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yahoo/TensorFlowOnSpark | examples/imagenet/inception/image_processing.py | eval_image | def eval_image(image, height, width, scope=None):
"""Prepare one image for evaluation.
Args:
image: 3-D float Tensor
height: integer
width: integer
scope: Optional scope for name_scope.
Returns:
3-D float Tensor of prepared image.
"""
with tf.name_scope(values=[image, height, width], name... | python | def eval_image(image, height, width, scope=None):
"""Prepare one image for evaluation.
Args:
image: 3-D float Tensor
height: integer
width: integer
scope: Optional scope for name_scope.
Returns:
3-D float Tensor of prepared image.
"""
with tf.name_scope(values=[image, height, width], name... | [
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] | 5e4b6c185ab722fd0104ede0377e1149ea8d6f7c | https://github.com/yahoo/TensorFlowOnSpark/blob/5e4b6c185ab722fd0104ede0377e1149ea8d6f7c/examples/imagenet/inception/image_processing.py#L279-L301 | train | Prepare one image for evaluation. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
yahoo/TensorFlowOnSpark | examples/imagenet/inception/image_processing.py | image_preprocessing | def image_preprocessing(image_buffer, bbox, train, thread_id=0):
"""Decode and preprocess one image for evaluation or training.
Args:
image_buffer: JPEG encoded string Tensor
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates a... | python | def image_preprocessing(image_buffer, bbox, train, thread_id=0):
"""Decode and preprocess one image for evaluation or training.
Args:
image_buffer: JPEG encoded string Tensor
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates a... | [
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/image_processing.py | parse_example_proto | def parse_example_proto(example_serialized):
"""Parses an Example proto containing a training example of an image.
The output of the build_image_data.py image preprocessing script is a dataset
containing serialized Example protocol buffers. Each Example proto contains
the following fields:
image/height: 4... | python | def parse_example_proto(example_serialized):
"""Parses an Example proto containing a training example of an image.
The output of the build_image_data.py image preprocessing script is a dataset
containing serialized Example protocol buffers. Each Example proto contains
the following fields:
image/height: 4... | [
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/image_processing.py | batch_inputs | def batch_inputs(dataset, batch_size, train, num_preprocess_threads=None,
num_readers=1):
"""Contruct batches of training or evaluation examples from the image dataset.
Args:
dataset: instance of Dataset class specifying the dataset.
See dataset.py for details.
batch_size: integer
... | python | def batch_inputs(dataset, batch_size, train, num_preprocess_threads=None,
num_readers=1):
"""Contruct batches of training or evaluation examples from the image dataset.
Args:
dataset: instance of Dataset class specifying the dataset.
See dataset.py for details.
batch_size: integer
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yahoo/TensorFlowOnSpark | scripts/spark_ec2.py | setup_external_libs | def setup_external_libs(libs):
"""
Download external libraries from PyPI to SPARK_EC2_DIR/lib/ and prepend them to our PATH.
"""
PYPI_URL_PREFIX = "https://pypi.python.org/packages/source"
SPARK_EC2_LIB_DIR = os.path.join(SPARK_EC2_DIR, "lib")
if not os.path.exists(SPARK_EC2_LIB_DIR):
p... | python | def setup_external_libs(libs):
"""
Download external libraries from PyPI to SPARK_EC2_DIR/lib/ and prepend them to our PATH.
"""
PYPI_URL_PREFIX = "https://pypi.python.org/packages/source"
SPARK_EC2_LIB_DIR = os.path.join(SPARK_EC2_DIR, "lib")
if not os.path.exists(SPARK_EC2_LIB_DIR):
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yahoo/TensorFlowOnSpark | scripts/spark_ec2.py | get_existing_cluster | def get_existing_cluster(conn, opts, cluster_name, die_on_error=True):
"""
Get the EC2 instances in an existing cluster if available.
Returns a tuple of lists of EC2 instance objects for the masters and slaves.
"""
print("Searching for existing cluster {c} in region {r}...".format(
c=clust... | python | def get_existing_cluster(conn, opts, cluster_name, die_on_error=True):
"""
Get the EC2 instances in an existing cluster if available.
Returns a tuple of lists of EC2 instance objects for the masters and slaves.
"""
print("Searching for existing cluster {c} in region {r}...".format(
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yahoo/TensorFlowOnSpark | scripts/spark_ec2.py | is_ssh_available | def is_ssh_available(host, opts, print_ssh_output=True):
"""
Check if SSH is available on a host.
