INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Closes the record and index files. | def close(self):
"""Closes the record and index files."""
if not self.is_open:
return
super(IndexCreator, self).close()
self.fidx.close() |
Returns the current position of read head. | 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 |
Creates the index file from open record file | 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:'... |
Run commands raise exception if failed | 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 |
Run the doxygen make commands | 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) |
Build mxnet. so lib | 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/.. && ... |
build r pdf | 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... |
build scala for scala docs java docs and clojure docs to use | 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... |
build scala doc and then move the outdir | 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... |
build java docs and then move the outdir | 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... |
build clojure doc and then move the outdir | 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 ... |
Convert a markdown table to rst format | 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:... |
Find tables in a markdown and then convert them into the rst format | 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... |
A iterator that returns if a line is within a code block | 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
"""
... |
split lines into code and non - code 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, _)... |
Evaluate python source codes | 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
... |
Copies artifacts needed for website presentation | 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)
_... |
Download caffe model into disk by the given meta info | 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, ... |
Download convert and save a 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... |
Run _func with multi - process using params. | 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 ... |
Split the number ( sam_num ) into numbers by n_tile | 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... |
put worker | 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}) |
create a namedtuple with default values | 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... |
merge dict a b with b overriding keys in a | def merge_dict(a, b):
""" merge dict a, b, with b overriding keys in a """
c = a.copy()
c.update(b)
return c |
accept list of namedtuple return a dict of zipped fields | 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... |
convert raw configuration to unified dictionary | 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... |
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 | 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
... |
Returns the name and shape information of input and output tensors of the given ONNX model file. | 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
... |
wrapper for a small Convolution group | 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... |
wrapper for a small Convolution group | 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... |
Wrapper function to extract features from base network attaching extra layers and SSD specific layers | 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... |
the basic aggregation module for SSD detection. Takes in multiple layers generate multiple object detection targets by customized layers | 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... |
Apply weighting to loss. | 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... |
Reshapes x to the same shape as y. | 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) |
create TV gradient executor with input binded on img | 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... |
Train a neural style network. Args are from argparse and control input output hyper - parameters. callback allows for display of training progress. | 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(... |
Load data/ label from dataset | 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... |
perform data augmentations: crop mirror resize sub mean swap channels... | 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)
... |
Gets MNIST dataset | 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.... |
Get input slice from the input shape. | 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... |
Check the argument names of symbol. This function checks the duplication of arguments in Symbol. The check is done for feedforward net for now. | 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... |
Load a list of arrays into a list of arrays specified by slices. | 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], \
... |
bind executor for bucketing potentially sharing data with an existing executor. | 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... |
Load data and labels into arrays. | 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) |
Perform a forward pass on each executor. | 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) |
Update evaluation metric with label and current outputs. | 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]
... |
Install monitor on all executors. | 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) |
Set parameter and aux values. | 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_... |
Load data and labels into arrays. | 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 ... |
Update metric with the current executor. | def update_metric(self, metric, labels, pre_sliced=False):
"""Update metric with the current executor."""
self.curr_execgrp.update_metric(metric, labels, pre_sliced) |
Clear all contents in the relay memory | 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 |
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_LIBSVM_PARSER_H_ | 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_... |
Process a file. | 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:
... |
Main entry function. | 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 == ... |
Print summary of certain result 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... |
Process a cpp file. | 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':
... |
Process a python file. | 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)... |
Print summary of lint. | 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_... |
Start server/ scheduler. | 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() |
Return the server 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
... |
Run the server whose behavior is like. | 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(... |
Generate function for ndarray op by handle and function name. | 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... |
Create a NDArray function from the FunctionHandle. | 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... |
Counts tokens in the specified string. | 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<... |
Return a new array of given shape and type filled with 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... |
Returns a new array of given shape and type without initializing entries. | 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... |
Creates an array from any object exposing the array interface. | 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... |
Loads an array from file. | 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.
