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Encode a set of proposals with respect to some
reference boxes
Arguments:
reference_boxes (Tensor): reference boxes
proposals (Tensor): boxes to be encoded | def encode(self, reference_boxes, proposals):
TO_REMOVE = 1 # TODO remove
ex_widths = proposals[:, 2] - proposals[:, 0] + TO_REMOVE
ex_heights = proposals[:, 3] - proposals[:, 1] + TO_REMOVE
ex_ctr_x = proposals[:, 0] + 0.5 * ex_widths
ex_ctr_y = proposals[:, 1] + 0.5 * ex_heights
gt_widths = reference_boxes[:, 2] - reference_boxes[:, 0] + TO_REMOVE
gt_heights = reference_boxes[:, 3] - reference_boxes[:, 1] + TO_REMOVE
gt_ctr_x = reference_boxes[:, 0] + 0.5 * gt_widths
gt_ctr_y = reference_boxes[:, 1] + 0.5 * gt_heights
wx, wy, ww, wh = self.weights
targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths
targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights
targets_dw = ww * torch.log(gt_widths / ex_widths)
targets_dh = wh * torch.log(gt_heights / ex_heights)
targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1)
return targets | 123,156 |
From a set of original boxes and encoded relative box offsets,
get the decoded boxes.
Arguments:
rel_codes (Tensor): encoded boxes
boxes (Tensor): reference boxes. | def decode(self, rel_codes, boxes):
boxes = boxes.to(rel_codes.dtype)
TO_REMOVE = 1 # TODO remove
widths = boxes[:, 2] - boxes[:, 0] + TO_REMOVE
heights = boxes[:, 3] - boxes[:, 1] + TO_REMOVE
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
wx, wy, ww, wh = self.weights
dx = rel_codes[:, 0::4] / wx
dy = rel_codes[:, 1::4] / wy
dw = rel_codes[:, 2::4] / ww
dh = rel_codes[:, 3::4] / wh
# Prevent sending too large values into torch.exp()
dw = torch.clamp(dw, max=self.bbox_xform_clip)
dh = torch.clamp(dh, max=self.bbox_xform_clip)
pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
pred_w = torch.exp(dw) * widths[:, None]
pred_h = torch.exp(dh) * heights[:, None]
pred_boxes = torch.zeros_like(rel_codes)
# x1
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
# y1
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
# x2 (note: "- 1" is correct; don't be fooled by the asymmetry)
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1
# y2 (note: "- 1" is correct; don't be fooled by the asymmetry)
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1
return pred_boxes | 123,157 |
Create a single numpy array from a dataset.
The dataset must have only one dimension, that is,
the length of its `output_shapes` and `output_types`
is 1, and its output shape must be `[]`, that is,
every tensor in the dataset must be a scalar.
Args:
ds: a TF Dataset.
batch_size: how many elements to read per pass
Returns:
a single numpy array. | def make_single_array(ds, batch_size=8*1024):
if isinstance(ds.output_types, tuple) or isinstance(ds.output_shapes, tuple):
raise ValueError('Dataset must have a single type and shape')
nshapes = len(ds.output_shapes)
if nshapes > 0:
raise ValueError('Dataset must be comprised of scalars (TensorShape=[])')
batches = []
with tf.Session() as sess:
ds = ds.batch(batch_size)
iterator = ds.make_initializable_iterator()
sess.run(iterator.initializer)
get_next = iterator.get_next()
with tqdm(desc='Elements', unit_scale=1) as pbar:
try:
while True:
batches.append(sess.run(get_next))
pbar.update(len(batches[-1]))
except tf.errors.OutOfRangeError:
pass
if batches:
return np.concatenate(batches)
return np.array([], dtype=ds.output_types.as_numpy_dtype) | 123,179 |
Given dataset of key names, return histogram of moves/game.
Move counts are written by the game players, so
this is mostly useful for repair or backfill.
Args:
sess: TF session
ds: TF dataset containing game move keys.
batch_size: performance tuning parameter | def _histogram_move_keys_by_game(sess, ds, batch_size=8*1024):
ds = ds.batch(batch_size)
# Turns 'g_0000001234_m_133' into 'g_0000001234'
ds = ds.map(lambda x: tf.strings.substr(x, 0, 12))
iterator = ds.make_initializable_iterator()
sess.run(iterator.initializer)
get_next = iterator.get_next()
h = collections.Counter()
try:
while True:
h.update(sess.run(get_next))
except tf.errors.OutOfRangeError:
pass
# NOTE: Cannot be truly sure the count is right till the end.
return h | 123,180 |
Constructor.
Args:
project_name: string name of GCP project having table.
instance_name: string name of CBT instance in project.
table_name: string name of CBT table in instance. | def __init__(self, project_name, instance_name, table_name):
self.btspec = BigtableSpec(project_name, instance_name, table_name)
self.bt_table = bigtable.Client(
self.btspec.project, admin=True).instance(
self.btspec.instance).table(self.btspec.table)
self.tf_table = tf.contrib.cloud.BigtableClient(
self.btspec.project,
self.btspec.instance).table(self.btspec.table) | 123,186 |
Delete rows related to the given game range.
Args:
format_str: a string to `.format()` by the game numbers
in order to create the row prefixes.
start_game: the starting game number of the deletion.
end_game: the ending game number of the deletion. | def delete_row_range(self, format_str, start_game, end_game):
row_keys = make_single_array(
self.tf_table.keys_by_range_dataset(
format_str.format(start_game),
format_str.format(end_game)))
row_keys = list(row_keys)
if not row_keys:
utils.dbg('No rows left for games %d..%d' % (
start_game, end_game))
return
utils.dbg('Deleting %d rows: %s..%s' % (
len(row_keys), row_keys[0], row_keys[-1]))
# Reverse the keys so that the queue is left in a more
# sensible end state if you change your mind (say, due to a
# mistake in the timestamp) and abort the process: there will
# be a bit trimmed from the end, rather than a bit
# trimmed out of the middle.
row_keys.reverse()
total_keys = len(row_keys)
utils.dbg('Deleting total of %d keys' % total_keys)
concurrency = min(MAX_BT_CONCURRENCY,
multiprocessing.cpu_count() * 2)
with multiprocessing.Pool(processes=concurrency) as pool:
batches = []
with tqdm(desc='Keys', unit_scale=2, total=total_keys) as pbar:
for b in utils.iter_chunks(bigtable.row.MAX_MUTATIONS,
row_keys):
pbar.update(len(b))
batches.append((self.btspec, b))
if len(batches) >= concurrency:
pool.map(_delete_rows, batches)
batches = []
pool.map(_delete_rows, batches)
batches = [] | 123,191 |
Require a given number of fresh games to be played.
Args:
number_fresh: integer, number of new fresh games needed
Increments the cell `table_state=metadata:wait_for_game_number`
by the given number of games. This will cause
`self.wait_for_fresh_games()` to block until the game
counter has reached this number. | def require_fresh_games(self, number_fresh):
latest = self.latest_game_number
table_state = self.bt_table.row(TABLE_STATE)
table_state.set_cell(METADATA, WAIT_CELL, int(latest + number_fresh))
table_state.commit()
print("== Setting wait cell to ", int(latest + number_fresh), flush=True) | 123,194 |
Block caller until required new games have been played.
Args:
poll_interval: number of seconds to wait between checks
If the cell `table_state=metadata:wait_for_game_number` exists,
then block the caller, checking every `poll_interval` seconds,
until `table_state=metadata:game_counter is at least the value
in that cell. | def wait_for_fresh_games(self, poll_interval=15.0):
wait_until_game = self.read_wait_cell()
if not wait_until_game:
return
latest_game = self.latest_game_number
last_latest = latest_game
while latest_game < wait_until_game:
utils.dbg('Latest game {} not yet at required game {} '
'(+{}, {:0.3f} games/sec)'.format(
latest_game,
wait_until_game,
latest_game - last_latest,
(latest_game - last_latest) / poll_interval
))
time.sleep(poll_interval)
last_latest = latest_game
latest_game = self.latest_game_number | 123,195 |
Count the total moves in a game range.
Args:
game_begin: integer, starting game
game_end: integer, ending game
Uses the `ct_` keyspace for rapid move summary. | def count_moves_in_game_range(self, game_begin, game_end):
rows = self.bt_table.read_rows(
ROWCOUNT_PREFIX.format(game_begin),
ROWCOUNT_PREFIX.format(game_end),
filter_=bigtable_row_filters.ColumnRangeFilter(
METADATA, MOVE_COUNT, MOVE_COUNT))
return sum([int(r.cell_value(METADATA, MOVE_COUNT)) for r in rows]) | 123,197 |
Randomly choose a given number of moves from the last n games.
Args:
n: number of games at the end of this GameQueue to source.
moves: number of moves to be sampled from `n` games.
shuffle: if True, shuffle the selected moves.
column_family: name of the column family containing move examples.
column: name of the column containing move examples.
Returns:
a dataset containing the selected moves. | def moves_from_last_n_games(self, n, moves, shuffle,
column_family, column):
self.wait_for_fresh_games()
latest_game = self.latest_game_number
utils.dbg('Latest game in %s: %s' % (self.btspec.table, latest_game))
if latest_game == 0:
raise ValueError('Cannot find a latest game in the table')
start = int(max(0, latest_game - n))
ds = self.moves_from_games(start, latest_game, moves, shuffle,
column_family, column)
return ds | 123,199 |
Add move counts from the given histogram to the table.
Used to update the move counts in an existing table. Should
not be needed except for backfill or repair.
Args:
sess: TF session to use for doing a Bigtable write.
tf_table: TF Cloud Bigtable to use for writing.
h: a dictionary keyed by game row prefix ("g_0023561") whose values
are the move counts for each game. | def _write_move_counts(self, sess, h):
def gen():
for k, v in h.items():
# The keys in the histogram may be of type 'bytes'
k = str(k, 'utf-8')
vs = str(v)
yield (k.replace('g_', 'ct_') + '_%d' % v, vs)
yield (k + '_m_000', vs)
mc = tf.data.Dataset.from_generator(gen, (tf.string, tf.string))
wr_op = self.tf_table.write(mc,
column_families=[METADATA],
columns=[MOVE_COUNT])
sess.run(wr_op) | 123,200 |
Input function which provides batches for train or eval.
Args:
is_training: A boolean denoting whether the input is for training.
data_dir: The directory containing the input data.
batch_size: The number of samples per batch.
num_epochs: The number of epochs to repeat the dataset.
num_gpus: The number of gpus used for training.
dtype: Data type to use for images/features
Returns:
A dataset that can be used for iteration. | def input_fn(is_training, data_dir, batch_size, num_epochs=1, num_gpus=None,
dtype=tf.float32):
mlperf_log.resnet_print(key=mlperf_log.INPUT_ORDER)
filenames = get_filenames(is_training, data_dir)
dataset = tf.data.Dataset.from_tensor_slices(filenames)
if is_training:
# Shuffle the input files
dataset = dataset.shuffle(buffer_size=_NUM_TRAIN_FILES)
# Convert to individual records
dataset = dataset.flat_map(tf.data.TFRecordDataset)
return resnet_run_loop.process_record_dataset(
dataset=dataset,
is_training=is_training,
batch_size=batch_size,
shuffle_buffer=_SHUFFLE_BUFFER,
parse_record_fn=parse_record,
num_epochs=num_epochs,
num_gpus=num_gpus,
examples_per_epoch=_NUM_IMAGES['train'] if is_training else None,
dtype=dtype
) | 123,205 |
Retrieve the size of each block_layer in the ResNet model.
