text_prompt
stringlengths 100
17.7k
⌀ | code_prompt
stringlengths 7
9.86k
⌀ |
|---|---|
<SYSTEM_TASK:>
Set the "death signal" of the current process, so that
<END_TASK>
<USER_TASK:>
Description:
def enable_death_signal(_warn=True):
"""
Set the "death signal" of the current process, so that
the current process will be cleaned with guarantee
in case the parent dies accidentally.
""" |
if platform.system() != 'Linux':
return
try:
import prctl # pip install python-prctl
except ImportError:
if _warn:
log_once('"import prctl" failed! Install python-prctl so that processes can be cleaned with guarantee.',
'warn')
return
else:
assert hasattr(prctl, 'set_pdeathsig'), \
"prctl.set_pdeathsig does not exist! Note that you need to install 'python-prctl' instead of 'prctl'."
# is SIGHUP a good choice?
prctl.set_pdeathsig(signal.SIGHUP) |
<SYSTEM_TASK:>
Execute a command with timeout, and return STDOUT and STDERR
<END_TASK>
<USER_TASK:>
Description:
def subproc_call(cmd, timeout=None):
"""
Execute a command with timeout, and return STDOUT and STDERR
Args:
cmd(str): the command to execute.
timeout(float): timeout in seconds.
Returns:
output(bytes), retcode(int). If timeout, retcode is -1.
""" |
try:
output = subprocess.check_output(
cmd, stderr=subprocess.STDOUT,
shell=True, timeout=timeout)
return output, 0
except subprocess.TimeoutExpired as e:
logger.warn("Command '{}' timeout!".format(cmd))
logger.warn(e.output.decode('utf-8'))
return e.output, -1
except subprocess.CalledProcessError as e:
logger.warn("Command '{}' failed, return code={}".format(cmd, e.returncode))
logger.warn(e.output.decode('utf-8'))
return e.output, e.returncode
except Exception:
logger.warn("Command '{}' failed to run.".format(cmd))
return "", -2 |
<SYSTEM_TASK:>
Put obj to queue, but will give up when the thread is stopped
<END_TASK>
<USER_TASK:>
Description:
def queue_put_stoppable(self, q, obj):
""" Put obj to queue, but will give up when the thread is stopped""" |
while not self.stopped():
try:
q.put(obj, timeout=5)
break
except queue.Full:
pass |
<SYSTEM_TASK:>
Take obj from queue, but will give up when the thread is stopped
<END_TASK>
<USER_TASK:>
Description:
def queue_get_stoppable(self, q):
""" Take obj from queue, but will give up when the thread is stopped""" |
while not self.stopped():
try:
return q.get(timeout=5)
except queue.Empty:
pass |
<SYSTEM_TASK:>
Visualize use weights in convolution filters.
<END_TASK>
<USER_TASK:>
Description:
def visualize_conv_weights(filters, name):
"""Visualize use weights in convolution filters.
Args:
filters: tensor containing the weights [H,W,Cin,Cout]
name: label for tensorboard
Returns:
image of all weight
""" |
with tf.name_scope('visualize_w_' + name):
filters = tf.transpose(filters, (3, 2, 0, 1)) # [h, w, cin, cout] -> [cout, cin, h, w]
filters = tf.unstack(filters) # --> cout * [cin, h, w]
filters = tf.concat(filters, 1) # --> [cin, cout * h, w]
filters = tf.unstack(filters) # --> cin * [cout * h, w]
filters = tf.concat(filters, 1) # --> [cout * h, cin * w]
filters = tf.expand_dims(filters, 0)
filters = tf.expand_dims(filters, -1)
tf.summary.image('visualize_w_' + name, filters) |
<SYSTEM_TASK:>
Visualize activations for convolution layers.
<END_TASK>
<USER_TASK:>
Description:
def visualize_conv_activations(activation, name):
"""Visualize activations for convolution layers.
Remarks:
This tries to place all activations into a square.
Args:
activation: tensor with the activation [B,H,W,C]
name: label for tensorboard
Returns:
image of almost all activations
""" |
import math
with tf.name_scope('visualize_act_' + name):
_, h, w, c = activation.get_shape().as_list()
rows = []
c_per_row = int(math.sqrt(c))
for y in range(0, c - c_per_row, c_per_row):
row = activation[:, :, :, y:y + c_per_row] # [?, H, W, 32] --> [?, H, W, 5]
cols = tf.unstack(row, axis=3) # [?, H, W, 5] --> 5 * [?, H, W]
row = tf.concat(cols, 1)
rows.append(row)
viz = tf.concat(rows, 2)
tf.summary.image('visualize_act_' + name, tf.expand_dims(viz, -1)) |
<SYSTEM_TASK:>
Make the static shape of a tensor less specific.
<END_TASK>
<USER_TASK:>
Description:
def shapeless_placeholder(x, axis, name):
"""
Make the static shape of a tensor less specific.
If you want to feed to a tensor, the shape of the feed value must match
the tensor's static shape. This function creates a placeholder which
defaults to x if not fed, but has a less specific static shape than x.
See also `tensorflow#5680 <https://github.com/tensorflow/tensorflow/issues/5680>`_.
Args:
x: a tensor
axis(int or list of ints): these axes of ``x.get_shape()`` will become
None in the output.
name(str): name of the output tensor
Returns:
a tensor equal to x, but shape information is partially cleared.
""" |
shp = x.get_shape().as_list()
if not isinstance(axis, list):
axis = [axis]
for a in axis:
if shp[a] is None:
raise ValueError("Axis {} of shape {} is already unknown!".format(a, shp))
shp[a] = None
x = tf.placeholder_with_default(x, shape=shp, name=name)
return x |
<SYSTEM_TASK:>
OpenAI official code actually models the "uniform" latent code as
<END_TASK>
<USER_TASK:>
Description:
def sample_prior(batch_size):
cat, _ = get_distributions(DIST_PRIOR_PARAM[:NUM_CLASS], DIST_PRIOR_PARAM[NUM_CLASS:])
sample_cat = tf.one_hot(cat.sample(batch_size), NUM_CLASS)
"""
OpenAI official code actually models the "uniform" latent code as
a Gaussian distribution, but obtain the samples from a uniform distribution.
""" |
sample_uni = tf.random_uniform([batch_size, NUM_UNIFORM], -1, 1)
samples = tf.concat([sample_cat, sample_uni], axis=1)
return samples |
<SYSTEM_TASK:>
Estimate filters for convolution layers
<END_TASK>
<USER_TASK:>
Description:
def _parameter_net(self, theta, kernel_shape=9):
"""Estimate filters for convolution layers
Args:
theta: angle of filter
kernel_shape: size of each filter
Returns:
learned filter as [B, k, k, 1]
""" |
with argscope(FullyConnected, nl=tf.nn.leaky_relu):
net = FullyConnected('fc1', theta, 64)
net = FullyConnected('fc2', net, 128)
pred_filter = FullyConnected('fc3', net, kernel_shape ** 2, nl=tf.identity)
pred_filter = tf.reshape(pred_filter, [BATCH, kernel_shape, kernel_shape, 1], name="pred_filter")
logger.info('Parameter net output: {}'.format(pred_filter.get_shape().as_list()))
return pred_filter |
<SYSTEM_TASK:>
Implements a steerable Gaussian filter.
<END_TASK>
<USER_TASK:>
Description:
def filter_with_theta(image, theta, sigma=1., filter_size=9):
"""Implements a steerable Gaussian filter.
This function can be used to evaluate the first
directional derivative of an image, using the
method outlined in
W. T. Freeman and E. H. Adelson, "The Design
and Use of Steerable Filters", IEEE PAMI, 1991.
It evaluates the directional derivative of the input
image I, oriented at THETA degrees with respect to the
image rows. The standard deviation of the Gaussian kernel
is given by SIGMA (assumed to be equal to unity by default).
Args:
image: any input image (only one channel)
theta: orientation of filter [0, 2 * pi]
sigma (float, optional): standard derivation of Gaussian
filter_size (int, optional): filter support
Returns:
filtered image and the filter
""" |
x = np.arange(-filter_size // 2 + 1, filter_size // 2 + 1)
# 1D Gaussian
g = np.array([np.exp(-(x**2) / (2 * sigma**2))])
# first-derivative of 1D Gaussian
gp = np.array([-(x / sigma) * np.exp(-(x**2) / (2 * sigma**2))])
ix = convolve2d(image, -gp, mode='same', boundary='fill', fillvalue=0)
ix = convolve2d(ix, g.T, mode='same', boundary='fill', fillvalue=0)
iy = convolve2d(image, g, mode='same', boundary='fill', fillvalue=0)
iy = convolve2d(iy, -gp.T, mode='same', boundary='fill', fillvalue=0)
output = np.cos(theta) * ix + np.sin(theta) * iy
# np.cos(theta) * np.matmul(g.T, gp) + np.sin(theta) * np.matmul(gp.T, g)
gt_filter = np.matmul(g.T, gp)
gt_filter = np.cos(theta) * gt_filter + np.sin(theta) * gt_filter.T
return output, gt_filter |
<SYSTEM_TASK:>
Assign `self.g_vars` to the parameters under scope `g_scope`,
<END_TASK>
<USER_TASK:>
Description:
def collect_variables(self, g_scope='gen', d_scope='discrim'):
"""
Assign `self.g_vars` to the parameters under scope `g_scope`,
and same with `self.d_vars`.
""" |
self.g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, g_scope)
assert self.g_vars
self.d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, d_scope)
assert self.d_vars |
<SYSTEM_TASK:>
Build standard GAN loss and set `self.g_loss` and `self.d_loss`.
<END_TASK>
<USER_TASK:>
Description:
def build_losses(self, logits_real, logits_fake):
"""
Build standard GAN loss and set `self.g_loss` and `self.d_loss`.
D and G play two-player minimax game with value function V(G,D)
min_G max _D V(D, G) = IE_{x ~ p_data} [log D(x)] + IE_{z ~ p_fake} [log (1 - D(G(z)))]
Args:
logits_real (tf.Tensor): discrim logits from real samples
logits_fake (tf.Tensor): discrim logits from fake samples produced by generator
""" |
with tf.name_scope("GAN_loss"):
score_real = tf.sigmoid(logits_real)
score_fake = tf.sigmoid(logits_fake)
tf.summary.histogram('score-real', score_real)
tf.summary.histogram('score-fake', score_fake)
with tf.name_scope("discrim"):
d_loss_pos = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_real, labels=tf.ones_like(logits_real)), name='loss_real')
d_loss_neg = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake, labels=tf.zeros_like(logits_fake)), name='loss_fake')
d_pos_acc = tf.reduce_mean(tf.cast(score_real > 0.5, tf.float32), name='accuracy_real')
d_neg_acc = tf.reduce_mean(tf.cast(score_fake < 0.5, tf.float32), name='accuracy_fake')
d_accuracy = tf.add(.5 * d_pos_acc, .5 * d_neg_acc, name='accuracy')
self.d_loss = tf.add(.5 * d_loss_pos, .5 * d_loss_neg, name='loss')
with tf.name_scope("gen"):
self.g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake, labels=tf.ones_like(logits_fake)), name='loss')
g_accuracy = tf.reduce_mean(tf.cast(score_fake > 0.5, tf.float32), name='accuracy')
add_moving_summary(self.g_loss, self.d_loss, d_accuracy, g_accuracy) |
<SYSTEM_TASK:>
We need to set tower_func because it's a TowerTrainer,
<END_TASK>
<USER_TASK:>
Description:
def _build_gan_trainer(self, input, model):
"""
We need to set tower_func because it's a TowerTrainer,
and only TowerTrainer supports automatic graph creation for inference during training.
If we don't care about inference during training, using tower_func is
not needed. Just calling model.build_graph directly is OK.
""" |
# Build the graph
self.tower_func = TowerFuncWrapper(model.build_graph, model.get_input_signature())
with TowerContext('', is_training=True):
self.tower_func(*input.get_input_tensors())
opt = model.get_optimizer()
# Define the training iteration
# by default, run one d_min after one g_min
with tf.name_scope('optimize'):
g_min = opt.minimize(model.g_loss, var_list=model.g_vars, name='g_op')
with tf.control_dependencies([g_min]):
d_min = opt.minimize(model.d_loss, var_list=model.d_vars, name='d_op')
self.train_op = d_min |
<SYSTEM_TASK:>
Get the cost from the regularizers in ``tf.GraphKeys.REGULARIZATION_LOSSES``.
<END_TASK>
<USER_TASK:>
Description:
def regularize_cost_from_collection(name='regularize_cost'):
"""
Get the cost from the regularizers in ``tf.GraphKeys.REGULARIZATION_LOSSES``.
If in replicated mode, will only regularize variables created within the current tower.
Args:
name (str): the name of the returned tensor
Returns:
tf.Tensor: a scalar, the total regularization cost.
