INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Returns a list of contexts this parameter is initialized on. | def list_ctx(self):
"""Returns a list of contexts this parameter is initialized on."""
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_lis... |
Sets gradient buffer on all contexts to 0. No action is taken if
parameter is uninitialized or doesn't require gradient. | def zero_grad(self):
"""Sets gradient buffer on all contexts to 0. No action is taken if
parameter is uninitialized or doesn't require gradient."""
if self._grad is None:
return
for i in self._grad:
ndarray.zeros_like(i, out=i) |
Returns a symbol representing this parameter. | def var(self):
"""Returns a symbol representing this parameter."""
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, sty... |
Cast data and gradient of this Parameter to a new data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type. | def cast(self, dtype):
"""Cast data and gradient of this Parameter to a new data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type.
"""
self.dtype = dtype
if self._data is None:
return
with autograd.pause():
... |
Retrieves a :py:class:`Parameter` with name ``self.prefix+name``. If not found,
:py:func:`get` will first try to retrieve it from "shared" dict. If still not
found, :py:func:`get` will create a new :py:class:`Parameter` with key-word arguments and
insert it to self.
Parameters
-... | def get(self, name, **kwargs):
"""Retrieves a :py:class:`Parameter` with name ``self.prefix+name``. If not found,
:py:func:`get` will first try to retrieve it from "shared" dict. If still not
found, :py:func:`get` will create a new :py:class:`Parameter` with key-word arguments and
insert... |
Retrieves a :py:class:`.Constant` with name ``self.prefix+name``. If not found,
:py:func:`get` will first try to retrieve it from "shared" dict. If still not
found, :py:func:`get` will create a new :py:class:`.Constant` with key-word
arguments and insert it to self.
Parameters
-... | def get_constant(self, name, value=None):
"""Retrieves a :py:class:`.Constant` with name ``self.prefix+name``. If not found,
:py:func:`get` will first try to retrieve it from "shared" dict. If still not
found, :py:func:`get` will create a new :py:class:`.Constant` with key-word
arguments... |
Copies all Parameters in ``other`` to self. | def update(self, other):
"""Copies all Parameters in ``other`` to self."""
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 t... |
Initializes all Parameters managed by this dictionary to be used for :py:class:`NDArray`
API. It has no effect when using :py:class:`Symbol` API.
Parameters
----------
init : Initializer
Global default Initializer to be used when :py:meth:`Parameter.init` is ``None``.
... | def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False,
force_reinit=False):
"""Initializes all Parameters managed by this dictionary to be used for :py:class:`NDArray`
API. It has no effect when using :py:class:`Symbol` API.
Parameters
----------
... |
Set an attribute to a new value for all Parameters.
For example, set grad_req to null if you don't need gradient w.r.t a
model's Parameters::
model.collect_params().setattr('grad_req', 'null')
or change the learning rate multiplier::
model.collect_params().setattr('lr... | def setattr(self, name, value):
"""Set an attribute to a new value for all Parameters.
For example, set grad_req to null if you don't need gradient w.r.t a
model's Parameters::
model.collect_params().setattr('grad_req', 'null')
or change the learning rate multiplier::
... |
Save parameters to file.
Parameters
----------
filename : str
Path to parameter file.
strip_prefix : str, default ''
Strip prefix from parameter names before saving. | def save(self, filename, strip_prefix=''):
"""Save parameters to file.
Parameters
----------
filename : str
Path to parameter file.
strip_prefix : str, default ''
Strip prefix from parameter names before saving.
"""
arg_dict = {}
f... |
Load parameters from file.
Parameters
----------
filename : str
Path to parameter file.
ctx : Context or list of Context
Context(s) initialize loaded parameters on.
allow_missing : bool, default False
Whether to silently skip loading parameter... | def load(self, filename, ctx=None, allow_missing=False,
ignore_extra=False, restore_prefix=''):
"""Load parameters from file.
Parameters
----------
filename : str
Path to parameter file.
ctx : Context or list of Context
Context(s) initialize ... |
List and add all the torch backed ndarray functions to current module. | def _init_torch_module():
"""List and add all the torch backed ndarray functions to current module."""
plist = ctypes.POINTER(FunctionHandle)()
size = ctypes.c_uint()
check_call(_LIB.MXListFunctions(ctypes.byref(size),
ctypes.byref(plist)))
module_obj = sys.modul... |
Create a Torch function from the FunctionHandle. | def _make_torch_function(handle):
"""Create a Torch function from the FunctionHandle."""