... |
Loads an array dictionary or list from a buffer | 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 ... |
Saves a list of arrays or a dict of str - > array to file. | 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... |
Get the common prefix for all names | 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 |
Utility function that helps in inferring DType of args and auxs params from given input param. | 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... |
Creates prefix and params for new Block. | 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.... |
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. | 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... |
[ Deprecated ] Please use save_parameters. Note that if you want load from SymbolBlock later please use export instead. | 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... |
Load parameters from file previously saved by save_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... |
[ Deprecated ] Please use load_parameters. | def load_params(self, filename, ctx=None, allow_missing=False,
ignore_extra=False):
"""[Deprecated] Please use load_parameters.
Load parameters from file.
filename : str
Path to parameter file.
ctx : Context or list of Context, default cpu()
... |
Registers block as a child of self.: py: class: Block s assigned to self as attributes will be registered automatically. | def register_child(self, block, name=None):
"""Registers block as a child of self. :py:class:`Block` s assigned to self as
attributes will be registered automatically."""
if name is None:
name = str(len(self._children))
self._children[name] = block |
r Registers a forward pre - hook on the block. | def register_forward_pre_hook(self, hook):
r"""Registers a forward pre-hook on the block.
The hook function is called immediately before :func:`forward`.
It should not modify the input or output.
Parameters
----------
hook : callable
The forward hook functio... |
r Registers a forward hook on the block. | def register_forward_hook(self, hook):
r"""Registers a forward hook on the block.
The hook function is called immediately after :func:`forward`.
It should not modify the input or output.
Parameters
----------
hook : callable
The forward hook function of form... |
r Applies fn recursively to every child block as well as self. | def apply(self, fn):
r"""Applies ``fn`` recursively to every child block as well as self.
Parameters
----------
fn : callable
Function to be applied to each submodule, of form `fn(block)`.
Returns
-------
this block
"""
for cld in sel... |
Initializes: py: class: Parameter s of this: py: class: Block and its children. Equivalent to block. collect_params (). initialize (... ) | def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False,
force_reinit=False):
"""Initializes :py:class:`Parameter` s of this :py:class:`Block` and its children.
Equivalent to ``block.collect_params().initialize(...)``
Parameters
----------
ini... |
Activates or deactivates: py: class: HybridBlock s recursively. Has no effect on non - hybrid children. | def hybridize(self, active=True, **kwargs):
"""Activates or deactivates :py:class:`HybridBlock` s recursively. Has no effect on
non-hybrid children.
Parameters
----------
active : bool, default True
Whether to turn hybrid on or off.
static_alloc : bool, defau... |
Cast this Block to use another data type. | def cast(self, dtype):
"""Cast this Block to use another data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type.
"""
for child in self._children.values():
child.cast(dtype)
for _, param in self.params.items():
... |
Print the summary of the model s output and parameters. | def summary(self, *inputs):
"""Print the summary of the model's output and parameters.
The network must have been initialized, and must not have been hybridized.
Parameters
----------
inputs : object
Any input that the model supports. For any tensor in the input, on... |
Generic infer attributes. | def _infer_attrs(self, infer_fn, attr, *args):
"""Generic infer attributes."""
inputs, out = self._get_graph(*args)
args, _ = _flatten(args, "input")
with warnings.catch_warnings(record=True) as w:
arg_attrs, _, aux_attrs = getattr(out, infer_fn)(
**{i.name: g... |
Export HybridBlock to json format that can be loaded by SymbolBlock. imports mxnet. mod. Module or the C ++ interface. | def export(self, path, epoch=0):
"""Export HybridBlock to json format that can be loaded by
`SymbolBlock.imports`, `mxnet.mod.Module` or the C++ interface.
.. note:: When there are only one input, it will have name `data`. When there
Are more than one inputs, they will be name... |
Defines the forward computation. Arguments can be either: py: class: NDArray or: py: class: Symbol. | def forward(self, x, *args):
"""Defines the forward computation. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`."""
if isinstance(x, NDArray):
with x.context as ctx:
if self._active:
return self._call_cached_op(x, *args)
... |
Import model previously saved by HybridBlock. export or Module. save_checkpoint as a SymbolBlock for use in Gluon. | def imports(symbol_file, input_names, param_file=None, ctx=None):
"""Import model previously saved by `HybridBlock.export` or
`Module.save_checkpoint` as a SymbolBlock for use in Gluon.