"""
s = subprocess.Popen(
ssh_command(opts) + ['-t', '-t', '-o', 'ConnectTimeout=3',
'%s@%s' % (opts.user, host), stringify_command('true')],
stdout=subprocess.P... | python | def is_ssh_available(host, opts, print_ssh_output=True):
"""
Check if SSH is available on a host.
"""
s = subprocess.Popen(
ssh_command(opts) + ['-t', '-t', '-o', 'ConnectTimeout=3',
'%s@%s' % (opts.user, host), stringify_command('true')],
stdout=subprocess.P... | [
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yahoo/TensorFlowOnSpark | scripts/spark_ec2.py | is_cluster_ssh_available | def is_cluster_ssh_available(cluster_instances, opts):
"""
Check if SSH is available on all the instances in a cluster.
"""
for i in cluster_instances:
dns_name = get_dns_name(i, opts.private_ips)
if not is_ssh_available(host=dns_name, opts=opts):
return False
else:
... | python | def is_cluster_ssh_available(cluster_instances, opts):
"""
Check if SSH is available on all the instances in a cluster.
"""
for i in cluster_instances:
dns_name = get_dns_name(i, opts.private_ips)
if not is_ssh_available(host=dns_name, opts=opts):
return False
else:
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yahoo/TensorFlowOnSpark | scripts/spark_ec2.py | wait_for_cluster_state | def wait_for_cluster_state(conn, opts, cluster_instances, cluster_state):
"""
Wait for all the instances in the cluster to reach a designated state.
cluster_instances: a list of boto.ec2.instance.Instance
cluster_state: a string representing the desired state of all the instances in the cluster
... | python | def wait_for_cluster_state(conn, opts, cluster_instances, cluster_state):
"""
Wait for all the instances in the cluster to reach a designated state.
cluster_instances: a list of boto.ec2.instance.Instance
cluster_state: a string representing the desired state of all the instances in the cluster
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/imagenet_data.py | ImagenetData.download_message | def download_message(self):
"""Instruction to download and extract the tarball from Flowers website."""
print('Failed to find any ImageNet %s files'% self.subset)
print('')
print('If you have already downloaded and processed the data, then make '
'sure to set --data_dir to point to the direct... | python | def download_message(self):
"""Instruction to download and extract the tarball from Flowers website."""
print('Failed to find any ImageNet %s files'% self.subset)
print('')
print('If you have already downloaded and processed the data, then make '
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yahoo/TensorFlowOnSpark | examples/wide_deep/wide_deep_run_loop.py | define_wide_deep_flags | def define_wide_deep_flags():
"""Add supervised learning flags, as well as wide-deep model type."""
flags_core.define_base()
flags_core.define_benchmark()
flags_core.define_performance(
num_parallel_calls=False, inter_op=True, intra_op=True,
synthetic_data=False, max_train_steps=False, dtype=False,
... | python | def define_wide_deep_flags():
"""Add supervised learning flags, as well as wide-deep model type."""
flags_core.define_base()
flags_core.define_benchmark()
flags_core.define_performance(
num_parallel_calls=False, inter_op=True, intra_op=True,
synthetic_data=False, max_train_steps=False, dtype=False,
... | [
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yahoo/TensorFlowOnSpark | examples/wide_deep/wide_deep_run_loop.py | export_model | def export_model(model, model_type, export_dir, model_column_fn):
"""Export to SavedModel format.
Args:
model: Estimator object
model_type: string indicating model type. "wide", "deep" or "wide_deep"
export_dir: directory to export the model.
model_column_fn: Function to generate model feature colu... | python | def export_model(model, model_type, export_dir, model_column_fn):
"""Export to SavedModel format.
Args:
model: Estimator object
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yahoo/TensorFlowOnSpark | examples/wide_deep/wide_deep_run_loop.py | run_loop | def run_loop(name, train_input_fn, eval_input_fn, model_column_fn,
build_estimator_fn, flags_obj, tensors_to_log, early_stop=False):
"""Define training loop."""
model_helpers.apply_clean(flags.FLAGS)
model = build_estimator_fn(
model_dir=flags_obj.model_dir, model_type=flags_obj.model_type,
... | python | def run_loop(name, train_input_fn, eval_input_fn, model_column_fn,
build_estimator_fn, flags_obj, tensors_to_log, early_stop=False):
"""Define training loop."""
model_helpers.apply_clean(flags.FLAGS)
model = build_estimator_fn(
model_dir=flags_obj.model_dir, model_type=flags_obj.model_type,
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/inception_train.py | _tower_loss | def _tower_loss(images, labels, num_classes, scope, reuse_variables=None):
"""Calculate the total loss on a single tower running the ImageNet model.
