partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
train | ExampleBuffer.parallel_fill | games is a list of .tfrecord.zz game records. | reinforcement/tensorflow/minigo/rl_loop/example_buffer.py | def parallel_fill(self, games, threads=8):
""" games is a list of .tfrecord.zz game records. """
games.sort(key=os.path.basename)
# A couple extra in case parsing fails
max_games = int(self.max_size / self.sampling_frac / 200) + 480
if len(games) > max_games:
games = ... | def parallel_fill(self, games, threads=8):
""" games is a list of .tfrecord.zz game records. """
games.sort(key=os.path.basename)
# A couple extra in case parsing fails
max_games = int(self.max_size / self.sampling_frac / 200) + 480
if len(games) > max_games:
games = ... | [
"games",
"is",
"a",
"list",
"of",
".",
"tfrecord",
".",
"zz",
"game",
"records",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/rl_loop/example_buffer.py#L87-L98 | [
"def",
"parallel_fill",
"(",
"self",
",",
"games",
",",
"threads",
"=",
"8",
")",
":",
"games",
".",
"sort",
"(",
"key",
"=",
"os",
".",
"path",
".",
"basename",
")",
"# A couple extra in case parsing fails",
"max_games",
"=",
"int",
"(",
"self",
".",
"m... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | ExampleBuffer.update | new_games is a list of .tfrecord.zz new game records. | reinforcement/tensorflow/minigo/rl_loop/example_buffer.py | def update(self, new_games):
""" new_games is a list of .tfrecord.zz new game records. """
new_games.sort(key=os.path.basename)
first_new_game = None
for idx, game in enumerate(new_games):
timestamp = file_timestamp(game)
if timestamp <= self.examples[-1][0]:
... | def update(self, new_games):
""" new_games is a list of .tfrecord.zz new game records. """
new_games.sort(key=os.path.basename)
first_new_game = None
for idx, game in enumerate(new_games):
timestamp = file_timestamp(game)
if timestamp <= self.examples[-1][0]:
... | [
"new_games",
"is",
"a",
"list",
"of",
".",
"tfrecord",
".",
"zz",
"new",
"game",
"records",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/rl_loop/example_buffer.py#L100-L117 | [
"def",
"update",
"(",
"self",
",",
"new_games",
")",
":",
"new_games",
".",
"sort",
"(",
"key",
"=",
"os",
".",
"path",
".",
"basename",
")",
"first_new_game",
"=",
"None",
"for",
"idx",
",",
"game",
"in",
"enumerate",
"(",
"new_games",
")",
":",
"ti... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | RetinaNetPostProcessor.forward_for_single_feature_map | Arguments:
anchors: list[BoxList]
box_cls: tensor of size N, A * C, H, W
box_regression: tensor of size N, A * 4, H, W | object_detection/pytorch/maskrcnn_benchmark/modeling/rpn/retinanet/inference.py | def forward_for_single_feature_map(
self, anchors, box_cls, box_regression):
"""
Arguments:
anchors: list[BoxList]
box_cls: tensor of size N, A * C, H, W
box_regression: tensor of size N, A * 4, H, W
"""
device = box_cls.device
N, _... | def forward_for_single_feature_map(
self, anchors, box_cls, box_regression):
"""
Arguments:
anchors: list[BoxList]
box_cls: tensor of size N, A * C, H, W
box_regression: tensor of size N, A * 4, H, W
"""
device = box_cls.device
N, _... | [
"Arguments",
":",
"anchors",
":",
"list",
"[",
"BoxList",
"]",
"box_cls",
":",
"tensor",
"of",
"size",
"N",
"A",
"*",
"C",
"H",
"W",
"box_regression",
":",
"tensor",
"of",
"size",
"N",
"A",
"*",
"4",
"H",
"W"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/modeling/rpn/retinanet/inference.py#L59-L125 | [
"def",
"forward_for_single_feature_map",
"(",
"self",
",",
"anchors",
",",
"box_cls",
",",
"box_regression",
")",
":",
"device",
"=",
"box_cls",
".",
"device",
"N",
",",
"_",
",",
"H",
",",
"W",
"=",
"box_cls",
".",
"shape",
"A",
"=",
"box_regression",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | make_tf_example | Args:
features: [N, N, FEATURE_DIM] nparray of uint8
pi: [N * N + 1] nparray of float32
value: float | reinforcement/tensorflow/minigo/preprocessing.py | def make_tf_example(features, pi, value):
"""
Args:
features: [N, N, FEATURE_DIM] nparray of uint8
pi: [N * N + 1] nparray of float32
value: float
"""
return tf.train.Example(features=tf.train.Features(feature={
'x': tf.train.Feature(
bytes_list=tf.train.Bytes... | def make_tf_example(features, pi, value):
"""
Args:
features: [N, N, FEATURE_DIM] nparray of uint8
pi: [N * N + 1] nparray of float32
value: float
"""
return tf.train.Example(features=tf.train.Features(feature={
'x': tf.train.Feature(
bytes_list=tf.train.Bytes... | [
"Args",
":",
"features",
":",
"[",
"N",
"N",
"FEATURE_DIM",
"]",
"nparray",
"of",
"uint8",
"pi",
":",
"[",
"N",
"*",
"N",
"+",
"1",
"]",
"nparray",
"of",
"float32",
"value",
":",
"float"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/preprocessing.py#L39-L55 | [
"def",
"make_tf_example",
"(",
"features",
",",
"pi",
",",
"value",
")",
":",
"return",
"tf",
".",
"train",
".",
"Example",
"(",
"features",
"=",
"tf",
".",
"train",
".",
"Features",
"(",
"feature",
"=",
"{",
"'x'",
":",
"tf",
".",
"train",
".",
"F... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | write_tf_examples | Args:
filename: Where to write tf.records
tf_examples: An iterable of tf.Example
serialize: whether to serialize the examples. | reinforcement/tensorflow/minigo/preprocessing.py | def write_tf_examples(filename, tf_examples, serialize=True):
"""
Args:
filename: Where to write tf.records
tf_examples: An iterable of tf.Example
serialize: whether to serialize the examples.
"""
with tf.python_io.TFRecordWriter(
filename, options=TF_RECORD_CONFIG) a... | def write_tf_examples(filename, tf_examples, serialize=True):
"""
Args:
filename: Where to write tf.records
tf_examples: An iterable of tf.Example
serialize: whether to serialize the examples.
"""
with tf.python_io.TFRecordWriter(
filename, options=TF_RECORD_CONFIG) a... | [
"Args",
":",
"filename",
":",
"Where",
"to",
"write",
"tf",
".",
"records",
"tf_examples",
":",
"An",
"iterable",
"of",
"tf",
".",
"Example",
"serialize",
":",
"whether",
"to",
"serialize",
"the",
"examples",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/preprocessing.py#L58-L71 | [
"def",
"write_tf_examples",
"(",
"filename",
",",
"tf_examples",
",",
"serialize",
"=",
"True",
")",
":",
"with",
"tf",
".",
"python_io",
".",
"TFRecordWriter",
"(",
"filename",
",",
"options",
"=",
"TF_RECORD_CONFIG",
")",
"as",
"writer",
":",
"for",
"ex",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | batch_parse_tf_example | Args:
example_batch: a batch of tf.Example
Returns:
A tuple (feature_tensor, dict of output tensors) | reinforcement/tensorflow/minigo/preprocessing.py | def batch_parse_tf_example(batch_size, example_batch):
"""
Args:
example_batch: a batch of tf.Example
Returns:
A tuple (feature_tensor, dict of output tensors)
"""
features = {
'x': tf.FixedLenFeature([], tf.string),
'pi': tf.FixedLenFeature([], tf.string),
'o... | def batch_parse_tf_example(batch_size, example_batch):
"""
Args:
example_batch: a batch of tf.Example
Returns:
A tuple (feature_tensor, dict of output tensors)
"""
features = {
'x': tf.FixedLenFeature([], tf.string),
'pi': tf.FixedLenFeature([], tf.string),
'o... | [
"Args",
":",
"example_batch",
":",
"a",
"batch",
"of",
"tf",
".",
"Example",
"Returns",
":",
"A",
"tuple",
"(",
"feature_tensor",
"dict",
"of",
"output",
"tensors",
")"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/preprocessing.py#L74-L95 | [
"def",
"batch_parse_tf_example",
"(",
"batch_size",
",",
"example_batch",
")",
":",
"features",
"=",
"{",
"'x'",
":",
"tf",
".",
"FixedLenFeature",
"(",
"[",
"]",
",",
"tf",
".",
"string",
")",
",",
"'pi'",
":",
"tf",
".",
"FixedLenFeature",
"(",
"[",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | read_tf_records | Args:
batch_size: batch size to return
tf_records: a list of tf_record filenames
num_repeats: how many times the data should be read (default: One)
shuffle_records: whether to shuffle the order of files read
shuffle_examples: whether to shuffle the tf.Examples
shuffle_buf... | reinforcement/tensorflow/minigo/preprocessing.py | def read_tf_records(batch_size, tf_records, num_repeats=1,
shuffle_records=True, shuffle_examples=True,
shuffle_buffer_size=None, interleave=True,
filter_amount=1.0):
"""
Args:
batch_size: batch size to return
tf_records: a list of tf_r... | def read_tf_records(batch_size, tf_records, num_repeats=1,
shuffle_records=True, shuffle_examples=True,
shuffle_buffer_size=None, interleave=True,
filter_amount=1.0):
"""
Args:
batch_size: batch size to return
tf_records: a list of tf_r... | [
"Args",
":",
"batch_size",
":",
"batch",
"size",
"to",
"return",
"tf_records",
":",
"a",
"list",
"of",
"tf_record",
"filenames",
"num_repeats",
":",
"how",
"many",
"times",
"the",
"data",
"should",
"be",
"read",
"(",
"default",
":",
"One",
")",
"shuffle_re... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/preprocessing.py#L98-L147 | [
"def",
"read_tf_records",
"(",
"batch_size",
",",
"tf_records",
",",
"num_repeats",
"=",
"1",
",",
"shuffle_records",
"=",
"True",
",",
"shuffle_examples",
"=",
"True",
",",
"shuffle_buffer_size",
"=",
"None",
",",
"interleave",
"=",
"True",
",",
"filter_amount"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | get_input_tensors | Read tf.Records and prepare them for ingestion by dual_net.
See `read_tf_records` for parameter documentation.
Returns a dict of tensors (see return value of batch_parse_tf_example) | reinforcement/tensorflow/minigo/preprocessing.py | def get_input_tensors(batch_size, tf_records, num_repeats=1,
shuffle_records=True, shuffle_examples=True,
shuffle_buffer_size=None,
filter_amount=0.05, random_rotation=True):
"""Read tf.Records and prepare them for ingestion by dual_net.
See `re... | def get_input_tensors(batch_size, tf_records, num_repeats=1,
shuffle_records=True, shuffle_examples=True,
shuffle_buffer_size=None,
filter_amount=0.05, random_rotation=True):
"""Read tf.Records and prepare them for ingestion by dual_net.
See `re... | [
"Read",
"tf",
".",
"Records",
"and",
"prepare",
"them",
"for",
"ingestion",
"by",
"dual_net",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/preprocessing.py#L181-L207 | [
"def",
"get_input_tensors",
"(",
"batch_size",
",",
"tf_records",
",",
"num_repeats",
"=",
"1",
",",
"shuffle_records",
"=",
"True",
",",
"shuffle_examples",
"=",
"True",
",",
"shuffle_buffer_size",
"=",
"None",
",",
"filter_amount",
"=",
"0.05",
",",
"random_ro... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | make_dataset_from_selfplay | Returns an iterable of tf.Examples.
Args:
data_extracts: An iterable of (position, pi, result) tuples | reinforcement/tensorflow/minigo/preprocessing.py | def make_dataset_from_selfplay(data_extracts):
"""
Returns an iterable of tf.Examples.
Args:
data_extracts: An iterable of (position, pi, result) tuples
"""
tf_examples = (make_tf_example(features_lib.extract_features(pos), pi, result)
for pos, pi, result in data_extracts)... | def make_dataset_from_selfplay(data_extracts):
"""
Returns an iterable of tf.Examples.
Args:
data_extracts: An iterable of (position, pi, result) tuples
"""
tf_examples = (make_tf_example(features_lib.extract_features(pos), pi, result)
for pos, pi, result in data_extracts)... | [
"Returns",
"an",
"iterable",
"of",
"tf",
".",
"Examples",
".",
"Args",
":",
"data_extracts",
":",
"An",
"iterable",
"of",
"(",
"position",
"pi",
"result",
")",
"tuples"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/preprocessing.py#L265-L273 | [
"def",
"make_dataset_from_selfplay",
"(",
"data_extracts",
")",
":",
"tf_examples",
"=",
"(",
"make_tf_example",
"(",
"features_lib",
".",
"extract_features",
"(",
"pos",
")",
",",
"pi",
",",
"result",
")",
"for",
"pos",
",",
"pi",
",",
"result",
"in",
"data... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | ROIBoxHead.forward | Arguments:
features (list[Tensor]): feature-maps from possibly several levels
proposals (list[BoxList]): proposal boxes
targets (list[BoxList], optional): the ground-truth targets.
Returns:
x (Tensor): the result of the feature extractor
proposals (li... | object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py | def forward(self, features, proposals, targets=None):
"""
Arguments:
features (list[Tensor]): feature-maps from possibly several levels
proposals (list[BoxList]): proposal boxes
targets (list[BoxList], optional): the ground-truth targets.
Returns:
... | def forward(self, features, proposals, targets=None):
"""
Arguments:
features (list[Tensor]): feature-maps from possibly several levels
proposals (list[BoxList]): proposal boxes
targets (list[BoxList], optional): the ground-truth targets.
Returns:
... | [
"Arguments",
":",
"features",
"(",
"list",
"[",
"Tensor",
"]",
")",
":",
"feature",
"-",
"maps",
"from",
"possibly",
"several",
"levels",
"proposals",
"(",
"list",
"[",
"BoxList",
"]",
")",
":",
"proposal",
"boxes",
"targets",
"(",
"list",
"[",
"BoxList"... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py#L23-L61 | [
"def",
"forward",
"(",
"self",
",",
"features",
",",
"proposals",
",",
"targets",
"=",
"None",
")",
":",
"if",
"self",
".",
"training",
":",
"# Faster R-CNN subsamples during training the proposals with a fixed",
"# positive / negative ratio",
"with",
"torch",
".",
"n... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | past_stop_threshold | Return a boolean representing whether a model should be stopped.
Args:
stop_threshold: float, the threshold above which a model should stop
training.
eval_metric: float, the current value of the relevant metric to check.
Returns:
True if training should stop, False otherwise.
Raises:
Valu... | image_classification/tensorflow/official/utils/misc/model_helpers.py | def past_stop_threshold(stop_threshold, eval_metric):
"""Return a boolean representing whether a model should be stopped.
Args:
stop_threshold: float, the threshold above which a model should stop
training.
eval_metric: float, the current value of the relevant metric to check.
Returns:
True if... | def past_stop_threshold(stop_threshold, eval_metric):
"""Return a boolean representing whether a model should be stopped.
Args:
stop_threshold: float, the threshold above which a model should stop
training.
eval_metric: float, the current value of the relevant metric to check.
