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pytorch/text | torchtext/vocab.py | Vocab.set_vectors | def set_vectors(self, stoi, vectors, dim, unk_init=torch.Tensor.zero_):
"""
Set the vectors for the Vocab instance from a collection of Tensors.
Arguments:
stoi: A dictionary of string to the index of the associated vector
in the `vectors` input argument.
... | python | def set_vectors(self, stoi, vectors, dim, unk_init=torch.Tensor.zero_):
"""
Set the vectors for the Vocab instance from a collection of Tensors.
Arguments:
stoi: A dictionary of string to the index of the associated vector
in the `vectors` input argument.
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pytorch/text | torchtext/datasets/sst.py | SST.splits | def splits(cls, text_field, label_field, root='.data',
train='train.txt', validation='dev.txt', test='test.txt',
train_subtrees=False, **kwargs):
"""Create dataset objects for splits of the SST dataset.
Arguments:
text_field: The field that will be used for the... | python | def splits(cls, text_field, label_field, root='.data',
train='train.txt', validation='dev.txt', test='test.txt',
train_subtrees=False, **kwargs):
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pytorch/text | torchtext/datasets/sst.py | SST.iters | def iters(cls, batch_size=32, device=0, root='.data', vectors=None, **kwargs):
"""Create iterator objects for splits of the SST dataset.
Arguments:
batch_size: Batch_size
device: Device to create batches on. Use - 1 for CPU and None for
the currently active GPU d... | python | def iters(cls, batch_size=32, device=0, root='.data', vectors=None, **kwargs):
"""Create iterator objects for splits of the SST dataset.
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batch_size: Batch_size
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pytorch/text | torchtext/data/utils.py | interleave_keys | def interleave_keys(a, b):
"""Interleave bits from two sort keys to form a joint sort key.
Examples that are similar in both of the provided keys will have similar
values for the key defined by this function. Useful for tasks with two
text fields like machine translation or natural language inference.
... | python | def interleave_keys(a, b):
"""Interleave bits from two sort keys to form a joint sort key.
Examples that are similar in both of the provided keys will have similar
values for the key defined by this function. Useful for tasks with two
text fields like machine translation or natural language inference.
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pytorch/text | torchtext/data/utils.py | RandomShuffler.use_internal_state | def use_internal_state(self):
"""Use a specific RNG state."""
old_state = random.getstate()
random.setstate(self._random_state)
yield
self._random_state = random.getstate()
random.setstate(old_state) | python | def use_internal_state(self):
"""Use a specific RNG state."""
old_state = random.getstate()
random.setstate(self._random_state)
yield
self._random_state = random.getstate()
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pytorch/text | torchtext/utils.py | download_from_url | def download_from_url(url, path):
"""Download file, with logic (from tensor2tensor) for Google Drive"""
def process_response(r):
chunk_size = 16 * 1024
total_size = int(r.headers.get('Content-length', 0))
with open(path, "wb") as file:
with tqdm(total=total_size, unit='B',
... | python | def download_from_url(url, path):
"""Download file, with logic (from tensor2tensor) for Google Drive"""
def process_response(r):
chunk_size = 16 * 1024
total_size = int(r.headers.get('Content-length', 0))
with open(path, "wb") as file:
with tqdm(total=total_size, unit='B',
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pytorch/text | torchtext/utils.py | unicode_csv_reader | def unicode_csv_reader(unicode_csv_data, **kwargs):
"""Since the standard csv library does not handle unicode in Python 2, we need a wrapper.
Borrowed and slightly modified from the Python docs:
https://docs.python.org/2/library/csv.html#csv-examples"""
if six.PY2:
# csv.py doesn't do Unicode; e... | python | def unicode_csv_reader(unicode_csv_data, **kwargs):
"""Since the standard csv library does not handle unicode in Python 2, we need a wrapper.
Borrowed and slightly modified from the Python docs:
https://docs.python.org/2/library/csv.html#csv-examples"""
if six.PY2:
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pytorch/text | torchtext/datasets/translation.py | TranslationDataset.splits | def splits(cls, exts, fields, path=None, root='.data',
train='train', validation='val', test='test', **kwargs):
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Arguments:
exts: A tuple containing the extension to path for each language.
fields: A tup... | python | def splits(cls, exts, fields, path=None, root='.data',
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pytorch/text | torchtext/datasets/translation.py | Multi30k.splits | def splits(cls, exts, fields, root='.data',
train='train', validation='val', test='test2016', **kwargs):
"""Create dataset objects for splits of the Multi30k dataset.
Arguments:
exts: A tuple containing the extension to path for each language.
fields: A tuple cont... | python | def splits(cls, exts, fields, root='.data',
train='train', validation='val', test='test2016', **kwargs):
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pytorch/text | torchtext/datasets/translation.py | IWSLT.splits | def splits(cls, exts, fields, root='.data',
train='train', validation='IWSLT16.TED.tst2013',
test='IWSLT16.TED.tst2014', **kwargs):
"""Create dataset objects for splits of the IWSLT dataset.
Arguments:
exts: A tuple containing the extension to path for each lan... | python | def splits(cls, exts, fields, root='.data',
train='train', validation='IWSLT16.TED.tst2013',
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pytorch/text | torchtext/data/field.py | NestedField.build_vocab | def build_vocab(self, *args, **kwargs):
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Positional arguments: Dataset objects or other iterable data
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pytorch/text | torchtext/data/field.py | NestedField.numericalize | def numericalize(self, arrs, device=None):
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tensors will be stacked at the first dimension.
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return count
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minibatch.append(ex)
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pytorch/text | torchtext/data/iterator.py | Iterator.splits | def splits(cls, datasets, batch_sizes=None, **kwargs):
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datasets: Tuple of Dataset objects corresponding to the splits. The
first such object should be the train set.
