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mlperf/training | image_classification/tensorflow/official/resnet/resnet_run_loop.py | process_record_dataset | def process_record_dataset(dataset, is_training, batch_size, shuffle_buffer,
parse_record_fn, num_epochs=1, num_gpus=None,
examples_per_epoch=None, dtype=tf.float32):
"""Given a Dataset with raw records, return an iterator over the records.
Args:
dataset: A... | python | def process_record_dataset(dataset, is_training, batch_size, shuffle_buffer,
parse_record_fn, num_epochs=1, num_gpus=None,
examples_per_epoch=None, dtype=tf.float32):
"""Given a Dataset with raw records, return an iterator over the records.
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_run_loop.py | get_synth_input_fn | def get_synth_input_fn(height, width, num_channels, num_classes):
"""Returns an input function that returns a dataset with zeroes.
This is useful in debugging input pipeline performance, as it removes all
elements of file reading and image preprocessing.
Args:
height: Integer height that will be used to c... | python | def get_synth_input_fn(height, width, num_channels, num_classes):
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_run_loop.py | learning_rate_with_decay | def learning_rate_with_decay(
batch_size, batch_denom, num_images, boundary_epochs, decay_rates,
base_lr=0.1, enable_lars=False):
"""Get a learning rate that decays step-wise as training progresses.
Args:
batch_size: the number of examples processed in each training batch.
batch_denom: this value w... | python | def learning_rate_with_decay(
batch_size, batch_denom, num_images, boundary_epochs, decay_rates,
base_lr=0.1, enable_lars=False):
"""Get a learning rate that decays step-wise as training progresses.
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batch_size: the number of examples processed in each training batch.
batch_denom: this value w... | [
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_run_loop.py | resnet_model_fn | def resnet_model_fn(features, labels, mode, model_class,
resnet_size, weight_decay, learning_rate_fn, momentum,
data_format, version, loss_scale, loss_filter_fn=None,
dtype=resnet_model.DEFAULT_DTYPE,
label_smoothing=0.0, enable_lars=False)... | python | def resnet_model_fn(features, labels, mode, model_class,
resnet_size, weight_decay, learning_rate_fn, momentum,
data_format, version, loss_scale, loss_filter_fn=None,
dtype=resnet_model.DEFAULT_DTYPE,
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_run_loop.py | per_device_batch_size | def per_device_batch_size(batch_size, num_gpus):
"""For multi-gpu, batch-size must be a multiple of the number of GPUs.
Note that this should eventually be handled by DistributionStrategies
directly. Multi-GPU support is currently experimental, however,
so doing the work here until that feature is in place.
... | python | def per_device_batch_size(batch_size, num_gpus):
"""For multi-gpu, batch-size must be a multiple of the number of GPUs.
Note that this should eventually be handled by DistributionStrategies
directly. Multi-GPU support is currently experimental, however,
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_run_loop.py | resnet_main | def resnet_main(seed, flags, model_function, input_function, shape=None):
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Args:
flags: FLAGS object that contains the params for running. See
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model_function: the function that instantiates the Model and builds the
ops for... | python | def resnet_main(seed, flags, model_function, input_function, shape=None):
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flags: FLAGS object that contains the params for running. See
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mlperf/training | reinforcement/tensorflow/minigo/ratings/ratings.py | compute_ratings | def compute_ratings(data=None):
""" Returns the tuples of (model_id, rating, sigma)
N.B. that `model_id` here is NOT the model number in the run
'data' is tuples of (winner, loser) model_ids (not model numbers)
"""
if data is None:
with sqlite3.connect("ratings.db") as db:
data ... | python | def compute_ratings(data=None):
""" Returns the tuples of (model_id, rating, sigma)
N.B. that `model_id` here is NOT the model number in the run
'data' is tuples of (winner, loser) model_ids (not model numbers)
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if data is None:
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mlperf/training | reinforcement/tensorflow/minigo/ratings/ratings.py | suggest_pairs | def suggest_pairs(top_n=10, per_n=3, ignore_before=300):
""" Find the maximally interesting pairs of players to match up
First, sort the ratings by uncertainty.
Then, take the ten highest players with the highest uncertainty
For each of them, call them `p1`
Sort all the models by their distance from... | python | def suggest_pairs(top_n=10, per_n=3, ignore_before=300):
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First, sort the ratings by uncertainty.
Then, take the ten highest players with the highest uncertainty
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mlperf/training | reinforcement/tensorflow/minigo/strategies.py | time_recommendation | def time_recommendation(move_num, seconds_per_move=5, time_limit=15 * 60,
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"""Given the current move number and the 'desired' seconds per move, return
how much time should actually be used. This is intended specifically for
CGOS time controls, which has an absolute... | python | def time_recommendation(move_num, seconds_per_move=5, time_limit=15 * 60,
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mlperf/training | reinforcement/tensorflow/minigo/strategies.py | MCTSPlayer.suggest_move | def suggest_move(self, position):
"""Used for playing a single game.
For parallel play, use initialize_move, select_leaf,
incorporate_results, and pick_move
"""
start = time.time()
if self.timed_match:
while time.time() - start < self.seconds_per_move:
... | python | def suggest_move(self, position):
"""Used for playing a single game.
