Code stringlengths 103 85.9k | Summary listlengths 0 94 |
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Please provide a description of the function:def tokenize(self, path):
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r') as f:
tokens = 0
for line in f:
words = line.split() + ['<eos>']
tokens += len(wo... | [
"Tokenizes a text file."
] |
Please provide a description of the function:def _build_doc(func_name,
desc,
arg_names,
arg_types,
arg_desc,
key_var_num_args=None,
ret_type=None):
param_str = _build_param_doc(arg_names, arg_types, arg_desc)
if key_v... | [
"Build docstring for symbolic functions."
] |
Please provide a description of the function:def get_output_shape(sym, **input_shapes):
_, s_outputs, _ = sym.infer_shape(**input_shapes)
return dict(zip(sym.list_outputs(), s_outputs)) | [
"Get user friendly information of the output shapes."
] |
Please provide a description of the function:def num_gpus():
count = ctypes.c_int()
check_call(_LIB.MXGetGPUCount(ctypes.byref(count)))
return count.value | [
"Query CUDA for the number of GPUs present.\n\n Raises\n ------\n Will raise an exception on any CUDA error.\n\n Returns\n -------\n count : int\n The number of GPUs.\n\n "
] |
Please provide a description of the function:def gpu_memory_info(device_id=0):
free = ctypes.c_uint64()
total = ctypes.c_uint64()
dev_id = ctypes.c_int(device_id)
check_call(_LIB.MXGetGPUMemoryInformation64(dev_id, ctypes.byref(free), ctypes.byref(total)))
return (free.value, total.value) | [
"Query CUDA for the free and total bytes of GPU global memory.\n\n Parameters\n ----------\n device_id : int, optional\n The device id of the GPU device.\n\n Raises\n ------\n Will raise an exception on any CUDA error.\n\n Returns\n -------\n (free, total) : (int, int)\n The... |
Please provide a description of the function:def current_context():
if not hasattr(Context._default_ctx, "value"):
Context._default_ctx.value = Context('cpu', 0)
return Context._default_ctx.value | [
"Returns the current context.\n\n By default, `mx.cpu()` is used for all the computations\n and it can be overridden by using `with mx.Context(x)` statement where\n x can be cpu(device_id) or gpu(device_id).\n\n Examples\n -------\n >>> mx.current_context()\n cpu(0)\n >>> with mx.Context('gp... |
Please provide a description of the function:def _list_audio_files(self, root, skip_rows=0):
self.synsets = []
self.items = []
if not self._train_csv:
# The audio files are organized in folder structure with
# directory name as label and audios in them
... | [
"Populates synsets - a map of index to label for the data items.\n Populates the data in the dataset, making tuples of (data, label)\n "
] |
Please provide a description of the function:def transform_first(self, fn, lazy=False):
return super(AudioFolderDataset, self).transform_first(fn, lazy=lazy) | [
"Returns a new dataset with the first element of each sample\n transformed by the transformer function `fn`.\n\n This is useful, for example, when you only want to transform data\n while keeping label as is.\n lazy=False is passed to transform_first for dataset so that all tramsforms cou... |
Please provide a description of the function:def config_cython():
if not with_cython:
return []
# pylint: disable=unreachable
if os.name == 'nt':
print("WARNING: Cython is not supported on Windows, will compile without cython module")
return []
try:
from Cython.Buil... | [
"Try to configure cython and return cython configuration"
] |
Please provide a description of the function:def _compose(self, *args, **kwargs):
name = kwargs.pop('name', None)
if name:
name = c_str(name)
if len(args) != 0 and len(kwargs) != 0:
raise TypeError('compose only accept input Symbols \
either as p... | [
"Compose symbol on inputs.\n\n This call mutates the current symbol.\n\n Parameters\n ----------\n args:\n provide positional arguments\n\n kwargs:\n provide keyword arguments\n\n Returns\n -------\n the resulting symbol\n "
] |
Please provide a description of the function:def _set_attr(self, **kwargs):
keys = c_str_array(kwargs.keys())
vals = c_str_array([str(s) for s in kwargs.values()])
num_args = mx_uint(len(kwargs))
check_call(_LIB.MXSymbolSetAttrs(
self.handle, num_args, keys, vals)) | [
"Set the attribute of the symbol.\n\n Parameters\n ----------\n **kwargs\n The attributes to set\n "
] |
Please provide a description of the function:def get_config(network, data_shape, **kwargs):
if network == 'vgg16_reduced':
if data_shape >= 448:
from_layers = ['relu4_3', 'relu7', '', '', '', '', '']
num_filters = [512, -1, 512, 256, 256, 256, 256]
strides = [-1, -1,... | [
"Configuration factory for various networks\n\n Parameters\n ----------\n network : str\n base network name, such as vgg_reduced, inceptionv3, resnet...\n data_shape : int\n input data dimension\n kwargs : dict\n extra arguments\n "
] |
Please provide a description of the function:def get_symbol_train(network, data_shape, **kwargs):
if network.startswith('legacy'):
logging.warn('Using legacy model.')