# 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_us... |
r"""Inception v3 model from
`"Rethinking the Inception Architecture for Computer Vision"
<http://arxiv.org/abs/1512.00567>`_ paper.
Parameters
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in ... | def inception_v3(pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""Inception v3 model from
`"Rethinking the Inception Architecture for Computer Vision"
<http://arxiv.org/abs/1512.00567>`_ paper.
Parameters
----------
pretrained : bool, de... |
Pack a string into MXImageRecord.
Parameters
----------
header : IRHeader
Header of the image record.
``header.label`` can be a number or an array. See more detail in ``IRHeader``.
s : str
Raw image string to be packed.
Returns
-------
s : str
The packed str... | def pack(header, s):
"""Pack a string into MXImageRecord.
Parameters
----------
header : IRHeader
Header of the image record.
``header.label`` can be a number or an array. See more detail in ``IRHeader``.
s : str
Raw image string to be packed.
Returns
-------
s ... |
Unpack a MXImageRecord to string.
Parameters
----------
s : str
String buffer from ``MXRecordIO.read``.
Returns
-------
header : IRHeader
Header of the image record.
s : str
Unpacked string.
Examples
--------
>>> record = mx.recordio.MXRecordIO('test.re... | def unpack(s):
"""Unpack a MXImageRecord to string.
Parameters
----------
s : str
String buffer from ``MXRecordIO.read``.
Returns
-------
header : IRHeader
Header of the image record.
s : str
Unpacked string.
Examples
--------
>>> record = mx.record... |
Unpack a MXImageRecord to image.
Parameters
----------
s : str
String buffer from ``MXRecordIO.read``.
iscolor : int
Image format option for ``cv2.imdecode``.
Returns
-------
header : IRHeader
Header of the image record.
img : numpy.ndarray
Unpacked imag... | def unpack_img(s, iscolor=-1):
"""Unpack a MXImageRecord to image.
Parameters
----------
s : str
String buffer from ``MXRecordIO.read``.
iscolor : int
Image format option for ``cv2.imdecode``.
Returns
-------
header : IRHeader
Header of the image record.
img... |
Pack an image into ``MXImageRecord``.
Parameters
----------
header : IRHeader
Header of the image record.
``header.label`` can be a number or an array. See more detail in ``IRHeader``.
img : numpy.ndarray
Image to be packed.
quality : int
Quality for JPEG encoding in... | def pack_img(header, img, quality=95, img_fmt='.jpg'):
"""Pack an image into ``MXImageRecord``.
Parameters
----------
header : IRHeader
Header of the image record.
``header.label`` can be a number or an array. See more detail in ``IRHeader``.
img : numpy.ndarray
Image to be ... |
Opens the record file. | def open(self):
"""Opens the record file."""
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.byref(self.handle... |
Closes the record file. | def close(self):
"""Closes the record file."""
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
self.pid = Non... |
Check process id to ensure integrity, reset if in new process. | def _check_pid(self, allow_reset=False):
"""Check process id to ensure integrity, reset if in new process."""
if not self.pid == current_process().pid:
if allow_reset:
self.reset()
else:
raise RuntimeError("Forbidden operation in multiple processes... |
Inserts a string buffer as a record.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'w')
>>> for i in range(5):
... record.write('record_%d'%i)
>>> record.close()
Parameters
----------
buf : string (python2), bytes (python3)... | def write(self, buf):
"""Inserts a string buffer as a record.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'w')
>>> for i in range(5):
... record.write('record_%d'%i)
>>> record.close()
Parameters
----------
buf : ... |
Returns record as a string.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'r')
>>> for i in range(5):
... item = record.read()
... print(item)
record_0
record_1
record_2
record_3
record_4
>>> recor... | def read(self):
"""Returns record as a string.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'r')
>>> for i in range(5):
... item = record.read()
... print(item)
record_0
record_1
record_2
record_3
... |
Closes the record file. | def close(self):
"""Closes the record file."""
if not self.is_open:
return
super(MXIndexedRecordIO, self).close()
self.fidx.close() |
Sets the current read pointer position.