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datasets: Tuple of Dataset objects corresponding to the splits. The
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pytorch/text | torchtext/data/iterator.py | Iterator.data | def data(self):
"""Return the examples in the dataset in order, sorted, or shuffled."""
if self.sort:
xs = sorted(self.dataset, key=self.sort_key)
elif self.shuffle:
xs = [self.dataset[i] for i in self.random_shuffler(range(len(self.dataset)))]
else:
x... | python | def data(self):
"""Return the examples in the dataset in order, sorted, or shuffled."""
if self.sort:
xs = sorted(self.dataset, key=self.sort_key)
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xs = [self.dataset[i] for i in self.random_shuffler(range(len(self.dataset)))]
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pytorch/text | torchtext/data/iterator.py | Iterator.init_epoch | def init_epoch(self):
"""Set up the batch generator for a new epoch."""
if self._restored_from_state:
self.random_shuffler.random_state = self._random_state_this_epoch
else:
self._random_state_this_epoch = self.random_shuffler.random_state
self.create_batches()
... | python | def init_epoch(self):
"""Set up the batch generator for a new epoch."""
if self._restored_from_state:
self.random_shuffler.random_state = self._random_state_this_epoch
else:
self._random_state_this_epoch = self.random_shuffler.random_state
self.create_batches()
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pytorch/text | torchtext/data/batch.py | Batch.fromvars | def fromvars(cls, dataset, batch_size, train=None, **kwargs):
"""Create a Batch directly from a number of Variables."""
batch = cls()
batch.batch_size = batch_size
batch.dataset = dataset
batch.fields = dataset.fields.keys()
for k, v in kwargs.items():
setattr... | python | def fromvars(cls, dataset, batch_size, train=None, **kwargs):
"""Create a Batch directly from a number of Variables."""
batch = cls()
batch.batch_size = batch_size
batch.dataset = dataset
batch.fields = dataset.fields.keys()
for k, v in kwargs.items():
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pytorch/text | torchtext/datasets/sequence_tagging.py | UDPOS.splits | def splits(cls, fields, root=".data", train="en-ud-tag.v2.train.txt",
validation="en-ud-tag.v2.dev.txt",
test="en-ud-tag.v2.test.txt", **kwargs):
"""Downloads and loads the Universal Dependencies Version 2 POS Tagged
data.
"""
return super(UDPOS, cls).split... | python | def splits(cls, fields, root=".data", train="en-ud-tag.v2.train.txt",
validation="en-ud-tag.v2.dev.txt",
test="en-ud-tag.v2.test.txt", **kwargs):
"""Downloads and loads the Universal Dependencies Version 2 POS Tagged
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pytorch/text | torchtext/datasets/sequence_tagging.py | CoNLL2000Chunking.splits | def splits(cls, fields, root=".data", train="train.txt",
test="test.txt", validation_frac=0.1, **kwargs):
"""Downloads and loads the CoNLL 2000 Chunking dataset.
NOTE: There is only a train and test dataset so we use
10% of the train set as validation
"""
tr... | python | def splits(cls, fields, root=".data", train="train.txt",
test="test.txt", validation_frac=0.1, **kwargs):
"""Downloads and loads the CoNLL 2000 Chunking dataset.
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pytorch/text | torchtext/datasets/imdb.py | IMDB.splits | def splits(cls, text_field, label_field, root='.data',
train='train', test='test', **kwargs):
"""Create dataset objects for splits of the IMDB dataset.
Arguments:
text_field: The field that will be used for the sentence.
label_field: The field that will be used fo... | python | def splits(cls, text_field, label_field, root='.data',
train='train', test='test', **kwargs):
"""Create dataset objects for splits of the IMDB dataset.
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text_field: The field that will be used for the sentence.
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pytorch/text | torchtext/datasets/language_modeling.py | WikiText2.splits | def splits(cls, text_field, root='.data', train='wiki.train.tokens',
validation='wiki.valid.tokens', test='wiki.test.tokens',
**kwargs):
"""Create dataset objects for splits of the WikiText-2 dataset.
This is the most flexible way to use the dataset.
Arguments:
... | python | def splits(cls, text_field, root='.data', train='wiki.train.tokens',
validation='wiki.valid.tokens', test='wiki.test.tokens',
**kwargs):
"""Create dataset objects for splits of the WikiText-2 dataset.
This is the most flexible way to use the dataset.
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pytorch/text | torchtext/datasets/language_modeling.py | WikiText2.iters | def iters(cls, batch_size=32, bptt_len=35, device=0, root='.data',
vectors=None, **kwargs):
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This is the simplest way to use the dataset, and assumes common
defaults for field, vocabulary, and iterator parameters.
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x: The input to apply the... | python | def call(self, x, *args):
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Arguments:
pipeline: The Pipeline or callable to apply before this
Pipeline.
"""
if not isinstance(pipeline, Pipeline):
pipeline = Pipeline(pipeline)
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"""Add a Pipeline to be applied before this processing pipeline.
Arguments:
pipeline: The Pipeline or callable to apply before this
Pipeline.
"""
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Arguments:
pipeline: The Pipeline or callable to apply after this
Pipeline.
"""
if not isinstance(pipeline, Pipeline):
pipeline = Pipeline(pipeline)
... | python | def add_after(self, pipeline):
"""Add a Pipeline to be applied after this processing pipeline.
Arguments:
pipeline: The Pipeline or callable to apply after this
Pipeline.
"""
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pytorch/text | torchtext/data/dataset.py | check_split_ratio | def check_split_ratio(split_ratio):
"""Check that the split ratio argument is not malformed"""
valid_ratio = 0.