Returns:
True if... | [
"Return",
"a",
"boolean",
"representing",
"whether",
"a",
"model",
"should",
"be",
"stopped",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/misc/model_helpers.py#L26-L55 | [
"def",
"past_stop_threshold",
"(",
"stop_threshold",
",",
"eval_metric",
")",
":",
"if",
"stop_threshold",
"is",
"None",
":",
"return",
"False",
"if",
"not",
"isinstance",
"(",
"stop_threshold",
",",
"numbers",
".",
"Number",
")",
":",
"raise",
"ValueError",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | RPNModule.forward | Arguments:
images (ImageList): images for which we want to compute the predictions
features (list[Tensor]): features computed from the images that are
used for computing the predictions. Each tensor in the list
correspond to different feature levels
ta... | object_detection/pytorch/maskrcnn_benchmark/modeling/rpn/rpn.py | def forward(self, images, features, targets=None):
"""
Arguments:
images (ImageList): images for which we want to compute the predictions
features (list[Tensor]): features computed from the images that are
used for computing the predictions. Each tensor in the lis... | def forward(self, images, features, targets=None):
"""
Arguments:
images (ImageList): images for which we want to compute the predictions
features (list[Tensor]): features computed from the images that are
used for computing the predictions. Each tensor in the lis... | [
"Arguments",
":",
"images",
"(",
"ImageList",
")",
":",
"images",
"for",
"which",
"we",
"want",
"to",
"compute",
"the",
"predictions",
"features",
"(",
"list",
"[",
"Tensor",
"]",
")",
":",
"features",
"computed",
"from",
"the",
"images",
"that",
"are",
... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/modeling/rpn/rpn.py#L81-L102 | [
"def",
"forward",
"(",
"self",
",",
"images",
",",
"features",
",",
"targets",
"=",
"None",
")",
":",
"objectness",
",",
"rpn_box_regression",
"=",
"self",
".",
"head",
"(",
"features",
")",
"anchors",
"=",
"self",
".",
"anchor_generator",
"(",
"images",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | synchronize | Helper function to synchronize (barrier) among all processes when
using distributed training | object_detection/pytorch/maskrcnn_benchmark/utils/comm.py | def synchronize():
"""
Helper function to synchronize (barrier) among all processes when
using distributed training
"""
if not dist.is_available():
return
if not dist.is_initialized():
return
world_size = dist.get_world_size()
if world_size == 1:
return
dist.b... | def synchronize():
"""
Helper function to synchronize (barrier) among all processes when
using distributed training
"""
if not dist.is_available():
return
if not dist.is_initialized():
return
world_size = dist.get_world_size()
if world_size == 1:
return
dist.b... | [
"Helper",
"function",
"to",
"synchronize",
"(",
"barrier",
")",
"among",
"all",
"processes",
"when",
"using",
"distributed",
"training"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/utils/comm.py#L33-L45 | [
"def",
"synchronize",
"(",
")",
":",
"if",
"not",
"dist",
".",
"is_available",
"(",
")",
":",
"return",
"if",
"not",
"dist",
".",
"is_initialized",
"(",
")",
":",
"return",
"world_size",
"=",
"dist",
".",
"get_world_size",
"(",
")",
"if",
"world_size",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | all_gather | Run all_gather on arbitrary picklable data (not necessarily tensors)
Args:
data: any picklable object
Returns:
list[data]: list of data gathered from each rank | object_detection/pytorch/maskrcnn_benchmark/utils/comm.py | def all_gather(data):
"""
Run all_gather on arbitrary picklable data (not necessarily tensors)
Args:
data: any picklable object
Returns:
list[data]: list of data gathered from each rank
"""
world_size = get_world_size()
if world_size == 1:
return [data]
# seriali... | def all_gather(data):
"""
Run all_gather on arbitrary picklable data (not necessarily tensors)
Args:
data: any picklable object
Returns:
list[data]: list of data gathered from each rank
"""
world_size = get_world_size()
if world_size == 1:
return [data]
# seriali... | [
"Run",
"all_gather",
"on",
"arbitrary",
"picklable",
"data",
"(",
"not",
"necessarily",
"tensors",
")",
"Args",
":",
"data",
":",
"any",
"picklable",
"object",
"Returns",
":",
"list",
"[",
"data",
"]",
":",
"list",
"of",
"data",
"gathered",
"from",
"each",... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/utils/comm.py#L48-L88 | [
"def",
"all_gather",
"(",
"data",
")",
":",
"world_size",
"=",
"get_world_size",
"(",
")",
"if",
"world_size",
"==",
"1",
":",
"return",
"[",
"data",
"]",
"# serialized to a Tensor",
"buffer",
"=",
"pickle",
".",
"dumps",
"(",
"data",
")",
"storage",
"=",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | reduce_dict | Args:
input_dict (dict): all the values will be reduced
average (bool): whether to do average or sum
Reduce the values in the dictionary from all processes so that process with rank
0 has the averaged results. Returns a dict with the same fields as
input_dict, after reduction. | object_detection/pytorch/maskrcnn_benchmark/utils/comm.py | def reduce_dict(input_dict, average=True):
"""
Args:
input_dict (dict): all the values will be reduced
average (bool): whether to do average or sum
Reduce the values in the dictionary from all processes so that process with rank
0 has the averaged results. Returns a dict with the same fi... | def reduce_dict(input_dict, average=True):
"""
Args:
input_dict (dict): all the values will be reduced
average (bool): whether to do average or sum
Reduce the values in the dictionary from all processes so that process with rank
0 has the averaged results. Returns a dict with the same fi... | [
"Args",
":",
"input_dict",
"(",
"dict",
")",
":",
"all",
"the",
"values",
"will",
"be",
"reduced",
"average",
"(",
"bool",
")",
":",
"whether",
"to",
"do",
"average",
"or",
"sum",
"Reduce",
"the",
"values",
"in",
"the",
"dictionary",
"from",
"all",
"pr... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/utils/comm.py#L91-L117 | [
"def",
"reduce_dict",
"(",
"input_dict",
",",
"average",
"=",
"True",
")",
":",
"world_size",
"=",
"get_world_size",
"(",
")",
"if",
"world_size",
"<",
"2",
":",
"return",
"input_dict",
"with",
"torch",
".",
"no_grad",
"(",
")",
":",
"names",
"=",
"[",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | main | Bootstrap random weights. | reinforcement/tensorflow/minigo/bootstrap.py | def main(unused_argv):
"""Bootstrap random weights."""
utils.ensure_dir_exists(os.path.dirname(FLAGS.export_path))
if FLAGS.create_bootstrap:
dual_net.bootstrap()
dual_net.export_model(FLAGS.export_path) | def main(unused_argv):
"""Bootstrap random weights."""
utils.ensure_dir_exists(os.path.dirname(FLAGS.export_path))
if FLAGS.create_bootstrap:
dual_net.bootstrap()
dual_net.export_model(FLAGS.export_path) | [
"Bootstrap",
"random",
"weights",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/bootstrap.py#L39-L44 | [
"def",
"main",
"(",
"unused_argv",
")",
":",
"utils",
".",
"ensure_dir_exists",
"(",
"os",
".",
"path",
".",
"dirname",
"(",
"FLAGS",
".",
"export_path",
")",
")",
"if",
"FLAGS",
".",
"create_bootstrap",
":",
"dual_net",
".",
"bootstrap",
"(",
")",
"dual... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | SequenceGenerator.greedy_search | Greedy decoder.
:param batch_size: decoder batch size
:param initial_input: initial input, usually tensor of BOS tokens
:param initial_context: initial context, usually [encoder_context,
src_seq_lengths, None]
returns: (translation, lengths, counter)
translation... | rnn_translator/pytorch/seq2seq/inference/beam_search.py | def greedy_search(self, batch_size, initial_input, initial_context=None):
"""
Greedy decoder.
:param batch_size: decoder batch size
:param initial_input: initial input, usually tensor of BOS tokens
:param initial_context: initial context, usually [encoder_context,
sr... | def greedy_search(self, batch_size, initial_input, initial_context=None):
"""
Greedy decoder.
:param batch_size: decoder batch size
:param initial_input: initial input, usually tensor of BOS tokens
:param initial_context: initial context, usually [encoder_context,
sr... | [
"Greedy",
"decoder",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/inference/beam_search.py#L53-L118 | [
"def",
"greedy_search",
"(",
"self",
",",
"batch_size",
",",
"initial_input",
",",
"initial_context",
"=",
"None",
")",
":",
"max_seq_len",
"=",
"self",
".",
"max_seq_len",
"translation",
"=",
"torch",
".",
"zeros",
"(",
"batch_size",
",",
"max_seq_len",
",",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | SequenceGenerator.beam_search | Beam search decoder.
:param batch_size: decoder batch size
:param initial_input: initial input, usually tensor of BOS tokens
:param initial_context: initial context, usually [encoder_context,
src_seq_lengths, None]
returns: (translation, lengths, counter)
transl... | rnn_translator/pytorch/seq2seq/inference/beam_search.py | def beam_search(self, batch_size, initial_input, initial_context=None):
"""
Beam search decoder.
:param batch_size: decoder batch size
:param initial_input: initial input, usually tensor of BOS tokens
:param initial_context: initial context, usually [encoder_context,
... | def beam_search(self, batch_size, initial_input, initial_context=None):
"""
Beam search decoder.
:param batch_size: decoder batch size
:param initial_input: initial input, usually tensor of BOS tokens
:param initial_context: initial context, usually [encoder_context,
... | [
"Beam",
"search",
"decoder",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/inference/beam_search.py#L120-L303 | [
"def",
"beam_search",
"(",
"self",
",",
"batch_size",
",",
"initial_input",
",",
"initial_context",
"=",
"None",
")",
":",
"beam_size",
"=",
"self",
".",
"beam_size",
"norm_const",
"=",
"self",
".",
"len_norm_const",
"norm_factor",
"=",
"self",
".",
"len_norm_... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | evaluate | evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
predictions(list[BoxList]): each item in the list represents the
prediction results for one image.
output_folder: output folder, to save evaluation files or results.
**kwargs: ot... | object_detection/pytorch/maskrcnn_benchmark/data/datasets/evaluation/__init__.py | def evaluate(dataset, predictions, output_folder, **kwargs):
"""evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
predictions(list[BoxList]): each item in the list represents the
prediction results for one image.
output_folder: outp... | def evaluate(dataset, predictions, output_folder, **kwargs):
"""evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
predictions(list[BoxList]): each item in the list represents the
prediction results for one image.
output_folder: outp... | [
"evaluate",
"dataset",
"using",
"different",
"methods",
"based",
"on",
"dataset",
"type",
".",
"Args",
":",
"dataset",
":",
"Dataset",
"object",
"predictions",
"(",
"list",
"[",
"BoxList",
"]",
")",
":",
"each",
"item",
"in",
"the",
"list",
"represents",
"... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/data/datasets/evaluation/__init__.py#L7-L27 | [
"def",
"evaluate",
"(",
"dataset",
",",
"predictions",
",",
"output_folder",
",",
"*",
"*",
"kwargs",
")",
":",
"args",
"=",
"dict",
"(",
"dataset",
"=",
"dataset",
",",
"predictions",
"=",
"predictions",
",",
"output_folder",
"=",
"output_folder",
",",
"*... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | cat | Efficient version of torch.cat that avoids a copy if there is only a single element in a list | object_detection/pytorch/maskrcnn_benchmark/modeling/utils.py | def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
assert isinstance(tensors, (list, tuple))
if len(tensors) == 1:
return tensors[0]
return torch.cat(tensors, dim) | def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
assert isinstance(tensors, (list, tuple))
if len(tensors) == 1:
return tensors[0]
return torch.cat(tensors, dim) | [
"Efficient",
"version",
"of",
"torch",
".",
"cat",
"that",
"avoids",
"a",
"copy",
"if",
"there",
"is",
"only",
"a",
"single",
"element",
"in",
"a",
"list"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/modeling/utils.py#L9-L16 | [
"def",
"cat",
"(",
"tensors",
",",
"dim",
"=",
"0",
")",
":",
"assert",
"isinstance",
"(",
"tensors",
",",
"(",
"list",
",",
"tuple",
")",
")",
"if",
"len",
"(",
"tensors",
")",
"==",
"1",
":",
"return",
"tensors",
"[",
"0",
"]",
"return",
"torch... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | validate_examples | Validate that examples are well formed.
Pi should sum to 1.0
value should be {-1,1}
Usage:
validate_examples("../data/300.tfrecord.zz") | reinforcement/tensorflow/minigo/oneoffs/validate_misc.py | def validate_examples(example_file):
"""Validate that examples are well formed.
Pi should sum to 1.0
value should be {-1,1}
Usage:
validate_examples("../data/300.tfrecord.zz")
"""
def test_example(raw):
example = tf.train.Example()
example.ParseFromString(raw)
... | def validate_examples(example_file):
"""Validate that examples are well formed.
Pi should sum to 1.0
value should be {-1,1}
Usage:
validate_examples("../data/300.tfrecord.zz")
"""
def test_example(raw):
example = tf.train.Example()
example.ParseFromString(raw)
... | [
"Validate",
"that",
"examples",
"are",
"well",
"formed",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/validate_misc.py#L22-L43 | [
"def",
"validate_examples",
"(",
"example_file",
")",
":",
"def",
"test_example",
"(",
"raw",
")",
":",
"example",
"=",
"tf",
".",
"train",
".",
"Example",
"(",
")",
"example",
".",
"ParseFromString",
"(",
"raw",
")",
"pi",
"=",
"np",
".",
"frombuffer",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | get_train_hooks | Factory for getting a list of TensorFlow hooks for training by name.
Args:
name_list: a list of strings to name desired hook classes. Allowed:
LoggingTensorHook, ProfilerHook, ExamplesPerSecondHook, which are defined
as keys in HOOKS
**kwargs: a dictionary of arguments to the hooks.
Returns:
... | image_classification/tensorflow/official/utils/logs/hooks_helper.py | def get_train_hooks(name_list, **kwargs):
"""Factory for getting a list of TensorFlow hooks for training by name.
Args:
name_list: a list of strings to name desired hook classes. Allowed:
LoggingTensorHook, ProfilerHook, ExamplesPerSecondHook, which are defined
as keys in HOOKS
**kwargs: a dict... | def get_train_hooks(name_list, **kwargs):
"""Factory for getting a list of TensorFlow hooks for training by name.
Args:
name_list: a list of strings to name desired hook classes. Allowed:
LoggingTensorHook, ProfilerHook, ExamplesPerSecondHook, which are defined
as keys in HOOKS
**kwargs: a dict... | [
"Factory",
"for",
"getting",
"a",
"list",
"of",
"TensorFlow",
"hooks",
"for",
"training",
"by",
"name",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/hooks_helper.py#L37-L64 | [
"def",
"get_train_hooks",
"(",
"name_list",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"not",
"name_list",
":",
"return",
"[",
"]",
"train_hooks",
"=",
"[",
"]",
"for",
"name",
"in",
"name_list",
":",
"hook_name",
"=",
"HOOKS",
".",
"get",
"(",
"name",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | get_logging_tensor_hook | Function to get LoggingTensorHook.
Args:
every_n_iter: `int`, print the values of `tensors` once every N local
steps taken on the current worker.
tensors_to_log: List of tensor names or dictionary mapping labels to tensor
names. If not set, log _TENSORS_TO_LOG by default.
**kwargs: a dictiona... | image_classification/tensorflow/official/utils/logs/hooks_helper.py | def get_logging_tensor_hook(every_n_iter=100, tensors_to_log=None, **kwargs): # pylint: disable=unused-argument
"""Function to get LoggingTensorHook.
Args:
every_n_iter: `int`, print the values of `tensors` once every N local
steps taken on the current worker.
tensors_to_log: List of tensor names or... | def get_logging_tensor_hook(every_n_iter=100, tensors_to_log=None, **kwargs): # pylint: disable=unused-argument
"""Function to get LoggingTensorHook.
Args:
every_n_iter: `int`, print the values of `tensors` once every N local
steps taken on the current worker.
tensors_to_log: List of tensor names or... | [
"Function",
"to",
"get",
"LoggingTensorHook",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/hooks_helper.py#L67-L86 | [
"def",
"get_logging_tensor_hook",
"(",
"every_n_iter",
"=",
"100",
",",
"tensors_to_log",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"# pylint: disable=unused-argument",
"if",
"tensors_to_log",
"is",
"None",
":",
"tensors_to_log",
"=",
"_TENSORS_TO_LOG",
"retur... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | get_examples_per_second_hook | Function to get ExamplesPerSecondHook.
Args:
every_n_steps: `int`, print current and average examples per second every
N steps.
batch_size: `int`, total batch size used to calculate examples/second from
global time.
warm_steps: skip this number of steps before logging and running average.
... | image_classification/tensorflow/official/utils/logs/hooks_helper.py | def get_examples_per_second_hook(every_n_steps=100,
batch_size=128,
warm_steps=5,
**kwargs): # pylint: disable=unused-argument
"""Function to get ExamplesPerSecondHook.