This function is internally called by `read_idx(idx)` to find the current
reader pointer position. It doesn't return anything. | def seek(self, idx):
"""Sets the current read pointer position.
This function is internally called by `read_idx(idx)` to find the current
reader pointer position. It doesn't return anything."""
assert not self.writable
self._check_pid(allow_reset=True)
pos = ctypes.c_siz... |
Returns the current position of write head.
Examples
---------
>>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
>>> print(record.tell())
0
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
... print(record.tell())
... | def tell(self):
"""Returns the current position of write head.
Examples
---------
>>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
>>> print(record.tell())
0
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
..... |
Inserts input record at given index.
Examples
---------
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
>>> record.close()
Parameters
----------
idx : int
Index of a file.
buf :
Record to write. | def write_idx(self, idx, buf):
"""Inserts input record at given index.
Examples
---------
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
>>> record.close()
Parameters
----------
idx : int
Index of a file.
bu... |
Add new metrics as new columns to selected pandas dataframe.
Parameters
----------
dataframe : pandas.DataFrame
Selected dataframe needs to be modified.
metrics : metric.EvalMetric
New metrics to be added. | def _add_new_columns(dataframe, metrics):
"""Add new metrics as new columns to selected pandas dataframe.
Parameters
----------
dataframe : pandas.DataFrame
Selected dataframe needs to be modified.
metrics : metric.EvalMetric
New metrics to be added.
"""
#TODO(leodirac): we ... |
Generates callback arguments for model.fit()
for a set of callback objects.
Callback objects like PandasLogger(), LiveLearningCurve()
get passed in. This assembles all their callback arguments. | def args_wrapper(*args):
"""Generates callback arguments for model.fit()
for a set of callback objects.
Callback objects like PandasLogger(), LiveLearningCurve()
get passed in. This assembles all their callback arguments.
"""
out = defaultdict(list)
for callback in args:
callback_ar... |
Append new metrics to selected dataframes.
Parameters
----------
metrics : metric.EvalMetric
New metrics to be added.
df_name : str
Name of the dataframe to be modified. | def append_metrics(self, metrics, df_name):
"""Append new metrics to selected dataframes.
Parameters
----------
metrics : metric.EvalMetric
New metrics to be added.
df_name : str
Name of the dataframe to be modified.
"""
dataframe = self._... |
Callback funtion for training. | def train_cb(self, param):
"""Callback funtion for training.
"""
if param.nbatch % self.frequent == 0:
self._process_batch(param, 'train') |
Update parameters for selected dataframe after a completed batch
Parameters
----------
dataframe : pandas.DataFrame
Selected dataframe needs to be modified. | def _process_batch(self, param, dataframe):
"""Update parameters for selected dataframe after a completed batch
Parameters
----------
dataframe : pandas.DataFrame
Selected dataframe needs to be modified.
"""
now = time.time()
if param.eval_metric is no... |
Callback function after each epoch. Now it records each epoch time
and append it to epoch dataframe. | def epoch_cb(self):
"""Callback function after each epoch. Now it records each epoch time
and append it to epoch dataframe.
"""
metrics = {}
metrics['elapsed'] = self.elapsed()
now = datetime.datetime.now()
metrics['epoch_time'] = now - self.last_epoch_time
... |
Render the plot with bokeh.io and push to notebook. | def _push_render(self):
"""Render the plot with bokeh.io and push to notebook.
"""
bokeh.io.push_notebook(handle=self.handle)
self.last_update = time.time() |
Update selected dataframe after a completed batch
Parameters
----------
df_name : str
Selected dataframe name needs to be modified. | def _process_batch(self, param, df_name):
"""Update selected dataframe after a completed batch
Parameters
----------
df_name : str
Selected dataframe name needs to be modified.
"""
if param.eval_metric is not None:
metrics = dict(param.eval_metric.... |
:param nested_list: list of list of string
:return: dictionary mapping from string to int, inverse of that dictionary | def build_vocab(nested_list):
"""
:param nested_list: list of list of string
:return: dictionary mapping from string to int, inverse of that dictionary
"""
# Build vocabulary
word_counts = Counter(itertools.chain(*nested_list))
# Mapping from index to label
vocabulary_inv = [x[0] for x ... |
Reads a csv of sentences/tag sequences into a pandas dataframe.