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# Only the train set relative ratio is provided
# Assert in bounds, validation size is zero
assert 0. < split_ratio < 1., (
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"""Check that the split ratio argument is not malformed"""
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pytorch/text | torchtext/data/dataset.py | Dataset.split | def split(self, split_ratio=0.7, stratified=False, strata_field='label',
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pytorch/text | torchtext/data/dataset.py | Dataset.download | def download(cls, root, check=None):
"""Download and unzip an online archive (.zip, .gz, or .tgz).
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root (str): Folder to download data to.
check (str or None): Folder whose existence indicates
that the dataset has already been downloaded, or
... | python | def download(cls, root, check=None):
"""Download and unzip an online archive (.zip, .gz, or .tgz).
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root (str): Folder to download data to.
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pytorch/text | torchtext/data/dataset.py | Dataset.filter_examples | def filter_examples(self, field_names):
"""Remove unknown words from dataset examples with respect to given field.
Arguments:
field_names (list(str)): Within example only the parts with field names in
field_names will have their unknown words deleted.
"""
for... | python | def filter_examples(self, field_names):
"""Remove unknown words from dataset examples with respect to given field.
Arguments:
field_names (list(str)): Within example only the parts with field names in
field_names will have their unknown words deleted.
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pytorch/text | torchtext/datasets/nli.py | NLIDataset.splits | def splits(cls, text_field, label_field, parse_field=None,
extra_fields={}, root='.data', train='train.jsonl',
validation='val.jsonl', test='test.jsonl'):
"""Create dataset objects for splits of the SNLI dataset.
This is the most flexible way to use the dataset.
A... | python | def splits(cls, text_field, label_field, parse_field=None,
extra_fields={}, root='.data', train='train.jsonl',
validation='val.jsonl', test='test.jsonl'):
"""Create dataset objects for splits of the SNLI dataset.
This is the most flexible way to use the dataset.
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pytorch/text | torchtext/datasets/nli.py | NLIDataset.iters | def iters(cls, batch_size=32, device=0, root='.data',
vectors=None, trees=False, **kwargs):
"""Create iterator objects for splits of the SNLI dataset.
This is the simplest way to use the dataset, and assumes common
defaults for field, vocabulary, and iterator parameters.
... | python | def iters(cls, batch_size=32, device=0, root='.data',
vectors=None, trees=False, **kwargs):
"""Create iterator objects for splits of the SNLI dataset.
This is the simplest way to use the dataset, and assumes common
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sripathikrishnan/redis-rdb-tools | rdbtools/encodehelpers.py | bytes_to_unicode | def bytes_to_unicode(byte_data, escape, skip_printable=False):
"""
Decode given bytes using specified escaping method.
:param byte_data: The byte-like object with bytes to decode.
:param escape: The escape method to use.
:param skip_printable: If True, don't escape byte_data with all 'printable ASCI... | python | def bytes_to_unicode(byte_data, escape, skip_printable=False):
"""
Decode given bytes using specified escaping method.
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sripathikrishnan/redis-rdb-tools | rdbtools/encodehelpers.py | apply_escape_bytes | def apply_escape_bytes(byte_data, escape, skip_printable=False):
"""
Apply the specified escape method on the given bytes.
:param byte_data: The byte-like object with bytes to escape.
:param escape: The escape method to use.
:param skip_printable: If True, don't escape byte_data with all 'printable ... | python | def apply_escape_bytes(byte_data, escape, skip_printable=False):
"""
Apply the specified escape method on the given bytes.
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:param escape: The escape method to use.
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sripathikrishnan/redis-rdb-tools | rdbtools/parser.py | RdbParser._decode_module_id | def _decode_module_id(self, module_id):
"""
decode module id to string
based on @antirez moduleTypeNameByID function from redis/src/module.c
:param module_id: 64bit integer
:return: string
"""
name = [''] * 9
module_id >>= 10
for i in reversed(rang... | python | def _decode_module_id(self, module_id):
"""
decode module id to string
based on @antirez moduleTypeNameByID function from redis/src/module.c
:param module_id: 64bit integer
:return: string
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waleedka/hiddenlayer | hiddenlayer/history.py | format_step | def format_step(step, zero_prefix=False):
"""Return the step value in format suitable for display."""
if isinstance(step, int):
return "{:06}".format(step) if zero_prefix else "{}".format(step)
elif isinstance(step, tuple):
return "{:04}:{:06}".format(*step) if zero_prefix else "{}:{}".forma... | python | def format_step(step, zero_prefix=False):
"""Return the step value in format suitable for display."""
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waleedka/hiddenlayer | hiddenlayer/history.py | History.log | def log(self, step, **kwargs):
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my_history.log(34, loss=2.3, accuracy=0.2)
Okay to call multiple times for the same step. New values overwrite
older ones if they have the same metric name.
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"""Record metrics at a specific step. E.g.
my_history.log(34, loss=2.3, accuracy=0.2)
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waleedka/hiddenlayer | hiddenlayer/history.py | History.get_total_time | def get_total_time(self):
"""Returns the total period between when the first and last steps
where logged. This usually correspnods to the total training time
if there were no gaps in the training.
"""
first_step = self.steps[0]
last_step = self.steps[-1]
seconds =... | python | def get_total_time(self):
"""Returns the total period between when the first and last steps
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"""
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waleedka/hiddenlayer | hiddenlayer/graph.py | Graph.outgoing | def outgoing(self, node):
"""Returns nodes connecting out of the given node (or list of nodes)."""
nodes = node if isinstance(node, list) else [node]
node_ids = [self.id(n) for n in nodes]
# Find edges outgoing from this group but not incoming to it
outgoing = [self[e[1]] for e i... | python | def outgoing(self, node):
"""Returns nodes connecting out of the given node (or list of nodes)."""
nodes = node if isinstance(node, list) else [node]
node_ids = [self.id(n) for n in nodes]
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waleedka/hiddenlayer | hiddenlayer/graph.py | Graph.siblings | def siblings(self, node):
"""Returns all nodes that share the same parent (incoming node) with
the given node, including the node itself.