For parallel play, use initialize_move, select_leaf,
incorporate_results, and pick_move
"""
start = time.time()
if self.timed_match:
while time.time() - start < self.seconds_per_move:
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mlperf/training | reinforcement/tensorflow/minigo/strategies.py | MCTSPlayer.play_move | def play_move(self, c):
"""Notable side effects:
- finalizes the probability distribution according to
this roots visit counts into the class' running tally, `searches_pi`
- Makes the node associated with this move the root, for future
`inject_noise` calls.
"""
... | python | def play_move(self, c):
"""Notable side effects:
- finalizes the probability distribution according to
this roots visit counts into the class' running tally, `searches_pi`
- Makes the node associated with this move the root, for future
`inject_noise` calls.
"""
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mlperf/training | reinforcement/tensorflow/minigo/strategies.py | MCTSPlayer.pick_move | def pick_move(self):
"""Picks a move to play, based on MCTS readout statistics.
Highest N is most robust indicator. In the early stage of the game, pick
a move weighted by visit count; later on, pick the absolute max."""
if self.root.position.n >= self.temp_threshold:
fcoord... | python | def pick_move(self):
"""Picks a move to play, based on MCTS readout statistics.
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mlperf/training | rnn_translator/pytorch/seq2seq/models/decoder.py | RecurrentAttention.forward | def forward(self, inputs, hidden, context, context_len):
"""
Execute RecurrentAttention.
:param inputs: tensor with inputs
:param hidden: hidden state for LSTM layer
:param context: context tensor from encoder
:param context_len: vector of encoder sequence lengths
... | python | def forward(self, inputs, hidden, context, context_len):
"""
Execute RecurrentAttention.
:param inputs: tensor with inputs
:param hidden: hidden state for LSTM layer
:param context: context tensor from encoder
:param context_len: vector of encoder sequence lengths
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mlperf/training | rnn_translator/pytorch/seq2seq/models/decoder.py | ResidualRecurrentDecoder.init_hidden | def init_hidden(self, hidden):
"""
Converts flattened hidden state (from sequence generator) into a tuple
of hidden states.
:param hidden: None or flattened hidden state for decoder RNN layers
"""
if hidden is not None:
# per-layer chunks
hidden =... | python | def init_hidden(self, hidden):
"""
Converts flattened hidden state (from sequence generator) into a tuple
of hidden states.
:param hidden: None or flattened hidden state for decoder RNN layers
"""
if hidden is not None:
# per-layer chunks
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mlperf/training | rnn_translator/pytorch/seq2seq/models/decoder.py | ResidualRecurrentDecoder.package_hidden | def package_hidden(self):
"""
Flattens the hidden state from all LSTM layers into one tensor (for
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"""
if self.inference:
hidden = torch.cat(tuple(itertools.chain(*self.next_hidden)))
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hidden = None
return hidde... | python | def package_hidden(self):
"""
Flattens the hidden state from all LSTM layers into one tensor (for
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"""
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mlperf/training | compliance/mlperf_compliance/tf_mlperf_log.py | log_deferred | def log_deferred(op, log_id, every_n=1, first_n=None):
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/utils/model_zoo.py | cache_url | def cache_url(url, model_dir=None, progress=True):
r"""Loads the Torch serialized object at the given URL.
If the object is already present in `model_dir`, it's deserialized and
returned. The filename part of the URL should follow the naming convention
``filename-<sha256>.ext`` where ``<sha256>`` is the... | python | def cache_url(url, model_dir=None, progress=True):
r"""Loads the Torch serialized object at the given URL.
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mlperf/training | translation/tensorflow/transformer/model/attention_layer.py | Attention.split_heads | def split_heads(self, x):
"""Split x into different heads, and transpose the resulting value.
The tensor is transposed to insure the inner dimensions hold the correct
values during the matrix multiplication.
Args:
x: A tensor with shape [batch_size, length, hidden_size]
Returns:
A ten... | python | def split_heads(self, x):
"""Split x into different heads, and transpose the resulting value.
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Args:
x: A tensor with shape [batch_size, length, hidden_size]
Returns:
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mlperf/training | translation/tensorflow/transformer/model/attention_layer.py | Attention.combine_heads | def combine_heads(self, x):
"""Combine tensor that has been split.
Args:
x: A tensor [batch_size, num_heads, length, hidden_size/num_heads]
Returns:
A tensor with shape [batch_size, length, hidden_size]
"""
with tf.name_scope("combine_heads"):
batch_size = tf.shape(x)[0]
le... | python | def combine_heads(self, x):
"""Combine tensor that has been split.
Args:
x: A tensor [batch_size, num_heads, length, hidden_size/num_heads]
Returns:
A tensor with shape [batch_size, length, hidden_size]
"""
with tf.name_scope("combine_heads"):
batch_size = tf.shape(x)[0]
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mlperf/training | translation/tensorflow/transformer/model/attention_layer.py | Attention.call | def call(self, x, y, bias, cache=None):
"""Apply attention mechanism to x and y.