Converts into X = array(list(int)) & Y = array(list(int))
Splits into training and test sets
Builds dictionaries mapping from index labels to labels/ indexed features to features
:param data_dir: directory to read in csv data from
:para... | def build_iters(data_dir, max_records, train_fraction, batch_size, buckets=None):
"""
Reads a csv of sentences/tag sequences into a pandas dataframe.
Converts into X = array(list(int)) & Y = array(list(int))
Splits into training and test sets
Builds dictionaries mapping from index labels to labels/ ... |
Build NN symbol depending on the length of the input sequence | def sym_gen(seq_len):
"""
Build NN symbol depending on the length of the input sequence
"""
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[... |
Draw random samples from an approximately log-uniform or Zipfian distribution.
This operation randomly samples *num_sampled* candidates the range of integers [0, range_max).
The elements of sampled_candidates are drawn with replacement from the base distribution.
The base distribution for this operator is... | def rand_zipfian(true_classes, num_sampled, range_max):
"""Draw random samples from an approximately log-uniform or Zipfian distribution.
This operation randomly samples *num_sampled* candidates the range of integers [0, range_max).
The elements of sampled_candidates are drawn with replacement from the bas... |
Run a for loop with user-defined computation over Symbols on dimension 0.
This operator simulates a for loop and body has the computation for an iteration
of the for loop. It runs the computation in body on each slice from the input
NDArrays.
body takes two arguments as input and outputs a tuple of tw... | def foreach(body, data, init_states, name="foreach"):
"""Run a for loop with user-defined computation over Symbols on dimension 0.
This operator simulates a for loop and body has the computation for an iteration
of the for loop. It runs the computation in body on each slice from the input
NDArrays.
... |
Run a while loop with user-defined computation and loop condition.
This operator simulates a while loop which iterately does customized computation
as long as the condition is satisfied.
`loop_vars` is a Symbol or nested lists of Symbols on which the computation uses.
`cond` is a user-defined functio... | def while_loop(cond, func, loop_vars, max_iterations=None, name="while_loop"):
"""Run a while loop with user-defined computation and loop condition.
This operator simulates a while loop which iterately does customized computation
as long as the condition is satisfied.
`loop_vars` is a Symbol or nested... |
Run an if-then-else using user-defined condition and computation
This operator simulates a if-like branch which chooses to do one of
the two customized computations according to the specified condition.
`pred` is a scalar MXNet Symbol,
indicating which branch of computation should be used.
`then_... | def cond(pred, then_func, else_func, name="cond"):
"""Run an if-then-else using user-defined condition and computation
This operator simulates a if-like branch which chooses to do one of
the two customized computations according to the specified condition.
`pred` is a scalar MXNet Symbol,
indicati... |
Indexes unknown and reserved tokens. | def _index_unknown_and_reserved_tokens(self, unknown_token, reserved_tokens):
"""Indexes unknown and 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:
sel... |
Indexes keys of `counter`.
Indexes keys of `counter` according to frequency thresholds such as `most_freq_count` and
`min_freq`. | def _index_counter_keys(self, counter, unknown_token, reserved_tokens, most_freq_count,
min_freq):
"""Indexes keys of `counter`.
Indexes keys of `counter` according to frequency thresholds such as `most_freq_count` and
`min_freq`.
"""
assert isinsta... |
Converts tokens to indices according to the vocabulary.
Parameters
----------
tokens : str or list of strs
A source token or tokens to be converted.
Returns
-------
int or list of ints
A token index or a list of token indices according to the v... | def to_indices(self, tokens):
"""Converts tokens to indices according to the vocabulary.
Parameters
----------
tokens : str or list of strs
A source token or tokens to be converted.
Returns
-------
int or list of ints
A token index or a... |
Converts token indices to tokens according to the vocabulary.
Parameters
----------
indices : int or list of ints
A source token index or token indices to be converted.
Returns
-------
str or list of strs
A token or a list of tokens according t... | def to_tokens(self, indices):
"""Converts token indices to tokens according to the vocabulary.
Parameters
----------
indices : int or list of ints
A source token index or token indices to be converted.
Returns
-------
str or list of strs
... |
Create an io iterator by handle. | def _make_io_iterator(handle):
"""Create an io iterator by 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)()
check_ca... |
List and add all the data iterators to current module. | def _init_io_module():
"""List and add all the data iterators to current 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):
hd... |
Get DataDesc list from attribute lists.