Parameters
----------
symbol_file : str
Path to symbol file.
input_names ... |
Calculates the expectation of the gradients per epoch for each parameter w. r. t number of batches | def calc_expectation(grad_dict, num_batches):
"""Calculates the expectation of the gradients per epoch for each parameter w.r.t number of batches
Parameters
----------
grad_dict: dict
dictionary that maps parameter name to gradients in the mod executor group
num_batches: int
number ... |
Calculates the variance of the gradients per epoch for each parameter w. r. t number of batches | def calc_variance(grad_dict, num_batches, param_names):
"""Calculates the variance of the gradients per epoch for each parameter w.r.t number of batches
Parameters
----------
grad_dict: dict
dictionary that maps parameter name to gradients in the mod executor group
num_batches: int
... |
Create directories recursively if they don t exist. os. makedirs ( exist_ok = True ) is not available in Python2 | def makedirs(d):
"""Create directories recursively if they don't exist. os.makedirs(exist_ok=True) is not
available in Python2"""
if sys.version_info[0] < 3:
from distutils.dir_util import mkpath
mkpath(d)
else:
os.makedirs(d, exist_ok=True) |
r AlexNet model from the One weird trick... <https:// arxiv. org/ abs/ 1404. 5997 > _ paper. | def alexnet(pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""AlexNet model from the `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
Parameters
----------
pretrained : bool, default False
Whether to load the pretrained weights... |
computes f1 precision and recall on the entity class | def classifer_metrics(label, pred):
"""
computes f1, precision and recall on the entity class
"""
prediction = np.argmax(pred, axis=1)
label = label.astype(int)
pred_is_entity = prediction != not_entity_index
label_is_entity = label != not_entity_index
corr_pred = (prediction == label)... |
Construct data iter | def data_iter(batch_size, num_embed, pre_trained_word2vec=False):
"""Construct data iter
Parameters
----------
batch_size: int
num_embed: int
pre_trained_word2vec: boolean
identify the pre-trained layers or not
Returns
----------
train_set: DataIter
... |
Generate network symbol | def sym_gen(batch_size, sentences_size, num_embed, vocabulary_size,
num_label=2, filter_list=None, num_filter=100,
dropout=0.0, pre_trained_word2vec=False):
"""Generate network symbol
Parameters
----------
batch_size: int
sentences_size: int
num_embed: int
vocabulary... |
Train cnn model | def train(symbol_data, train_iterator, valid_iterator, data_column_names, target_names):
"""Train cnn model
Parameters
----------
symbol_data: symbol
train_iterator: DataIter
Train DataIter
valid_iterator: DataIter
Valid DataIter
data_column_names: li... |
convert the caltech101 mat file to images Examples -------- python convert_data. py -- dataset/ home/ ubuntu/ datasets/ caltech101/ data/ caltech101_silhouettes_28. mat -- save_path/ home/ ubuntu/ datasets/ caltech101/ data/ -- invert -- height 32 -- width 32 | def convert_mat_to_images(args):
'''convert the caltech101 mat file to images
Examples
--------
python convert_data.py --dataset /home/ubuntu/datasets/caltech101/data/caltech101_silhouettes_28.mat --save_path /home/ubuntu/datasets/caltech101/data/ --invert --height 32 --width 32
'''
dataset = sc... |
Build using CMake | def build(args) -> None:
"""Build using CMake"""
venv_exe = shutil.which('virtualenv')
pyexe = shutil.which(args.pyexe)
if not venv_exe:
logging.warn("virtualenv wasn't found in path, it's recommended to install virtualenv to manage python environments")
if not pyexe:
logging.warn("P... |
Create a linear regression network for performing SVRG optimization. Parameters ---------- batch_size: int Size of data split update_freq: int Update Frequency for calculating full gradients | def create_network(batch_size, update_freq):
"""Create a linear regression network for performing SVRG optimization.
Parameters
----------
batch_size: int
Size of data split
update_freq: int
Update Frequency for calculating full gradients
Returns
----------
di: mx.io.NDA... |
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