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train | _generate_subtokens_with_target_vocab_size | Generate subtoken vocabulary close to the target size. | translation/tensorflow/transformer/utils/tokenizer.py | def _generate_subtokens_with_target_vocab_size(
token_counts, alphabet, target_size, threshold, min_count=None,
reserved_tokens=None):
"""Generate subtoken vocabulary close to the target size."""
if reserved_tokens is None:
reserved_tokens = RESERVED_TOKENS
if min_count is not None:
tf.logging.in... | def _generate_subtokens_with_target_vocab_size(
token_counts, alphabet, target_size, threshold, min_count=None,
reserved_tokens=None):
"""Generate subtoken vocabulary close to the target size."""
if reserved_tokens is None:
reserved_tokens = RESERVED_TOKENS
if min_count is not None:
tf.logging.in... | [
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train | _generate_alphabet_dict | Create set of characters that appear in any element in the iterable. | translation/tensorflow/transformer/utils/tokenizer.py | def _generate_alphabet_dict(iterable, reserved_tokens=None):
"""Create set of characters that appear in any element in the iterable."""
if reserved_tokens is None:
reserved_tokens = RESERVED_TOKENS
alphabet = {c for token in iterable for c in token}
alphabet |= {c for token in reserved_tokens for c in token... | def _generate_alphabet_dict(iterable, reserved_tokens=None):
"""Create set of characters that appear in any element in the iterable."""
if reserved_tokens is None:
reserved_tokens = RESERVED_TOKENS
alphabet = {c for token in iterable for c in token}
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train | _count_and_gen_subtokens | Count number of times subtokens appear, and generate new subtokens.
Args:
token_counts: dict mapping tokens to the number of times they appear in the
original files.
alphabet: list of allowed characters. Used to escape the tokens, which
guarantees that all tokens can be split into subtokens.
... | translation/tensorflow/transformer/utils/tokenizer.py | def _count_and_gen_subtokens(
token_counts, alphabet, subtoken_dict, max_subtoken_length):
"""Count number of times subtokens appear, and generate new subtokens.
Args:
token_counts: dict mapping tokens to the number of times they appear in the
original files.
alphabet: list of allowed characters.... | def _count_and_gen_subtokens(
token_counts, alphabet, subtoken_dict, max_subtoken_length):
"""Count number of times subtokens appear, and generate new subtokens.
Args:
token_counts: dict mapping tokens to the number of times they appear in the
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train | _filter_and_bucket_subtokens | Return a bucketed list of subtokens that are filtered by count.
Args:
subtoken_counts: defaultdict mapping subtokens to their counts
min_count: int count used to filter subtokens
Returns:
List of subtoken sets, where subtokens in set i have the same length=i. | translation/tensorflow/transformer/utils/tokenizer.py | def _filter_and_bucket_subtokens(subtoken_counts, min_count):
"""Return a bucketed list of subtokens that are filtered by count.
Args:
subtoken_counts: defaultdict mapping subtokens to their counts
min_count: int count used to filter subtokens
Returns:
List of subtoken sets, where subtokens in set i... | def _filter_and_bucket_subtokens(subtoken_counts, min_count):
"""Return a bucketed list of subtokens that are filtered by count.
Args:
subtoken_counts: defaultdict mapping subtokens to their counts
min_count: int count used to filter subtokens
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train | _gen_new_subtoken_list | Generate candidate subtokens ordered by count, and new max subtoken length.
Add subtokens to the candiate list in order of length (longest subtokens
first). When a subtoken is added, the counts of each of its prefixes are
decreased. Prefixes that don't appear much outside the subtoken are not added
to the cand... | translation/tensorflow/transformer/utils/tokenizer.py | def _gen_new_subtoken_list(
subtoken_counts, min_count, alphabet, reserved_tokens=None):
"""Generate candidate subtokens ordered by count, and new max subtoken length.
Add subtokens to the candiate list in order of length (longest subtokens
first). When a subtoken is added, the counts of each of its prefixes... | def _gen_new_subtoken_list(
subtoken_counts, min_count, alphabet, reserved_tokens=None):
"""Generate candidate subtokens ordered by count, and new max subtoken length.
Add subtokens to the candiate list in order of length (longest subtokens
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train | _generate_subtokens | Create a list of subtokens in decreasing order of frequency.
Args:
token_counts: dict mapping str tokens -> int count
alphabet: set of characters
min_count: int minimum number of times a subtoken must appear before it is
added to the vocabulary.
num_iterations: int number of iterations to gener... | translation/tensorflow/transformer/utils/tokenizer.py | def _generate_subtokens(
token_counts, alphabet, min_count, num_iterations=4,
reserved_tokens=None):
"""Create a list of subtokens in decreasing order of frequency.
Args:
token_counts: dict mapping str tokens -> int count
alphabet: set of characters
min_count: int minimum number of times a subt... | def _generate_subtokens(
token_counts, alphabet, min_count, num_iterations=4,
reserved_tokens=None):
"""Create a list of subtokens in decreasing order of frequency.