"""
incoming = self.incoming(node)
# TODO: Not handling the case of multiple incoming nodes yet
if len(incoming) == 1:
inc... | python | def siblings(self, node):
"""Returns all nodes that share the same parent (incoming node) with
the given node, including the node itself.
"""
incoming = self.incoming(node)
# TODO: Not handling the case of multiple incoming nodes yet
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the given node including the itself. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
waleedka/hiddenlayer | hiddenlayer/graph.py | Graph.remove | def remove(self, nodes):
"""Remove a node and its edges."""
nodes = nodes if isinstance(nodes, list) else [nodes]
for node in nodes:
k = self.id(node)
self.edges = list(filter(lambda e: e[0] != k and e[1] != k, self.edges))
del self.nodes[k] | python | def remove(self, nodes):
"""Remove a node and its edges."""
nodes = nodes if isinstance(nodes, list) else [nodes]
for node in nodes:
k = self.id(node)
self.edges = list(filter(lambda e: e[0] != k and e[1] != k, self.edges))
del self.nodes[k] | [
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waleedka/hiddenlayer | hiddenlayer/graph.py | Graph.search | def search(self, pattern):
"""Searches the graph for a sub-graph that matches the given pattern
and returns the first match it finds.
"""
for node in self.nodes.values():
match, following = pattern.match(self, node)
if match:
return match, followin... | python | def search(self, pattern):
"""Searches the graph for a sub-graph that matches the given pattern
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"""
for node in self.nodes.values():
match, following = pattern.match(self, node)
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waleedka/hiddenlayer | hiddenlayer/graph.py | Graph.sequence_id | def sequence_id(self, sequence):
"""Make up an ID for a sequence (list) of nodes.
Note: `getrandbits()` is very uninformative as a "readable" ID. Here, we build a name
such that when the mouse hovers over the drawn node in Jupyter, one can figure out
which original nodes make up the sequ... | python | def sequence_id(self, sequence):
"""Make up an ID for a sequence (list) of nodes.
Note: `getrandbits()` is very uninformative as a "readable" ID. Here, we build a name
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waleedka/hiddenlayer | hiddenlayer/graph.py | Graph.build_dot | def build_dot(self):
"""Generate a GraphViz Dot graph.
Returns a GraphViz Digraph object.
"""
from graphviz import Digraph
# Build GraphViz Digraph
dot = Digraph()
dot.attr("graph",
bgcolor=self.theme["background_color"],
color... | python | def build_dot(self):
"""Generate a GraphViz Dot graph.
Returns a GraphViz Digraph object.
"""
from graphviz import Digraph
# Build GraphViz Digraph
dot = Digraph()
dot.attr("graph",
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waleedka/hiddenlayer | hiddenlayer/utils.py | to_data | def to_data(value):
"""Standardize data types. Converts PyTorch tensors to Numpy arrays,
and Numpy scalars to Python scalars."""
# TODO: Use get_framework() for better detection.
if value.__class__.__module__.startswith("torch"):
import torch
if isinstance(value, torch.nn.parameter.Param... | python | def to_data(value):
"""Standardize data types. Converts PyTorch tensors to Numpy arrays,
and Numpy scalars to Python scalars."""
# TODO: Use get_framework() for better detection.
if value.__class__.__module__.startswith("torch"):
import torch
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waleedka/hiddenlayer | hiddenlayer/utils.py | write | def write(*args):
"""Like print(), but recognizes tensors and arrays and show
more details about them.
Example:
hl.write("My Tensor", my_tensor)
Prints:
My Tensor float32 (10, 3, 224, 224) min: 0.0 max: 1.0
"""
s = ""
for a in args:
# Convert tensors ... | python | def write(*args):
"""Like print(), but recognizes tensors and arrays and show
more details about them.
Example:
hl.write("My Tensor", my_tensor)
Prints:
My Tensor float32 (10, 3, 224, 224) min: 0.0 max: 1.0
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s = ""
for a in args:
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waleedka/hiddenlayer | hiddenlayer/canvas.py | norm | def norm(image):
"""Normalize an image to [0, 1] range."""
min_value = image.min()
max_value = image.max()
if min_value == max_value:
return image - min_value
return (image - min_value) / (max_value - min_value) | python | def norm(image):
"""Normalize an image to [0, 1] range."""
min_value = image.min()
max_value = image.max()
if min_value == max_value:
return image - min_value
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waleedka/hiddenlayer | hiddenlayer/canvas.py | show_images | def show_images(images, titles=None, cols=5, **kwargs):
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- A list of lists of Numpy arrays. In this case, the images in
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"""
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"""
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- A list of lists of Numpy arrays. In this case, the images in
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waleedka/hiddenlayer | hiddenlayer/canvas.py | Canvas.draw_summary | def draw_summary(self, history, title=""):
"""Inserts a text summary at the top that lists the number of steps and total
training time."""
# Generate summary string
time_str = str(history.get_total_time()).split(".")[0] # remove microseconds
summary = "Step: {} Time: {}".fo... | python | def draw_summary(self, history, title=""):
"""Inserts a text summary at the top that lists the number of steps and total
training time."""
# Generate summary string
time_str = str(history.get_total_time()).split(".")[0] # remove microseconds
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waleedka/hiddenlayer | hiddenlayer/canvas.py | Canvas.draw_plot | def draw_plot(self, metrics, labels=None, ylabel=""):
"""
metrics: One or more metrics parameters. Each represents the history
of one metric.