Parameters
----------
shapes : a tuple of (name_, shape_)
types : a tuple of (name_, np.dtype) | def get_list(shapes, types):
"""Get DataDesc list from attribute lists.
Parameters
----------
shapes : a tuple of (name_, shape_)
types : a tuple of (name_, np.dtype)
"""
if types is not None:
type_dict = dict(types)
return [DataDesc(x[0]... |
Get next data batch from iterator.
Returns
-------
DataBatch
The data of next batch.
Raises
------
StopIteration
If the end of the data is reached. | def next(self):
"""Get next data batch from iterator.
Returns
-------
DataBatch
The data of next batch.
Raises
------
StopIteration
If the end of the data is reached.
"""
if self.iter_next():
return DataBatch(d... |
Ignore roll over data and set to start. | def hard_reset(self):
"""Ignore roll over data and set to start."""
if self.shuffle:
self._shuffle_data()
self.cursor = -self.batch_size
self._cache_data = None
self._cache_label = None |
Resets the iterator to the beginning of the data. | def reset(self):
"""Resets the iterator to the beginning of the data."""
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:
... |
Increments the coursor by batch_size for next batch
and check current cursor if it exceed the number of data points. | def iter_next(self):
"""Increments the coursor by batch_size for next batch
and check current cursor if it exceed the number of data points."""
self.cursor += self.batch_size
return self.cursor < self.num_data |
Returns the next batch of data. | def next(self):
"""Returns the next batch of data."""
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 this case, ... |
Load data from underlying arrays. | def _getdata(self, data_source, start=None, end=None):
"""Load data from underlying arrays."""
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] if dat... |
Helper function to concat two NDArrays. | def _concat(self, first_data, second_data):
"""Helper function to concat two NDArrays."""
assert len(first_data) == len(
second_data), 'data source should contain the same size'
if first_data and second_data:
return [
concat(
first_data... |
Load data from underlying arrays, internal use only. | def _batchify(self, data_source):
"""Load data from underlying arrays, internal use only."""
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 ... |
Get pad value of DataBatch. | def getpad(self):
"""Get pad value of DataBatch."""
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_over' and \... |
Shuffle the data. | def _shuffle_data(self):
"""Shuffle the data."""
# 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) |
Given a quantized symbol and a dict of params that have not been quantized,
generate quantized params. Currently only supports quantizing the arg_params
with names of `weight` or `bias`, not aux_params. If `qsym` contains symbols
that are excluded from being quantized, their corresponding params will
no... | def _quantize_params(qsym, params, th_dict):
"""Given a quantized symbol and a dict of params that have not been quantized,
generate quantized params. Currently only supports quantizing the arg_params
with names of `weight` or `bias`, not aux_params. If `qsym` contains symbols
that are excluded from bei... |
Given a symbol object representing a neural network of data type FP32,
quantize it into a INT8 network.
Parameters
----------
sym : Symbol
FP32 neural network symbol.
excluded_sym_names : list of strings
A list of strings representing the names of the symbols that users want to excl... | def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_dtype='int8'):
"""Given a symbol object representing a neural network of data type FP32,
quantize it into a INT8 network.
Parameters
----------
sym : Symbol
FP32 neural network symbol.
excluded_sym_names : l... |
Given a dictionary containing the thresholds for quantizing the layers,
set the thresholds into the quantized symbol as the params of requantize operators. | def _calibrate_quantized_sym(qsym, th_dict):
"""Given a dictionary containing the thresholds for quantizing the layers,
set the thresholds into the quantized symbol as the params of requantize operators.
"""
if th_dict is None or len(th_dict) == 0:
return qsym
num_layer_outputs = len(th_dict... |
Collect min and max values from layer outputs and save them in
a dictionary mapped by layer names. | def _collect_layer_output_min_max(mod, data, include_layer=None,
max_num_examples=None, logger=None):
"""Collect min and max values from layer outputs and save them in
a dictionary mapped by layer names.