Args:
every_n_steps: `int`, print current ... | def get_examples_per_second_hook(every_n_steps=100,
batch_size=128,
warm_steps=5,
**kwargs): # pylint: disable=unused-argument
"""Function to get ExamplesPerSecondHook.
Args:
every_n_steps: `int`, print current ... | [
"Function",
"to",
"get",
"ExamplesPerSecondHook",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/hooks_helper.py#L103-L123 | [
"def",
"get_examples_per_second_hook",
"(",
"every_n_steps",
"=",
"100",
",",
"batch_size",
"=",
"128",
",",
"warm_steps",
"=",
"5",
",",
"*",
"*",
"kwargs",
")",
":",
"# pylint: disable=unused-argument",
"return",
"hooks",
".",
"ExamplesPerSecondHook",
"(",
"ever... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | get_logging_metric_hook | Function to get LoggingMetricHook.
Args:
benchmark_log_dir: `string`, directory path to save the metric log.
tensors_to_log: List of tensor names or dictionary mapping labels to tensor
names. If not set, log _TENSORS_TO_LOG by default.
every_n_secs: `int`, the frequency for logging the metric. Defa... | image_classification/tensorflow/official/utils/logs/hooks_helper.py | def get_logging_metric_hook(benchmark_log_dir=None,
tensors_to_log=None,
every_n_secs=600,
**kwargs): # pylint: disable=unused-argument
"""Function to get LoggingMetricHook.
Args:
benchmark_log_dir: `string`, directory path to... | def get_logging_metric_hook(benchmark_log_dir=None,
tensors_to_log=None,
every_n_secs=600,
**kwargs): # pylint: disable=unused-argument
"""Function to get LoggingMetricHook.
Args:
benchmark_log_dir: `string`, directory path to... | [
"Function",
"to",
"get",
"LoggingMetricHook",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/hooks_helper.py#L126-L150 | [
"def",
"get_logging_metric_hook",
"(",
"benchmark_log_dir",
"=",
"None",
",",
"tensors_to_log",
"=",
"None",
",",
"every_n_secs",
"=",
"600",
",",
"*",
"*",
"kwargs",
")",
":",
"# pylint: disable=unused-argument",
"if",
"benchmark_log_dir",
"is",
"None",
":",
"rai... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | parse_rec | Parse a PASCAL VOC xml file | single_stage_detector/ssd/eval.py | def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.f... | def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.f... | [
"Parse",
"a",
"PASCAL",
"VOC",
"xml",
"file"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/single_stage_detector/ssd/eval.py#L40-L57 | [
"def",
"parse_rec",
"(",
"filename",
")",
":",
"tree",
"=",
"ET",
".",
"parse",
"(",
"filename",
")",
"objects",
"=",
"[",
"]",
"for",
"obj",
"in",
"tree",
".",
"findall",
"(",
"'object'",
")",
":",
"obj_struct",
"=",
"{",
"}",
"obj_struct",
"[",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | voc_eval | rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
... | single_stage_detector/ssd/eval.py | def voc_eval(detpath,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=True):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
... | def voc_eval(detpath,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=True):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
... | [
"rec",
"prec",
"ap",
"=",
"voc_eval",
"(",
"detpath",
"annopath",
"imagesetfile",
"classname",
"[",
"ovthresh",
"]",
"[",
"use_07_metric",
"]",
")",
"Top",
"level",
"function",
"that",
"does",
"the",
"PASCAL",
"VOC",
"evaluation",
".",
"detpath",
":",
"Path"... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/single_stage_detector/ssd/eval.py#L59-L192 | [
"def",
"voc_eval",
"(",
"detpath",
",",
"annopath",
",",
"imagesetfile",
",",
"classname",
",",
"cachedir",
",",
"ovthresh",
"=",
"0.5",
",",
"use_07_metric",
"=",
"True",
")",
":",
"# assumes detections are in detpath.format(classname)",
"# assumes annotations are in a... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | build_collate_fn | Factory for collate_fn functions.
:param batch_first: if True returns batches in (batch, seq) format, if
False returns in (seq, batch) format
:param parallel: if True builds batches from parallel corpus (src, tgt)
:param sort: if True sorts by src sequence length within each batch | rnn_translator/pytorch/seq2seq/data/dataset.py | def build_collate_fn(batch_first=False, parallel=True, sort=False):
"""
Factory for collate_fn functions.
:param batch_first: if True returns batches in (batch, seq) format, if
False returns in (seq, batch) format
:param parallel: if True builds batches from parallel corpus (src, tgt)
:para... | def build_collate_fn(batch_first=False, parallel=True, sort=False):
"""
Factory for collate_fn functions.
:param batch_first: if True returns batches in (batch, seq) format, if
False returns in (seq, batch) format
:param parallel: if True builds batches from parallel corpus (src, tgt)
:para... | [
"Factory",
"for",
"collate_fn",
"functions",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/data/dataset.py#L15-L81 | [
"def",
"build_collate_fn",
"(",
"batch_first",
"=",
"False",
",",
"parallel",
"=",
"True",
",",
"sort",
"=",
"False",
")",
":",
"def",
"collate_seq",
"(",
"seq",
")",
":",
"\"\"\"\n Builds batches for training or inference.\n Batches are returned as pytorch ... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | TextDataset.sort_by_length | Sorts dataset by the sequence length. | rnn_translator/pytorch/seq2seq/data/dataset.py | def sort_by_length(self):
"""
Sorts dataset by the sequence length.
"""
self.lengths, indices = self.lengths.sort(descending=True)
self.src = [self.src[idx] for idx in indices]
self.indices = indices.tolist()
self.sorted = True | def sort_by_length(self):
"""
Sorts dataset by the sequence length.
"""
self.lengths, indices = self.lengths.sort(descending=True)
self.src = [self.src[idx] for idx in indices]
self.indices = indices.tolist()
self.sorted = True | [
"Sorts",
"dataset",
"by",
"the",
"sequence",
"length",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/data/dataset.py#L115-L123 | [
"def",
"sort_by_length",
"(",
"self",
")",
":",
"self",
".",
"lengths",
",",
"indices",
"=",
"self",
".",
"lengths",
".",
"sort",
"(",
"descending",
"=",
"True",
")",
"self",
".",
"src",
"=",
"[",
"self",
".",
"src",
"[",
"idx",
"]",
"for",
"idx",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | TextDataset.unsort | "Unsorts" given array (restores original order of elements before
dataset was sorted by sequence length).
:param array: array to be "unsorted" | rnn_translator/pytorch/seq2seq/data/dataset.py | def unsort(self, array):
"""
"Unsorts" given array (restores original order of elements before
dataset was sorted by sequence length).
:param array: array to be "unsorted"
"""
if self.sorted:
inverse = sorted(enumerate(self.indices), key=itemgetter(1))
... | def unsort(self, array):
"""
"Unsorts" given array (restores original order of elements before
dataset was sorted by sequence length).
:param array: array to be "unsorted"
"""
if self.sorted:
inverse = sorted(enumerate(self.indices), key=itemgetter(1))
... | [
"Unsorts",
"given",
"array",
"(",
"restores",
"original",
"order",
"of",
"elements",
"before",
"dataset",
"was",
"sorted",
"by",
"sequence",
"length",
")",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/data/dataset.py#L125-L135 | [
"def",
"unsort",
"(",
"self",
",",
"array",
")",
":",
"if",
"self",
".",
"sorted",
":",
"inverse",
"=",
"sorted",
"(",
"enumerate",
"(",
"self",
".",
"indices",
")",
",",
"key",
"=",
"itemgetter",
"(",
"1",
")",
")",
"array",
"=",
"[",
"array",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | TextDataset.filter_data | Preserves only samples which satisfy the following inequality:
min_len <= sample sequence length <= max_len
:param min_len: minimum sequence length
:param max_len: maximum sequence length | rnn_translator/pytorch/seq2seq/data/dataset.py | def filter_data(self, min_len, max_len):
"""
Preserves only samples which satisfy the following inequality:
min_len <= sample sequence length <= max_len
:param min_len: minimum sequence length
:param max_len: maximum sequence length
"""
logging.info(f'Filteri... | def filter_data(self, min_len, max_len):
"""
Preserves only samples which satisfy the following inequality:
min_len <= sample sequence length <= max_len
:param min_len: minimum sequence length
:param max_len: maximum sequence length
"""
logging.info(f'Filteri... | [
"Preserves",
"only",
"samples",
"which",
"satisfy",
"the",
"following",
"inequality",
":",
"min_len",
"<",
"=",
"sample",
"sequence",
"length",
"<",
"=",
"max_len"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/data/dataset.py#L137-L155 | [
"def",
"filter_data",
"(",
"self",
",",
"min_len",
",",
"max_len",
")",
":",
"logging",
".",
"info",
"(",
"f'Filtering data, min len: {min_len}, max len: {max_len}'",
")",
"initial_len",
"=",
"len",
"(",
"self",
".",
"src",
")",
"filtered_src",
"=",
"[",
"]",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | TextDataset.process_data | Loads data from the input file.
:param fname: input file name
:param tokenizer: tokenizer
:param max_size: loads at most 'max_size' samples from the input file,
if None loads the entire dataset | rnn_translator/pytorch/seq2seq/data/dataset.py | def process_data(self, fname, tokenizer, max_size):
"""
Loads data from the input file.
:param fname: input file name
:param tokenizer: tokenizer
:param max_size: loads at most 'max_size' samples from the input file,
if None loads the entire dataset
"""
... | def process_data(self, fname, tokenizer, max_size):
"""
Loads data from the input file.
:param fname: input file name
:param tokenizer: tokenizer
:param max_size: loads at most 'max_size' samples from the input file,
if None loads the entire dataset
"""
... | [
"Loads",
"data",
"from",
"the",
"input",
"file",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/data/dataset.py#L157-L175 | [
"def",
"process_data",
"(",
"self",
",",
"fname",
",",
"tokenizer",
",",
"max_size",
")",
":",
"logging",
".",
"info",
"(",
"f'Processing data from {fname}'",
")",
"data",
"=",
"[",
"]",
"with",
"open",
"(",
"fname",
")",
"as",
"dfile",
":",
"for",
"idx"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | ParallelDataset.sort_by_length | Sorts dataset by the sequence length. | rnn_translator/pytorch/seq2seq/data/dataset.py | def sort_by_length(self):
"""
Sorts dataset by the sequence length.
"""
self.lengths, indices = self.lengths.sort(descending=True)
self.src = [self.src[idx] for idx in indices]
self.tgt = [self.tgt[idx] for idx in indices]
self.src_lengths = [self.src_lengths[idx... | def sort_by_length(self):
"""
Sorts dataset by the sequence length.
"""
self.lengths, indices = self.lengths.sort(descending=True)
self.src = [self.src[idx] for idx in indices]
self.tgt = [self.tgt[idx] for idx in indices]
self.src_lengths = [self.src_lengths[idx... | [
"Sorts",
"dataset",
"by",
"the",
"sequence",
"length",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/data/dataset.py#L251-L262 | [
"def",
"sort_by_length",
"(",
"self",
")",
":",
"self",
".",
"lengths",
",",
"indices",
"=",
"self",
".",
"lengths",
".",
"sort",
"(",
"descending",
"=",
"True",
")",
"self",
".",
"src",
"=",
"[",
"self",
".",
"src",
"[",
"idx",
"]",
"for",
"idx",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | ParallelDataset.filter_data | Preserves only samples which satisfy the following inequality:
min_len <= src sample sequence length <= max_len AND
min_len <= tgt sample sequence length <= max_len
:param min_len: minimum sequence length
:param max_len: maximum sequence length | rnn_translator/pytorch/seq2seq/data/dataset.py | def filter_data(self, min_len, max_len):
"""
Preserves only samples which satisfy the following inequality:
min_len <= src sample sequence length <= max_len AND
min_len <= tgt sample sequence length <= max_len
:param min_len: minimum sequence length
:param max_le... | def filter_data(self, min_len, max_len):
"""
Preserves only samples which satisfy the following inequality:
min_len <= src sample sequence length <= max_len AND
min_len <= tgt sample sequence length <= max_len
:param min_len: minimum sequence length
:param max_le... | [
"Preserves",
"only",
"samples",
"which",
"satisfy",
"the",
"following",
"inequality",
":",
"min_len",
"<",
"=",
"src",
"sample",
"sequence",
"length",
"<",
"=",
"max_len",
"AND",
"min_len",
"<",
"=",
"tgt",
"sample",
"sequence",
"length",
"<",
"=",
"max_len"... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/data/dataset.py#L264-L287 | [
"def",
"filter_data",
"(",
"self",
",",
"min_len",
",",
"max_len",
")",
":",
"logging",
".",
"info",
"(",
"f'Filtering data, min len: {min_len}, max len: {max_len}'",
")",
"initial_len",
"=",
"len",
"(",
"self",
".",
"src",
")",
"filtered_src",
"=",
"[",
"]",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | LazyParallelDataset.process_raw_data | Loads data from the input file.
:param fname: input file name
:param max_size: loads at most 'max_size' samples from the input file,
if None loads the entire dataset | rnn_translator/pytorch/seq2seq/data/dataset.py | def process_raw_data(self, fname, max_size):
"""
Loads data from the input file.
:param fname: input file name
:param max_size: loads at most 'max_size' samples from the input file,
if None loads the entire dataset
"""
logging.info(f'Processing data from {fna... | def process_raw_data(self, fname, max_size):
"""
Loads data from the input file.
:param fname: input file name
:param max_size: loads at most 'max_size' samples from the input file,
if None loads the entire dataset
"""
logging.info(f'Processing data from {fna... | [
"Loads",
"data",
"from",
"the",
"input",
"file",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/data/dataset.py#L331-L346 | [
"def",
"process_raw_data",
"(",
"self",
",",
"fname",
",",
"max_size",
")",
":",
"logging",
".",
"info",
"(",
"f'Processing data from {fname}'",
")",
"data",
"=",
"[",
"]",
"with",
"open",
"(",
"fname",
")",
"as",
"dfile",
":",
"for",
"idx",
",",
"line",... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | LazyParallelDataset.filter_raw_data | Preserves only samples which satisfy the following inequality:
min_len <= src sample sequence length <= max_len AND
min_len <= tgt sample sequence length <= max_len
:param min_len: minimum sequence length
:param max_len: maximum sequence length | rnn_translator/pytorch/seq2seq/data/dataset.py | def filter_raw_data(self, min_len, max_len):
"""
Preserves only samples which satisfy the following inequality:
min_len <= src sample sequence length <= max_len AND
min_len <= tgt sample sequence length <= max_len
:param min_len: minimum sequence length
:param ma... | def filter_raw_data(self, min_len, max_len):
"""
Preserves only samples which satisfy the following inequality:
min_len <= src sample sequence length <= max_len AND
min_len <= tgt sample sequence length <= max_len
:param min_len: minimum sequence length
:param ma... | [
"Preserves",
"only",
"samples",
"which",
"satisfy",
"the",
"following",
"inequality",
":",
"min_len",
"<",
"=",
"src",
"sample",
"sequence",
"length",
"<",
"=",
"max_len",
"AND",
"min_len",
"<",
"=",
"tgt",
"sample",
"sequence",
"length",
"<",
"=",
"max_len"... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/data/dataset.py#L348-L377 | [
"def",
"filter_raw_data",
"(",
"self",
",",
"min_len",
",",
"max_len",
")",
":",
"initial_len",
"=",
"len",
"(",
"self",
".",
"raw_src",
")",
"filtered_src",
"=",
"[",
"]",
"filtered_tgt",
"=",
"[",
"]",
"filtered_src_len",
"=",
"[",
"]",
"filtered_tgt_len... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | heatmaps_to_keypoints | Extract predicted keypoint locations from heatmaps. Output has shape
(#rois, 4, #keypoints) with the 4 rows corresponding to (x, y, logit, prob)
for each keypoint. | object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/inference.py | def heatmaps_to_keypoints(maps, rois):
"""Extract predicted keypoint locations from heatmaps. Output has shape
(#rois, 4, #keypoints) with the 4 rows corresponding to (x, y, logit, prob)
for each keypoint.