"""
metrics = metrics if isinstance(metrics, list) else [metrics]
# Loop through metrics
title = ""
for i, m i... | python | def draw_plot(self, metrics, labels=None, ylabel=""):
"""
metrics: One or more metrics parameters. Each represents the history
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"""
metrics = metrics if isinstance(metrics, list) else [metrics]
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waleedka/hiddenlayer | hiddenlayer/canvas.py | Canvas.draw_image | def draw_image(self, metric, limit=5):
"""Display a series of images at different time steps."""
rows = 1
cols = limit
self.ax.axis("off")
# Take the Axes gridspec and divide it into a grid
gs = matplotlib.gridspec.GridSpecFromSubplotSpec(
rows, cols, subplot_... | python | def draw_image(self, metric, limit=5):
"""Display a series of images at different time steps."""
rows = 1
cols = limit
self.ax.axis("off")
# Take the Axes gridspec and divide it into a grid
gs = matplotlib.gridspec.GridSpecFromSubplotSpec(
rows, cols, subplot_... | [
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waleedka/hiddenlayer | hiddenlayer/canvas.py | Canvas.draw_hist | def draw_hist(self, metric, title=""):
"""Draw a series of histograms of the selected keys over different
training steps.
"""
# TODO: assert isinstance(list(values.values())[0], np.ndarray)
rows = 1
cols = 1
limit = 10 # max steps to show
# We need a 3D... | python | def draw_hist(self, metric, title=""):
"""Draw a series of histograms of the selected keys over different
training steps.
"""
# TODO: assert isinstance(list(values.values())[0], np.ndarray)
rows = 1
cols = 1
limit = 10 # max steps to show
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waleedka/hiddenlayer | demos/tf_cifar10.py | CIFAR10._load | def _load(self, dataset='train'):
"""Load the data in memory.
Args:
dataset: string in ['train', 'test']
"""
data, labels = None, None
if dataset is 'train':
files = [os.path.join(self.cifar10_dir, 'data_batch_%d' % i) for i in range(1, 6)]
else:
... | python | def _load(self, dataset='train'):
"""Load the data in memory.
Args:
dataset: string in ['train', 'test']
"""
data, labels = None, None
if dataset is 'train':
files = [os.path.join(self.cifar10_dir, 'data_batch_%d' % i) for i in range(1, 6)]
else:
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waleedka/hiddenlayer | demos/tf_cifar10.py | CIFAR10.model | def model(self, inputs, mode='train'):
"""Build a simple convnet (BN before ReLU).
Args:
inputs: a tensor of size [batch_size, height, width, channels]
mode: string in ['train', 'test']
Returns:
the last op containing the predictions
Note:
... | python | def model(self, inputs, mode='train'):
"""Build a simple convnet (BN before ReLU).
Args:
inputs: a tensor of size [batch_size, height, width, channels]
mode: string in ['train', 'test']
Returns:
the last op containing the predictions
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waleedka/hiddenlayer | demos/tf_cifar10.py | CIFAR10.maybe_download_and_extract | def maybe_download_and_extract(self):
"""Download and extract the tarball from Alex Krizhevsky's website."""
if not os.path.exists(self.cifar10_dir):
if not os.path.exists(self.data_dir):
os.makedirs(self.data_dir)
def _progress(count, block_size, total_size):
... | python | def maybe_download_and_extract(self):
"""Download and extract the tarball from Alex Krizhevsky's website."""
if not os.path.exists(self.cifar10_dir):
if not os.path.exists(self.data_dir):
os.makedirs(self.data_dir)
def _progress(count, block_size, total_size):
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waleedka/hiddenlayer | hiddenlayer/pytorch_builder.py | dump_pytorch_graph | def dump_pytorch_graph(graph):
"""List all the nodes in a PyTorch graph."""
f = "{:25} {:40} {} -> {}"
print(f.format("kind", "scopeName", "inputs", "outputs"))
for node in graph.nodes():
print(f.format(node.kind(), node.scopeName(),
[i.unique() for i in node.inputs()],
... | python | def dump_pytorch_graph(graph):
"""List all the nodes in a PyTorch graph."""
f = "{:25} {:40} {} -> {}"
print(f.format("kind", "scopeName", "inputs", "outputs"))
for node in graph.nodes():
print(f.format(node.kind(), node.scopeName(),
[i.unique() for i in node.inputs()],
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waleedka/hiddenlayer | hiddenlayer/pytorch_builder.py | pytorch_id | def pytorch_id(node):
"""Returns a unique ID for a node."""
# After ONNX simplification, the scopeName is not unique anymore
# so append node outputs to guarantee uniqueness
return node.scopeName() + "/outputs/" + "/".join([o.uniqueName() for o in node.outputs()]) | python | def pytorch_id(node):
"""Returns a unique ID for a node."""
# After ONNX simplification, the scopeName is not unique anymore
# so append node outputs to guarantee uniqueness
return node.scopeName() + "/outputs/" + "/".join([o.uniqueName() for o in node.outputs()]) | [
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waleedka/hiddenlayer | hiddenlayer/pytorch_builder.py | get_shape | def get_shape(torch_node):
"""Return the output shape of the given Pytorch node."""
# Extract node output shape from the node string representation
# This is a hack because there doesn't seem to be an official way to do it.
# See my quesiton in the PyTorch forum:
# https://discuss.pytorch.org/t/node... | python | def get_shape(torch_node):
"""Return the output shape of the given Pytorch node."""
# Extract node output shape from the node string representation
# This is a hack because there doesn't seem to be an official way to do it.
# See my quesiton in the PyTorch forum:
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waleedka/hiddenlayer | hiddenlayer/tf_builder.py | dump_tf_graph | def dump_tf_graph(tfgraph, tfgraphdef):
"""List all the nodes in a TF graph.
tfgraph: A TF Graph object.
tfgraphdef: A TF GraphDef object.
"""
print("Nodes ({})".format(len(tfgraphdef.node)))
f = "{:15} {:59} {:20} {}"
print(f.format("kind", "scopeName", "shape", "inputs"))
for node in t... | python | def dump_tf_graph(tfgraph, tfgraphdef):
"""List all the nodes in a TF graph.
tfgraph: A TF Graph object.
tfgraphdef: A TF GraphDef object.