""" |
ctx = get_current_tower_context()
if not ctx.is_training:
# TODO Currently cannot build the wd_cost correctly at inference,
# because ths vs_name used in inference can be '', therefore the
# variable filter will fail
return tf.constant(0, dtype=tf.float32, name='empty_' + name)
# NOTE: this collection doesn't always grow with towers.
# It only grows with actual variable creation, but not get_variable call.
if ctx.has_own_variables: # be careful of the first tower (name='')
losses = ctx.get_collection_in_tower(tfv1.GraphKeys.REGULARIZATION_LOSSES)
else:
losses = tfv1.get_collection(tfv1.GraphKeys.REGULARIZATION_LOSSES)
if len(losses) > 0:
logger.info("regularize_cost_from_collection() found {} regularizers "
"in REGULARIZATION_LOSSES collection.".format(len(losses)))
def maploss(l):
assert l.dtype.is_floating, l
if l.dtype != tf.float32:
l = tf.cast(l, tf.float32)
return l
losses = [maploss(l) for l in losses]
reg_loss = tf.add_n(losses, name=name)
return reg_loss
else:
return tf.constant(0, dtype=tf.float32, name='empty_' + name) |
<SYSTEM_TASK:>
Same as `tf.layers.dropout`.
<END_TASK>
<USER_TASK:>
Description:
def Dropout(x, *args, **kwargs):
"""
Same as `tf.layers.dropout`.
However, for historical reasons, the first positional argument is
interpreted as keep_prob rather than drop_prob.
Explicitly use `rate=` keyword arguments to ensure things are consistent.
""" |
if 'is_training' in kwargs:
kwargs['training'] = kwargs.pop('is_training')
if len(args) > 0:
if args[0] != 0.5:
logger.warn(
"The first positional argument to tensorpack.Dropout is the probability to keep, rather than to drop. "
"This is different from the rate argument in tf.layers.Dropout due to historical reasons. "
"To mimic tf.layers.Dropout, explicitly use keyword argument 'rate' instead")
rate = 1 - args[0]
elif 'keep_prob' in kwargs:
assert 'rate' not in kwargs, "Cannot set both keep_prob and rate!"
rate = 1 - kwargs.pop('keep_prob')
elif 'rate' in kwargs:
rate = kwargs.pop('rate')
else:
rate = 0.5
if kwargs.get('training', None) is None:
kwargs['training'] = get_current_tower_context().is_training
if get_tf_version_tuple() <= (1, 12):
return tf.layers.dropout(x, rate=rate, **kwargs)
else:
return tf.nn.dropout(x, rate=rate if kwargs['training'] else 0.) |
<SYSTEM_TASK:>
Return a proper background image of background_shape, given img.
<END_TASK>
<USER_TASK:>
Description:
def fill(self, background_shape, img):
"""
Return a proper background image of background_shape, given img.
Args:
background_shape (tuple): a shape (h, w)
img: an image
Returns:
a background image
""" |
background_shape = tuple(background_shape)
return self._fill(background_shape, img) |
<SYSTEM_TASK:>
Apply a function on the wrapped tensor.
<END_TASK>
<USER_TASK:>
Description:
def apply(self, func, *args, **kwargs):
"""
Apply a function on the wrapped tensor.
Returns:
LinearWrap: ``LinearWrap(func(self.tensor(), *args, **kwargs))``.
""" |
ret = func(self._t, *args, **kwargs)
return LinearWrap(ret) |
<SYSTEM_TASK:>
Apply a function on the wrapped tensor. The tensor
<END_TASK>
<USER_TASK:>
Description:
def apply2(self, func, *args, **kwargs):
"""
Apply a function on the wrapped tensor. The tensor
will be the second argument of func.
This is because many symbolic functions
(such as tensorpack's layers) takes 'scope' as the first argument.
Returns:
LinearWrap: ``LinearWrap(func(args[0], self.tensor(), *args[1:], **kwargs))``.
""" |
ret = func(args[0], self._t, *(args[1:]), **kwargs)
return LinearWrap(ret) |
<SYSTEM_TASK:>
Will setup the assign operator for that variable.
<END_TASK>
<USER_TASK:>
Description:
def setup_graph(self):
""" Will setup the assign operator for that variable. """ |
all_vars = tfv1.global_variables() + tfv1.local_variables()
for v in all_vars:
if v.name == self.var_name:
self.var = v
break
else:
raise ValueError("{} is not a variable in the graph!".format(self.var_name)) |
<SYSTEM_TASK:>
Using schedule, compute the value to be set at a given point.
<END_TASK>
<USER_TASK:>
Description:
def _get_value_to_set_at_point(self, point):
"""
Using schedule, compute the value to be set at a given point.
""" |
laste, lastv = None, None
for e, v in self.schedule:
if e == point:
return v # meet the exact boundary, return directly
if e > point:
break
laste, lastv = e, v
if laste is None or laste == e:
# hasn't reached the first scheduled point, or reached the end of all scheduled points
return None
if self.interp is None:
# If no interpolation, nothing to do.
return None
v = (point - laste) * 1. / (e - laste) * (v - lastv) + lastv
return v |
<SYSTEM_TASK:>
Convert a caffe parameter name to a tensorflow parameter name as
<END_TASK>
<USER_TASK:>
Description:
def name_conversion(caffe_layer_name):
""" Convert a caffe parameter name to a tensorflow parameter name as
defined in the above model """ |
# beginning & end mapping
NAME_MAP = {'bn_conv1/beta': 'conv0/bn/beta',
'bn_conv1/gamma': 'conv0/bn/gamma',
'bn_conv1/mean/EMA': 'conv0/bn/mean/EMA',
'bn_conv1/variance/EMA': 'conv0/bn/variance/EMA',
'conv1/W': 'conv0/W', 'conv1/b': 'conv0/b',
'fc1000/W': 'linear/W', 'fc1000/b': 'linear/b'}
if caffe_layer_name in NAME_MAP:
return NAME_MAP[caffe_layer_name]
s = re.search('([a-z]+)([0-9]+)([a-z]+)_', caffe_layer_name)
if s is None:
s = re.search('([a-z]+)([0-9]+)([a-z]+)([0-9]+)_', caffe_layer_name)
layer_block_part1 = s.group(3)
layer_block_part2 = s.group(4)
assert layer_block_part1 in ['a', 'b']
layer_block = 0 if layer_block_part1 == 'a' else int(layer_block_part2)
else:
layer_block = ord(s.group(3)) - ord('a')
layer_type = s.group(1)
layer_group = s.group(2)
layer_branch = int(re.search('_branch([0-9])', caffe_layer_name).group(1))
assert layer_branch in [1, 2]
if layer_branch == 2:
layer_id = re.search('_branch[0-9]([a-z])/', caffe_layer_name).group(1)
layer_id = ord(layer_id) - ord('a') + 1
TYPE_DICT = {'res': 'conv{}', 'bn': 'conv{}/bn'}
layer_type = TYPE_DICT[layer_type].format(layer_id if layer_branch == 2 else 'shortcut')
tf_name = caffe_layer_name[caffe_layer_name.index('/'):]
tf_name = 'group{}/block{}/{}'.format(
int(layer_group) - 2, layer_block, layer_type) + tf_name
return tf_name |
<SYSTEM_TASK:>
Use fn to map the output of any variable getter.
<END_TASK>
<USER_TASK:>
Description:
def remap_variables(fn):
"""
Use fn to map the output of any variable getter.
Args:
fn (tf.Variable -> tf.Tensor)
Returns:
The current variable scope with a custom_getter that maps
all the variables by fn.
Example:
.. code-block:: python
with varreplace.remap_variables(lambda var: quantize(var)):
x = FullyConnected('fc', x, 1000) # fc/{W,b} will be quantized
""" |
def custom_getter(getter, *args, **kwargs):
v = getter(*args, **kwargs)
return fn(v)
return custom_getter_scope(custom_getter) |
<SYSTEM_TASK:>
Return a context to freeze variables,
<END_TASK>
<USER_TASK:>
Description:
def freeze_variables(stop_gradient=True, skip_collection=False):
"""
Return a context to freeze variables,
by wrapping ``tf.get_variable`` with a custom getter.
It works by either applying ``tf.stop_gradient`` on the variables,
or by keeping them out of the ``TRAINABLE_VARIABLES`` collection, or
both.
Example:
.. code-block:: python
with varreplace.freeze_variable(stop_gradient=False, skip_collection=True):
x = FullyConnected('fc', x, 1000) # fc/* will not be trained
Args:
stop_gradient (bool): if True, variables returned from `get_variable`
will be wrapped with `tf.stop_gradient` and therefore has no
gradient when used later.
Note that the created variables may still have gradient when accessed
by other approaches (e.g. by name, or by collection).
Also note that this makes `tf.get_variable` returns a Tensor instead of a Variable,
which may break existing code.
Therefore, it's recommended to use the `skip_collection` option instead.
skip_collection (bool): if True, do not add the variable to
``TRAINABLE_VARIABLES`` collection, but to ``MODEL_VARIABLES``
collection. As a result they will not be trained by default.
""" |
def custom_getter(getter, *args, **kwargs):
trainable = kwargs.get('trainable', True)
name = args[0] if len(args) else kwargs.get('name')
if skip_collection:
kwargs['trainable'] = False
v = getter(*args, **kwargs)
if skip_collection:
tf.add_to_collection(tf.GraphKeys.MODEL_VARIABLES, v)
if trainable and stop_gradient:
v = tf.stop_gradient(v, name='freezed_' + name)
return v
return custom_getter_scope(custom_getter) |
<SYSTEM_TASK:>
Convert to a nested dict.
<END_TASK>
<USER_TASK:>
Description:
def to_dict(self):
"""Convert to a nested dict. """ |
return {k: v.to_dict() if isinstance(v, AttrDict) else v
for k, v in self.__dict__.items() if not k.startswith('_')} |
<SYSTEM_TASK:>
Update from command line args.
<END_TASK>
<USER_TASK:>
Description:
def update_args(self, args):
"""Update from command line args. """ |
for cfg in args:
keys, v = cfg.split('=', maxsplit=1)
keylist = keys.split('.')
dic = self
for i, k in enumerate(keylist[:-1]):
assert k in dir(dic), "Unknown config key: {}".format(keys)
dic = getattr(dic, k)
key = keylist[-1]
oldv = getattr(dic, key)
if not isinstance(oldv, str):
v = eval(v)
setattr(dic, key, v) |
<SYSTEM_TASK:>
Get a corresponding model loader by looking at the file name.
<END_TASK>
<USER_TASK:>
Description:
def get_model_loader(filename):
"""
Get a corresponding model loader by looking at the file name.
Returns:
SessInit: either a :class:`DictRestore` (if name ends with 'npy/npz') or
:class:`SaverRestore` (otherwise).
""" |
assert isinstance(filename, six.string_types), filename
filename = os.path.expanduser(filename)
if filename.endswith('.npy'):
assert tf.gfile.Exists(filename), filename
return DictRestore(np.load(filename, encoding='latin1').item())
elif filename.endswith('.npz'):
assert tf.gfile.Exists(filename), filename
obj = np.load(filename)
return DictRestore(dict(obj))
else:
return SaverRestore(filename) |
<SYSTEM_TASK:>
return a set of strings
<END_TASK>
<USER_TASK:>
Description:
def _read_checkpoint_vars(model_path):
""" return a set of strings """ |
reader = tf.train.NewCheckpointReader(model_path)
reader = CheckpointReaderAdapter(reader) # use an adapter to standardize the name
ckpt_vars = reader.get_variable_to_shape_map().keys()
return reader, set(ckpt_vars) |
<SYSTEM_TASK:>
Decorator for function to support argscope
<END_TASK>
<USER_TASK:>
Description:
def enable_argscope_for_function(func, log_shape=True):
"""Decorator for function to support argscope
Example:
.. code-block:: python
from mylib import myfunc
myfunc = enable_argscope_for_function(myfunc)
Args:
func: A function mapping one or multiple tensors to one or multiple
tensors.
log_shape (bool): Specify whether the first input resp. output tensor
shape should be printed once.
Remarks:
If the function ``func`` returns multiple input or output tensors,
only the first input/output tensor shape is displayed during logging.
Returns:
The decorated function.
""" |
assert callable(func), "func should be a callable"
@wraps(func)
def wrapped_func(*args, **kwargs):
actual_args = copy.copy(get_arg_scope()[func.__name__])
actual_args.update(kwargs)
out_tensor = func(*args, **actual_args)
in_tensor = args[0]
ctx = get_current_tower_context()
name = func.__name__ if 'name' not in kwargs else kwargs['name']
if log_shape:
if ('tower' not in ctx.ns_name.lower()) or ctx.is_main_training_tower:
# we assume the first parameter is the most interesting
if isinstance(out_tensor, tuple):
out_tensor_descr = out_tensor[0]
else:
out_tensor_descr = out_tensor
logger.info('%20s: %20s -> %20s' %
(name, in_tensor.shape.as_list(),
out_tensor_descr.shape.as_list()))
return out_tensor
# argscope requires this property
wrapped_func.symbolic_function = None
return wrapped_func |
<SYSTEM_TASK:>
Overwrite all functions of a given module to support argscope.
<END_TASK>
<USER_TASK:>
Description:
def enable_argscope_for_module(module, log_shape=True):
"""
Overwrite all functions of a given module to support argscope.
Note that this function monkey-patches the module and therefore could
have unexpected consequences.
It has been only tested to work well with ``tf.layers`` module.
Example:
.. code-block:: python
import tensorflow as tf
enable_argscope_for_module(tf.layers)
Args:
log_shape (bool): print input/output shapes of each function.