We perform 'batch splitting'. This means that we cut up a batch across
multiple GPU's. For instance, if the batch size = 32 and num_gpus = 2,
then each tower will... | python | def _tower_loss(images, labels, num_classes, scope, reuse_variables=None):
"""Calculate the total loss on a single tower running the ImageNet model.
We perform 'batch splitting'. This means that we cut up a batch across
multiple GPU's. For instance, if the batch size = 32 and num_gpus = 2,
then each tower will... | [
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/inception_train.py | train | def train(dataset):
"""Train on dataset for a number of steps."""
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable(
'global_step', [],
... | python | def train(dataset):
"""Train on dataset for a number of steps."""
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable(
'global_step', [],
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/ops.py | batch_norm | def batch_norm(inputs,
decay=0.999,
center=True,
scale=False,
epsilon=0.001,
moving_vars='moving_vars',
activation=None,
is_training=True,
trainable=True,
restore=True,
s... | python | def batch_norm(inputs,
decay=0.999,
center=True,
scale=False,
epsilon=0.001,
moving_vars='moving_vars',
activation=None,
is_training=True,
trainable=True,
restore=True,
s... | [
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/ops.py | _two_element_tuple | def _two_element_tuple(int_or_tuple):
"""Converts `int_or_tuple` to height, width.
Several of the functions that follow accept arguments as either
a tuple of 2 integers or a single integer. A single integer
indicates that the 2 values of the tuple are the same.
This functions normalizes the input value by ... | python | def _two_element_tuple(int_or_tuple):
"""Converts `int_or_tuple` to height, width.
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a tuple of 2 integers or a single integer. A single integer
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/ops.py | conv2d | def conv2d(inputs,
num_filters_out,
kernel_size,
stride=1,
padding='SAME',
activation=tf.nn.relu,
stddev=0.01,
bias=0.0,
weight_decay=0,
batch_norm_params=None,
is_training=True,
trainable=True,
... | python | def conv2d(inputs,
num_filters_out,
kernel_size,
stride=1,
padding='SAME',
activation=tf.nn.relu,
stddev=0.01,
bias=0.0,
weight_decay=0,
batch_norm_params=None,
is_training=True,
trainable=True,
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/ops.py | fc | def fc(inputs,
num_units_out,
activation=tf.nn.relu,
stddev=0.01,
bias=0.0,
weight_decay=0,
batch_norm_params=None,
is_training=True,
trainable=True,
restore=True,
scope=None,
reuse=None):
"""Adds a fully connected layer followed by an optio... | python | def fc(inputs,
num_units_out,
activation=tf.nn.relu,
stddev=0.01,
bias=0.0,
weight_decay=0,
batch_norm_params=None,
is_training=True,
trainable=True,
restore=True,
scope=None,
reuse=None):
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/ops.py | one_hot_encoding | def one_hot_encoding(labels, num_classes, scope=None):
"""Transform numeric labels into onehot_labels.
Args:
labels: [batch_size] target labels.
num_classes: total number of classes.
scope: Optional scope for name_scope.
Returns:
one hot encoding of the labels.
"""
with tf.name_scope(scope, '... | python | def one_hot_encoding(labels, num_classes, scope=None):
"""Transform numeric labels into onehot_labels.
Args:
labels: [batch_size] target labels.
num_classes: total number of classes.
scope: Optional scope for name_scope.
Returns:
one hot encoding of the labels.
"""
with tf.name_scope(scope, '... | [
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/ops.py | max_pool | def max_pool(inputs, kernel_size, stride=2, padding='VALID', scope=None):
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Args:
inputs: a tensor of size [batch_size, height, width, depth].
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/ops.py | dropout | def dropout(inputs, keep_prob=0.5, is_training=True, scope=None):
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Args:
inputs: the tensor to pass to the Dropout layer.
keep_prob: the probability of keeping each input unit.
is_training: whether or not the model is in training mode. If so, dropout is
... | python | def dropout(inputs, keep_prob=0.5, is_training=True, scope=None):
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/ops.py | flatten | def flatten(inputs, scope=None):
"""Flattens the input while maintaining the batch_size.
Assumes that the first dimension represents the batch.
Args:
inputs: a tensor of size [batch_size, ...].
scope: Optional scope for name_scope.
Returns:
a flattened tensor with shape [batch_size, k].
Raise... | python | def flatten(inputs, scope=None):
"""Flattens the input while maintaining the batch_size.