"""
print("Nodes ({})".format(len(tfgraphdef.node)))
f = "{:15} {:59} {:20} {}"
print(f.format("kind", "scopeName", "shape", "inputs"))
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waleedka/hiddenlayer | hiddenlayer/tf_builder.py | import_graph | def import_graph(hl_graph, tf_graph, output=None, verbose=False):
"""Convert TF graph to directed graph
tfgraph: A TF Graph object.
output: Name of the output node (string).
verbose: Set to True for debug print output
"""
# Get clean(er) list of nodes
graph_def = tf_graph.as_graph_def(add_sh... | python | def import_graph(hl_graph, tf_graph, output=None, verbose=False):
"""Convert TF graph to directed graph
tfgraph: A TF Graph object.
output: Name of the output node (string).
verbose: Set to True for debug print output
"""
# Get clean(er) list of nodes
graph_def = tf_graph.as_graph_def(add_sh... | [
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher.send_fetches | def send_fetches(self):
"""Send FetchRequests for all assigned partitions that do not already have
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Returns:
List of Futures: each future resolves to a FetchResponse
"""
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"""Send FetchRequests for all assigned partitions that do not already have
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Returns:
List of Futures: each future resolves to a FetchResponse
"""
futures = []
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher.reset_offsets_if_needed | def reset_offsets_if_needed(self, partitions):
"""Lookup and set offsets for any partitions which are awaiting an
explicit reset.
Arguments:
partitions (set of TopicPartitions): the partitions to reset
"""
for tp in partitions:
# TODO: If there are severa... | python | def reset_offsets_if_needed(self, partitions):
"""Lookup and set offsets for any partitions which are awaiting an
explicit reset.
Arguments:
partitions (set of TopicPartitions): the partitions to reset
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher.update_fetch_positions | def update_fetch_positions(self, partitions):
"""Update the fetch positions for the provided partitions.
Arguments:
partitions (list of TopicPartitions): partitions to update
Raises:
NoOffsetForPartitionError: if no offset is stored for a given
partition... | python | def update_fetch_positions(self, partitions):
"""Update the fetch positions for the provided partitions.
Arguments:
partitions (list of TopicPartitions): partitions to update
Raises:
NoOffsetForPartitionError: if no offset is stored for a given
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher._reset_offset | def _reset_offset(self, partition):
"""Reset offsets for the given partition using the offset reset strategy.
Arguments:
partition (TopicPartition): the partition that needs reset offset
Raises:
NoOffsetForPartitionError: if no offset reset strategy is defined
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"""Reset offsets for the given partition using the offset reset strategy.
Arguments:
partition (TopicPartition): the partition that needs reset offset
Raises:
NoOffsetForPartitionError: if no offset reset strategy is defined
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher._retrieve_offsets | def _retrieve_offsets(self, timestamps, timeout_ms=float("inf")):
"""Fetch offset for each partition passed in ``timestamps`` map.
Blocks until offsets are obtained, a non-retriable exception is raised
or ``timeout_ms`` passed.
Arguments:
timestamps: {TopicPartition: int} d... | python | def _retrieve_offsets(self, timestamps, timeout_ms=float("inf")):
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher.fetched_records | def fetched_records(self, max_records=None):
"""Returns previously fetched records and updates consumed offsets.
Arguments:
max_records (int): Maximum number of records returned. Defaults
to max_poll_records configuration.
Raises:
OffsetOutOfRangeError: ... | python | def fetched_records(self, max_records=None):
"""Returns previously fetched records and updates consumed offsets.
Arguments:
max_records (int): Maximum number of records returned. Defaults
to max_poll_records configuration.
Raises:
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher._message_generator | def _message_generator(self):
"""Iterate over fetched_records"""
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completion = self._completed_fetches.popleft()
self._next_partition_records = self._parse_fetc... | python | def _message_generator(self):
"""Iterate over fetched_records"""
while self._next_partition_records or self._completed_fetches:
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completion = self._completed_fetches.popleft()
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher._send_offset_requests | def _send_offset_requests(self, timestamps):
"""Fetch offsets for each partition in timestamps dict. This may send
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Arguments:
timestamps (dict): {TopicPartition: int} mapping of fetching
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... | python | def _send_offset_requests(self, timestamps):
"""Fetch offsets for each partition in timestamps dict. This may send
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Arguments:
timestamps (dict): {TopicPartition: int} mapping of fetching
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher._handle_offset_response | def _handle_offset_response(self, future, response):
"""Callback for the response of the list offset call above.
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future (Future): the future to update based on response
response (OffsetResponse): response from the server
Raises:
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future (Future): the future to update based on response
response (OffsetResponse): response from the server
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher._create_fetch_requests | def _create_fetch_requests(self):
"""Create fetch requests for all assigned partitions, grouped by node.
FetchRequests skipped if no leader, or node has requests in flight
Returns:
dict: {node_id: FetchRequest, ...} (version depends on api_version)
"""
# create the ... | python | def _create_fetch_requests(self):
"""Create fetch requests for all assigned partitions, grouped by node.
FetchRequests skipped if no leader, or node has requests in flight
Returns:
dict: {node_id: FetchRequest, ...} (version depends on api_version)
"""
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dpkp/kafka-python | kafka/consumer/fetcher.py | Fetcher._handle_fetch_response | def _handle_fetch_response(self, request, send_time, response):
"""The callback for fetch completion"""
fetch_offsets = {}
for topic, partitions in request.topics:
for partition_data in partitions:
partition, offset = partition_data[:2]
fetch_offsets[T... | python | def _handle_fetch_response(self, request, send_time, response):
"""The callback for fetch completion"""
fetch_offsets = {}
for topic, partitions in request.topics:
for partition_data in partitions:
partition, offset = partition_data[:2]
fetch_offsets[T... | [
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dpkp/kafka-python | kafka/consumer/fetcher.py | FetchResponseMetricAggregator.record | def record(self, partition, num_bytes, num_records):
"""
After each partition is parsed, we update the current metric totals
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have reported, we write the metric.