return symbol_builder.import_module(network).get_symbol_train(**kwargs)
config = get_config(network, data_shape, **kwargs).c... | [
"Wrapper for get symbol for train\n\n Parameters\n ----------\n network : str\n name for the base network symbol\n data_shape : int\n input shape\n kwargs : dict\n see symbol_builder.get_symbol_train for more details\n "
] |
Please provide a description of the function:def _set_trainer(self, trainer):
# trainer cannot be replaced for sparse params
if self._stype != 'default' and self._trainer and trainer and self._trainer is not trainer:
raise RuntimeError(
"Failed to set the trainer for... | [
" Set the trainer this parameter is associated with. "
] |
Please provide a description of the function:def _get_row_sparse(self, arr_list, ctx, row_id):
# get row sparse params based on row ids
if not isinstance(row_id, ndarray.NDArray):
raise TypeError("row_id must have NDArray type, but %s is given"%(type(row_id)))
if not self._t... | [
" Get row_sparse data from row_sparse parameters based on row_id. "
] |
Please provide a description of the function:def _load_init(self, data, ctx):
if self.shape:
for self_dim, data_dim in zip(self.shape, data.shape):
assert self_dim in (0, data_dim), \
"Failed loading Parameter '%s' from saved params: " \
... | [
"(Re)initializes by loading from data."
] |
Please provide a description of the function:def _finish_deferred_init(self):
if not self._deferred_init:
return
init, ctx, default_init, data = self._deferred_init
self._deferred_init = ()
assert self.shape is not None and np.prod(self.shape) > 0, \
"Can... | [
"Finishes deferred initialization."
] |
Please provide a description of the function:def _init_impl(self, data, ctx_list):
self._ctx_list = list(ctx_list)
self._ctx_map = [[], []]
for i, ctx in enumerate(self._ctx_list):
dev_list = self._ctx_map[ctx.device_typeid&1]
while len(dev_list) <= ctx.device_id... | [
"Sets data and grad."
] |
Please provide a description of the function:def _init_grad(self):
if self.grad_req == 'null':
self._grad = None
return
self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context,
stype=self._grad_stype) for i in self._da... | [
"Initialize grad buffers."
] |
Please provide a description of the function:def _reduce(self):
ctx = context.cpu()
if self._stype == 'default':
block = self.list_data()
data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block)
else:
# fetch all rows for 'row_sparse' param
... | [
"Reduce data from multiple context to cpu."
] |
Please provide a description of the function:def initialize(self, init=None, ctx=None, default_init=initializer.Uniform(),
force_reinit=False):
if self._data is not None and not force_reinit:
warnings.warn("Parameter '%s' is already initialized, ignoring. " \
... | [
"Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API.\n\n Parameters\n ----------\n init : Initializer\n The initializer to use. Overrides :py:meth:`Parameter.init` and default_init.\n ctx : Context or list of Context, defaults to :py:meth:`context... |
Please provide a description of the function:def reset_ctx(self, ctx):
if ctx is None:
ctx = [context.current_context()]
if isinstance(ctx, Context):
ctx = [ctx]
if self._data:
data = self._reduce()
with autograd.pause():
s... | [
"Re-assign Parameter to other contexts.\n\n Parameters\n ----------\n ctx : Context or list of Context, default ``context.current_context()``.\n Assign Parameter to given context. If ctx is a list of Context, a\n copy will be made for each context.\n "
] |
Please provide a description of the function:def set_data(self, data):
self.shape = data.shape
if self._data is None:
assert self._deferred_init, \
"Parameter '%s' has not been initialized"%self.name
self._deferred_init = self._deferred_init[:3] + (data,... | [
"Sets this parameter's value on all contexts."
] |
Please provide a description of the function:def row_sparse_data(self, row_id):
if self._stype != 'row_sparse':
raise RuntimeError("Cannot return a copy of Parameter %s via row_sparse_data() " \
"because its storage type is %s. Please use data() instead." \
... | [
"Returns a copy of the 'row_sparse' parameter on the same context as row_id's.\n The copy only retains rows whose ids occur in provided row ids.\n The parameter must have been initialized on this context before.\n\n Parameters\n ----------\n row_id: NDArray\n Row ids to... |
Please provide a description of the function:def list_row_sparse_data(self, row_id):
if self._stype != 'row_sparse':
raise RuntimeError("Cannot return copies of Parameter '%s' on all contexts via " \
"list_row_sparse_data() because its storage type is %s. Plea... | [
"Returns copies of the 'row_sparse' parameter on all contexts, in the same order\n as creation. The copy only retains rows whose ids occur in provided row ids.\n The parameter must have been initialized before.\n\n Parameters\n ----------\n row_id: NDArray\n Row ids to ... |
Please provide a description of the function:def data(self, ctx=None):
if self._stype != 'default':
raise RuntimeError("Cannot return a copy of Parameter '%s' on ctx %s via data() " \
"because its storage type is %s. Please use row_sparse_data() " \
... | [
"Returns a copy of this parameter on one context. Must have been\n initialized on this context before. For sparse parameters, use\n :py:meth:`Parameter.row_sparse_data` instead.\n\n Parameters\n ----------\n ctx : Context\n Desired context.\n\n Returns\n -... |
Please provide a description of the function:def list_data(self):
if self._stype != 'default':
raise RuntimeError("Cannot return copies of Parameter '%s' on all contexts via " \
"list_data() because its storage type is %s. Please use " \
... | [
"Returns copies of this parameter on all contexts, in the same order\n as creation. For sparse parameters, use :py:meth:`Parameter.list_row_sparse_data`\n instead.\n\n Returns\n -------\n list of NDArrays\n "
] |
Please provide a description of the function:def grad(self, ctx=None):
if self._data is not None and self._grad is None:
raise RuntimeError(
"Cannot get gradient array for Parameter '%s' " \
"because grad_req='null'"%(self.name))
return self._check_an... | [
"Returns a gradient buffer for this parameter on one context.\n\n Parameters\n ----------\n ctx : Context\n Desired context.\n "
] |
Please provide a description of the function:def list_grad(self):
if self._data is not None and self._grad is None:
raise RuntimeError(
"Cannot get gradient array for Parameter '%s' " \
"because grad_req='null'"%(self.name))
return self._check_and_get... | [
"Returns gradient buffers on all contexts, in the same order\n as :py:meth:`values`."