"""
# This function converts a discrete image coordinate in a HEATMAP_SIZE x
# HEATMAP_SIZ... | def heatmaps_to_keypoints(maps, rois):
"""Extract predicted keypoint locations from heatmaps. Output has shape
(#rois, 4, #keypoints) with the 4 rows corresponding to (x, y, logit, prob)
for each keypoint.
"""
# This function converts a discrete image coordinate in a HEATMAP_SIZE x
# HEATMAP_SIZ... | [
"Extract",
"predicted",
"keypoint",
"locations",
"from",
"heatmaps",
".",
"Output",
"has",
"shape",
"(",
"#rois",
"4",
"#keypoints",
")",
"with",
"the",
"4",
"rows",
"corresponding",
"to",
"(",
"x",
"y",
"logit",
"prob",
")",
"for",
"each",
"keypoint",
"."... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/inference.py#L40-L94 | [
"def",
"heatmaps_to_keypoints",
"(",
"maps",
",",
"rois",
")",
":",
"# This function converts a discrete image coordinate in a HEATMAP_SIZE x",
"# HEATMAP_SIZE image to a continuous keypoint coordinate. We maintain",
"# consistency with keypoints_to_heatmap_labels by using the conversion from",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | build_tensor_serving_input_receiver_fn | Returns a input_receiver_fn that can be used during serving.
This expects examples to come through as float tensors, and simply
wraps them as TensorServingInputReceivers.
Arguably, this should live in tf.estimator.export. Testing here first.
Args:
shape: list representing target size of a single example.... | image_classification/tensorflow/official/utils/export/export.py | def build_tensor_serving_input_receiver_fn(shape, dtype=tf.float32,
batch_size=1):
"""Returns a input_receiver_fn that can be used during serving.
This expects examples to come through as float tensors, and simply
wraps them as TensorServingInputReceivers.
Arguably, ... | def build_tensor_serving_input_receiver_fn(shape, dtype=tf.float32,
batch_size=1):
"""Returns a input_receiver_fn that can be used during serving.
This expects examples to come through as float tensors, and simply
wraps them as TensorServingInputReceivers.
Arguably, ... | [
"Returns",
"a",
"input_receiver_fn",
"that",
"can",
"be",
"used",
"during",
"serving",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/export/export.py#L24-L49 | [
"def",
"build_tensor_serving_input_receiver_fn",
"(",
"shape",
",",
"dtype",
"=",
"tf",
".",
"float32",
",",
"batch_size",
"=",
"1",
")",
":",
"def",
"serving_input_receiver_fn",
"(",
")",
":",
"# Prep a placeholder where the input example will be fed in",
"features",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | _within_box | Validate which keypoints are contained inside a given box.
points: NxKx2
boxes: Nx4
output: NxK | object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/loss.py | def _within_box(points, boxes):
"""Validate which keypoints are contained inside a given box.
points: NxKx2
boxes: Nx4
output: NxK
"""
x_within = (points[..., 0] >= boxes[:, 0, None]) & (
points[..., 0] <= boxes[:, 2, None]
)
y_within = (points[..., 1] >= boxes[:, 1, None]) & (
... | def _within_box(points, boxes):
"""Validate which keypoints are contained inside a given box.
points: NxKx2
boxes: Nx4
output: NxK
"""
x_within = (points[..., 0] >= boxes[:, 0, None]) & (
points[..., 0] <= boxes[:, 2, None]
)
y_within = (points[..., 1] >= boxes[:, 1, None]) & (
... | [
"Validate",
"which",
"keypoints",
"are",
"contained",
"inside",
"a",
"given",
"box",
".",
"points",
":",
"NxKx2",
"boxes",
":",
"Nx4",
"output",
":",
"NxK"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/loss.py#L39-L51 | [
"def",
"_within_box",
"(",
"points",
",",
"boxes",
")",
":",
"x_within",
"=",
"(",
"points",
"[",
"...",
",",
"0",
"]",
">=",
"boxes",
"[",
":",
",",
"0",
",",
"None",
"]",
")",
"&",
"(",
"points",
"[",
"...",
",",
"0",
"]",
"<=",
"boxes",
"[... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | ExamplesPerSecondHook.begin | Called once before using the session to check global step. | image_classification/tensorflow/official/utils/logs/hooks.py | def begin(self):
"""Called once before using the session to check global step."""
self._global_step_tensor = tf.train.get_global_step()
if self._global_step_tensor is None:
raise RuntimeError(
'Global step should be created to use StepCounterHook.') | def begin(self):
"""Called once before using the session to check global step."""
self._global_step_tensor = tf.train.get_global_step()
if self._global_step_tensor is None:
raise RuntimeError(
'Global step should be created to use StepCounterHook.') | [
"Called",
"once",
"before",
"using",
"the",
"session",
"to",
"check",
"global",
"step",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/hooks.py#L69-L74 | [
"def",
"begin",
"(",
"self",
")",
":",
"self",
".",
"_global_step_tensor",
"=",
"tf",
".",
"train",
".",
"get_global_step",
"(",
")",
"if",
"self",
".",
"_global_step_tensor",
"is",
"None",
":",
"raise",
"RuntimeError",
"(",
"'Global step should be created to us... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | ExamplesPerSecondHook.after_run | Called after each call to run().
Args:
run_context: A SessionRunContext object.
run_values: A SessionRunValues object. | image_classification/tensorflow/official/utils/logs/hooks.py | def after_run(self, run_context, run_values): # pylint: disable=unused-argument
"""Called after each call to run().
Args:
run_context: A SessionRunContext object.
run_values: A SessionRunValues object.
"""
global_step = run_values.results
if self._timer.should_trigger_for_step(
... | def after_run(self, run_context, run_values): # pylint: disable=unused-argument
"""Called after each call to run().
Args:
run_context: A SessionRunContext object.
run_values: A SessionRunValues object.
"""
global_step = run_values.results
if self._timer.should_trigger_for_step(
... | [
"Called",
"after",
"each",
"call",
"to",
"run",
"()",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/hooks.py#L87-L115 | [
"def",
"after_run",
"(",
"self",
",",
"run_context",
",",
"run_values",
")",
":",
"# pylint: disable=unused-argument",
"global_step",
"=",
"run_values",
".",
"results",
"if",
"self",
".",
"_timer",
".",
"should_trigger_for_step",
"(",
"global_step",
")",
"and",
"g... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | vis_keypoints | Visualizes keypoints (adapted from vis_one_image).
kps has shape (4, #keypoints) where 4 rows are (x, y, logit, prob). | object_detection/pytorch/demo/predictor.py | def vis_keypoints(img, kps, kp_thresh=2, alpha=0.7):
"""Visualizes keypoints (adapted from vis_one_image).
kps has shape (4, #keypoints) where 4 rows are (x, y, logit, prob).
"""
dataset_keypoints = PersonKeypoints.NAMES
kp_lines = PersonKeypoints.CONNECTIONS
# Convert from plt 0-1 RGBA colors ... | def vis_keypoints(img, kps, kp_thresh=2, alpha=0.7):
"""Visualizes keypoints (adapted from vis_one_image).
kps has shape (4, #keypoints) where 4 rows are (x, y, logit, prob).
"""
dataset_keypoints = PersonKeypoints.NAMES
kp_lines = PersonKeypoints.CONNECTIONS
# Convert from plt 0-1 RGBA colors ... | [
"Visualizes",
"keypoints",
"(",
"adapted",
"from",
"vis_one_image",
")",
".",
"kps",
"has",
"shape",
"(",
"4",
"#keypoints",
")",
"where",
"4",
"rows",
"are",
"(",
"x",
"y",
"logit",
"prob",
")",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/demo/predictor.py#L376-L434 | [
"def",
"vis_keypoints",
"(",
"img",
",",
"kps",
",",
"kp_thresh",
"=",
"2",
",",
"alpha",
"=",
"0.7",
")",
":",
"dataset_keypoints",
"=",
"PersonKeypoints",
".",
"NAMES",
"kp_lines",
"=",
"PersonKeypoints",
".",
"CONNECTIONS",
"# Convert from plt 0-1 RGBA colors t... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | COCODemo.build_transform | Creates a basic transformation that was used to train the models | object_detection/pytorch/demo/predictor.py | def build_transform(self):
"""
Creates a basic transformation that was used to train the models
"""
cfg = self.cfg
# we are loading images with OpenCV, so we don't need to convert them
# to BGR, they are already! So all we need to do is to normalize
# by 255 if w... | def build_transform(self):
"""
Creates a basic transformation that was used to train the models
"""
cfg = self.cfg
# we are loading images with OpenCV, so we don't need to convert them
# to BGR, they are already! So all we need to do is to normalize
# by 255 if w... | [
"Creates",
"a",
"basic",
"transformation",
"that",
"was",
"used",
"to",
"train",
"the",
"models"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/demo/predictor.py#L132-L160 | [
"def",
"build_transform",
"(",
"self",
")",
":",
"cfg",
"=",
"self",
".",
"cfg",
"# we are loading images with OpenCV, so we don't need to convert them",
"# to BGR, they are already! So all we need to do is to normalize",
"# by 255 if we want to convert to BGR255 format, or flip the channe... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | COCODemo.run_on_opencv_image | Arguments:
image (np.ndarray): an image as returned by OpenCV
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
the BoxList via `prediction.fields()` | object_detection/pytorch/demo/predictor.py | def run_on_opencv_image(self, image):
"""
Arguments:
image (np.ndarray): an image as returned by OpenCV
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
t... | def run_on_opencv_image(self, image):
"""
Arguments:
image (np.ndarray): an image as returned by OpenCV
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
t... | [
"Arguments",
":",
"image",
"(",
"np",
".",
"ndarray",
")",
":",
"an",
"image",
"as",
"returned",
"by",
"OpenCV"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/demo/predictor.py#L162-L185 | [
"def",
"run_on_opencv_image",
"(",
"self",
",",
"image",
")",
":",
"predictions",
"=",
"self",
".",
"compute_prediction",
"(",
"image",
")",
"top_predictions",
"=",
"self",
".",
"select_top_predictions",
"(",
"predictions",
")",
"result",
"=",
"image",
".",
"c... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | COCODemo.compute_prediction | Arguments:
original_image (np.ndarray): an image as returned by OpenCV
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
the BoxList via `prediction.fields()` | object_detection/pytorch/demo/predictor.py | def compute_prediction(self, original_image):
"""
Arguments:
original_image (np.ndarray): an image as returned by OpenCV
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
... | def compute_prediction(self, original_image):
"""
Arguments:
original_image (np.ndarray): an image as returned by OpenCV
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
... | [
"Arguments",
":",
"original_image",
"(",
"np",
".",
"ndarray",
")",
":",
"an",
"image",
"as",
"returned",
"by",
"OpenCV"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/demo/predictor.py#L187-L222 | [
"def",
"compute_prediction",
"(",
"self",
",",
"original_image",
")",
":",
"# apply pre-processing to image",
"image",
"=",
"self",
".",
"transforms",
"(",
"original_image",
")",
"# convert to an ImageList, padded so that it is divisible by",
"# cfg.DATALOADER.SIZE_DIVISIBILITY",... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | COCODemo.select_top_predictions | Select only predictions which have a `score` > self.confidence_threshold,
and returns the predictions in descending order of score
Arguments:
predictions (BoxList): the result of the computation by the model.
It should contain the field `scores`.
Returns:
... | object_detection/pytorch/demo/predictor.py | def select_top_predictions(self, predictions):
"""
Select only predictions which have a `score` > self.confidence_threshold,
and returns the predictions in descending order of score
Arguments:
predictions (BoxList): the result of the computation by the model.
... | def select_top_predictions(self, predictions):
"""
Select only predictions which have a `score` > self.confidence_threshold,
and returns the predictions in descending order of score
Arguments:
predictions (BoxList): the result of the computation by the model.
... | [
"Select",
"only",
"predictions",
"which",
"have",
"a",
"score",
">",
"self",
".",
"confidence_threshold",
"and",
"returns",
"the",
"predictions",
"in",
"descending",
"order",
"of",
"score"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/demo/predictor.py#L224-L243 | [
"def",
"select_top_predictions",
"(",
"self",
",",
"predictions",
")",
":",
"scores",
"=",
"predictions",
".",
"get_field",
"(",
"\"scores\"",
")",
"keep",
"=",
"torch",
".",
"nonzero",
"(",
"scores",
">",
"self",
".",
"confidence_threshold",
")",
".",
"sque... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | COCODemo.compute_colors_for_labels | Simple function that adds fixed colors depending on the class | object_detection/pytorch/demo/predictor.py | def compute_colors_for_labels(self, labels):
"""
Simple function that adds fixed colors depending on the class
"""
colors = labels[:, None] * self.palette
colors = (colors % 255).numpy().astype("uint8")
return colors | def compute_colors_for_labels(self, labels):
"""
Simple function that adds fixed colors depending on the class
"""
colors = labels[:, None] * self.palette
colors = (colors % 255).numpy().astype("uint8")
return colors | [
"Simple",
"function",
"that",
"adds",
"fixed",
"colors",
"depending",
"on",
"the",
"class"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/demo/predictor.py#L245-L251 | [
"def",
"compute_colors_for_labels",
"(",
"self",
",",
"labels",
")",
":",
"colors",
"=",
"labels",
"[",
":",
",",
"None",
"]",
"*",
"self",
".",
"palette",
"colors",
"=",
"(",
"colors",
"%",
"255",
")",
".",
"numpy",
"(",
")",
".",
"astype",
"(",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | COCODemo.overlay_boxes | Adds the predicted boxes on top of the image
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `labels`. | object_detection/pytorch/demo/predictor.py | def overlay_boxes(self, image, predictions):
"""
Adds the predicted boxes on top of the image
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `lab... | def overlay_boxes(self, image, predictions):
"""
Adds the predicted boxes on top of the image
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `lab... | [
"Adds",
"the",
"predicted",
"boxes",
"on",
"top",
"of",
"the",
"image"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/demo/predictor.py#L253-L274 | [
"def",
"overlay_boxes",
"(",
"self",
",",
"image",
",",
"predictions",
")",
":",
"labels",
"=",
"predictions",
".",
"get_field",
"(",
"\"labels\"",
")",
"boxes",
"=",
"predictions",
".",
"bbox",
"colors",
"=",
"self",
".",
"compute_colors_for_labels",
"(",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | COCODemo.overlay_mask | Adds the instances contours for each predicted object.
Each label has a different color.
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `mask` and `label... | object_detection/pytorch/demo/predictor.py | def overlay_mask(self, image, predictions):
"""
Adds the instances contours for each predicted object.
Each label has a different color.
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the mode... | def overlay_mask(self, image, predictions):
"""
Adds the instances contours for each predicted object.
Each label has a different color.