""" |
if is_tfv2() and module == tf.layers:
module = tf.compat.v1.layers
for name, obj in getmembers(module):
if isfunction(obj):
setattr(module, name, enable_argscope_for_function(obj,
log_shape=log_shape)) |
<SYSTEM_TASK:>
Pad tensor in H, W
<END_TASK>
<USER_TASK:>
Description:
def pad(x, p=3):
"""Pad tensor in H, W
Remarks:
TensorFlow uses "ceil(input_spatial_shape[i] / strides[i])" rather than explicit padding
like Caffe, pyTorch does. Hence, we need to pad here beforehand.
Args:
x (tf.tensor): incoming tensor
p (int, optional): padding for H, W
Returns:
tf.tensor: padded tensor
""" |
return tf.pad(x, [[0, 0], [0, 0], [p, p], [p, p]]) |
<SYSTEM_TASK:>
Correlation Cost Volume computation.
<END_TASK>
<USER_TASK:>
Description:
def correlation(ina, inb,
kernel_size, max_displacement,
stride_1, stride_2,
pad, data_format):
"""
Correlation Cost Volume computation.
This is a fallback Python-only implementation, specialized just for FlowNet2.
It takes a lot of memory and is slow.
If you know to compile a custom op yourself, it's better to use the cuda implementation here:
https://github.com/PatWie/tensorflow-recipes/tree/master/OpticalFlow/user_ops
""" |
assert pad == max_displacement
assert kernel_size == 1
assert data_format == 'NCHW'
assert max_displacement % stride_2 == 0
assert stride_1 == 1
D = int(max_displacement / stride_2 * 2) + 1 # D^2 == number of correlations per spatial location
b, c, h, w = ina.shape.as_list()
inb = tf.pad(inb, [[0, 0], [0, 0], [pad, pad], [pad, pad]])
res = []
for k1 in range(0, D):
start_h = k1 * stride_2
for k2 in range(0, D):
start_w = k2 * stride_2
s = tf.slice(inb, [0, 0, start_h, start_w], [-1, -1, h, w])
ans = tf.reduce_mean(ina * s, axis=1, keepdims=True)
res.append(ans)
res = tf.concat(res, axis=1) # ND^2HW
return res |
<SYSTEM_TASK:>
Resize input tensor with unkown input-shape by a factor
<END_TASK>
<USER_TASK:>
Description:
def resize(x, mode, factor=4):
"""Resize input tensor with unkown input-shape by a factor
Args:
x (tf.Tensor): tensor NCHW
factor (int, optional): resize factor for H, W
Note:
Differences here against Caffe have huge impacts on the
quality of the predictions.
Returns:
tf.Tensor: resized tensor NCHW
""" |
assert mode in ['bilinear', 'nearest'], mode
shp = tf.shape(x)[2:] * factor
# NCHW -> NHWC
x = tf.transpose(x, [0, 2, 3, 1])
if mode == 'bilinear':
x = tf.image.resize_bilinear(x, shp, align_corners=True)
else:
# better approximation of what Caffe is doing
x = tf.image.resize_nearest_neighbor(x, shp, align_corners=False)
# NHWC -> NCHW
return tf.transpose(x, [0, 3, 1, 2]) |
<SYSTEM_TASK:>
Architecture in Table 4 of FlowNet 2.0.
<END_TASK>
<USER_TASK:>
Description:
def flownet2_fusion(self, x):
"""
Architecture in Table 4 of FlowNet 2.0.
Args:
x: NCHW tensor, where C=11 is the concatenation of 7 items of [3, 2, 2, 1, 1, 1, 1] channels.
""" |
with argscope([tf.layers.conv2d], activation=lambda x: tf.nn.leaky_relu(x, 0.1),
padding='valid', strides=2, kernel_size=3,
data_format='channels_first'), \
argscope([tf.layers.conv2d_transpose], padding='same', activation=tf.identity,
data_format='channels_first', strides=2, kernel_size=4):
conv0 = tf.layers.conv2d(pad(x, 1), 64, name='conv0', strides=1)
x = tf.layers.conv2d(pad(conv0, 1), 64, name='conv1')
conv1 = tf.layers.conv2d(pad(x, 1), 128, name='conv1_1', strides=1)
x = tf.layers.conv2d(pad(conv1, 1), 128, name='conv2')
conv2 = tf.layers.conv2d(pad(x, 1), 128, name='conv2_1', strides=1)
flow2 = tf.layers.conv2d(pad(conv2, 1), 2, name='predict_flow2', strides=1, activation=tf.identity)
flow2_up = tf.layers.conv2d_transpose(flow2, 2, name='upsampled_flow2_to_1')
x = tf.layers.conv2d_transpose(conv2, 32, name='deconv1', activation=lambda x: tf.nn.leaky_relu(x, 0.1))
concat1 = tf.concat([conv1, x, flow2_up], axis=1, name='concat1')
interconv1 = tf.layers.conv2d(pad(concat1, 1), 32, strides=1, name='inter_conv1', activation=tf.identity)
flow1 = tf.layers.conv2d(pad(interconv1, 1), 2, name='predict_flow1', strides=1, activation=tf.identity)
flow1_up = tf.layers.conv2d_transpose(flow1, 2, name='upsampled_flow1_to_0')
x = tf.layers.conv2d_transpose(concat1, 16, name='deconv0', activation=lambda x: tf.nn.leaky_relu(x, 0.1))
concat0 = tf.concat([conv0, x, flow1_up], axis=1, name='concat0')
interconv0 = tf.layers.conv2d(pad(concat0, 1), 16, strides=1, name='inter_conv0', activation=tf.identity)
flow0 = tf.layers.conv2d(pad(interconv0, 1), 2, name='predict_flow0', strides=1, activation=tf.identity)
return tf.identity(flow0, name='flow2') |
<SYSTEM_TASK:>
Overlay a mask on top of the image.
<END_TASK>
<USER_TASK:>
Description:
def draw_mask(im, mask, alpha=0.5, color=None):
"""
Overlay a mask on top of the image.
Args:
im: a 3-channel uint8 image in BGR
mask: a binary 1-channel image of the same size
color: if None, will choose automatically
""" |
if color is None:
color = PALETTE_RGB[np.random.choice(len(PALETTE_RGB))][::-1]
im = np.where(np.repeat((mask > 0)[:, :, None], 3, axis=2),
im * (1 - alpha) + color * alpha, im)
im = im.astype('uint8')
return im |
<SYSTEM_TASK:>
Run DataFlow and send data to a ZMQ socket addr.
<END_TASK>
<USER_TASK:>
Description:
def send_dataflow_zmq(df, addr, hwm=50, format=None, bind=False):
"""
Run DataFlow and send data to a ZMQ socket addr.
It will serialize and send each datapoint to this address with a PUSH socket.
This function never returns.
Args:
df (DataFlow): Will infinitely loop over the DataFlow.
addr: a ZMQ socket endpoint.
hwm (int): ZMQ high-water mark (buffer size)
format (str): The serialization format.
Default format uses :mod:`tensorpack.utils.serialize`.
This format works with :class:`dataflow.RemoteDataZMQ`.
An alternate format is 'zmq_ops', used by https://github.com/tensorpack/zmq_ops
and :class:`input_source.ZMQInput`.
bind (bool): whether to bind or connect to the endpoint address.
""" |
assert format in [None, 'zmq_op', 'zmq_ops']
if format is None:
dump_fn = dumps
else:
from zmq_ops import dump_arrays
dump_fn = dump_arrays
ctx = zmq.Context()
socket = ctx.socket(zmq.PUSH)
socket.set_hwm(hwm)
if bind:
socket.bind(addr)
else:
socket.connect(addr)
try:
df.reset_state()
logger.info("Serving data to {} with {} format ...".format(
addr, 'default' if format is None else 'zmq_ops'))
INTERVAL = 200
q = deque(maxlen=INTERVAL)
try:
total = len(df)
except NotImplementedError:
total = 0
tqdm_args = get_tqdm_kwargs(leave=True, smoothing=0.8)
tqdm_args['bar_format'] = tqdm_args['bar_format'] + "{postfix}"
while True:
with tqdm.trange(total, **tqdm_args) as pbar:
for dp in df:
start = time.time()
socket.send(dump_fn(dp), copy=False)
q.append(time.time() - start)
pbar.update(1)
if pbar.n % INTERVAL == 0:
avg = "{:.3f}".format(sum(q) / len(q))
pbar.set_postfix({'AvgSendLat': avg})
finally:
logger.info("Exiting send_dataflow_zmq ...")
socket.setsockopt(zmq.LINGER, 0)
socket.close()
if not ctx.closed:
ctx.destroy(0) |
<SYSTEM_TASK:>
Aligned version of tf.image.crop_and_resize, following our definition of floating point boxes.
<END_TASK>
<USER_TASK:>
Description:
def crop_and_resize(image, boxes, box_ind, crop_size, pad_border=True):
"""
Aligned version of tf.image.crop_and_resize, following our definition of floating point boxes.
Args:
image: NCHW
boxes: nx4, x1y1x2y2
box_ind: (n,)
crop_size (int):
Returns:
n,C,size,size
""" |
assert isinstance(crop_size, int), crop_size
boxes = tf.stop_gradient(boxes)
# TF's crop_and_resize produces zeros on border
if pad_border:
# this can be quite slow
image = tf.pad(image, [[0, 0], [0, 0], [1, 1], [1, 1]], mode='SYMMETRIC')
boxes = boxes + 1
@under_name_scope()
def transform_fpcoor_for_tf(boxes, image_shape, crop_shape):
"""
The way tf.image.crop_and_resize works (with normalized box):
Initial point (the value of output[0]): x0_box * (W_img - 1)
Spacing: w_box * (W_img - 1) / (W_crop - 1)
Use the above grid to bilinear sample.
However, what we want is (with fpcoor box):
Spacing: w_box / W_crop
Initial point: x0_box + spacing/2 - 0.5
(-0.5 because bilinear sample (in my definition) assumes floating point coordinate
(0.0, 0.0) is the same as pixel value (0, 0))
This function transform fpcoor boxes to a format to be used by tf.image.crop_and_resize
Returns:
y1x1y2x2
"""
x0, y0, x1, y1 = tf.split(boxes, 4, axis=1)
spacing_w = (x1 - x0) / tf.cast(crop_shape[1], tf.float32)
spacing_h = (y1 - y0) / tf.cast(crop_shape[0], tf.float32)
imshape = [tf.cast(image_shape[0] - 1, tf.float32), tf.cast(image_shape[1] - 1, tf.float32)]
nx0 = (x0 + spacing_w / 2 - 0.5) / imshape[1]
ny0 = (y0 + spacing_h / 2 - 0.5) / imshape[0]
nw = spacing_w * tf.cast(crop_shape[1] - 1, tf.float32) / imshape[1]
nh = spacing_h * tf.cast(crop_shape[0] - 1, tf.float32) / imshape[0]
return tf.concat([ny0, nx0, ny0 + nh, nx0 + nw], axis=1)
# Expand bbox to a minium size of 1
# boxes_x1y1, boxes_x2y2 = tf.split(boxes, 2, axis=1)
# boxes_wh = boxes_x2y2 - boxes_x1y1
# boxes_center = tf.reshape((boxes_x2y2 + boxes_x1y1) * 0.5, [-1, 2])
# boxes_newwh = tf.maximum(boxes_wh, 1.)
# boxes_x1y1new = boxes_center - boxes_newwh * 0.5
# boxes_x2y2new = boxes_center + boxes_newwh * 0.5
# boxes = tf.concat([boxes_x1y1new, boxes_x2y2new], axis=1)
image_shape = tf.shape(image)[2:]
boxes = transform_fpcoor_for_tf(boxes, image_shape, [crop_size, crop_size])
image = tf.transpose(image, [0, 2, 3, 1]) # nhwc
ret = tf.image.crop_and_resize(
image, boxes, tf.cast(box_ind, tf.int32),
crop_size=[crop_size, crop_size])
ret = tf.transpose(ret, [0, 3, 1, 2]) # ncss
return ret |
<SYSTEM_TASK:>
Slice anchors to the spatial size of this featuremap.
<END_TASK>
<USER_TASK:>
Description:
def narrow_to(self, featuremap):
"""
Slice anchors to the spatial size of this featuremap.
""" |
shape2d = tf.shape(featuremap)[2:] # h,w
slice3d = tf.concat([shape2d, [-1]], axis=0)
slice4d = tf.concat([shape2d, [-1, -1]], axis=0)
boxes = tf.slice(self.boxes, [0, 0, 0, 0], slice4d)
gt_labels = tf.slice(self.gt_labels, [0, 0, 0], slice3d)
gt_boxes = tf.slice(self.gt_boxes, [0, 0, 0, 0], slice4d)
return RPNAnchors(boxes, gt_labels, gt_boxes) |
<SYSTEM_TASK:>
Apply a mapping on certain argument before calling the original function.
<END_TASK>
<USER_TASK:>
Description:
def map_arg(**maps):
"""
Apply a mapping on certain argument before calling the original function.