] |
Please provide a description of the function:def list_ctx(self):
if self._data is None:
if self._deferred_init:
return self._deferred_init[1]
raise RuntimeError("Parameter '%s' has not been initialized"%self.name)
return self._ctx_list | [
"Returns a list of contexts this parameter is initialized on."
] |
Please provide a description of the function:def zero_grad(self):
if self._grad is None:
return
for i in self._grad:
ndarray.zeros_like(i, out=i) | [
"Sets gradient buffer on all contexts to 0. No action is taken if\n parameter is uninitialized or doesn't require gradient."
] |
Please provide a description of the function:def var(self):
if self._var is None:
self._var = symbol.var(self.name, shape=self.shape, dtype=self.dtype,
lr_mult=self.lr_mult, wd_mult=self.wd_mult,
init=self.init, stype=sel... | [
"Returns a symbol representing this parameter."
] |
Please provide a description of the function:def cast(self, dtype):
self.dtype = dtype
if self._data is None:
return
with autograd.pause():
self._data = [i.astype(dtype) for i in self._data]
if self._grad is None:
return
se... | [
"Cast data and gradient of this Parameter to a new data type.\n\n Parameters\n ----------\n dtype : str or numpy.dtype\n The new data type.\n "
] |
Please provide a description of the function:def get(self, name, **kwargs):
name = self.prefix + name
param = self._get_impl(name)
if param is None: # pylint: disable=too-many-nested-blocks
param = Parameter(name, **kwargs)
self._params[name] = param
else... | [
"Retrieves a :py:class:`Parameter` with name ``self.prefix+name``. If not found,\n :py:func:`get` will first try to retrieve it from \"shared\" dict. If still not\n found, :py:func:`get` will create a new :py:class:`Parameter` with key-word arguments and\n insert it to self.\n\n Paramete... |
Please provide a description of the function:def get_constant(self, name, value=None):
name = self.prefix + name
param = self._get_impl(name)
if param is None:
if value is None:
raise KeyError("No constant named '{}'. Please specify value " \
... | [
"Retrieves a :py:class:`.Constant` with name ``self.prefix+name``. If not found,\n :py:func:`get` will first try to retrieve it from \"shared\" dict. If still not\n found, :py:func:`get` will create a new :py:class:`.Constant` with key-word\n arguments and insert it to self.\n\n Paramete... |
Please provide a description of the function:def update(self, other):
for k, v in other.items():
if k in self._params:
assert self._params[k] is v, \
"Cannot update self with other because they have different " \
"Parameters with the s... | [
"Copies all Parameters in ``other`` to self."
] |
Please provide a description of the function:def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False,
force_reinit=False):
if verbose:
init.set_verbosity(verbose=verbose)
for _, v in self.items():
v.initialize(None, ctx, init, force_re... | [
"Initializes all Parameters managed by this dictionary to be used for :py:class:`NDArray`\n API. It has no effect when using :py:class:`Symbol` API.\n\n Parameters\n ----------\n init : Initializer\n Global default Initializer to be used when :py:meth:`Parameter.init` is ``Non... |
Please provide a description of the function:def save(self, filename, strip_prefix=''):
arg_dict = {}
for param in self.values():
weight = param._reduce()
if not param.name.startswith(strip_prefix):
raise ValueError(
"Prefix '%s' is to... | [
"Save parameters to file.\n\n Parameters\n ----------\n filename : str\n Path to parameter file.\n strip_prefix : str, default ''\n Strip prefix from parameter names before saving.\n "
] |
Please provide a description of the function:def load(self, filename, ctx=None, allow_missing=False,
ignore_extra=False, restore_prefix=''):
if restore_prefix:
for name in self.keys():
assert name.startswith(restore_prefix), \
"restore_prefix... | [
"Load parameters from file.\n\n Parameters\n ----------\n filename : str\n Path to parameter file.\n ctx : Context or list of Context\n Context(s) initialize loaded parameters on.\n allow_missing : bool, default False\n Whether to silently skip loa... |
Please provide a description of the function:def _make_torch_function(handle):
# Get the property of function
n_used_vars = mx_uint()
n_scalars = mx_uint()
n_mutate_vars = mx_uint()
type_mask = ctypes.c_int()
check_call(_LIB.MXFuncDescribe(
handle,
ctypes.byref(n_used_vars),... | [
"Create a Torch function from the FunctionHandle.",
"Invoke this function by passing in parameters.\n\n Parameters\n ----------\n *args\n Positional arguments of inputs (both scalar and `NDArray`).\n\n Returns\n -------\n out : NDArray\n The result N... |
Please provide a description of the function:def _init_torch_module():
plist = ctypes.POINTER(FunctionHandle)()
size = ctypes.c_uint()
check_call(_LIB.MXListFunctions(ctypes.byref(size),
ctypes.byref(plist)))
module_obj = sys.modules[__name__]
for i in range... | [
"List and add all the torch backed ndarray functions to current module."