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the mode... | [
"Adds",
"the",
"instances",
"contours",
"for",
"each",
"predicted",
"object",
".",
"Each",
"label",
"has",
"a",
"different",
"color",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/demo/predictor.py#L276-L300 | [
"def",
"overlay_mask",
"(",
"self",
",",
"image",
",",
"predictions",
")",
":",
"masks",
"=",
"predictions",
".",
"get_field",
"(",
"\"mask\"",
")",
".",
"numpy",
"(",
")",
"labels",
"=",
"predictions",
".",
"get_field",
"(",
"\"labels\"",
")",
"colors",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | COCODemo.create_mask_montage | Create a montage showing the probability heatmaps for each one one of the
detected objects
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `mask`. | object_detection/pytorch/demo/predictor.py | def create_mask_montage(self, image, predictions):
"""
Create a montage showing the probability heatmaps for each one one of the
detected objects
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by... | def create_mask_montage(self, image, predictions):
"""
Create a montage showing the probability heatmaps for each one one of the
detected objects
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by... | [
"Create",
"a",
"montage",
"showing",
"the",
"probability",
"heatmaps",
"for",
"each",
"one",
"one",
"of",
"the",
"detected",
"objects"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/demo/predictor.py#L311-L345 | [
"def",
"create_mask_montage",
"(",
"self",
",",
"image",
",",
"predictions",
")",
":",
"masks",
"=",
"predictions",
".",
"get_field",
"(",
"\"mask\"",
")",
"masks_per_dim",
"=",
"self",
".",
"masks_per_dim",
"masks",
"=",
"L",
".",
"interpolate",
"(",
"masks... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | COCODemo.overlay_class_names | Adds detected class names and scores in the positions defined by the
top-left corner of the predicted bounding box
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain t... | object_detection/pytorch/demo/predictor.py | def overlay_class_names(self, image, predictions):
"""
Adds detected class names and scores in the positions defined by the
top-left corner of the predicted bounding box
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the resu... | def overlay_class_names(self, image, predictions):
"""
Adds detected class names and scores in the positions defined by the
top-left corner of the predicted bounding box
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the resu... | [
"Adds",
"detected",
"class",
"names",
"and",
"scores",
"in",
"the",
"positions",
"defined",
"by",
"the",
"top",
"-",
"left",
"corner",
"of",
"the",
"predicted",
"bounding",
"box"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/demo/predictor.py#L347-L370 | [
"def",
"overlay_class_names",
"(",
"self",
",",
"image",
",",
"predictions",
")",
":",
"scores",
"=",
"predictions",
".",
"get_field",
"(",
"\"scores\"",
")",
".",
"tolist",
"(",
")",
"labels",
"=",
"predictions",
".",
"get_field",
"(",
"\"labels\"",
")",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | _collect_cpu_info | Collect the CPU information for the local environment. | image_classification/tensorflow/official/utils/logs/logger.py | def _collect_cpu_info(run_info):
"""Collect the CPU information for the local environment."""
cpu_info = {}
cpu_info["num_cores"] = multiprocessing.cpu_count()
# Note: cpuinfo is not installed in the TensorFlow OSS tree.
# It is installable via pip.
import cpuinfo # pylint: disable=g-import-not-at-top
... | def _collect_cpu_info(run_info):
"""Collect the CPU information for the local environment."""
cpu_info = {}
cpu_info["num_cores"] = multiprocessing.cpu_count()
# Note: cpuinfo is not installed in the TensorFlow OSS tree.
# It is installable via pip.
import cpuinfo # pylint: disable=g-import-not-at-top
... | [
"Collect",
"the",
"CPU",
"information",
"for",
"the",
"local",
"environment",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/logger.py#L146-L160 | [
"def",
"_collect_cpu_info",
"(",
"run_info",
")",
":",
"cpu_info",
"=",
"{",
"}",
"cpu_info",
"[",
"\"num_cores\"",
"]",
"=",
"multiprocessing",
".",
"cpu_count",
"(",
")",
"# Note: cpuinfo is not installed in the TensorFlow OSS tree.",
"# It is installable via pip.",
"im... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | _collect_gpu_info | Collect local GPU information by TF device library. | image_classification/tensorflow/official/utils/logs/logger.py | def _collect_gpu_info(run_info):
"""Collect local GPU information by TF device library."""
gpu_info = {}
local_device_protos = device_lib.list_local_devices()
gpu_info["count"] = len([d for d in local_device_protos
if d.device_type == "GPU"])
# The device description usually is a J... | def _collect_gpu_info(run_info):
"""Collect local GPU information by TF device library."""
gpu_info = {}
local_device_protos = device_lib.list_local_devices()
gpu_info["count"] = len([d for d in local_device_protos
if d.device_type == "GPU"])
# The device description usually is a J... | [
"Collect",
"local",
"GPU",
"information",
"by",
"TF",
"device",
"library",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/logger.py#L163-L178 | [
"def",
"_collect_gpu_info",
"(",
"run_info",
")",
":",
"gpu_info",
"=",
"{",
"}",
"local_device_protos",
"=",
"device_lib",
".",
"list_local_devices",
"(",
")",
"gpu_info",
"[",
"\"count\"",
"]",
"=",
"len",
"(",
"[",
"d",
"for",
"d",
"in",
"local_device_pro... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | BenchmarkLogger.log_estimator_evaluation_result | Log the evaluation result for a estimator.
The evaluate result is a directory that contains metrics defined in
model_fn. It also contains a entry for global_step which contains the value
of the global step when evaluation was performed.
Args:
eval_results: dict, the result of evaluate() from a e... | image_classification/tensorflow/official/utils/logs/logger.py | def log_estimator_evaluation_result(self, eval_results):
"""Log the evaluation result for a estimator.
The evaluate result is a directory that contains metrics defined in
model_fn. It also contains a entry for global_step which contains the value
of the global step when evaluation was performed.
A... | def log_estimator_evaluation_result(self, eval_results):
"""Log the evaluation result for a estimator.
The evaluate result is a directory that contains metrics defined in
model_fn. It also contains a entry for global_step which contains the value
of the global step when evaluation was performed.
A... | [
"Log",
"the",
"evaluation",
"result",
"for",
"a",
"estimator",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/logger.py#L47-L64 | [
"def",
"log_estimator_evaluation_result",
"(",
"self",
",",
"eval_results",
")",
":",
"if",
"not",
"isinstance",
"(",
"eval_results",
",",
"dict",
")",
":",
"tf",
".",
"logging",
".",
"warning",
"(",
"\"eval_results should be directory for logging. Got %s\"",
",",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | BenchmarkLogger.log_metric | Log the benchmark metric information to local file.
Currently the logging is done in a synchronized way. This should be updated
to log asynchronously.
Args:
name: string, the name of the metric to log.
value: number, the value of the metric. The value will not be logged if it
is not a ... | image_classification/tensorflow/official/utils/logs/logger.py | def log_metric(self, name, value, unit=None, global_step=None, extras=None):
"""Log the benchmark metric information to local file.
Currently the logging is done in a synchronized way. This should be updated
to log asynchronously.
Args:
name: string, the name of the metric to log.
value: n... | def log_metric(self, name, value, unit=None, global_step=None, extras=None):
"""Log the benchmark metric information to local file.
Currently the logging is done in a synchronized way. This should be updated
to log asynchronously.
Args:
name: string, the name of the metric to log.
value: n... | [
"Log",
"the",
"benchmark",
"metric",
"information",
"to",
"local",
"file",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/logger.py#L66-L103 | [
"def",
"log_metric",
"(",
"self",
",",
"name",
",",
"value",
",",
"unit",
"=",
"None",
",",
"global_step",
"=",
"None",
",",
"extras",
"=",
"None",
")",
":",
"if",
"not",
"isinstance",
"(",
"value",
",",
"numbers",
".",
"Number",
")",
":",
"tf",
".... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | BenchmarkLogger.log_run_info | Collect most of the TF runtime information for the local env.
The schema of the run info follows official/benchmark/datastore/schema.
Args:
model_name: string, the name of the model. | image_classification/tensorflow/official/utils/logs/logger.py | def log_run_info(self, model_name):
"""Collect most of the TF runtime information for the local env.
The schema of the run info follows official/benchmark/datastore/schema.
Args:
model_name: string, the name of the model.
"""
run_info = {
"model_name": model_name,
"machine_co... | def log_run_info(self, model_name):
"""Collect most of the TF runtime information for the local env.
The schema of the run info follows official/benchmark/datastore/schema.
Args:
model_name: string, the name of the model.
"""
run_info = {
"model_name": model_name,
"machine_co... | [
"Collect",
"most",
"of",
"the",
"TF",
"runtime",
"information",
"for",
"the",
"local",
"env",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/image_classification/tensorflow/official/utils/logs/logger.py#L105-L130 | [
"def",
"log_run_info",
"(",
"self",
",",
"model_name",
")",
":",
"run_info",
"=",
"{",
"\"model_name\"",
":",
"model_name",
",",
"\"machine_config\"",
":",
"{",
"}",
",",
"\"run_date\"",
":",
"datetime",
".",
"datetime",
".",
"now",
"(",
")",
".",
"strftim... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | read_existing_paths | Return the SGF filename for each existing eval record. | reinforcement/tensorflow/minigo/oneoffs/eval_sgf_to_cbt.py | def read_existing_paths(bt_table):
"""Return the SGF filename for each existing eval record."""
rows = bt_table.read_rows(
filter_=row_filters.ColumnRangeFilter(
METADATA, SGF_FILENAME, SGF_FILENAME))
names = (row.cell_value(METADATA, SGF_FILENAME).decode() for row in rows)
processed... | def read_existing_paths(bt_table):
"""Return the SGF filename for each existing eval record."""
rows = bt_table.read_rows(
filter_=row_filters.ColumnRangeFilter(
METADATA, SGF_FILENAME, SGF_FILENAME))
names = (row.cell_value(METADATA, SGF_FILENAME).decode() for row in rows)
processed... | [
"Return",
"the",
"SGF",
"filename",
"for",
"each",
"existing",
"eval",
"record",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/eval_sgf_to_cbt.py#L87-L94 | [
"def",
"read_existing_paths",
"(",
"bt_table",
")",
":",
"rows",
"=",
"bt_table",
".",
"read_rows",
"(",
"filter_",
"=",
"row_filters",
".",
"ColumnRangeFilter",
"(",
"METADATA",
",",
"SGF_FILENAME",
",",
"SGF_FILENAME",
")",
")",
"names",
"=",
"(",
"row",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | canonical_name | Keep filename and some date folders | reinforcement/tensorflow/minigo/oneoffs/eval_sgf_to_cbt.py | def canonical_name(sgf_name):
"""Keep filename and some date folders"""
sgf_name = os.path.normpath(sgf_name)
assert sgf_name.endswith('.sgf'), sgf_name
# Strip off '.sgf'
sgf_name = sgf_name[:-4]
# Often eval is inside a folder with the run name.
# include from folder before /eval/ if part... | def canonical_name(sgf_name):
"""Keep filename and some date folders"""
sgf_name = os.path.normpath(sgf_name)
assert sgf_name.endswith('.sgf'), sgf_name
# Strip off '.sgf'
sgf_name = sgf_name[:-4]
# Often eval is inside a folder with the run name.
# include from folder before /eval/ if part... | [
"Keep",
"filename",
"and",
"some",
"date",
"folders"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/eval_sgf_to_cbt.py#L97-L111 | [
"def",
"canonical_name",
"(",
"sgf_name",
")",
":",
"sgf_name",
"=",
"os",
".",
"path",
".",
"normpath",
"(",
"sgf_name",
")",
"assert",
"sgf_name",
".",
"endswith",
"(",
"'.sgf'",
")",
",",
"sgf_name",
"# Strip off '.sgf'",
"sgf_name",
"=",
"sgf_name",
"[",... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | read_games | Read all SGFs that match glob
Parse each game and extract relevant metadata for eval games table. | reinforcement/tensorflow/minigo/oneoffs/eval_sgf_to_cbt.py | def read_games(glob, existing_paths):
"""Read all SGFs that match glob
Parse each game and extract relevant metadata for eval games table.
"""
globbed = sorted(gfile.Glob(glob))
skipped = 0
to_parse = []
for sgf_name in tqdm(globbed):
assert sgf_name.lower().endswith('.sgf'), sgf_... | def read_games(glob, existing_paths):
"""Read all SGFs that match glob
Parse each game and extract relevant metadata for eval games table.
"""
globbed = sorted(gfile.Glob(glob))
skipped = 0
to_parse = []
for sgf_name in tqdm(globbed):
assert sgf_name.lower().endswith('.sgf'), sgf_... | [
"Read",
"all",
"SGFs",
"that",
"match",
"glob"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/eval_sgf_to_cbt.py#L149-L176 | [
"def",
"read_games",
"(",
"glob",
",",
"existing_paths",
")",
":",
"globbed",
"=",
"sorted",
"(",
"gfile",
".",
"Glob",
"(",
"glob",
")",
")",
"skipped",
"=",
"0",
"to_parse",
"=",
"[",
"]",
"for",
"sgf_name",
"in",
"tqdm",
"(",
"globbed",
")",
":",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | write_eval_records | Write all eval_records to eval_table
In addition to writing new rows table_state must be updated in
row `table_state` columns `metadata:eval_game_counter`
Args:
bt_table: bigtable table to add rows to.
game_data: metadata pairs (column name, value) for each eval record.
last_game: last... | reinforcement/tensorflow/minigo/oneoffs/eval_sgf_to_cbt.py | def write_eval_records(bt_table, game_data, last_game):
"""Write all eval_records to eval_table
In addition to writing new rows table_state must be updated in
row `table_state` columns `metadata:eval_game_counter`
Args:
bt_table: bigtable table to add rows to.
game_data: metadata pairs (c... | def write_eval_records(bt_table, game_data, last_game):
"""Write all eval_records to eval_table
In addition to writing new rows table_state must be updated in
row `table_state` columns `metadata:eval_game_counter`
Args:
bt_table: bigtable table to add rows to.
game_data: metadata pairs (c... | [
"Write",
"all",
"eval_records",
"to",
"eval_table"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/eval_sgf_to_cbt.py#L179-L233 | [
"def",
"write_eval_records",
"(",
"bt_table",
",",
"game_data",
",",
"last_game",
")",
":",
"eval_num",
"=",
"last_game",
"# Each column counts as a mutation so max rows is ~10000",
"GAMES_PER_COMMIT",
"=",
"2000",
"for",
"games",
"in",
"grouper",
"(",
"tqdm",
"(",
"g... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | main | All of the magic together. | reinforcement/tensorflow/minigo/oneoffs/eval_sgf_to_cbt.py | def main(unusedargv):
"""All of the magic together."""
del unusedargv
bt_table = (bigtable
.Client(FLAGS.cbt_project, admin=True)
.instance(FLAGS.cbt_instance)
.table(FLAGS.cbt_table))
assert bt_table.exists(), "Table doesn't exist"
# Get current gam... | def main(unusedargv):
"""All of the magic together."""
del unusedargv
bt_table = (bigtable
.Client(FLAGS.cbt_project, admin=True)
.instance(FLAGS.cbt_instance)
.table(FLAGS.cbt_table))
assert bt_table.exists(), "Table doesn't exist"
# Get current gam... | [
"All",
"of",
"the",
"magic",
"together",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/eval_sgf_to_cbt.py#L236-L270 | [
"def",
"main",
"(",
"unusedargv",
")",
":",
"del",
"unusedargv",
"bt_table",
"=",
"(",
"bigtable",
".",
"Client",
"(",
"FLAGS",
".",
"cbt_project",
",",
"admin",
"=",
"True",
")",
".",
"instance",
"(",
"FLAGS",
".",
"cbt_instance",
")",
".",
"table",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | initialize_from_checkpoint | Initialize the reinforcement learning loop from a checkpoint. | reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py | def initialize_from_checkpoint(state):
"""Initialize the reinforcement learning loop from a checkpoint."""
# The checkpoint's work_dir should contain the most recently trained model.
model_paths = glob.glob(os.path.join(FLAGS.checkpoint_dir,
'work_dir/model.ckpt-*.pb'))
i... | def initialize_from_checkpoint(state):
"""Initialize the reinforcement learning loop from a checkpoint."""
# The checkpoint's work_dir should contain the most recently trained model.
model_paths = glob.glob(os.path.join(FLAGS.checkpoint_dir,
'work_dir/model.ckpt-*.pb'))
i... | [
"Initialize",
"the",
"reinforcement",
"learning",
"loop",
"from",
"a",
"checkpoint",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py#L144-L171 | [
"def",
"initialize_from_checkpoint",
"(",
"state",
")",
":",
"# The checkpoint's work_dir should contain the most recently trained model.",
"model_paths",
"=",
"glob",
".",
"glob",
"(",
"os",
".",
"path",
".",
"join",
"(",
"FLAGS",
".",
"checkpoint_dir",
",",
"'work_dir... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | run | Run the given subprocess command in a coroutine.
Args:
*cmd: the command to run and its arguments.
Returns:
The output that the command wrote to stdout as a list of strings, one line
per element (stderr output is piped to stdout).
Raises:
RuntimeError: if the command returns a non-zero result. | reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py | async def run(*cmd):
"""Run the given subprocess command in a coroutine.
Args:
*cmd: the command to run and its arguments.
Returns:
The output that the command wrote to stdout as a list of strings, one line
per element (stderr output is piped to stdout).
Raises:
RuntimeError: if the command r... | async def run(*cmd):
"""Run the given subprocess command in a coroutine.
Args:
*cmd: the command to run and its arguments.
Returns:
The output that the command wrote to stdout as a list of strings, one line
per element (stderr output is piped to stdout).