"""
collector = _LayerOutputMinMaxCollector(include_layer=include_... |
Collect layer outputs and save them in a dictionary mapped by layer names. | def _collect_layer_outputs(mod, data, include_layer=None, max_num_examples=None, logger=None):
"""Collect layer outputs and save them in a dictionary mapped by layer names."""
collector = _LayerOutputCollector(include_layer=include_layer, logger=logger)
num_examples = _collect_layer_statistics(mod, data, co... |
Given a discrete distribution (may have not been normalized to 1),
smooth it by replacing zeros with eps multiplied by a scaling factor and taking the
corresponding amount off the non-zero values.
Ref: http://web.engr.illinois.edu/~hanj/cs412/bk3/KL-divergence.pdf | def _smooth_distribution(p, eps=0.0001):
"""Given a discrete distribution (may have not been normalized to 1),
smooth it by replacing zeros with eps multiplied by a scaling factor and taking the
corresponding amount off the non-zero values.
Ref: http://web.engr.illinois.edu/~hanj/cs412/bk3/KL-divergence... |
Given a dataset, find the optimal threshold for quantizing it.
The reference distribution is `q`, and the candidate distribution is `p`.
`q` is a truncated version of the original distribution.
Ref: http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf | def _get_optimal_threshold(arr, quantized_dtype, num_bins=8001, num_quantized_bins=255):
"""Given a dataset, find the optimal threshold for quantizing it.
The reference distribution is `q`, and the candidate distribution is `p`.
`q` is a truncated version of the original distribution.
Ref: http://on-de... |
Given a ndarray dict, find the optimal threshold for quantizing each value of the key. | def _get_optimal_thresholds(nd_dict, quantized_dtype, num_bins=8001, num_quantized_bins=255, logger=None):
"""Given a ndarray dict, find the optimal threshold for quantizing each value of the key."""
if stats is None:
raise ImportError('scipy.stats is required for running entropy mode of calculating'
... |
Given a str as a path the symbol .json file or a symbol, returns a Symbol object. | def _load_sym(sym, logger=logging):
"""Given a str as a path the symbol .json file or a symbol, returns a Symbol object."""
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.i... |
Given a str as a path to the .params file or a pair of params,
returns two dictionaries representing arg_params and aux_params. | def _load_params(params, logger=logging):
"""Given a str as a path to the .params file or a pair of params,
returns two dictionaries representing arg_params and aux_params.
"""
if isinstance(params, str):
cur_path = os.path.dirname(os.path.realpath(__file__))
param_file_path = os.path.jo... |
User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.
The backend quantized operators are only enabled for Linux systems. Please do not run
inference using the quantized models on Windows for now.
The quantization implementation adopts the TensorFlow's approach:
https:... | 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,
quantized_dtype='int8', ... |
Callback function for collecting layer output NDArrays. | def collect(self, name, arr):
"""Callback function for collecting layer output NDArrays."""
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).co... |
Callback function for collecting min and max values from an NDArray. | def collect(self, name, arr):
"""Callback function for collecting min and max values from an NDArray."""
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, writa... |
The encoder is a CNN which takes 32x32 image as input
generates the 100 dimensional shape embedding as a sample from normal distribution
using predicted meand and variance | def encoder(nef, z_dim, batch_size, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''The encoder is a CNN which takes 32x32 image as input
generates the 100 dimensional shape embedding as a sample from normal distribution
using predicted meand and variance
'''
BatchNorm = mx.sym.BatchNorm
da... |
The genrator is a CNN which takes 100 dimensional embedding as input
and reconstructs the input image given to the encoder | 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.BatchNorm
rand = mx.sym.Variable('rand')
... |
First part of the discriminator which takes a 32x32 image as input
and output a convolutional feature map, this is required to calculate
the layer loss | def discriminator1(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''First part of the discriminator which takes a 32x32 image as input
and output a convolutional feature map, this is required to calculate
the layer loss'''
BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
d1 ... |