] |
Please provide a description of the function:def inception_v3(pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r
net = Inception3(**kwargs)
if pretrained:
from ..model_store import get_model_file
net.load_parameters(get_model_file('incept... | [
"Inception v3 model from\n `\"Rethinking the Inception Architecture for Computer Vision\"\n <http://arxiv.org/abs/1512.00567>`_ paper.\n\n Parameters\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n ctx : Context, default CPU\n The... |
Please provide a description of the function:def pack(header, s):
header = IRHeader(*header)
if isinstance(header.label, numbers.Number):
header = header._replace(flag=0)
else:
label = np.asarray(header.label, dtype=np.float32)
header = header._replace(flag=label.size, label=0)
... | [
"Pack a string into MXImageRecord.\n\n Parameters\n ----------\n header : IRHeader\n Header of the image record.\n ``header.label`` can be a number or an array. See more detail in ``IRHeader``.\n s : str\n Raw image string to be packed.\n\n Returns\n -------\n s : str\n ... |
Please provide a description of the function:def unpack(s):
header = IRHeader(*struct.unpack(_IR_FORMAT, s[:_IR_SIZE]))
s = s[_IR_SIZE:]
if header.flag > 0:
header = header._replace(label=np.frombuffer(s, np.float32, header.flag))
s = s[header.flag*4:]
return header, s | [
"Unpack a MXImageRecord to string.\n\n Parameters\n ----------\n s : str\n String buffer from ``MXRecordIO.read``.\n\n Returns\n -------\n header : IRHeader\n Header of the image record.\n s : str\n Unpacked string.\n\n Examples\n --------\n >>> record = mx.recordi... |
Please provide a description of the function:def unpack_img(s, iscolor=-1):
header, s = unpack(s)
img = np.frombuffer(s, dtype=np.uint8)
assert cv2 is not None
img = cv2.imdecode(img, iscolor)
return header, img | [
"Unpack a MXImageRecord to image.\n\n Parameters\n ----------\n s : str\n String buffer from ``MXRecordIO.read``.\n iscolor : int\n Image format option for ``cv2.imdecode``.\n\n Returns\n -------\n header : IRHeader\n Header of the image record.\n img : numpy.ndarray\n ... |
Please provide a description of the function:def pack_img(header, img, quality=95, img_fmt='.jpg'):
assert cv2 is not None
jpg_formats = ['.JPG', '.JPEG']
png_formats = ['.PNG']
encode_params = None
if img_fmt.upper() in jpg_formats:
encode_params = [cv2.IMWRITE_JPEG_QUALITY, quality]
... | [
"Pack an image into ``MXImageRecord``.\n\n Parameters\n ----------\n header : IRHeader\n Header of the image record.\n ``header.label`` can be a number or an array. See more detail in ``IRHeader``.\n img : numpy.ndarray\n Image to be packed.\n quality : int\n Quality for J... |
Please provide a description of the function:def open(self):
if self.flag == "w":
check_call(_LIB.MXRecordIOWriterCreate(self.uri, ctypes.byref(self.handle)))
self.writable = True
elif self.flag == "r":
check_call(_LIB.MXRecordIOReaderCreate(self.uri, ctypes.... | [
"Opens the record file."
] |
Please provide a description of the function:def _check_pid(self, allow_reset=False):
if not self.pid == current_process().pid:
if allow_reset:
self.reset()
else:
raise RuntimeError("Forbidden operation in multiple processes") | [
"Check process id to ensure integrity, reset if in new process."
] |
Please provide a description of the function:def close(self):
if not self.is_open:
return
if self.writable:
check_call(_LIB.MXRecordIOWriterFree(self.handle))
else:
check_call(_LIB.MXRecordIOReaderFree(self.handle))
self.is_open = False
... | [
"Closes the record file."