Args:
x: a tensor with shape [batch_size, length_x, hidden_size]
y: a tensor with shape [batch_size, length_y, hidden_size]
bias: attention bias that will be added to the result of the dot product.
cache: (... | python | def call(self, x, y, bias, cache=None):
"""Apply attention mechanism to x and y.
Args:
x: a tensor with shape [batch_size, length_x, hidden_size]
y: a tensor with shape [batch_size, length_y, hidden_size]
bias: attention bias that will be added to the result of the dot product.
cache: (... | [
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _save_vocab_file | def _save_vocab_file(vocab_file, subtoken_list):
"""Save subtokens to file."""
with tf.gfile.Open(vocab_file, mode="w") as f:
for subtoken in subtoken_list:
f.write("'%s'\n" % _unicode_to_native(subtoken)) | python | def _save_vocab_file(vocab_file, subtoken_list):
"""Save subtokens to file."""
with tf.gfile.Open(vocab_file, mode="w") as f:
for subtoken in subtoken_list:
f.write("'%s'\n" % _unicode_to_native(subtoken)) | [
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _load_vocab_file | def _load_vocab_file(vocab_file, reserved_tokens=None):
"""Load vocabulary while ensuring reserved tokens are at the top."""
if reserved_tokens is None:
reserved_tokens = RESERVED_TOKENS
subtoken_list = []
with tf.gfile.Open(vocab_file, mode="r") as f:
for line in f:
subtoken = _native_to_unicode... | python | def _load_vocab_file(vocab_file, reserved_tokens=None):
"""Load vocabulary while ensuring reserved tokens are at the top."""
if reserved_tokens is None:
reserved_tokens = RESERVED_TOKENS
subtoken_list = []
with tf.gfile.Open(vocab_file, mode="r") as f:
for line in f:
subtoken = _native_to_unicode... | [
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _native_to_unicode | def _native_to_unicode(s):
"""Convert string to unicode (required in Python 2)."""
if six.PY2:
return s if isinstance(s, unicode) else s.decode("utf-8")
else:
return s | python | def _native_to_unicode(s):
"""Convert string to unicode (required in Python 2)."""
if six.PY2:
return s if isinstance(s, unicode) else s.decode("utf-8")
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return s | [
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _unicode_to_native | def _unicode_to_native(s):
"""Convert string from unicode to native format (required in Python 2)."""
if six.PY2:
return s.encode("utf-8") if isinstance(s, unicode) else s
else:
return s | python | def _unicode_to_native(s):
"""Convert string from unicode to native format (required in Python 2)."""
if six.PY2:
return s.encode("utf-8") if isinstance(s, unicode) else s
else:
return s | [
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _split_string_to_tokens | def _split_string_to_tokens(text):
"""Splits text to a list of string tokens."""
if not text:
return []
ret = []
token_start = 0
# Classify each character in the input string
is_alnum = [c in _ALPHANUMERIC_CHAR_SET for c in text]
for pos in xrange(1, len(text)):
if is_alnum[pos] != is_alnum[pos - ... | python | def _split_string_to_tokens(text):
"""Splits text to a list of string tokens."""
if not text:
return []
ret = []
token_start = 0
# Classify each character in the input string
is_alnum = [c in _ALPHANUMERIC_CHAR_SET for c in text]
for pos in xrange(1, len(text)):
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _escape_token | def _escape_token(token, alphabet):
r"""Replace characters that aren't in the alphabet and append "_" to token.
Apply three transformations to the token:
1. Replace underline character "_" with "\u", and backslash "\" with "\\".
2. Replace characters outside of the alphabet with "\###;", where ### is the
... | python | def _escape_token(token, alphabet):
r"""Replace characters that aren't in the alphabet and append "_" to token.
Apply three transformations to the token:
1. Replace underline character "_" with "\u", and backslash "\" with "\\".
2. Replace characters outside of the alphabet with "\###;", where ### is the
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _unescape_token | def _unescape_token(token):
r"""Replaces escaped characters in the token with their unescaped versions.
Applies inverse transformations as _escape_token():
1. Replace "\u" with "_", and "\\" with "\".
2. Replace "\###;" with the unicode character the ### refers to.
Args:
token: escaped string
Ret... | python | def _unescape_token(token):
r"""Replaces escaped characters in the token with their unescaped versions.
Applies inverse transformations as _escape_token():
1. Replace "\u" with "_", and "\\" with "\".
2. Replace "\###;" with the unicode character the ### refers to.
Args:
token: escaped string
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _count_tokens | def _count_tokens(files, file_byte_limit=1e6):
"""Return token counts of words in the files.
Samples file_byte_limit bytes from each file, and counts the words that appear
in the samples. The samples are semi-evenly distributed across the file.
Args:
files: List of filepaths
file_byte_limit: Max numbe... | python | def _count_tokens(files, file_byte_limit=1e6):
"""Return token counts of words in the files.
Samples file_byte_limit bytes from each file, and counts the words that appear
in the samples. The samples are semi-evenly distributed across the file.