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inputs: a tensor of size [batch_size, ...].
scope: Optional scope for name_scope.
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yahoo/TensorFlowOnSpark | examples/imagenet/inception/slim/ops.py | repeat_op | def repeat_op(repetitions, inputs, op, *args, **kwargs):
"""Build a sequential Tower starting from inputs by using an op repeatedly.
It creates new scopes for each operation by increasing the counter.
Example: given repeat_op(3, _, ops.conv2d, 64, [3, 3], scope='conv1')
it will repeat the given op under the ... | python | def repeat_op(repetitions, inputs, op, *args, **kwargs):
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/rpc_manager.py | RPCManager.do_rpc | def do_rpc(self, name: str, rpc_function: Callable[..., Awaitable[None]]) -> Callable[..., Awaitable[None]]:
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/resource.py | register_resource | def register_resource(res: 'Resource', ty: str, name: str, custom: bool, props: 'Inputs', opts: Optional['ResourceOptions']):
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/settings.py | configure | def configure(settings: Settings):
"""
Configure sets the current ambient settings bag to the one given.
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if not settings or not isinstance(settings, Settings):
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global SETTINGS # pylint: disable=global-statement
... | python | def configure(settings: Settings):
"""
Configure sets the current ambient settings bag to the one given.
"""
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/settings.py | get_project | def get_project() -> Optional[str]:
"""
Returns the current project name.
"""
project = SETTINGS.project
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return project | python | def get_project() -> Optional[str]:
"""
Returns the current project name.
"""
project = SETTINGS.project
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require_test_mode_enabled()
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/settings.py | get_stack | def get_stack() -> Optional[str]:
"""
Returns the current stack name.
"""
stack = SETTINGS.stack
if not stack:
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return stack | python | def get_stack() -> Optional[str]:
"""
Returns the current stack name.
"""
stack = SETTINGS.stack
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/settings.py | get_monitor | def get_monitor() -> Optional[resource_pb2_grpc.ResourceMonitorStub]:
"""
Returns the current resource monitoring service client for RPC communications.
"""
monitor = SETTINGS.monitor
if not monitor:
require_test_mode_enabled()
return monitor | python | def get_monitor() -> Optional[resource_pb2_grpc.ResourceMonitorStub]:
"""
Returns the current resource monitoring service client for RPC communications.
"""
monitor = SETTINGS.monitor
if not monitor:
require_test_mode_enabled()
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/rpc.py | serialize_properties | async def serialize_properties(inputs: 'Inputs',
property_deps: Dict[str, List['Resource']],
input_transformer: Optional[Callable[[str], str]] = None) -> struct_pb2.Struct:
"""
Serializes an arbitrary Input bag into a Protobuf structure, keeping trac... | python | async def serialize_properties(inputs: 'Inputs',
property_deps: Dict[str, List['Resource']],
input_transformer: Optional[Callable[[str], str]] = None) -> struct_pb2.Struct:
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/rpc.py | serialize_property | async def serialize_property(value: 'Input[Any]',
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"""
Serializes a single Input into a form suitable for remoting to the engine, awaiting
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/rpc.py | deserialize_properties | def deserialize_properties(props_struct: struct_pb2.Struct) -> Any:
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Python types.
"""
# Check out this link for details on what sort of types Protobuf is going to generate:
# https://developers.google.com/... | python | def deserialize_properties(props_struct: struct_pb2.Struct) -> Any:
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Deserializes a protobuf `struct_pb2.Struct` into a Python dictionary containing normal
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/rpc.py | deserialize_property | def deserialize_property(value: Any) -> Any:
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if value == UNKNOWN:
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"""
Deserializes a single protobuf value (either `Struct` or `ListValue`) into idiomatic
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if value == UNKNOWN:
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/rpc.py | translate_output_properties | def translate_output_properties(res: 'Resource', output: Any) -> Any:
"""
Recursively rewrite keys of objects returned by the engine to conform with a naming
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/rpc.py | resolve_outputs_due_to_exception | def resolve_outputs_due_to_exception(resolvers: Dict[str, Resolver], exn: Exception):
"""
Resolves all outputs with resolvers exceptionally, using the given exception as the reason why the resolver has
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:param resolvers: Resolvers associated with a resource's outputs.
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pulumi/pulumi | sdk/python/lib/pulumi/config.py | Config.get | def get(self, key: str) -> Optional[str]:
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pulumi/pulumi | sdk/python/lib/pulumi/config.py | Config.get_bool | def get_bool(self, key: str) -> Optional[bool]:
"""
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:param str key: The requested configuration key.