"""
self.unrecorded_partitions.remove(p... | python | def record(self, partition, num_bytes, num_records):
"""
After each partition is parsed, we update the current metric totals
with the total bytes and number of records parsed. After all partitions
have reported, we write the metric.
"""
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dpkp/kafka-python | kafka/consumer/base.py | Consumer._auto_commit | def _auto_commit(self):
"""
Check if we have to commit based on number of messages and commit
"""
# Check if we are supposed to do an auto-commit
if not self.auto_commit or self.auto_commit_every_n is None:
return
if self.count_since_commit >= self.auto_comm... | python | def _auto_commit(self):
"""
Check if we have to commit based on number of messages and commit
"""
# Check if we are supposed to do an auto-commit
if not self.auto_commit or self.auto_commit_every_n is None:
return
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dpkp/kafka-python | kafka/consumer/base.py | Consumer.pending | def pending(self, partitions=None):
"""
Gets the pending message count
Keyword Arguments:
partitions (list): list of partitions to check for, default is to check all
"""
if partitions is None:
partitions = self.offsets.keys()
total = 0
re... | python | def pending(self, partitions=None):
"""
Gets the pending message count
Keyword Arguments:
partitions (list): list of partitions to check for, default is to check all
"""
if partitions is None:
partitions = self.offsets.keys()
total = 0
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dpkp/kafka-python | kafka/partitioner/hashed.py | murmur2 | def murmur2(data):
"""Pure-python Murmur2 implementation.
Based on java client, see org.apache.kafka.common.utils.Utils.murmur2
Args:
data (bytes): opaque bytes
Returns: MurmurHash2 of data
"""
# Python2 bytes is really a str, causing the bitwise operations below to fail
# so conv... | python | def murmur2(data):
"""Pure-python Murmur2 implementation.
Based on java client, see org.apache.kafka.common.utils.Utils.murmur2
Args:
data (bytes): opaque bytes
Returns: MurmurHash2 of data
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dpkp/kafka-python | kafka/consumer/multiprocess.py | MultiProcessConsumer.get_messages | def get_messages(self, count=1, block=True, timeout=10):
"""
Fetch the specified number of messages
Keyword Arguments:
count: Indicates the maximum number of messages to be fetched
block: If True, the API will block till all messages are fetched.
If block... | python | def get_messages(self, count=1, block=True, timeout=10):
"""
Fetch the specified number of messages
Keyword Arguments:
count: Indicates the maximum number of messages to be fetched
block: If True, the API will block till all messages are fetched.
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dpkp/kafka-python | kafka/context.py | OffsetCommitContext.mark | def mark(self, partition, offset):
"""
Set the high-water mark in the current context.
In order to know the current partition, it is helpful to initialize
the consumer to provide partition info via:
.. code:: python
consumer.provide_partition_info()
"""
... | python | def mark(self, partition, offset):
"""
Set the high-water mark in the current context.
In order to know the current partition, it is helpful to initialize
the consumer to provide partition info via:
.. code:: python
consumer.provide_partition_info()
"""
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dpkp/kafka-python | kafka/context.py | OffsetCommitContext.commit | def commit(self):
"""
Commit this context's offsets:
- If the high-water mark has moved, commit up to and position the
consumer at the high-water mark.
- Otherwise, reset to the consumer to the initial offsets.
"""
if self.high_water_mark:
sel... | python | def commit(self):
"""
Commit this context's offsets:
- If the high-water mark has moved, commit up to and position the
consumer at the high-water mark.
- Otherwise, reset to the consumer to the initial offsets.
"""
if self.high_water_mark:
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dpkp/kafka-python | kafka/context.py | OffsetCommitContext.rollback | def rollback(self):
"""
Rollback this context:
- Position the consumer at the initial offsets.
"""
self.logger.info("Rolling back context: %s", self.initial_offsets)
self.update_consumer_offsets(self.initial_offsets) | python | def rollback(self):
"""
Rollback this context:
- Position the consumer at the initial offsets.
"""
self.logger.info("Rolling back context: %s", self.initial_offsets)
self.update_consumer_offsets(self.initial_offsets) | [
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dpkp/kafka-python | kafka/context.py | OffsetCommitContext.commit_partition_offsets | def commit_partition_offsets(self, partition_offsets):
"""
Commit explicit partition/offset pairs.
"""
self.logger.debug("Committing partition offsets: %s", partition_offsets)
commit_requests = [
OffsetCommitRequestPayload(self.consumer.topic, partition, offset, None... | python | def commit_partition_offsets(self, partition_offsets):
"""
Commit explicit partition/offset pairs.
"""
self.logger.debug("Committing partition offsets: %s", partition_offsets)
commit_requests = [
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dpkp/kafka-python | kafka/context.py | OffsetCommitContext.update_consumer_offsets | def update_consumer_offsets(self, partition_offsets):
"""
Update consumer offsets to explicit positions.
"""
self.logger.debug("Updating consumer offsets to: %s", partition_offsets)
for partition, offset in partition_offsets.items():
self.consumer.offsets[partition] ... | python | def update_consumer_offsets(self, partition_offsets):
"""
Update consumer offsets to explicit positions.