Args:
maps (dict): {argument_name: map_func}
""" |
def deco(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
if six.PY2:
argmap = inspect.getcallargs(func, *args, **kwargs)
else:
# getcallargs was deprecated since 3.5
sig = inspect.signature(func)
argmap = sig.bind_partial(*args, **kwargs).arguments
for k, map_func in six.iteritems(maps):
if k in argmap:
argmap[k] = map_func(argmap[k])
return func(**argmap)
return wrapper
return deco |
<SYSTEM_TASK:>
A decorator. It performs memoization ignoring the arguments used to call
<END_TASK>
<USER_TASK:>
Description:
def memoized_ignoreargs(func):
"""
A decorator. It performs memoization ignoring the arguments used to call
the function.
""" |
def wrapper(*args, **kwargs):
if func not in _MEMOIZED_NOARGS:
res = func(*args, **kwargs)
_MEMOIZED_NOARGS[func] = res
return res
return _MEMOIZED_NOARGS[func]
return wrapper |
<SYSTEM_TASK:>
Ensure a 2D shape.
<END_TASK>
<USER_TASK:>
Description:
def shape2d(a):
"""
Ensure a 2D shape.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 2. if ``a`` is a int, return ``[a, a]``.
""" |
if type(a) == int:
return [a, a]
if isinstance(a, (list, tuple)):
assert len(a) == 2
return list(a)
raise RuntimeError("Illegal shape: {}".format(a)) |
<SYSTEM_TASK:>
Ensuer a 4D shape, to use with 4D symbolic functions.
<END_TASK>
<USER_TASK:>
Description:
def shape4d(a, data_format='NHWC'):
"""
Ensuer a 4D shape, to use with 4D symbolic functions.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 4. if ``a`` is a int, return ``[1, a, a, 1]``
or ``[1, 1, a, a]`` depending on data_format.
""" |
s2d = shape2d(a)
if get_data_format(data_format, False) == 'NHWC':
return [1] + s2d + [1]
else:
return [1, 1] + s2d |
<SYSTEM_TASK:>
Decorate a method or property of a class, so that this method can only
<END_TASK>
<USER_TASK:>
Description:
def call_only_once(func):
"""
Decorate a method or property of a class, so that this method can only
be called once for every instance.
Calling it more than once will result in exception.
""" |
@functools.wraps(func)
def wrapper(*args, **kwargs):
self = args[0]
# cannot use hasattr here, because hasattr tries to getattr, which
# fails if func is a property
assert func.__name__ in dir(self), "call_only_once can only be used on method or property!"
if not hasattr(self, '_CALL_ONLY_ONCE_CACHE'):
cache = self._CALL_ONLY_ONCE_CACHE = set()
else:
cache = self._CALL_ONLY_ONCE_CACHE
cls = type(self)
# cannot use ismethod(), because decorated method becomes a function
is_method = inspect.isfunction(getattr(cls, func.__name__))
assert func not in cache, \
"{} {}.{} can only be called once per object!".format(
'Method' if is_method else 'Property',
cls.__name__, func.__name__)
cache.add(func)
return func(*args, **kwargs)
return wrapper |
<SYSTEM_TASK:>
A decorator that performs memoization on methods. It stores the cache on the object instance itself.
<END_TASK>
<USER_TASK:>
Description:
def memoized_method(func):
"""
A decorator that performs memoization on methods. It stores the cache on the object instance itself.
""" |
@functools.wraps(func)
def wrapper(*args, **kwargs):
self = args[0]
assert func.__name__ in dir(self), "memoized_method can only be used on method!"
if not hasattr(self, '_MEMOIZED_CACHE'):
cache = self._MEMOIZED_CACHE = {}
else:
cache = self._MEMOIZED_CACHE
key = (func, ) + args[1:] + tuple(kwargs)
ret = cache.get(key, None)
if ret is not None:
return ret
value = func(*args, **kwargs)
cache[key] = value
return value
return wrapper |
<SYSTEM_TASK:>
A decorator which automatically reuses the current variable scope if the
<END_TASK>
<USER_TASK:>
Description:
def auto_reuse_variable_scope(func):
"""
A decorator which automatically reuses the current variable scope if the
function has been called with the same variable scope before.
Example:
.. code-block:: python
@auto_reuse_variable_scope
def myfunc(x):
return tf.layers.conv2d(x, 128, 3)
myfunc(x1) # will inherit parent scope reuse
myfunc(x2) # will reuse
with tf.variable_scope('newscope'):
myfunc(x3) # will inherit parent scope reuse
myfunc(x4) # will reuse
""" |
used_scope = set()
@functools.wraps(func)
def wrapper(*args, **kwargs):
scope = tf.get_variable_scope()
h = hash((tf.get_default_graph(), scope.name))
# print("Entering " + scope.name + " reuse: " + str(h in used_scope))
if h in used_scope:
if get_tf_version_tuple() >= (1, 5):
with tf.variable_scope(scope, reuse=True, auxiliary_name_scope=False):
return func(*args, **kwargs)
else:
ns = tf.get_default_graph().get_name_scope()
with tf.variable_scope(scope, reuse=True), \
tf.name_scope(ns + '/' if ns else ''):
return func(*args, **kwargs)
else:
used_scope.add(h)
return func(*args, **kwargs)
return wrapper |
<SYSTEM_TASK:>
Return a context which either opens and caches a new name scope,
<END_TASK>
<USER_TASK:>
Description:
def cached_name_scope(name, top_level=True):
"""
Return a context which either opens and caches a new name scope,
or reenter an existing one.
Args:
top_level(bool): if True, the name scope will always be top-level.
It will not be nested under any existing name scope of the caller.
""" |
if not top_level:
current_ns = tf.get_default_graph().get_name_scope()
if current_ns:
name = current_ns + '/' + name
ns = _get_cached_ns(name)
with tf.name_scope(ns):
yield ns |
<SYSTEM_TASK:>
Copy values of variables on GPU 0 to other GPUs.
<END_TASK>
<USER_TASK:>
Description:
def get_post_init_ops():
"""
Copy values of variables on GPU 0 to other GPUs.
""" |
# literally all variables, because it's better to sync optimizer-internal variables as well
all_vars = tf.global_variables() + tf.local_variables()
var_by_name = dict([(v.name, v) for v in all_vars])
trainable_names = set([x.name for x in tf.trainable_variables()])
post_init_ops = []
def log_failure(name, reason):
logger.warn("[ReplicatedTrainer] Do not know how to sync variable '{}' across GPUs. "
"Reason: {} ".format(name, reason))
assert name not in trainable_names, \
"The aforementioned variable is trainable, so this is probably a fatal error."
logger.warn(
"[ReplicatedTrainer] This variable is non-trainable. "
"Ignore this warning if you know it's OK to leave it out-of-sync.")
for v in all_vars:
if not v.name.startswith('tower'):
continue
if v.name.startswith('tower0'):
# in this trainer, the master name doesn't have the towerx/ prefix
log_failure(v.name, "Name should not have prefix 'tower0' in this trainer!")
continue # TODO some vars (EMA) may still startswith tower0
split_name = v.name.split('/')
prefix = split_name[0]
realname = '/'.join(split_name[1:])
if prefix in realname:
log_failure(v.name, "Prefix {} appears multiple times in its name!".format(prefix))
continue
copy_from = var_by_name.get(realname)
if copy_from is not None:
post_init_ops.append(v.assign(copy_from.read_value()))
else:
log_failure(v.name, "Cannot find {} in the graph!".format(realname))
logger.info(
"'sync_variables_from_main_tower' includes {} operations.".format(len(post_init_ops)))
return tf.group(*post_init_ops, name='sync_variables_from_main_tower') |
<SYSTEM_TASK:>
Humanize timedelta given in seconds
<END_TASK>
<USER_TASK:>
Description:
def humanize_time_delta(sec):
"""Humanize timedelta given in seconds
Args:
sec (float): time difference in seconds. Must be positive.
Returns:
str - time difference as a readable string
Example:
.. code-block:: python
print(humanize_time_delta(1)) # 1 second
print(humanize_time_delta(60 + 1)) # 1 minute 1 second
print(humanize_time_delta(87.6)) # 1 minute 27 seconds
print(humanize_time_delta(0.01)) # 0.01 seconds
print(humanize_time_delta(60 * 60 + 1)) # 1 hour 1 second
print(humanize_time_delta(60 * 60 * 24 + 1)) # 1 day 1 second
print(humanize_time_delta(60 * 60 * 24 + 60 * 2 + 60*60*9 + 3)) # 1 day 9 hours 2 minutes 3 seconds
""" |
if sec < 0:
logger.warn("humanize_time_delta() obtains negative seconds!")
return "{:.3g} seconds".format(sec)
if sec == 0:
return "0 second"
time = datetime(2000, 1, 1) + timedelta(seconds=int(sec))
units = ['day', 'hour', 'minute', 'second']
vals = [int(sec // 86400), time.hour, time.minute, time.second]
if sec < 60:
vals[-1] = sec
def _format(v, u):
return "{:.3g} {}{}".format(v, u, "s" if v > 1 else "")
ans = []
for v, u in zip(vals, units):
if v > 0:
ans.append(_format(v, u))
return " ".join(ans) |
<SYSTEM_TASK:>
Get a good RNG seeded with time, pid and the object.
<END_TASK>
<USER_TASK:>
Description:
def get_rng(obj=None):
"""
Get a good RNG seeded with time, pid and the object.
Args:
obj: some object to use to generate random seed.
Returns:
np.random.RandomState: the RNG.
""" |
seed = (id(obj) + os.getpid() +
int(datetime.now().strftime("%Y%m%d%H%M%S%f"))) % 4294967295
if _RNG_SEED is not None:
seed = _RNG_SEED
return np.random.RandomState(seed) |
<SYSTEM_TASK:>
Each called in the code to this function is guaranteed to return True the
<END_TASK>
<USER_TASK:>
Description:
def execute_only_once():
"""
Each called in the code to this function is guaranteed to return True the
first time and False afterwards.
Returns:
bool: whether this is the first time this function gets called from this line of code.
Example:
.. code-block:: python
if execute_only_once():
# do something only once
""" |
f = inspect.currentframe().f_back
ident = (f.f_code.co_filename, f.f_lineno)
if ident in _EXECUTE_HISTORY:
return False
_EXECUTE_HISTORY.add(ident)
return True |
<SYSTEM_TASK:>
Return default arguments to be used with tqdm.
<END_TASK>
<USER_TASK:>
Description:
def get_tqdm_kwargs(**kwargs):
"""
Return default arguments to be used with tqdm.
Args:
kwargs: extra arguments to be used.
Returns:
dict:
""" |
default = dict(
smoothing=0.5,
dynamic_ncols=True,
ascii=True,
bar_format='{l_bar}{bar}|{n_fmt}/{total_fmt}[{elapsed}<{remaining},{rate_noinv_fmt}]'
)
try:
# Use this env var to override the refresh interval setting
interval = float(os.environ['TENSORPACK_PROGRESS_REFRESH'])
except KeyError:
interval = _pick_tqdm_interval(kwargs.get('file', sys.stderr))
default['mininterval'] = interval
default.update(kwargs)
return default |
<SYSTEM_TASK:>
Similar to `from ctypes.util import find_library`, but try
<END_TASK>
<USER_TASK:>
Description:
def find_library_full_path(name):
"""
Similar to `from ctypes.util import find_library`, but try
to return full path if possible.
""" |
from ctypes.util import find_library
if os.name == "posix" and sys.platform == "darwin":
# on Mac, ctypes already returns full path
return find_library(name)
def _use_proc_maps(name):
"""
Find so from /proc/pid/maps
Only works with libraries that has already been loaded.
But this is the most accurate method -- it finds the exact library that's being used.
"""
procmap = os.path.join('/proc', str(os.getpid()), 'maps')
if not os.path.isfile(procmap):
return None
with open(procmap, 'r') as f:
for line in f:
line = line.strip().split(' ')
sofile = line[-1]
basename = os.path.basename(sofile)
if 'lib' + name + '.so' in basename:
if os.path.isfile(sofile):
return os.path.realpath(sofile)
# The following two methods come from https://github.com/python/cpython/blob/master/Lib/ctypes/util.py
def _use_ld(name):
"""
Find so with `ld -lname -Lpath`.
It will search for files in LD_LIBRARY_PATH, but not in ldconfig.
"""
cmd = "ld -t -l{} -o {}".format(name, os.devnull)
ld_lib_path = os.environ.get('LD_LIBRARY_PATH', '')
for d in ld_lib_path.split(':'):
cmd = cmd + " -L " + d
result, ret = subproc_call(cmd + '|| true')
expr = r'[^\(\)\s]*lib%s\.[^\(\)\s]*' % re.escape(name)
res = re.search(expr, result.decode('utf-8'))
if res:
res = res.group(0)
if not os.path.isfile(res):
return None
return os.path.realpath(res)
def _use_ldconfig(name):
"""
Find so in `ldconfig -p`.
It does not handle LD_LIBRARY_PATH.