The number of block layers used for the Resnet model varies according
to the size of the model. This helper grabs the layer set we want, throwing
an error if a non-standard size has been selected.
Args:
resnet_size: The number of convolutional layers needed in the model.
Returns:
A list of block sizes to use in building the model.
Raises:
KeyError: if invalid resnet_size is received. | def _get_block_sizes(resnet_size):
choices = {
18: [2, 2, 2, 2],
34: [3, 4, 6, 3],
50: [3, 4, 6, 3],
101: [3, 4, 23, 3],
152: [3, 8, 36, 3],
200: [3, 24, 36, 3]
}
try:
return choices[resnet_size]
except KeyError:
err = ('Could not find layers for selected Resnet size.\n'
'Size received: {}; sizes allowed: {}.'.format(
resnet_size, choices.keys()))
raise ValueError(err) | 123,206 |
Builds just the inference part of the model graph.
Args:
features: input features tensor.
training: True if the model is training.
params: A dictionary
Returns:
(policy_output, value_output, logits) tuple of tensors. | def model_inference_fn(features, training, params):
mg_batchn = functools.partial(
tf.layers.batch_normalization,
axis=-1,
momentum=.95,
epsilon=1e-5,
center=True,
scale=True,
fused=True,
training=training)
mg_conv2d = functools.partial(
tf.layers.conv2d,
filters=params['conv_width'],
kernel_size=3,
padding="same",
data_format="channels_last",
use_bias=False)
mg_global_avgpool2d = functools.partial(
tf.layers.average_pooling2d,
pool_size=go.N,
strides=1,
padding="valid",
data_format="channels_last")
def mg_activation(inputs):
if FLAGS.use_swish:
return tf.nn.swish(inputs)
return tf.nn.relu(inputs)
def residual_inner(inputs):
conv_layer1 = mg_batchn(mg_conv2d(inputs))
initial_output = mg_activation(conv_layer1)
conv_layer2 = mg_batchn(mg_conv2d(initial_output))
return conv_layer2
def mg_res_layer(inputs):
residual = residual_inner(inputs)
output = mg_activation(inputs + residual)
return output
def mg_squeeze_excitation_layer(inputs):
# Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-Excitation Networks.
# 2018 IEEE/CVF Conference on Computer Vision, 7132-7141.
# arXiv:1709.01507 [cs.CV]
channels = params['conv_width']
ratio = FLAGS.SE_ratio
assert channels % ratio == 0
residual = residual_inner(inputs)
pool = mg_global_avgpool2d(residual)
fc1 = tf.layers.dense(pool, units=channels // ratio)
squeeze = mg_activation(fc1)
if FLAGS.use_SE_bias:
fc2 = tf.layers.dense(squeeze, units=2*channels)
# Channels_last so axis = 3 = -1
gamma, bias = tf.split(fc2, 2, axis=3)
else:
gamma = tf.layers.dense(squeeze, units=channels)
bias = 0
sig = tf.nn.sigmoid(gamma)
# Explicitly signal the broadcast.
scale = tf.reshape(sig, [-1, 1, 1, channels])
excitation = tf.multiply(scale, residual) + bias
return mg_activation(inputs + excitation)
initial_block = mg_activation(mg_batchn(mg_conv2d(features)))
# the shared stack
shared_output = initial_block
for _ in range(params['trunk_layers']):
if FLAGS.use_SE or FLAGS.use_SE_bias:
shared_output = mg_squeeze_excitation_layer(shared_output)
else:
shared_output = mg_res_layer(shared_output)
# Policy head
policy_conv = mg_conv2d(
shared_output, filters=params['policy_conv_width'], kernel_size=1)
policy_conv = mg_activation(mg_batchn(policy_conv, center=False, scale=False))
logits = tf.layers.dense(
tf.reshape(
policy_conv, [-1, params['policy_conv_width'] * go.N * go.N]),
go.N * go.N + 1)
policy_output = tf.nn.softmax(logits, name='policy_output')
# Value head
value_conv = mg_conv2d(
shared_output, filters=params['value_conv_width'], kernel_size=1)
value_conv = mg_activation(mg_batchn(value_conv, center=False, scale=False))
value_fc_hidden = mg_activation(tf.layers.dense(
tf.reshape(value_conv, [-1, params['value_conv_width'] * go.N * go.N]),
params['fc_width']))
value_output = tf.nn.tanh(
tf.reshape(tf.layers.dense(value_fc_hidden, 1), [-1]),
name='value_output')
return policy_output, value_output, logits | 123,248 |
Take the latest checkpoint and copy it to model_path.
Assumes that all relevant model files are prefixed by the same name.
(For example, foo.index, foo.meta and foo.data-00000-of-00001).
Args:
model_path: The path (can be a gs:// path) to export model | def export_model(model_path):
estimator = tf.estimator.Estimator(model_fn, model_dir=FLAGS.work_dir,
params=FLAGS.flag_values_dict())
latest_checkpoint = estimator.latest_checkpoint()
all_checkpoint_files = tf.gfile.Glob(latest_checkpoint + '*')
for filename in all_checkpoint_files:
suffix = filename.partition(latest_checkpoint)[2]
destination_path = model_path + suffix
print("Copying {} to {}".format(filename, destination_path))
tf.gfile.Copy(filename, destination_path) | 123,254 |
Parses the output of --helpfull.
Args:
help_output: str, the full output of --helpfull.
Returns:
A set of flags that are valid flags. | def parse_helpfull_output(help_output, regex=FLAG_HELP_RE_PY):
valid_flags = set()
for _, no_prefix, flag_name in regex.findall(help_output):
valid_flags.add('--' + flag_name)
if no_prefix:
valid_flags.add('--no' + flag_name)
return valid_flags | 123,274 |
Prepares a subprocess command by running --helpfull and masking flags.
Args:
subprocess_cmd: List[str], what would be passed into subprocess.call()
i.e. ['python', 'train.py', '--flagfile=flags']
Returns:
['python', 'train.py', '--train_flag=blah', '--more_flags'] | def prepare_subprocess_cmd(subprocess_cmd):
help_cmd = subprocess_cmd + ['--helpfull']
help_output = subprocess.run(help_cmd, stdout=subprocess.PIPE).stdout
help_output = help_output.decode('ascii')
if 'python' in subprocess_cmd[0]:
valid_flags = parse_helpfull_output(help_output)
else:
valid_flags = parse_helpfull_output(help_output, regex=FLAG_HELP_RE_CC)
parsed_flags = flags.FlagValues().read_flags_from_files(subprocess_cmd[1:])
filtered_flags = filter_flags(parsed_flags, valid_flags)
return [subprocess_cmd[0]] + filtered_flags | 123,276 |
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 tensor with shape [batch_size, num_heads, length, hidden_size/num_heads] | def split_heads(self, x):
with tf.name_scope("split_heads"):
batch_size = tf.shape(x)[0]
length = tf.shape(x)[1]
# Calculate depth of last dimension after it has been split.
depth = (self.hidden_size // self.num_heads)
# Split the last dimension
x = tf.reshape(x, [batch_size, length, self.num_heads, depth])
# Transpose the result
return tf.transpose(x, [0, 2, 1, 3]) | 123,343 |
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] | def combine_heads(self, x):
with tf.name_scope("combine_heads"):
batch_size = tf.shape(x)[0]
length = tf.shape(x)[2]
x = tf.transpose(x, [0, 2, 1, 3]) # --> [batch, length, num_heads, depth]
return tf.reshape(x, [batch_size, length, self.hidden_size]) | 123,344 |
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
character's Unicode code point.
3. Appends "_" to mark the end of a token.
Args:
token: unicode string to be escaped
alphabet: list of all known characters
Returns:
escaped string | def _escape_token(token, alphabet):
r
token = token.replace(u"\\", u"\\\\").replace(u"_", u"\\u")
ret = [c if c in alphabet and c != u"\n" else r"\%d;" % ord(c) for c in token]
return u"".join(ret) + "_" | 123,352 |
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
Returns:
unescaped string | def _unescape_token(token):
r
def match(m):
r
# Check if the matched strings are '\u' or '\\'.
if m.group(1) is None:
return u"_" if m.group(0) == u"\\u" else u"\\"
# If m.group(1) exists, try and return unicode character.
try:
return six.unichr(int(m.group(1)))
except (ValueError, OverflowError) as _:
return _UNDEFINED_UNICODE
# Use match function to replace escaped substrings in the token.
return _UNESCAPE_REGEX.sub(match, token) | 123,353 |
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 number of bytes that will be read from each file.
Returns:
Dictionary mapping tokens to the number of times they appear in the sampled
lines from the files. | def _count_tokens(files, file_byte_limit=1e6):
token_counts = collections.defaultdict(int)
for filepath in files:
with tf.gfile.Open(filepath, mode="r") as reader:
file_byte_budget = file_byte_limit
counter = 0
lines_to_skip = int(reader.size() / (file_byte_budget * 2))
for line in reader:
if counter < lines_to_skip:
counter += 1
else:
if file_byte_budget < 0:
break
line = line.strip()
file_byte_budget -= len(line)
counter = 0
# Add words to token counts
for token in _split_string_to_tokens(_native_to_unicode(line)):
token_counts[token] += 1
return token_counts | 123,354 |
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. | def _filter_and_bucket_subtokens(subtoken_counts, min_count):
# Create list of buckets, where subtokens in bucket i have length i.
subtoken_buckets = []
for subtoken, count in six.iteritems(subtoken_counts):
if count < min_count: # Filter out subtokens that don't appear enough
continue
while len(subtoken_buckets) <= len(subtoken):
subtoken_buckets.append(set())
subtoken_buckets[len(subtoken)].add(subtoken)
return subtoken_buckets | 123,359 |
Add operations to classify a batch of input images.
Args:
inputs: A Tensor representing a batch of input images.
training: A boolean. Set to True to add operations required only when
training the classifier.