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"""
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pulumi/pulumi | sdk/python/lib/pulumi/config.py | Config.get_int | def get_int(self, key: str) -> Optional[int]:
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pulumi/pulumi | sdk/python/lib/pulumi/config.py | Config.get_float | def get_float(self, key: str) -> Optional[float]:
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pulumi/pulumi | sdk/python/lib/pulumi/config.py | Config.require | def require(self, key: str) -> str:
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pulumi/pulumi | sdk/python/lib/pulumi/config.py | Config.require_bool | def require_bool(self, key: str) -> bool:
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pulumi/pulumi | sdk/python/lib/pulumi/config.py | Config.require_int | def require_int(self, key: str) -> int:
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/known_types.py | asset | def asset(class_obj: type) -> type:
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return class_obj | python | def asset(class_obj: type) -> type:
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Decorator to annotate the Asset class. Registers the decorated class
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Decorator to annotate the StringAsset class. Registers the decorated class
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/known_types.py | remote_asset | def remote_asset(class_obj: type) -> type:
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Decorator to annotate the RemoteAsset class. Registers the decorated class
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/known_types.py | archive | def archive(class_obj: type) -> type:
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Decorator to annotate the Archive class. Registers the decorated class
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assert isinstance(class_obj, type), "class_obj is not a Class"
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return class_obj | python | def archive(class_obj: type) -> type:
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Decorator to annotate the Archive class. Registers the decorated class
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/known_types.py | asset_archive | def asset_archive(class_obj: type) -> type:
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Decorator to annotate the AssetArchive class. Registers the decorated class
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Decorator to annotate the AssetArchive class. Registers the decorated class
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/known_types.py | file_archive | def file_archive(class_obj: type) -> type:
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Decorator to annotate the FileArchive class. Registers the decorated class
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Decorator to annotate the FileArchive class. Registers the decorated class
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/known_types.py | remote_archive | def remote_archive(class_obj: type) -> type:
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Decorator to annotate the RemoteArchive class. Registers the decorated class
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Decorator to annotate the RemoteArchive class. Registers the decorated class
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/known_types.py | custom_resource | def custom_resource(class_obj: type) -> type:
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Decorator to annotate the CustomResource class. Registers the decorated class
as the CustomResource known type.
"""
assert isinstance(class_obj, type), "class_obj is not a Class"
global _custom_resource_type
_custom_resource_type = class_obj
... | python | def custom_resource(class_obj: type) -> type:
"""
Decorator to annotate the CustomResource class. Registers the decorated class
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pulumi/pulumi | sdk/python/lib/pulumi/log.py | debug | def debug(msg: str, resource: Optional['Resource'] = None, stream_id: Optional[int] = None) -> None:
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Logs a message to the Pulumi CLI's debug channel, associating it with a resource
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:param str msg: The message to send to the Pulumi CLI.
:param Optional[Resource] resou... | python | def debug(msg: str, resource: Optional['Resource'] = None, stream_id: Optional[int] = None) -> None:
"""
Logs a message to the Pulumi CLI's debug channel, associating it with a resource
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"""
Logs a message to the Pulumi CLI's info channel, associating it with a resource
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Logs a message to the Pulumi CLI's info channel, associating it with a resource
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pulumi/pulumi | sdk/python/lib/pulumi/log.py | error | def error(msg: str, resource: Optional['Resource'] = None, stream_id: Optional[int] = None):
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/config.py | get_config_env | def get_config_env() -> Dict[str, Any]:
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return dict() | python | def get_config_env() -> Dict[str, Any]:
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/stack.py | run_in_stack | async def run_in_stack(func: Callable):
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pulumi/pulumi | sdk/python/lib/pulumi/runtime/stack.py | Stack.output | def output(self, name: str, value: Any):
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pulumi/pulumi | sdk/python/lib/pulumi/resource.py | export | def export(name: str, value: Any):
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:param Any value: The value of this output.
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stack = get_root_resource()
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pulumi/pulumi | sdk/python/lib/pulumi/resource.py | Resource.get_provider | def get_provider(self, module_member: str) -> Optional['ProviderResource']:
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pytorch/ignite | ignite/engine/engine.py | Engine.has_event_handler | def has_event_handler(self, handler, event_name=None):
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handler (callable): the callable event handler.
event_name: The event the handler attached to. Set this
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"""Check if the specified event has the specified handler.
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handler (callable): the callable event handler that should be removed
event_name: The event the handler attached to.