"""
with change_env('LC_ALL', 'C'), change_env('LANG', 'C'):
ldconfig, ret = subproc_call("ldconfig -p")
ldconfig = ldconfig.decode('utf-8')
if ret != 0:
return None
expr = r'\s+(lib%s\.[^\s]+)\s+\(.*=>\s+(.*)' % (re.escape(name))
res = re.search(expr, ldconfig)
if not res:
return None
else:
ret = res.group(2)
return os.path.realpath(ret)
if sys.platform.startswith('linux'):
return _use_proc_maps(name) or _use_ld(name) or _use_ldconfig(name) or find_library(name)
return find_library(name) |
<SYSTEM_TASK:>
Return the three quantization functions fw, fa, fg, for weights, activations and gradients respectively
<END_TASK>
<USER_TASK:>
Description:
def get_dorefa(bitW, bitA, bitG):
"""
Return the three quantization functions fw, fa, fg, for weights, activations and gradients respectively
""" |
def quantize(x, k):
n = float(2 ** k - 1)
@tf.custom_gradient
def _quantize(x):
return tf.round(x * n) / n, lambda dy: dy
return _quantize(x)
def fw(x):
if bitW == 32:
return x
if bitW == 1: # BWN
E = tf.stop_gradient(tf.reduce_mean(tf.abs(x)))
@tf.custom_gradient
def _sign(x):
return tf.where(tf.equal(x, 0), tf.ones_like(x), tf.sign(x / E)) * E, lambda dy: dy
return _sign(x)
x = tf.tanh(x)
x = x / tf.reduce_max(tf.abs(x)) * 0.5 + 0.5
return 2 * quantize(x, bitW) - 1
def fa(x):
if bitA == 32:
return x
return quantize(x, bitA)
def fg(x):
if bitG == 32:
return x
@tf.custom_gradient
def _identity(input):
def grad_fg(x):
rank = x.get_shape().ndims
assert rank is not None
maxx = tf.reduce_max(tf.abs(x), list(range(1, rank)), keep_dims=True)
x = x / maxx
n = float(2**bitG - 1)
x = x * 0.5 + 0.5 + tf.random_uniform(
tf.shape(x), minval=-0.5 / n, maxval=0.5 / n)
x = tf.clip_by_value(x, 0.0, 1.0)
x = quantize(x, bitG) - 0.5
return x * maxx * 2
return input, grad_fg
return _identity(x)
return fw, fa, fg |
<SYSTEM_TASK:>
Draw text on an image.
<END_TASK>
<USER_TASK:>
Description:
def draw_text(img, pos, text, color, font_scale=0.4):
"""
Draw text on an image.
Args:
pos (tuple): x, y; the position of the text
text (str):
font_scale (float):
color (tuple): a 3-tuple BGR color in [0, 255]
""" |
img = img.astype(np.uint8)
x0, y0 = int(pos[0]), int(pos[1])
# Compute text size.
font = cv2.FONT_HERSHEY_SIMPLEX
((text_w, text_h), _) = cv2.getTextSize(text, font, font_scale, 1)
# Place text background.
if x0 + text_w > img.shape[1]:
x0 = img.shape[1] - text_w
if y0 - int(1.15 * text_h) < 0:
y0 = int(1.15 * text_h)
back_topleft = x0, y0 - int(1.3 * text_h)
back_bottomright = x0 + text_w, y0
cv2.rectangle(img, back_topleft, back_bottomright, color, -1)
# Show text.
text_bottomleft = x0, y0 - int(0.25 * text_h)
cv2.putText(img, text, text_bottomleft, font, font_scale, (222, 222, 222), lineType=cv2.LINE_AA)
return img |
<SYSTEM_TASK:>
Convert polygons to binary masks.
<END_TASK>
<USER_TASK:>
Description:
def segmentation_to_mask(polys, height, width):
"""
Convert polygons to binary masks.
Args:
polys: a list of nx2 float array. Each array contains many (x, y) coordinates.
Returns:
a binary matrix of (height, width)
""" |
polys = [p.flatten().tolist() for p in polys]
assert len(polys) > 0, "Polygons are empty!"
import pycocotools.mask as cocomask
rles = cocomask.frPyObjects(polys, height, width)
rle = cocomask.merge(rles)
return cocomask.decode(rle) |
<SYSTEM_TASK:>
Same as `tf.layers.MaxPooling2D`. Default strides is equal to pool_size.
<END_TASK>
<USER_TASK:>
Description:
def MaxPooling(
inputs,
pool_size,
strides=None,
padding='valid',
data_format='channels_last'):
"""
Same as `tf.layers.MaxPooling2D`. Default strides is equal to pool_size.
""" |
if strides is None:
strides = pool_size
layer = tf.layers.MaxPooling2D(pool_size, strides, padding=padding, data_format=data_format)
ret = layer.apply(inputs, scope=tf.get_variable_scope())
return tf.identity(ret, name='output') |
<SYSTEM_TASK:>
Same as `tf.layers.AveragePooling2D`. Default strides is equal to pool_size.
<END_TASK>
<USER_TASK:>
Description:
def AvgPooling(
inputs,
pool_size,
strides=None,
padding='valid',
data_format='channels_last'):
"""
Same as `tf.layers.AveragePooling2D`. Default strides is equal to pool_size.
""" |
if strides is None:
strides = pool_size
layer = tf.layers.AveragePooling2D(pool_size, strides, padding=padding, data_format=data_format)
ret = layer.apply(inputs, scope=tf.get_variable_scope())
return tf.identity(ret, name='output') |
<SYSTEM_TASK:>
Unpool the input with a fixed matrix to perform kronecker product with.
<END_TASK>
<USER_TASK:>
Description:
def FixedUnPooling(x, shape, unpool_mat=None, data_format='channels_last'):
"""
Unpool the input with a fixed matrix to perform kronecker product with.
Args:
x (tf.Tensor): a 4D image tensor
shape: int or (h, w) tuple
unpool_mat: a tf.Tensor or np.ndarray 2D matrix with size=shape.
If is None, will use a matrix with 1 at top-left corner.
Returns:
tf.Tensor: a 4D image tensor.
""" |
data_format = get_data_format(data_format, keras_mode=False)
shape = shape2d(shape)
output_shape = StaticDynamicShape(x)
output_shape.apply(1 if data_format == 'NHWC' else 2, lambda x: x * shape[0])
output_shape.apply(2 if data_format == 'NHWC' else 3, lambda x: x * shape[1])
# a faster implementation for this special case
if shape[0] == 2 and shape[1] == 2 and unpool_mat is None and data_format == 'NHWC':
ret = UnPooling2x2ZeroFilled(x)
else:
# check unpool_mat
if unpool_mat is None:
mat = np.zeros(shape, dtype='float32')
mat[0][0] = 1
unpool_mat = tf.constant(mat, name='unpool_mat')
elif isinstance(unpool_mat, np.ndarray):
unpool_mat = tf.constant(unpool_mat, name='unpool_mat')
assert unpool_mat.shape.as_list() == list(shape)
if data_format == 'NHWC':
x = tf.transpose(x, [0, 3, 1, 2])
# perform a tensor-matrix kronecker product
x = tf.expand_dims(x, -1) # bchwx1
mat = tf.expand_dims(unpool_mat, 0) # 1xshxsw
ret = tf.tensordot(x, mat, axes=1) # bxcxhxwxshxsw
if data_format == 'NHWC':
ret = tf.transpose(ret, [0, 2, 4, 3, 5, 1])
else:
ret = tf.transpose(ret, [0, 1, 2, 4, 3, 5])
shape3_dyn = [output_shape.get_dynamic(k) for k in range(1, 4)]
ret = tf.reshape(ret, tf.stack([-1] + shape3_dyn))
ret.set_shape(tf.TensorShape(output_shape.get_static()))
return ret |
<SYSTEM_TASK:>
Save variables in dic to path.
<END_TASK>
<USER_TASK:>
Description:
def save_chkpt_vars(dic, path):
"""
Save variables in dic to path.
Args:
dic: {name: value}
path: save as npz if the name ends with '.npz', otherwise save as a checkpoint.
""" |
logger.info("Variables to save to {}:".format(path))
keys = sorted(list(dic.keys()))
logger.info(pprint.pformat(keys))
assert not path.endswith('.npy')
if path.endswith('.npz'):
np.savez_compressed(path, **dic)
else:
with tf.Graph().as_default(), \
tf.Session() as sess:
for k, v in six.iteritems(dic):
k = get_op_tensor_name(k)[0]
_ = tf.Variable(name=k, initial_value=v) # noqa
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.save(sess, path, write_meta_graph=False) |
<SYSTEM_TASK:>
Work around TF problems in checkpoint path handling.
<END_TASK>
<USER_TASK:>
Description:
def get_checkpoint_path(model_path):
"""
Work around TF problems in checkpoint path handling.
Args:
model_path: a user-input path
Returns:
str: the argument that can be passed to NewCheckpointReader
""" |
if os.path.basename(model_path) == model_path:
model_path = os.path.join('.', model_path) # avoid #4921 and #6142
if os.path.basename(model_path) == 'checkpoint':
assert tfv1.gfile.Exists(model_path), model_path
model_path = tf.train.latest_checkpoint(os.path.dirname(model_path))
# to be consistent with either v1 or v2
# fix paths if provided a wrong one
new_path = model_path
if '00000-of-00001' in model_path:
new_path = model_path.split('.data')[0]
elif model_path.endswith('.index'):
new_path = model_path.split('.index')[0]
if new_path != model_path:
logger.info(
"Checkpoint path {} is auto-corrected to {}.".format(model_path, new_path))
model_path = new_path
assert tfv1.gfile.Exists(model_path) or tfv1.gfile.Exists(model_path + '.index'), model_path
return model_path |
<SYSTEM_TASK:>
Load all variables from a checkpoint to a dict.
<END_TASK>
<USER_TASK:>
Description:
def load_chkpt_vars(model_path):
""" Load all variables from a checkpoint to a dict.
Args:
model_path(str): path to a checkpoint.
Returns:
dict: a name:value dict
""" |
model_path = get_checkpoint_path(model_path)
reader = tfv1.train.NewCheckpointReader(model_path)
var_names = reader.get_variable_to_shape_map().keys()
result = {}
for n in var_names:
result[n] = reader.get_tensor(n)
return result |
<SYSTEM_TASK:>
Put an image.
<END_TASK>
<USER_TASK:>
Description:
def put_image(self, name, val):
"""
Put an image.
Args:
name (str):
val (np.ndarray): 2D, 3D (HWC) or 4D (NHWC) numpy array of images
in range [0,255]. If channel is 3, assumed to be RGB.
""" |
assert isinstance(val, np.ndarray)
arr = image_to_nhwc(val)
self._dispatch(lambda m: m.process_image(name, arr))
s = create_image_summary(name, arr)
self._dispatch(lambda m: m.process_summary(s)) |
<SYSTEM_TASK:>
Add stats to json and dump to disk.
<END_TASK>
<USER_TASK:>
Description:
def _trigger(self):
"""
Add stats to json and dump to disk.
Note that this method is idempotent.
""" |
if len(self._stat_now):
self._stat_now['epoch_num'] = self.epoch_num
self._stat_now['global_step'] = self.global_step
self._stats.append(self._stat_now)
self._stat_now = {}
self._write_stat() |
<SYSTEM_TASK:>
Delegate property to self.loop
<END_TASK>
<USER_TASK:>
Description:
def _get_property(name):
"""
Delegate property to self.loop
""" |
ret = property(
lambda self: getattr(self.loop, name))
if six.PY3: # __doc__ is readonly in Py2
try:
ret.__doc__ = getattr(TrainLoop, name).__doc__
except AttributeError:
pass
return ret |
<SYSTEM_TASK:>
Configure the loop given the settings.
<END_TASK>
<USER_TASK:>
Description:
def config(self, steps_per_epoch, starting_epoch, max_epoch):
"""
Configure the loop given the settings.
""" |
self.starting_epoch = int(starting_epoch)
self.max_epoch = int(max_epoch)
self.steps_per_epoch = int(steps_per_epoch)
# Allow empty epoch (no steps), if we want to run the callbacks only.
assert self.steps_per_epoch >= 0 and self.max_epoch >= 0
self._epoch_num = starting_epoch - 1 |
<SYSTEM_TASK:>
Setup callbacks and monitors. Must be called after the main graph is built.
<END_TASK>
<USER_TASK:>
Description:
def setup_callbacks(self, callbacks, monitors):
"""
Setup callbacks and monitors. Must be called after the main graph is built.
Args:
callbacks ([Callback]):
monitors ([MonitorBase]):
""" |
assert isinstance(callbacks, list), callbacks
assert isinstance(monitors, list), monitors
describe_trainable_vars() # TODO weird
self.register_callback(MaintainStepCounter())
for cb in callbacks:
self.register_callback(cb)
for cb in self._callbacks:
assert not isinstance(cb, MonitorBase), "Monitor cannot be pre-registered for now!"
registered_monitors = []
for m in monitors:
if self.register_callback(m):
registered_monitors.append(m)
self.monitors = Monitors(registered_monitors)
self.register_callback(self.monitors) # monitors is also a callback
# some final operations that might modify the graph
logger.info("Setup callbacks graph ...")
self._callbacks = Callbacks(self._callbacks)
self._callbacks.setup_graph(weakref.proxy(self)) |
<SYSTEM_TASK:>
Create SessionRunHooks for all callbacks, and hook it onto `self.sess` to create `self.hooked_sess`.
<END_TASK>
<USER_TASK:>
Description:
def initialize_hooks(self):
"""
Create SessionRunHooks for all callbacks, and hook it onto `self.sess` to create `self.hooked_sess`.
A new trainer may override this method to create multiple groups of hooks,
which can be useful when the training is not done by a single `train_op`.