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token_counts: dict mapping str tokens -> int count
alphabet: set of characters
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train | Subtokenizer.init_from_files | Create subtoken vocabulary based on files, and save vocab to file.
Args:
vocab_file: String name of vocab file to store subtoken vocabulary.
files: List of file paths that will be used to generate vocabulary.
target_vocab_size: target vocabulary size to generate.
threshold: int threshold of... | translation/tensorflow/transformer/utils/tokenizer.py | def init_from_files(
vocab_file, files, target_vocab_size, threshold, min_count=None,
file_byte_limit=1e6, reserved_tokens=None):
"""Create subtoken vocabulary based on files, and save vocab to file.
Args:
vocab_file: String name of vocab file to store subtoken vocabulary.
files: List o... | def init_from_files(
vocab_file, files, target_vocab_size, threshold, min_count=None,
file_byte_limit=1e6, reserved_tokens=None):
"""Create subtoken vocabulary based on files, and save vocab to file.
Args:
vocab_file: String name of vocab file to store subtoken vocabulary.
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train | Subtokenizer.encode | Encodes a string into a list of int subtoken ids. | translation/tensorflow/transformer/utils/tokenizer.py | def encode(self, raw_string, add_eos=False):
"""Encodes a string into a list of int subtoken ids."""
ret = []
tokens = _split_string_to_tokens(_native_to_unicode(raw_string))
for token in tokens:
ret.extend(self._token_to_subtoken_ids(token))
if add_eos:
ret.append(EOS_ID)
return ret | def encode(self, raw_string, add_eos=False):
"""Encodes a string into a list of int subtoken ids."""
ret = []
tokens = _split_string_to_tokens(_native_to_unicode(raw_string))
for token in tokens:
ret.extend(self._token_to_subtoken_ids(token))
if add_eos:
ret.append(EOS_ID)
return ret | [
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train | Subtokenizer._token_to_subtoken_ids | Encode a single token into a list of subtoken ids. | translation/tensorflow/transformer/utils/tokenizer.py | def _token_to_subtoken_ids(self, token):
"""Encode a single token into a list of subtoken ids."""
cache_location = hash(token) % self._cache_size
cache_key, cache_value = self._cache[cache_location]
if cache_key == token:
return cache_value
ret = _split_token_to_subtokens(
_escape_tok... | def _token_to_subtoken_ids(self, token):
"""Encode a single token into a list of subtoken ids."""
cache_location = hash(token) % self._cache_size
cache_key, cache_value = self._cache[cache_location]
if cache_key == token:
return cache_value
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train | Subtokenizer.decode | Converts list of int subtokens ids into a string. | translation/tensorflow/transformer/utils/tokenizer.py | def decode(self, subtokens):
"""Converts list of int subtokens ids into a string."""
if isinstance(subtokens, np.ndarray):
# Note that list(subtokens) converts subtokens to a python list, but the
# items remain as np.int32. This converts both the array and its items.
subtokens = subtokens.toli... | def decode(self, subtokens):
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train | Subtokenizer._subtoken_ids_to_tokens | Convert list of int subtoken ids to a list of string tokens. | translation/tensorflow/transformer/utils/tokenizer.py | def _subtoken_ids_to_tokens(self, subtokens):
"""Convert list of int subtoken ids to a list of string tokens."""
escaped_tokens = "".join([
self.subtoken_list[s] for s in subtokens
if s < len(self.subtoken_list)])
escaped_tokens = escaped_tokens.split("_")
# All tokens in the vocabulary... | def _subtoken_ids_to_tokens(self, subtokens):
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escaped_tokens = "".join([
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train | batch_run_many | Used to avoid a memory oveflow issue when running the network
on too many positions. TODO: This should be a member function of
player.network? | reinforcement/tensorflow/minigo/oneoffs/training_curve.py | def batch_run_many(player, positions, batch_size=100):
"""Used to avoid a memory oveflow issue when running the network
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player.network?"""
prob_list = []
value_list = []
for idx in range(0, len(positions), batch_size):
prob... | def batch_run_many(player, positions, batch_size=100):
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prob_list = []
value_list = []
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train | MaskPostProcessor.forward | Arguments:
x (Tensor): the mask logits
boxes (list[BoxList]): bounding boxes that are used as
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Returns:
results (list[BoxList]): one BoxList for each image, containing
the extra field mask | object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py | def forward(self, x, boxes):
"""
Arguments:
x (Tensor): the mask logits
boxes (list[BoxList]): bounding boxes that are used as
reference, one for ech image
Returns:
results (list[BoxList]): one BoxList for each image, containing
... | def forward(self, x, boxes):
"""
Arguments:
x (Tensor): the mask logits
boxes (list[BoxList]): bounding boxes that are used as
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Returns:
results (list[BoxList]): one BoxList for each image, containing
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train | get_caller | Returns file.py:lineno of your caller. A stack_index of 2 will provide
the caller of the function calling this function. Notice that stack_index
of 2 or more will fail if called from global scope. | compliance/mlperf_compliance/mlperf_log.py | def get_caller(stack_index=2, root_dir=None):
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the caller of the function calling this function. Notice that stack_index
of 2 or more will fail if called from global scope. '''
caller = inspect.getframeinfo(inspect.stack()[stack_i... | def get_caller(stack_index=2, root_dir=None):
''' Returns file.py:lineno of your caller. A stack_index of 2 will provide
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caller = inspect.getframeinfo(inspect.stack()[stack_i... | [
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train | _mlperf_print | Prints out an MLPerf Log Line.