Args:
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _split_token_to_subtokens | def _split_token_to_subtokens(token, subtoken_dict, max_subtoken_length):
"""Splits a token into subtokens defined in the subtoken dict."""
ret = []
start = 0
token_len = len(token)
while start < token_len:
# Find the longest subtoken, so iterate backwards.
for end in xrange(min(token_len, start + max... | python | def _split_token_to_subtokens(token, subtoken_dict, max_subtoken_length):
"""Splits a token into subtokens defined in the subtoken dict."""
ret = []
start = 0
token_len = len(token)
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _generate_subtokens_with_target_vocab_size | 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... | python | 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:
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _generate_alphabet_dict | 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... | python | 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... | [
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _count_and_gen_subtokens | 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.... | python | def _count_and_gen_subtokens(
token_counts, alphabet, subtoken_dict, max_subtoken_length):
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Args:
token_counts: dict mapping tokens to the number of times they appear in the
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _filter_and_bucket_subtokens | 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... | python | def _filter_and_bucket_subtokens(subtoken_counts, min_count):
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subtoken_counts: defaultdict mapping subtokens to their counts
min_count: int count used to filter subtokens
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _gen_new_subtoken_list | 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... | python | def _gen_new_subtoken_list(
subtoken_counts, min_count, alphabet, reserved_tokens=None):
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | _generate_subtokens | 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... | python | def _generate_subtokens(
token_counts, alphabet, min_count, num_iterations=4,
reserved_tokens=None):
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token_counts: dict mapping str tokens -> int count
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | Subtokenizer.init_from_files | 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... | python | 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... | [
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | Subtokenizer.encode | 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 | python | 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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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | Subtokenizer._token_to_subtoken_ids | 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... | python | 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(
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | Subtokenizer.decode | 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... | python | 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
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subtokens = subtokens.toli... | [
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mlperf/training | translation/tensorflow/transformer/utils/tokenizer.py | Subtokenizer._subtoken_ids_to_tokens | 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... | python | def _subtoken_ids_to_tokens(self, subtokens):
"""Convert list of int subtoken ids to a list of string tokens."""
escaped_tokens = "".join([
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if s < len(self.subtoken_list)])
escaped_tokens = escaped_tokens.split("_")
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mlperf/training | reinforcement/tensorflow/minigo/oneoffs/training_curve.py | batch_run_many | def batch_run_many(player, positions, batch_size=100):
"""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?"""
prob_list = []
value_list = []
for idx in range(0, len(positions), batch_size):
prob... | python | def batch_run_many(player, positions, batch_size=100):
"""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?"""
prob_list = []
value_list = []
for idx in range(0, len(positions), batch_size):
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py | MaskPostProcessor.forward | 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
... | python | def forward(self, x, boxes):
"""
Arguments:
x (Tensor): the mask logits
boxes (list[BoxList]): bounding boxes that are used as
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results (list[BoxList]): one BoxList for each image, containing
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mlperf/training | compliance/mlperf_compliance/mlperf_log.py | get_caller | def get_caller(stack_index=2, root_dir=None):
''' 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. '''
caller = inspect.getframeinfo(inspect.stack()[stack_i... | python | 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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mlperf/training | compliance/mlperf_compliance/mlperf_log.py | _mlperf_print | def _mlperf_print(key, value=None, benchmark=None, stack_offset=0,
tag_set=None, deferred=False, root_dir=None,
extra_print=False, prefix=""):
''' Prints out an MLPerf Log Line.
key: The MLPerf log key such as 'CLOCK' or 'QUALITY'. See the list of log keys in the spec.
value: ... | python | def _mlperf_print(key, value=None, benchmark=None, stack_offset=0,
tag_set=None, deferred=False, root_dir=None,
extra_print=False, prefix=""):
''' Prints out an MLPerf Log Line.
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mlperf/training | reinforcement/tensorflow/minigo/rl_loop/shipname.py | generate | def generate(model_num):
"""Generates a new model name, given the model number."""
if model_num == 0:
new_name = 'bootstrap'
else:
new_name = random.choice(NAMES)
full_name = "%06d-%s" % (model_num, new_name)
return full_name | python | def generate(model_num):
"""Generates a new model name, given the model number."""
if model_num == 0:
new_name = 'bootstrap'
else:
new_name = random.choice(NAMES)
full_name = "%06d-%s" % (model_num, new_name)
return full_name | [
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mlperf/training | reinforcement/tensorflow/minigo/rl_loop/shipname.py | detect_model_num | def detect_model_num(string):
"""Takes a string related to a model name and extract its model number.
For example:
'000000-bootstrap.index' => 0
"""
match = re.match(MODEL_NUM_REGEX, string)
if match:
return int(match.group())
return None | python | def detect_model_num(string):
"""Takes a string related to a model name and extract its model number.
For example:
'000000-bootstrap.index' => 0
"""
match = re.match(MODEL_NUM_REGEX, string)
if match:
return int(match.group())
return None | [
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mlperf/training | reinforcement/tensorflow/minigo/rl_loop/shipname.py | detect_model_name | def detect_model_name(string):
"""Takes a string related to a model name and extract its model name.
For example:
'000000-bootstrap.index' => '000000-bootstrap'
"""
match = re.match(MODEL_NAME_REGEX, string)
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return None | python | def detect_model_name(string):
"""Takes a string related to a model name and extract its model name.
