypes.POI... | python | def _generate_ndarray_function_code(handle, name, func_name, signature_only=False):
"""Generate function for ndarray op by handle and function name."""
real_name = ctypes.c_char_p()
desc = ctypes.c_char_p()
num_args = mx_uint()
arg_names = ctypes.POINTER(ctypes.c_char_p)()
arg_types = ctypes.POI... | [
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apache/incubator-mxnet | python/mxnet/ndarray/register.py | _make_ndarray_function | def _make_ndarray_function(handle, name, func_name):
"""Create a NDArray function from the FunctionHandle."""
code, doc_str = _generate_ndarray_function_code(handle, name, func_name)
local = {}
exec(code, None, local) # pylint: disable=exec-used
ndarray_function = local[func_name]
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"""Create a NDArray function from the FunctionHandle."""
code, doc_str = _generate_ndarray_function_code(handle, name, func_name)
local = {}
exec(code, None, local) # pylint: disable=exec-used
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apache/incubator-mxnet | python/mxnet/contrib/text/utils.py | count_tokens_from_str | def count_tokens_from_str(source_str, token_delim=' ', seq_delim='\n',
to_lower=False, counter_to_update=None):
"""Counts tokens in the specified string.
For token_delim=\'<td>\' and seq_delim=\'<sd>\', a specified string of two sequences of
tokens may look like::
<td>token1<... | python | def count_tokens_from_str(source_str, token_delim=' ', seq_delim='\n',
to_lower=False, counter_to_update=None):
"""Counts tokens in the specified string.
For token_delim=\'<td>\' and seq_delim=\'<sd>\', a specified string of two sequences of
tokens may look like::
<td>token1<... | [
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apache/incubator-mxnet | python/mxnet/ndarray/utils.py | zeros | def zeros(shape, ctx=None, dtype=None, stype=None, **kwargs):
"""Return a new array of given shape and type, filled with zeros.
Parameters
----------
shape : int or tuple of int
The shape of the empty array
ctx : Context, optional
An optional device context (default is the current d... | python | def zeros(shape, ctx=None, dtype=None, stype=None, **kwargs):
"""Return a new array of given shape and type, filled with zeros.
Parameters
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shape : int or tuple of int
The shape of the empty array
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apache/incubator-mxnet | python/mxnet/ndarray/utils.py | empty | def empty(shape, ctx=None, dtype=None, stype=None):
"""Returns a new array of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
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shape : int or tuple of int
The shape of the empty array.
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apache/incubator-mxnet | python/mxnet/ndarray/utils.py | array | def array(source_array, ctx=None, dtype=None):
"""Creates an array from any object exposing the array interface.
Parameters
----------
source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
ctx... | python | def array(source_array, ctx=None, dtype=None):
"""Creates an array from any object exposing the array interface.
Parameters
----------
source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
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apache/incubator-mxnet | python/mxnet/ndarray/utils.py | load | def load(fname):
"""Loads an array from file.
See more details in ``save``.
Parameters
----------
fname : str
The filename.
Returns
-------
list of NDArray, RowSparseNDArray or CSRNDArray, or \
dict of str to NDArray, RowSparseNDArray or CSRNDArray
Loaded data.
... | python | def load(fname):
"""Loads an array from file.
See more details in ``save``.
Parameters
----------
fname : str
The filename.
Returns
-------
list of NDArray, RowSparseNDArray or CSRNDArray, or \
dict of str to NDArray, RowSparseNDArray or CSRNDArray
Loaded data.
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apache/incubator-mxnet | python/mxnet/ndarray/utils.py | load_frombuffer | def load_frombuffer(buf):
"""Loads an array dictionary or list from a buffer
See more details in ``save``.
Parameters
----------
buf : str
Buffer containing contents of a file as a string or bytes.
Returns
-------
list of NDArray, RowSparseNDArray or CSRNDArray, or \
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"""Loads an array dictionary or list from a buffer
See more details in ``save``.
Parameters
----------
buf : str
Buffer containing contents of a file as a string or bytes.
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apache/incubator-mxnet | python/mxnet/ndarray/utils.py | save | def save(fname, data):
"""Saves a list of arrays or a dict of str->array to file.