Second part of the discriminator which takes a 256x8x8 feature map as input
and generates the loss based on whether the input image was a real one or fake one | def discriminator2(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''Second part of the discriminator which takes a 256x8x8 feature map as input
and generates the loss based on whether the input image was a real one or fake one'''
BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
... |
GaussianLogDensity loss calculation for layer wise loss | def GaussianLogDensity(x, mu, log_var, name='GaussianLogDensity', EPSILON = 1e-6):
'''GaussianLogDensity loss calculation for layer wise loss
'''
c = mx.sym.ones_like(log_var)*2.0 * 3.1416
c = mx.symbol.log(c)
var = mx.sym.exp(log_var)
x_mu2 = mx.symbol.square(x - mu) # [Issue] not sure the di... |
Calculate the discriminator layer loss | def DiscriminatorLayerLoss():
'''Calculate the discriminator layer loss
'''
data = mx.sym.Variable('data')
label = mx.sym.Variable('label')
data = mx.sym.Flatten(data)
label = mx.sym.Flatten(label)
label = mx.sym.BlockGrad(label)
zeros = mx.sym.zeros_like(data)
output = -Gaussi... |
KLDivergenceLoss loss | def KLDivergenceLoss():
'''KLDivergenceLoss loss
'''
data = mx.sym.Variable('data')
mu1, lv1 = mx.sym.split(data, num_outputs=2, axis=0)
mu2 = mx.sym.zeros_like(mu1)
lv2 = mx.sym.zeros_like(lv1)
v1 = mx.sym.exp(lv1)
v2 = mx.sym.exp(lv2)
mu_diff_sq = mx.sym.square(mu1 - mu2)
di... |
Get the dataset | def get_data(path, activation):
'''Get the dataset
'''
data = []
image_names = []
for filename in os.listdir(path):
img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE)
image_names.append(filename)
if img is not None:
data.append(img)
data = np... |
fill the ith grid of the buffer matrix with the values from the img
buf : buffer matrix
i : serial of the image in the 2D grid
img : image data
shape : ( height width depth ) of image | def fill_buf(buf, i, img, shape):
'''fill the ith grid of the buffer matrix with the values from the img
buf : buffer matrix
i : serial of the image in the 2D grid
img : image data
shape : ( height width depth ) of image'''
# grid height is a multiple of individual image height
m = buf.shap... |
create a grid of images and save it as a final image
title : grid image name
X : array of images | def visual(title, X, activation):
'''create a grid of images and save it as a final image
title : grid image name
X : array of images
'''
assert len(X.shape) == 4
X = X.transpose((0, 2, 3, 1))
if activation == 'sigmoid':
X = np.clip((X)*(255.0), 0, 255).astype(np.uint8)
elif act... |
adversarial training of the VAE | def train(dataset, nef, ndf, ngf, nc, batch_size, Z, lr, beta1, epsilon, ctx, check_point, g_dl_weight, output_path, checkpoint_path, data_path, activation,num_epoch, save_after_every, visualize_after_every, show_after_every):
'''adversarial training of the VAE
'''
#encoder
z_mu, z_lv, z = encoder(nef,... |
Creates/Validates dir | def create_and_validate_dir(data_dir):
'''Creates/Validates dir
'''
if data_dir != "":
if not os.path.exists(data_dir):
try:
logging.info('create directory %s', data_dir)
os.makedirs(data_dir)
except OSError as exc:
if exc.errno... |
Parse args | def parse_args():
'''Parse args
'''
parser = argparse.ArgumentParser(description='Train and Test an Adversarial Variatiional Encoder')
parser.add_argument('--train', help='train the network', action='store_true')
parser.add_argument('--test', help='test the network', action='store_true')
parser... |
Gets root mse between the logarithms of the prediction and the truth. | def get_rmse_log(net, X_train, y_train):
"""Gets root mse between the logarithms of the prediction and the truth."""
num_train = X_train.shape[0]
clipped_preds = nd.clip(net(X_train), 1, float('inf'))
return np.sqrt(2 * nd.sum(square_loss(
nd.log(clipped_preds), nd.log(y_train))).asscalar() / nu... |
Gets a neural network. Better results are obtained with modifications. | def get_net():
"""Gets a neural network. Better results are obtained with modifications."""
net = gluon.nn.Sequential()
with net.name_scope():
net.add(gluon.nn.Dense(50, activation="relu"))
net.add(gluon.nn.Dense(1))
net.initialize()
return net |
Trains the model. | def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
weight_decay, batch_size):
"""Trains the model."""
dataset_train = gluon.data.ArrayDataset(X_train, y_train)
data_iter_train = gluon.data.DataLoader(dataset_train, batch_size,
shuffle... |
Conducts k-fold cross validation for the model. | def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,
learning_rate, weight_decay, batch_size):
"""Conducts k-fold cross validation for the model."""