For example:
'000000-bootstrap.index' => '000000-bootstrap'
"""
match = re.match(MODEL_NAME_REGEX, string)
if match:
return match.group()
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_model.py | batch_norm | def batch_norm(inputs, training, data_format):
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# We set fused=True for a significant performance boost. See
# https://www.tensorflow.org/performance/performance_guide#common_fused_ops
outputs = tf.layers.batch_normalization(
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# https://www.tensorflow.org/performance/performance_guide#common_fused_ops
outputs = tf.layers.batch_normalization(
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_model.py | fixed_padding | def fixed_padding(inputs, kernel_size, data_format):
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Args:
inputs: A tensor of size [batch, channels, height_in, width_in] or
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_model.py | conv2d_fixed_padding | def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
"""Strided 2-D convolution with explicit padding."""
# The padding is consistent and is based only on `kernel_size`, not on the
# dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
inputs_for_logging = inputs
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"""Strided 2-D convolution with explicit padding."""
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_model.py | _bottleneck_block_v1 | def _bottleneck_block_v1(inputs, filters, training, projection_shortcut,
strides, data_format):
"""A single block for ResNet v1, with a bottleneck.
Similar to _building_block_v1(), except using the "bottleneck" blocks
described in:
Convolution then batch normalization then ReLU as de... | python | def _bottleneck_block_v1(inputs, filters, training, projection_shortcut,
strides, data_format):
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_model.py | block_layer | def block_layer(inputs, filters, bottleneck, block_fn, blocks, strides,
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"""Creates one layer of blocks for the ResNet model.
Args:
inputs: A tensor of size [batch, channels, height_in, width_in] or
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mlperf/training | image_classification/tensorflow/official/resnet/resnet_model.py | Model._custom_dtype_getter | def _custom_dtype_getter(self, getter, name, shape=None, dtype=DEFAULT_DTYPE,
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This function is a custom getter. A custom getter is a function with the
same signature as tf.get_variable, except it has... | python | def _custom_dtype_getter(self, getter, name, shape=None, dtype=DEFAULT_DTYPE,
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mlperf/training | rnn_translator/pytorch/seq2seq/data/sampler.py | DistributedSampler.init_rng | def init_rng(self):
"""
Creates new RNG, seed depends on current epoch idx.
"""
rng = torch.Generator()
seed = self.seeds[self.epoch]
logging.info(f'Sampler for epoch {self.epoch} uses seed {seed}')
rng.manual_seed(seed)
return rng | python | def init_rng(self):
"""
Creates new RNG, seed depends on current epoch idx.
"""
rng = torch.Generator()
seed = self.seeds[self.epoch]
logging.info(f'Sampler for epoch {self.epoch} uses seed {seed}')
rng.manual_seed(seed)
return rng | [
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mlperf/training | rnn_translator/pytorch/seq2seq/data/sampler.py | DistributedSampler.distribute_batches | def distribute_batches(self, indices):
"""
Assigns batches to workers.
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:param indices: torch.tensor with batch indices
"""
assert len(indices) == self.num_samples
indices = indices.view(-1, self.batch_size)
... | python | def distribute_batches(self, indices):
"""
Assigns batches to workers.
Consecutive ranks are getting consecutive batches.
:param indices: torch.tensor with batch indices
"""
assert len(indices) == self.num_samples
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mlperf/training | rnn_translator/pytorch/seq2seq/data/sampler.py | DistributedSampler.reshuffle_batches | def reshuffle_batches(self, indices, rng):
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Permutes global batches
:param indices: torch.tensor with batch indices
:param rng: instance of torch.Generator
"""
indices = indices.view(-1, self.global_batch_size)
num_batches = indices.shape[0]
order = to... | python | def reshuffle_batches(self, indices, rng):
"""
Permutes global batches
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indices = indices.view(-1, self.global_batch_size)
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/modeling/rpn/retinanet/retinanet.py | RetinaNetModule.forward | 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... | python | 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
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/layers/smooth_l1_loss.py | smooth_l1_loss | def smooth_l1_loss(input, target, beta=1. / 9, size_average=True):
"""
very similar to the smooth_l1_loss from pytorch, but with
the extra beta parameter
"""
n = torch.abs(input - target)
cond = n < beta
loss = torch.where(cond, 0.5 * n ** 2 / beta, n - 0.5 * beta)
if size_average:
... | python | def smooth_l1_loss(input, target, beta=1. / 9, size_average=True):
"""
very similar to the smooth_l1_loss from pytorch, but with
the extra beta parameter
"""
n = torch.abs(input - target)
cond = n < beta
loss = torch.where(cond, 0.5 * n ** 2 / beta, n - 0.5 * beta)
if size_average:
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mlperf/training | reinforcement/tensorflow/minigo/oneoffs/heatmap.py | eval_policy | def eval_policy(eval_positions):
"""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
Policy network outp... | python | def eval_policy(eval_positions):
"""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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mlperf/training | reinforcement/tensorflow/minigo/utils.py | dbg | def dbg(*objects, file=sys.stderr, flush=True, **kwargs):
"Helper function to print to stderr and flush"
print(*objects, file=file, flush=flush, **kwargs) | python | def dbg(*objects, file=sys.stderr, flush=True, **kwargs):
"Helper function to print to stderr and flush"
print(*objects, file=file, flush=flush, **kwargs) | [
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mlperf/training | reinforcement/tensorflow/minigo/utils.py | ensure_dir_exists | 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) | python | 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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mlperf/training | reinforcement/tensorflow/minigo/utils.py | parse_game_result | 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 | python | 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 | [
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mlperf/training | reinforcement/tensorflow/minigo/utils.py | iter_chunks | 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
... | python | 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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mlperf/training | reinforcement/tensorflow/minigo/utils.py | timer | def timer(message):
"Context manager for timing snippets of code."
