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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 |
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 |
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... |
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... |
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... |
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
returned as new NDArrays instead of stored into `variable.grad`.
Supports recording gradie... |
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... |
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... |
def ParseAllOps():
"""
MXNET_DLL int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
AtomicSymbolCreator **out_array);
MXNET_DLL int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
const char **nam... |
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.')
... |
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])) |
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) |
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) |
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) |
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... |
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)
:param num_classes: ... |
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."""
fb_functype = CFUNCTYPE(c_int, c_int, POINTER(c_void_p), POINTER(c_int),
POI... |
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.
out_dat... |
def assign(self, dst, req, src):
"""Helper function for assigning into dst depending on requirements."""
if req == 'null':
return
elif req in ('write', 'inplace'):
dst[:] = src
elif req == 'add':
dst[:] += src |
def infer_type(self, in_type):
"""infer_type interface. override to create new operators
Parameters
----------
in_type : list of np.dtype
list of argument types in the same order as
declared in list_arguments.
Returns
-------
in_type : li... |
def infer_storage_type(self, in_stype):
"""infer_storage_type interface. Used to infer storage type of
inputs and outputs in the forward pass. When this interface is not implemented,
all stypes will be inferred as default.
Parameters
----------
in_stype : list of stypes,... |
def infer_storage_type_backward(self, ograd_stype, in_stype, out_stype, igrad_stype, aux_stype):
"""infer_storage_type_backward interface. Used to infer storage
type of inputs and outputs in the backward pass.
Will raise an error if undefined storage type is returned.
Returned lists hav... |
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.
out_dat... |
def inc(self):
"""Get index for new entry."""
self.lock.acquire()
cur = self.counter
self.counter += 1
self.lock.release()
return cur |
def close(self):
"""Closes the record and index files."""
if not self.is_open:
return
super(IndexCreator, self).close()
self.fidx.close() |
def tell(self):
"""Returns the current position of read head.
"""
pos = ctypes.c_size_t()
check_call(_LIB.MXRecordIOReaderTell(self.handle, ctypes.byref(pos)))
return pos.value |
def create_index(self):
"""Creates the index file from open record file
"""
self.reset()
counter = 0
pre_time = time.time()
while True:
if counter % 1000 == 0:
cur_time = time.time()
print('time:', cur_time - pre_time, ' count:'... |
def _run_cmd(cmds):
"""Run commands, raise exception if failed"""
if not isinstance(cmds, str):
cmds = "".join(cmds)
print("Execute \"%s\"" % cmds)
try:
subprocess.check_call(cmds, shell=True)
except subprocess.CalledProcessError as err:
print(err)
raise err |
def generate_doxygen(app):
"""Run the doxygen make commands"""
_run_cmd("cd %s/.. && make doxygen" % app.builder.srcdir)
_run_cmd("cp -rf doxygen/html %s/doxygen" % app.builder.outdir) |
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/.. && ... |
def build_r_docs(app):
"""build r pdf"""
r_root = app.builder.srcdir + '/../R-package'
pdf_path = app.builder.srcdir + '/api/r/mxnet-r-reference-manual.pdf'
_run_cmd('cd ' + r_root +
'; R -e "roxygen2::roxygenize()"; R CMD Rd2pdf . --no-preview -o ' + pdf_path)
dest_path = app.builder.o... |
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)
_run_cmd("cd %s/.. && make scalainstall" % app.builder.srcdir)
else:
_run_c... |
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([
'`fi... |
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([
'`find nativ... |
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 ... |
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:... |
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... |
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
"""
... |
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, _)... |
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
... |
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)
_... |
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, ... |
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... |
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 ... |
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)))]
end_num = start_num[1::]
end_num.append(len(sam_num))
return [[i, j] for i, j in zip(s... |
def put_worker(func, from_idx, to_idx, params, out_q):
"""
put worker
"""
succ, fail = func(from_idx, to_idx, params)
return out_q.put({'succ': succ, 'fail': fail}) |
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):
prototype = T(**default... |
def merge_dict(a, b):
""" merge dict a, b, with b overriding keys in a """
c = a.copy()
c.update(b)
return c |
def zip_namedtuple(nt_list):
""" accept list of namedtuple, return a dict of zipped fields """
if not nt_list:
return dict()
if not isinstance(nt_list, list):
nt_list = [nt_list]
for nt in nt_list:
assert type(nt) == type(nt_list[0])
ret = {k : [v] for k, v in nt_list[0]._asd... |
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... |
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
... |
def get_model_metadata(model_file):
"""
Returns the name and shape information of input and output tensors of the given ONNX model file.
Notes
-----
This method is available when you ``import mxnet.contrib.onnx``
Parameters
----------
model_file : str
ONNX model file name
... |
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... |
def legacy_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... |
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... |
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... |
def _apply_weighting(F, loss, weight=None, sample_weight=None):
"""Apply weighting to loss.
Parameters
----------
loss : Symbol
The loss to be weighted.
weight : float or None
Global scalar weight for loss.
sample_weight : Symbol or None
Per sample weighting. Must be bro... |
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) |
def get_tv_grad_executor(img, ctx, tv_weight):
"""create TV gradient executor with input binded on img
"""
if tv_weight <= 0.0:
return None
nchannel = img.shape[1]
simg = mx.sym.Variable("img")
skernel = mx.sym.Variable("kernel")
channels = mx.sym.SliceChannel(simg, num_outputs=nchan... |
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(... |
def _get_batch(self):
"""
Load data/label from dataset
"""
batch_data = mx.nd.zeros((self.batch_size, 3, self._data_shape[0], self._data_shape[1]))
batch_label = []
for i in range(self.batch_size):
if (self._current + i) >= self._size:
if not s... |
def _data_augmentation(self, data, label):
"""
perform data augmentations: crop, mirror, resize, sub mean, swap channels...
"""
if self.is_train and self._rand_samplers:
rand_crops = []
for rs in self._rand_samplers:
rand_crops += rs.sample(label)
... |
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.... |
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,
in the same... |
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... |
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):
if isinstance(d_targets, nd.NDArray):
d_src.copyto(d_targets)
else:
assert d_targets[-1][0].stop == d_src.shape[0], \
... |
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... |
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) |
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) |
def update_metric(self, metric, labels, pre_sliced=False):
"""Update evaluation metric with label and current outputs."""
for current_exec, (texec, islice) in enumerate(zip(self.train_execs, self.slices)):
if not pre_sliced:
labels_slice = [label[islice] for label in labels]
... |
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) |
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_... |
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 ... |
def update_metric(self, metric, labels, pre_sliced=False):
"""Update metric with the current executor."""
self.curr_execgrp.update_metric(metric, labels, pre_sliced) |
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 |
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_... |
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:
... |
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 == ... |
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... |
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':
... |
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)... |
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_... |
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() |
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
... |
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(... |
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... |
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]
ndarray_func... |
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<... |
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... |
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
An optional device context (default is the curren... |
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... |
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.
... |
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 \
dict ... |
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... |
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 |
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... |
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.... |
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... |
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... |
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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