assert k > 1
fold_size = X_train.shape[0] // k
train_loss_sum = 0.0
test_loss_sum = 0.0
for test_idx in range... |
Trains the model and predicts on the test data set. | def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,
weight_decay, batch_size):
"""Trains the model and predicts on the test data set."""
net = get_net()
_ = train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
weight_decay, batch_size)
preds =... |
Create CapsNet | def capsnet(batch_size, n_class, num_routing, recon_loss_weight):
"""Create CapsNet"""
# data.shape = [batch_size, 1, 28, 28]
data = mx.sym.Variable('data')
input_shape = (1, 28, 28)
# Conv2D layer
# net.shape = [batch_size, 256, 20, 20]
conv1 = mx.sym.Convolution(data=data,
... |
Perform CapsNet training | def do_training(num_epoch, optimizer, kvstore, learning_rate, model_prefix, decay):
"""Perform CapsNet training"""
summary_writer = SummaryWriter(args.tblog_dir)
lr_scheduler = SimpleLRScheduler(learning_rate)
optimizer_params = {'lr_scheduler': lr_scheduler}
module.init_params()
module.init_opt... |
Shuffle the data. | def _shuffle(data, idx):
"""Shuffle the data."""
shuffle_data = []
for idx_k, idx_v in data:
shuffle_data.append((idx_k, mx.ndarray.array(idx_v.asnumpy()[idx], idx_v.context)))
return shuffle_data |
Reset class MNISTCustomIter(mx.io.NDArrayIter): | def reset(self):
"""Reset class MNISTCustomIter(mx.io.NDArrayIter):"""
# shuffle data
if self.is_train:
np.random.shuffle(self.idx)
self.data = _shuffle(self.data, self.idx)
self.label = _shuffle(self.label, self.idx)
if self.last_batch_handle == 'rol... |
Update the hyper-parameters and loss of CapsNet | def update(self, labels, preds):
"""Update the hyper-parameters and loss of CapsNet"""
batch_sum_metric = 0
batch_num_inst = 0
for label, pred_outcaps in zip(labels[0], preds[0]):
label_np = int(label.asnumpy())
pred_label = int(np.argmax(pred_outcaps.asnumpy()))
... |
Generate next of iterator | def next(self):
"""Generate next of iterator"""
if self.iter_next():
if self.is_train:
data_raw_list = self.getdata()
data_shifted = []
for data_raw in data_raw_list[0]:
data_shifted.append(random_shift(data_raw.asnumpy(), 0... |
Get the attribute dict given the attribute set by the symbol.
Parameters
----------
attr : dict of string to string
The attribute passed in by user during symbol creation.
Returns
-------
attr : dict of string to string
Updated attributes to add ... | def get(self, attr):
"""
Get the attribute dict given the attribute set by the symbol.
Parameters
----------
attr : dict of string to string
The attribute passed in by user during symbol creation.
Returns
-------
attr : dict of string to stri... |
Create kvstore assuming some parameters' storage types are row_sparse.
Parameters
----------
kvstore : KVStore or str
The kvstore.
Returns
-------
kvstore : KVStore
update_on_kvstore : bool. Always True. | def _create_sparse_kvstore(kvstore):
"""Create kvstore assuming some parameters' storage types are row_sparse.
Parameters
----------
kvstore : KVStore or str
The kvstore.
Returns
-------
kvstore : KVStore
update_on_kvstore : bool. Always True.
"""
# always update on kvs... |
Create kvstore
This function select and create a proper kvstore if given the kvstore type.
Parameters
----------
kvstore : KVStore or str
The kvstore.
num_device : int
The number of devices
arg_params : dict of str to `NDArray`.
Model parameter, dict of name to `NDArray`... | def _create_kvstore(kvstore, num_device, arg_params):
"""Create kvstore
This function select and create a proper kvstore if given the kvstore type.
Parameters
----------
kvstore : KVStore or str
The kvstore.
num_device : int
The number of devices
arg_params : dict of str to ... |
Initialize kvstore | def _initialize_kvstore(kvstore, param_arrays, arg_params, param_names, update_on_kvstore):
"""Initialize kvstore"""
for idx, param_on_devs in enumerate(param_arrays):
name = param_names[idx]
kvstore.init(name, arg_params[name])
if update_on_kvstore:
kvstore.pull(name, param... |
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