] |
Please provide a description of the function:def write(self, buf):
assert self.writable
self._check_pid(allow_reset=False)
check_call(_LIB.MXRecordIOWriterWriteRecord(self.handle,
ctypes.c_char_p(buf),
... | [
"Inserts a string buffer as a record.\n\n Examples\n ---------\n >>> record = mx.recordio.MXRecordIO('tmp.rec', 'w')\n >>> for i in range(5):\n ... record.write('record_%d'%i)\n >>> record.close()\n\n Parameters\n ----------\n buf : string (python2),... |
Please provide a description of the function:def read(self):
assert not self.writable
# trying to implicitly read from multiple processes is forbidden,
# there's no elegant way to handle unless lock is introduced
self._check_pid(allow_reset=False)
buf = ctypes.c_char_p()... | [
"Returns record as a string.\n\n Examples\n ---------\n >>> record = mx.recordio.MXRecordIO('tmp.rec', 'r')\n >>> for i in range(5):\n ... item = record.read()\n ... print(item)\n record_0\n record_1\n record_2\n record_3\n record_4\... |
Please provide a description of the function:def close(self):
if not self.is_open:
return
super(MXIndexedRecordIO, self).close()
self.fidx.close() | [
"Closes the record file."
] |
Please provide a description of the function:def seek(self, idx):
assert not self.writable
self._check_pid(allow_reset=True)
pos = ctypes.c_size_t(self.idx[idx])
check_call(_LIB.MXRecordIOReaderSeek(self.handle, pos)) | [
"Sets the current read pointer position.\n\n This function is internally called by `read_idx(idx)` to find the current\n reader pointer position. It doesn't return anything."
] |
Please provide a description of the function:def tell(self):
assert self.writable
pos = ctypes.c_size_t()
check_call(_LIB.MXRecordIOWriterTell(self.handle, ctypes.byref(pos)))
return pos.value | [
"Returns the current position of write head.\n\n Examples\n ---------\n >>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')\n >>> print(record.tell())\n 0\n >>> for i in range(5):\n ... record.write_idx(i, 'record_%d'%i)\n ... print(r... |
Please provide a description of the function:def write_idx(self, idx, buf):
key = self.key_type(idx)
pos = self.tell()
self.write(buf)
self.fidx.write('%s\t%d\n'%(str(key), pos))
self.idx[key] = pos
self.keys.append(key) | [
"Inserts input record at given index.\n\n Examples\n ---------\n >>> for i in range(5):\n ... record.write_idx(i, 'record_%d'%i)\n >>> record.close()\n\n Parameters\n ----------\n idx : int\n Index of a file.\n buf :\n Record t... |
Please provide a description of the function:def _add_new_columns(dataframe, metrics):
#TODO(leodirac): we don't really need to do this on every update. Optimize
new_columns = set(metrics.keys()) - set(dataframe.columns)
for col in new_columns:
dataframe[col] = None | [
"Add new metrics as new columns to selected pandas dataframe.\n\n Parameters\n ----------\n dataframe : pandas.DataFrame\n Selected dataframe needs to be modified.\n metrics : metric.EvalMetric\n New metrics to be added.\n "
] |
Please provide a description of the function:def args_wrapper(*args):
out = defaultdict(list)
for callback in args:
callback_args = callback.callback_args()
for k, v in callback_args.items():
out[k].append(v)
return dict(out) | [
"Generates callback arguments for model.fit()\n for a set of callback objects.\n Callback objects like PandasLogger(), LiveLearningCurve()\n get passed in. This assembles all their callback arguments.\n "
] |
Please provide a description of the function:def append_metrics(self, metrics, df_name):
dataframe = self._dataframes[df_name]
_add_new_columns(dataframe, metrics)
dataframe.loc[len(dataframe)] = metrics | [
"Append new metrics to selected dataframes.\n\n Parameters\n ----------\n metrics : metric.EvalMetric\n New metrics to be added.\n df_name : str\n Name of the dataframe to be modified.\n "
] |
Please provide a description of the function:def train_cb(self, param):
if param.nbatch % self.frequent == 0:
self._process_batch(param, 'train') | [
"Callback funtion for training.\n "
] |
Please provide a description of the function:def _process_batch(self, param, dataframe):
now = time.time()
if param.eval_metric is not None:
metrics = dict(param.eval_metric.get_name_value())
param.eval_metric.reset()
else:
metrics = {}
# #115... | [
"Update parameters for selected dataframe after a completed batch\n Parameters\n ----------\n dataframe : pandas.DataFrame\n Selected dataframe needs to be modified.\n "
] |
Please provide a description of the function:def epoch_cb(self):
metrics = {}
metrics['elapsed'] = self.elapsed()
now = datetime.datetime.now()
metrics['epoch_time'] = now - self.last_epoch_time
self.append_metrics(metrics, 'epoch')
self.last_epoch_time = now | [
"Callback function after each epoch. Now it records each epoch time\n and append it to epoch dataframe.\n "
] |
Please provide a description of the function:def _push_render(self):
bokeh.io.push_notebook(handle=self.handle)
self.last_update = time.time() | [
"Render the plot with bokeh.io and push to notebook.\n "
] |
Please provide a description of the function:def _process_batch(self, param, df_name):
if param.eval_metric is not None:
metrics = dict(param.eval_metric.get_name_value())
param.eval_metric.reset()
else:
metrics = {}
metrics['elapsed'] = datetime.date... | [
"Update selected dataframe after a completed batch\n Parameters\n ----------\n df_name : str\n Selected dataframe name needs to be modified.\n "
] |
Please provide a description of the function:def build_vocab(nested_list):
# Build vocabulary
word_counts = Counter(itertools.chain(*nested_list))