Raises:
RuntimeError: if the command r... | [
"Run",
"the",
"given",
"subprocess",
"command",
"in",
"a",
"coroutine",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py#L191-L215 | [
"async",
"def",
"run",
"(",
"*",
"cmd",
")",
":",
"stdout",
"=",
"await",
"checked_run",
"(",
"*",
"cmd",
")",
"log_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"FLAGS",
".",
"base_dir",
",",
"get_cmd_name",
"(",
"cmd",
")",
"+",
"'.log'",
")",... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | get_golden_chunk_records | Return up to num_records of golden chunks to train on.
Returns:
A list of golden chunks up to num_records in length, sorted by path. | reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py | def get_golden_chunk_records():
"""Return up to num_records of golden chunks to train on.
Returns:
A list of golden chunks up to num_records in length, sorted by path.
"""
pattern = os.path.join(fsdb.golden_chunk_dir(), '*.zz')
return sorted(tf.gfile.Glob(pattern), reverse=True)[:FLAGS.window_size] | def get_golden_chunk_records():
"""Return up to num_records of golden chunks to train on.
Returns:
A list of golden chunks up to num_records in length, sorted by path.
"""
pattern = os.path.join(fsdb.golden_chunk_dir(), '*.zz')
return sorted(tf.gfile.Glob(pattern), reverse=True)[:FLAGS.window_size] | [
"Return",
"up",
"to",
"num_records",
"of",
"golden",
"chunks",
"to",
"train",
"on",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py#L218-L226 | [
"def",
"get_golden_chunk_records",
"(",
")",
":",
"pattern",
"=",
"os",
".",
"path",
".",
"join",
"(",
"fsdb",
".",
"golden_chunk_dir",
"(",
")",
",",
"'*.zz'",
")",
"return",
"sorted",
"(",
"tf",
".",
"gfile",
".",
"Glob",
"(",
"pattern",
")",
",",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | selfplay | Run selfplay and write a training chunk to the fsdb golden_chunk_dir.
Args:
state: the RL loop State instance.
flagfile: the name of the flagfile to use for selfplay, either 'selfplay'
(the default) or 'boostrap'. | reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py | async def selfplay(state, flagfile='selfplay'):
"""Run selfplay and write a training chunk to the fsdb golden_chunk_dir.
Args:
state: the RL loop State instance.
flagfile: the name of the flagfile to use for selfplay, either 'selfplay'
(the default) or 'boostrap'.
"""
output_dir = os.path.join... | async def selfplay(state, flagfile='selfplay'):
"""Run selfplay and write a training chunk to the fsdb golden_chunk_dir.
Args:
state: the RL loop State instance.
flagfile: the name of the flagfile to use for selfplay, either 'selfplay'
(the default) or 'boostrap'.
"""
output_dir = os.path.join... | [
"Run",
"selfplay",
"and",
"write",
"a",
"training",
"chunk",
"to",
"the",
"fsdb",
"golden_chunk_dir",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py#L230-L273 | [
"async",
"def",
"selfplay",
"(",
"state",
",",
"flagfile",
"=",
"'selfplay'",
")",
":",
"output_dir",
"=",
"os",
".",
"path",
".",
"join",
"(",
"fsdb",
".",
"selfplay_dir",
"(",
")",
",",
"state",
".",
"output_model_name",
")",
"holdout_dir",
"=",
"os",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | train | Run training and write a new model to the fsdb models_dir.
Args:
state: the RL loop State instance.
tf_records: a list of paths to TensorFlow records to train on. | reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py | async def train(state, tf_records):
"""Run training and write a new model to the fsdb models_dir.
Args:
state: the RL loop State instance.
tf_records: a list of paths to TensorFlow records to train on.
"""
model_path = os.path.join(fsdb.models_dir(), state.train_model_name)
await run(
'python3... | async def train(state, tf_records):
"""Run training and write a new model to the fsdb models_dir.
Args:
state: the RL loop State instance.
tf_records: a list of paths to TensorFlow records to train on.
"""
model_path = os.path.join(fsdb.models_dir(), state.train_model_name)
await run(
'python3... | [
"Run",
"training",
"and",
"write",
"a",
"new",
"model",
"to",
"the",
"fsdb",
"models_dir",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py#L276-L297 | [
"async",
"def",
"train",
"(",
"state",
",",
"tf_records",
")",
":",
"model_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"fsdb",
".",
"models_dir",
"(",
")",
",",
"state",
".",
"train_model_name",
")",
"await",
"run",
"(",
"'python3'",
",",
"'train.... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | validate | Validate the trained model against holdout games.
Args:
state: the RL loop State instance.
holdout_glob: a glob that matches holdout games. | reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py | async def validate(state, holdout_glob):
"""Validate the trained model against holdout games.
Args:
state: the RL loop State instance.
holdout_glob: a glob that matches holdout games.
"""
if not glob.glob(holdout_glob):
print('Glob "{}" didn\'t match any files, skipping validation'.format(
... | async def validate(state, holdout_glob):
"""Validate the trained model against holdout games.
Args:
state: the RL loop State instance.
holdout_glob: a glob that matches holdout games.
"""
if not glob.glob(holdout_glob):
print('Glob "{}" didn\'t match any files, skipping validation'.format(
... | [
"Validate",
"the",
"trained",
"model",
"against",
"holdout",
"games",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py#L300-L315 | [
"async",
"def",
"validate",
"(",
"state",
",",
"holdout_glob",
")",
":",
"if",
"not",
"glob",
".",
"glob",
"(",
"holdout_glob",
")",
":",
"print",
"(",
"'Glob \"{}\" didn\\'t match any files, skipping validation'",
".",
"format",
"(",
"holdout_glob",
")",
")",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | evaluate_model | Evaluate one model against a target.
Args:
eval_model_path: the path to the model to evaluate.
target_model_path: the path to the model to compare to.
sgf_dif: directory path to write SGF output to.
seed: random seed to use when running eval.
Returns:
The win-rate of eval_model against target_... | reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py | async def evaluate_model(eval_model_path, target_model_path, sgf_dir, seed):
"""Evaluate one model against a target.
Args:
eval_model_path: the path to the model to evaluate.
target_model_path: the path to the model to compare to.
sgf_dif: directory path to write SGF output to.
seed: random seed to... | async def evaluate_model(eval_model_path, target_model_path, sgf_dir, seed):
"""Evaluate one model against a target.
Args:
eval_model_path: the path to the model to evaluate.
target_model_path: the path to the model to compare to.
sgf_dif: directory path to write SGF output to.
seed: random seed to... | [
"Evaluate",
"one",
"model",
"against",
"a",
"target",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py#L318-L345 | [
"async",
"def",
"evaluate_model",
"(",
"eval_model_path",
",",
"target_model_path",
",",
"sgf_dir",
",",
"seed",
")",
":",
"lines",
"=",
"await",
"run",
"(",
"'bazel-bin/cc/eval'",
",",
"'--flagfile={}'",
".",
"format",
"(",
"os",
".",
"path",
".",
"join",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | evaluate_trained_model | Evaluate the most recently trained model against the current best model.
Args:
state: the RL loop State instance. | reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py | async def evaluate_trained_model(state):
"""Evaluate the most recently trained model against the current best model.
Args:
state: the RL loop State instance.
"""
return await evaluate_model(
state.train_model_path, state.best_model_path,
os.path.join(fsdb.eval_dir(), state.train_model_name), s... | async def evaluate_trained_model(state):
"""Evaluate the most recently trained model against the current best model.
Args:
state: the RL loop State instance.
"""
return await evaluate_model(
state.train_model_path, state.best_model_path,
os.path.join(fsdb.eval_dir(), state.train_model_name), s... | [
"Evaluate",
"the",
"most",
"recently",
"trained",
"model",
"against",
"the",
"current",
"best",
"model",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py#L348-L357 | [
"async",
"def",
"evaluate_trained_model",
"(",
"state",
")",
":",
"return",
"await",
"evaluate_model",
"(",
"state",
".",
"train_model_path",
",",
"state",
".",
"best_model_path",
",",
"os",
".",
"path",
".",
"join",
"(",
"fsdb",
".",
"eval_dir",
"(",
")",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | rl_loop | The main reinforcement learning (RL) loop. | reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py | def rl_loop():
"""The main reinforcement learning (RL) loop."""
state = State()
if FLAGS.checkpoint_dir:
# Start from a partially trained model.
initialize_from_checkpoint(state)
else:
# Play the first round of selfplay games with a fake model that returns
# random noise. We do this instead of... | def rl_loop():
"""The main reinforcement learning (RL) loop."""
state = State()
if FLAGS.checkpoint_dir:
# Start from a partially trained model.
initialize_from_checkpoint(state)
else:
# Play the first round of selfplay games with a fake model that returns
# random noise. We do this instead of... | [
"The",
"main",
"reinforcement",
"learning",
"(",
"RL",
")",
"loop",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py#L360-L414 | [
"def",
"rl_loop",
"(",
")",
":",
"state",
"=",
"State",
"(",
")",
"if",
"FLAGS",
".",
"checkpoint_dir",
":",
"# Start from a partially trained model.",
"initialize_from_checkpoint",
"(",
"state",
")",
"else",
":",
"# Play the first round of selfplay games with a fake mode... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | main | Run the reinforcement learning loop. | reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py | def main(unused_argv):
"""Run the reinforcement learning loop."""
print('Wiping dir %s' % FLAGS.base_dir, flush=True)
shutil.rmtree(FLAGS.base_dir, ignore_errors=True)
dirs = [fsdb.models_dir(), fsdb.selfplay_dir(), fsdb.holdout_dir(),
fsdb.eval_dir(), fsdb.golden_chunk_dir(), fsdb.working_dir()]
f... | def main(unused_argv):
"""Run the reinforcement learning loop."""
print('Wiping dir %s' % FLAGS.base_dir, flush=True)
shutil.rmtree(FLAGS.base_dir, ignore_errors=True)
dirs = [fsdb.models_dir(), fsdb.selfplay_dir(), fsdb.holdout_dir(),
fsdb.eval_dir(), fsdb.golden_chunk_dir(), fsdb.working_dir()]
f... | [
"Run",
"the",
"reinforcement",
"learning",
"loop",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/ml_perf/reference_implementation.py#L417-L447 | [
"def",
"main",
"(",
"unused_argv",
")",
":",
"print",
"(",
"'Wiping dir %s'",
"%",
"FLAGS",
".",
"base_dir",
",",
"flush",
"=",
"True",
")",
"shutil",
".",
"rmtree",
"(",
"FLAGS",
".",
"base_dir",
",",
"ignore_errors",
"=",
"True",
")",
"dirs",
"=",
"[... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | replay_position | Wrapper for a go.Position which replays its history.
Assumes an empty start position! (i.e. no handicap, and history must be exhaustive.)
Result must be passed in, since a resign cannot be inferred from position
history alone.
for position_w_context in replay_position(position):
print(position... | reinforcement/tensorflow/minigo/go.py | def replay_position(position, result):
"""
Wrapper for a go.Position which replays its history.
Assumes an empty start position! (i.e. no handicap, and history must be exhaustive.)
Result must be passed in, since a resign cannot be inferred from position
history alone.
for position_w_context i... | def replay_position(position, result):
"""
Wrapper for a go.Position which replays its history.
Assumes an empty start position! (i.e. no handicap, and history must be exhaustive.)
Result must be passed in, since a resign cannot be inferred from position
history alone.
for position_w_context i... | [
"Wrapper",
"for",
"a",
"go",
".",
"Position",
"which",
"replays",
"its",
"history",
".",
"Assumes",
"an",
"empty",
"start",
"position!",
"(",
"i",
".",
"e",
".",
"no",
"handicap",
"and",
"history",
"must",
"be",
"exhaustive",
".",
")"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/go.py#L71-L87 | [
"def",
"replay_position",
"(",
"position",
",",
"result",
")",
":",
"assert",
"position",
".",
"n",
"==",
"len",
"(",
"position",
".",
"recent",
")",
",",
"\"Position history is incomplete\"",
"pos",
"=",
"Position",
"(",
"komi",
"=",
"position",
".",
"komi"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | is_koish | Check if c is surrounded on all sides by 1 color, and return that color | reinforcement/tensorflow/minigo/go.py | def is_koish(board, c):
'Check if c is surrounded on all sides by 1 color, and return that color'
if board[c] != EMPTY:
return None
neighbors = {board[n] for n in NEIGHBORS[c]}
if len(neighbors) == 1 and EMPTY not in neighbors:
return list(neighbors)[0]
else:
return None | def is_koish(board, c):
'Check if c is surrounded on all sides by 1 color, and return that color'
if board[c] != EMPTY:
return None
neighbors = {board[n] for n in NEIGHBORS[c]}
if len(neighbors) == 1 and EMPTY not in neighbors:
return list(neighbors)[0]
else:
return None | [
"Check",
"if",
"c",
"is",
"surrounded",
"on",
"all",
"sides",
"by",
"1",
"color",
"and",
"return",
"that",
"color"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/go.py#L106-L114 | [
"def",
"is_koish",
"(",
"board",
",",
"c",
")",
":",
"if",
"board",
"[",
"c",
"]",
"!=",
"EMPTY",
":",
"return",
"None",
"neighbors",
"=",
"{",
"board",
"[",
"n",
"]",
"for",
"n",
"in",
"NEIGHBORS",
"[",
"c",
"]",
"}",
"if",
"len",
"(",
"neighb... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | is_eyeish | Check if c is an eye, for the purpose of restricting MC rollouts. | reinforcement/tensorflow/minigo/go.py | def is_eyeish(board, c):
'Check if c is an eye, for the purpose of restricting MC rollouts.'
# pass is fine.
if c is None:
return
color = is_koish(board, c)
if color is None:
return None
diagonal_faults = 0
diagonals = DIAGONALS[c]
if len(diagonals) < 4:
diagonal_... | def is_eyeish(board, c):
'Check if c is an eye, for the purpose of restricting MC rollouts.'