Returns:
A logits Tensor with shape [<batch_size>, self.num_classes]. | def __call__(self, inputs, training):
# Drop batch size from shape logging.
mlperf_log.resnet_print(key=mlperf_log.MODEL_HP_INITIAL_SHAPE,
value=inputs.shape.as_list()[1:])
with self._model_variable_scope():
if self.data_format == 'channels_first':
# Convert the inputs from channels_last (NHWC) to channels_first (NCHW).
# This provides a large performance boost on GPU. See
# https://www.tensorflow.org/performance/performance_guide#data_formats
inputs = tf.transpose(inputs, [0, 3, 1, 2])
inputs = conv2d_fixed_padding(
inputs=inputs, filters=self.num_filters, kernel_size=self.kernel_size,
strides=self.conv_stride, data_format=self.data_format)
inputs = tf.identity(inputs, 'initial_conv')
# We do not include batch normalization or activation functions in V2
# for the initial conv1 because the first ResNet unit will perform these
# for both the shortcut and non-shortcut paths as part of the first
# block's projection. Cf. Appendix of [2].
if self.resnet_version == 1:
inputs = batch_norm(inputs, training, self.data_format)
mlperf_log.resnet_print(key=mlperf_log.MODEL_HP_RELU)
inputs = tf.nn.relu(inputs)
if self.first_pool_size:
pooled_inputs = tf.layers.max_pooling2d(
inputs=inputs, pool_size=self.first_pool_size,
strides=self.first_pool_stride, padding='SAME',
data_format=self.data_format)
resnet_log_helper.log_max_pool(input_tensor=inputs, output_tensor=pooled_inputs)
inputs = tf.identity(pooled_inputs, 'initial_max_pool')
for i, num_blocks in enumerate(self.block_sizes):
num_filters = self.num_filters * (2**i)
inputs = block_layer(
inputs=inputs, filters=num_filters, bottleneck=self.bottleneck,
block_fn=self.block_fn, blocks=num_blocks,
strides=self.block_strides[i], training=training,
name='block_layer{}'.format(i + 1), data_format=self.data_format)
# Only apply the BN and ReLU for model that does pre_activation in each
# building/bottleneck block, eg resnet V2.
if self.pre_activation:
inputs = batch_norm(inputs, training, self.data_format)
mlperf_log.resnet_print(key=mlperf_log.MODEL_HP_RELU)
inputs = tf.nn.relu(inputs)
# The current top layer has shape
# `batch_size x pool_size x pool_size x final_size`.
# ResNet does an Average Pooling layer over pool_size,
# but that is the same as doing a reduce_mean. We do a reduce_mean
# here because it performs better than AveragePooling2D.
axes = [2, 3] if self.data_format == 'channels_first' else [1, 2]
inputs = tf.reduce_mean(inputs, axes, keepdims=True)
inputs = tf.identity(inputs, 'final_reduce_mean')
inputs = tf.reshape(inputs, [-1, self.final_size])
mlperf_log.resnet_print(key=mlperf_log.MODEL_HP_DENSE,
value=self.num_classes)
inputs = tf.layers.dense(inputs=inputs, units=self.num_classes)
inputs = tf.identity(inputs, 'final_dense')
# Drop batch size from shape logging.
mlperf_log.resnet_print(key=mlperf_log.MODEL_HP_FINAL_SHAPE,
value=inputs.shape.as_list()[1:])
return inputs | 123,431 |
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 target language
}
tag: String to append to the compiled filename.
Returns:
Full path of compiled input and target files. | def compile_files(raw_dir, raw_files, tag):
tf.logging.info("Compiling files with tag %s." % tag)
filename = "%s-%s" % (_PREFIX, tag)
input_compiled_file = os.path.join(raw_dir, filename + ".lang1")
target_compiled_file = os.path.join(raw_dir, filename + ".lang2")
with tf.gfile.Open(input_compiled_file, mode="w") as input_writer:
with tf.gfile.Open(target_compiled_file, mode="w") as target_writer:
for i in range(len(raw_files["inputs"])):
input_file = raw_files["inputs"][i]
target_file = raw_files["targets"][i]
tf.logging.info("Reading files %s and %s." % (input_file, target_file))
write_file(input_writer, input_file)
write_file(target_writer, target_file)
return input_compiled_file, target_compiled_file | 123,465 |
Initialize layers to build Transformer model.
Args:
params: hyperparameter object defining layer sizes, dropout values, etc.
train: boolean indicating whether the model is in training mode. Used to
determine if dropout layers should be added. | def __init__(self, params, train):
self.train = train
self.params = params
self.embedding_softmax_layer = embedding_layer.EmbeddingSharedWeights(
params.vocab_size, params.hidden_size)
self.encoder_stack = EncoderStack(params, train)
self.decoder_stack = DecoderStack(params, train) | 123,474 |
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]
Returns:
float tensor with shape [batch_size, input_length, hidden_size] | def encode(self, inputs, attention_bias):
with tf.name_scope("encode"):
# Prepare inputs to the layer stack by adding positional encodings and
# applying dropout.
embedded_inputs = self.embedding_softmax_layer(inputs)
inputs_padding = model_utils.get_padding(inputs)
with tf.name_scope("add_pos_encoding"):
length = tf.shape(embedded_inputs)[1]
pos_encoding = model_utils.get_position_encoding(
length, self.params.hidden_size)
encoder_inputs = embedded_inputs + pos_encoding
if self.train:
mlperf_log.transformer_print(
key=mlperf_log.MODEL_HP_LAYER_POSTPROCESS_DROPOUT,
value=self.params.layer_postprocess_dropout)
encoder_inputs = tf.nn.dropout(
encoder_inputs, 1 - self.params.layer_postprocess_dropout)
return self.encoder_stack(encoder_inputs, attention_bias, inputs_padding) | 123,476 |
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]
encoder_outputs: continuous representation of input sequence.
float tensor with shape [batch_size, input_length, hidden_size]
attention_bias: float tensor with shape [batch_size, 1, 1, input_length]
Returns:
float32 tensor with shape [batch_size, target_length, vocab_size] | def decode(self, targets, encoder_outputs, attention_bias):
with tf.name_scope("decode"):
# Prepare inputs to decoder layers by shifting targets, adding positional
# encoding and applying dropout.
decoder_inputs = self.embedding_softmax_layer(targets)
with tf.name_scope("shift_targets"):
# Shift targets to the right, and remove the last element
decoder_inputs = tf.pad(
decoder_inputs, [[0, 0], [1, 0], [0, 0]])[:, :-1, :]
with tf.name_scope("add_pos_encoding"):
length = tf.shape(decoder_inputs)[1]
decoder_inputs += model_utils.get_position_encoding(
length, self.params.hidden_size)
if self.train:
mlperf_log.transformer_print(
key=mlperf_log.MODEL_HP_LAYER_POSTPROCESS_DROPOUT,
value=self.params.layer_postprocess_dropout)
decoder_inputs = tf.nn.dropout(
decoder_inputs, 1 - self.params.layer_postprocess_dropout)
# Run values
decoder_self_attention_bias = model_utils.get_decoder_self_attention_bias(
length)
outputs = self.decoder_stack(
decoder_inputs, encoder_outputs, decoder_self_attention_bias,
attention_bias)
logits = self.embedding_softmax_layer.linear(outputs)
return logits | 123,477 |
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: pandas dataframe with columns 'userId', 'movieId', 'rating',
'timestamp'.
Returns:
tuple of dataframes (filtered_ratings, train_ratings, test_ratings). | def _preprocess_movie_lens(ratings_df):
ratings_df["data"] = 1.0
num_timestamps = ratings_df[["userId", "timestamp"]].groupby(
"userId").nunique()
last_user_timestamp = ratings_df[["userId", "timestamp"]].groupby(
"userId").max()
ratings_df["numberOfTimestamps"] = ratings_df["userId"].apply(
lambda x: num_timestamps["timestamp"][x])
ratings_df["lastTimestamp"] = ratings_df["userId"].apply(
lambda x: last_user_timestamp["timestamp"][x])
ratings_df = ratings_df[ratings_df["numberOfTimestamps"] > 2]
ratings_df = _create_row_col_indices(ratings_df)
train_ratings_df = ratings_df[
ratings_df["timestamp"] < ratings_df["lastTimestamp"]]
test_ratings_df = ratings_df[
ratings_df["timestamp"] == ratings_df["lastTimestamp"]]
return ratings_df, train_ratings_df, test_ratings_df | 123,490 |
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 padding tokens in x. | def call(self, x):
with tf.name_scope("embedding"):
embeddings = tf.gather(self.shared_weights, x)
# Scale embedding by the sqrt of the hidden size
embeddings *= self.hidden_size ** 0.5
# Create binary array of size [batch_size, length]
# where 1 = padding, 0 = not padding
padding = model_utils.get_padding(x)
# Set all padding embedding values to 0
embeddings *= tf.expand_dims(1 - padding, -1)
return embeddings | 123,516 |
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]. | def linear(self, x):
with tf.name_scope("presoftmax_linear"):
batch_size = tf.shape(x)[0]
length = tf.shape(x)[1]
x = tf.reshape(x, [-1, self.hidden_size])
logits = tf.matmul(x, self.shared_weights, transpose_b=True)
return tf.reshape(logits, [batch_size, length, self.vocab_size]) | 123,517 |
Given a set of BoxList containing the `labels` field,
return a set of BoxList for which `labels > 0`.
Arguments:
boxes (list of BoxList) | def keep_only_positive_boxes(boxes):
assert isinstance(boxes, (list, tuple))
assert isinstance(boxes[0], BoxList)
assert boxes[0].has_field("labels")
positive_boxes = []
positive_inds = []
num_boxes = 0
for boxes_per_image in boxes:
labels = boxes_per_image.get_field("labels")
inds_mask = labels > 0
inds = inds_mask.nonzero().squeeze(1)
positive_boxes.append(boxes_per_image[inds])
positive_inds.append(inds_mask)
return positive_boxes, positive_inds | 123,526 |
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_search(). | def _minigui_report_search_status(self, leaves):
root = self._player.get_root()
msg = {
"id": hex(id(root)),
"n": int(root.N),
"q": float(root.Q),
}
msg["childQ"] = [int(round(q * 1000)) for q in root.child_Q]
msg["childN"] = [int(n) for n in root.child_N]
ranked_children = root.rank_children()
variations = {}
for i in ranked_children[:15]:
if root.child_N[i] == 0 or i not in root.children:
break
c = coords.to_gtp(coords.from_flat(i))
child = root.children[i]
nodes = child.most_visited_path_nodes()
moves = [coords.to_gtp(coords.from_flat(m.fmove)) for m in nodes]
variations[c] = {
"n": int(root.child_N[i]),
"q": float(root.child_Q[i]),
"moves": [c] + moves,
}
if leaves:
path = []
leaf = leaves[0]
while leaf != root:
path.append(leaf.fmove)
leaf = leaf.parent
if path:
path.reverse()
variations["live"] = {
"n": int(root.child_N[path[0]]),
"q": float(root.child_Q[path[0]]),
"moves": [coords.to_gtp(coords.from_flat(m)) for m in path]
}
if variations:
msg["variations"] = variations
dbg("mg-update:%s" % json.dumps(msg, sort_keys=True)) | 123,552 |
Given a list of strings, removes blanks and replace space character with space.
Option to remove repetitions (e.g. 'abbca' -> 'abca').
Arguments:
sequences: list of 1-d array of integers
remove_repetitions (boolean, optional): If true, repeating characters
are removed. Defaults to False. | def process_strings(self, sequences, remove_repetitions=False):
processed_strings = []
for sequence in sequences:
string = self.process_string(remove_repetitions, sequence).strip()
processed_strings.append(string)
return processed_strings | 123,589 |
Computes the Word Error Rate, defined as the edit distance between the
two provided sentences after tokenizing to words.
Arguments:
s1 (string): space-separated sentence
s2 (string): space-separated sentence | def wer(self, s1, s2):
# build mapping of words to integers
b = set(s1.split() + s2.split())
word2char = dict(zip(b, range(len(b))))
# map the words to a char array (Levenshtein packages only accepts
# strings)
w1 = [chr(word2char[w]) for w in s1.split()]
w2 = [chr(word2char[w]) for w in s2.split()]
return Lev.distance(''.join(w1), ''.join(w2)) | 123,591 |
Returns the argmax decoding given the probability matrix. Removes
repeated elements in the sequence, as well as blanks.
Arguments:
probs: Tensor of character probabilities from the network. Expected shape of seq_length x batch x output_dim
sizes(optional): Size of each sequence in the mini-batch
Returns:
strings: sequences of the model's best guess for the transcription on inputs | def decode(self, probs, sizes=None):
_, max_probs = torch.max(probs.transpose(0, 1), 2)
strings = self.convert_to_strings(max_probs.view(max_probs.size(0), max_probs.size(1)), sizes)
return self.process_strings(strings, remove_repetitions=True) | 123,594 |
Returns an iterable of tf.Examples.