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value: The value which contains no newlines.
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train | generate | Generates a new model name, given the model number. | reinforcement/tensorflow/minigo/rl_loop/shipname.py | def generate(model_num):
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train | detect_model_num | Takes a string related to a model name and extract its model number.
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train | batch_norm | Performs a batch normalization using a standard set of parameters. | image_classification/tensorflow/official/resnet/resnet_model.py | def batch_norm(inputs, training, data_format):
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# We set fused=True for a significant performance boost. See
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train | conv2d_fixed_padding | Strided 2-D convolution with explicit padding. | image_classification/tensorflow/official/resnet/resnet_model.py | def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
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train | block_layer | Creates one layer of blocks for the ResNet model.
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inputs: A tensor of size [batch, channels, height_in, width_in] or
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filters: The number of filters for the first convolution of the layer.
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Args:
inputs: A tensor of size [batch, channels, height_in, width_in] or
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train | Model._custom_dtype_getter | Creates variables in fp32, then casts to fp16 if necessary.
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train | DistributedSampler.init_rng | Creates new RNG, seed depends on current epoch idx. | rnn_translator/pytorch/seq2seq/data/sampler.py | def init_rng(self):
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"""
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"""
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rng = torch.Generator()
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train | DistributedSampler.distribute_batches | Assigns batches to workers.
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train | DistributedSampler.reshuffle_batches | Permutes global batches
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features (list[Tensor]): features computed from the images that are
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train | smooth_l1_loss | very similar to the smooth_l1_loss from pytorch, but with
the extra beta parameter | object_detection/pytorch/maskrcnn_benchmark/layers/smooth_l1_loss.py | def smooth_l1_loss(input, target, beta=1. / 9, size_average=True):
"""
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"""
n = torch.abs(input - target)
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if size_average:
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"""
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the extra beta parameter
"""
n = torch.abs(input - target)
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loss = torch.where(cond, 0.5 * n ** 2 / beta, n - 0.5 * beta)
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train | eval_policy | Evaluate all positions with all models save the policy heatmaps as CSVs
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CSV format is: model number, value network output, policy network outputs
position_name is taken from the SGF file
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"""Evaluate all positions with all models save the policy heatmaps as CSVs
CSV name is "heatmap-<position_name>-<model-index>.csv"
CSV format is: model number, value network output, policy network outputs
position_name is taken from the SGF file
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"""Evaluate all positions with all models save the policy heatmaps as CSVs
CSV name is "heatmap-<position_name>-<model-index>.csv"
CSV format is: model number, value network output, policy network outputs
position_name is taken from the SGF file
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train | dbg | Helper function to print to stderr and flush | reinforcement/tensorflow/minigo/utils.py | def dbg(*objects, file=sys.stderr, flush=True, **kwargs):
"Helper function to print to stderr and flush"
print(*objects, file=file, flush=flush, **kwargs) | def dbg(*objects, file=sys.stderr, flush=True, **kwargs):
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train | ensure_dir_exists | Creates local directories if they don't exist. | reinforcement/tensorflow/minigo/utils.py | def ensure_dir_exists(directory):
"Creates local directories if they don't exist."
if directory.startswith('gs://'):
return
if not os.path.exists(directory):
dbg("Making dir {}".format(directory))
os.makedirs(directory, exist_ok=True) | def ensure_dir_exists(directory):
"Creates local directories if they don't exist."
if directory.startswith('gs://'):
return
if not os.path.exists(directory):
dbg("Making dir {}".format(directory))
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train | parse_game_result | Parse an SGF result string into value target. | reinforcement/tensorflow/minigo/utils.py | def parse_game_result(result):
"Parse an SGF result string into value target."
if re.match(r'[bB]\+', result):
return 1
if re.match(r'[wW]\+', result):
return -1
return 0 | def parse_game_result(result):
"Parse an SGF result string into value target."
if re.match(r'[bB]\+', result):
return 1
if re.match(r'[wW]\+', result):
return -1
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train | iter_chunks | Yield from an iterator in chunks of chunk_size. | reinforcement/tensorflow/minigo/utils.py | def iter_chunks(chunk_size, iterator):
"Yield from an iterator in chunks of chunk_size."