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handler (callable): the callable event handler that should be removed
event_name: The event the handler attached to.
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pytorch/ignite | ignite/engine/engine.py | Engine._fire_event | def _fire_event(self, event_name, *event_args, **event_kwargs):
"""Execute all the handlers associated with given event.
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data (Iterable): Collection of batches allowing repeated iteration (e.g., list or `DataLoader`).
max_epochs (int, optional): max epochs to run for (default: 1).
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max_epochs (int, optional): max epochs to run for (default: 1).
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pytorch/ignite | ignite/metrics/confusion_matrix.py | IoU | def IoU(cm, ignore_index=None):
"""Calculates Intersection over Union
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
ignore_index (int, optional): index to ignore, e.g. background index
Returns:
MetricsLambda
Examples:
.. code-block:: python
trai... | python | def IoU(cm, ignore_index=None):
"""Calculates Intersection over Union
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
ignore_index (int, optional): index to ignore, e.g. background index
Returns:
MetricsLambda
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pytorch/ignite | ignite/metrics/confusion_matrix.py | cmAccuracy | def cmAccuracy(cm):
"""
Calculates accuracy using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
Returns:
MetricsLambda
"""
# Increase floating point precision
cm = cm.type(torch.float64)
return cm.diag().... | python | def cmAccuracy(cm):
"""
Calculates accuracy using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
Returns:
MetricsLambda
"""
# Increase floating point precision
cm = cm.type(torch.float64)
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"""
Calculates precision using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
Returns:
MetricsLambda
... | python | def cmPrecision(cm, average=True):
"""
Calculates precision using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
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cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
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"""
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cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
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pytorch/ignite | ignite/utils.py | convert_tensor | def convert_tensor(input_, device=None, non_blocking=False):
"""Move tensors to relevant device."""
def _func(tensor):
return tensor.to(device=device, non_blocking=non_blocking) if device else tensor
return apply_to_tensor(input_, _func) | python | def convert_tensor(input_, device=None, non_blocking=False):
"""Move tensors to relevant device."""
def _func(tensor):
return tensor.to(device=device, non_blocking=non_blocking) if device else tensor
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pytorch/ignite | ignite/utils.py | apply_to_type | def apply_to_type(input_, input_type, func):
"""Apply a function on a object of `input_type` or mapping, or sequence of objects of `input_type`.
"""
if isinstance(input_, input_type):
return func(input_)
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pytorch/ignite | ignite/utils.py | to_onehot | def to_onehot(indices, num_classes):
"""Convert a tensor of indices of any shape `(N, ...)` to a
tensor of one-hot indicators of shape `(N, num_classes, ...)`.
"""
onehot = torch.zeros(indices.shape[0], num_classes, *indices.shape[1:], device=indices.device)
return onehot.scatter_(1, indices.unsquee... | python | def to_onehot(indices, num_classes):
"""Convert a tensor of indices of any shape `(N, ...)` to a
tensor of one-hot indicators of shape `(N, num_classes, ...)`.
"""
onehot = torch.zeros(indices.shape[0], num_classes, *indices.shape[1:], device=indices.device)
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pytorch/ignite | ignite/contrib/handlers/tqdm_logger.py | ProgressBar.attach | def attach(self, engine, metric_names=None, output_transform=None,
event_name=Events.ITERATION_COMPLETED,
closing_event_name=Events.EPOCH_COMPLETED):
"""
Attaches the progress bar to an engine object.
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engine (Engine): engine object.
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Attaches the progress bar to an engine object.
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pytorch/ignite | ignite/contrib/engines/tbptt.py | create_supervised_tbptt_trainer | def create_supervised_tbptt_trainer(
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dim=0,
device=None,
non_blocking=False,
prepare_batch=_prepare_batch
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"""Create a trainer for truncated backprop through time supervised models.
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non_blocking=False,
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pytorch/ignite | ignite/engine/__init__.py | _prepare_batch | def _prepare_batch(batch, device=None, non_blocking=False):
"""Prepare batch for training: pass to a device with options.
"""
x, y = batch
return (convert_tensor(x, device=device, non_blocking=non_blocking),
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"""Prepare batch for training: pass to a device with options.