Args:
data_extracts: An iterable of (position, pi, result) tuples | def make_dataset_from_selfplay(data_extracts):
tf_examples = (make_tf_example(features_lib.extract_features(pos), pi, result)
for pos, pi, result in data_extracts)
return tf_examples | 123,638 |
Return a boolean representing whether a model should be stopped.
Args:
stop_threshold: float, the threshold above which a model should stop
training.
eval_metric: float, the current value of the relevant metric to check.
Returns:
True if training should stop, False otherwise.
Raises:
ValueError: if either stop_threshold or eval_metric is not a number | def past_stop_threshold(stop_threshold, eval_metric):
if stop_threshold is None:
return False
if not isinstance(stop_threshold, numbers.Number):
raise ValueError("Threshold for checking stop conditions must be a number.")
if not isinstance(eval_metric, numbers.Number):
raise ValueError("Eval metric being checked against stop conditions "
"must be a number.")
if eval_metric >= stop_threshold:
tf.logging.info(
"Stop threshold of {} was passed with metric value {}.".format(
stop_threshold, eval_metric))
return True
return False | 123,649 |
Run all_gather on arbitrary picklable data (not necessarily tensors)
Args:
data: any picklable object
Returns:
list[data]: list of data gathered from each rank | def all_gather(data):
world_size = get_world_size()
if world_size == 1:
return [data]
# serialized to a Tensor
buffer = pickle.dumps(data)
storage = torch.ByteStorage.from_buffer(buffer)
tensor = torch.ByteTensor(storage).to("cuda")
# obtain Tensor size of each rank
local_size = torch.IntTensor([tensor.numel()]).to("cuda")
size_list = [torch.IntTensor([0]).to("cuda") for _ in range(world_size)]
dist.all_gather(size_list, local_size)
size_list = [int(size.item()) for size in size_list]
max_size = max(size_list)
# receiving Tensor from all ranks
# we pad the tensor because torch all_gather does not support
# gathering tensors of different shapes
tensor_list = []
for _ in size_list:
tensor_list.append(torch.ByteTensor(size=(max_size,)).to("cuda"))
if local_size != max_size:
padding = torch.ByteTensor(size=(max_size - local_size,)).to("cuda")
tensor = torch.cat((tensor, padding), dim=0)
dist.all_gather(tensor_list, tensor)
data_list = []
for size, tensor in zip(size_list, tensor_list):
buffer = tensor.cpu().numpy().tobytes()[:size]
data_list.append(pickle.loads(buffer))
return data_list | 123,655 |
evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
predictions(list[BoxList]): each item in the list represents the
prediction results for one image.
output_folder: output folder, to save evaluation files or results.
**kwargs: other args.
Returns:
evaluation result | def evaluate(dataset, predictions, output_folder, **kwargs):
args = dict(
dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
)
if isinstance(dataset, datasets.COCODataset):
return coco_evaluation(**args)
elif isinstance(dataset, datasets.PascalVOCDataset):
return voc_evaluation(**args)
else:
dataset_name = dataset.__class__.__name__
raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name)) | 123,669 |
Factory for getting a list of TensorFlow hooks for training by name.
Args:
name_list: a list of strings to name desired hook classes. Allowed:
LoggingTensorHook, ProfilerHook, ExamplesPerSecondHook, which are defined
as keys in HOOKS
**kwargs: a dictionary of arguments to the hooks.
Returns:
list of instantiated hooks, ready to be used in a classifier.train call.
Raises:
ValueError: if an unrecognized name is passed. | def get_train_hooks(name_list, **kwargs):
if not name_list:
return []
train_hooks = []
for name in name_list:
hook_name = HOOKS.get(name.strip().lower())
if hook_name is None:
raise ValueError('Unrecognized training hook requested: {}'.format(name))
else:
train_hooks.append(hook_name(**kwargs))
return train_hooks | 123,672 |
Function to get LoggingTensorHook.
Args:
every_n_iter: `int`, print the values of `tensors` once every N local
steps taken on the current worker.
tensors_to_log: List of tensor names or dictionary mapping labels to tensor
names. If not set, log _TENSORS_TO_LOG by default.
**kwargs: a dictionary of arguments to LoggingTensorHook.
Returns:
Returns a LoggingTensorHook with a standard set of tensors that will be
printed to stdout. | def get_logging_tensor_hook(every_n_iter=100, tensors_to_log=None, **kwargs): # pylint: disable=unused-argument
if tensors_to_log is None:
tensors_to_log = _TENSORS_TO_LOG
return tf.train.LoggingTensorHook(
tensors=tensors_to_log,
every_n_iter=every_n_iter) | 123,673 |
Function to get LoggingMetricHook.
Args:
benchmark_log_dir: `string`, directory path to save the metric log.
tensors_to_log: List of tensor names or dictionary mapping labels to tensor
names. If not set, log _TENSORS_TO_LOG by default.
every_n_secs: `int`, the frequency for logging the metric. Default to every
10 mins.
Returns:
Returns a ProfilerHook that writes out timelines that can be loaded into
profiling tools like chrome://tracing. | def get_logging_metric_hook(benchmark_log_dir=None,
tensors_to_log=None,
every_n_secs=600,
**kwargs): # pylint: disable=unused-argument
if benchmark_log_dir is None:
raise ValueError("metric_log_dir should be provided to use metric logger")
if tensors_to_log is None:
tensors_to_log = _TENSORS_TO_LOG
return metric_hook.LoggingMetricHook(
tensors=tensors_to_log,
log_dir=benchmark_log_dir,
every_n_secs=every_n_secs) | 123,675 |
Called after each call to run().
Args:
run_context: A SessionRunContext object.
run_values: A SessionRunValues object. | def after_run(self, run_context, run_values): # pylint: disable=unused-argument
global_step = run_values.results
if self._timer.should_trigger_for_step(
global_step) and global_step > self._warm_steps:
elapsed_time, elapsed_steps = self._timer.update_last_triggered_step(
global_step)
if elapsed_time is not None:
self._step_train_time += elapsed_time
self._total_steps += elapsed_steps
# average examples per second is based on the total (accumulative)
# training steps and training time so far
average_examples_per_sec = self._batch_size * (
self._total_steps / self._step_train_time)
# current examples per second is based on the elapsed training steps
# and training time per batch
current_examples_per_sec = self._batch_size * (
elapsed_steps / elapsed_time)
# Current examples/sec followed by average examples/sec
tf.logging.info('Batch [%g]: current exp/sec = %g, average exp/sec = '
'%g', self._total_steps, current_examples_per_sec,
average_examples_per_sec) | 123,750 |
Adds the predicted boxes on top of the image
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `labels`. | def overlay_boxes(self, image, predictions):
labels = predictions.get_field("labels")
boxes = predictions.bbox
colors = self.compute_colors_for_labels(labels).tolist()
for box, color in zip(boxes, colors):
box = box.to(torch.int64)
top_left, bottom_right = box[:2].tolist(), box[2:].tolist()
image = cv2.rectangle(
image, tuple(top_left), tuple(bottom_right), tuple(color), 1
)
return image | 123,758 |
Adds the instances contours for each predicted object.
Each label has a different color.
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `mask` and `labels`. | def overlay_mask(self, image, predictions):
masks = predictions.get_field("mask").numpy()
labels = predictions.get_field("labels")
colors = self.compute_colors_for_labels(labels).tolist()
for mask, color in zip(masks, colors):
thresh = mask[0, :, :, None]
contours, hierarchy = cv2_util.findContours(
thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
)
image = cv2.drawContours(image, contours, -1, color, 3)
composite = image
return composite | 123,759 |
Create a montage showing the probability heatmaps for each one one of the
detected objects
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `mask`. | def create_mask_montage(self, image, predictions):
masks = predictions.get_field("mask")
masks_per_dim = self.masks_per_dim
masks = L.interpolate(
masks.float(), scale_factor=1 / masks_per_dim
).byte()
height, width = masks.shape[-2:]
max_masks = masks_per_dim ** 2
masks = masks[:max_masks]
# handle case where we have less detections than max_masks
if len(masks) < max_masks:
masks_padded = torch.zeros(max_masks, 1, height, width, dtype=torch.uint8)
masks_padded[: len(masks)] = masks
masks = masks_padded
masks = masks.reshape(masks_per_dim, masks_per_dim, height, width)
result = torch.zeros(
(masks_per_dim * height, masks_per_dim * width), dtype=torch.uint8
)
for y in range(masks_per_dim):
start_y = y * height
end_y = (y + 1) * height
for x in range(masks_per_dim):
start_x = x * width
end_x = (x + 1) * width
result[start_y:end_y, start_x:end_x] = masks[y, x]
return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET) | 123,761 |
Adds detected class names and scores in the positions defined by the
top-left corner of the predicted bounding box
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `scores` and `labels`. | def overlay_class_names(self, image, predictions):
scores = predictions.get_field("scores").tolist()
labels = predictions.get_field("labels").tolist()
labels = [self.CATEGORIES[i] for i in labels]
boxes = predictions.bbox
template = "{}: {:.2f}"
for box, score, label in zip(boxes, scores, labels):
x, y = box[:2]
s = template.format(label, score)
cv2.putText(
image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1
)
return image | 123,762 |
Log the evaluation result for a estimator.
The evaluate result is a directory that contains metrics defined in
model_fn. It also contains a entry for global_step which contains the value
of the global step when evaluation was performed.
Args:
eval_results: dict, the result of evaluate() from a estimator. | def log_estimator_evaluation_result(self, eval_results):
if not isinstance(eval_results, dict):
tf.logging.warning("eval_results should be directory for logging. Got %s",
type(eval_results))
return
global_step = eval_results[tf.GraphKeys.GLOBAL_STEP]
for key in sorted(eval_results):
if key != tf.GraphKeys.GLOBAL_STEP:
self.log_metric(key, eval_results[key], global_step=global_step) | 123,772 |
Collect most of the TF runtime information for the local env.
The schema of the run info follows official/benchmark/datastore/schema.