""" |
hooks = self._callbacks.get_hooks()
self.hooked_sess = tfv1.train.MonitoredSession(
session_creator=ReuseSessionCreator(self.sess), hooks=hooks) |
<SYSTEM_TASK:>
Get a list of tensors in the default graph by a list of names.
<END_TASK>
<USER_TASK:>
Description:
def get_tensors_by_names(names):
"""
Get a list of tensors in the default graph by a list of names.
Args:
names (list):
""" |
ret = []
G = tfv1.get_default_graph()
for n in names:
opn, varn = get_op_tensor_name(n)
ret.append(G.get_tensor_by_name(varn))
return ret |
<SYSTEM_TASK:>
Get either tf.Operation of tf.Tensor from names.
<END_TASK>
<USER_TASK:>
Description:
def get_op_or_tensor_by_name(name):
"""
Get either tf.Operation of tf.Tensor from names.
Args:
name (list[str] or str): names of operations or tensors.
Raises:
KeyError, if the name doesn't exist
""" |
G = tfv1.get_default_graph()
def f(n):
if len(n) >= 3 and n[-2] == ':':
return G.get_tensor_by_name(n)
else:
return G.get_operation_by_name(n)
if not isinstance(name, list):
return f(name)
else:
return list(map(f, name)) |
<SYSTEM_TASK:>
Adds ops to enqueue on all worker queues.
<END_TASK>
<USER_TASK:>
Description:
def _add_sync_queues_and_barrier(self, name, dependencies):
"""Adds ops to enqueue on all worker queues.
Args:
name: prefixed for the shared_name of ops.
dependencies: control dependency from ops.
Returns:
an op that should be used as control dependency before starting next step.
""" |
self._sync_queue_counter += 1
with tf.device(self.sync_queue_devices[self._sync_queue_counter % len(self.sync_queue_devices)]):
sync_queues = [
tf.FIFOQueue(self.num_worker, [tf.bool], shapes=[[]],
shared_name='%s%s' % (name, i))
for i in range(self.num_worker)]
queue_ops = []
# For each other worker, add an entry in a queue, signaling that it can finish this step.
token = tf.constant(False)
with tf.control_dependencies(dependencies):
for i, q in enumerate(sync_queues):
if i != self.task_index:
queue_ops.append(q.enqueue(token))
# Drain tokens off queue for this worker, one for each other worker.
queue_ops.append(
sync_queues[self.task_index].dequeue_many(len(sync_queues) - 1))
return tf.group(*queue_ops, name=name) |
<SYSTEM_TASK:>
Create shadow variables on PS, and replace variables in avg_grads
<END_TASK>
<USER_TASK:>
Description:
def _apply_shadow_vars(avg_grads):
"""
Create shadow variables on PS, and replace variables in avg_grads
by these shadow variables.
Args:
avg_grads: list of (grad, var) tuples
""" |
ps_var_grads = []
for grad, var in avg_grads:
assert var.name.startswith('tower'), var.name
my_name = '/'.join(var.name.split('/')[1:])
my_name = get_op_tensor_name(my_name)[0]
new_v = tf.get_variable(my_name, dtype=var.dtype.base_dtype,
initializer=var.initial_value,
trainable=True)
# (g, v) to be applied, where v is global (ps vars)
ps_var_grads.append((grad, new_v))
return ps_var_grads |
<SYSTEM_TASK:>
Create shadow vars for model_variables as well, and add to the list of ``shadow_vars``.
<END_TASK>
<USER_TASK:>
Description:
def _shadow_model_variables(shadow_vars):
"""
Create shadow vars for model_variables as well, and add to the list of ``shadow_vars``.
Returns:
list of (shadow_model_var, local_model_var) used for syncing.
""" |
G = tf.get_default_graph()
curr_shadow_vars = set([v.name for v in shadow_vars])
model_vars = tf.model_variables()
shadow_model_vars = []
for v in model_vars:
assert v.name.startswith('tower'), "Found some MODEL_VARIABLES created outside of the tower function!"
stripped_op_name, stripped_var_name = get_op_tensor_name(re.sub('^tower[0-9]+/', '', v.name))
if stripped_op_name in curr_shadow_vars:
continue
try:
G.get_tensor_by_name(stripped_var_name)
logger.warn("Model Variable {} also appears in other collections.".format(stripped_var_name))
continue
except KeyError:
pass
new_v = tf.get_variable(stripped_op_name, dtype=v.dtype.base_dtype,
initializer=v.initial_value,
trainable=False)
curr_shadow_vars.add(stripped_op_name) # avoid duplicated shadow_model_vars
shadow_vars.append(new_v)
shadow_model_vars.append((new_v, v)) # only need to sync model_var from one tower
return shadow_model_vars |
<SYSTEM_TASK:>
Apply averaged gradients to ps vars, and then copy the updated
<END_TASK>
<USER_TASK:>
Description:
def _apply_gradients_and_copy(self, opt, raw_grad_list, ps_var_grads):
"""
Apply averaged gradients to ps vars, and then copy the updated
variables back to each tower.
Args:
raw_grad_list: Ngpu x Nvar x 2 gradient list from all towers
ps_var_grads: Nvar x 2 (grad, ps_var)
Returns:
list of copy ops
""" |
# TODO do this for variables together?
with tf.name_scope('apply_gradients'):
var_update_ops = []
for vid, (g, v) in enumerate(ps_var_grads):
# TODO do we put momentum variables into local or global?
apply_gradient_op = opt.apply_gradients([(g, v)])
barrier = self._add_sync_queues_and_barrier(
'param_update_barrier_{}'.format(vid), [apply_gradient_op])
with tf.control_dependencies([barrier]), \
tf.device(self.cpu_device):
updated_value = v.read_value()
for towerid in range(self.nr_gpu):
var_update_ops.append(
raw_grad_list[towerid][vid][1].assign(updated_value))
return var_update_ops |
<SYSTEM_TASK:>
Get the op to copy-initialized all local variables from PS.
<END_TASK>
<USER_TASK:>
Description:
def _get_initial_sync_op(self):
"""
Get the op to copy-initialized all local variables from PS.
""" |
def strip_port(s):
if s.endswith(':0'):
return s[:-2]
return s
local_vars = tf.local_variables()
local_var_by_name = dict([(strip_port(v.name), v) for v in local_vars])
ops = []
nr_shadow_vars = len(self._shadow_vars)
for v in self._shadow_vars:
vname = strip_port(v.name)
for i in range(self.nr_gpu):
name = 'tower%s/%s' % (i, vname)
assert name in local_var_by_name, \
"Shadow variable {} doesn't match a corresponding local variable!".format(v.name)
copy_to = local_var_by_name[name]
# logger.info("{} -> {}".format(v.name, copy_to.name))
ops.append(copy_to.assign(v.read_value()))
return tf.group(*ops, name='sync_{}_variables_from_ps'.format(nr_shadow_vars)) |
<SYSTEM_TASK:>
Get the op to sync local model_variables to PS.
<END_TASK>
<USER_TASK:>
Description:
def _get_sync_model_vars_op(self):
"""
Get the op to sync local model_variables to PS.
""" |
ops = []
for (shadow_v, local_v) in self._shadow_model_vars:
ops.append(shadow_v.assign(local_v.read_value()))
assert len(ops)
return tf.group(*ops, name='sync_{}_model_variables_to_ps'.format(len(ops))) |
<SYSTEM_TASK:>
This callback is enabled by default.
<END_TASK>
<USER_TASK:>
Description:
def MergeAllSummaries(period=0, run_alone=False, key=None):
"""
This callback is enabled by default.
Evaluate all summaries by ``tf.summary.merge_all``, and write them to logs.
Args:
period (int): by default the callback summarizes once every epoch.
This option (if not set to 0) makes it additionally summarize every ``period`` steps.
run_alone (bool): whether to evaluate the summaries alone.
If True, summaries will be evaluated after each epoch alone.
If False, summaries will be evaluated together with the
`sess.run` calls, in the last step of each epoch.
For :class:`SimpleTrainer`, it needs to be False because summary may
depend on inputs.
key (str): the collection of summary tensors. Same as in ``tf.summary.merge_all``.
Default is ``tf.GraphKeys.SUMMARIES``.
""" |
if key is None:
key = tf.GraphKeys.SUMMARIES
period = int(period)
if run_alone:
return MergeAllSummaries_RunAlone(period, key)
else:
return MergeAllSummaries_RunWithOp(period, key) |
<SYSTEM_TASK:>
Run the environment for one step.
<END_TASK>
<USER_TASK:>
Description:
def step(self, exploration):
"""
Run the environment for one step.
If the episode ends, store the entire episode to the replay memory.
""" |
old_s = self._current_ob
if self.rng.rand() <= exploration:
act = self.rng.choice(range(self.num_actions))
else:
history = self.recent_state()
history.append(old_s)
history = np.stack(history, axis=-1) # state_shape + (Hist,)
# assume batched network
history = np.expand_dims(history, axis=0)
q_values = self.predictor(history)[0][0] # this is the bottleneck
act = np.argmax(q_values)
self._current_ob, reward, isOver, info = self.player.step(act)
self._current_game_score.feed(reward)
self._current_episode.append(Experience(old_s, act, reward, isOver))
if isOver:
flush_experience = True
if 'ale.lives' in info: # if running Atari, do something special
if info['ale.lives'] != 0:
# only record score and flush experience
# when a whole game is over (not when an episode is over)
flush_experience = False
self.player.reset()
if flush_experience:
self.total_scores.append(self._current_game_score.sum)
self._current_game_score.reset()
# Ensure that the whole episode of experience is continuous in the replay buffer
with self.memory.writer_lock:
for exp in self._current_episode:
self.memory.append(exp)
self._current_episode.clear() |
<SYSTEM_TASK:>
Execute one step in any of the runners.
<END_TASK>
<USER_TASK:>
Description:
def step(self, exploration):
"""
Execute one step in any of the runners.
""" |
if len(self._runners) > 1:
self._populate_job_queue.put(exploration)
else:
self._runners[0].step(exploration) |
<SYSTEM_TASK:>
log the time of some heavy callbacks
<END_TASK>
<USER_TASK:>
Description:
def log(self):
""" log the time of some heavy callbacks """ |
if self.tot < 3:
return
msgs = []
for name, t in self.times:
if t / self.tot > 0.3 and t > 1:
msgs.append(name + ": " + humanize_time_delta(t))
logger.info(
"Callbacks took {:.3f} sec in total. {}".format(
self.tot, '; '.join(msgs))) |
<SYSTEM_TASK:>
Get a variable used in this tower.
<END_TASK>
<USER_TASK:>
Description:
def get_variable(self, name):
"""
Get a variable used in this tower.
The name should not contain the variable scope prefix of the tower.
When the tower has the same variable scope and name scope, this is equivalent to
:meth:`get_tensor`.
""" |
name = get_op_tensor_name(name)[1]
if len(self.vs_name):
name_with_vs = self.vs_name + "/" + name
else:
name_with_vs = name
return get_op_or_tensor_by_name(name_with_vs) |
<SYSTEM_TASK:>
Download URL to a directory.
<END_TASK>
<USER_TASK:>
Description:
def download(url, dir, filename=None, expect_size=None):
"""
Download URL to a directory.
Will figure out the filename automatically from URL, if not given.
""" |
mkdir_p(dir)
if filename is None:
filename = url.split('/')[-1]
fpath = os.path.join(dir, filename)
if os.path.isfile(fpath):
if expect_size is not None and os.stat(fpath).st_size == expect_size:
logger.info("File {} exists! Skip download.".format(filename))
return fpath
else:
logger.warn("File {} exists. Will overwrite with a new download!".format(filename))
def hook(t):
last_b = [0]
def inner(b, bsize, tsize=None):
if tsize is not None:
t.total = tsize
t.update((b - last_b[0]) * bsize)
last_b[0] = b
return inner
try:
with tqdm.tqdm(unit='B', unit_scale=True, miniters=1, desc=filename) as t:
fpath, _ = urllib.request.urlretrieve(url, fpath, reporthook=hook(t))
statinfo = os.stat(fpath)
size = statinfo.st_size
except IOError:
logger.error("Failed to download {}".format(url))
raise
assert size > 0, "Downloaded an empty file from {}!".format(url)
if expect_size is not None and size != expect_size:
logger.error("File downloaded from {} does not match the expected size!".format(url))
logger.error("You may have downloaded a broken file, or the upstream may have modified the file.")
# TODO human-readable size
logger.info('Succesfully downloaded ' + filename + ". " + str(size) + ' bytes.')
return fpath |
<SYSTEM_TASK:>
Get items from this collection that are added in the current tower.
<END_TASK>
<USER_TASK:>
Description:
def get_collection_in_tower(self, key):
"""
Get items from this collection that are added in the current tower.
""" |
new = tf.get_collection(key)
old = set(self.original.get(key, []))
# persist the order in new
return [x for x in new if x not in old] |
<SYSTEM_TASK:>
Iterate on the raw PTB data.
<END_TASK>
<USER_TASK:>
Description:
def ptb_producer(raw_data, batch_size, num_steps, name=None):
"""Iterate on the raw PTB data.
This chunks up raw_data into batches of examples and returns Tensors that
are drawn from these batches.