iterator = iter(iterator)
while True:
next_chunk = _take_n(chunk_size, iterator)
# If len(iterable) % chunk_size == 0, don't return an empty chunk.
if next_chunk:
yield next_chunk
... | def iter_chunks(chunk_size, iterator):
"Yield from an iterator in chunks of chunk_size."
iterator = iter(iterator)
while True:
next_chunk = _take_n(chunk_size, iterator)
# If len(iterable) % chunk_size == 0, don't return an empty chunk.
if next_chunk:
yield next_chunk
... | [
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train | timer | Context manager for timing snippets of code. | reinforcement/tensorflow/minigo/utils.py | def timer(message):
"Context manager for timing snippets of code."
tick = time.time()
yield
tock = time.time()
print("%s: %.3f seconds" % (message, (tock - tick))) | def timer(message):
"Context manager for timing snippets of code."
tick = time.time()
yield
tock = time.time()
print("%s: %.3f seconds" % (message, (tock - tick))) | [
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train | logged_timer | Context manager for timing snippets of code. Echos to logging module. | reinforcement/tensorflow/minigo/utils.py | def logged_timer(message):
"Context manager for timing snippets of code. Echos to logging module."
tick = time.time()
yield
tock = time.time()
logging.info("%s: %.3f seconds", message, (tock - tick)) | def logged_timer(message):
"Context manager for timing snippets of code. Echos to logging module."
tick = time.time()
yield
tock = time.time()
logging.info("%s: %.3f seconds", message, (tock - tick)) | [
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train | find_file | Returns full filepath if the file is in path or a subdirectory. | translation/tensorflow/transformer/data_download.py | def find_file(path, filename, max_depth=5):
"""Returns full filepath if the file is in path or a subdirectory."""
for root, dirs, files in os.walk(path):
if filename in files:
return os.path.join(root, filename)
# Don't search past max_depth
depth = root[len(path) + 1:].count(os.sep)
if depth... | def find_file(path, filename, max_depth=5):
"""Returns full filepath if the file is in path or a subdirectory."""
for root, dirs, files in os.walk(path):
if filename in files:
return os.path.join(root, filename)
# Don't search past max_depth
depth = root[len(path) + 1:].count(os.sep)
if depth... | [
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train | get_raw_files | Return raw files from source. Downloads/extracts if needed.
Args:
raw_dir: string directory to store raw files
data_source: dictionary with
{"url": url of compressed dataset containing input and target files
"input": file with data in input language
"target": file with data in target lang... | translation/tensorflow/transformer/data_download.py | def get_raw_files(raw_dir, data_source):
"""Return raw files from source. Downloads/extracts if needed.
Args:
raw_dir: string directory to store raw files
data_source: dictionary with
{"url": url of compressed dataset containing input and target files
"input": file with data in input language
... | def get_raw_files(raw_dir, data_source):
"""Return raw files from source. Downloads/extracts if needed.
Args:
raw_dir: string directory to store raw files
data_source: dictionary with
{"url": url of compressed dataset containing input and target files
"input": file with data in input language
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train | txt_line_iterator | Iterate through lines of file. | translation/tensorflow/transformer/data_download.py | def txt_line_iterator(path):
"""Iterate through lines of file."""
with tf.gfile.Open(path) as f:
for line in f:
yield line.strip() | def txt_line_iterator(path):
"""Iterate through lines of file."""
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train | compile_files | Compile raw files into a single file for each language.
Args:
raw_dir: Directory containing downloaded raw files.
raw_files: Dict containing filenames of input and target data.
{"inputs": list of files containing data in input language
"targets": list of files containing corresponding data in ta... | translation/tensorflow/transformer/data_download.py | def compile_files(raw_dir, raw_files, tag):
"""Compile raw files into a single file for each language.
Args:
raw_dir: Directory containing downloaded raw files.
raw_files: Dict containing filenames of input and target data.
{"inputs": list of files containing data in input language
"targets": ... | def compile_files(raw_dir, raw_files, tag):
"""Compile raw files into a single file for each language.
Args:
raw_dir: Directory containing downloaded raw files.
raw_files: Dict containing filenames of input and target data.
{"inputs": list of files containing data in input language
"targets": ... | [
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train | write_file | Write all of lines from file using the writer. | translation/tensorflow/transformer/data_download.py | def write_file(writer, filename):
"""Write all of lines from file using the writer."""
for line in txt_line_iterator(filename):
writer.write(line)
writer.write("\n") | def write_file(writer, filename):
"""Write all of lines from file using the writer."""
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train | encode_and_save_files | Save data from files as encoded Examples in TFrecord format.
Args:
subtokenizer: Subtokenizer object that will be used to encode the strings.
data_dir: The directory in which to write the examples
raw_files: A tuple of (input, target) data files. Each line in the input and
the corresponding line in... | translation/tensorflow/transformer/data_download.py | def encode_and_save_files(
subtokenizer, data_dir, raw_files, tag, total_shards):
"""Save data from files as encoded Examples in TFrecord format.