# Mapping from index to label
vocabulary_inv = [x[0] for x in word_counts.most_common()]
# Mapping from label to index
vocabulary = {x: i for ... | [
"\n :param nested_list: list of list of string\n :return: dictionary mapping from string to int, inverse of that dictionary\n "
] |
Please provide a description of the function:def build_iters(data_dir, max_records, train_fraction, batch_size, buckets=None):
# Read in data as numpy array
df = pd.read_pickle(os.path.join(data_dir, "ner_data.pkl"))[:max_records]
# Get feature lists
entities=[list(array) for array in df["BILOU_ta... | [
"\n Reads a csv of sentences/tag sequences into a pandas dataframe.\n Converts into X = array(list(int)) & Y = array(list(int))\n Splits into training and test sets\n Builds dictionaries mapping from index labels to labels/ indexed features to features\n :param data_dir: directory to read in csv data... |
Please provide a description of the function:def sym_gen(seq_len):
sentence_shape = train_iter.provide_data[0][1]
char_sentence_shape = train_iter.provide_data[1][1]
entities_shape = train_iter.provide_label[0][1]
X_sent = mx.symbol.Variable(train_iter.provide_data[0].name)
X_char_sent = mx.sy... | [
"\n Build NN symbol depending on the length of the input sequence\n "
] |
Please provide a description of the function:def rand_zipfian(true_classes, num_sampled, range_max):
assert(isinstance(true_classes, Symbol)), "unexpected type %s" % type(true_classes)
log_range = math.log(range_max + 1)
rand = uniform(0, log_range, shape=(num_sampled,), dtype='float64')
# make sur... | [
"Draw random samples from an approximately log-uniform or Zipfian distribution.\n\n This operation randomly samples *num_sampled* candidates the range of integers [0, range_max).\n The elements of sampled_candidates are drawn with replacement from the base distribution.\n\n The base distribution for this o... |
Please provide a description of the function:def while_loop(cond, func, loop_vars, max_iterations=None, name="while_loop"):
def _to_python_scalar(inputs, type_, name):
if hasattr(inputs, "asscalar"):
inputs = inputs.asscalar()
try:
inputs = type_(inputs)
... | [
"Run a while loop with user-defined computation and loop condition.\n\n This operator simulates a while loop which iterately does customized computation\n as long as the condition is satisfied.\n\n `loop_vars` is a Symbol or nested lists of Symbols on which the computation uses.\n\n `cond` is a user-def... |
Please provide a description of the function:def cond(pred, then_func, else_func, name="cond"):
def _create_subgraph(graph_vars, graph_func, subgraph_name):
subgraph_name = _get_unique_subgraph_name(subgraph_name)
with AttrScope(__subgraph_name__=subgraph_name):
# create new variab... | [
"Run an if-then-else using user-defined condition and computation\n\n This operator simulates a if-like branch which chooses to do one of\n the two customized computations according to the specified condition.\n\n `pred` is a scalar MXNet Symbol,\n indicating which branch of computation should be used.\... |
Please provide a description of the function:def _index_unknown_and_reserved_tokens(self, unknown_token, reserved_tokens):
self._unknown_token = unknown_token
# Thus, constants.UNKNOWN_IDX must be 0.
self._idx_to_token = [unknown_token]
if reserved_tokens is None:
... | [
"Indexes unknown and reserved tokens."
] |
Please provide a description of the function:def _index_counter_keys(self, counter, unknown_token, reserved_tokens, most_freq_count,
min_freq):
assert isinstance(counter, collections.Counter), \
'`counter` must be an instance of collections.Counter.'
un... | [
"Indexes keys of `counter`.\n\n\n Indexes keys of `counter` according to frequency thresholds such as `most_freq_count` and\n `min_freq`.\n "
] |
Please provide a description of the function:def to_indices(self, tokens):
to_reduce = False
if not isinstance(tokens, list):
tokens = [tokens]
to_reduce = True
indices = [self.token_to_idx[token] if token in self.token_to_idx
else C.UNKNOWN_... | [
"Converts tokens to indices according to the vocabulary.\n\n\n Parameters\n ----------\n tokens : str or list of strs\n A source token or tokens to be converted.\n\n\n Returns\n -------\n int or list of ints\n A token index or a list of token indices a... |
Please provide a description of the function:def to_tokens(self, indices):
to_reduce = False
if not isinstance(indices, list):
indices = [indices]
to_reduce = True
max_idx = len(self.idx_to_token) - 1
tokens = []
for idx in indices:
... | [
"Converts token indices to tokens according to the vocabulary.\n\n\n Parameters\n ----------\n indices : int or list of ints\n A source token index or token indices to be converted.\n\n\n Returns\n -------\n str or list of strs\n A token or a list of t... |
Please provide a description of the function:def _make_io_iterator(handle):
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.POINTER(ctypes.c_char_p)()
arg_descs = ctypes.POINTER(ctypes.c_char_p)()
c... | [
"Create an io iterator by handle.",
"Create an iterator.\n The parameters listed below can be passed in as keyword arguments.\n\n Parameters\n ----------\n name : string, required.\n Name of the resulting data iterator.\n\n Returns\n -------\n dataiter: ... |
Please provide a description of the function:def _init_io_module():
plist = ctypes.POINTER(ctypes.c_void_p)()
size = ctypes.c_uint()
check_call(_LIB.MXListDataIters(ctypes.byref(size), ctypes.byref(plist)))
module_obj = sys.modules[__name__]
for i in range(size.value):
hdl = ctypes.c_vo... | [
"List and add all the data iterators to current module."