Args:
model_name: string, the name of the model. | def log_run_info(self, model_name):
run_info = {
"model_name": model_name,
"machine_config": {},
"run_date": datetime.datetime.now().strftime(_DATE_TIME_FORMAT_PATTERN)}
_collect_tensorflow_info(run_info)
_collect_tensorflow_environment_variables(run_info)
_collect_cpu_info(run_info)
_collect_gpu_info(run_info)
_collect_memory_info(run_info)
with tf.gfile.GFile(os.path.join(
self._logging_dir, BENCHMARK_RUN_LOG_FILE_NAME), "w") as f:
try:
json.dump(run_info, f)
f.write("\n")
except (TypeError, ValueError) as e:
tf.logging.warning("Failed to dump benchmark run info to log file: %s",
e) | 123,774 |
Write all eval_records to eval_table
In addition to writing new rows table_state must be updated in
row `table_state` columns `metadata:eval_game_counter`
Args:
bt_table: bigtable table to add rows to.
game_data: metadata pairs (column name, value) for each eval record.
last_game: last_game in metadata:table_state | def write_eval_records(bt_table, game_data, last_game):
eval_num = last_game
# Each column counts as a mutation so max rows is ~10000
GAMES_PER_COMMIT = 2000
for games in grouper(tqdm(game_data), GAMES_PER_COMMIT):
assert bt_table.read_row(EVAL_PREFIX.format(eval_num)), "Prev row doesn't exists"
assert bt_table.read_row(EVAL_PREFIX.format(eval_num+1)) is None, "Row already exists"
rows = []
for i, metadata in enumerate(games):
eval_num += 1
row_name = EVAL_PREFIX.format(eval_num)
row = bt_table.row(row_name)
for column, value in metadata:
row.set_cell(METADATA, column, value)
rows.append(row)
# For each batch of games print a couple of the rows being added.
if i < 5 or i + 5 > len(games):
print("\t", i, row_name, metadata[6][1])
if eval_num == last_game + len(games):
test = input("Commit ('y'/'yes' required): ")
if test.lower() not in ('y', 'yes'):
break
# TODO(derek): Figure out how to condition on atomic counter update.
# Condition all updates on the current value of last_game
game_num_update = bt_table.row(TABLE_STATE)
game_num_update.set_cell(METADATA, EVAL_GAME_COUNTER, eval_num)
print(TABLE_STATE, eval_num)
response = bt_table.mutate_rows(rows)
# validate that all rows written successfully
any_bad = False
for i, status in enumerate(response):
if status.code is not 0:
print("Row number {} failed to write {}".format(i, status))
any_bad = True
if any_bad:
break
game_num_update.commit() | 123,781 |
Run the given subprocess command in a coroutine.
Args:
*cmd: the command to run and its arguments.
Returns:
The output that the command wrote to stdout as a list of strings, one line
per element (stderr output is piped to stdout).
Raises:
RuntimeError: if the command returns a non-zero result. | async def run(*cmd):
stdout = await checked_run(*cmd)
log_path = os.path.join(FLAGS.base_dir, get_cmd_name(cmd) + '.log')
with gfile.Open(log_path, 'a') as f:
f.write(expand_cmd_str(cmd))
f.write('\n')
f.write(stdout)
f.write('\n')
# Split stdout into lines.
return stdout.split('\n') | 123,785 |
Run selfplay and write a training chunk to the fsdb golden_chunk_dir.
Args:
state: the RL loop State instance.
flagfile: the name of the flagfile to use for selfplay, either 'selfplay'
(the default) or 'boostrap'. | async def selfplay(state, flagfile='selfplay'):
output_dir = os.path.join(fsdb.selfplay_dir(), state.output_model_name)
holdout_dir = os.path.join(fsdb.holdout_dir(), state.output_model_name)
lines = await run(
'bazel-bin/cc/selfplay',
'--flagfile={}.flags'.format(os.path.join(FLAGS.flags_dir, flagfile)),
'--model={}'.format(state.best_model_path),
'--output_dir={}'.format(output_dir),
'--holdout_dir={}'.format(holdout_dir),
'--seed={}'.format(state.seed))
result = '\n'.join(lines[-6:])
logging.info(result)
stats = parse_win_stats_table(result, 1)[0]
num_games = stats.total_wins
logging.info('Black won %0.3f, white won %0.3f',
stats.black_wins.total / num_games,
stats.white_wins.total / num_games)
# Write examples to a single record.
pattern = os.path.join(output_dir, '*', '*.zz')
random.seed(state.seed)
tf.set_random_seed(state.seed)
np.random.seed(state.seed)
# TODO(tommadams): This method of generating one golden chunk per generation
# is sub-optimal because each chunk gets reused multiple times for training,
# introducing bias. Instead, a fresh dataset should be uniformly sampled out
# of *all* games in the training window before the start of each training run.
buffer = example_buffer.ExampleBuffer(sampling_frac=1.0)
# TODO(tommadams): parallel_fill is currently non-deterministic. Make it not
# so.
logging.info('Writing golden chunk from "{}"'.format(pattern))
buffer.parallel_fill(tf.gfile.Glob(pattern))
buffer.flush(os.path.join(fsdb.golden_chunk_dir(),
state.output_model_name + '.tfrecord.zz')) | 123,787 |
Run training and write a new model to the fsdb models_dir.
Args:
state: the RL loop State instance.
tf_records: a list of paths to TensorFlow records to train on. | async def train(state, tf_records):
model_path = os.path.join(fsdb.models_dir(), state.train_model_name)
await run(
'python3', 'train.py', *tf_records,
'--flagfile={}'.format(os.path.join(FLAGS.flags_dir, 'train.flags')),
'--work_dir={}'.format(fsdb.working_dir()),
'--export_path={}'.format(model_path),
'--training_seed={}'.format(state.seed),
'--freeze=true')
# Append the time elapsed from when the RL was started to when this model
# was trained.
elapsed = time.time() - state.start_time
timestamps_path = os.path.join(fsdb.models_dir(), 'train_times.txt')
with gfile.Open(timestamps_path, 'a') as f:
print('{:.3f} {}'.format(elapsed, state.train_model_name), file=f) | 123,788 |
Validate the trained model against holdout games.
Args:
state: the RL loop State instance.
holdout_glob: a glob that matches holdout games. | async def validate(state, holdout_glob):
if not glob.glob(holdout_glob):
print('Glob "{}" didn\'t match any files, skipping validation'.format(
holdout_glob))
else:
await run(
'python3', 'validate.py', holdout_glob,
'--flagfile={}'.format(os.path.join(FLAGS.flags_dir, 'validate.flags')),
'--work_dir={}'.format(fsdb.working_dir())) | 123,789 |
Evaluate one model against a target.
Args:
eval_model_path: the path to the model to evaluate.
target_model_path: the path to the model to compare to.
sgf_dif: directory path to write SGF output to.
seed: random seed to use when running eval.
Returns:
The win-rate of eval_model against target_model in the range [0, 1]. | async def evaluate_model(eval_model_path, target_model_path, sgf_dir, seed):
lines = await run(
'bazel-bin/cc/eval',
'--flagfile={}'.format(os.path.join(FLAGS.flags_dir, 'eval.flags')),
'--model={}'.format(eval_model_path),
'--model_two={}'.format(target_model_path),
'--sgf_dir={}'.format(sgf_dir),
'--seed={}'.format(seed))
result = '\n'.join(lines[-7:])
logging.info(result)
eval_stats, target_stats = parse_win_stats_table(result, 2)
num_games = eval_stats.total_wins + target_stats.total_wins
win_rate = eval_stats.total_wins / num_games
logging.info('Win rate %s vs %s: %.3f', eval_stats.model_name,
target_stats.model_name, win_rate)
return win_rate | 123,790 |
Evaluate the most recently trained model against the current best model.
Args:
state: the RL loop State instance. | async def evaluate_trained_model(state):
return await evaluate_model(
state.train_model_path, state.best_model_path,
os.path.join(fsdb.eval_dir(), state.train_model_name), state.seed) | 123,791 |
Turn a game into SGF.
Doesn't handle handicap games or positions with incomplete history.
Args:
move_history: iterable of PlayerMoves
result_string: "B+R", "W+0.5", etc.
comments: iterable of string/None. Will be zipped with move_history. | def make_sgf(
move_history,
result_string,
ruleset="Chinese",
komi=7.5,
white_name=PROGRAM_IDENTIFIER,
black_name=PROGRAM_IDENTIFIER,
comments=[]
):
boardsize = go.N
game_moves = ''.join(translate_sgf_move(*z)
for z in itertools.zip_longest(move_history, comments))
result = result_string
return SGF_TEMPLATE.format(**locals()) | 123,836 |
Download content from a url.
Args:
path: string directory where file will be downloaded
url: string url
Returns:
Full path to downloaded file | def download_from_url(path, url):
filename = url.split("/")[-1]
found_file = find_file(path, filename, max_depth=0)
if found_file is None:
filename = os.path.join(path, filename)
tf.logging.info("Downloading from %s to %s." % (url, filename))
inprogress_filepath = filename + ".incomplete"
inprogress_filepath, _ = urllib.request.urlretrieve(
url, inprogress_filepath, reporthook=download_report_hook)
# Print newline to clear the carriage return from the download progress.
print()
tf.gfile.Rename(inprogress_filepath, filename)
return filename
else:
tf.logging.info("Already downloaded: %s (at %s)." % (url, found_file))
return found_file | 123,889 |
Given segmentation masks and the bounding boxes corresponding
to the location of the masks in the image, this function
crops and resizes the masks in the position defined by the
boxes. This prepares the masks for them to be fed to the
loss computation as the targets.
Arguments:
segmentation_masks: an instance of SegmentationMask
proposals: an instance of BoxList | def project_masks_on_boxes(segmentation_masks, proposals, discretization_size):
masks = []
M = discretization_size
device = proposals.bbox.device
proposals = proposals.convert("xyxy")
assert segmentation_masks.size == proposals.size, "{}, {}".format(
segmentation_masks, proposals
)
# TODO put the proposals on the CPU, as the representation for the
# masks is not efficient GPU-wise (possibly several small tensors for
# representing a single instance mask)
proposals = proposals.bbox.to(torch.device("cpu"))
for segmentation_mask, proposal in zip(segmentation_masks, proposals):
# crop the masks, resize them to the desired resolution and
# then convert them to the tensor representation,
# instead of the list representation that was used
cropped_mask = segmentation_mask.crop(proposal)
scaled_mask = cropped_mask.resize((M, M))
mask = scaled_mask.convert(mode="mask")
masks.append(mask)
if len(masks) == 0:
return torch.empty(0, dtype=torch.float32, device=device)
return torch.stack(masks, dim=0).to(device, dtype=torch.float32) | 123,891 |
Crops the given image to a random part of the image, and randomly flips.
We use the fused decode_and_crop op, which performs better than the two ops
used separately in series, but note that this requires that the image be
passed in as an un-decoded string Tensor.
Args:
image_buffer: scalar string Tensor representing the raw JPEG image buffer.
num_channels: Integer depth of the image buffer for decoding.
Returns:
3-D tensor with cropped image. | def _decode_crop_and_flip(image_buffer, num_channels):
# A large fraction of image datasets contain a human-annotated bounding box
# delineating the region of the image containing the object of interest. We
# choose to create a new bounding box for the object which is a randomly
# distorted version of the human-annotated bounding box that obeys an
# allowed range of aspect ratios, sizes and overlap with the human-annotated
# bounding box. If no box is supplied, then we assume the bounding box is
# the entire image.
min_object_covered=0.1
aspect_ratio_range=[0.75, 1.33]
area_range=[0.05, 1.0]
max_attempts=100
mlperf_log.resnet_print(key=mlperf_log.INPUT_DISTORTED_CROP_MIN_OBJ_COV,
value=min_object_covered)
mlperf_log.resnet_print(key=mlperf_log.INPUT_DISTORTED_CROP_RATIO_RANGE,
value=aspect_ratio_range)
mlperf_log.resnet_print(key=mlperf_log.INPUT_DISTORTED_CROP_AREA_RANGE,
value=area_range)
mlperf_log.resnet_print(key=mlperf_log.INPUT_DISTORTED_CROP_MAX_ATTEMPTS,
value=max_attempts)
mlperf_log.resnet_print(key=mlperf_log.INPUT_CROP_USES_BBOXES, value=False)
bbox = tf.constant([0.0, 0.0, 1.0, 1.0],
dtype=tf.float32, shape=[1, 1, 4]) #From the entire image
sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
tf.image.extract_jpeg_shape(image_buffer),
bounding_boxes=bbox,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
max_attempts=max_attempts,
use_image_if_no_bounding_boxes=True)
bbox_begin, bbox_size, _ = sample_distorted_bounding_box
# Reassemble the bounding box in the format the crop op requires.
offset_y, offset_x, _ = tf.unstack(bbox_begin)
target_height, target_width, _ = tf.unstack(bbox_size)
crop_window = tf.stack([offset_y, offset_x, target_height, target_width])
# Use the fused decode and crop op here, which is faster than each in series.
cropped = tf.image.decode_and_crop_jpeg(
image_buffer, crop_window, channels=num_channels)
# Flip to add a little more random distortion in.
mlperf_log.resnet_print(key=mlperf_log.INPUT_RANDOM_FLIP)
cropped = tf.image.random_flip_left_right(cropped)
return cropped | 123,910 |
Performs central crops of the given image list.