Args:
subtokenizer: Subtokenizer object that will be used to encode the strings.
data_dir: The directory in which to write the examples
raw_files: A tupl... | def encode_and_save_files(
subtokenizer, data_dir, raw_files, tag, total_shards):
"""Save data from files as encoded Examples in TFrecord format.
Args:
subtokenizer: Subtokenizer object that will be used to encode the strings.
data_dir: The directory in which to write the examples
raw_files: A tupl... | [
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train | shard_filename | Create filename for data shard. | translation/tensorflow/transformer/data_download.py | def shard_filename(path, tag, shard_num, total_shards):
"""Create filename for data shard."""
return os.path.join(
path, "%s-%s-%s-%.5d-of-%.5d" % (_PREFIX, _ENCODE_TAG, tag, shard_num, total_shards)) | def shard_filename(path, tag, shard_num, total_shards):
"""Create filename for data shard."""
return os.path.join(
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train | shuffle_records | Shuffle records in a single file. | translation/tensorflow/transformer/data_download.py | def shuffle_records(fname):
"""Shuffle records in a single file."""
tf.logging.info("Shuffling records in file %s" % fname)
# Rename file prior to shuffling
tmp_fname = fname + ".unshuffled"
tf.gfile.Rename(fname, tmp_fname)
reader = tf.python_io.tf_record_iterator(tmp_fname)
records = []
for record i... | def shuffle_records(fname):
"""Shuffle records in a single file."""
tf.logging.info("Shuffling records in file %s" % fname)
# Rename file prior to shuffling
tmp_fname = fname + ".unshuffled"
tf.gfile.Rename(fname, tmp_fname)
reader = tf.python_io.tf_record_iterator(tmp_fname)
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train | dict_to_example | Converts a dictionary of string->int to a tf.Example. | translation/tensorflow/transformer/data_download.py | def dict_to_example(dictionary):
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train | all_exist | Returns true if all files in the list exist. | translation/tensorflow/transformer/data_download.py | def all_exist(filepaths):
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train | main | Obtain training and evaluation data for the Transformer model. | translation/tensorflow/transformer/data_download.py | def main(unused_argv):
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int tensor with shape [batch_size, target_length]
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train | Transformer._get_symbols_to_logits_fn | Returns a decoding function that calculates logits of the next tokens. | translation/tensorflow/transformer/model/transformer.py | def _get_symbols_to_logits_fn(self, max_decode_length):
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train | Transformer.predict | Return predicted sequence. | translation/tensorflow/transformer/model/transformer.py | def predict(self, encoder_outputs, encoder_decoder_attention_bias):
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train | _create_row_col_indices | Maps user and items ids to their locations in the rating matrix. | data_generation/fractal_graph_expansions/run_expansion.py | def _create_row_col_indices(ratings_df):
"""Maps user and items ids to their locations in the rating matrix."""
user_id_to_user_idx = _create_index(ratings_df, "userId")
item_id_to_item_idx = _create_index(ratings_df, "movieId")
ratings_df["row"] = ratings_df["userId"].apply(
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"""Maps user and items ids to their locations in the rating matrix."""
user_id_to_user_idx = _create_index(ratings_df, "userId")
item_id_to_item_idx = _create_index(ratings_df, "movieId")
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ratings_df: pandas dataframe with columns 'userId', 'movie... | data_generation/fractal_graph_expansions/run_expansion.py | def _preprocess_movie_lens(ratings_df):
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train | COCO.getAnnIds | Get ann ids that satisfy given filter conditions. default skips that filter
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catIds (int array) : get anns for given cats
areaRng (float array) : get anns for given area range (e.g. [0 inf])
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Get ann ids that satisfy given filter conditions. default skips that filter
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catIds (int array) : get anns for given cats
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train | COCO.getCatIds | filtering parameters. default skips that filter.
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train | COCO.getImgIds | Get img ids that satisfy given filter conditions.
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:param catIds (int array) : get imgs with all given cats
:return: ids (int array) : integer array of img ids | single_stage_detector/ssd/coco.py | def getImgIds(self, imgIds=[], catIds=[]):
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Get img ids that satisfy given filter conditions.
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:param catIds (int array) : get imgs with all given cats
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'''
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Get img ids that satisfy given filter conditions.
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train | COCO.loadAnns | Load anns with the specified ids.
:param ids (int array) : integer ids specifying anns
:return: anns (object array) : loaded ann objects | single_stage_detector/ssd/coco.py | def loadAnns(self, ids=[]):
"""
Load anns with the specified ids.
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:return: anns (object array) : loaded ann objects
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if _isArrayLike(ids):
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Load anns with the specified ids.
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train | COCO.loadCats | Load cats with the specified ids.