"""
x, y = batch
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pytorch/ignite | ignite/engine/__init__.py | create_supervised_trainer | def create_supervised_trainer(model, optimizer, loss_fn,
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"""
Factory function for creating a trainer ... | python | def create_supervised_trainer(model, optimizer, loss_fn,
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pytorch/ignite | ignite/engine/__init__.py | create_supervised_evaluator | def create_supervised_evaluator(model, metrics=None,
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"""
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pytorch/ignite | ignite/contrib/handlers/param_scheduler.py | create_lr_scheduler_with_warmup | def create_lr_scheduler_with_warmup(lr_scheduler, warmup_start_value, warmup_end_value, warmup_duration,
save_history=False,
output_simulated_values=None):
"""
Helper method to create a LR scheduler with a linear warm-up.
Args:
... | python | def create_lr_scheduler_with_warmup(lr_scheduler, warmup_start_value, warmup_end_value, warmup_duration,
save_history=False,
output_simulated_values=None):
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Helper method to create a LR scheduler with a linear warm-up.
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pytorch/ignite | ignite/contrib/handlers/param_scheduler.py | ParamScheduler.simulate_values | def simulate_values(cls, num_events, **scheduler_kwargs):
"""Method to simulate scheduled values during num_events events.
Args:
num_events (int): number of events during the simulation.
**scheduler_kwargs : parameter scheduler configuration kwargs.
Returns:
... | python | def simulate_values(cls, num_events, **scheduler_kwargs):
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Args:
num_events (int): number of events during the simulation.
**scheduler_kwargs : parameter scheduler configuration kwargs.
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pytorch/ignite | ignite/contrib/handlers/param_scheduler.py | CosineAnnealingScheduler.get_param | def get_param(self):
"""Method to get current optimizer's parameter value
"""
cycle_progress = self.event_index / self.cycle_size
return self.start_value + ((self.end_value - self.start_value) / 2) * (1 - math.cos(math.pi * cycle_progress)) | python | def get_param(self):
"""Method to get current optimizer's parameter value
"""
cycle_progress = self.event_index / self.cycle_size
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pytorch/ignite | ignite/contrib/handlers/param_scheduler.py | ConcatScheduler.simulate_values | def simulate_values(cls, num_events, schedulers, durations, param_names=None, **kwargs):
"""Method to simulate scheduled values during num_events events.
Args:
num_events (int): number of events during the simulation.
schedulers (list of ParamScheduler): list of parameter schedu... | python | def simulate_values(cls, num_events, schedulers, durations, param_names=None, **kwargs):
"""Method to simulate scheduled values during num_events events.
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num_events (int): number of events during the simulation.
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pytorch/ignite | ignite/contrib/handlers/param_scheduler.py | LRScheduler.get_param | def get_param(self):
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"""Method to get current optimizer's parameter value
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pytorch/ignite | ignite/contrib/handlers/param_scheduler.py | LRScheduler.simulate_values | def simulate_values(cls, num_events, lr_scheduler, **kwargs):
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num_events (int): number of events during the simulation.
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pytorch/ignite | examples/gan/dcgan.py | check_manual_seed | def check_manual_seed(seed):
""" If manual seed is not specified, choose a random one and communicate it to the user.
"""
seed = seed or random.randint(1, 10000)
random.seed(seed)
torch.manual_seed(seed)
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""" If manual seed is not specified, choose a random one and communicate it to the user.
"""
seed = seed or random.randint(1, 10000)
random.seed(seed)
torch.manual_seed(seed)
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pytorch/ignite | ignite/contrib/handlers/base_logger.py | BaseLogger.attach | def attach(self, engine, log_handler, event_name):
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Args:
engine (Engine): engine object.
log_handler (callable): a logging handler to execute
event_name: event to attach the... | python | def attach(self, engine, log_handler, event_name):
"""Attach the logger to the engine and execute `log_handler` function at `event_name` events.
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engine (Engine): engine object.
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pytorch/ignite | ignite/contrib/handlers/base_logger.py | BaseOutputHandler._setup_output_metrics | def _setup_output_metrics(self, engine):
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"""
metrics = {}
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"""Helper method to setup metrics to log
"""
metrics = {}
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pytorch/ignite | ignite/handlers/timing.py | Timer.attach | def attach(self, engine, start=Events.STARTED, pause=Events.COMPLETED, resume=None, step=None):
""" Register callbacks to control the timer.
Args:
engine (Engine):
Engine that this timer will be attached to.
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Event which should star... | python | def attach(self, engine, start=Events.STARTED, pause=Events.COMPLETED, resume=None, step=None):
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engine (Engine):
Engine that this timer will be attached to.
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pytorch/ignite | ignite/contrib/handlers/visdom_logger.py | _BaseVisDrawer.add_scalar | def add_scalar(self, logger, k, v, event_name, global_step):
"""
Helper method to log a scalar with VisdomLogger.