Args:
image: a 3-D image tensor
crop_height: the height of the image following the crop.
crop_width: the width of the image following the crop.
Returns:
3-D tensor with cropped image. | def _central_crop(image, crop_height, crop_width):
shape = tf.shape(image)
height, width = shape[0], shape[1]
mlperf_log.resnet_print(key=mlperf_log.INPUT_CENTRAL_CROP,
value=[crop_height, crop_width])
amount_to_be_cropped_h = (height - crop_height)
crop_top = amount_to_be_cropped_h // 2
amount_to_be_cropped_w = (width - crop_width)
crop_left = amount_to_be_cropped_w // 2
return tf.slice(
image, [crop_top, crop_left, 0], [crop_height, crop_width, -1]) | 123,911 |
Resize images preserving the original aspect ratio.
Args:
image: A 3-D image `Tensor`.
resize_min: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
resized_image: A 3-D tensor containing the resized image. | def _aspect_preserving_resize(image, resize_min):
mlperf_log.resnet_print(key=mlperf_log.INPUT_RESIZE_ASPECT_PRESERVING,
value={"min": resize_min})
shape = tf.shape(image)
height, width = shape[0], shape[1]
new_height, new_width = _smallest_size_at_least(height, width, resize_min)
return _resize_image(image, new_height, new_width) | 123,914 |
Simple wrapper around tf.resize_images.
This is primarily to make sure we use the same `ResizeMethod` and other
details each time.
Args:
image: A 3-D image `Tensor`.
height: The target height for the resized image.
width: The target width for the resized image.
Returns:
resized_image: A 3-D tensor containing the resized image. The first two
dimensions have the shape [height, width]. | def _resize_image(image, height, width):
return tf.image.resize_images(
image, [height, width], method=tf.image.ResizeMethod.BILINEAR,
align_corners=False) | 123,915 |
Reshapes first two dimensions in to single dimension.
Args:
tensor: Tensor to reshape of shape [A, B, ...]
Returns:
Reshaped tensor of shape [A*B, ...] | def _flatten_beam_dim(tensor):
shape = _shape_list(tensor)
shape[0] *= shape[1]
shape.pop(1) # Remove beam dim
return tf.reshape(tensor, shape) | 123,920 |
Reshapes first dimension back to [batch_size, beam_size].
Args:
tensor: Tensor to reshape of shape [batch_size*beam_size, ...]
batch_size: Tensor, original batch size.
beam_size: int, original beam size.
Returns:
Reshaped tensor of shape [batch_size, beam_size, ...] | def _unflatten_beam_dim(tensor, batch_size, beam_size):
shape = _shape_list(tensor)
new_shape = [batch_size, beam_size] + shape[1:]
return tf.reshape(tensor, new_shape) | 123,921 |
Return initial state dictionary and its shape invariants.
Args:
initial_ids: initial ids to pass into the symbols_to_logits_fn.
int tensor with shape [batch_size, 1]
initial_cache: dictionary storing values to be passed into the
symbols_to_logits_fn.
Returns:
state and shape invariant dictionaries with keys from _StateKeys | def _create_initial_state(self, initial_ids, initial_cache):
# Current loop index (starts at 0)
cur_index = tf.constant(0)
# Create alive sequence with shape [batch_size, beam_size, 1]
alive_seq = _expand_to_beam_size(initial_ids, self.beam_size)
alive_seq = tf.expand_dims(alive_seq, axis=2)
# Create tensor for storing initial log probabilities.
# Assume initial_ids are prob 1.0
initial_log_probs = tf.constant(
[[0.] + [-float("inf")] * (self.beam_size - 1)])
alive_log_probs = tf.tile(initial_log_probs, [self.batch_size, 1])
# Expand all values stored in the dictionary to the beam size, so that each
# beam has a separate cache.
alive_cache = nest.map_structure(
lambda t: _expand_to_beam_size(t, self.beam_size), initial_cache)
# Initialize tensor storing finished sequences with filler values.
finished_seq = tf.zeros(tf.shape(alive_seq), tf.int32)
# Set scores of the initial finished seqs to negative infinity.
finished_scores = tf.ones([self.batch_size, self.beam_size]) * -INF
# Initialize finished flags with all False values.
finished_flags = tf.zeros([self.batch_size, self.beam_size], tf.bool)
# Create state dictionary
state = {
_StateKeys.CUR_INDEX: cur_index,
_StateKeys.ALIVE_SEQ: alive_seq,
_StateKeys.ALIVE_LOG_PROBS: alive_log_probs,
_StateKeys.ALIVE_CACHE: alive_cache,
_StateKeys.FINISHED_SEQ: finished_seq,
_StateKeys.FINISHED_SCORES: finished_scores,
_StateKeys.FINISHED_FLAGS: finished_flags
}
# Create state invariants for each value in the state dictionary. Each
# dimension must be a constant or None. A None dimension means either:
# 1) the dimension's value is a tensor that remains the same but may
# depend on the input sequence to the model (e.g. batch size).
# 2) the dimension may have different values on different iterations.
state_shape_invariants = {
_StateKeys.CUR_INDEX: tf.TensorShape([]),
_StateKeys.ALIVE_SEQ: tf.TensorShape([None, self.beam_size, None]),
_StateKeys.ALIVE_LOG_PROBS: tf.TensorShape([None, self.beam_size]),
_StateKeys.ALIVE_CACHE: nest.map_structure(
_get_shape_keep_last_dim, alive_cache),
_StateKeys.FINISHED_SEQ: tf.TensorShape([None, self.beam_size, None]),
_StateKeys.FINISHED_SCORES: tf.TensorShape([None, self.beam_size]),
_StateKeys.FINISHED_FLAGS: tf.TensorShape([None, self.beam_size])
}
return state, state_shape_invariants | 123,926 |
Return whether to continue the search loop.
The loops should terminate when
1) when decode length has been reached, or
2) when the worst score in the finished sequences is better than the best
score in the alive sequences (i.e. the finished sequences are provably
unchanging)
Args:
state: A dictionary with the current loop state.
Returns:
Bool tensor with value True if loop should continue, False if loop should
terminate. | def _continue_search(self, state):
i = state[_StateKeys.CUR_INDEX]
alive_log_probs = state[_StateKeys.ALIVE_LOG_PROBS]
finished_scores = state[_StateKeys.FINISHED_SCORES]
finished_flags = state[_StateKeys.FINISHED_FLAGS]
not_at_max_decode_length = tf.less(i, self.max_decode_length)
# Calculate largest length penalty (the larger penalty, the better score).
max_length_norm = _length_normalization(self.alpha, self.max_decode_length)
# Get the best possible scores from alive sequences.
best_alive_scores = alive_log_probs[:, 0] / max_length_norm
# Compute worst score in finished sequences for each batch element
finished_scores *= tf.to_float(finished_flags) # set filler scores to zero
lowest_finished_scores = tf.reduce_min(finished_scores, axis=1)
# If there are no finished sequences in a batch element, then set the lowest
# finished score to -INF for that element.
finished_batches = tf.reduce_any(finished_flags, 1)
lowest_finished_scores += (1. - tf.to_float(finished_batches)) * -INF
worst_finished_score_better_than_best_alive_score = tf.reduce_all(
tf.greater(lowest_finished_scores, best_alive_scores)
)
return tf.logical_and(
not_at_max_decode_length,
tf.logical_not(worst_finished_score_better_than_best_alive_score)
) | 123,927 |
Return positional encoding.
Calculates the position encoding as a mix of sine and cosine functions with
geometrically increasing wavelengths.
Defined and formulized in Attention is All You Need, section 3.5.
Args:
length: Sequence length.
hidden_size: Size of the
min_timescale: Minimum scale that will be applied at each position
max_timescale: Maximum scale that will be applied at each position
Returns:
Tensor with shape [length, hidden_size] | def get_position_encoding(
length, hidden_size, min_timescale=1.0, max_timescale=1.0e4):
position = tf.to_float(tf.range(length))
num_timescales = hidden_size // 2
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
(tf.to_float(num_timescales) - 1))
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(inv_timescales, 0)
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
return signal | 123,941 |
Calculate bias for decoder that maintains model's autoregressive property.
Creates a tensor that masks out locations that correspond to illegal
connections, so prediction at position i cannot draw information from future
positions.
Args:
length: int length of sequences in batch.
Returns:
float tensor of shape [1, 1, length, length] | def get_decoder_self_attention_bias(length):
with tf.name_scope("decoder_self_attention_bias"):
valid_locs = tf.matrix_band_part(tf.ones([length, length]), -1, 0)
valid_locs = tf.reshape(valid_locs, [1, 1, length, length])
decoder_bias = _NEG_INF * (1.0 - valid_locs)
return decoder_bias | 123,942 |
Return float tensor representing the padding values in x.
Args:
x: int tensor with any shape
padding_value: int value that
Returns:
flaot tensor with same shape as x containing values 0 or 1.
0 -> non-padding, 1 -> padding | def get_padding(x, padding_value=0):
with tf.name_scope("padding"):
return tf.to_float(tf.equal(x, padding_value)) | 123,943 |
Calculate bias tensor from padding values in tensor.
Bias tensor that is added to the pre-softmax multi-headed attention logits,
which has shape [batch_size, num_heads, length, length]. The tensor is zero at
non-padding locations, and -1e9 (negative infinity) at padding locations.
Args:
x: int tensor with shape [batch_size, length]
Returns:
Attention bias tensor of shape [batch_size, 1, 1, length]. | def get_padding_bias(x):
with tf.name_scope("attention_bias"):
padding = get_padding(x)
attention_bias = padding * _NEG_INF
attention_bias = tf.expand_dims(
tf.expand_dims(attention_bias, axis=1), axis=1)
return attention_bias | 123,944 |
Adds a result to the dictionary.
Args:
dict_entry: main dict to add entry
entry: slot for this entry (likely an integer)
dt: the timing for the entry
start_time: when the entry started unix time float | def _add_result(self, dict_entry, entry, dt, start_time):
time_entry = {}
time_entry['dt'] = dt
time_entry['start_time'] = start_time
dict_entry[entry] = time_entry | 123,953 |
Sorts dict of results based on log start_time.
Sorts the results and returns an array with only the values but sorted
by oldest value first.value
Args:
results_dicts: List of result dicts
Returns:
List of only the time but sorted oldest first. | def _sorted_results(self, results_dicts):
print('results dicts:', results_dicts)
sorted_dict = sorted(results_dicts, key=lambda k: k['start_time'])
results = []
for entry in sorted_dict:
results.append(entry['dt'])
return results | 123,954 |
Verifies and result and returns timing.