] |
Please provide a description of the function:def get_list(shapes, types):
if types is not None:
type_dict = dict(types)
return [DataDesc(x[0], x[1], type_dict[x[0]]) for x in shapes]
else:
return [DataDesc(x[0], x[1]) for x in shapes] | [
"Get DataDesc list from attribute lists.\n\n Parameters\n ----------\n shapes : a tuple of (name_, shape_)\n types : a tuple of (name_, np.dtype)\n "
] |
Please provide a description of the function:def next(self):
if self.iter_next():
return DataBatch(data=self.getdata(), label=self.getlabel(), \
pad=self.getpad(), index=self.getindex())
else:
raise StopIteration | [
"Get next data batch from iterator.\n\n Returns\n -------\n DataBatch\n The data of next batch.\n\n Raises\n ------\n StopIteration\n If the end of the data is reached.\n "
] |
Please provide a description of the function:def hard_reset(self):
if self.shuffle:
self._shuffle_data()
self.cursor = -self.batch_size
self._cache_data = None
self._cache_label = None | [
"Ignore roll over data and set to start."
] |
Please provide a description of the function:def reset(self):
if self.shuffle:
self._shuffle_data()
# the range below indicate the last batch
if self.last_batch_handle == 'roll_over' and \
self.num_data - self.batch_size < self.cursor < self.num_data:
... | [
"Resets the iterator to the beginning of the data."
] |
Please provide a description of the function:def iter_next(self):
self.cursor += self.batch_size
return self.cursor < self.num_data | [
"Increments the coursor by batch_size for next batch\n and check current cursor if it exceed the number of data points."
] |
Please provide a description of the function:def next(self):
if not self.iter_next():
raise StopIteration
data = self.getdata()
label = self.getlabel()
# iter should stop when last batch is not complete
if data[0].shape[0] != self.batch_size:
# in thi... | [
"Returns the next batch of data."
] |
Please provide a description of the function:def _getdata(self, data_source, start=None, end=None):
assert start is not None or end is not None, 'should at least specify start or end'
start = start if start is not None else 0
if end is None:
end = data_source[0][1].shape[0] ... | [
"Load data from underlying arrays."
] |
Please provide a description of the function:def _concat(self, first_data, second_data):
assert len(first_data) == len(
second_data), 'data source should contain the same size'
if first_data and second_data:
return [
concat(
first_data... | [
"Helper function to concat two NDArrays."
] |
Please provide a description of the function:def _batchify(self, data_source):
assert self.cursor < self.num_data, 'DataIter needs reset.'
# first batch of next epoch with 'roll_over'
if self.last_batch_handle == 'roll_over' and \
-self.batch_size < self.cursor < 0:
... | [
"Load data from underlying arrays, internal use only."
] |
Please provide a description of the function:def getpad(self):
if self.last_batch_handle == 'pad' and \
self.cursor + self.batch_size > self.num_data:
return self.cursor + self.batch_size - self.num_data
# check the first batch
elif self.last_batch_handle == 'roll... | [
"Get pad value of DataBatch."
] |
Please provide a description of the function:def _shuffle_data(self):
# shuffle index
np.random.shuffle(self.idx)
# get the data by corresponding index
self.data = _getdata_by_idx(self.data, self.idx)
self.label = _getdata_by_idx(self.label, self.idx) | [
"Shuffle the data."