Args:
raw_data: one of the raw data outputs from ptb_raw_data.
batch_size: int, the batch size.
num_steps: int, the number of unrolls.
name: the name of this operation (optional).
Returns:
A pair of Tensors, each shaped [batch_size, num_steps]. The second element
of the tuple is the same data time-shifted to the right by one.
Raises:
tf.errors.InvalidArgumentError: if batch_size or num_steps are too high.
""" |
with tf.name_scope(name, "PTBProducer", [raw_data, batch_size, num_steps]):
raw_data = tf.convert_to_tensor(raw_data, name="raw_data", dtype=tf.int32)
data_len = tf.size(raw_data)
batch_len = data_len // batch_size
data = tf.reshape(raw_data[0 : batch_size * batch_len],
[batch_size, batch_len])
epoch_size = (batch_len - 1) // num_steps
assertion = tf.assert_positive(
epoch_size,
message="epoch_size == 0, decrease batch_size or num_steps")
with tf.control_dependencies([assertion]):
epoch_size = tf.identity(epoch_size, name="epoch_size")
i = tf.train.range_input_producer(epoch_size, shuffle=False).dequeue()
x = tf.strided_slice(data, [0, i * num_steps],
[batch_size, (i + 1) * num_steps])
x.set_shape([batch_size, num_steps])
y = tf.strided_slice(data, [0, i * num_steps + 1],
[batch_size, (i + 1) * num_steps + 1])
y.set_shape([batch_size, num_steps])
return x, y |
<SYSTEM_TASK:>
Deterministic bilinearly-upsample the input images.
<END_TASK>
<USER_TASK:>
Description:
def CaffeBilinearUpSample(x, shape):
"""
Deterministic bilinearly-upsample the input images.
It is implemented by deconvolution with "BilinearFiller" in Caffe.
It is aimed to mimic caffe behavior.
Args:
x (tf.Tensor): a NCHW tensor
shape (int): the upsample factor
Returns:
tf.Tensor: a NCHW tensor.
""" |
inp_shape = x.shape.as_list()
ch = inp_shape[1]
assert ch == 1, "This layer only works for channel=1"
# for a version that supports >1 channels, see:
# https://github.com/tensorpack/tensorpack/issues/1040#issuecomment-452798180
shape = int(shape)
filter_shape = 2 * shape
def bilinear_conv_filler(s):
"""
s: width, height of the conv filter
https://github.com/BVLC/caffe/blob/99bd99795dcdf0b1d3086a8d67ab1782a8a08383/include/caffe/filler.hpp#L219-L268
"""
f = np.ceil(float(s) / 2)
c = float(2 * f - 1 - f % 2) / (2 * f)
ret = np.zeros((s, s), dtype='float32')
for x in range(s):
for y in range(s):
ret[x, y] = (1 - abs(x / f - c)) * (1 - abs(y / f - c))
return ret
w = bilinear_conv_filler(filter_shape)
w = np.repeat(w, ch * ch).reshape((filter_shape, filter_shape, ch, ch))
weight_var = tf.constant(w, tf.float32,
shape=(filter_shape, filter_shape, ch, ch),
name='bilinear_upsample_filter')
x = tf.pad(x, [[0, 0], [0, 0], [shape - 1, shape - 1], [shape - 1, shape - 1]], mode='SYMMETRIC')
out_shape = tf.shape(x) * tf.constant([1, 1, shape, shape], tf.int32)
deconv = tf.nn.conv2d_transpose(x, weight_var, out_shape,
[1, 1, shape, shape], 'SAME', data_format='NCHW')
edge = shape * (shape - 1)
deconv = deconv[:, :, edge:-edge, edge:-edge]
if inp_shape[2]:
inp_shape[2] *= shape
if inp_shape[3]:
inp_shape[3] *= shape
deconv.set_shape(inp_shape)
return deconv |
<SYSTEM_TASK:>
Returns True if spec_or_tensor is compatible with this TensorSpec.
<END_TASK>
<USER_TASK:>
Description:
def is_compatible_with(self, spec_or_tensor):
"""Returns True if spec_or_tensor is compatible with this TensorSpec.
Two tensors are considered compatible if they have the same dtype
and their shapes are compatible (see `tf.TensorShape.is_compatible_with`).
Args:
spec_or_tensor: A tf.TensorSpec or a tf.Tensor
Returns:
True if spec_or_tensor is compatible with self.
""" |
return (self._dtype.is_compatible_with(spec_or_tensor.dtype) and
self._shape.is_compatible_with(spec_or_tensor.shape)) |
<SYSTEM_TASK:>
Print a description of the current model parameters.
<END_TASK>
<USER_TASK:>
Description:
def describe_trainable_vars():
"""
Print a description of the current model parameters.
Skip variables starting with "tower", as they are just duplicates built by data-parallel logic.
""" |
train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
if len(train_vars) == 0:
logger.warn("No trainable variables in the graph!")
return
total = 0
total_bytes = 0
data = []
for v in train_vars:
if v.name.startswith('tower'):
continue
shape = v.get_shape()
ele = shape.num_elements()
if ele is None:
logger.warn("Shape of variable {} is not fully defined but {}.".format(v.name, shape))
ele = 0
try:
shape = shape.as_list()
except ValueError:
shape = '<unknown>'
total += ele
total_bytes += ele * v.dtype.size
data.append([v.name, shape, ele, v.device, v.dtype.base_dtype.name])
headers = ['name', 'shape', '#elements', 'device', 'dtype']
dtypes = list(set([x[4] for x in data]))
if len(dtypes) == 1 and dtypes[0] == "float32":
# don't log the dtype if all vars are float32 (default dtype)
for x in data:
del x[4]
del headers[4]
devices = set([x[3] for x in data])
if len(devices) == 1:
# don't log the device if all vars on the same device
for x in data:
del x[3]
del headers[3]
table = tabulate(data, headers=headers)
size_mb = total_bytes / 1024.0**2
summary_msg = colored(
"\nNumber of trainable variables: {}".format(len(data)) +
"\nNumber of parameters (elements): {}".format(total) +
"\nStorage space needed for all trainable variables: {:.02f}MB".format(size_mb),
'cyan')
logger.info(colored("List of Trainable Variables: \n", 'cyan') + table + summary_msg) |
<SYSTEM_TASK:>
Embed all given tensors into an nfeatures-dim space.
<END_TASK>
<USER_TASK:>
Description:
def embed(self, x, nfeatures=2):
"""Embed all given tensors into an nfeatures-dim space. """ |
list_split = 0
if isinstance(x, list):
list_split = len(x)
x = tf.concat(x, 0)
# pre-process MNIST dataflow data
x = tf.expand_dims(x, 3)
x = x * 2 - 1
# the embedding network
net = slim.layers.conv2d(x, 20, 5, scope='conv1')
net = slim.layers.max_pool2d(net, 2, scope='pool1')
net = slim.layers.conv2d(net, 50, 5, scope='conv2')
net = slim.layers.max_pool2d(net, 2, scope='pool2')
net = slim.layers.flatten(net, scope='flatten3')
net = slim.layers.fully_connected(net, 500, scope='fully_connected4')
embeddings = slim.layers.fully_connected(net, nfeatures, activation_fn=None, scope='fully_connected5')
# if "x" was a list of tensors, then split the embeddings
if list_split > 0:
embeddings = tf.split(embeddings, list_split, 0)
return embeddings |
<SYSTEM_TASK:>
All-reduce average the gradients among K devices. Results are broadcasted to all devices.
<END_TASK>
<USER_TASK:>
Description:
def allreduce_grads(all_grads, average):
"""
All-reduce average the gradients among K devices. Results are broadcasted to all devices.
Args:
all_grads (K x N): List of list of gradients. N is the number of variables.
average (bool): average gradients or not.
Returns:
K x N: same as input, but each grad is replaced by the average over K devices.
""" |
if get_tf_version_tuple() <= (1, 12):
from tensorflow.contrib import nccl
else:
from tensorflow.python.ops import nccl_ops as nccl
nr_tower = len(all_grads)
if nr_tower == 1:
return all_grads
new_all_grads = [] # N x K
for grads in zip(*all_grads):
summed = nccl.all_sum(grads)
grads_for_devices = [] # K
for g in summed:
with tf.device(g.device):
# tensorflow/benchmarks didn't average gradients
if average:
g = tf.multiply(g, 1.0 / nr_tower)
grads_for_devices.append(g)
new_all_grads.append(grads_for_devices)
# transpose to K x N
ret = list(zip(*new_all_grads))
return ret |
<SYSTEM_TASK:>
Hierarchical allreduce for DGX-1 system.
<END_TASK>
<USER_TASK:>
Description:
def allreduce_grads_hierarchical(all_grads, devices, average=False):
"""
Hierarchical allreduce for DGX-1 system.
Args:
all_grads (K x N): List of list of gradients. N is the number of variables.
devices ([str]): K str for the K devices.
average (bool): average gradients or not.
Returns:
(K x N): same as input, but each grad is replaced by the average over K lists.
""" |
num_gpu = len(devices)
assert num_gpu == 8, num_gpu
assert len(all_grads) == num_gpu, len(all_grads)
group_size = num_gpu // 2
agg_all_grads = [] # N x K
for varid, grads in enumerate(zip(*all_grads)):
# grads: K gradients
g0_main_gpu = varid % num_gpu
g1_main_gpu = (g0_main_gpu + group_size) % num_gpu
g0_start = 0 if g0_main_gpu < group_size else group_size
g1_start = 0 if g1_main_gpu < group_size else group_size
assert g0_start != g1_start
g0_grads = grads[g0_start: g0_start + group_size]
g1_grads = grads[g1_start: g1_start + group_size]
with tf.device(devices[g0_main_gpu]):
g0_agg = tf.add_n(g0_grads, name='group0_agg')
with tf.device(devices[g1_main_gpu]):
g1_agg = tf.add_n(g1_grads, name='group1_agg')
g1_total_agg = tf.add(g0_agg, g1_agg, name='group1_total_agg')
with tf.device(devices[g0_main_gpu]):
g0_total_agg = tf.identity(g1_total_agg, name='group0_total_agg')
agg_grads = [] # K aggregated grads
for k in range(num_gpu):
if (k < group_size) == (g0_main_gpu < group_size):
main_gpu = g0_total_agg
else:
main_gpu = g1_total_agg
with tf.device(devices[k]):
if not average:
device_total_agg = tf.identity(
main_gpu, name='device{}_total_agg'.format(k))
else:
# TODO where to put average?
device_total_agg = tf.multiply(
main_gpu, 1.0 / num_gpu, name='device{}_total_agg'.format(k))
agg_grads.append(device_total_agg)
agg_all_grads.append(agg_grads)
# transpose
agg_all_grads = list(zip(*agg_all_grads)) # K x Nvar
return agg_all_grads |
<SYSTEM_TASK:>
Average the gradients.
<END_TASK>
<USER_TASK:>
Description:
def aggregate_grads(all_grads,
colocation=False,
devices=None,
average=True):
"""
Average the gradients.
Args:
all_grads (K x N x 2): A list of K lists. Each of the list is a list of N (grad, var) tuples.
The variables have to be the same across the K lists.
colocation (bool): colocate gradient averaging on the device of the variable.
devices (list[str]): assign the averaging to these device in
round-robin. Cannot be used together with ``colocation``.
average (bool): do average or sum
Returns:
(N x 2): A list of N (grad, var) tuples, where grad is averaged or summed over K.
""" |
assert not (devices is not None and colocation)
if devices is not None:
assert isinstance(devices, list), devices
nr_tower = len(all_grads)
if nr_tower == 1:
return all_grads[0]
def aggregate(grads):
if average:
return tf.multiply(tf.add_n(grads), 1.0 / nr_tower)
else:
return tf.add_n(grads)
ret = []
for idx, grad_and_vars in enumerate(zip(*all_grads)):
# Ngpu * 2
v = grad_and_vars[0][1]
grads = [g for (g, _) in grad_and_vars]
if colocation:
with tf.device(v.device): # colocate summed grad with var
grad = aggregate(grads)
elif devices is None:
grad = aggregate(grads)
else:
dev = devices[idx % len(devices)]
with tf.device(dev):
grad = aggregate(grads)
ret.append((grad, v))
return ret |
<SYSTEM_TASK:>
Assign boxes to level 2~5.
<END_TASK>
<USER_TASK:>
Description:
def fpn_map_rois_to_levels(boxes):
"""
Assign boxes to level 2~5.
Args:
boxes (nx4):
Returns:
[tf.Tensor]: 4 tensors for level 2-5. Each tensor is a vector of indices of boxes in its level.
[tf.Tensor]: 4 tensors, the gathered boxes in each level.
Be careful that the returned tensor could be empty.
""" |
sqrtarea = tf.sqrt(tf_area(boxes))
level = tf.cast(tf.floor(
4 + tf.log(sqrtarea * (1. / 224) + 1e-6) * (1.0 / np.log(2))), tf.int32)
# RoI levels range from 2~5 (not 6)
level_ids = [
tf.where(level <= 2),
tf.where(tf.equal(level, 3)), # == is not supported
tf.where(tf.equal(level, 4)),
tf.where(level >= 5)]
level_ids = [tf.reshape(x, [-1], name='roi_level{}_id'.format(i + 2))
for i, x in enumerate(level_ids)]
num_in_levels = [tf.size(x, name='num_roi_level{}'.format(i + 2))
for i, x in enumerate(level_ids)]
add_moving_summary(*num_in_levels)
level_boxes = [tf.gather(boxes, ids) for ids in level_ids]
return level_ids, level_boxes |
<SYSTEM_TASK:>
Add summaries for RPN proposals.