tick = time.time()
yield
tock = time.time()
print("%s: %.3f seconds" % (message, (tock - tick))) | python | 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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mlperf/training | reinforcement/tensorflow/minigo/utils.py | logged_timer | 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)) | python | 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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mlperf/training | translation/tensorflow/transformer/data_download.py | find_file | 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... | python | 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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mlperf/training | translation/tensorflow/transformer/data_download.py | get_raw_files | 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
... | python | 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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mlperf/training | translation/tensorflow/transformer/data_download.py | txt_line_iterator | def txt_line_iterator(path):
"""Iterate through lines of file."""
with tf.gfile.Open(path) as f:
for line in f:
yield line.strip() | python | def txt_line_iterator(path):
"""Iterate through lines of file."""
with tf.gfile.Open(path) as f:
for line in f:
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mlperf/training | translation/tensorflow/transformer/data_download.py | compile_files | 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": ... | python | 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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mlperf/training | translation/tensorflow/transformer/data_download.py | write_file | 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") | python | 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") | [
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mlperf/training | translation/tensorflow/transformer/data_download.py | encode_and_save_files | 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... | python | 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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mlperf/training | translation/tensorflow/transformer/data_download.py | shard_filename | 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)) | python | 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)) | [
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mlperf/training | translation/tensorflow/transformer/data_download.py | shuffle_records | 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... | python | 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 = []
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mlperf/training | translation/tensorflow/transformer/data_download.py | dict_to_example | def dict_to_example(dictionary):
"""Converts a dictionary of string->int to a tf.Example."""
features = {}
for k, v in six.iteritems(dictionary):
features[k] = tf.train.Feature(int64_list=tf.train.Int64List(value=v))
return tf.train.Example(features=tf.train.Features(feature=features)) | python | def dict_to_example(dictionary):
"""Converts a dictionary of string->int to a tf.Example."""
features = {}
for k, v in six.iteritems(dictionary):
features[k] = tf.train.Feature(int64_list=tf.train.Int64List(value=v))
return tf.train.Example(features=tf.train.Features(feature=features)) | [
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mlperf/training | translation/tensorflow/transformer/data_download.py | all_exist | def all_exist(filepaths):
"""Returns true if all files in the list exist."""
for fname in filepaths:
if not tf.gfile.Exists(fname):
return False
return True | python | def all_exist(filepaths):
"""Returns true if all files in the list exist."""
for fname in filepaths:
if not tf.gfile.Exists(fname):
return False
return True | [
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mlperf/training | translation/tensorflow/transformer/data_download.py | main | def main(unused_argv):
"""Obtain training and evaluation data for the Transformer model."""
tf.logging.set_verbosity(tf.logging.INFO)
make_dir(FLAGS.raw_dir)
make_dir(FLAGS.data_dir)
# Get paths of download/extracted training and evaluation files.
tf.logging.info("Step 1/4: Downloading data from source")
... | python | def main(unused_argv):
"""Obtain training and evaluation data for the Transformer model."""
tf.logging.set_verbosity(tf.logging.INFO)
make_dir(FLAGS.raw_dir)
make_dir(FLAGS.data_dir)
# Get paths of download/extracted training and evaluation files.
tf.logging.info("Step 1/4: Downloading data from source")
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mlperf/training | translation/tensorflow/transformer/model/transformer.py | Transformer.encode | def encode(self, inputs, attention_bias):
"""Generate continuous representation for inputs.
Args:
inputs: int tensor with shape [batch_size, input_length].
attention_bias: float tensor with shape [batch_size, 1, 1, input_length]
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float tensor with shape [batch_size, input_length,... | python | def encode(self, inputs, attention_bias):
"""Generate continuous representation for inputs.
Args:
inputs: int tensor with shape [batch_size, input_length].
attention_bias: float tensor with shape [batch_size, 1, 1, input_length]
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mlperf/training | translation/tensorflow/transformer/model/transformer.py | Transformer.decode | def decode(self, targets, encoder_outputs, attention_bias):
"""Generate logits for each value in the target sequence.