] |
Please provide a description of the function:def _quantize_params(qsym, params, th_dict):
inputs_name = qsym.list_arguments()
quantized_params = {}
for name in inputs_name:
if name.endswith(('weight_quantize', 'bias_quantize')):
original_name = name[:-len('_quantize')]
p... | [
"Given a quantized symbol and a dict of params that have not been quantized,\n generate quantized params. Currently only supports quantizing the arg_params\n with names of `weight` or `bias`, not aux_params. If `qsym` contains symbols\n that are excluded from being quantized, their corresponding params wil... |
Please provide a description of the function:def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_dtype='int8'):
num_excluded_symbols = 0
if excluded_symbols is not None:
assert isinstance(excluded_symbols, list)
num_excluded_symbols = len(excluded_symbols)
el... | [
"Given a symbol object representing a neural network of data type FP32,\n quantize it into a INT8 network.\n\n Parameters\n ----------\n sym : Symbol\n FP32 neural network symbol.\n excluded_sym_names : list of strings\n A list of strings representing the names of the symbols that users... |
Please provide a description of the function:def _calibrate_quantized_sym(qsym, th_dict):
if th_dict is None or len(th_dict) == 0:
return qsym
num_layer_outputs = len(th_dict)
layer_output_names = []
min_vals = []
max_vals = []
for k, v in th_dict.items():
layer_output_names... | [
"Given a dictionary containing the thresholds for quantizing the layers,\n set the thresholds into the quantized symbol as the params of requantize operators.\n "
] |
Please provide a description of the function:def _collect_layer_output_min_max(mod, data, include_layer=None,
max_num_examples=None, logger=None):
collector = _LayerOutputMinMaxCollector(include_layer=include_layer, logger=logger)
num_examples = _collect_layer_statistics(m... | [
"Collect min and max values from layer outputs and save them in\n a dictionary mapped by layer names.\n "
] |
Please provide a description of the function:def _collect_layer_outputs(mod, data, include_layer=None, max_num_examples=None, logger=None):
collector = _LayerOutputCollector(include_layer=include_layer, logger=logger)
num_examples = _collect_layer_statistics(mod, data, collector, max_num_examples, logger)
... | [
"Collect layer outputs and save them in a dictionary mapped by layer names."
] |
Please provide a description of the function:def _smooth_distribution(p, eps=0.0001):
is_zeros = (p == 0).astype(np.float32)
is_nonzeros = (p != 0).astype(np.float32)
n_zeros = is_zeros.sum()
n_nonzeros = p.size - n_zeros
if not n_nonzeros:
raise ValueError('The discrete probability dis... | [
"Given a discrete distribution (may have not been normalized to 1),\n smooth it by replacing zeros with eps multiplied by a scaling factor and taking the\n corresponding amount off the non-zero values.\n Ref: http://web.engr.illinois.edu/~hanj/cs412/bk3/KL-divergence.pdf\n "
] |
Please provide a description of the function:def _get_optimal_threshold(arr, quantized_dtype, num_bins=8001, num_quantized_bins=255):
if isinstance(arr, NDArray):
arr = arr.asnumpy()
elif isinstance(arr, list):
assert len(arr) != 0
for i, nd in enumerate(arr):
if isinsta... | [
"Given a dataset, find the optimal threshold for quantizing it.\n The reference distribution is `q`, and the candidate distribution is `p`.\n `q` is a truncated version of the original distribution.\n\n Ref: http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf\n ... |
Please provide a description of the function:def _get_optimal_thresholds(nd_dict, quantized_dtype, num_bins=8001, num_quantized_bins=255, logger=None):
if stats is None:
raise ImportError('scipy.stats is required for running entropy mode of calculating'
' the optimal threshold... | [
"Given a ndarray dict, find the optimal threshold for quantizing each value of the key."
] |
Please provide a description of the function:def _load_sym(sym, logger=logging):
if isinstance(sym, str): # sym is a symbol file path
cur_path = os.path.dirname(os.path.realpath(__file__))
symbol_file_path = os.path.join(cur_path, sym)
logger.info('Loading symbol from file %s' % symbol... | [
"Given a str as a path the symbol .json file or a symbol, returns a Symbol object."
] |
Please provide a description of the function:def _load_params(params, logger=logging):
if isinstance(params, str):
cur_path = os.path.dirname(os.path.realpath(__file__))
param_file_path = os.path.join(cur_path, params)
logger.info('Loading params from file %s' % param_file_path)
... | [
"Given a str as a path to the .params file or a pair of params,\n returns two dictionaries representing arg_params and aux_params.\n "
] |
Please provide a description of the function:def quantize_model(sym, arg_params, aux_params,
data_names=('data',), label_names=('softmax_label',),
ctx=cpu(), excluded_sym_names=None, calib_mode='entropy',
calib_data=None, num_calib_examples=None, calib_layer=None... | [
"User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.\n The backend quantized operators are only enabled for Linux systems. Please do not run\n inference using the quantized models on Windows for now.\n The quantization implementation adopts the TensorFlow's approach:\n ... |
Please provide a description of the function:def collect(self, name, arr):
name = py_str(name)
if self.include_layer is not None and not self.include_layer(name):
return
handle = ctypes.cast(arr, NDArrayHandle)
arr = NDArray(handle, writable=False).copyto(cpu())
... | [
"Callback function for collecting layer output NDArrays."
] |
Please provide a description of the function:def collect(self, name, arr):
name = py_str(name)
if self.include_layer is not None and not self.include_layer(name):
return
handle = ctypes.cast(arr, NDArrayHandle)
arr = NDArray(handle, writable=False)
min_range ... | [
"Callback function for collecting min and max values from an NDArray."
] |
Please provide a description of the function:def generator(ngf, nc, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12, z_dim=100, activation='sigmoid'):
'''The genrator is a CNN which takes 100 dimensional embedding as input
and reconstructs the input image given to the encoder
'''
BatchNorm = mx.sym.Batch... | [] |
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