# pass is fine.
if c is None:
return
color = is_koish(board, c)
if color is None:
return None
diagonal_faults = 0
diagonals = DIAGONALS[c]
if len(diagonals) < 4:
diagonal_... | [
"Check",
"if",
"c",
"is",
"an",
"eye",
"for",
"the",
"purpose",
"of",
"restricting",
"MC",
"rollouts",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/go.py#L117-L135 | [
"def",
"is_eyeish",
"(",
"board",
",",
"c",
")",
":",
"# pass is fine.",
"if",
"c",
"is",
"None",
":",
"return",
"color",
"=",
"is_koish",
"(",
"board",
",",
"c",
")",
"if",
"color",
"is",
"None",
":",
"return",
"None",
"diagonal_faults",
"=",
"0",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | Position.is_move_legal | Checks that a move is on an empty space, not on ko, and not suicide | reinforcement/tensorflow/minigo/go.py | def is_move_legal(self, move):
'Checks that a move is on an empty space, not on ko, and not suicide'
if move is None:
return True
if self.board[move] != EMPTY:
return False
if move == self.ko:
return False
if self.is_move_suicidal(move):
... | def is_move_legal(self, move):
'Checks that a move is on an empty space, not on ko, and not suicide'
if move is None:
return True
if self.board[move] != EMPTY:
return False
if move == self.ko:
return False
if self.is_move_suicidal(move):
... | [
"Checks",
"that",
"a",
"move",
"is",
"on",
"an",
"empty",
"space",
"not",
"on",
"ko",
"and",
"not",
"suicide"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/go.py#L384-L395 | [
"def",
"is_move_legal",
"(",
"self",
",",
"move",
")",
":",
"if",
"move",
"is",
"None",
":",
"return",
"True",
"if",
"self",
".",
"board",
"[",
"move",
"]",
"!=",
"EMPTY",
":",
"return",
"False",
"if",
"move",
"==",
"self",
".",
"ko",
":",
"return"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | Position.all_legal_moves | Returns a np.array of size go.N**2 + 1, with 1 = legal, 0 = illegal | reinforcement/tensorflow/minigo/go.py | def all_legal_moves(self):
'Returns a np.array of size go.N**2 + 1, with 1 = legal, 0 = illegal'
# by default, every move is legal
legal_moves = np.ones([N, N], dtype=np.int8)
# ...unless there is already a stone there
legal_moves[self.board != EMPTY] = 0
# calculate whic... | def all_legal_moves(self):
'Returns a np.array of size go.N**2 + 1, with 1 = legal, 0 = illegal'
# by default, every move is legal
legal_moves = np.ones([N, N], dtype=np.int8)
# ...unless there is already a stone there
legal_moves[self.board != EMPTY] = 0
# calculate whic... | [
"Returns",
"a",
"np",
".",
"array",
"of",
"size",
"go",
".",
"N",
"**",
"2",
"+",
"1",
"with",
"1",
"=",
"legal",
"0",
"=",
"illegal"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/go.py#L397-L424 | [
"def",
"all_legal_moves",
"(",
"self",
")",
":",
"# by default, every move is legal",
"legal_moves",
"=",
"np",
".",
"ones",
"(",
"[",
"N",
",",
"N",
"]",
",",
"dtype",
"=",
"np",
".",
"int8",
")",
"# ...unless there is already a stone there",
"legal_moves",
"["... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | Position.score | Return score from B perspective. If W is winning, score is negative. | reinforcement/tensorflow/minigo/go.py | def score(self):
'Return score from B perspective. If W is winning, score is negative.'
working_board = np.copy(self.board)
while EMPTY in working_board:
unassigned_spaces = np.where(working_board == EMPTY)
c = unassigned_spaces[0][0], unassigned_spaces[1][0]
... | def score(self):
'Return score from B perspective. If W is winning, score is negative.'
working_board = np.copy(self.board)
while EMPTY in working_board:
unassigned_spaces = np.where(working_board == EMPTY)
c = unassigned_spaces[0][0], unassigned_spaces[1][0]
... | [
"Return",
"score",
"from",
"B",
"perspective",
".",
"If",
"W",
"is",
"winning",
"score",
"is",
"negative",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/go.py#L505-L523 | [
"def",
"score",
"(",
"self",
")",
":",
"working_board",
"=",
"np",
".",
"copy",
"(",
"self",
".",
"board",
")",
"while",
"EMPTY",
"in",
"working_board",
":",
"unassigned_spaces",
"=",
"np",
".",
"where",
"(",
"working_board",
"==",
"EMPTY",
")",
"c",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | parse_sgf_to_examples | Return supervised examples from positions
NOTE: last move is not played because no p.next_move after. | reinforcement/tensorflow/minigo/oneoffs/oneoff_utils.py | def parse_sgf_to_examples(sgf_path):
"""Return supervised examples from positions
NOTE: last move is not played because no p.next_move after.
"""
return zip(*[(p.position, p.next_move, p.result)
for p in sgf_wrapper.replay_sgf_file(sgf_path)]) | def parse_sgf_to_examples(sgf_path):
"""Return supervised examples from positions
NOTE: last move is not played because no p.next_move after.
"""
return zip(*[(p.position, p.next_move, p.result)
for p in sgf_wrapper.replay_sgf_file(sgf_path)]) | [
"Return",
"supervised",
"examples",
"from",
"positions"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/oneoff_utils.py#L34-L41 | [
"def",
"parse_sgf_to_examples",
"(",
"sgf_path",
")",
":",
"return",
"zip",
"(",
"*",
"[",
"(",
"p",
".",
"position",
",",
"p",
".",
"next_move",
",",
"p",
".",
"result",
")",
"for",
"p",
"in",
"sgf_wrapper",
".",
"replay_sgf_file",
"(",
"sgf_path",
")... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | find_and_filter_sgf_files | Finds all sgf files in base_dir with year >= min_year and komi | reinforcement/tensorflow/minigo/oneoffs/oneoff_utils.py | def find_and_filter_sgf_files(base_dir, min_year=None, komi=None):
"""Finds all sgf files in base_dir with year >= min_year and komi"""
sgf_files = []
for dirpath, dirnames, filenames in os.walk(base_dir):
for filename in filenames:
if filename.endswith('.sgf'):
path = os... | def find_and_filter_sgf_files(base_dir, min_year=None, komi=None):
"""Finds all sgf files in base_dir with year >= min_year and komi"""
sgf_files = []
for dirpath, dirnames, filenames in os.walk(base_dir):
for filename in filenames:
if filename.endswith('.sgf'):
path = os... | [
"Finds",
"all",
"sgf",
"files",
"in",
"base_dir",
"with",
"year",
">",
"=",
"min_year",
"and",
"komi"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/oneoff_utils.py#L77-L95 | [
"def",
"find_and_filter_sgf_files",
"(",
"base_dir",
",",
"min_year",
"=",
"None",
",",
"komi",
"=",
"None",
")",
":",
"sgf_files",
"=",
"[",
"]",
"for",
"dirpath",
",",
"dirnames",
",",
"filenames",
"in",
"os",
".",
"walk",
"(",
"base_dir",
")",
":",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | get_model_paths | Returns all model paths in the model_dir. | reinforcement/tensorflow/minigo/oneoffs/oneoff_utils.py | def get_model_paths(model_dir):
"""Returns all model paths in the model_dir."""
all_models = gfile.Glob(os.path.join(model_dir, '*.meta'))
model_filenames = [os.path.basename(m) for m in all_models]
model_numbers_names = [
(shipname.detect_model_num(m), shipname.detect_model_name(m))
for... | def get_model_paths(model_dir):
"""Returns all model paths in the model_dir."""
all_models = gfile.Glob(os.path.join(model_dir, '*.meta'))
model_filenames = [os.path.basename(m) for m in all_models]
model_numbers_names = [
(shipname.detect_model_num(m), shipname.detect_model_name(m))
for... | [
"Returns",
"all",
"model",
"paths",
"in",
"the",
"model_dir",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/oneoff_utils.py#L98-L106 | [
"def",
"get_model_paths",
"(",
"model_dir",
")",
":",
"all_models",
"=",
"gfile",
".",
"Glob",
"(",
"os",
".",
"path",
".",
"join",
"(",
"model_dir",
",",
"'*.meta'",
")",
")",
"model_filenames",
"=",
"[",
"os",
".",
"path",
".",
"basename",
"(",
"m",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | make_sgf | Turn a game into SGF.
Doesn't handle handicap games or positions with incomplete history.
Args:
move_history: iterable of PlayerMoves
result_string: "B+R", "W+0.5", etc.
comments: iterable of string/None. Will be zipped with move_history. | reinforcement/tensorflow/minigo/sgf_wrapper.py | def make_sgf(
move_history,
result_string,
ruleset="Chinese",
komi=7.5,
white_name=PROGRAM_IDENTIFIER,
black_name=PROGRAM_IDENTIFIER,
comments=[]
):
"""Turn a game into SGF.
Doesn't handle handicap games or positions with incomplete history.
Args:
move_history: iterable... | def make_sgf(
move_history,
result_string,
ruleset="Chinese",
komi=7.5,
white_name=PROGRAM_IDENTIFIER,
black_name=PROGRAM_IDENTIFIER,
comments=[]
):
"""Turn a game into SGF.
Doesn't handle handicap games or positions with incomplete history.
Args:
move_history: iterable... | [
"Turn",
"a",
"game",
"into",
"SGF",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/sgf_wrapper.py#L54-L76 | [
"def",
"make_sgf",
"(",
"move_history",
",",
"result_string",
",",
"ruleset",
"=",
"\"Chinese\"",
",",
"komi",
"=",
"7.5",
",",
"white_name",
"=",
"PROGRAM_IDENTIFIER",
",",
"black_name",
"=",
"PROGRAM_IDENTIFIER",
",",
"comments",
"=",
"[",
"]",
")",
":",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | handle_node | A node can either add B+W stones, play as B, or play as W. | reinforcement/tensorflow/minigo/sgf_wrapper.py | def handle_node(pos, node):
'A node can either add B+W stones, play as B, or play as W.'
props = node.properties
black_stones_added = [coords.from_sgf(
c) for c in props.get('AB', [])]
white_stones_added = [coords.from_sgf(
c) for c in props.get('AW', [])]
if black_stones_added or wh... | def handle_node(pos, node):
'A node can either add B+W stones, play as B, or play as W.'
props = node.properties
black_stones_added = [coords.from_sgf(
c) for c in props.get('AB', [])]
white_stones_added = [coords.from_sgf(
c) for c in props.get('AW', [])]
if black_stones_added or wh... | [
"A",
"node",
"can",
"either",
"add",
"B",
"+",
"W",
"stones",
"play",
"as",
"B",
"or",
"play",
"as",
"W",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/sgf_wrapper.py#L93-L110 | [
"def",
"handle_node",
"(",
"pos",
",",
"node",
")",
":",
"props",
"=",
"node",
".",
"properties",
"black_stones_added",
"=",
"[",
"coords",
".",
"from_sgf",
"(",
"c",
")",
"for",
"c",
"in",
"props",
".",
"get",
"(",
"'AB'",
",",
"[",
"]",
")",
"]",... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | replay_sgf | Wrapper for sgf files, returning go.PositionWithContext instances.
It does NOT return the very final position, as there is no follow up.
To get the final position, call pwc.position.play_move(pwc.next_move)
on the last PositionWithContext returned.
Example usage:
with open(filename) as f:
... | reinforcement/tensorflow/minigo/sgf_wrapper.py | def replay_sgf(sgf_contents):
"""Wrapper for sgf files, returning go.PositionWithContext instances.
It does NOT return the very final position, as there is no follow up.
To get the final position, call pwc.position.play_move(pwc.next_move)
on the last PositionWithContext returned.
Example usage:
... | def replay_sgf(sgf_contents):
"""Wrapper for sgf files, returning go.PositionWithContext instances.
It does NOT return the very final position, as there is no follow up.
To get the final position, call pwc.position.play_move(pwc.next_move)
on the last PositionWithContext returned.
Example usage:
... | [
"Wrapper",
"for",
"sgf",
"files",
"returning",
"go",
".",
"PositionWithContext",
"instances",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/sgf_wrapper.py#L142-L170 | [
"def",
"replay_sgf",
"(",
"sgf_contents",
")",
":",
"root_node",
"=",
"get_sgf_root_node",
"(",
"sgf_contents",
")",
"props",
"=",
"root_node",
".",
"properties",
"assert",
"int",
"(",
"sgf_prop",
"(",
"props",
".",
"get",
"(",
"'GM'",
",",
"[",
"'1'",
"]"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | Seq2Seq.decode | Applies the decoder to inputs, given the context from the encoder.
:param inputs: tensor with inputs (batch, seq_len) if 'batch_first'
else (seq_len, batch)
:param context: context from the encoder
:param inference: if True inference mode, if False training mode | rnn_translator/pytorch/seq2seq/models/seq2seq_base.py | def decode(self, inputs, context, inference=False):
"""
Applies the decoder to inputs, given the context from the encoder.
:param inputs: tensor with inputs (batch, seq_len) if 'batch_first'
else (seq_len, batch)
:param context: context from the encoder
:param infere... | def decode(self, inputs, context, inference=False):
"""
Applies the decoder to inputs, given the context from the encoder.
:param inputs: tensor with inputs (batch, seq_len) if 'batch_first'
else (seq_len, batch)
:param context: context from the encoder
:param infere... | [
"Applies",
"the",
"decoder",
"to",
"inputs",
"given",
"the",
"context",
"from",
"the",
"encoder",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/models/seq2seq_base.py#L33-L42 | [
"def",
"decode",
"(",
"self",
",",
"inputs",
",",
"context",
",",
"inference",
"=",
"False",
")",
":",
"return",
"self",
".",
"decoder",
"(",
"inputs",
",",
"context",
",",
"inference",
")"
] | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | Seq2Seq.generate | Autoregressive generator, works with SequenceGenerator class.
Executes decoder (in inference mode), applies log_softmax and topK for
inference with beam search decoding.
:param inputs: tensor with inputs to the decoder
:param context: context from the encoder
:param beam_size: b... | rnn_translator/pytorch/seq2seq/models/seq2seq_base.py | def generate(self, inputs, context, beam_size):
"""
Autoregressive generator, works with SequenceGenerator class.
Executes decoder (in inference mode), applies log_softmax and topK for
inference with beam search decoding.
:param inputs: tensor with inputs to the decoder
... | def generate(self, inputs, context, beam_size):
"""
Autoregressive generator, works with SequenceGenerator class.
Executes decoder (in inference mode), applies log_softmax and topK for
inference with beam search decoding.
:param inputs: tensor with inputs to the decoder
... | [
"Autoregressive",
"generator",
"works",
"with",
"SequenceGenerator",
"class",
".",
"Executes",
"decoder",
"(",
"in",
"inference",
"mode",
")",
"applies",
"log_softmax",
"and",
"topK",
"for",
"inference",
"with",
"beam",
"search",
"decoding",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/models/seq2seq_base.py#L44-L64 | [
"def",
"generate",
"(",
"self",
",",
"inputs",
",",
"context",
",",
"beam_size",
")",
":",
"logits",
",",
"scores",
",",
"new_context",
"=",
"self",
".",
"decode",
"(",
"inputs",
",",
"context",
",",
"True",
")",
"logprobs",
"=",
"log_softmax",
"(",
"l... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | Translator.build_eval_path | Appends index of the current epoch and index of the current iteration
to the name of the file with results.
:param epoch: index of the current epoch
:param iteration: index of the current iteration | rnn_translator/pytorch/seq2seq/inference/inference.py | def build_eval_path(self, epoch, iteration):
"""
Appends index of the current epoch and index of the current iteration
to the name of the file with results.
:param epoch: index of the current epoch
:param iteration: index of the current iteration
"""
if iteration... | def build_eval_path(self, epoch, iteration):
"""
Appends index of the current epoch and index of the current iteration
to the name of the file with results.
:param epoch: index of the current epoch
:param iteration: index of the current iteration
"""
if iteration... | [
"Appends",
"index",
"of",
"the",
"current",
"epoch",
"and",
"index",
"of",
"the",
"current",
"iteration",
"to",
"the",
"name",
"of",
"the",
"file",
"with",
"results",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/inference/inference.py#L72-L85 | [
"def",
"build_eval_path",
"(",
"self",
",",
"epoch",
",",
"iteration",
")",
":",
"if",
"iteration",
"is",
"not",
"None",
":",
"eval_fname",
"=",
"f'eval_epoch_{epoch}_iter_{iteration}'",
"else",
":",
"eval_fname",
"=",
"f'eval_epoch_{epoch}'",
"eval_path",
"=",
"o... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | Translator.run | Runs translation on test dataset.
:param calc_bleu: if True compares results with reference and computes
BLEU score
:param epoch: index of the current epoch
:param iteration: index of the current iteration
:param eval_path: path to the file for saving results
:param ... | rnn_translator/pytorch/seq2seq/inference/inference.py | def run(self, calc_bleu=True, epoch=None, iteration=None, eval_path=None,
summary=False, reference_path=None):
"""
Runs translation on test dataset.
:param calc_bleu: if True compares results with reference and computes
BLEU score
:param epoch: index of the curre... | def run(self, calc_bleu=True, epoch=None, iteration=None, eval_path=None,
summary=False, reference_path=None):
"""
Runs translation on test dataset.
:param calc_bleu: if True compares results with reference and computes
BLEU score
:param epoch: index of the curre... | [
"Runs",
"translation",
"on",
"test",
"dataset",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/inference/inference.py#L87-L145 | [
"def",
"run",
"(",
"self",
",",
"calc_bleu",
"=",
"True",
",",
"epoch",
"=",
"None",
",",
"iteration",
"=",
"None",
",",
"eval_path",
"=",
"None",
",",
"summary",
"=",
"False",
",",
"reference_path",
"=",
"None",
")",
":",
"if",
"self",
".",
"cuda",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | Translator.evaluate | Runs evaluation on test dataset.
:param epoch: index of the current epoch
:param iteration: index of the current iteration
:param summary: if True prints summary | rnn_translator/pytorch/seq2seq/inference/inference.py | def evaluate(self, epoch, iteration, summary):
"""
Runs evaluation on test dataset.
:param epoch: index of the current epoch
:param iteration: index of the current iteration
:param summary: if True prints summary
"""
batch_time = AverageMeter(False)
tot_t... | def evaluate(self, epoch, iteration, summary):
"""
Runs evaluation on test dataset.