Args:
targets: target values for the output sequence.
int tensor with shape [batch_size, target_length]
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... | python | def decode(self, targets, encoder_outputs, attention_bias):
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targets: target values for the output sequence.
int tensor with shape [batch_size, target_length]
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mlperf/training | translation/tensorflow/transformer/model/transformer.py | Transformer._get_symbols_to_logits_fn | def _get_symbols_to_logits_fn(self, max_decode_length):
"""Returns a decoding function that calculates logits of the next tokens."""
timing_signal = model_utils.get_position_encoding(
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decoder_self_attention_bias = model_utils.get_decoder_self_attentio... | python | def _get_symbols_to_logits_fn(self, max_decode_length):
"""Returns a decoding function that calculates logits of the next tokens."""
timing_signal = model_utils.get_position_encoding(
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decoder_self_attention_bias = model_utils.get_decoder_self_attentio... | [
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mlperf/training | translation/tensorflow/transformer/model/transformer.py | Transformer.predict | def predict(self, encoder_outputs, encoder_decoder_attention_bias):
"""Return predicted sequence."""
batch_size = tf.shape(encoder_outputs)[0]
input_length = tf.shape(encoder_outputs)[1]
max_decode_length = input_length + self.params.extra_decode_length
symbols_to_logits_fn = self._get_symbols_to_l... | python | def predict(self, encoder_outputs, encoder_decoder_attention_bias):
"""Return predicted sequence."""
batch_size = tf.shape(encoder_outputs)[0]
input_length = tf.shape(encoder_outputs)[1]
max_decode_length = input_length + self.params.extra_decode_length
symbols_to_logits_fn = self._get_symbols_to_l... | [
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mlperf/training | data_generation/fractal_graph_expansions/run_expansion.py | _create_row_col_indices | 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(
lambda x: user_id_to_user_id... | python | 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(
lambda x: user_id_to_user_id... | [
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mlperf/training | data_generation/fractal_graph_expansions/run_expansion.py | _preprocess_movie_lens | def _preprocess_movie_lens(ratings_df):
"""Separate the rating datafram into train and test sets.
Filters out users with less than two distinct timestamps. Creates train set
and test set. The test set contains all the last interactions of users with
more than two distinct timestamps.
Args:
ratings_df: p... | python | def _preprocess_movie_lens(ratings_df):
"""Separate the rating datafram into train and test sets.
Filters out users with less than two distinct timestamps. Creates train set
and test set. The test set contains all the last interactions of users with
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ratings_df: p... | [
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mlperf/training | single_stage_detector/ssd/coco.py | COCO.getAnnIds | def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
"""
Get ann ids that satisfy given filter conditions. default skips that filter
:param imgIds (int array) : get anns for given imgs
catIds (int array) : get anns for given cats
areaRng (f... | python | def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
"""
Get ann ids that satisfy given filter conditions. default skips that filter
:param imgIds (int array) : get anns for given imgs
catIds (int array) : get anns for given cats
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mlperf/training | single_stage_detector/ssd/coco.py | COCO.getCatIds | def getCatIds(self, catNms=[], supNms=[], catIds=[]):
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filtering parameters. default skips that filter.
:param catNms (str array) : get cats for given cat names
:param supNms (str array) : get cats for given supercategory names
:param catIds (int array) : get cats for given... | python | def getCatIds(self, catNms=[], supNms=[], catIds=[]):
"""
filtering parameters. default skips that filter.
:param catNms (str array) : get cats for given cat names
:param supNms (str array) : get cats for given supercategory names
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mlperf/training | single_stage_detector/ssd/coco.py | COCO.getImgIds | def getImgIds(self, imgIds=[], catIds=[]):
'''
Get img ids that satisfy given filter conditions.
:param imgIds (int array) : get imgs for given ids
:param catIds (int array) : get imgs with all given cats
:return: ids (int array) : integer array of img ids
'''
im... | python | def getImgIds(self, imgIds=[], catIds=[]):
'''
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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mlperf/training | single_stage_detector/ssd/coco.py | COCO.loadAnns | def loadAnns(self, ids=[]):
"""
Load anns with the specified ids.
:param ids (int array) : integer ids specifying anns
:return: anns (object array) : loaded ann objects
"""
if _isArrayLike(ids):
return [self.anns[id] for id in ids]
elif type(ids)... | python | def loadAnns(self, ids=[]):
"""
Load anns with the specified ids.
:param ids (int array) : integer ids specifying anns
:return: anns (object array) : loaded ann objects
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mlperf/training | single_stage_detector/ssd/coco.py | COCO.loadCats | 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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elif type(ids)... | python | 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
"""
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mlperf/training | single_stage_detector/ssd/coco.py | COCO.loadImgs | def loadImgs(self, ids=[]):
"""
Load anns with the specified ids.
:param ids (int array) : integer ids specifying img
:return: imgs (object array) : loaded img objects
"""
if _isArrayLike(ids):
return [self.imgs[id] for id in ids]
elif type(ids) ... | python | def loadImgs(self, ids=[]):
"""
Load anns with the specified ids.
:param ids (int array) : integer ids specifying img
:return: imgs (object array) : loaded img objects
"""
if _isArrayLike(ids):
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mlperf/training | single_stage_detector/ssd/coco.py | COCO.showAnns | def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
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if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
datasetType = 'inst... | python | def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
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mlperf/training | single_stage_detector/ssd/coco.py | COCO.loadRes | def loadRes(self, resFile):
"""
Load result file and return a result api object.
:param resFile (str) : file name of result file
:return: res (obj) : result api object
"""
res = COCO()
res.dataset['images'] = [img for img in self.dataset['images']]
... | python | def loadRes(self, resFile):
"""
Load result file and return a result api object.