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:return: cats (object array) : loaded cat objects | single_stage_detector/ssd/coco.py | def loadCats(self, ids=[]):
"""
Load cats with the specified ids.
:param ids (int array) : integer ids specifying cats
:return: cats (object array) : loaded cat objects
"""
if _isArrayLike(ids):
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train | COCO.loadImgs | Load anns with the specified ids.
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train | COCO.showAnns | Display the specified annotations.
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train | COCO.loadRes | Load result file and return a result api object.
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train | COCO.annToRLE | Convert annotation which can be polygons, uncompressed RLE to RLE.
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train | COCO.annToMask | Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
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train | GeneralizedRCNN.forward | Arguments:
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train | EmbeddingSharedWeights.linear | Computes logits by running x through a linear layer.
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train | Seq2SeqTrainer.iterate | Performs one iteration of the training/validation.
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train | Seq2SeqTrainer.feed_data | Runs training or validation on batches from data_loader.
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train | Seq2SeqTrainer.preallocate | Generates maximum sequence length batch and runs forward and backward
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train | Seq2SeqTrainer.optimize | Sets model in training mode, preallocates memory and runs training on
data provided by data_loader.
:param data_loader: data loader | rnn_translator/pytorch/seq2seq/train/trainer.py | def optimize(self, data_loader):
"""
Sets model in training mode, preallocates memory and runs training on
data provided by data_loader.
:param data_loader: data loader
"""
torch.set_grad_enabled(True)
self.model.train()
torch.cuda.empty_cache()
s... | def optimize(self, data_loader):
"""
Sets model in training mode, preallocates memory and runs training on
data provided by data_loader.
:param data_loader: data loader
"""
torch.set_grad_enabled(True)
self.model.train()
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train | Seq2SeqTrainer.evaluate | Sets model in eval mode, disables gradients, preallocates memory and
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:param data_loader: data loader | rnn_translator/pytorch/seq2seq/train/trainer.py | def evaluate(self, data_loader):
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torch.cuda.empty... | def evaluate(self, data_loader):
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train | Seq2SeqTrainer.load | Loads checkpoint from filename.
:param filename: path to the checkpoint file | rnn_translator/pytorch/seq2seq/train/trainer.py | def load(self, filename):
"""
Loads checkpoint from filename.
:param filename: path to the checkpoint file
"""
if os.path.isfile(filename):
checkpoint = torch.load(filename, map_location={'cuda:0': 'cpu'})
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"""
Loads checkpoint from filename.
:param filename: path to the checkpoint file
"""
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train | Seq2SeqTrainer.save | Stores checkpoint to a file.
:param identifier: identifier for periodic checkpoint
:param is_best: if True stores checkpoint to 'model_best.pth'
:param save_all: if True stores checkpoint after completed training
epoch | rnn_translator/pytorch/seq2seq/train/trainer.py | def save(self, identifier=None, is_best=False, save_all=False):
"""
Stores checkpoint to a file.
:param identifier: identifier for periodic checkpoint
:param is_best: if True stores checkpoint to 'model_best.pth'
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train | keep_only_positive_boxes | Given a set of BoxList containing the `labels` field,
return a set of BoxList for which `labels > 0`.
Arguments:
boxes (list of BoxList) | object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py | def keep_only_positive_boxes(boxes):
"""
Given a set of BoxList containing the `labels` field,
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Arguments:
boxes (list of BoxList)
"""
assert isinstance(boxes, (list, tuple))
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assert boxes[0].has_... | def keep_only_positive_boxes(boxes):
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boxes (list of BoxList)
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train | ROIMaskHead.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/mask_head/mask_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:
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"""
Arguments:
features (list[Tensor]): feature-maps from possibly several levels
proposals (list[BoxList]): proposal boxes
targets (list[BoxList], optional): the ground-truth targets.
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train | get_95_percentile_bleak | Gets the 95th percentile of bleakest_eval from bigtable | reinforcement/tensorflow/minigo/rl_loop/update_resign_threshold.py | def get_95_percentile_bleak(games_nr, n_back=500):
"""Gets the 95th percentile of bleakest_eval from bigtable"""
end_game = int(games_nr.latest_game_number)
start_game = end_game - n_back if end_game >= n_back else 0
moves = games_nr.bleakest_moves(start_game, end_game)
evals = np.array([m[2] for m ... | def get_95_percentile_bleak(games_nr, n_back=500):
"""Gets the 95th percentile of bleakest_eval from bigtable"""
end_game = int(games_nr.latest_game_number)
start_game = end_game - n_back if end_game >= n_back else 0
moves = games_nr.bleakest_moves(start_game, end_game)
evals = np.array([m[2] for m ... | [
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train | update_flagfile | Updates the flagfile at `flags_path`, changing the value for
`resign_threshold` to `new_threshold` | reinforcement/tensorflow/minigo/rl_loop/update_resign_threshold.py | def update_flagfile(flags_path, new_threshold):
"""Updates the flagfile at `flags_path`, changing the value for
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"""
if abs(new_threshold) > 1:
raise ValueError("Invalid new percentile for resign threshold")
with tf.gfile.GFile(flags_path) as f:
l... | def update_flagfile(flags_path, new_threshold):
"""Updates the flagfile at `flags_path`, changing the value for
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"""
if abs(new_threshold) > 1:
raise ValueError("Invalid new percentile for resign threshold")
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train | MiniguiBasicCmdHandler._minigui_report_search_status | Prints the current MCTS search status to stderr.