<END_TASK>
<USER_TASK:>
Description:
def proposal_metrics(iou):
"""
Add summaries for RPN proposals.
Args:
iou: nxm, #proposal x #gt
""" |
# find best roi for each gt, for summary only
best_iou = tf.reduce_max(iou, axis=0)
mean_best_iou = tf.reduce_mean(best_iou, name='best_iou_per_gt')
summaries = [mean_best_iou]
with tf.device('/cpu:0'):
for th in [0.3, 0.5]:
recall = tf.truediv(
tf.count_nonzero(best_iou >= th),
tf.size(best_iou, out_type=tf.int64),
name='recall_iou{}'.format(th))
summaries.append(recall)
add_moving_summary(*summaries) |
<SYSTEM_TASK:>
Generate final results from predictions of all proposals.
<END_TASK>
<USER_TASK:>
Description:
def fastrcnn_predictions(boxes, scores):
"""
Generate final results from predictions of all proposals.
Args:
boxes: n#classx4 floatbox in float32
scores: nx#class
Returns:
boxes: Kx4
scores: K
labels: K
""" |
assert boxes.shape[1] == cfg.DATA.NUM_CLASS
assert scores.shape[1] == cfg.DATA.NUM_CLASS
boxes = tf.transpose(boxes, [1, 0, 2])[1:, :, :] # #catxnx4
scores = tf.transpose(scores[:, 1:], [1, 0]) # #catxn
def f(X):
"""
prob: n probabilities
box: nx4 boxes
Returns: n boolean, the selection
"""
prob, box = X
output_shape = tf.shape(prob, out_type=tf.int64)
# filter by score threshold
ids = tf.reshape(tf.where(prob > cfg.TEST.RESULT_SCORE_THRESH), [-1])
prob = tf.gather(prob, ids)
box = tf.gather(box, ids)
# NMS within each class
selection = tf.image.non_max_suppression(
box, prob, cfg.TEST.RESULTS_PER_IM, cfg.TEST.FRCNN_NMS_THRESH)
selection = tf.gather(ids, selection)
if get_tf_version_tuple() >= (1, 13):
sorted_selection = tf.sort(selection, direction='ASCENDING')
mask = tf.sparse.SparseTensor(indices=tf.expand_dims(sorted_selection, 1),
values=tf.ones_like(sorted_selection, dtype=tf.bool),
dense_shape=output_shape)
mask = tf.sparse.to_dense(mask, default_value=False)
else:
# this function is deprecated by TF
sorted_selection = -tf.nn.top_k(-selection, k=tf.size(selection))[0]
mask = tf.sparse_to_dense(
sparse_indices=sorted_selection,
output_shape=output_shape,
sparse_values=True,
default_value=False)
return mask
# TF bug in version 1.11, 1.12: https://github.com/tensorflow/tensorflow/issues/22750
buggy_tf = get_tf_version_tuple() in [(1, 11), (1, 12)]
masks = tf.map_fn(f, (scores, boxes), dtype=tf.bool,
parallel_iterations=1 if buggy_tf else 10) # #cat x N
selected_indices = tf.where(masks) # #selection x 2, each is (cat_id, box_id)
scores = tf.boolean_mask(scores, masks)
# filter again by sorting scores
topk_scores, topk_indices = tf.nn.top_k(
scores,
tf.minimum(cfg.TEST.RESULTS_PER_IM, tf.size(scores)),
sorted=False)
filtered_selection = tf.gather(selected_indices, topk_indices)
cat_ids, box_ids = tf.unstack(filtered_selection, axis=1)
final_scores = tf.identity(topk_scores, name='scores')
final_labels = tf.add(cat_ids, 1, name='labels')
final_ids = tf.stack([cat_ids, box_ids], axis=1, name='all_ids')
final_boxes = tf.gather_nd(boxes, final_ids, name='boxes')
return final_boxes, final_scores, final_labels |
<SYSTEM_TASK:>
Launch forward prediction for the new state given by some client.
<END_TASK>
<USER_TASK:>
Description:
def _on_state(self, state, client):
"""
Launch forward prediction for the new state given by some client.
""" |
def cb(outputs):
try:
distrib, value = outputs.result()
except CancelledError:
logger.info("Client {} cancelled.".format(client.ident))
return
assert np.all(np.isfinite(distrib)), distrib
action = np.random.choice(len(distrib), p=distrib)
client.memory.append(TransitionExperience(
state, action, reward=None, value=value, prob=distrib[action]))
self.send_queue.put([client.ident, dumps(action)])
self.async_predictor.put_task([state], cb) |
<SYSTEM_TASK:>
Process a message sent from some client.
<END_TASK>
<USER_TASK:>
Description:
def _process_msg(self, client, state, reward, isOver):
"""
Process a message sent from some client.
""" |
# in the first message, only state is valid,
# reward&isOver should be discarded
if len(client.memory) > 0:
client.memory[-1].reward = reward
if isOver:
# should clear client's memory and put to queue
self._parse_memory(0, client, True)
else:
if len(client.memory) == LOCAL_TIME_MAX + 1:
R = client.memory[-1].value
self._parse_memory(R, client, False)
# feed state and return action
self._on_state(state, client) |
<SYSTEM_TASK:>
Converts a checkpoint and graph to a servable for TensorFlow Serving.
<END_TASK>
<USER_TASK:>
Description:
def export_serving(self, filename,
tags=[tf.saved_model.SERVING if is_tfv2() else tf.saved_model.tag_constants.SERVING],
signature_name='prediction_pipeline'):
"""
Converts a checkpoint and graph to a servable for TensorFlow Serving.
Use TF's `SavedModelBuilder` to export a trained model without tensorpack dependency.
Args:
filename (str): path for export directory
tags (list): list of user specified tags
signature_name (str): name of signature for prediction
Note:
This produces
.. code-block:: none
variables/ # output from the vanilla Saver
variables.data-?????-of-?????
variables.index
saved_model.pb # a `SavedModel` protobuf
Currently, we only support a single signature, which is the general PredictSignatureDef:
https://github.com/tensorflow/serving/blob/master/tensorflow_serving/g3doc/signature_defs.md
""" |
self.graph = self.config._maybe_create_graph()
with self.graph.as_default():
input = PlaceholderInput()
input.setup(self.config.input_signature)
with PredictTowerContext(''):
self.config.tower_func(*input.get_input_tensors())
input_tensors = get_tensors_by_names(self.config.input_names)
saved_model = tfv1.saved_model.utils
inputs_signatures = {t.name: saved_model.build_tensor_info(t) for t in input_tensors}
output_tensors = get_tensors_by_names(self.config.output_names)
outputs_signatures = {t.name: saved_model.build_tensor_info(t) for t in output_tensors}
self.config.session_init._setup_graph()
# we cannot use "self.config.session_creator.create_session()" here since it finalizes the graph
sess = tfv1.Session(config=tfv1.ConfigProto(allow_soft_placement=True))
self.config.session_init._run_init(sess)
builder = tfv1.saved_model.builder.SavedModelBuilder(filename)
prediction_signature = tfv1.saved_model.signature_def_utils.build_signature_def(
inputs=inputs_signatures,
outputs=outputs_signatures,
method_name=tfv1.saved_model.signature_constants.PREDICT_METHOD_NAME)
builder.add_meta_graph_and_variables(
sess, tags,
signature_def_map={signature_name: prediction_signature})
builder.save()
logger.info("SavedModel created at {}.".format(filename)) |
<SYSTEM_TASK:>
This logs the time usage of a code block
<END_TASK>
<USER_TASK:>
Description:
def time_logger(name):
"""This logs the time usage of a code block""" |
start_time = time.time()
yield
end_time = time.time()
total_time = end_time - start_time
logging.info("%s; time: %ss", name, total_time) |
<SYSTEM_TASK:>
Initializes ray based on environment variables and internal defaults.
<END_TASK>
<USER_TASK:>
Description:
def initialize_ray():
"""Initializes ray based on environment variables and internal defaults.""" |
if threading.current_thread().name == "MainThread":
plasma_directory = None
object_store_memory = os.environ.get("MODIN_MEMORY", None)
if os.environ.get("MODIN_OUT_OF_CORE", "False").title() == "True":
from tempfile import gettempdir
plasma_directory = gettempdir()
# We may have already set the memory from the environment variable, we don't
# want to overwrite that value if we have.
if object_store_memory is None:
# Round down to the nearest Gigabyte.
mem_bytes = ray.utils.get_system_memory() // 10 ** 9 * 10 ** 9
# Default to 8x memory for out of core
object_store_memory = 8 * mem_bytes
# In case anything failed above, we can still improve the memory for Modin.
if object_store_memory is None:
# Round down to the nearest Gigabyte.
object_store_memory = int(
0.6 * ray.utils.get_system_memory() // 10 ** 9 * 10 ** 9
)
# If the memory pool is smaller than 2GB, just use the default in ray.
if object_store_memory == 0:
object_store_memory = None
else:
object_store_memory = int(object_store_memory)
ray.init(
include_webui=False,
ignore_reinit_error=True,
plasma_directory=plasma_directory,
object_store_memory=object_store_memory,
)
# Register custom serializer for method objects to avoid warning message.
# We serialize `MethodType` objects when we use AxisPartition operations.
ray.register_custom_serializer(types.MethodType, use_pickle=True) |
<SYSTEM_TASK:>
Applies func to the object.
<END_TASK>
<USER_TASK:>
Description:
def apply(
self,
func,
num_splits=None,
other_axis_partition=None,
maintain_partitioning=True,
**kwargs
):
"""Applies func to the object.
See notes in Parent class about this method.
Args:
func: The function to apply.
num_splits: The number of times to split the result object.
other_axis_partition: Another `DaskFrameAxisPartition` object to apply to
func with this one.
Returns:
A list of `DaskFramePartition` objects.
""" |
import dask
if num_splits is None:
num_splits = len(self.list_of_blocks)
if other_axis_partition is not None:
return [
DaskFramePartition(dask.delayed(obj))
for obj in deploy_func_between_two_axis_partitions(
self.axis,
func,
num_splits,
len(self.list_of_blocks),
kwargs,
*dask.compute(
*tuple(
self.list_of_blocks + other_axis_partition.list_of_blocks
)
)
)
]
args = [self.axis, func, num_splits, kwargs, maintain_partitioning]
args.extend(dask.compute(*self.list_of_blocks))
return [
DaskFramePartition(dask.delayed(obj)) for obj in deploy_axis_func(*args)
] |
<SYSTEM_TASK:>
Convert categorical variable into indicator variables.
<END_TASK>
<USER_TASK:>
Description:
def get_dummies(
data,
prefix=None,
prefix_sep="_",
dummy_na=False,
columns=None,
sparse=False,
drop_first=False,
dtype=None,
):
"""Convert categorical variable into indicator variables.
Args:
data (array-like, Series, or DataFrame): data to encode.
prefix (string, [string]): Prefix to apply to each encoded column
label.
prefix_sep (string, [string]): Separator between prefix and value.
dummy_na (bool): Add a column to indicate NaNs.
columns: Which columns to encode.
sparse (bool): Not Implemented: If True, returns SparseDataFrame.
drop_first (bool): Whether to remove the first level of encoded data.
dtype: The dtype for the get_dummies call.
Returns:
DataFrame or one-hot encoded data.
""" |
if sparse:
raise NotImplementedError(
"SparseDataFrame is not implemented. "
"To contribute to Modin, please visit "
"github.com/modin-project/modin."
)
if not isinstance(data, DataFrame):
ErrorMessage.default_to_pandas("`get_dummies` on non-DataFrame")
return DataFrame(
pandas.get_dummies(
data,
prefix=prefix,
prefix_sep=prefix_sep,
dummy_na=dummy_na,
columns=columns,
sparse=sparse,
drop_first=drop_first,
dtype=dtype,
)
)
else:
new_manager = data._query_compiler.get_dummies(
columns,
prefix=prefix,
prefix_sep=prefix_sep,
dummy_na=dummy_na,
drop_first=drop_first,
dtype=dtype,
)
return DataFrame(query_compiler=new_manager) |
<SYSTEM_TASK:>
Shuffle the order of the data in this axis based on the `lengths`.
<END_TASK>
<USER_TASK:>
Description:
def shuffle(self, func, lengths, **kwargs):
"""Shuffle the order of the data in this axis based on the `lengths`.
Extends `BaseFrameAxisPartition.shuffle`.
Args:
func: The function to apply before splitting.
lengths: The list of partition lengths to split the result into.
Returns:
A list of RemotePartition objects split by `lengths`.
""" |
num_splits = len(lengths)
# We add these to kwargs and will pop them off before performing the operation.
kwargs["manual_partition"] = True
kwargs["_lengths"] = lengths
args = [self.axis, func, num_splits, kwargs, False]
args.extend(self.list_of_blocks)
return self._wrap_partitions(self.deploy_axis_func(*args)) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.