Examples of filenames:
- ``/path/to/file``
- ``s3://my-bucket/path/to/file`` (if compiled with AWS S3 supports)
- ``hdfs://path/to/file`` (if compiled with HDFS supports)
Parameters
----------
fname : st... | python | def save(fname, data):
"""Saves a list of arrays or a dict of str->array to file.
Examples of filenames:
- ``/path/to/file``
- ``s3://my-bucket/path/to/file`` (if compiled with AWS S3 supports)
- ``hdfs://path/to/file`` (if compiled with HDFS supports)
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----------
fname : st... | [
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apache/incubator-mxnet | python/mxnet/gluon/block.py | _common_prefix | def _common_prefix(names):
"""Get the common prefix for all names"""
if not names:
return ''
prefix = names[0]
for name in names:
i = 0
while i < len(prefix) and i < len(name) and prefix[i] == name[i]:
i += 1
prefix = prefix[:i]
return prefix | python | def _common_prefix(names):
"""Get the common prefix for all names"""
if not names:
return ''
prefix = names[0]
for name in names:
i = 0
while i < len(prefix) and i < len(name) and prefix[i] == name[i]:
i += 1
prefix = prefix[:i]
return prefix | [
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apache/incubator-mxnet | python/mxnet/gluon/block.py | _infer_param_types | def _infer_param_types(in_params, out_params, arg_params, aux_params, default_dtype=mx_real_t):
"""Utility function that helps in inferring DType of args and auxs params
from given input param.
Parameters
----------
in_params: List of Symbol
List of input symbol variables.
out_params: S... | python | def _infer_param_types(in_params, out_params, arg_params, aux_params, default_dtype=mx_real_t):
"""Utility function that helps in inferring DType of args and auxs params
from given input param.
Parameters
----------
in_params: List of Symbol
List of input symbol variables.
out_params: S... | [
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apache/incubator-mxnet | python/mxnet/gluon/block.py | _BlockScope.create | def create(prefix, params, hint):
"""Creates prefix and params for new `Block`."""
current = getattr(_BlockScope._current, "value", None)
if current is None:
if prefix is None:
if not hasattr(_name.NameManager._current, "value"):
_name.NameManager.... | python | def create(prefix, params, hint):
"""Creates prefix and params for new `Block`."""
current = getattr(_BlockScope._current, "value", None)
if current is None:
if prefix is None:
if not hasattr(_name.NameManager._current, "value"):
_name.NameManager.... | [
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apache/incubator-mxnet | python/mxnet/gluon/block.py | Block.collect_params | def collect_params(self, select=None):
"""Returns a :py:class:`ParameterDict` containing this :py:class:`Block` and all of its
children's Parameters(default), also can returns the select :py:class:`ParameterDict`
which match some given regular expressions.
For example, collect the speci... | python | def collect_params(self, select=None):
"""Returns a :py:class:`ParameterDict` containing this :py:class:`Block` and all of its
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apache/incubator-mxnet | python/mxnet/gluon/block.py | Block.save_params | def save_params(self, filename):
"""[Deprecated] Please use save_parameters. Note that if you want load
from SymbolBlock later, please use export instead.
Save parameters to file.
filename : str
Path to file.
"""
warnings.warn("save_params is deprecated. Ple... | python | def save_params(self, filename):
"""[Deprecated] Please use save_parameters. Note that if you want load
from SymbolBlock later, please use export instead.
Save parameters to file.
filename : str
Path to file.
"""
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apache/incubator-mxnet | python/mxnet/gluon/block.py | Block.load_parameters | def load_parameters(self, filename, ctx=None, allow_missing=False,
ignore_extra=False):
"""Load parameters from file previously saved by `save_parameters`.
Parameters
----------
filename : str
Path to parameter file.
ctx : Context or list of C... | python | def load_parameters(self, filename, ctx=None, allow_missing=False,
ignore_extra=False):
"""Load parameters from file previously saved by `save_parameters`.
Parameters
----------
filename : str
Path to parameter file.
ctx : Context or list of C... | [
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