:param resFile (str) : file name of result file
:return: res (obj) : result api object
"""
res = COCO()
res.dataset['images'] = [img for img in self.dataset['images']]
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mlperf/training | single_stage_detector/ssd/coco.py | COCO.annToRLE | def annToRLE(self, ann):
"""
Convert annotation which can be polygons, uncompressed RLE to RLE.
:return: binary mask (numpy 2D array)
"""
t = self.imgs[ann['image_id']]
h, w = t['height'], t['width']
segm = ann['segmentation']
if type(segm) == list:
... | python | def annToRLE(self, ann):
"""
Convert annotation which can be polygons, uncompressed RLE to RLE.
:return: binary mask (numpy 2D array)
"""
t = self.imgs[ann['image_id']]
h, w = t['height'], t['width']
segm = ann['segmentation']
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mlperf/training | single_stage_detector/ssd/coco.py | COCO.annToMask | def annToMask(self, ann):
"""
Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
:return: binary mask (numpy 2D array)
"""
rle = self.annToRLE(ann)
m = maskUtils.decode(rle)
return m | python | def annToMask(self, ann):
"""
Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
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"""
rle = self.annToRLE(ann)
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py | GeneralizedRCNN.forward | def forward(self, images, targets=None):
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Arguments:
images (list[Tensor] or ImageList): images to be processed
targets (list[BoxList]): ground-truth boxes present in the image (optional)
Returns:
result (list[BoxList] or dict[Tensor]): the output from the... | python | def forward(self, images, targets=None):
"""
Arguments:
images (list[Tensor] or ImageList): images to be processed
targets (list[BoxList]): ground-truth boxes present in the image (optional)
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mlperf/training | data_generation/fractal_graph_expansions/graph_analysis.py | sparse_svd | def sparse_svd(sparse_matrix, num_values, max_iter):
"""Wrapper around SciPy's Singular Value Decomposition for sparse matrices.
Args:
sparse_matrix: a SciPy sparse matrix (typically large).
num_values: the number of largest singular values to compute.
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sparse_matrix: a SciPy sparse matrix (typically large).
num_values: the number of largest singular values to compute.
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mlperf/training | translation/tensorflow/transformer/model/embedding_layer.py | EmbeddingSharedWeights.call | def call(self, x):
"""Get token embeddings of x.
Args:
x: An int64 tensor with shape [batch_size, length]
Returns:
embeddings: float32 tensor with shape [batch_size, length, embedding_size]
padding: float32 tensor with shape [batch_size, length] indicating the
locations of the pad... | python | def call(self, x):
"""Get token embeddings of x.
Args:
x: An int64 tensor with shape [batch_size, length]
Returns:
embeddings: float32 tensor with shape [batch_size, length, embedding_size]
padding: float32 tensor with shape [batch_size, length] indicating the
locations of the pad... | [
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] | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/translation/tensorflow/transformer/model/embedding_layer.py#L53-L75 | train | Get token embeddings of x. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
mlperf/training | translation/tensorflow/transformer/model/embedding_layer.py | EmbeddingSharedWeights.linear | def linear(self, x):
"""Computes logits by running x through a linear layer.
Args:
x: A float32 tensor with shape [batch_size, length, hidden_size]
Returns:
float32 tensor with shape [batch_size, length, vocab_size].
"""
with tf.name_scope("presoftmax_linear"):
batch_size = tf.sha... | python | def linear(self, x):
"""Computes logits by running x through a linear layer.
Args:
x: A float32 tensor with shape [batch_size, length, hidden_size]
Returns:
float32 tensor with shape [batch_size, length, vocab_size].
"""
with tf.name_scope("presoftmax_linear"):
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] | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/translation/tensorflow/transformer/model/embedding_layer.py#L77-L92 | train | Computes logits by running x through a linear layer. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
mlperf/training | rnn_translator/pytorch/seq2seq/train/trainer.py | Seq2SeqTrainer.iterate | def iterate(self, src, tgt, update=True, training=True):
"""
Performs one iteration of the training/validation.
:param src: batch of examples from the source language
:param tgt: batch of examples from the target language
:param update: if True: optimizer does update of the weig... | python | def iterate(self, src, tgt, update=True, training=True):
"""
Performs one iteration of the training/validation.
:param src: batch of examples from the source language
:param tgt: batch of examples from the target language
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] | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/rnn_translator/pytorch/seq2seq/train/trainer.py#L128-L173 | train | Performs one iteration of the training and validation of the language. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
mlperf/training | rnn_translator/pytorch/seq2seq/train/trainer.py | Seq2SeqTrainer.preallocate | def preallocate(self, data_loader, training):
"""
Generates maximum sequence length batch and runs forward and backward
pass without updating model parameters.
:param data_loader: data loader
:param training: if True preallocates memory for backward pass
"""
batc... | python | def preallocate(self, data_loader, training):
"""
Generates maximum sequence length batch and runs forward and backward
pass without updating model parameters.
:param data_loader: data loader
:param training: if True preallocates memory for backward pass
"""
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mlperf/training | rnn_translator/pytorch/seq2seq/train/trainer.py | Seq2SeqTrainer.optimize | 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... | python | 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()
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