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logger (VisdomLogger): visdom logger
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"""
Helper method to log a scalar with VisdomLogger.
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logger (VisdomLogger): visdom logger
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pytorch/ignite | ignite/_utils.py | _to_hours_mins_secs | def _to_hours_mins_secs(time_taken):
"""Convert seconds to hours, mins, and seconds."""
mins, secs = divmod(time_taken, 60)
hours, mins = divmod(mins, 60)
return hours, mins, secs | python | def _to_hours_mins_secs(time_taken):
"""Convert seconds to hours, mins, and seconds."""
mins, secs = divmod(time_taken, 60)
hours, mins = divmod(mins, 60)
return hours, mins, secs | [
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coursera-dl/coursera-dl | coursera/commandline.py | parse_args | def parse_args(args=None):
"""
Parse the arguments/options passed to the program on the command line.
"""
parse_kwargs = {
"description": 'Download Coursera.org lecture material and resources.'
}
conf_file_path = os.path.join(os.getcwd(), LOCAL_CONF_FILE_NAME)
if os.path.isfile(co... | python | def parse_args(args=None):
"""
Parse the arguments/options passed to the program on the command line.
"""
parse_kwargs = {
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coursera-dl/coursera-dl | coursera/utils.py | random_string | def random_string(length):
"""
Return a pseudo-random string of specified length.
"""
valid_chars = string_ascii_letters + string_digits
return ''.join(random.choice(valid_chars) for i in range(length)) | python | def random_string(length):
"""
Return a pseudo-random string of specified length.
"""
valid_chars = string_ascii_letters + string_digits
return ''.join(random.choice(valid_chars) for i in range(length)) | [
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coursera-dl/coursera-dl | coursera/utils.py | clean_filename | def clean_filename(s, minimal_change=False):
"""
Sanitize a string to be used as a filename.
If minimal_change is set to true, then we only strip the bare minimum of
characters that are problematic for filesystems (namely, ':', '/' and
'\x00', '\n').
"""
# First, deal with URL encoded stri... | python | def clean_filename(s, minimal_change=False):
"""
Sanitize a string to be used as a filename.
If minimal_change is set to true, then we only strip the bare minimum of
characters that are problematic for filesystems (namely, ':', '/' and
'\x00', '\n').
"""
# First, deal with URL encoded stri... | [
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coursera-dl/coursera-dl | coursera/utils.py | normalize_path | def normalize_path(path):
"""
Normalizes path on Windows OS. This means prepending
<backslash><backslash>?<backslash> to the path to get access to
Win32 device namespace instead of Win32 file namespace.
See https://msdn.microsoft.com/en-us/library/aa365247%28v=vs.85%29.aspx#maxpath
@param path:... | python | def normalize_path(path):
"""
Normalizes path on Windows OS. This means prepending
<backslash><backslash>?<backslash> to the path to get access to
Win32 device namespace instead of Win32 file namespace.
See https://msdn.microsoft.com/en-us/library/aa365247%28v=vs.85%29.aspx#maxpath
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coursera-dl/coursera-dl | coursera/utils.py | get_anchor_format | def get_anchor_format(a):
"""
Extract the resource file-type format from the anchor.
"""
# (. or format=) then (file_extension) then (? or $)
# e.g. "...format=txt" or "...download.mp4?..."
fmt = re.search(r"(?:\.|format=)(\w+)(?:\?.*)?$", a)
return fmt.group(1) if fmt else None | python | def get_anchor_format(a):
"""
Extract the resource file-type format from the anchor.
"""
# (. or format=) then (file_extension) then (? or $)
# e.g. "...format=txt" or "...download.mp4?..."
fmt = re.search(r"(?:\.|format=)(\w+)(?:\?.*)?$", a)
return fmt.group(1) if fmt else None | [
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coursera-dl/coursera-dl | coursera/utils.py | clean_url | def clean_url(url):
"""
Remove params, query and fragment parts from URL so that `os.path.basename`
and `os.path.splitext` can work correctly.
@param url: URL to clean.
@type url: str
@return: Cleaned URL.
@rtype: str
"""
parsed = urlparse(url.strip())
reconstructed = ParseResu... | python | def clean_url(url):
"""
Remove params, query and fragment parts from URL so that `os.path.basename`
and `os.path.splitext` can work correctly.
@param url: URL to clean.
@type url: str
@return: Cleaned URL.
@rtype: str
"""
parsed = urlparse(url.strip())
reconstructed = ParseResu... | [
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