Reports the current search path, root node's child_Q, root node's
child_N, the most visited path in a format that can be parsed by
one of the STDERR_HANDLERS in minigui.ts.
Args:
leaves: list of leaf MCTSNodes returned by tree_... | reinforcement/tensorflow/minigo/gtp_cmd_handlers.py | def _minigui_report_search_status(self, leaves):
"""Prints the current MCTS search status to stderr.
Reports the current search path, root node's child_Q, root node's
child_N, the most visited path in a format that can be parsed by
one of the STDERR_HANDLERS in minigui.ts.
Args... | def _minigui_report_search_status(self, leaves):
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Reports the current search path, root node's child_Q, root node's
child_N, the most visited path in a format that can be parsed by
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train | parse_args | Parse commandline arguments. | rnn_translator/pytorch/train.py | def parse_args():
"""
Parse commandline arguments.
"""
def exclusive_group(group, name, default, help):
destname = name.replace('-', '_')
subgroup = group.add_mutually_exclusive_group(required=False)
subgroup.add_argument(f'--{name}', dest=f'{destname}',
... | def parse_args():
"""
Parse commandline arguments.
"""
def exclusive_group(group, name, default, help):
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subgroup = group.add_mutually_exclusive_group(required=False)
subgroup.add_argument(f'--{name}', dest=f'{destname}',
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train | main | Launches data-parallel multi-gpu training. | rnn_translator/pytorch/train.py | def main():
"""
Launches data-parallel multi-gpu training.
"""
mlperf_log.ROOT_DIR_GNMT = os.path.dirname(os.path.abspath(__file__))
mlperf_log.LOGGER.propagate = False
args = parse_args()
device = utils.set_device(args.cuda, args.local_rank)
distributed = utils.init_distributed(args.cu... | def main():
"""
Launches data-parallel multi-gpu training.
"""
mlperf_log.ROOT_DIR_GNMT = os.path.dirname(os.path.abspath(__file__))
mlperf_log.LOGGER.propagate = False
args = parse_args()
device = utils.set_device(args.cuda, args.local_rank)
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train | validate_holdout_selfplay | Validate on held-out selfplay data. | reinforcement/tensorflow/minigo/rl_loop/train_and_validate.py | def validate_holdout_selfplay():
"""Validate on held-out selfplay data."""
holdout_dirs = (os.path.join(fsdb.holdout_dir(), d)
for d in reversed(gfile.ListDirectory(fsdb.holdout_dir()))
if gfile.IsDirectory(os.path.join(fsdb.holdout_dir(), d))
for f in... | def validate_holdout_selfplay():
"""Validate on held-out selfplay data."""
holdout_dirs = (os.path.join(fsdb.holdout_dir(), d)
for d in reversed(gfile.ListDirectory(fsdb.holdout_dir()))
if gfile.IsDirectory(os.path.join(fsdb.holdout_dir(), d))
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train | validate_pro | Validate on professional data. | reinforcement/tensorflow/minigo/rl_loop/train_and_validate.py | def validate_pro():
"""Validate on professional data."""
cmd = ['python3', 'validate.py', FLAGS.pro_dataset,
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'--tpu_name={}'.format(TPU_NAME),
'--work_dir={}'.format(fsdb.working_dir()),
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'--validate_name=... | def validate_pro():
"""Validate on professional data."""
cmd = ['python3', 'validate.py', FLAGS.pro_dataset,
'--use_tpu',
'--tpu_name={}'.format(TPU_NAME),
'--work_dir={}'.format(fsdb.working_dir()),
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train | get_group_gn | get number of groups used by GroupNorm, based on number of channels. | object_detection/pytorch/maskrcnn_benchmark/modeling/make_layers.py | def get_group_gn(dim, dim_per_gp, num_groups):
"""get number of groups used by GroupNorm, based on number of channels."""
assert dim_per_gp == -1 or num_groups == -1, \
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if dim_per_gp > 0:
assert dim % dim_per_gp == 0, \
"dim: {}, dim_per_g... | def get_group_gn(dim, dim_per_gp, num_groups):
"""get number of groups used by GroupNorm, based on number of channels."""
assert dim_per_gp == -1 or num_groups == -1, \
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if dim_per_gp > 0:
assert dim % dim_per_gp == 0, \
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train | make_fc | Caffe2 implementation uses XavierFill, which in fact
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train | BoxList.transpose | Transpose bounding box (flip or rotate in 90 degree steps)
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train | parse_args | Parse commandline arguments. | rnn_translator/pytorch/translate.py | def parse_args():
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Parse commandline arguments.