"""
self.logger.debug("Updating consumer offsets to: %s", partition_offsets)
for partition, offset in partition_offsets.items():
self.consumer.offsets[partition] ... | [
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dpkp/kafka-python | kafka/coordinator/base.py | BaseCoordinator.coordinator | def coordinator(self):
"""Get the current coordinator
Returns: the current coordinator id or None if it is unknown
"""
if self.coordinator_id is None:
return None
elif self._client.is_disconnected(self.coordinator_id):
self.coordinator_dead('Node Disconne... | python | def coordinator(self):
"""Get the current coordinator
Returns: the current coordinator id or None if it is unknown
"""
if self.coordinator_id is None:
return None
elif self._client.is_disconnected(self.coordinator_id):
self.coordinator_dead('Node Disconne... | [
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dpkp/kafka-python | kafka/coordinator/base.py | BaseCoordinator.ensure_coordinator_ready | def ensure_coordinator_ready(self):
"""Block until the coordinator for this group is known
(and we have an active connection -- java client uses unsent queue).
"""
with self._client._lock, self._lock:
while self.coordinator_unknown():
# Prior to 0.8.2 there w... | python | def ensure_coordinator_ready(self):
"""Block until the coordinator for this group is known
(and we have an active connection -- java client uses unsent queue).
"""
with self._client._lock, self._lock:
while self.coordinator_unknown():
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dpkp/kafka-python | kafka/coordinator/base.py | BaseCoordinator.poll_heartbeat | def poll_heartbeat(self):
"""
Check the status of the heartbeat thread (if it is active) and indicate
the liveness of the client. This must be called periodically after
joining with :meth:`.ensure_active_group` to ensure that the member stays
in the group. If an interval of time ... | python | def poll_heartbeat(self):
"""
Check the status of the heartbeat thread (if it is active) and indicate
the liveness of the client. This must be called periodically after
joining with :meth:`.ensure_active_group` to ensure that the member stays
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dpkp/kafka-python | kafka/coordinator/base.py | BaseCoordinator.ensure_active_group | def ensure_active_group(self):
"""Ensure that the group is active (i.e. joined and synced)"""
with self._client._lock, self._lock:
if self._heartbeat_thread is None:
self._start_heartbeat_thread()
while self.need_rejoin() or self._rejoin_incomplete():
... | python | def ensure_active_group(self):
"""Ensure that the group is active (i.e. joined and synced)"""
with self._client._lock, self._lock:
if self._heartbeat_thread is None:
self._start_heartbeat_thread()
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dpkp/kafka-python | kafka/coordinator/base.py | BaseCoordinator._send_join_group_request | def _send_join_group_request(self):
"""Join the group and return the assignment for the next generation.
This function handles both JoinGroup and SyncGroup, delegating to
:meth:`._perform_assignment` if elected leader by the coordinator.
Returns:
Future: resolves to the enc... | python | def _send_join_group_request(self):
"""Join the group and return the assignment for the next generation.
This function handles both JoinGroup and SyncGroup, delegating to
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Future: resolves to the enc... | [
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dpkp/kafka-python | kafka/coordinator/base.py | BaseCoordinator._on_join_leader | def _on_join_leader(self, response):
"""
Perform leader synchronization and send back the assignment
for the group via SyncGroupRequest
Arguments:
response (JoinResponse): broker response to parse
Returns:
Future: resolves to member assignment encoded-by... | python | def _on_join_leader(self, response):
"""
Perform leader synchronization and send back the assignment
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Arguments:
response (JoinResponse): broker response to parse
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Future: resolves to member assignment encoded-by... | [
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dpkp/kafka-python | kafka/coordinator/base.py | BaseCoordinator._send_group_coordinator_request | def _send_group_coordinator_request(self):
"""Discover the current coordinator for the group.
Returns:
Future: resolves to the node id of the coordinator
"""
node_id = self._client.least_loaded_node()
if node_id is None:
return Future().failure(Errors.NoB... | python | def _send_group_coordinator_request(self):
"""Discover the current coordinator for the group.
Returns:
Future: resolves to the node id of the coordinator
"""
node_id = self._client.least_loaded_node()
if node_id is None:
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dpkp/kafka-python | kafka/coordinator/base.py | BaseCoordinator.coordinator_dead | def coordinator_dead(self, error):
"""Mark the current coordinator as dead."""
if self.coordinator_id is not None:
log.warning("Marking the coordinator dead (node %s) for group %s: %s.",
self.coordinator_id, self.group_id, error)
self.coordinator_id = None | python | def coordinator_dead(self, error):
"""Mark the current coordinator as dead."""
if self.coordinator_id is not None:
log.warning("Marking the coordinator dead (node %s) for group %s: %s.",
self.coordinator_id, self.group_id, error)
self.coordinator_id = None | [
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dpkp/kafka-python | kafka/coordinator/base.py | BaseCoordinator.generation | def generation(self):
"""Get the current generation state if the group is stable.
Returns: the current generation or None if the group is unjoined/rebalancing
"""
with self._lock:
if self.state is not MemberState.STABLE:
return None
return self._g... | python | def generation(self):
"""Get the current generation state if the group is stable.
Returns: the current generation or None if the group is unjoined/rebalancing
"""
with self._lock:
if self.state is not MemberState.STABLE:
return None
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dpkp/kafka-python | kafka/coordinator/base.py | BaseCoordinator.reset_generation | def reset_generation(self):
"""Reset the generation and memberId because we have fallen out of the group."""
with self._lock:
self._generation = Generation.NO_GENERATION
self.rejoin_needed = True
self.state = MemberState.UNJOINED | python | def reset_generation(self):
"""Reset the generation and memberId because we have fallen out of the group."""
with self._lock:
self._generation = Generation.NO_GENERATION
self.rejoin_needed = True
self.state = MemberState.UNJOINED | [
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"group",
"."
] | f6a8a38937688ea2cc5dc13d3d1039493be5c9b5 | https://github.com/dpkp/kafka-python/blob/f6a8a38937688ea2cc5dc13d3d1039493be5c9b5/kafka/coordinator/base.py#L723-L728 | train | Reset the generation and memberId because we have fallen out of the group. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
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