Uses submodule mlp_compliance (https://github.com/bitfort/mlp_compliance)
Args:
log_file: Absolute path to result file.
division: open, closed
result_name: name of the benchmark, ncf, ssd, etc
Returns:
Time for the result or `INFINITE_TIME` if not a success
Raises:
Exception: If expected compliance level is not hit or cannot figure
out expected compliance level. | def verify_and_extract_time(self, log_file, division, result_name):
expected_level = constants.DIVISION_COMPLIANCE_CHECK_LEVEL.get(
division, None)
print(result_name)
if expected_level is None:
raise Exception('Unknown division: {}'.format(division))
start_time, level, dt, _, success = self.get_compliance(log_file)
print(float(start_time))
if int(level) != expected_level:
raise Exception('Error Level {} does not match needed level {}:{}'.format(
level, expected_level, log_file))
# Sets failure to converge to "infinite time" per the rules
if success and dt:
return dt, start_time
else:
print('Result was not a success set to INFINITE_TIME({})'.format(
INFINITE_TIME))
return INFINITE_TIME, start_time | 123,959 |
Performs non-maximum suppression on a boxlist, with scores specified
in a boxlist field via score_field.
Arguments:
boxlist(BoxList)
nms_thresh (float)
max_proposals (int): if > 0, then only the top max_proposals are kept
after non-maximum suppression
score_field (str) | def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores"):
if nms_thresh <= 0:
return boxlist
mode = boxlist.mode
boxlist = boxlist.convert("xyxy")
boxes = boxlist.bbox
score = boxlist.get_field(score_field)
keep = _box_nms(boxes, score, nms_thresh)
if max_proposals > 0:
keep = keep[: max_proposals]
boxlist = boxlist[keep]
return boxlist.convert(mode) | 123,991 |
Only keep boxes with both sides >= min_size
Arguments:
boxlist (Boxlist)
min_size (int) | def remove_small_boxes(boxlist, min_size):
# TODO maybe add an API for querying the ws / hs
xywh_boxes = boxlist.convert("xywh").bbox
_, _, ws, hs = xywh_boxes.unbind(dim=1)
keep = (
(ws >= min_size) & (hs >= min_size)
).nonzero().squeeze(1)
return boxlist[keep] | 123,992 |
Compute the intersection over union of two set of boxes.
The box order must be (xmin, ymin, xmax, ymax).
Arguments:
box1: (BoxList) bounding boxes, sized [N,4].
box2: (BoxList) bounding boxes, sized [M,4].
Returns:
(tensor) iou, sized [N,M].
Reference:
https://github.com/chainer/chainercv/blob/master/chainercv/utils/bbox/bbox_iou.py | def boxlist_iou(boxlist1, boxlist2):
if boxlist1.size != boxlist2.size:
raise RuntimeError(
"boxlists should have same image size, got {}, {}".format(boxlist1, boxlist2))
N = len(boxlist1)
M = len(boxlist2)
area1 = boxlist1.area()
area2 = boxlist2.area()
box1, box2 = boxlist1.bbox, boxlist2.bbox
lt = torch.max(box1[:, None, :2], box2[:, :2]) # [N,M,2]
rb = torch.min(box1[:, None, 2:], box2[:, 2:]) # [N,M,2]
TO_REMOVE = 1
wh = (rb - lt + TO_REMOVE).clamp(min=0) # [N,M,2]
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
iou = inter / (area1[:, None] + area2 - inter)
return iou | 123,993 |
Concatenates a list of BoxList (having the same image size) into a
single BoxList
Arguments:
bboxes (list[BoxList]) | def cat_boxlist(bboxes):
assert isinstance(bboxes, (list, tuple))
assert all(isinstance(bbox, BoxList) for bbox in bboxes)
size = bboxes[0].size
assert all(bbox.size == size for bbox in bboxes)
mode = bboxes[0].mode
assert all(bbox.mode == mode for bbox in bboxes)
fields = set(bboxes[0].fields())
assert all(set(bbox.fields()) == fields for bbox in bboxes)
cat_boxes = BoxList(_cat([bbox.bbox for bbox in bboxes], dim=0), size, mode)
for field in fields:
data = _cat([bbox.get_field(field) for bbox in bboxes], dim=0)
cat_boxes.add_field(field, data)
return cat_boxes | 123,994 |
Create min and max boundary lists up to max_length.
For example, when max_length=24, min_boundary=4 and boundary_scale=2, the
returned values will be:
buckets_min = [0, 4, 8, 16, 24]
buckets_max = [4, 8, 16, 24, 25]
Args:
max_length: The maximum length of example in dataset.
min_boundary: Minimum length in boundary.
boundary_scale: Amount to scale consecutive boundaries in the list.
Returns:
min and max boundary lists | def _create_min_max_boundaries(
max_length, min_boundary=_MIN_BOUNDARY, boundary_scale=_BOUNDARY_SCALE):
# Create bucket boundaries list by scaling the previous boundary or adding 1
# (to ensure increasing boundary sizes).
bucket_boundaries = []
x = min_boundary
while x < max_length:
bucket_boundaries.append(x)
x = max(x + 1, int(x * boundary_scale))
# Create min and max boundary lists from the initial list.
buckets_min = [0] + bucket_boundaries
buckets_max = bucket_boundaries + [max_length + 1]
return buckets_min, buckets_max | 124,025 |
Convert dtype string to tf dtype, and set loss_scale default as needed.
Args:
flags: namespace object returned by arg parser.
Raises:
ValueError: If an invalid dtype is provided. | def parse_dtype_info(flags):
if flags.dtype in (i[0] for i in DTYPE_MAP.values()):
return # Make function idempotent
try:
flags.dtype, default_loss_scale = DTYPE_MAP[flags.dtype]
except KeyError:
raise ValueError("Invalid dtype: {}".format(flags.dtype))
flags.loss_scale = flags.loss_scale or default_loss_scale | 124,045 |
Propagate a virtual loss up to the root node.
Args:
up_to: The node to propagate until. (Keep track of this! You'll
need it to reverse the virtual loss later.) | def add_virtual_loss(self, up_to):
self.losses_applied += 1
# This is a "win" for the current node; hence a loss for its parent node
# who will be deciding whether to investigate this node again.
loss = self.position.to_play
self.W += loss
if self.parent is None or self is up_to:
return
self.parent.add_virtual_loss(up_to) | 124,102 |
Propagates a value estimation up to the root node.
Args:
value: the value to be propagated (1 = black wins, -1 = white wins)
up_to: the node to propagate until. | def backup_value(self, value, up_to):
self.N += 1
self.W += value
if self.parent is None or self is up_to:
return
self.parent.backup_value(value, up_to) | 124,105 |
Convert a list of command arguments to types specified by the handler.
Args:
handler: a command handler function.
args: the list of string arguments to pass to handler.
Returns:
A new list containing `args` that have been converted to the expected type
for `handler`. For each function parameter of `handler` that has either an
explicit type annotation or a non-None default value, the corresponding
element in `args` is converted to that type. | def _convert_args(handler, args):
args = list(args)
params = inspect.signature(handler).parameters
for i, (arg, name) in enumerate(zip(args, params)):
default = params[name].default
annotation = params[name].annotation
if annotation != inspect.Parameter.empty:
if isinstance(annotation, type) and annotation != str:
# The parameter is annotated with a type that isn't str: convert
# the arg to that type.
args[i] = annotation(arg)
elif default != inspect.Parameter.empty:
if default is not None and not isinstance(default, str):
# The parameter has a default value that isn't None or a str:
# convert the arg to the default value's type.
args[i] = type(default)(arg)
return args | 124,120 |
Registers a new command handler object.
All methods on `handler_obj` whose name starts with "cmd_" are
registered as a GTP command. For example, the method cmd_genmove will
be invoked when the engine receives a genmove command.
Args:
handler_obj: the handler object to register. | def add_cmd_handler(self, handler_obj):
for field in dir(handler_obj):
if field.startswith("cmd_"):
cmd = field[4:]
fn = getattr(handler_obj, field)
if cmd in self.cmds:
print('Replacing {} with {}'.format(
_handler_name(self.cmds[cmd]), _handler_name(fn)),
file=sys.stderr)
self.cmds[cmd] = fn | 124,121 |
Calculate cross entropy loss while ignoring padding.
Args:
logits: Tensor of size [batch_size, length_logits, vocab_size]
labels: Tensor of size [batch_size, length_labels]
smoothing: Label smoothing constant, used to determine the on and off values
vocab_size: int size of the vocabulary
Returns:
Returns a float32 tensor with shape
[batch_size, max(length_logits, length_labels)] | def padded_cross_entropy_loss(logits, labels, smoothing, vocab_size):
with tf.name_scope("loss", [logits, labels]):
logits, labels = _pad_tensors_to_same_length(logits, labels)
# Calculate smoothing cross entropy
with tf.name_scope("smoothing_cross_entropy", [logits, labels]):
confidence = 1.0 - smoothing
low_confidence = (1.0 - confidence) / tf.to_float(vocab_size - 1)
soft_targets = tf.one_hot(
tf.cast(labels, tf.int32),
depth=vocab_size,
on_value=confidence,
off_value=low_confidence)
xentropy = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=logits, labels=soft_targets)
# Calculate the best (lowest) possible value of cross entropy, and
# subtract from the cross entropy loss.
normalizing_constant = -(
confidence * tf.log(confidence) + tf.to_float(vocab_size - 1) *
low_confidence * tf.log(low_confidence + 1e-20))
xentropy -= normalizing_constant
weights = tf.to_float(tf.not_equal(labels, 0))
return xentropy * weights, weights | 124,133 |
Wrap a metric fn that returns scores and weights as an eval metric fn.
The input metric_fn returns values for the current batch. The wrapper
aggregates the return values collected over all of the batches evaluated.
Args:
metric_fn: function that returns scores and weights for the current batch's
logits and predicted labels.
Returns:
function that aggregates the scores and weights from metric_fn. | def _convert_to_eval_metric(metric_fn):
def problem_metric_fn(*args):
(scores, weights) = metric_fn(*args)
# The tf.metrics.mean function assures correct aggregation.
return tf.metrics.mean(scores, weights)
return problem_metric_fn | 124,134 |
Approximate BLEU score computation between labels and predictions.
An approximate BLEU scoring method since we do not glue word pieces or
decode the ids and tokenize the output. By default, we use ngram order of 4
and use brevity penalty. Also, this does not have beam search.
Args:
logits: Tensor of size [batch_size, length_logits, vocab_size]
labels: Tensor of size [batch-size, length_labels]
Returns:
bleu: int, approx bleu score | def bleu_score(logits, labels):
predictions = tf.to_int32(tf.argmax(logits, axis=-1))
# TODO: Look into removing use of py_func
bleu = tf.py_func(compute_bleu, (labels, predictions), tf.float32)
return bleu, tf.constant(1.0) | 124,140 |
Extracts all n-grams up to a given maximum order from an input segment.
Args:
segment: text segment from which n-grams will be extracted.
max_order: maximum length in tokens of the n-grams returned by this
methods.
Returns:
The Counter containing all n-grams upto max_order in segment
with a count of how many times each n-gram occurred. | def _get_ngrams_with_counter(segment, max_order):
ngram_counts = collections.Counter()
for order in xrange(1, max_order + 1):
for i in xrange(0, len(segment) - order + 1):
ngram = tuple(segment[i:i + order])
ngram_counts[ngram] += 1
return ngram_counts | 124,141 |
Computes BLEU score of translated segments against one or more references.