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Parse commandline arguments.
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train | main | Launches translation (inference).
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Launches translation (inference).
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Launches translation (inference).
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train | Decoder.convert_to_strings | Given a list of numeric sequences, returns the corresponding strings | speech_recognition/pytorch/decoder.py | def convert_to_strings(self, sequences, sizes=None):
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train | Decoder.process_strings | Given a list of strings, removes blanks and replace space character with space.
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sequences: list of 1-d array of integers
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"""
Given a list of strings, removes blanks and replace space character with space.
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Arguments:
sequences: list of 1-d array of integers
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Given a list of strings, removes blanks and replace space character with space.
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train | Decoder.wer | Computes the Word Error Rate, defined as the edit distance between the
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s1 (string): space-separated sentence
s2 (string): space-separated sentence | speech_recognition/pytorch/decoder.py | def wer(self, s1, s2):
"""
Computes the Word Error Rate, defined as the edit distance between the
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Arguments:
s1 (string): space-separated sentence
s2 (string): space-separated sentence
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# build ... | def wer(self, s1, s2):
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Computes the Word Error Rate, defined as the edit distance between the
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s1 (string): space-separated sentence
s2 (string): space-separated sentence
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train | PostProcessor.forward | Arguments:
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boxes (list[BoxList]): bounding boxes that are used as
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results (list[BoxList]): one BoxList for each... | object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py | def forward(self, x, boxes):
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Arguments:
x (tuple[tensor, tensor]): x contains the class logits
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boxes (list[BoxList]): bounding boxes that are used as
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train | PostProcessor.filter_results | Returns bounding-box detection results by thresholding on scores and
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"""
# unwrap the boxlist to avoid additional overhead.
# if we had multi-class NMS, we could perform this directly on th... | def filter_results(self, boxlist, num_classes):
"""Returns bounding-box detection results by thresholding on scores and
applying non-maximum suppression (NMS).
"""
# unwrap the boxlist to avoid additional overhead.
# if we had multi-class NMS, we could perform this directly on th... | [
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] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py#L105-L146 | [
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"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | _determine_chunk_to_make | Returns the full path of the chunk to make (gs://...)
and a boolean, indicating whether we should wait for a new model
or if we're 'behind' and should just write out our current chunk immediately
True == write immediately. | reinforcement/tensorflow/minigo/rl_loop/example_buffer.py | def _determine_chunk_to_make(write_dir):
"""
Returns the full path of the chunk to make (gs://...)
and a boolean, indicating whether we should wait for a new model
or if we're 'behind' and should just write out our current chunk immediately
True == write immediately.
"""
models = fsdb.get_mo... | def _determine_chunk_to_make(write_dir):
"""
Returns the full path of the chunk to make (gs://...)
and a boolean, indicating whether we should wait for a new model
or if we're 'behind' and should just write out our current chunk immediately
True == write immediately.
"""
models = fsdb.get_mo... | [
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"... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
train | fill_and_wait_models | Fills a ringbuffer with positions from the most recent games, then
continually rsync's and updates the buffer until a new model is promoted.
Once it detects a new model, iit then dumps its contents for training to
immediately begin on the next model. | reinforcement/tensorflow/minigo/rl_loop/example_buffer.py | def fill_and_wait_models(bufsize=EXAMPLES_PER_GENERATION,
write_dir=None,
threads=8,
model_window=100,
skip_first_rsync=False):
""" Fills a ringbuffer with positions from the most recent games, then
continually r... | def fill_and_wait_models(bufsize=EXAMPLES_PER_GENERATION,
write_dir=None,
threads=8,
model_window=100,
skip_first_rsync=False):
""" Fills a ringbuffer with positions from the most recent games, then
continually r... | [
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train | make_chunk_for | Explicitly make a golden chunk for a given model `model_num`
(not necessarily the most recent one).
While we haven't yet got enough samples (EXAMPLES_PER_GENERATION)
Add samples from the games of previous model. | reinforcement/tensorflow/minigo/rl_loop/example_buffer.py | def make_chunk_for(output_dir=LOCAL_DIR,
local_dir=LOCAL_DIR,
game_dir=None,
model_num=1,
positions=EXAMPLES_PER_GENERATION,
threads=8,
sampling_frac=0.02):
"""
Explicitly make a golden chunk for a ... | def make_chunk_for(output_dir=LOCAL_DIR,
local_dir=LOCAL_DIR,
game_dir=None,
model_num=1,
positions=EXAMPLES_PER_GENERATION,
threads=8,
sampling_frac=0.02):
"""
Explicitly make a golden chunk for a ... | [
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] | mlperf/training | python | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/rl_loop/example_buffer.py#L294-L329 | [
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... | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 |
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