:param epoch: index of the current epoch
:param iteration: index of the current iteration
:param summary: if True prints summary
"""
batch_time = AverageMeter(False)
tot_t... | [
"Runs",
"evaluation",
"on",
"test",
"dataset",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/inference/inference.py#L147-L255 | [
"def",
"evaluate",
"(",
"self",
",",
"epoch",
",",
"iteration",
",",
"summary",
")",
":",
"batch_time",
"=",
"AverageMeter",
"(",
"False",
")",
"tot_tok_per_sec",
"=",
"AverageMeter",
"(",
"False",
")",
"iterations",
"=",
"AverageMeter",
"(",
"False",
")",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | Translator.run_detokenizer | Executes moses detokenizer on eval_path file and saves result to
eval_path + ".detok" file.
:param eval_path: path to the tokenized input | rnn_translator/pytorch/seq2seq/inference/inference.py | def run_detokenizer(self, eval_path):
"""
Executes moses detokenizer on eval_path file and saves result to
eval_path + ".detok" file.
:param eval_path: path to the tokenized input
"""
logging.info('Running detokenizer')
detok_path = os.path.join(self.dataset_dir,... | def run_detokenizer(self, eval_path):
"""
Executes moses detokenizer on eval_path file and saves result to
eval_path + ".detok" file.
:param eval_path: path to the tokenized input
"""
logging.info('Running detokenizer')
detok_path = os.path.join(self.dataset_dir,... | [
"Executes",
"moses",
"detokenizer",
"on",
"eval_path",
"file",
"and",
"saves",
"result",
"to",
"eval_path",
"+",
".",
"detok",
"file",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/inference/inference.py#L257-L271 | [
"def",
"run_detokenizer",
"(",
"self",
",",
"eval_path",
")",
":",
"logging",
".",
"info",
"(",
"'Running detokenizer'",
")",
"detok_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"self",
".",
"dataset_dir",
",",
"config",
".",
"DETOKENIZER",
")",
"detok... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | Translator.run_sacrebleu | Executes sacrebleu and returns BLEU score.
:param detok_eval_path: path to the test file
:param reference_path: path to the reference file | rnn_translator/pytorch/seq2seq/inference/inference.py | def run_sacrebleu(self, detok_eval_path, reference_path):
"""
Executes sacrebleu and returns BLEU score.
:param detok_eval_path: path to the test file
:param reference_path: path to the reference file
"""
if reference_path is None:
reference_path = os.path.jo... | def run_sacrebleu(self, detok_eval_path, reference_path):
"""
Executes sacrebleu and returns BLEU score.
:param detok_eval_path: path to the test file
:param reference_path: path to the reference file
"""
if reference_path is None:
reference_path = os.path.jo... | [
"Executes",
"sacrebleu",
"and",
"returns",
"BLEU",
"score",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/inference/inference.py#L273-L289 | [
"def",
"run_sacrebleu",
"(",
"self",
",",
"detok_eval_path",
",",
"reference_path",
")",
":",
"if",
"reference_path",
"is",
"None",
":",
"reference_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"self",
".",
"dataset_dir",
",",
"config",
".",
"TGT_TEST_TAR... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | configure | Configures cloud logging
This is called for all main calls. If a $LOGGING_PROJECT is environment
variable configured, then STDERR and STDOUT are redirected to cloud
logging. | reinforcement/tensorflow/minigo/cloud_logging.py | def configure(project=LOGGING_PROJECT):
"""Configures cloud logging
This is called for all main calls. If a $LOGGING_PROJECT is environment
variable configured, then STDERR and STDOUT are redirected to cloud
logging.
"""
if not project:
sys.stderr.write('!! Error: The $LOGGING_PROJECT e... | def configure(project=LOGGING_PROJECT):
"""Configures cloud logging
This is called for all main calls. If a $LOGGING_PROJECT is environment
variable configured, then STDERR and STDOUT are redirected to cloud
logging.
"""
if not project:
sys.stderr.write('!! Error: The $LOGGING_PROJECT e... | [
"Configures",
"cloud",
"logging"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/cloud_logging.py#L28-L48 | [
"def",
"configure",
"(",
"project",
"=",
"LOGGING_PROJECT",
")",
":",
"if",
"not",
"project",
":",
"sys",
".",
"stderr",
".",
"write",
"(",
"'!! Error: The $LOGGING_PROJECT enviroment '",
"'variable is required in order to set up cloud logging. '",
"'Cloud logging is disabled... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | align_and_update_state_dicts | Strategy: suppose that the models that we will create will have prefixes appended
to each of its keys, for example due to an extra level of nesting that the original
pre-trained weights from ImageNet won't contain. For example, model.state_dict()
might return backbone[0].body.res2.conv1.weight, while the pr... | object_detection/pytorch/maskrcnn_benchmark/utils/model_serialization.py | def align_and_update_state_dicts(model_state_dict, loaded_state_dict):
"""
Strategy: suppose that the models that we will create will have prefixes appended
to each of its keys, for example due to an extra level of nesting that the original
pre-trained weights from ImageNet won't contain. For example, m... | def align_and_update_state_dicts(model_state_dict, loaded_state_dict):
"""
Strategy: suppose that the models that we will create will have prefixes appended
to each of its keys, for example due to an extra level of nesting that the original
pre-trained weights from ImageNet won't contain. For example, m... | [
"Strategy",
":",
"suppose",
"that",
"the",
"models",
"that",
"we",
"will",
"create",
"will",
"have",
"prefixes",
"appended",
"to",
"each",
"of",
"its",
"keys",
"for",
"example",
"due",
"to",
"an",
"extra",
"level",
"of",
"nesting",
"that",
"the",
"original... | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/utils/model_serialization.py#L10-L58 | [
"def",
"align_and_update_state_dicts",
"(",
"model_state_dict",
",",
"loaded_state_dict",
")",
":",
"current_keys",
"=",
"sorted",
"(",
"list",
"(",
"model_state_dict",
".",
"keys",
"(",
")",
")",
")",
"loaded_keys",
"=",
"sorted",
"(",
"list",
"(",
"loaded_stat... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | compute_update_ratio | Compute the ratio of gradient norm to weight norm. | reinforcement/tensorflow/minigo/train.py | def compute_update_ratio(weight_tensors, before_weights, after_weights):
"""Compute the ratio of gradient norm to weight norm."""
deltas = [after - before for after,
before in zip(after_weights, before_weights)]
delta_norms = [np.linalg.norm(d.ravel()) for d in deltas]
weight_norms = [np.l... | def compute_update_ratio(weight_tensors, before_weights, after_weights):
"""Compute the ratio of gradient norm to weight norm."""
deltas = [after - before for after,
before in zip(after_weights, before_weights)]
delta_norms = [np.linalg.norm(d.ravel()) for d in deltas]
weight_norms = [np.l... | [
"Compute",
"the",
"ratio",
"of",
"gradient",
"norm",
"to",
"weight",
"norm",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/train.py#L93-L104 | [
"def",
"compute_update_ratio",
"(",
"weight_tensors",
",",
"before_weights",
",",
"after_weights",
")",
":",
"deltas",
"=",
"[",
"after",
"-",
"before",
"for",
"after",
",",
"before",
"in",
"zip",
"(",
"after_weights",
",",
"before_weights",
")",
"]",
"delta_n... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | train | Train on examples. | reinforcement/tensorflow/minigo/train.py | def train(*tf_records: "Records to train on"):
"""Train on examples."""
tf.logging.set_verbosity(tf.logging.INFO)
estimator = dual_net.get_estimator()
effective_batch_size = FLAGS.train_batch_size
if FLAGS.use_tpu:
effective_batch_size *= FLAGS.num_tpu_cores
if FLAGS.use_tpu:
i... | def train(*tf_records: "Records to train on"):
"""Train on examples."""
tf.logging.set_verbosity(tf.logging.INFO)
estimator = dual_net.get_estimator()
effective_batch_size = FLAGS.train_batch_size
if FLAGS.use_tpu:
effective_batch_size *= FLAGS.num_tpu_cores
if FLAGS.use_tpu:
i... | [
"Train",
"on",
"examples",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/train.py#L142-L207 | [
"def",
"train",
"(",
"*",
"tf_records",
":",
"\"Records to train on\"",
")",
":",
"tf",
".",
"logging",
".",
"set_verbosity",
"(",
"tf",
".",
"logging",
".",
"INFO",
")",
"estimator",
"=",
"dual_net",
".",
"get_estimator",
"(",
")",
"effective_batch_size",
"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | main | Train on examples and export the updated model weights. | reinforcement/tensorflow/minigo/train.py | def main(argv):
"""Train on examples and export the updated model weights."""
tf_records = argv[1:]
logging.info("Training on %s records: %s to %s",
len(tf_records), tf_records[0], tf_records[-1])
with utils.logged_timer("Training"):
train(*tf_records)
if FLAGS.export_path:
... | def main(argv):
"""Train on examples and export the updated model weights."""
tf_records = argv[1:]
logging.info("Training on %s records: %s to %s",
len(tf_records), tf_records[0], tf_records[-1])
with utils.logged_timer("Training"):
train(*tf_records)
if FLAGS.export_path:
... | [
"Train",
"on",
"examples",
"and",
"export",
"the",
"updated",
"model",
"weights",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/train.py#L210-L223 | [
"def",
"main",
"(",
"argv",
")",
":",
"tf_records",
"=",
"argv",
"[",
"1",
":",
"]",
"logging",
".",
"info",
"(",
"\"Training on %s records: %s to %s\"",
",",
"len",
"(",
"tf_records",
")",
",",
"tf_records",
"[",
"0",
"]",
",",
"tf_records",
"[",
"-",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | main | Freeze a model to a GraphDef proto. | reinforcement/tensorflow/minigo/freeze_graph.py | def main(unused_argv):
"""Freeze a model to a GraphDef proto."""
if FLAGS.use_tpu:
dual_net.freeze_graph_tpu(FLAGS.model_path)
else:
dual_net.freeze_graph(FLAGS.model_path) | def main(unused_argv):
"""Freeze a model to a GraphDef proto."""
if FLAGS.use_tpu:
dual_net.freeze_graph_tpu(FLAGS.model_path)
else:
dual_net.freeze_graph(FLAGS.model_path) | [
"Freeze",
"a",
"model",
"to",
"a",
"GraphDef",
"proto",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/freeze_graph.py#L27-L32 | [
"def",
"main",
"(",
"unused_argv",
")",
":",
"if",
"FLAGS",
".",
"use_tpu",
":",
"dual_net",
".",
"freeze_graph_tpu",
"(",
"FLAGS",
".",
"model_path",
")",
"else",
":",
"dual_net",
".",
"freeze_graph",
"(",
"FLAGS",
".",
"model_path",
")"
] | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | grouper | Itertools recipe
>>> list(grouper(3, iter('ABCDEFG')))
[['A', 'B', 'C'], ['D', 'E', 'F'], ['G']] | reinforcement/tensorflow/minigo/oneoffs/sharp_positions.py | def grouper(n, iterable):
"""Itertools recipe
>>> list(grouper(3, iter('ABCDEFG')))
[['A', 'B', 'C'], ['D', 'E', 'F'], ['G']]
"""
return (iterable[i:i + n] for i in range(0, len(iterable), n)) | def grouper(n, iterable):
"""Itertools recipe
>>> list(grouper(3, iter('ABCDEFG')))
[['A', 'B', 'C'], ['D', 'E', 'F'], ['G']]
"""
return (iterable[i:i + n] for i in range(0, len(iterable), n)) | [
"Itertools",
"recipe",
">>>",
"list",
"(",
"grouper",
"(",
"3",
"iter",
"(",
"ABCDEFG",
")))",
"[[",
"A",
"B",
"C",
"]",
"[",
"D",
"E",
"F",
"]",
"[",
"G",
"]]"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/sharp_positions.py#L91-L96 | [
"def",
"grouper",
"(",
"n",
",",
"iterable",
")",
":",
"return",
"(",
"iterable",
"[",
"i",
":",
"i",
"+",
"n",
"]",
"for",
"i",
"in",
"range",
"(",
"0",
",",
"len",
"(",
"iterable",
")",
",",
"n",
")",
")"
] | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | subsample | Sample num_positions postions from each game in sgf_dir
Usage:
python3 sharp_positions.py subsample --num_positions 10 --sgf_dir data/s
NOTE(sethtroisi): see link for a script to truncate SGFs at move number
https://github.com/sethtroisi/go-scripts | reinforcement/tensorflow/minigo/oneoffs/sharp_positions.py | def subsample():
"""Sample num_positions postions from each game in sgf_dir
Usage:
python3 sharp_positions.py subsample --num_positions 10 --sgf_dir data/s
NOTE(sethtroisi): see link for a script to truncate SGFs at move number
https://github.com/sethtroisi/go-scripts
"""
sgf_file... | def subsample():
"""Sample num_positions postions from each game in sgf_dir
Usage:
python3 sharp_positions.py subsample --num_positions 10 --sgf_dir data/s
NOTE(sethtroisi): see link for a script to truncate SGFs at move number
https://github.com/sethtroisi/go-scripts
"""
sgf_file... | [
"Sample",
"num_positions",
"postions",
"from",
"each",
"game",
"in",
"sgf_dir"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/sharp_positions.py#L115-L143 | [
"def",
"subsample",
"(",
")",
":",
"sgf_files",
"=",
"oneoff_utils",
".",
"find_and_filter_sgf_files",
"(",
"FLAGS",
".",
"sgf_dir",
",",
"None",
",",
"None",
")",
"with",
"open",
"(",
"FLAG",
".",
"collection",
",",
"'w'",
")",
"as",
"collection",
":",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | evaluate | Get Policy and Value for each network, for each position
Usage:
python3 sharp_positions.py evaluate --sgf_dir data/s --model_dir models/ | reinforcement/tensorflow/minigo/oneoffs/sharp_positions.py | def evaluate():
"""Get Policy and Value for each network, for each position
Usage:
python3 sharp_positions.py evaluate --sgf_dir data/s --model_dir models/
"""
def short_str(v):
if isinstance(v, float):
return "{.3f}".format(v)
return str(v)
# Load positons
... | def evaluate():
"""Get Policy and Value for each network, for each position
Usage:
python3 sharp_positions.py evaluate --sgf_dir data/s --model_dir models/
"""
def short_str(v):
if isinstance(v, float):
return "{.3f}".format(v)
return str(v)
# Load positons
... | [
"Get",
"Policy",
"and",
"Value",
"for",
"each",
"network",
"for",
"each",
"position"
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/sharp_positions.py#L146-L206 | [
"def",
"evaluate",
"(",
")",
":",
"def",
"short_str",
"(",
"v",
")",
":",
"if",
"isinstance",
"(",
"v",
",",
"float",
")",
":",
"return",
"\"{.3f}\"",
".",
"format",
"(",
"v",
")",
"return",
"str",
"(",
"v",
")",
"# Load positons",
"sgf_names",
",",
... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | minimize | Find a subset of problems that maximal explains rating.
Usage:
python3 sharp_positions.py minimize \
--model_dir models --sgf_dir data/s
--rating_json ratings.json --results results.csv | reinforcement/tensorflow/minigo/oneoffs/sharp_positions.py | def minimize():
"""Find a subset of problems that maximal explains rating.
Usage:
python3 sharp_positions.py minimize \
--model_dir models --sgf_dir data/s
--rating_json ratings.json --results results.csv
"""
########################### HYPER PARAMETERS #################... | def minimize():
"""Find a subset of problems that maximal explains rating.
Usage:
python3 sharp_positions.py minimize \
--model_dir models --sgf_dir data/s
--rating_json ratings.json --results results.csv
"""
########################### HYPER PARAMETERS #################... | [
"Find",
"a",
"subset",
"of",
"problems",
"that",
"maximal",
"explains",
"rating",
"."
] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/oneoffs/sharp_positions.py#L209-L438 | [
"def",
"minimize",
"(",
")",
":",
"########################### HYPER PARAMETERS ###############################",
"# Stop when r2 is this much worse than full set of positions",
"r2_stopping_percent",
"=",
"0.96",
"# for this many iterations",
"stopping_iterations",
"=",
"5",
"# Limit SV... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.