Args:
reference_corpus: list of references for each translation. Each
reference should be tokenized into a list of tokens.
translation_corpus: list of translations to score. Each translation
should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
use_bp: boolean, whether to apply brevity penalty.
Returns:
BLEU score. | def compute_bleu(reference_corpus, translation_corpus, max_order=4,
use_bp=True):
reference_length = 0
translation_length = 0
bp = 1.0
geo_mean = 0
matches_by_order = [0] * max_order
possible_matches_by_order = [0] * max_order
precisions = []
for (references, translations) in zip(reference_corpus, translation_corpus):
reference_length += len(references)
translation_length += len(translations)
ref_ngram_counts = _get_ngrams_with_counter(references, max_order)
translation_ngram_counts = _get_ngrams_with_counter(translations, max_order)
overlap = dict((ngram,
min(count, translation_ngram_counts[ngram]))
for ngram, count in ref_ngram_counts.items())
for ngram in overlap:
matches_by_order[len(ngram) - 1] += overlap[ngram]
for ngram in translation_ngram_counts:
possible_matches_by_order[len(ngram) - 1] += translation_ngram_counts[
ngram]
precisions = [0] * max_order
smooth = 1.0
for i in xrange(0, max_order):
if possible_matches_by_order[i] > 0:
precisions[i] = float(matches_by_order[i]) / possible_matches_by_order[i]
if matches_by_order[i] > 0:
precisions[i] = float(matches_by_order[i]) / possible_matches_by_order[
i]
else:
smooth *= 2
precisions[i] = 1.0 / (smooth * possible_matches_by_order[i])
else:
precisions[i] = 0.0
if max(precisions) > 0:
p_log_sum = sum(math.log(p) for p in precisions if p)
geo_mean = math.exp(p_log_sum / max_order)
if use_bp:
ratio = translation_length / reference_length
bp = math.exp(1 - 1. / ratio) if ratio < 1.0 else 1.0
bleu = geo_mean * bp
return np.float32(bleu) | 124,142 |
ROUGE-2 F1 score computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
logits: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge2_fscore: approx rouge-2 f1 score. | def rouge_2_fscore(logits, labels):
predictions = tf.to_int32(tf.argmax(logits, axis=-1))
# TODO: Look into removing use of py_func
rouge_2_f_score = tf.py_func(rouge_n, (predictions, labels), tf.float32)
return rouge_2_f_score, tf.constant(1.0) | 124,143 |
Computes ROUGE-N f1 score of two text collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Args:
eval_sentences: Predicted sentences.
ref_sentences: Sentences from the reference set
n: Size of ngram. Defaults to 2.
Returns:
f1 score for ROUGE-N | def rouge_n(eval_sentences, ref_sentences, n=2):
f1_scores = []
for eval_sentence, ref_sentence in zip(eval_sentences, ref_sentences):
eval_ngrams = _get_ngrams(n, eval_sentence)
ref_ngrams = _get_ngrams(n, ref_sentence)
ref_count = len(ref_ngrams)
eval_count = len(eval_ngrams)
# Count the overlapping ngrams between evaluated and reference
overlapping_ngrams = eval_ngrams.intersection(ref_ngrams)
overlapping_count = len(overlapping_ngrams)
# Handle edge case. This isn't mathematically correct, but it's good enough
if eval_count == 0:
precision = 0.0
else:
precision = float(overlapping_count) / eval_count
if ref_count == 0:
recall = 0.0
else:
recall = float(overlapping_count) / ref_count
f1_scores.append(2.0 * ((precision * recall) / (precision + recall + 1e-8)))
# return overlapping_count / reference_count
return np.mean(f1_scores, dtype=np.float32) | 124,144 |
ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge_l_fscore: approx rouge-l f1 score. | def rouge_l_fscore(predictions, labels):
outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (outputs, labels),
tf.float32)
return rouge_l_f_score, tf.constant(1.0) | 124,145 |
Extract files from downloaded compressed archive file.
Args:
path: string directory where the files will be downloaded
url: url containing the compressed input and target files
input_filename: name of file containing data in source language
target_filename: name of file containing data in target language
Returns:
Full paths to extracted input and target files.
Raises:
OSError: if the the download/extraction fails. | def download_and_extract(path, url, input_filename, target_filename):
logging.info('Downloading and extracting data to: %s' % path)
# Check if extracted files already exist in path
input_file = find_file(path, input_filename)
target_file = find_file(path, target_filename)
if input_file and target_file:
logging.info("Already downloaded and extracted %s." % url)
return input_file, target_file
# Download archive file if it doesn't already exist.
compressed_file = download_from_url(path, url)
# Extract compressed files
logging.info("Extracting %s." % compressed_file)
with tarfile.open(compressed_file, "r:gz") as corpus_tar:
corpus_tar.extractall(path)
# Return filepaths of the requested files.
input_file = find_file(path, input_filename)
target_file = find_file(path, target_filename)
if input_file and target_file:
return input_file, target_file
raise OSError("Download/extraction failed for url %s to path %s" %
(url, path)) | 124,151 |
This method performs the positive/negative sampling, and return
the sampled proposals.
Note: this function keeps a state.
Arguments:
proposals (list[BoxList])
targets (list[BoxList]) | def subsample(self, proposals, targets):
labels, regression_targets = self.prepare_targets(proposals, targets)
sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
proposals = list(proposals)
# add corresponding label and regression_targets information to the bounding boxes
for labels_per_image, regression_targets_per_image, proposals_per_image in zip(
labels, regression_targets, proposals
):
proposals_per_image.add_field("labels", labels_per_image)
proposals_per_image.add_field(
"regression_targets", regression_targets_per_image
)
# distributed sampled proposals, that were obtained on all feature maps
# concatenated via the fg_bg_sampler, into individual feature map levels
for img_idx, (pos_inds_img, neg_inds_img) in enumerate(
zip(sampled_pos_inds, sampled_neg_inds)
):
img_sampled_inds = torch.nonzero(pos_inds_img | neg_inds_img).squeeze(1)
proposals_per_image = proposals[img_idx][img_sampled_inds]
proposals[img_idx] = proposals_per_image
self._proposals = proposals
return proposals | 124,164 |
Computes the loss for Faster R-CNN.
This requires that the subsample method has been called beforehand.
Arguments:
class_logits (list[Tensor])
box_regression (list[Tensor])
Returns:
classification_loss (Tensor)
box_loss (Tensor) | def __call__(self, class_logits, box_regression):
class_logits = cat(class_logits, dim=0)
box_regression = cat(box_regression, dim=0)
device = class_logits.device
if not hasattr(self, "_proposals"):
raise RuntimeError("subsample needs to be called before")
proposals = self._proposals
labels = cat([proposal.get_field("labels") for proposal in proposals], dim=0)
regression_targets = cat(
[proposal.get_field("regression_targets") for proposal in proposals], dim=0
)
classification_loss = F.cross_entropy(class_logits, labels)
# get indices that correspond to the regression targets for
# the corresponding ground truth labels, to be used with
# advanced indexing
sampled_pos_inds_subset = torch.nonzero(labels > 0).squeeze(1)
labels_pos = labels[sampled_pos_inds_subset]
if self.cls_agnostic_bbox_reg:
map_inds = torch.tensor([4, 5, 6, 7], device=device)
else:
map_inds = 4 * labels_pos[:, None] + torch.tensor(
[0, 1, 2, 3], device=device)
box_loss = smooth_l1_loss(
box_regression[sampled_pos_inds_subset[:, None], map_inds],
regression_targets[sampled_pos_inds_subset],
size_average=False,
beta=1,
)
box_loss = box_loss / labels.numel()
return classification_loss, box_loss | 124,165 |
Run the given subprocess command in a coroutine.
Args:
*cmd: the command to run and its arguments.
Returns:
The output that the command wrote to stdout.
Raises:
RuntimeError: if the command returns a non-zero result. | async def checked_run(*cmd):
# Start the subprocess.
logging.info('Running: %s', expand_cmd_str(cmd))
with logged_timer('{} finished'.format(get_cmd_name(cmd))):
p = await asyncio.create_subprocess_exec(
*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.STDOUT)
# Stream output from the process stdout.
chunks = []
while True:
chunk = await p.stdout.read(16 * 1024)
if not chunk:
break
chunks.append(chunk)
# Wait for the process to finish, check it was successful & build stdout.
await p.wait()
stdout = b''.join(chunks).decode()[:-1]
if p.returncode:
raise RuntimeError('Return code {} from process: {}\n{}'.format(
p.returncode, expand_cmd_str(cmd), stdout))
return stdout | 124,169 |
Get frame by index.
Args:
frame_id (int): Index of the expected frame, 0-based.
Returns:
ndarray or None: Return the frame if successful, otherwise None. | def get_frame(self, frame_id):
if frame_id < 0 or frame_id >= self._frame_cnt:
raise IndexError(
'"frame_id" must be between 0 and {}'.format(self._frame_cnt -
1))
if frame_id == self._position:
return self.read()
if self._cache:
img = self._cache.get(frame_id)
if img is not None:
self._position = frame_id + 1
return img
self._set_real_position(frame_id)
ret, img = self._vcap.read()
if ret:
if self._cache:
self._cache.put(self._position, img)
self._position += 1
return img | 126,334 |
Convert a video to frame images
Args:
frame_dir (str): Output directory to store all the frame images.
file_start (int): Filenames will start from the specified number.
filename_tmpl (str): Filename template with the index as the
placeholder.
start (int): The starting frame index.
max_num (int): Maximum number of frames to be written.
show_progress (bool): Whether to show a progress bar. | def cvt2frames(self,
frame_dir,
file_start=0,
filename_tmpl='{:06d}.jpg',
start=0,
max_num=0,
show_progress=True):
mkdir_or_exist(frame_dir)
if max_num == 0:
task_num = self.frame_cnt - start
else:
task_num = min(self.frame_cnt - start, max_num)
if task_num <= 0:
raise ValueError('start must be less than total frame number')
if start > 0:
self._set_real_position(start)
def write_frame(file_idx):
img = self.read()
filename = osp.join(frame_dir, filename_tmpl.format(file_idx))
cv2.imwrite(filename, img)
if show_progress:
track_progress(write_frame, range(file_start,
file_start + task_num))
else:
for i in range(task_num):
img = self.read()
if img is None:
break
filename = osp.join(frame_dir,
filename_tmpl.format(i + file_start))
cv2.imwrite(filename, img) | 126,335 |
Track the progress of tasks execution with a progress bar.
Tasks are done with a simple for-loop.
Args:
func (callable): The function to be applied to each task.
tasks (list or tuple[Iterable, int]): A list of tasks or
(tasks, total num).
bar_width (int): Width of progress bar.
Returns:
list: The task results. | def track_progress(func, tasks, bar_width=50, **kwargs):
if isinstance(tasks, tuple):
assert len(tasks) == 2
assert isinstance(tasks[0], collections_abc.Iterable)
assert isinstance(tasks[1], int)
task_num = tasks[1]
tasks = tasks[0]
elif isinstance(tasks, collections_abc.Iterable):
task_num = len(tasks)
else:
raise TypeError(
'"tasks" must be an iterable object or a (iterator, int) tuple')
prog_bar = ProgressBar(task_num, bar_width)
results = []
for task in tasks:
results.append(func(task, **kwargs))
prog_bar.update()
sys.stdout.write('\n')
return results | 126,341 |
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